[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"_public_publisher_byId_8e6ed142-4ce1-41f3-a598-8a4a9b37e2e5":3,"_public_publication_all{\"sortAscending\":false,\"sortField\":\"updateTime\",\"page\":0,\"size\":10,\"facet\":true,\"searchKey\":\"publisherId:8e6ed142-4ce1-41f3-a598-8a4a9b37e2e5,\"}":52},{"code":4,"data":5,"meta":20},"SUCCESS",{"id":6,"createTime":7,"updateTime":8,"relativeEntities":9,"slug":10,"properties":11,"entityType":18,"verifyStatus":19,"verifyTime":20,"verifyNote":20,"syncStatus":19,"languages":20,"translateLanguages":20,"viewCount":21,"subjectFields":22,"manageAffiliations":23,"indexDatabases":24,"url":20,"thumbnailPath":20,"statistic":42,"gsStatistic":20,"type":51,"analyzePriority":20},"8e6ed142-4ce1-41f3-a598-8a4a9b37e2e5","2024-04-06T09:27:30.582+00:00","2025-11-21T10:02:11.069+00:00",[],"Discover-Internet-of-Things",{"issn":12,"title":14,"url":16},{"VOID":13},"27307239",{"EN":15},"Discover Internet of Things",{"VOID":17},"https:\u002F\u002Flink.springer.com\u002Fjournal\u002F43926","PUBLISHER","PENDING",null,0,[],[],[25],{"id":26,"indexDatabase":27,"url":39,"indexYears":40,"academicFieldIds":20,"indexDatabaseRanking":41},"4e3227cf-5945-4fa9-bef3-b3654c5946bf",{"id":28,"createTime":29,"updateTime":30,"relativeEntities":31,"label":32,"description":34,"key":36,"publicationTags":37,"standard":20},"3c7051d4-eb7d-4c57-a56b-36fc74c5d1e9","2023-05-22T09:57:18.509+00:00","2025-11-21T10:07:52.274+00:00",[],{"EN":33,"VI":33},"Scopus - Elsevier",{"EN":33,"VI":35},"Cơ sở dữ liệu Scopus thuộc Elsevier","scopus",[38],"SCOPUS","https:\u002F\u002Fwww.scopus.com\u002Fsourceid\u002F21101212766","2021-2025","NONE",{"impactFactor":21,"impactFactorByYear":43,"i10Index":21,"i10IndexLast5Year":21,"totalPublication":44,"totalPublicationByYear":45,"totalCitation":21,"totalCitationByYear":49,"totalCitationPerPublication":21,"totalCitationPerPublicationByYear":50,"hindexLast5Year":21,"hindex":21},{},31,{"2021":46,"2022":47,"2023":48},10,5,16,{},{},"JOURNAL",{"meta":53,"data":55},{"total":54},"36",[56,151,246,513,682,817,1055,1159,1445,1611],{"id":57,"createTime":58,"updateTime":59,"relativeEntities":60,"slug":61,"properties":62,"entityType":71,"verifyStatus":72,"verifyTime":59,"verifyNote":73,"syncStatus":19,"languages":20,"translateLanguages":20,"viewCount":21,"primaryUrl":74,"fullTextUrl":20,"authors":75,"publicationType":121,"publisherRelationship":122,"citationCount":20,"citationInfo":20,"publishDate":148,"publishYear":149,"citationAnalyzeStatus":19,"lastCitationAnalyze":20,"indexDatabases":20,"openAccess":20,"references":20,"isForceReanalyzing":150},"1f139756-4ce4-4397-a513-6f4d9611582c","2024-02-12T02:09:41.199+00:00","2025-01-30T23:40:48.231+00:00",[],"An-interpolation-based-reversible-data-hiding-scheme-for-internet-of-things-applications",{"references":63,"abstract":65,"title":67,"doi":69},{"VOID":64},"Lee C-F, Huang Y-L. Reversible data hiding scheme based on dual stegano-images using orientation combinations. Telecommun Syst. 2013;52(4):2237–47.\nHassan FS, Gutub A. Novel embedding secrecy within images utilizing an improved interpolation-based reversible data hiding scheme. J King Saud Univ-Comput Inform Sci. 2020;34:2017–30.\nZhang X, Sun Z, Tang Z, Chunqiang Yu, Wang X. High capacity data hiding based on interpolated image. Multimed Tools Appl. 2017;76(7):9195–218.\nWang J, Mao N, Chen X, Ni J, Wang C, Shi Y. Multiple histograms based reversible data hiding by using FCM clustering. Signal Process. 2019;159:193–203.\nJung K-H, Yoo K-Y. Data hiding method using image interpolation. Comput Stand Interfac. 2009;31(2):465–70.\nLiu Y-C, Hsien-Chu Wu, Shyr-Shen Yu. Adaptive DE-based reversible steganographic technique using bilinear interpolation and simplified location map. Multimed Tools Appl. 2011;52(2):263–76.\nMurthy VMMK, Rama S, Manikandan VM. Reversible data hiding using block-wise histogram shifting and run-length encoding. Int J Adv Computer Sci Appl. 2021;12(5):74.\nMalik A, Sikka G, Verma HK. An image interpolation based reversible data hiding scheme using pixel value adjusting feature. Multimed Tools Appl. 2017;76(11):13025–46.\nMalik A, Sikka G, Verma HK. Image interpolation based high capacity reversible data hiding scheme. Multimed Tools Appl. 2017;76(22):24107–23.\nGutub A, Al-Shaarani F. Efficient implementation of multi-image secret hiding based on LSB and DWT steganography comparisons. Arab J Sci Eng. 2020;45(4):2631–44.\nKumar R, Jung K-H. Robust reversible data hiding scheme based on two-layer embedding strategy. Inf Sci. 2020;512:96–107.\nMalik A, Sikka G, Verma HK. A reversible data hiding scheme for interpolated images based on pixel intensity range. Multimed Tools Appl. 2020;79(25):18005–31.\nWu D-C, Tsai W-H. A steganographic method for images by pixel-value differencing. Pattern Recogn Lett. 2003;24(9–10):1613–26.\nHu J, Li T. Reversible steganography using extended image interpolation technique. Comput Electr Eng. 2015;46:447–55.\nYang C-H, Wang S-J, Weng C-Y. Capacity-raising steganography using multi-pixel differencing and pixel-value shifting operations. Fund Inform. 2010;98(2–3):321–36.\nShen S, Huang L, Tian Q. A novel data hiding for color images based on pixel value difference and modulus function. Multimed Tools Appl. 2015;74(3):707–28.\nSwain G. Adaptive pixel value differencing steganography using both vertical and horizontal edges. Multimed Tools Appl. 2016;75(21):13541–56.\nKhodaei M, Bigham BS, Faez K. Adaptive data hiding, using pixel-value-differencing and LSB substitution. Cybern Syst. 2016;47(8):617–28.\nTsai P, Yu-Chen Hu, Yeh H-L. Reversible image hiding scheme using predictive coding and histogram shifting. Signal Process. 2009;89(6):1129–43.\nLu T-C, Chang C-C, Huang Y-H. High capacity reversible hiding scheme based on interpolation, difference expansion, and histogram shifting. Multimed Tools Appl. 2014;72(1):417–35.\nRukundo O, Cao H. Nearest neighbor value interpolation. arxiv preprint arXiv:12111768. 2012. https:\u002F\u002Fdoi.org\u002F10.14569\u002FIJACSA.2012.030405.\nChang Y-T, Huang C-T, Lee C-F, Wang S-J. Image interpolating based data hiding in conjunction with pixel-shifting of histogram. J Supercomput. 2013;66(2):1093–110.\nChan C-K, Cheng L-M. Hiding data in images by simple LSB substitution. Pattern Recogn. 2004;37(3):469–74.\nHussain M, Wahab AWA, Idris YIB, Ho ATS, Jung K-H. Image steganography in spatial domain: a survey. Signal Process Image Commun. 2018;65:46–66.\nAl-Dmour H, Al-Ani A. A steganography embedding method based on edge identification and XOR coding. Expert Syst Appl. 2016;46:293–306.\nCox I, Miller M, Bloom J, Fridrich J, Kalker T. Digital watermarking and steganography. Cambridge: Morgan kaufmann; 2007.\nSingh S. Adaptive PVD and LSB based high capacity data hiding scheme. Multimed Tools Appl. 2020;79(25):18815–37.\nMandal PC, Mukherjee I, Chatterji BN. High capacity reversible and secured data hiding in images using interpolation and difference expansion technique. Multimed Tools Appl. 2021;80:3623–44.\nBenseddik ML, Zebbiche K, Azzaz MS, et al. Interpolation-based reversible data hiding in the transform domain for fingerprint images. Multimed Tools Appl. 2022;81:20329–56.\nMohammad AA. A high quality interpolation-based reversible data hiding technique using dual images. Multimed Tools Appl. 2023. https:\u002F\u002Fdoi.org\u002F10.1007\u002Fs11042-023-15092-8.\nShastri S, Thanikaiselvan V. Interpolation based dual image reversible data hiding using trinary encoding. Multimed Tools Appl. 2023. https:\u002F\u002Fdoi.org\u002F10.1007\u002Fs11042-023-15574-9.\nRoselinkiruba R. Reversible data hiding using optimization, interpolation and binary image encryption techniques. Multimed Tools Appl. 2023;82:35757–80.",{"EN":66},"The exponential growth of the Internet of Things (IoT) has led to an increased demand for secure and efficient data transmission methods. However, there is a tradeoff in the image quality and hiding capacity in the data hiding methods. Therefore, the maximum amount of data that could be stored in the image media is a difficult challenge while maintaining the image quality. Thus, to make the balance between the quality of the images and the embedding capacity, a novel interpolation-based revisable data hiding (RDH) approach is developed for IoT applications. The proposed interpolation technique takes the average of the root value for the product of two neighboring original pixel values and the third original pixel value. And for the central pixel, it takes an average of two interpolated pixels. By doing so, most of the original pixels are considered and the calculated interpolated pixel is much enhanced as its average value. Furthermore, the data hiding is performed in two stages. In the first stage, RSA is performed on the secret message, and then embedding is done based on which intensity range group. The experimental results indicate that the proposed technique enhanced the embedding capacity by 17.58% and produced 7.80% higher PSNR values for the test images as compared to the baseline methods.",{"EN":68},"An interpolation-based reversible data hiding scheme for internet of things applications",{"VOID":70},"10.1007\u002Fs43926-023-00048-z","PUBLICATION","VERIFIED","Auto Verify","https:\u002F\u002Flink.springer.com\u002Farticle\u002F10.1007\u002Fs43926-023-00048-z",[76,95,108],{"id":77,"sortIndex":21,"researcher":20,"roles":78,"affiliations":80,"properties":92},"084ce6eb-4399-415b-944d-e97f5ead3530",[79],"AUTHOR",[81],{"id":20,"sortIndex":21,"affiliation":82,"properties":20},{"id":83,"createTime":84,"updateTime":85,"relativeEntities":86,"slug":87,"properties":88,"entityType":91,"verifyStatus":19,"verifyTime":20,"verifyNote":20,"syncStatus":19,"languages":20,"translateLanguages":20,"viewCount":21},"0ae370c3-bf69-4a7d-9b35-e1dfca105775","2023-12-29T00:42:11.435+00:00","2025-06-11T23:46:59.547+00:00",[],"Department-of-Computer-Science-Engineering-Dr-B-R-Ambedkar-National-Institute-of-Technology-Jalandhar-Punjab-India",{"title":89},{"VI":90},"Department of Computer Science & Engineering, Dr B R Ambedkar National Institute of Technology Jalandhar, Punjab, India","AFFILIATION",{"title":93},{"VI":94},"Riya Punia",{"id":96,"sortIndex":97,"researcher":20,"roles":98,"affiliations":99,"properties":105},"c95d38ce-a8df-49dc-a0f7-e9b64a93e1a1",2,[79],[100],{"id":20,"sortIndex":21,"affiliation":101,"properties":20},{"id":83,"createTime":84,"updateTime":85,"relativeEntities":102,"slug":87,"properties":103,"entityType":91,"verifyStatus":19,"verifyTime":20,"verifyNote":20,"syncStatus":19,"languages":20,"translateLanguages":20,"viewCount":21},[],{"title":104},{"VI":90},{"title":106},{"VI":107},"Samayveer Singh",{"id":109,"sortIndex":110,"researcher":20,"roles":111,"affiliations":112,"properties":118},"dc8ca001-9f27-42f3-8789-8011052b2427",1,[79],[113],{"id":20,"sortIndex":21,"affiliation":114,"properties":20},{"id":83,"createTime":84,"updateTime":85,"relativeEntities":115,"slug":87,"properties":116,"entityType":91,"verifyStatus":19,"verifyTime":20,"verifyNote":20,"syncStatus":19,"languages":20,"translateLanguages":20,"viewCount":21},[],{"title":117},{"VI":90},{"title":119},{"VI":120},"Aruna Malik","ARTICLE",{"url":74,"publisher":123,"properties":143},{"id":6,"createTime":7,"updateTime":8,"relativeEntities":124,"slug":10,"properties":125,"entityType":18,"verifyStatus":19,"verifyTime":20,"verifyNote":20,"syncStatus":19,"languages":20,"translateLanguages":20,"viewCount":21,"subjectFields":129,"manageAffiliations":130,"indexDatabases":131,"url":20,"thumbnailPath":20,"statistic":138,"gsStatistic":20,"type":51,"analyzePriority":20},[],{"issn":126,"title":127,"url":128},{"VOID":13},{"EN":15},{"VOID":17},[],[],[132],{"id":26,"indexDatabase":133,"url":39,"indexYears":40,"academicFieldIds":20,"indexDatabaseRanking":41},{"id":28,"createTime":29,"updateTime":30,"relativeEntities":134,"label":135,"description":136,"key":36,"publicationTags":137,"standard":20},[],{"EN":33,"VI":33},{"EN":33,"VI":35},[38],{"impactFactor":21,"impactFactorByYear":139,"i10Index":21,"i10IndexLast5Year":21,"totalPublication":44,"totalPublicationByYear":140,"totalCitation":21,"totalCitationByYear":141,"totalCitationPerPublication":21,"totalCitationPerPublicationByYear":142,"hindexLast5Year":21,"hindex":21},{},{"2021":46,"2022":47,"2023":48},{},{},{"volume":144,"pages":146},{"VOID":145},"3",{"VOID":147},"1-16","2023-11-09",2023,false,{"id":152,"createTime":153,"updateTime":154,"relativeEntities":155,"slug":156,"properties":157,"entityType":71,"verifyStatus":72,"verifyTime":154,"verifyNote":73,"syncStatus":19,"languages":20,"translateLanguages":20,"viewCount":21,"primaryUrl":166,"fullTextUrl":20,"authors":167,"publicationType":121,"publisherRelationship":220,"citationCount":20,"citationInfo":20,"publishDate":245,"publishYear":149,"citationAnalyzeStatus":19,"lastCitationAnalyze":20,"indexDatabases":20,"openAccess":20,"references":20,"isForceReanalyzing":150},"18f6abe5-9c10-468a-8aa3-e0f53741e718","2024-01-03T16:31:41.071+00:00","2024-12-17T22:21:37.667+00:00",[],"A-pragmatic-ensemble-learning-approach-for-rainfall-prediction",{"references":158,"abstract":160,"title":162,"doi":164},{"VOID":159},"Yilmaz AG. The effects of climate change on historical and future extreme rainfall in Antalya Turkey. Hydrol Sci J. 2015;60(12):2148–62.\nLoo YY, Billa L, Singh A. Effect of climate change on seasonal monsoon in Asia and its impact on the variability of monsoon rainfall in Southeast Asia. Geosci Front. 2015;6(6):817–23.\nMeynecke JO, Lee SY, Duke NC, Warnken J. Effect of rainfall as a component of climate change on estuarine fish production in Queensland, Australia. Estuar Coast Shelf Sci. 2006;69(3–4):491–504.\nKotz M, Levermann A, Wenz L. The effect of rainfall changes on economic production. Nature. 2022;601(7892):223–7.\nTheis L, Oord AVD, Bethge M. 2015. A note on the evaluation of generative models. arXiv preprint arXiv:1511.01844. 2015\nPazos N, Favara M, Sánchez A, Scott D, Behrman J. Long-term effects of rainfall shocks on foundational cognitive skills: evidence from Peru. SSRN Electron J. 2023. https:\u002F\u002Fdoi.org\u002F10.2139\u002Fssrn.4360823.\nPariyar SK, Keenlyside N, Sorteberg A, Spengler T, Bhatt BC, Ogawa F. Factors affecting extreme rainfall events in the South Pacific. Weather Clim Extremes. 2020;29:100262.\nYue W, Wang Z, Chen H, Payne A, Liu X. Machine learning with applications in breast cancer diagnosis and prognosis. Designs. 2018;2(2):13.\nLiyew CM, Melese HA. Machine learning techniques to predict daily rainfall amount. J Big Data. 2021;8:1–11.\nManandhar S, Dev S, Lee YH, Meng YS, Winkler S. A data-driven approach for accurate rainfall prediction. IEEE Trans Geosci Remote Sens. 2019;5(11):9323–31.\nZainudin S, Jasim DS, Bakar AA. Comparative analysis of data mining techniques for Malaysian rainfall prediction. Int J AdvSciEng Inform Technol. 2016;6(6):1148–53.\nChandra S, Gourisaria MK, Gm H, Konar D, Gao X, Wang T, Xu M. Prolificacy assessment of spermatozoan via state-of-the-art deep learning frameworks. IEEE Access. 2022;10:13715–27.\nJee G, Harshvardhan GM, Gourisaria MK. Juxtaposing inference capabilities of deep neural models over posteroanterior chest radiographs facilitating COVID-19 detection. J Interdiscip Math. 2021;24(2):299–325.\nAgrawal R, Singh V, Gourisaria MK, Sharma A, Das H. Comparative analysis of CNN Architectures for maize crop disease. In: 2022 10th International conference on emerging trends in engineering and technology-signal and information processing (ICETET-SIP-22). IEEE. 2022. pp. 1–7\nKhare S, Gourisaria MK, Harshvardhan GM, Joardar S, Singh V. Real estate cost estimation through data mining techniques. IOP Conf series Mater Sci Eng. 2021;1099(1):012053.\nPirone D, Cimorelli L, Del Giudice G, Pianese D. Short-term rainfall forecasting using cumulative precipitation fields from station data: a probabilistic machine learning approach. J Hydrol. 2023;617:128949.\nBasha CZ, Bhavana N, Bhavya P, Sowmya V. Rainfall prediction using machine learning & deep learning techniques. In: 2020 international conference on electronics and sustainable communication systems (ICESC). IEEE. 2020. pp. 92–97\nFahad S, Su F, Khan SU, Naeem MR, Wei K. Implementing a novel deep learning technique for rainfall forecasting via climatic variables: an approach via hierarchical clustering analysis. Sci Total Environ. 2023;854:158760.\nRahman AU, Abbas S, Gollapalli M, Ahmed R, Aftab S, Ahmad M, Khan MA, Mosavi A. Rainfall prediction system using machine learning fusion for smart cities. Sensors. 2022;22(9):3504.\nBarrera-Animas AY, Oyedele LO, Bilal M, Akinosho TD, Delgado JMD, Akanbi LA. Rainfall prediction: a comparative analysis of modern machine learning algorithms for time-series forecasting. Mach Learn Appl. 2022;7:100204.\nManna T, Anitha A. Precipitation prediction by integrating rough set on Fuzzy approximation space with deep learning techniques. Appl Soft Comput. 2023;139:110253.\nSuparta W, Samah AA. Rainfall prediction by using ANFIS times series technique in South Tangerang Indonesia. Geod Geodyn. 2020;11(6):411–7.\nVenkatachalam K, Trojovský P, Pamucar D, Bacanin N, Simic V. DWFH: an improved data-driven deep weather forecasting hybrid model using transductive long short term memory (T-LSTM). Expert Syst Appl. 2023;213:119270.\nKashiwao T, Nakayama K, Ando S, Ikeda K, Lee M, Bahadori A. A neural network-based local rainfall prediction system using meteorological data on the Internet: a case study using data from the Japan meteorological agency. Appl Soft Comput. 2017;56:317–30.\nVan SP, Le HM, Thanh DV, Dang TD, Loc HH, Anh DT. Deep learning convolutional neural network in rainfall–runoff modelling. J Hydroinf. 2020;22(3):541–61.\nHudnurkar S, Rayavarapu N. On the performance analysis of rainfall prediction using mutual information with artificial neural network. Intl J Electr Computer Eng. 2023;13(2):2101.\nTran Anh D, Duc Dang T, Van Pham S. Improved rainfall prediction using combined pre-processing methods and feed-forward neural networks. J. 2019;2(1):65–83.\nKhan MI, Maity R. Hybrid deep learning approach for multi-step-ahead daily rainfall prediction using GCM simulations. IEEE Access. 2020;8:52774–84.\nKaur H, Kumar M, Gupta A, Sachdeva M, Mittal A, Kumar K. Bagging: an ensemble approach for recognition of handwritten place-names in gurumukhi script. ACM Trans Asian Low-Resour Lang Inf Process. 2023. https:\u002F\u002Fdoi.org\u002F10.1145\u002F3593024.\nSarah S, Gourisaria MK, Khare S, Das H. Heart disease prediction using core machine learning techniques—a comparative study in advances in data and Information sciences proceedings of ICDIS 2021. Singapore: Springer Singapore; 2022. p. 247–60.\nUkey N, Yang Z, Li B, Zhang G, Hu Y, Zhang W. Survey on exact knn queries over high-dimensional data space. Sensors. 2023;23(2):629.\nAzam Z, Islam MM, Huda MN. Comparative analysis of intrusion detection systems and machine learning based model analysis through decision tree. IEEE Access. 2023. https:\u002F\u002Fdoi.org\u002F10.1109\u002FACCESS.2023.3296444.\nJain N, Jana PK. LRF: a logically randomized forest algorithm for classification and regression problems. Expert Syst Appl. 2023;213:119225.\nSingh V, Gourisaria MK, Das H. Performance analysis of machine learning algorithms for prediction of liver disease. In: 2021 IEEE 4th international conference on computing, power and communication technologies (GUCON). IEEE. 2021. pp. 1–7\nJhaveri S, Khedkar I, Kantharia Y, Jaswal S. Success prediction using random forest, catboost, xgboost and adaboost for Kickstarter campaigns. In: 2019 3rd International conference on computing methodologies and communication (ICCMC). IEEE. 2019. pp. 1170–1173\nHancock J, Khoshgoftaar TM. Medicare fraud detection using catboost. In: 2020 IEEE 21st international conference on information reuse and Integration for data science (IRI). IEEE. 2020. pp. 97–103\nNeo TKC, Ventura D. A direct boosting algorithm for the k-nearest neighbor classifier via local warping of the distance metric. Pattern Recogn Lett. 2012;33(1):92–102.",{"EN":161},"Heavy rainfall and precipitation play a massive role in shaping the socio-agricultural landscape of a country. Being one of the key indicators of climate change, natural disasters, and of the general topology of a region, rainfall prediction is a gift of estimation that can be used for multiple beneficial causes. Machine learning has an impressive repertoire in aiding prediction and estimation of rainfall. This paper aims to find the effect of ensemble learning, a subset of machine learning, on a rainfall prediction dataset, to increase the predictability of the models used. The classification models used in this paper were tested once individually, and then with applied ensemble techniques like bagging and boosting, on a rainfall dataset based in Australia. The objective of this paper is to demonstrate a reduction in bias and variance via ensemble learning techniques while also analyzing the increase or decrease in the aforementioned metrics. The study shows an overall reduction in bias by an average of 6% using boosting, and an average reduction in variance by 13.6%. Model performance was observed to become more generalized by lowering the false negative rate by an average of more than 20%. The techniques explored in this paper can be further utilized to improve model performance even further via hyper-parameter tuning.",{"EN":163},"A pragmatic ensemble learning approach for rainfall prediction",{"VOID":165},"10.1007\u002Fs43926-023-00044-3","https:\u002F\u002Flink.springer.com\u002Farticle\u002F10.1007\u002Fs43926-023-00044-3",[168,183,196,208],{"id":169,"sortIndex":21,"researcher":20,"roles":170,"affiliations":171,"properties":180},"7c2da8bd-327d-4d31-ab6c-c35327306624",[79],[172],{"id":20,"sortIndex":21,"affiliation":173,"properties":20},{"id":174,"createTime":175,"updateTime":175,"relativeEntities":176,"slug":20,"properties":177,"entityType":91,"verifyStatus":19,"verifyTime":20,"verifyNote":20,"syncStatus":19,"languages":20,"translateLanguages":20,"viewCount":21},"484acc86-bd4a-4a20-987b-059676c2f9ca","2023-12-05T23:28:18.791+00:00",[],{"title":178},{"VI":179},"School of Computer Engineering, KIIT Deemed to be University, Bhubaneswar, India",{"title":181},{"VI":182},"Soumili Ghosh",{"id":184,"sortIndex":185,"researcher":20,"roles":186,"affiliations":187,"properties":193},"3cb50d0c-451b-4332-883f-e6f6b0124e56",3,[79],[188],{"id":20,"sortIndex":21,"affiliation":189,"properties":20},{"id":174,"createTime":175,"updateTime":175,"relativeEntities":190,"slug":20,"properties":191,"entityType":91,"verifyStatus":19,"verifyTime":20,"verifyNote":20,"syncStatus":19,"languages":20,"translateLanguages":20,"viewCount":21},[],{"title":192},{"VI":179},{"title":194},{"VI":195},"Himansu Das",{"id":197,"sortIndex":110,"researcher":20,"roles":198,"affiliations":199,"properties":205},"993cd6d8-4a60-49de-9f4b-9cc9aae65636",[79],[200],{"id":20,"sortIndex":21,"affiliation":201,"properties":20},{"id":174,"createTime":175,"updateTime":175,"relativeEntities":202,"slug":20,"properties":203,"entityType":91,"verifyStatus":19,"verifyTime":20,"verifyNote":20,"syncStatus":19,"languages":20,"translateLanguages":20,"viewCount":21},[],{"title":204},{"VI":179},{"title":206},{"VI":207},"Mahendra Kumar Gourisaria",{"id":209,"sortIndex":97,"researcher":20,"roles":210,"affiliations":211,"properties":217},"dab6d357-a359-4237-a853-1355919fc9ad",[79],[212],{"id":20,"sortIndex":21,"affiliation":213,"properties":20},{"id":174,"createTime":175,"updateTime":175,"relativeEntities":214,"slug":20,"properties":215,"entityType":91,"verifyStatus":19,"verifyTime":20,"verifyNote":20,"syncStatus":19,"languages":20,"translateLanguages":20,"viewCount":21},[],{"title":216},{"VI":179},{"title":218},{"VI":219},"Biswajit Sahoo",{"url":166,"publisher":221,"properties":241},{"id":6,"createTime":7,"updateTime":8,"relativeEntities":222,"slug":10,"properties":223,"entityType":18,"verifyStatus":19,"verifyTime":20,"verifyNote":20,"syncStatus":19,"languages":20,"translateLanguages":20,"viewCount":21,"subjectFields":227,"manageAffiliations":228,"indexDatabases":229,"url":20,"thumbnailPath":20,"statistic":236,"gsStatistic":20,"type":51,"analyzePriority":20},[],{"issn":224,"title":225,"url":226},{"VOID":13},{"EN":15},{"VOID":17},[],[],[230],{"id":26,"indexDatabase":231,"url":39,"indexYears":40,"academicFieldIds":20,"indexDatabaseRanking":41},{"id":28,"createTime":29,"updateTime":30,"relativeEntities":232,"label":233,"description":234,"key":36,"publicationTags":235,"standard":20},[],{"EN":33,"VI":33},{"EN":33,"VI":35},[38],{"impactFactor":21,"impactFactorByYear":237,"i10Index":21,"i10IndexLast5Year":21,"totalPublication":44,"totalPublicationByYear":238,"totalCitation":21,"totalCitationByYear":239,"totalCitationPerPublication":21,"totalCitationPerPublicationByYear":240,"hindexLast5Year":21,"hindex":21},{},{"2021":46,"2022":47,"2023":48},{},{},{"volume":242,"pages":243},{"VOID":145},{"VOID":244},"1-15","2023-10-09",{"id":247,"createTime":248,"updateTime":249,"relativeEntities":250,"slug":251,"properties":252,"entityType":71,"verifyStatus":72,"verifyTime":249,"verifyNote":73,"syncStatus":19,"languages":262,"translateLanguages":20,"viewCount":21,"primaryUrl":264,"fullTextUrl":20,"authors":265,"publicationType":121,"publisherRelationship":339,"citationCount":360,"citationInfo":361,"publishDate":365,"publishYear":366,"citationAnalyzeStatus":367,"lastCitationAnalyze":368,"indexDatabases":20,"openAccess":20,"references":369,"isForceReanalyzing":150},"fe5df635-617a-4051-9fcc-c464fc53bcca","2024-04-13T10:28:54.266+00:00","2025-01-21T22:13:05.481+00:00",[],"Swarm-based-counter-UAV-defense-system",{"keywords":253,"openalex":254,"abstract":256,"title":258,"doi":260},{},{"VOID":255},"W4235146370",{"EN":257},"\u003Cjats:title>Abstract\u003C\u002Fjats:title>\u003Cjats:p>Unmanned Aerial Vehicles (UAVs) have quickly become one of the promising Internet-of-Things (IoT) devices for smart cities. Thanks to their mobility, agility, and onboard sensors’ customizability, UAVs have already demonstrated immense potential for numerous commercial applications. The UAVs expansion will come at the price of a dense, high-speed and dynamic traffic prone to UAVs going rogue or deployed with malicious intent. Counter UAV systems (C-UAS) are thus required to ensure their operations are safe. Existing C-UAS, which for the majority come from the military domain, lack scalability or induce collateral damages. This paper proposes a C-UAS able to intercept and escort intruders. It relies on an autonomous defense UAV swarm, capable of self-organizing their defense formation and to intercept the malicious UAV. This fully localized and GPS-free approach follows a modular design regarding the defense phases and it uses a newly developed balanced clustering to realize the intercept- and capture-formation. The resulting networked defense UAV swarm is resilient to communication losses. Finally, a prototype UAV simulator has been implemented. Through extensive simulations, we demonstrate the feasibility and performance of our approach.\u003C\u002Fjats:p>",{"EN":259},"Swarm-based counter UAV defense system",{"VOID":261},"10.1007\u002Fs43926-021-00002-x",[263],"EN","http:\u002F\u002Flink.springer.com\u002F10.1007\u002Fs43926-021-00002-x",[266,287,307,323],{"id":267,"sortIndex":21,"researcher":20,"roles":268,"affiliations":269,"properties":280},"017c4886-b126-4a0b-b2be-10edf31b60e7",[],[270],{"id":20,"sortIndex":21,"affiliation":271,"properties":20},{"id":272,"createTime":273,"updateTime":274,"relativeEntities":275,"slug":276,"properties":277,"entityType":91,"verifyStatus":19,"verifyTime":20,"verifyNote":20,"syncStatus":19,"languages":20,"translateLanguages":20,"viewCount":21},"b5621ead-b613-4223-98cd-9ed9c2ac7d7b","2024-01-09T16:08:38.621+00:00","2024-09-12T14:39:34.333+00:00",[],"SnT-University-of-Luxembourg-Esch-sur-Alzette-Luxembourg",{"title":278},{"VI":279},"SnT, University of Luxembourg, Esch-sur-Alzette, Luxembourg",{"openalex":281,"orcid":283,"title":285},{"VOID":282},"A5072267754",{"VOID":284},"https:\u002F\u002Forcid.org\u002F0000-0001-8155-0626",{"EN":286},"Matthias R. Brust",{"id":288,"sortIndex":110,"researcher":20,"roles":289,"affiliations":290,"properties":300},"912440d6-01f0-4c53-8b20-6c11c3f94ac3",[],[291],{"id":20,"sortIndex":21,"affiliation":292,"properties":20},{"id":293,"createTime":294,"updateTime":294,"relativeEntities":295,"slug":296,"properties":297,"entityType":91,"verifyStatus":19,"verifyTime":20,"verifyNote":20,"syncStatus":19,"languages":20,"translateLanguages":20,"viewCount":21},"a54fc80f-d9b7-4007-8e13-35fbc25cd399","2024-04-13T10:28:54.289+00:00",[],"FSTM-DCS-and-SnT-University-of-Luxembourg-Esch-sur-Alzette-Luxembourg",{"title":298},{"EN":299},"FSTM-DCS and SnT, University of Luxembourg, Esch-sur-Alzette, Luxembourg",{"openalex":301,"orcid":303,"title":305},{"VOID":302},"A5034295129",{"VOID":304},"https:\u002F\u002Forcid.org\u002F0000-0001-9419-4210",{"EN":306},"Grégoire Danoy",{"id":308,"sortIndex":185,"researcher":20,"roles":309,"affiliations":310,"properties":316},"6af16cf6-b5e6-428f-bd6f-c3ea5b16588a",[],[311],{"id":20,"sortIndex":21,"affiliation":312,"properties":20},{"id":293,"createTime":294,"updateTime":294,"relativeEntities":313,"slug":296,"properties":314,"entityType":91,"verifyStatus":19,"verifyTime":20,"verifyNote":20,"syncStatus":19,"languages":20,"translateLanguages":20,"viewCount":21},[],{"title":315},{"EN":299},{"openalex":317,"orcid":319,"title":321},{"VOID":318},"A5058311932",{"VOID":320},"https:\u002F\u002Forcid.org\u002F0000-0001-9338-2834",{"EN":322},"Pascal Bouvry",{"id":324,"sortIndex":97,"researcher":20,"roles":325,"affiliations":326,"properties":332},"7f8ce0c9-f782-43d9-bf54-e3a0c9106fa0",[],[327],{"id":20,"sortIndex":21,"affiliation":328,"properties":20},{"id":272,"createTime":273,"updateTime":274,"relativeEntities":329,"slug":276,"properties":330,"entityType":91,"verifyStatus":19,"verifyTime":20,"verifyNote":20,"syncStatus":19,"languages":20,"translateLanguages":20,"viewCount":21},[],{"title":331},{"VI":279},{"openalex":333,"orcid":335,"title":337},{"VOID":334},"A5084319765",{"VOID":336},"https:\u002F\u002Forcid.org\u002F0000-0002-1138-8130",{"EN":338},"Daniel H. Stolfi",{"url":20,"publisher":340,"properties":20},{"id":6,"createTime":7,"updateTime":8,"relativeEntities":341,"slug":10,"properties":342,"entityType":18,"verifyStatus":19,"verifyTime":20,"verifyNote":20,"syncStatus":19,"languages":20,"translateLanguages":20,"viewCount":21,"subjectFields":346,"manageAffiliations":347,"indexDatabases":348,"url":20,"thumbnailPath":20,"statistic":355,"gsStatistic":20,"type":51,"analyzePriority":20},[],{"issn":343,"title":344,"url":345},{"VOID":13},{"EN":15},{"VOID":17},[],[],[349],{"id":26,"indexDatabase":350,"url":39,"indexYears":40,"academicFieldIds":20,"indexDatabaseRanking":41},{"id":28,"createTime":29,"updateTime":30,"relativeEntities":351,"label":352,"description":353,"key":36,"publicationTags":354,"standard":20},[],{"EN":33,"VI":33},{"EN":33,"VI":35},[38],{"impactFactor":21,"impactFactorByYear":356,"i10Index":21,"i10IndexLast5Year":21,"totalPublication":44,"totalPublicationByYear":357,"totalCitation":21,"totalCitationByYear":358,"totalCitationPerPublication":21,"totalCitationPerPublicationByYear":359,"hindexLast5Year":21,"hindex":21},{},{"2021":46,"2022":47,"2023":48},{},{},24,{"total":360,"publishYear":20,"statisticByYear":362},{"2021":97,"2022":363,"2023":364,"2024":97},13,7,"2021-12-01",2021,"ERROR_IN_ANALYZE_CITATION","2024-04-13T19:51:34.388+00:00",[370,374,378,382,386,390,394,397,401,405,409,413,417,421,425,429,433,437,441,444,447,451,454,458,462,465,468,471,474,478,482,486,490,494,498,501,505,509],{"id":20,"text":371,"url":20,"identifiers":372},"Labib NS, Danoy G, Musial J, Brust MR, Bouvry P. A multilayer low-altitude airspace model for uav traffic management. DIVANet ’19. ACM; 2019.",{"doi":373},"10.1145\u002F3345838.3355998",{"id":20,"text":375,"url":20,"identifiers":376},"Sharma A, Singh PK, Kumar Y. An integrated fire detection system using iot and image processing technique for smart cities. Sustain Cities Soc. 2020;61:102332.",{"doi":377},"10.1016\u002Fj.scs.2020.102332",{"id":20,"text":379,"url":20,"identifiers":380},"Hernández-Vega J, Varela ER, Romero NH, Hernández-Santos C. Internet of things (iot) for monitoring air pollutants with an unmanned aerial vehicle (uav) in a smart city. Smart technology. Berlin: Springer International Publishing; 2018. p. 108–120.",{"doi":381},"10.1007\u002F978-3-319-73323-4_11",{"id":20,"text":383,"url":20,"identifiers":384},"Aweiss A, Homola J, Rios J, Jung J, Johnson M, Mercer J, Modi H, Torres E, Ishihara A. Flight demonstration of unmanned aircraft system (UAS) traffic management (UTM) at technical capability level 3. In: IEEE\u002FAIAA 38th Digital Avionics Systems Conference (DASC); 2019.",{"doi":385},"10.1109\u002FDASC43569.2019.9081718",{"id":20,"text":387,"url":20,"identifiers":388},"Lappas V, Zoumponos G, Kostopoulos V, Shin H, Tsourdos A, Tan-tarini M, Shmoko D, Munoz J, Amoratis N, Maragkakis A, Machairas T, Trifas A. EuroDRONE, a european UTM testbed for U-Space. In: International conference on unmanned aircraft systems (ICUAS); 2020.",{"doi":389},"10.1109\u002FICUAS48674.2020.9214020",{"id":20,"text":391,"url":20,"identifiers":392},"Prlin K, Alam MM, Le Moullec Y. Jamming of uav remote control systems using software defined radio. In: International conference on military communications and information systems (ICMCIS); 2018.",{"doi":393},"10.1109\u002FICMCIS.2018.8398711",{"id":20,"text":395,"url":20,"identifiers":396},"Droptec. The Dropster Net gun. https:\u002F\u002Fwww.droptec.ch\u002Fproduct. Accessed 14 Nov 2020.",{},{"id":20,"text":398,"url":20,"identifiers":399},"Li A, Wu Q, Zhang R. UAV-enabled cooperative jamming for improving secrecy of ground wiretap channel. IEEE Wireless Commun Lett. 2019;8(1):181–4.",{"doi":400},"10.1109\u002FLWC.2018.2865774",{"id":20,"text":402,"url":20,"identifiers":403},"Brust MR, Frey H, Rothkugel S. Dynamic multi-hop clustering for mobile hybrid wireless networks. In: Proceedings of the international conference on ubiquitous information management and communication. New York: ACM; 2008.",{"doi":404},"10.1145\u002F1352793.1352820",{"id":20,"text":406,"url":20,"identifiers":407},"Brust MR, Frey H, Rothkugel S. Adaptive multi-hop clustering in mobile networks. In: Proceedings of the 4th international conference on mobile technology, applications, and systems, Mobility ’07; 2007. p. 132–8.",{"doi":408},"10.1145\u002F1378063.1378086",{"id":20,"text":410,"url":20,"identifiers":411},"Birch Gabriel C, Griffin John C, Erdman Matthew K. UAS detection, classification, and neutralization: Market survey 2015. Sandia National Laboratories, USA: Technical report; 2016.",{"doi":412},"10.2172\u002F1222445",{"id":20,"text":414,"url":20,"identifiers":415},"Birch GC, Woo BL. Counter unmanned aerial systems testing: evaluation of vis swir mwir and lwir passive imagers. Technical report, Sandia National Laboratory, USA; 2017.",{"doi":416},"10.2172\u002F1342469",{"id":20,"text":418,"url":20,"identifiers":419},"Altawy R, Youssef AM. Security, privacy, and safety aspects of civilian drones: a survey. ACM Trans Cyber-Phys Syst. 2016;1(2):1–25.",{"doi":420},"10.1145\u002F3001836",{"id":20,"text":422,"url":20,"identifiers":423},"Guvenc I, Koohifar F, Singh S, Sichitiu ML, Matolak D. Detection, tracking, and interdiction for amateur drones. IEEE Commun Mag. 2018;56(4):75–81.",{"doi":424},"10.1109\u002FMCOM.2018.1700455",{"id":20,"text":426,"url":20,"identifiers":427},"Ding G, Wu Q, Zhang L, Lin Y, Tsiftsis TA, Yao Y. An amateur drone surveillance system based on the cognitive internet of things. IEEE Commun Mag. 2018;56(1):29–35.",{"doi":428},"10.1109\u002FMCOM.2017.1700452",{"id":20,"text":430,"url":20,"identifiers":431},"Shi X, Yang C, Xie W, Liang C, Shi Z, Chen J. Anti-drone system with multiple surveillance technologies: architecture, implementation, and challenges. IEEE Commun Mag. 2018;56(4):68–74.",{"doi":432},"10.1109\u002FMCOM.2018.1700430",{"id":20,"text":434,"url":20,"identifiers":435},"Kang H, Joung J, Kim J, Kang J, Cho YS. Protect your sky: a survey of counter unmanned aerial vehicle systems. IEEE Access. 2020;8:168671–71010.",{"doi":436},"10.1109\u002FACCESS.2020.3023473",{"id":20,"text":438,"url":20,"identifiers":439},"Noh J, Kwon Y, Son Y, Shin H, Kim D, Choi J, Kim Y. Tractor beam: safe-hijacking of consumer drones with adaptive gps spoofing. ACM Trans Priv Secur. 2019;22:1–26.",{"doi":440},"10.1145\u002F3309735",{"id":20,"text":442,"url":20,"identifiers":443},"Pljonkin AP. Vulnerability of the synchronization process in the quantum key distribution system. Int J Cloud Appl Comput IJCAC. 2019;9(1):50–8.",{},{"id":20,"text":445,"url":20,"identifiers":446},"Zohuri B. High-power microwave energy as weapon. Cham: Springer International Publishing; 2019. p. 269–308.",{},{"id":20,"text":448,"url":20,"identifiers":449},"Extance A. Military technology: laser weapons get real. Nature. 2015;521:408–10.",{"doi":450},"10.1038\u002F521408a",{"id":20,"text":452,"url":20,"identifiers":453},"Delft Dynamics. Drone catcher. https:\u002F\u002Fdronecatcher.nl\u002F. Accessed 14 Nov 2020.",{},{"id":20,"text":455,"url":20,"identifiers":456},"Rothe J, Strohmeier M, Montenegro S. A concept for catching drones with a net carried by cooperative UAVs. In: 2019 IEEE International Symposium on Safety, Security, and Rescue Robotics (SSRR); 2019.",{"doi":457},"10.1109\u002FSSRR.2019.8848973",{"id":20,"text":459,"url":20,"identifiers":460},"The New Indian Express. Now, eagles to take down illegal drones in Telangana. https:\u002F\u002Fwww.newindianexpress.com\u002Fstates\u002Ftelangana\u002F2020\u002Faug\u002F01\u002Fnow-eagles-to-take-down-illegal-drones-in-telangana-2177572.html. Accessed 14 Nov 2020.",{"doi":461},"10.26611\u002F10211411",{"id":20,"text":463,"url":20,"identifiers":464},"CNBC. A swarm of armed drones attacked a Russian military base in Syria. https:\u002F\u002Fwww.cnbc.com\u002F2018\u002F01\u002F11\u002Fswarm-of-armed-diy-drones-attacks-russian-military-base-in-syria.html. Accessed 14 Nov 2020.",{},{"id":20,"text":466,"url":20,"identifiers":467},"Scharre P. Counter-swarm: a guide to defeating robotic swarms—war on the rocks; 2017.",{},{"id":20,"text":469,"url":20,"identifiers":470},"Padgett NE. Defensive swarm: an agent-based modeling analysis. Master’s thesis, Naval Postgraduate School, USA; 2017.",{},{"id":20,"text":472,"url":20,"identifiers":473},"Farid AM, Egerton S, Barca JC, Kamal MAS. Adaptive multi-objective search in a swarm vs swarm context. In: IEEE international conference on systems, man, and cybernetics (SMC); 2018.",{},{"id":20,"text":475,"url":20,"identifiers":476},"Pozniak M, Ranganathan P. Counter UAS solutions through UAV swarm environments. In: 2019 IEEE international conference on electro information technology (EIT). 2019. p. 351–356.",{"doi":477},"10.1109\u002FEIT.2019.8834140",{"id":20,"text":479,"url":20,"identifiers":480},"Brust MR, Akbas MI, Turgut D. Vbca: a virtual forces clustering algorithm for autonomous aerial drone systems. In: IEEE SysCon; 2016.",{"doi":481},"10.1109\u002FSYSCON.2016.7490517",{"id":20,"text":483,"url":20,"identifiers":484},"Gillespie RJ. Fifty years of the vsepr model. Coord Chem Rev. 2008;252(12–14):1315–27.",{"doi":485},"10.1016\u002Fj.ccr.2007.07.007",{"id":20,"text":487,"url":20,"identifiers":488},"Al-Turjman FM, Hassanein HS, Ibnkahla MA. Connectivity optimization with realistic lifetime constraints for node placement in environmental monitoring. In: IEEE LCN; 2009.",{"doi":489},"10.1109\u002FLCN.2009.5355140",{"id":20,"text":491,"url":20,"identifiers":492},"Barnes L, Garcia R, Fields M, Valavanis K. Swarm formation control utilizing ground and aerial unmanned systems. In: IEEE\u002FRSJ international conference on intelligent robots and systems; 2008.",{"doi":493},"10.1109\u002FIROS.2008.4651260",{"id":20,"text":495,"url":20,"identifiers":496},"Kim H, Ahn H. Realization of swarm formation flying and optimal trajectory generation for multi-drone performance show. In: Symposium on system integration (SII): in IEEE\u002FSICE international; 2016.",{"doi":497},"10.1109\u002FSII.2016.7844106",{"id":20,"text":499,"url":20,"identifiers":500},"Maxa JA, Mahmoud MSB, Larrieu N. Survey on UAANET routing protocols and network security challenges. Ad Hoc Sens Wireless Netw; 2017.",{},{"id":20,"text":502,"url":20,"identifiers":503},"Brust MR, Danoy G, Bouvry P, Gashi D, Pathak H, Gonçalves M. Defending against intrusion of malicious uavs with networked uav defense swarms. In: 2017 IEEE 42nd conference on local computer networks workshops (LCN Workshops). New York: IEEE; 2017. p. 103–11.",{"doi":504},"10.1109\u002FLCN.Workshops.2017.71",{"id":20,"text":506,"url":20,"identifiers":507},"Brust MR, Turgut D, Ribeiro CHC, Kaiser M. Is the clustering coefficient a measure for fault tolerance in wireless sensor networks? In: IEEE internationa; conference on communications (ICC); 2012. p. 183–7.",{"doi":508},"10.1109\u002FICC.2012.6364474",{"id":20,"text":510,"url":20,"identifiers":511},"Brust MR, Ribeiro CHC, Turgut D, Rothkugel S. Lswtc: a local small-world topology control algorithm for backbone-assisted mobile ad hoc networks. In: IEEE local computer network conference (LCN); 2010.",{"doi":512},"10.1109\u002FLCN.2010.5735688",{"id":514,"createTime":515,"updateTime":516,"relativeEntities":517,"slug":518,"properties":519,"entityType":71,"verifyStatus":72,"verifyTime":516,"verifyNote":73,"syncStatus":19,"languages":20,"translateLanguages":20,"viewCount":21,"primaryUrl":530,"fullTextUrl":20,"authors":531,"publicationType":121,"publisherRelationship":660,"citationCount":20,"citationInfo":20,"publishDate":681,"publishYear":149,"citationAnalyzeStatus":19,"lastCitationAnalyze":20,"indexDatabases":20,"openAccess":20,"references":20,"isForceReanalyzing":150},"f27a2a06-8a50-4d36-b120-e15c8f7b382f","2024-04-06T09:27:30.505+00:00","2025-02-19T22:12:19.795+00:00",[],"An-efficient-hybrid-approach-for-optimization-using-simulated-annealing-and-grasshopper-algorithm-for-IoT-applications",{"references":520,"keywords":522,"abstract":524,"title":526,"doi":528},{"VOID":521},"Gharehchopogh FS, Abdollahzadeh B. An efficient harris hawk optimization algorithm for solving the travelling salesman problem. Clust Comput. 2022;25(3):1981–2005.\nOsaba E, Villar-Rodriguez E, Del Ser J, Nebro AJ, Molina D, LaTorre A, Herrera F. A tutorial on the design, experimentation and application of metaheuristic algorithms to real-world optimization problems. Swarm Evolut Comput. 2021;64:100888.\nHuang W, Zhang Y, Li L. Survey on multi-objective evolutionary algorithms. J phys Conf Series. 2019;1288(1):012057.\nGu ZM, Wang GG. Improving NSGA-III algorithms with information feedback models for large-scale many-objective optimization. Futur Gener Comput Syst. 2020;107:49–69.\nHe Z, Yen GG, Yi Z. Robust multiobjective optimization via evolutionary algorithms. IEEE Trans Evol Comput. 2018;23(2):316–30.\nDemir K, Nguyen BH, Xue B, Zhang M. A decomposition based multi-objective evolutionary algorithm with relieff based local search and solution repair mechanism for feature selection. In 2020 IEEE congress on evolutionary computation (CEC). IEEE. 2020;1–8.\nMorales-Castañeda B, Zaldivar D, Cuevas E, Fausto F, Rodríguez A. A better balance in metaheuristic algorithms: does it exist? Swarm Evol Comput. 2020;54:100671.\nSalih SQ, Alsewari AA. A new algorithm for normal and large-scale optimization problems: nomadic people optimizer. Neural Comput Appl. 2020;32(14):10359–86.\nHussain K, Salleh MNM, Cheng S, Shi Y. On the exploration and exploitation in popular swarm-based metaheuristic algorithms. Neural Comput Appl. 2019;31(11):7665–83.\nSalgotra R, Singh U, Saha S. New cuckoo search algorithms with enhanced exploration and exploitation properties. Expert Syst Appl. 2018;95:384–420.\nPrimeau N, Falcon R, Abielmona R, Petriu EM. A review of computational intelligence techniques in wireless sensor and actuator networks. IEEE Commun Surveys Tutorials. 2018;20(4):2822–54.\nHoussein EH, Mahdy MA, Shebl D, Mohamed WM. A survey of metaheuristic algorithms for solving optimization problems in metaheuristics. in machine learning: theory and applications. Cham: Springer; 2021.\nRauf HT, Bangyal WHK, Lali MI. An adaptive hybrid differential evolution algorithm for continuous optimization and classification problems. Neural Comput Appl. 2021;33(17):10841–67.\nOliveira PM, Solteiro Pires EJ, Boaventura-Cunha J, Pinho TM. Review of nature and biologically inspired metaheuristics for greenhouse environment control. Trans Inst Meas Control. 2020;42(12):2338–58.\nGodzik M, Dajda J, Kisiel-Dorohinicki M, Byrski A, Rutkowski L, Orzechowski P, Moore JH. Applying autonomous hybrid agent-based computing to difficult optimization problems. J Comput Sci. 2022. https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.jocs.2022.101858.\nTalbi EG. Machine learning into metaheuristics: a survey and taxonomy. ACM Comput Surveys (CSUR). 2021;54(6):1–32.\nMoayedi H, Le Van B. The applicability of biogeography-based optimization and earthworm optimization algorithm hybridized with ANFIS as Reliable Solutions in Estimation of Cooling Load in Buildings. Energies. 2022;15(19):7323.\nAbbasi A, Firouzi B, Sendur P, Heidari AA, Chen H, Tiwari R. Multi-strategy Gaussian Harris hawks optimization for fatigue life of tapered roller bearings. Eng Comput. 2022;38:4387–413.\nAldosari F, Abualigah L, Almotairi KH. A Normal distributed dwarf mongoose optimization algorithm for global optimization and data clustering applications. Symmetry. 2022;14(5):1021.\nGharehchopogh FS, Gholizadeh H. A comprehensive survey: whale optimization algorithm and its applications. Swarm Evol Comput. 2019;48:1–24.\nXiong H, Qiu B, Liu J. An improved multi-swarm particle swarm optimizer for optimizing the electric field distribution of multichannel transcranial magnetic stimulation. Artif Intell Med. 2020;104:101790.\nXin J, Li S, Sheng J, Zhang Y, Cui Y. Application of improved particle swarm optimization for navigation of unmanned surface vehicles. Sensors. 2019;19(14):3096.\nDereli S, Köker R. Strengthening the PSO algorithm with a new technique inspired by the golf game and solving the complex engineering problem. Complex Intell Syst. 2021;7(3):1515–26.\nMarzoughi A, Savkin AV. Autonomous navigation of a team of unmanned surface vehicles for intercepting intruders on a region boundary. Sensors. 2021;21(1):297.\nKarim AA, Isa NAM, Lim WH. Modified particle swarm optimization with effective guides. IEEE Access. 2020;8:188699–725.\nJi Y, Liew AWC, Yang L. A novel improved particle swarm optimization with long-short term memory hybrid model for stock indices forecast. IEEE Access. 2021;9:23660–71.\nZhang J, Sheng J, Lu J, Shen L. UCPSO: a uniform initialized particle swarm optimization algorithm with cosine inertia weight. Comput Intell Neurosci. 2021. https:\u002F\u002Fdoi.org\u002F10.1155\u002F2021\u002F8819333.\nLee JH, Delbruck T, Pfeiffer M. Training deep spiking neural networks using backpropagation. Front Neurosci. 2016;10:508.\nMirjalili SZ, Mirjalili S, Saremi S, Faris H, Aljarah I. Grasshopper optimization algorithm for multi-objective optimization problems. Appl Intell. 2018;48(4):805–20.\nDel Ser J, Osaba E, Molina D, Yang XS, Salcedo-Sanz S, Camacho D, Herrera F. Bio-inspired computation: Where we stand and what’s next. Swarm Evolut Comput. 2019;48:220–50.\nOkuyama T, Hayashi M, Yamaoka M. An Ising computer based on simulated quantum annealing by path integral Monte Carlo method. In 2017 IEEE international conference on rebooting computing (ICRC). IEEE. 201;1–6.\nVökler S, Baier D. Investigating machine learning techniques for solving product-line optimization problems. Archives Data Sci. 2020;6:1–11.\nPilatowsky-Cameo S, Villaseñor D, Bastarrachea-Magnani MA, Lerma-Hernández S, Santos LF, Hirsch JG. Ubiquitous quantum scarring does not prevent ergodicity. Nat Commun. 2021;12(1):1–8.\nAsghari K, Masdari M, Gharehchopogh FS, Saneifard R. A chaotic and hybrid gray wolf-whale algorithm for solving continuous optimization problems. Progress Artificial Intell. 2021;10(3):349–74.\nSayed GI, Khoriba G, Haggag MH. A novel chaotic salp swarm algorithm for global optimization and feature selection. Appl Intell. 2018;48(10):3462–81.\nSchaffer JD. \"Multiple Objective Optimization with Vector Evaluated Genetic Algorithms,\" in Proceedings of the 1st International Conference on Genetic Algorithms, New Jersey, USA.\nSrinivas N, Deb K. Multiobjective optimization using Nondominated sorting in genetic algorithms. Evol Comput. 1994;2(3):221–48.\nCao YJ, Wu QH. teaching genetic algorithm using matlab. Int J Elect Enging Educ. 1999;36:139–53.\nFonseca CM, Fleming PJ. Multiobjective genetic algorithms. In IEEE colloquium on genetic algorithms for control systems engineering. 1993;6–1.\nFonseca CM, Fleming PJ. Genetic algorithms for multiobjective optimization: formulation. Discuss General. 1993;93:416–23.\nYang XS, He X. Bat algorithm: literature review and applications. Int J Bio-Inspired Comput. 2013;5(3):141–9.\nDeb K, Agrawal S, Pratap A, Meyarivan T. (2000). A fast elitist non-dominated sorting genetic algorithm for multi-objective optimization: NSGA-II. In Parallel Problem Solving from Nature PPSN VI: 6th International Conference Paris, France, September 18–20, 2000 Proceedings , 849–858.\nCoello CC, Lechuga MS. MOPSO: a proposal for multiple objective particle swarm optimization. Proce Congress Evolut Comput. 2002;2:1051–6.\nPulido GT, Coello CA. Using Clustering Techniques to Improve the Performance of a Multi-objective Particle Swarm Optimizer. In: Deb K, editor. Genetic and Evolutionary Computation–GECCO 2004: Genetic and Evolutionary Computation Conference. Seattle: Springer; 2004.\nLeong WF, Yen GG. Dynamic population size in PSO-based multiobjective optimization. Vancouver: In IEEE Congress on Evolutionary Computation; 2006. p. 1718–25.\nSakib N, Kabir MWU, Subbir M, Alam S. A comparative study of flower pollination algorithm and bat algorithm on continuous optimization problems. Int J Soft Comput Eng. 2014;4:13–9.\nYang XS, Karamanoglu M, He X. Flower pollination algorithm: a novel approach for multiobjective optimization. Eng Optim. 2014;46(9):1222–37.\nParamasivan P, Santhi RK. Non-dominated sorting flower pollination algorithm for dynamic economic emission dispatch. Int J Comput Appl. 2015;130(9):19–26.\nBensouyad M, Saidouni DE. A discrete flower pollination algorithm for graph coloring problem. In IEEE 2nd international conference on cybernetics (CYBCONF). 2015.\nKabir MN, Ali J, Alsewari AA, Zamli KZ. An adaptive flower pollination algorithm for software test suite minimization. In 2017 3rd international conference on electrical information and communication technology (EICT). 2017;1–5.\nKabir MWU, Sakib N, Chowdhury SMR, Alam MS. A novel adaptive bat algorithm to control explorations and exploitations for continuous optimization problems,\". Int J Comput Appl. 2014. https:\u002F\u002Fdoi.org\u002F10.5120\u002F16402-6079.\nWang G-G, Gandomi AH, Yang X-S, Alavi AH. A new hybrid method based on krill herd and cuckoo search for global optimisation tasks,\". Int Bio-Inspired Comput. 2016;8(5):286–99.\nMirjalili S, Jangir P, Saremi S. Multi-objective ant lion optimizer: a multi-objective optimization algorithm for solving engineering problems. Appl Intell. 2017;46(1):79–95.\nDorigo M, Birattari M, Stutzle T. Ant colony optimization. IEEE Comput Intell Mag. 2007;1(4):28–39.\nKaraboga D, Basturk B. A powerful and efficient algorithm for numerical function optimization: artificial bee colony (ABC) algorithm. J Global Optim. 2007;39(3):459–71.\nStorn R, Price K. Differential evolution–a simple and efficient heuristic for global optimization over continuous spaces. J Global Optim. 1997;11(4):341–59.\nNicoară E. (2007). Performance measures for multi-objective optimization algorithms Buletinul Universităţii Petrol–Gaze din Ploieşti. Seria Matematică-Informatică-Fizică. 59(1):19–28.\nRahman CM, Rashid TA. A new evolutionary algorithm: learner performance based behavior algorithm. Egypt Inf J. 2021;22(2):213–23.\nRahman CM, Rashid TA. Dragonfly algorithm and its applications in applied science survey. Comput Intell Neurosci. 2019;2019:9293617.\nAhmed AM, Rashid TA, Saeed SM. Cat swarm optimization algorithm: a survey and performance evaluation. Comput Intell Neurosci. 2020;2020:20.\nHassan BA, Rashid TA. Operational framework for recent advances in backtracking search optimisation algorithm: a systematic review and performance evaluation. Appl Math Comput. 2019;370:124919.\nShamsaldin AS, Rashid TA, Al-Rashid RA, Al-Salihi NK, Mohammadi M. Donkey and smuggler optimization algorithm: a collaborative working approach to path finding. J Comput Design Eng. 2019;6:562–83.\nAbdullah JM, Rashid T. Fitness dependent optimizer: inspired by the bee swarming reproductive process. IEEE Access. 2019;7:43473–86.\nMuhammed DA, Saeed SAM, Rashid TA. Improved fitness-dependent optimizer algorithm. IEEE Access. 2020;8:19074–88.\nSharifai AG, Zainol ZB. Multiple filter-based rankers to guide hybrid grasshopper optimization algorithm and simulated annealing for feature selection with high dimensional multi-class imbalanced datasets. IEEE Access. 2021;9:74127–42. https:\u002F\u002Fdoi.org\u002F10.1109\u002FACCESS.2021.3081366.\nRashno A, Shafipour M, Fadaei S. Particle ranking: an efficient method for multi-objective particle swarm optimization feature selection. Knowl-Based Syst. 2022;245:108640.\nShafipour M, Rashno A, Fadaei S. Particle distance rank feature selection by particle swarm optimization. Expert Syst Appl. 2021;185:115620.\nGharehchopogh FS, Ucan A, Ibrikci T, Arasteh B, Isik G. Slime mould algorithm: a comprehensive survey of its variants and applications. Archives Comput Methods Eng. 2023. https:\u002F\u002Fdoi.org\u002F10.1007\u002Fs11831-023-09883-3.\nZaman HRR, Gharehchopogh FS. An improved particle swarm optimization with backtracking search optimization algorithm for solving continuous optimization problems. Eng Comput. 2022;38(Suppl 4):2797–831.\nShishavan ST, Gharehchopogh FS. An improved cuckoo search optimization algorithm with genetic algorithm for community detection in complex networks. Multimedia Tools Appl. 2022;81(18):25205–31.\nGharehchopogh FS. Quantum-inspired metaheuristic algorithms: comprehensive survey and classification. Artif Intell Rev. 2023;56(6):5479–543.\nMohammadzadeh H, Gharehchopogh FS. Feature selection with binary symbiotic organisms search algorithm for email spam detection. Int J Inf Technol Decis Mak. 2021;20(01):469–515.",{"EN":523},"",{"EN":525},"The multi-objective grasshopper optimization algorithm (MOGOA) is a relatively new algorithm inspired by the collective behavior of grasshoppers, which aims to solve multi-objective optimization problems in IoT applications. In order to enhance its performance and improve global convergence speed, the algorithm integrates simulated annealing (SA). Simulated annealing is a metaheuristic algorithm that is commonly used to improve the search capability of optimization algorithms. In the case of MOGOA, simulated annealing is integrated by employing symmetric perturbation to control the movement of grasshoppers. This helps in effectively balancing exploration and exploitation, leading to better convergence and improved performance. The paper proposes two hybrid algorithms based on MOGOA, which utilize simulated annealing for solving multi-objective optimization problems. One of these hybrid algorithms combines chaotic maps with simulated annealing and MOGOA. The purpose of incorporating simulated annealing and chaotic maps is to address the issue of slow convergence and enhance exploitation by searching high-quality regions identified by MOGOA. Experimental evaluations were conducted on thirteen different benchmark functions to assess the performance of the proposed algorithms. The results demonstrated that the introduction of simulated annealing significantly improved the convergence of MOGOA. Specifically, the IDG (Inverse Distance Generational distance) values for benchmark functions ZDT1, ZDT2, and ZDT3 were smaller than the IDG values obtained by using MOGOA alone, indicating better performance in terms of convergence. Overall, the proposed algorithms exhibit promise in solving multi-objective optimization problems.",{"EN":527},"An efficient hybrid approach for optimization using simulated annealing and grasshopper algorithm for IoT applications",{"VOID":529},"10.1007\u002Fs43926-023-00036-3","https:\u002F\u002Flink.springer.com\u002Farticle\u002F10.1007\u002Fs43926-023-00036-3",[532,548,561,586,598,611,623,635,647],{"id":533,"sortIndex":47,"researcher":20,"roles":534,"affiliations":535,"properties":545},"fe78a8c6-2a3a-4866-b1b9-1419e08ac355",[79],[536],{"id":20,"sortIndex":21,"affiliation":537,"properties":20},{"id":538,"createTime":539,"updateTime":539,"relativeEntities":540,"slug":541,"properties":542,"entityType":91,"verifyStatus":19,"verifyTime":20,"verifyNote":20,"syncStatus":19,"languages":20,"translateLanguages":20,"viewCount":21},"5ba805ec-e381-48e2-b2f6-ca4be146175c","2024-04-06T09:27:30.551+00:00",[],"National-University-of-Technology-Islamabad-Pakistan",{"title":543},{"VI":544},"National University of Technology, Islamabad, Pakistan",{"title":546},{"VI":547},"Ali Arshad",{"id":549,"sortIndex":550,"researcher":20,"roles":551,"affiliations":552,"properties":558},"9cb55bba-89ae-4a19-bd90-79234ccefd58",6,[79],[553],{"id":20,"sortIndex":21,"affiliation":554,"properties":20},{"id":538,"createTime":539,"updateTime":539,"relativeEntities":555,"slug":541,"properties":556,"entityType":91,"verifyStatus":19,"verifyTime":20,"verifyNote":20,"syncStatus":19,"languages":20,"translateLanguages":20,"viewCount":21},[],{"title":557},{"VI":544},{"title":559},{"VI":560},"Saman Riaz",{"id":562,"sortIndex":364,"researcher":20,"roles":563,"affiliations":564,"properties":583},"ffc9406d-6236-4709-b3eb-d163c6933b94",[79],[565,574],{"id":20,"sortIndex":21,"affiliation":566,"properties":20},{"id":567,"createTime":568,"updateTime":568,"relativeEntities":569,"slug":570,"properties":571,"entityType":91,"verifyStatus":19,"verifyTime":20,"verifyNote":20,"syncStatus":19,"languages":20,"translateLanguages":20,"viewCount":21},"f769281f-8fcc-453b-9a2a-d220ca152742","2024-04-06T09:27:30.576+00:00",[],"COPELABS-Lus%C3%B3fona-University-Campo-Grande-376-Lisbon-Portugal",{"title":572},{"VI":573},"COPELABS, Lusófona University, Campo Grande 376, Lisbon, Portugal",{"id":20,"sortIndex":21,"affiliation":575,"properties":20},{"id":576,"createTime":577,"updateTime":577,"relativeEntities":578,"slug":579,"properties":580,"entityType":91,"verifyStatus":19,"verifyTime":20,"verifyNote":20,"syncStatus":19,"languages":20,"translateLanguages":20,"viewCount":21},"3481b9f6-f2a9-44e9-9850-e85fb39c6afc","2024-04-06T09:27:30.567+00:00",[],"Department-of-Computer-Science-and-Information-Systems-College-of-Applied-Sciences-AlMaarefa-University-Ad-Diriyah-Riyadh-Saudi-Arabia",{"title":581},{"VI":582},"Department of Computer Science and Information Systems, College of Applied Sciences, AlMaarefa University, Ad Diriyah, Riyadh, Saudi Arabia",{"title":584},{"VI":585},"Ashit Kumar Dutta",{"id":587,"sortIndex":110,"researcher":20,"roles":588,"affiliations":589,"properties":595},"f4a0b34a-e334-4903-9ea8-ee8a85ea1d53",[79],[590],{"id":20,"sortIndex":21,"affiliation":591,"properties":20},{"id":538,"createTime":539,"updateTime":539,"relativeEntities":592,"slug":541,"properties":593,"entityType":91,"verifyStatus":19,"verifyTime":20,"verifyNote":20,"syncStatus":19,"languages":20,"translateLanguages":20,"viewCount":21},[],{"title":594},{"VI":544},{"title":596},{"VI":597},"Muhammad Rashid",{"id":599,"sortIndex":600,"researcher":20,"roles":601,"affiliations":602,"properties":608},"a1942722-b964-4947-8d6e-9e067d7a0a22",8,[79],[603],{"id":20,"sortIndex":21,"affiliation":604,"properties":20},{"id":567,"createTime":568,"updateTime":568,"relativeEntities":605,"slug":570,"properties":606,"entityType":91,"verifyStatus":19,"verifyTime":20,"verifyNote":20,"syncStatus":19,"languages":20,"translateLanguages":20,"viewCount":21},[],{"title":607},{"VI":573},{"title":609},{"VI":610},"Joel J. P. C. Rodrigues",{"id":612,"sortIndex":97,"researcher":20,"roles":613,"affiliations":614,"properties":620},"e24f4bee-34ca-4688-8d99-f04f398768de",[79],[615],{"id":20,"sortIndex":21,"affiliation":616,"properties":20},{"id":538,"createTime":539,"updateTime":539,"relativeEntities":617,"slug":541,"properties":618,"entityType":91,"verifyStatus":19,"verifyTime":20,"verifyNote":20,"syncStatus":19,"languages":20,"translateLanguages":20,"viewCount":21},[],{"title":619},{"VI":544},{"title":621},{"VI":622},"Afia Zafar",{"id":624,"sortIndex":185,"researcher":20,"roles":625,"affiliations":626,"properties":632},"740f55a4-6cac-4393-8cc2-3115c318d6ff",[79],[627],{"id":20,"sortIndex":21,"affiliation":628,"properties":20},{"id":538,"createTime":539,"updateTime":539,"relativeEntities":629,"slug":541,"properties":630,"entityType":91,"verifyStatus":19,"verifyTime":20,"verifyNote":20,"syncStatus":19,"languages":20,"translateLanguages":20,"viewCount":21},[],{"title":631},{"VI":544},{"title":633},{"VI":634},"Kainat Zafar",{"id":636,"sortIndex":21,"researcher":20,"roles":637,"affiliations":638,"properties":644},"1a3eef3f-106e-49b5-a0a0-d36b9a102058",[79],[639],{"id":20,"sortIndex":21,"affiliation":640,"properties":20},{"id":538,"createTime":539,"updateTime":539,"relativeEntities":641,"slug":541,"properties":642,"entityType":91,"verifyStatus":19,"verifyTime":20,"verifyNote":20,"syncStatus":19,"languages":20,"translateLanguages":20,"viewCount":21},[],{"title":643},{"VI":544},{"title":645},{"VI":646},"Faria Sajjad",{"id":648,"sortIndex":649,"researcher":20,"roles":650,"affiliations":651,"properties":657},"53076e0a-2eb5-4cc1-969f-b68411ad23ae",4,[79],[652],{"id":20,"sortIndex":21,"affiliation":653,"properties":20},{"id":538,"createTime":539,"updateTime":539,"relativeEntities":654,"slug":541,"properties":655,"entityType":91,"verifyStatus":19,"verifyTime":20,"verifyNote":20,"syncStatus":19,"languages":20,"translateLanguages":20,"viewCount":21},[],{"title":656},{"VI":544},{"title":658},{"VI":659},"Benish Fida",{"url":20,"publisher":661,"properties":20},{"id":6,"createTime":7,"updateTime":8,"relativeEntities":662,"slug":10,"properties":663,"entityType":18,"verifyStatus":19,"verifyTime":20,"verifyNote":20,"syncStatus":19,"languages":20,"translateLanguages":20,"viewCount":21,"subjectFields":667,"manageAffiliations":668,"indexDatabases":669,"url":20,"thumbnailPath":20,"statistic":676,"gsStatistic":20,"type":51,"analyzePriority":20},[],{"issn":664,"title":665,"url":666},{"VOID":13},{"EN":15},{"VOID":17},[],[],[670],{"id":26,"indexDatabase":671,"url":39,"indexYears":40,"academicFieldIds":20,"indexDatabaseRanking":41},{"id":28,"createTime":29,"updateTime":30,"relativeEntities":672,"label":673,"description":674,"key":36,"publicationTags":675,"standard":20},[],{"EN":33,"VI":33},{"EN":33,"VI":35},[38],{"impactFactor":21,"impactFactorByYear":677,"i10Index":21,"i10IndexLast5Year":21,"totalPublication":44,"totalPublicationByYear":678,"totalCitation":21,"totalCitationByYear":679,"totalCitationPerPublication":21,"totalCitationPerPublicationByYear":680,"hindexLast5Year":21,"hindex":21},{},{"2021":46,"2022":47,"2023":48},{},{},"2023-07-17",{"id":683,"createTime":684,"updateTime":685,"relativeEntities":686,"slug":687,"properties":688,"entityType":71,"verifyStatus":72,"verifyTime":685,"verifyNote":73,"syncStatus":19,"languages":20,"translateLanguages":20,"viewCount":21,"primaryUrl":697,"fullTextUrl":20,"authors":698,"publicationType":121,"publisherRelationship":790,"citationCount":20,"citationInfo":20,"publishDate":816,"publishYear":366,"citationAnalyzeStatus":19,"lastCitationAnalyze":20,"indexDatabases":20,"openAccess":20,"references":20,"isForceReanalyzing":150},"8b5218aa-608f-42b4-8e32-40abcb0fd1bf","2023-12-06T09:42:48.089+00:00","2024-12-23T21:53:04.425+00:00",[],"Correction-to-Bayesian-Topology-Learning-and-noise-removal-from-network-data",{"references":689,"abstract":691,"title":693,"doi":695},{"VOID":690},"Ramezani Mayiami M, Hajimirsadeghi M, Skretting K, Dong X, Blum RS, Poor HV. Bayesian Topology Learning and noise removal from network data. Discov Internet Things. 2021;1:11. https:\u002F\u002Fdoi.org\u002F10.1007\u002Fs43926-021-00011-w.",{"EN":692},"A correction to this paper has been published: https:\u002F\u002Fdoi.org\u002F10.1007\u002Fs43926-021-00013-8",{"EN":694},"Correction to: Bayesian Topology Learning and noise removal from network data",{"VOID":696},"10.1007\u002Fs43926-021-00013-8","https:\u002F\u002Flink.springer.com\u002F10.1007\u002Fs43926-021-00013-8",[699,714,729,746,761,773],{"id":700,"sortIndex":110,"researcher":20,"roles":701,"affiliations":702,"properties":711},"884fc9f3-bbc7-45dc-a5cc-995c5f91ae4a",[79],[703],{"id":20,"sortIndex":21,"affiliation":704,"properties":20},{"id":705,"createTime":706,"updateTime":706,"relativeEntities":707,"slug":20,"properties":708,"entityType":91,"verifyStatus":19,"verifyTime":20,"verifyNote":20,"syncStatus":19,"languages":20,"translateLanguages":20,"viewCount":21},"afc86bcb-189b-4be0-b31b-3d17737a1629","2023-12-06T09:42:48.118+00:00",[],{"title":709},{"VI":710},"Department of ECE, Lehigh University, Bethlehem, USA",{"title":712},{"VI":713},"Mohammad Hajimirsadeghi",{"id":715,"sortIndex":97,"researcher":20,"roles":716,"affiliations":717,"properties":726},"12b5b6f7-3401-41c5-adc2-7c1830b87c14",[79],[718],{"id":20,"sortIndex":21,"affiliation":719,"properties":20},{"id":720,"createTime":721,"updateTime":721,"relativeEntities":722,"slug":20,"properties":723,"entityType":91,"verifyStatus":19,"verifyTime":20,"verifyNote":20,"syncStatus":19,"languages":20,"translateLanguages":20,"viewCount":21},"9ee41fca-1c33-4290-8c4e-3a1f2233c865","2023-12-06T09:42:48.130+00:00",[],{"title":724},{"VI":725},"Department of EECS, University of Stavanger, Stavanger, Norway",{"title":727},{"VI":728},"Karl Skretting",{"id":730,"sortIndex":47,"researcher":20,"roles":731,"affiliations":732,"properties":743},"940bb050-dc9a-4f58-a5d6-55e5f9e07c33",[79],[733],{"id":20,"sortIndex":21,"affiliation":734,"properties":20},{"id":735,"createTime":736,"updateTime":737,"relativeEntities":738,"slug":739,"properties":740,"entityType":91,"verifyStatus":19,"verifyTime":20,"verifyNote":20,"syncStatus":19,"languages":20,"translateLanguages":20,"viewCount":21},"17b25672-6082-4c68-8773-b4e6d27780bd","2024-04-18T04:16:51.834+00:00","2025-02-01T17:18:28.188+00:00",[],"Department-of-Electrical-Engineering-Princeton-University-Princeton-USA-",{"title":741},{"EN":742},"Department of Electrical Engineering Princeton University Princeton (USA)",{"title":744},{"VI":745},"H. Vincent Poor",{"id":747,"sortIndex":21,"researcher":20,"roles":748,"affiliations":749,"properties":758},"f8c151cc-3753-43f3-a808-e0e97715e130",[79],[750],{"id":20,"sortIndex":21,"affiliation":751,"properties":20},{"id":752,"createTime":753,"updateTime":753,"relativeEntities":754,"slug":20,"properties":755,"entityType":91,"verifyStatus":19,"verifyTime":20,"verifyNote":20,"syncStatus":19,"languages":20,"translateLanguages":20,"viewCount":21},"63ac12bc-6f9b-4f01-a41f-4ffed7604399","2023-12-28T04:52:23.921+00:00",[],{"title":756},{"VI":757},"Department of ICT, University of Agder, Grimstad, Norway",{"title":759},{"VI":760},"Mahmoud Ramezani-Mayiami",{"id":762,"sortIndex":649,"researcher":20,"roles":763,"affiliations":764,"properties":770},"83fbb18f-37cb-419a-935c-8b5d3625fe25",[79],[765],{"id":20,"sortIndex":21,"affiliation":766,"properties":20},{"id":705,"createTime":706,"updateTime":706,"relativeEntities":767,"slug":20,"properties":768,"entityType":91,"verifyStatus":19,"verifyTime":20,"verifyNote":20,"syncStatus":19,"languages":20,"translateLanguages":20,"viewCount":21},[],{"title":769},{"VI":710},{"title":771},{"VI":772},"Rick S. Blum",{"id":774,"sortIndex":185,"researcher":20,"roles":775,"affiliations":776,"properties":787},"a60d8752-20b4-44d4-88a0-16c0ca303893",[79],[777],{"id":20,"sortIndex":21,"affiliation":778,"properties":20},{"id":779,"createTime":780,"updateTime":781,"relativeEntities":782,"slug":783,"properties":784,"entityType":91,"verifyStatus":19,"verifyTime":20,"verifyNote":20,"syncStatus":19,"languages":20,"translateLanguages":20,"viewCount":21},"b1c36f3d-badf-4d92-8daf-c40eb576285f","2023-12-06T18:52:59.370+00:00","2024-12-26T21:22:10.480+00:00",[],"Department-of-Engineering-Science-University-of-Oxford-Oxford-UK",{"title":785},{"VI":786},"Department of Engineering Science, University of Oxford, Oxford, UK",{"title":788},{"VI":789},"Xiaowen Dong",{"url":697,"publisher":791,"properties":811},{"id":6,"createTime":7,"updateTime":8,"relativeEntities":792,"slug":10,"properties":793,"entityType":18,"verifyStatus":19,"verifyTime":20,"verifyNote":20,"syncStatus":19,"languages":20,"translateLanguages":20,"viewCount":21,"subjectFields":797,"manageAffiliations":798,"indexDatabases":799,"url":20,"thumbnailPath":20,"statistic":806,"gsStatistic":20,"type":51,"analyzePriority":20},[],{"issn":794,"title":795,"url":796},{"VOID":13},{"EN":15},{"VOID":17},[],[],[800],{"id":26,"indexDatabase":801,"url":39,"indexYears":40,"academicFieldIds":20,"indexDatabaseRanking":41},{"id":28,"createTime":29,"updateTime":30,"relativeEntities":802,"label":803,"description":804,"key":36,"publicationTags":805,"standard":20},[],{"EN":33,"VI":33},{"EN":33,"VI":35},[38],{"impactFactor":21,"impactFactorByYear":807,"i10Index":21,"i10IndexLast5Year":21,"totalPublication":44,"totalPublicationByYear":808,"totalCitation":21,"totalCitationByYear":809,"totalCitationPerPublication":21,"totalCitationPerPublicationByYear":810,"hindexLast5Year":21,"hindex":21},{},{"2021":46,"2022":47,"2023":48},{},{},{"volume":812,"pages":814},{"VOID":813},"1",{"VOID":815},"1-1","2021-05-10",{"id":818,"createTime":819,"updateTime":820,"relativeEntities":821,"slug":822,"properties":823,"entityType":71,"verifyStatus":72,"verifyTime":820,"verifyNote":73,"syncStatus":19,"languages":833,"translateLanguages":20,"viewCount":21,"primaryUrl":834,"fullTextUrl":20,"authors":835,"publicationType":121,"publisherRelationship":884,"citationCount":97,"citationInfo":905,"publishDate":20,"publishYear":20,"citationAnalyzeStatus":19,"lastCitationAnalyze":20,"indexDatabases":20,"openAccess":20,"references":907,"isForceReanalyzing":150},"e810ff2d-004f-466e-ac13-755807eb1625","2024-04-15T22:59:20.690+00:00","2025-02-11T21:10:15.945+00:00",[],"arHateDetector-detection-of-hate-speech-from-standard-and-dialectal-Arabic-Tweets",{"keywords":824,"openalex":825,"abstract":827,"title":829,"doi":831},{},{"VOID":826},"W4327945737",{"EN":828},"\u003Cjats:title>Abstract\u003C\u002Fjats:title>\u003Cjats:p>Hate speech has become a phenomenon on social media platforms, such as Twitter. These websites and apps that were initially designed to facilitate our expression of free speech, are sometimes being used to spread hate towards each other. In the Arab region, Twitter is a very popular social media platform and thus the number of tweets that contain hate speech is increasing rapidly. Many tweets are written either in standard, dialectal Arabic, or mix. Existing work on Arabic hate speech are targeted towards either standard or single dialectal text, but not both. To fight hate speech more efficiently, in this paper, we conducted extensive experiments to investigate Arabic hate speech in tweets. Therefore, we propose a framework, called arHateDetector, that detects hate speech in the Arabic text of tweets. The proposed arHateDetector supports both standard and several dialectal Arabic. A large Arabic hate speech dataset, called arHateDataset, was compiled from several Arabic standard and dialectal tweets. The tweets are preprocessed to remove the unwanted content. We investigated the use of recent machine learning and deep learning models such as AraBERT to detect hate speech. All classification models used in the investigation are trained with the compiled dataset. Our experiments shows that AraBERT outperformed the other models producing the best performance across seven different datasets including the compiled arHateDataset with an accuracy of 93%. CNN and LinearSVC produced 88% and 89% respectively.\u003C\u002Fjats:p>",{"EN":830},"arHateDetector: detection of hate speech from standard and dialectal Arabic Tweets",{"VOID":832},"10.1007\u002Fs43926-023-00030-9",[263],"https:\u002F\u002Flink.springer.com\u002F10.1007\u002Fs43926-023-00030-9",[836,854,870],{"id":837,"sortIndex":110,"researcher":20,"roles":838,"affiliations":839,"properties":849},"5355f2d9-1899-4fe3-b96f-7b982407e722",[],[840],{"id":20,"sortIndex":21,"affiliation":841,"properties":20},{"id":842,"createTime":843,"updateTime":843,"relativeEntities":844,"slug":845,"properties":846,"entityType":91,"verifyStatus":19,"verifyTime":20,"verifyNote":20,"syncStatus":19,"languages":20,"translateLanguages":20,"viewCount":21},"df9da738-bfb2-468e-864e-320a0663b5b8","2024-04-15T22:59:20.720+00:00",[],"Department-of-Computer-Science-University-of-Sharjah-Street-Sharjah-UAE",{"title":847},{"EN":848},"Department of Computer Science, University of Sharjah, Street, Sharjah, UAE",{"openalex":850,"title":852},{"VOID":851},"A5015696952",{"EN":853},"Abdelrahman Moursi",{"id":855,"sortIndex":97,"researcher":20,"roles":856,"affiliations":857,"properties":863},"d6ab452a-6342-49c2-bbb8-83bb486640b4",[],[858],{"id":20,"sortIndex":21,"affiliation":859,"properties":20},{"id":842,"createTime":843,"updateTime":843,"relativeEntities":860,"slug":845,"properties":861,"entityType":91,"verifyStatus":19,"verifyTime":20,"verifyNote":20,"syncStatus":19,"languages":20,"translateLanguages":20,"viewCount":21},[],{"title":862},{"EN":848},{"openalex":864,"orcid":866,"title":868},{"VOID":865},"A5069814126",{"VOID":867},"https:\u002F\u002Forcid.org\u002F0000-0003-2285-953X",{"EN":869},"Zaher Al Aghbari",{"id":871,"sortIndex":21,"researcher":20,"roles":872,"affiliations":873,"properties":879},"d8b65991-b936-42a7-aed7-181f061dc428",[],[874],{"id":20,"sortIndex":21,"affiliation":875,"properties":20},{"id":842,"createTime":843,"updateTime":843,"relativeEntities":876,"slug":845,"properties":877,"entityType":91,"verifyStatus":19,"verifyTime":20,"verifyNote":20,"syncStatus":19,"languages":20,"translateLanguages":20,"viewCount":21},[],{"title":878},{"EN":848},{"openalex":880,"title":882},{"VOID":881},"A5081300413",{"EN":883},"Ramzi Khezzar",{"url":20,"publisher":885,"properties":20},{"id":6,"createTime":7,"updateTime":8,"relativeEntities":886,"slug":10,"properties":887,"entityType":18,"verifyStatus":19,"verifyTime":20,"verifyNote":20,"syncStatus":19,"languages":20,"translateLanguages":20,"viewCount":21,"subjectFields":891,"manageAffiliations":892,"indexDatabases":893,"url":20,"thumbnailPath":20,"statistic":900,"gsStatistic":20,"type":51,"analyzePriority":20},[],{"issn":888,"title":889,"url":890},{"VOID":13},{"EN":15},{"VOID":17},[],[],[894],{"id":26,"indexDatabase":895,"url":39,"indexYears":40,"academicFieldIds":20,"indexDatabaseRanking":41},{"id":28,"createTime":29,"updateTime":30,"relativeEntities":896,"label":897,"description":898,"key":36,"publicationTags":899,"standard":20},[],{"EN":33,"VI":33},{"EN":33,"VI":35},[38],{"impactFactor":21,"impactFactorByYear":901,"i10Index":21,"i10IndexLast5Year":21,"totalPublication":44,"totalPublicationByYear":902,"totalCitation":21,"totalCitationByYear":903,"totalCitationPerPublication":21,"totalCitationPerPublicationByYear":904,"hindexLast5Year":21,"hindex":21},{},{"2021":46,"2022":47,"2023":48},{},{},{"total":97,"publishYear":20,"statisticByYear":906},{"2024":97},[908,912,915,918,922,925,929,933,937,940,944,948,952,955,959,963,967,971,975,978,982,986,990,993,996,999,1002,1005,1008,1011,1015,1019,1022,1026,1030,1034,1037,1040,1043,1046,1049,1052],{"id":20,"text":909,"url":20,"identifiers":910},"Saeed MM, Al Aghbari Z. Artc: feature selection using association rules for text classification. Neural Comput Appl. 2022;34(24):22519–29.",{"doi":911},"10.1007\u002Fs00521-022-07669-5",{"id":20,"text":913,"url":20,"identifiers":914},"Cambridge-Dictionary https:\u002F\u002Fdictionary.cambridge.org\u002Fus\u002Fdictionary\u002Fenglish\u002Fhate-speech.",{},{"id":20,"text":916,"url":20,"identifiers":917},"Statista-Inc: The Most Common Languages on the Internet, https:\u002F\u002Fwww.statista.com\u002Fstatistics\u002F262946\u002Fshare-of-the-most-common-languages-on-the-internet. 2019.",{},{"id":20,"text":919,"url":20,"identifiers":920},"Elzobi M, Al-Hamadi A, Al Aghbari Z, Dings L, Saeed A. Gabor wavelet recognition approach for off-line handwritten arabic using explicit segmentation. In: S. Choras, R. (ed.) Image Processing and Communications Challenges. Springer, Heidelberg 2014; pp. 245–254.",{"doi":921},"10.1007\u002F978-3-319-01622-1_29",{"id":20,"text":923,"url":20,"identifiers":924},"Dinges L, Al-Hamadi A, Elzobi M, Al Aghbari Z, Mustafa H. Offline automatic segmentation based recognition of handwritten arabic words. Int J Sign Process Image Processing Pattern Recogn. 2011;4(4):131–43.",{},{"id":20,"text":926,"url":20,"identifiers":927},"Zampieri M, Malmasi S, Nakov P, Rosenthal S, Farra N, Kumar R. Predicting the type and target of offensive posts in social media. Proceedings of Human Language Technologies: The Annual Conference of the North American Chapter of the Association for Computational Linguistics (NAACL-HLT), p. 1415-1420. 2019.",{"doi":928},"10.18653\u002Fv1\u002FN19-1144",{"id":20,"text":930,"url":20,"identifiers":931},"Davidson T, Warmsley D, Macy M, Weber I. Automated hate speech detection and the problem of offensive language. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 11; 2017. p. 512–5.",{"doi":932},"10.1609\u002Ficwsm.v11i1.14955",{"id":20,"text":934,"url":20,"identifiers":935},"Mulki H, Haddad H, Ali CB, Alshabani H. L-hsab: A levantine twitter dataset for hate speech and abusive language. In: Proceedings of the Third Workshop on Abusive Language Online. 2019. p. 111–8.",{"doi":936},"10.18653\u002Fv1\u002FW19-3512",{"id":20,"text":938,"url":20,"identifiers":939},"Mubarak H, Rashed A, Darwish K, Samih Y, Abdelali A. Arabic offensive language on Twitter: Analysis and experiments. In: Proceedings of the Sixth Arabic Natural Language Processing Workshop, pp. 126–135. Association for Computational Linguistics, Kyiv, Ukraine (Virtual). 2021.",{},{"id":20,"text":941,"url":20,"identifiers":942},"Haddad H, Mulki H, Oueslati A. T-hsab: A tunisian hate speech and abusive dataset. In: International Conference on Arabic Language Processing, Springer. 2019; p. 251–63.",{"doi":943},"10.1007\u002F978-3-030-32959-4_18",{"id":20,"text":945,"url":20,"identifiers":946},"Boulouard Z, Ouaissa M, Ouaissa M. Machine learning for hate speech detection in arabic social media. In: Computational Intelligence in Recent Communication Networks. Springer, New York. 2022. p. 147–62.",{"doi":947},"10.1007\u002F978-3-030-77185-0_10",{"id":20,"text":949,"url":20,"identifiers":950},"Albadi N, Kurdi M, Mishra S. Are they our brothers? analysis and detection of religious hate speech in the arabic twittersphere. In: 2018 IEEE\u002FACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM). 2018; p. 69–76.",{"doi":951},"10.1109\u002FASONAM.2018.8508247",{"id":20,"text":953,"url":20,"identifiers":954},"Chowdhury AG, Didolkar A, Sawhney R, Shah R. Arhnet-leveraging community interaction for detection of religious hate speech in arabic. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics: Student Research Workshop, 2019. p. 273–80.",{},{"id":20,"text":956,"url":20,"identifiers":957},"Alsafari S, Sadaoui S, Mouhoub M. Hate and offensive speech detection on arabic social media. Online Soc Netw Media. 2020;19: 100096.",{"doi":958},"10.1016\u002Fj.osnem.2020.100096",{"id":20,"text":960,"url":20,"identifiers":961},"Anezi FYA. Arabic hate speech detection using deep recurrent neural networks. Appl Sci. 2022;12(12):6010.",{"doi":962},"10.3390\u002Fapp12126010",{"id":20,"text":964,"url":20,"identifiers":965},"Aldjanabi W, Dahou A, Al-qaness MA, Elaziz MA, Helmi AM, Damaševičius R. Arabic offensive and hate speech detection using a cross-corpora multi-task learning model. Informatics. 2021;8:69.",{"doi":966},"10.3390\u002Finformatics8040069",{"id":20,"text":968,"url":20,"identifiers":969},"Husain F, Uzuner O. Investigating the effect of preprocessing arabic text on offensive language and hate speech detection. Trans Asian Low-Resource Language Inform Process. 2022;21(4):1–20.",{"doi":970},"10.1145\u002F3501398",{"id":20,"text":972,"url":20,"identifiers":973},"Alsafari S, Sadaoui S. Semi-supervised self-learning for arabic hate speech detection. In: 2021 IEEE International Conference on Systems, Man, and Cybernetics (SMC), 2021. p. 863–8.",{"doi":974},"10.1109\u002FSMC52423.2021.9659134",{"id":20,"text":976,"url":20,"identifiers":977},"Mostafa A, Mohamed O, Ashraf A. Gof at arabic hate speech 2022: breaking the loss function convention for data-imbalanced arabic offensive text detection. In: Proceedinsg of the 5th Workshop on Open-Source Arabic Corpora and Processing Tools with Shared Tasks on Qur’an QA and Fine-Grained Hate Speech Detection, 2022. p. 167–75.",{},{"id":20,"text":979,"url":20,"identifiers":980},"Mursi KT, Alahmadi MD, Alsubaei FS, Alghamdi AS. Detecting islamic radicalism arabic tweets using natural language processing. IEEE Access. 2022;10:72526–34.",{"doi":981},"10.1109\u002FACCESS.2022.3188688",{"id":20,"text":983,"url":20,"identifiers":984},"Omar A, Mahmoud TM, Abd-El-Hafeez T, Mahfouz A. Multi-label arabic text classification in online social networks. Inform Syst. 2021;100: 101785.",{"doi":985},"10.1016\u002Fj.is.2021.101785",{"id":20,"text":987,"url":20,"identifiers":988},"AbdelHamid M, Jafar A, Rahal Y. Levantine hate speech detection in twitter. Soc Netw Anal Mining. 2022;12(1):1–13.",{"doi":989},"10.1007\u002Fs13278-021-00834-z",{"id":20,"text":991,"url":20,"identifiers":992},"Bennessir MA, Rhouma M, Haddad H, Fourati C. icompass at arabic hate speech 2022: Detect hate speech using qrnn and transformers. In: Proceedinsg of the 5th Workshop on Open-Source Arabic Corpora and Processing Tools with Shared Tasks on Qur’an QA and Fine-Grained Hate Speech Detection, pp. 176–180; 2022.",{},{"id":20,"text":994,"url":20,"identifiers":995},"Dataset: Arabic Levantine Hate Speech. https:\u002F\u002Fdictionary.cambridge.org\u002Fus\u002Fdictionary\u002Fenglish\u002Fhate-speech.",{},{"id":20,"text":997,"url":20,"identifiers":998},"Dataset: Hate Speech Detection in Arabic Twittersphere. https:\u002F\u002Fgithub.com\u002Fraghadsh\u002FArabic-Hate-speech",{},{"id":20,"text":1000,"url":20,"identifiers":1001},"Dataset: Religious Hate Speech Detection for Arabic Tweets. https:\u002F\u002Fgithub.com\u002Fnuhaalbadi\u002FArabic_hatespeech",{},{"id":20,"text":1003,"url":20,"identifiers":1004},"Dataset: Hate and Offensive Speech Detection on Arabic Social Media. https:\u002F\u002Fgithub.com\u002Fsbalsefri\u002FArabicHateSpeechDataset.",{},{"id":20,"text":1006,"url":20,"identifiers":1007},"Dataset: AraCOVID19-MFH: Arabic COVID-19 Multi-label Fake News Hate Speech Detection. https:\u002F\u002Fgithub.com\u002FMohamedHadjAmeur\u002FAraCOVID19MFH.",{},{"id":20,"text":1009,"url":20,"identifiers":1010},"Dataset: Multi-lingual Hate Speech. https:\u002F\u002Fwww.kaggle.com\u002Fdatasets\u002Fwajidhassanmoosa\u002Fmultilingual-hatespeech-dataset?resource=download.",{},{"id":20,"text":1012,"url":20,"identifiers":1013},"Ousidhoum N, Lin Z, Zhang H, Song Y, Yeung D-Y. Multilingual and multi-aspect hate speech analysis. arXiv preprint arXiv:1908.11049. 2019.",{"doi":1014},"10.18653\u002Fv1\u002FD19-1474",{"id":20,"text":1016,"url":20,"identifiers":1017},"Alshalan R, Al-Khalifa H. A deep learning approach for automatic hate speech detection in the saudi twittersphere. Appl Sci. 2020;10(23):8614.",{"doi":1018},"10.3390\u002Fapp10238614",{"id":20,"text":1020,"url":20,"identifiers":1021},"Stop-Words: List of Arabic Stop Words on Github. https:\u002F\u002Fgithub.com\u002Fnuhaalbadi\u002FArabic_hatespeech\u002Fblob\u002Fmaster\u002Fstop_words.csv.",{},{"id":20,"text":1023,"url":20,"identifiers":1024},"El Mahdaouy A, El Alaoui SO, Gaussier E. Word-embedding-based pseudo-relevance feedback for arabic information retrieval. J inform Sci. 2019;45(4):429–42.",{"doi":1025},"10.1177\u002F0165551518792210",{"id":20,"text":1027,"url":20,"identifiers":1028},"Kim Y. Convolutional neural networks for sentence classification. CoRR arXiv:abs\u002F1408.5882. 2014.",{"doi":1029},"10.3115\u002Fv1\u002FD14-1181",{"id":20,"text":1031,"url":20,"identifiers":1032},"Alkouz B, Al Aghbari Z, Al-Garadi MA, Sarker A. Deepluenza: Deep learning for influenza detection from twitter. Expert Syst Appl. 2022;198: 116845.",{"doi":1033},"10.1016\u002Fj.eswa.2022.116845",{"id":20,"text":1035,"url":20,"identifiers":1036},"Antoun W, Baly F, Hajj H. Arabert: Transformer-based model for arabic language understanding. arXiv preprint arXiv:2003.00104. 2020.",{},{"id":20,"text":1038,"url":20,"identifiers":1039},"NumPy: The Fundamental Package for Scientific Computing with Python. https:\u002F\u002Fnumpy.org\u002F",{},{"id":20,"text":1041,"url":20,"identifiers":1042},"NLTK: Natural Language Toolkit. https:\u002F\u002Fwww.nltk.org\u002F.",{},{"id":20,"text":1044,"url":20,"identifiers":1045},"scikit-learn: Tools for Predictive Data Analysis. https:\u002F\u002Fscikit-learn.org\u002Fstable\u002F.",{},{"id":20,"text":1047,"url":20,"identifiers":1048},"TensorFlow: Open Source Platform for Machine Learning. https:\u002F\u002Fwww.tensorflow.org\u002Foverview.",{},{"id":20,"text":1050,"url":20,"identifiers":1051},"Keras: Deep Learning API Written in Python. https:\u002F\u002Fkeras.io\u002Fapi\u002F.",{},{"id":20,"text":1053,"url":20,"identifiers":1054},"AraBERT: Arabic Pretrained Language Model Based on Google’s BERT. https:\u002F\u002Fgithub.com\u002Faub-mind\u002Farabert#AraBERT.",{},{"id":1056,"createTime":1057,"updateTime":1058,"relativeEntities":1059,"slug":1060,"properties":1061,"entityType":71,"verifyStatus":72,"verifyTime":1074,"verifyNote":73,"syncStatus":19,"languages":20,"translateLanguages":1075,"viewCount":21,"primaryUrl":1077,"fullTextUrl":20,"authors":1078,"publicationType":121,"publisherRelationship":1133,"citationCount":20,"citationInfo":20,"publishDate":1158,"publishYear":149,"citationAnalyzeStatus":19,"lastCitationAnalyze":20,"indexDatabases":20,"openAccess":20,"references":20,"isForceReanalyzing":150},"ad20fbef-25be-4ddc-a8c4-205826859e05","2024-01-19T10:17:16.961+00:00","2025-02-26T20:16:00.900+00:00",[],"Anomaly-based-intrusion-detection-system-for-IoT-application",{"references":1062,"abstract":1064,"title":1067,"doi":1070,"keywords":1072},{"VOID":1063},"Kyriazis D, Varvarigou T, White D, Rossi A, Cooper J. “Sustainable smart city IoT applications: Heat and electricity management & Eco-conscious cruise control for public transportation,”. IEEE 14th International Symposium on “A World of Wireless, Mobile and Multimedia Networks” (WoWMoM). Madrid, Spain. 2013;2013:1–5. https:\u002F\u002Fdoi.org\u002F10.1109\u002FWoWMoM.2013.6583500.\nShanzhi Chen, et al. A vision of IoT: Applications, challenges, and opportunities with china perspective’’. IEEE Int Things J. 2014;1(4):349–59.\nMalche Timothy, Maheshwary Priti. “Internet of Things (IoT) for building smart home system.” 2017 International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud)(I-SMAC). IEEE, 2017.\nFotios Zantalis, et al. A review of machine learning and IoT in smart transportation’’. Future Int. 2019;11(4):94.\nMezghani Emna, Exposito Ernesto, Drira Khalil. A model-driven methodology for the design of autonomic and cognitive IoT-based systems: Application to healthcare. IEEE Trans Emerg Topics Comput Intel. 2017;1(3):224–34.\nZhao Ji-chun et al. “The study and application of the IOT technology in agriculture.” 2010 3rd international conference on computer science and information technology. Vol. 2. IEEE, 2010.\nOu Qinghai et al. “Application of internet of things in smart grid power transmission.” 2012 third FTRA international conference on mobile, ubiquitous, and intelligent computing. IEEE, 2012.\nBichi BY, Islam SU, Kademi AM, Ahmad I. An energy-aware application module for the fog-based internet of military things. Discov Internet Things. 2022;2(1):4.\nKhanna A, Kaur S. Internet of Things (IoT), Applications and Challenges: A Comprehensive Review. Wireless Pers Commun. 2020;114:1687–762. https:\u002F\u002Fdoi.org\u002F10.1007\u002Fs11277-020-07446-4.\nSaba T, Saba T, et al. Real-time anomalies detection in the crowd using convolutional extended short-term memory network. J Inform Sci. 2021. https:\u002F\u002Fdoi.org\u002F10.1177\u002F01655515211022665.\nKhanna Abhishek, Kaur Sanmeet. Internet of things (IoT), applications and challenges: a comprehensive review. Wirel Pers Commun. 2020;114:1687–762.\nKraijak Surapon, Tuwanut Panwit. “A survey on IoT architectures, protocols, applications, security, privacy, real-world implementation and future trends.” 11th international conference on wireless communications, networking and mobile computing (WiCOM 2015). IET, 2015.\nSchneider S. The industrial internet of things (iiot) applications and taxonomy. Internet Things Data Anal Handb. 2017;41–81.\nKhan WZ, et al. Industrial internet of things Recent advances enabling technologies and open challenges. Comput Electr Eng. 2020;81: 106522.\nLi Shan, Iqbal Muddesar, Saxena Neetesh. “Future industry internet of things with zero-trust security.” Information Systems Frontiers 2022;1–14.\nAhmad I. Discover Internet of Things editorial, inaugural issue: Welcome from Editor-in-Chief. Discov Internet Things. 2021;1:1–4.\nAlkahtani H, Aldhyani THH, Al-Yaari M. Adaptive anomaly detection framework model objects in cyberspace. Appl Bionics Biomech. 2020;6660489:14.\nTang M, Alazab M, Luo Y. Big data for cybersecurity: vulnerability disclosure trends and dependencies. Inst Electr Electron Eng Trans Big Data. 2019;5(3):317–29.\nZerihun BM, Olwal TO, Hassen MR. Design and Analysis of IoT-Based Modern Agriculture Monitoring System for Real-Time Data Collection. In: Computer Vision and Machine Learning in Agriculture, vol. 2. Singapore: Springer Singapore; 2022. p. 73–82.\nAL-Sarawi Shadi, Anbar Mohammed, Abdullah Rosni, AI Hawari Ahmed B. Internet of things market analysis forecasts,2020-2030. Worlds4, 2020 Fourth World Conference on Smart Trends in Systems, Security and Sustainability, IEEE,2020, 978-1-7281-6823-4\u002F20.\nTanzila Saba, Amjad Rehman, Tariq Sadad, Hoshang Kolivand, Ali Bahaj Saeed. Anomaly-based intrusion detection system for IoT networks through deep learning model. Comput Electr Eng. 2022;99: 107810.\nMisbahuddin S, Zubairi JA, Saggaf A, Basuni J, A-Wadany S, Al-Sofi A. “IoT based dynamic road traffic management for smart cities,” 2015 12th International Conference on High-capacity Optical Networks and Enabling\u002FEmerging Technologies (HONET), Islamabad, 2015:1- 5, DOIurlhttps:\u002F\u002Fdoi.org\u002F10.1109\u002FHONET.2015.7395434.\nAhmed Bahaa, et al. Monitoring real time security attacks for IoT systems using DevSecOps: a systematic literature review’’. Information. 2021;12(4):154.\nMarzano Artur et al. “The evolution of bashlite and mirai iot botnets.” 2018 IEEE Symposium on Computers and Communications (ISCC). IEEE, 2018.\nTomer Vikas, Sharma Sachin. Detecting iot attacks using an ensemble machine learning model. Future Int. 2022;14(4):102.\nJacobs S. “Researchers Hack Into Michigan’s Traffic Lights.” MIT technology review. https:\u002F\u002Fwww.technologyreview.com\u002F2014\u002F08\u002F19\u002F171586\u002Fresearchershack-into-michigans-traffic-lights\u002F. Accessed 29 Sept 2020.\nKhan MA, Salah K. IoT security: Review, blockchain solutions, and open challenges. Future Gen Comput Syst. 2018;82:395–411. https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.future.2017.11.022.\nZahra A, Shah MA. “IoT-based ransomware growth rate evaluation and detection using command and control blacklisting,” 2017 23rd International Conference on Automation and Computing (ICAC), Huddersfield, 2017;1-6, DOIurlhttps:\u002F\u002Fdoi.org\u002F10.23919\u002FIConAC.2017.8082013\nGurulakshmi K, Nesarani A. “Analysis of IoT Bots Against DDOS Attack Using Machine Learning Algorithm,” 2018 2nd International Conference on Trends in Electronics and Informatics (ICOEI), 1 Sept. 2017, https:\u002F\u002Fdoi.org\u002F10.1109\u002Ficoei.2018.8553722\nDong Bo, Wang Xue. “Comparison deep learning method to traditional methods using for network intrusion detection.” 2016 8th IEEE international conference on communication software and networks (ICCSN). IEEE, 2016.\nKruegel C, Valeur F, Vigna G. Intrusion detection and correlation: challenges and solutions, vol. 14. Springer Science & Business Media; 2004.\nBenkhelifa Elhadj, Welsh Thomas, Hamouda Walaa. A critical review of practices and challenges in intrusion detection systems for IoT: Toward universal and resilient systems. IEEE Commun Surv Tutor. 2018;20(4):3496–509.\nKocher Geeta, Kumar Gulshan. Machine learning and deep learning methods for intrusion detection systems: recent developments and challenges. Soft Computing. 2021;25(15):9731–63.\nMargolis J, Oh TT, Jadhav S, Kim YH, Kim JN. “An In-Depth Analysis of the Mirai Botnet,” 2017 International Conference on Software Security and Assurance (ICSSA), Altoona, PA, 2017, pp. 6-12, https:\u002F\u002Fdoi.org\u002F10.1109\u002FICSSA.2017.12\nAnthi E, Williams L, Slowinska M, Theodorakopoulos G, Burnap p. A supervised intrusion detection system for smart home IoT devices. IEEE Int Things J. 2019;6(5):9042–53.\nArshad J, Azad MA, Abdeltaif MM, Salah K. An intrusion detection Framework for energy-constrained IoT devices. Mech Syst Signal process. 2020;136: 106436.\nLiao H-J, Lin C-HR, Lin Y-C, Tung K-Y. Intrusion Detection System: A Comprehensive Review. J Netw Comput Appl. 2013;36(1):16–24. https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.jnca.2012.09.004.\nShakhov V, Jan SU, Ahmed S, Koo I. “On Lightweight Method for Intrusions Detection in the Internet of Things,” 2019 IEEE International Black Sea Conference on Communications and Networking (BlackSeaCom), Sochi, Russia, 2019;1-5.\nMell Peter. Understanding intrusion detection systems, IS Management Handbook, 2003;409–418, Auerbach Publications\nJyothsna VVRPV, Rama Prasad, Munivara Prasad K. A review of anomaly based intrusion detection systems’’. Int J Comput Appl. 2011;28(7):26–35.\nAnhtuan Le, et al. A specification-based IDS for detecting attacks on RPL-based network topology’’. Information. 2016;7(2):25.\nMehmood Yasi, et al. “Intrusion detection system in cloud computing: challenges and opportunities.” 2013 2nd national conference on information assurance (NCIA). IEEE, 2013.\nMorin Benjamin, Me Ludovic, Debar Herve, Ducasse Mireille. M2D2: A formal data model for IDS alert correlation, International Workshop on Recent Advances in Intrusion Detection, 115–137,2002\nPacheco Jesus, Hariri Salim. “IoT security framework for smart cyber infrastructures.” 2016 IEEE 1st International Workshops on Foundations and Applications of Self Systems (FAS W). IEEE, 2016.\nFadlullah ZM, et al. State-of-the-art deep learning Evolving machine intelligence toward tomorrow intelligent network traffic control systems. IEEE Commun Surv Tutor. 2017;19(4):2432–55.\nWang X, Davidson NJ. “The Upper and Lower Bounds of the Prediction Accuracies of Ensemble Methods for Binary Classification,” 2010 Ninth International Conference on Machine Learning and Applications, Washington, DC, 2010:373-378.\nPeddabachigari S, Abraham A, Grosan C, Thomas J. Modeling intrusion detection system using hybrid intelligent systems. J Netw Comput Appl. 2007;30(1):114–32. https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.jnca.2005.06.003.\nGang Kou, et al. Multiple criteria mathematical programming for multi-class classification and application in network intrusion detection. Inform Sci. 2009;179(4):371–81.\nMulay Snehal A, Devale PR, Garje GV. Intrusion detection system using support vector machine and decision tree. Int J Comput Appl. 2010;3(3):40–3.\nUma M, Ganapathi P. A Survey on Various Cyber Attacks and their Classification. Int J Netw Secur. 2013;15(5):390–6.\nJhaveri Rutvij H, Patel Sankita J, Jinwala Devesh C. “DoS attacks in mobile ad hoc networks: A survey.” 2012 second international conference on advanced computing & communication technologies. IEEE, 2012.\nGoyal Priyanka, Batra Sahil, Singh Ajit. A literature review of security attack in mobile ad-hoc networks. Int J Comput Appl. 2010;9(12):11–5.\nPradip JM, et al. A survey of mobile ad hoc network attacks. Int J Eng Sci Technol. 2010;2(9):4063–71.\nDasGupta Bhaskar, Mobasheri Nasim, Yero Ismael G. On analyzing and evaluating privacy measures for social networks under active attack. Inform Sci. 2019;473:87–100.\nKeerthika M, Shanmugapriya D. Wireless sensor networks: Active and passive attacks-vulnerabilities and countermeasures. Global Trans Proc. 2021;2(2):362–7.\nKeerthika M, Shanmugapriya D. Wireless sensor networks: Active and passive attacks-vulnerabilities and countermeasures. Global Trans Proc. 2021;2(2):362–7.\nRaich Anagha, Gadicha Vijay. “Overview of passive attacks in cloud environment.” AIP Conference Proceedings. Vol. 2424. No. 1. AIP Publishing LLC, 2022.\nBhavsar M, Roy K, Liu Z, Kelly J, Gokaraju B. Intrusion-Based Attack Detection Using Machine Learning Techniques for Connected Autonomous Vehicle. In Fujita,H., Fournier-Viger, P., Ali, M., Wang, Y. (eds) Advances and Trends in Artificial Intelligence. Theory and Practices in Artificial Intelligence. IEA\u002FAIE 2022. Lecture Notes in Computer Science(), 2022;13343. Springer, Cham. https:\u002F\u002Fdoi.org\u002F10.1007\u002F978-3-031-08530-7_43.\nAI-Hamar Y, Kolivand H, Tajdini M, Saba T, Ramachandran V. Enterprise Credential Spear-phishing attack detection. Comput Electr Eng. 2021;94:107363.\nTabassum A, Lebda W. Security framework for iot devices against cyber-attacks. arXiv preprint arXiv:1912.01712. 2019.\nVemuri VR. Enhancing computer security with smart technology. Auerbach Publications; 2005.\nXiaocong Qian, Jidong Zhang. “Study on the structure of “Internet of Things(IOT)” business operation support platform,” 2010 IEEE 12th International Conference on Communication Technology, Nanjing, China, 2010;1068-1071, https:\u002F\u002Fdoi.org\u002F10.1109\u002FICCT.2010.5688537.\nAziz Rao Tariq, Haq EU. Security challenges facing IoT layers and its protective measures’’. Int J Comput Appl. 2018;179(27):31–5.\nZhong C-L, Zhu Z, Huang R-G. Study on the IOT Architecture and Gateway Technology,.” 14th International Symposium on Distributed Computing and Applications for Business Engineering and Science (DCABES). Guiyang, China. 2015;2015:196–9. https:\u002F\u002Fdoi.org\u002F10.1109\u002FDCABES.2015.56.\nWu Miao, Lu Ting-Jie, Ling Fei-Yang, Sun Jing, Du Hui-Ying. “Research on the architecture of Internet of Things,” 2010 3rd International Conference on Advanced Computer Theory and Engineering(ICACTE), Chengdu, 2010;V5-484-V5-487, https:\u002F\u002Fdoi.org\u002F10.1109\u002FICACTE.2010.5579493.\nZhong C-l, Zhu Z, Huang R-G. “Study on the IOT Architecture and Access Technology,.” 16th International Symposium on Distributed Computing and Applications to Business, Engineering and Science (DCABES). Anyang, China. 2017;2017:113–6. https:\u002F\u002Fdoi.org\u002F10.1109\u002FDCABES.2017.32.\nAlaba FA, Othman M, Hashem IA, Alotaibi F. Internet of Things security: A survey. J Netw Comput Appl. 2017;15(88):10–28.\nSethi P, Sarangi SR. Internet of things: architectures, protocols, and applications. J Electr Comput Eng. 2017;2017.\nKarthik Kumar Vaigandla. SandyaRani Bolla, Radhakrishna Karne, “A Survey on Future Generation Wireless Communications-6G: Requirements, Technologies, Challenges and Applications\". Int J Adv Trends Comput Sci Eng. 2021;10(5):3067–76.\nSula Edvald. A Review of Network Layer and Transport Layer Attacks on Wireless Networks. Int J Mod Eng Res (IJMER). 2019;8(12):23–7.\nFarhad AS, Maede A-T, Hamid M. DoS, impersonation and de-synchronization attacks against an ultra-lightweight RFID mutual authentication protocol for IoT. J Supercomput. 2018;74:509–25.\nEirini Anthi, et al. A Supervised Intrusion Detection System for Smart Home IoT Devices’’. IEEE Int Things J. 2019;6(5):9042–53.\nMadoka S, Ramaswamy R, Tripathi S. Internet of Things (IoT): A literature review. J Comput Commun. 2015;3(5):164.\nMahmoud R, Yousuf T, Aloul F, Zualkernan I. Internet of things (IoT) security: current status, challenges, and prospective measures. 10th International Conference for Internet Technology and Secured Transactions (ICITST) 2015, Dec 14 (pp. 336-341). IEEE.\nIoannou Christiana, Vassiliou Vasos. “The impact of network layer attacks in wireless sensor networks.” 2016 International Workshop on Secure Internet of Things (SIoT). IEEE, 2016.\nKing J, Awad AI. A distributed security mechanism for resource constrained IoT devices. Informatica (Slovenia). 2016;40(1):133–43.\nVaigandla K, Azmi N, Karne R. Investigation on intrusion detection systems (IDSs) in IoT. Int J Emerg Trends Eng Res. 2022;10(3).\nPea G-T. Anomaly-based network intrusion detection: Techniques, systems and challenges. Comput Secur. 2009;28(1):18–28.\nNguyen TTaGA. “A survey of techniques for internet traffic classification using machine learning,” IEEE Communications Surveys & Tutorials, 2008;10.4:56-76.\nLei JZaAG. “Network intrusion detection using an improved competitive learning neural network,” Communication Networks and Services Research, Vols. Second Annual Conference on. IEEE, 2004., p. Proceedings, 2004.\nDeng CaHQ. “Network security intrusion detection system based on incremental improved convolutional neural network model,” Communication and Electronics Systems (ICCES), pp. International Conference on. IEEE, 2016.\nSpadaccino P, Cuomo F. Intrusion detection systems for iot: opportunities and challenges offered by edge computing. J Future Evol Technol; 2022\nGhorbani AA, Lu W, Tavallaee M. Network Attacks. In: Network Intrusion Detection and Prevention. Advances in Information Security, vol. 47. Springer: Boston, MA; 2010. https:\u002F\u002Fdoi.org\u002F10.1007\u002F978-0-387-88771-5_1.\nAnwar S, Mohamad Zain J, Zolkipli MF, Inayat Z, Khan S, Anthony B, Chang V. From intrusion detection to an intrusion response system: Fundamentals, requirements, and future directions. Algorithms. 2017;10(2):1–24.\nShaver A, Liu Z, Thapa N, Roy K, Gokaraju B, Yuan X. Anomaly based intrusion detection for iot with machine learning. In: 2020 IEEE Applied Imagery Pattern Recognition Workshop (AIPR). IEEE; 2020. p. 1–6.\nTabassum Aliya, Erbad Aiman, Guizani Mohsen. “A Survey on Recent Approaches in Intrusion Detection System in IoTs.” 2019 15th International Wireless Communications & Mobile Computing Conference (IWCMC). IEEE, 2019.\nWu H, Schwab S, Peckham RL. U.S. Patent No. 7,424,744. Washington, DC: U.S. Patent and Trademark Office; 2008.\nKruegel C, Toth T. Using Decision Trees to Improve Signaturebased Intrusion Detection,\" in Recent Advances in Intrusion Detection, RAID 2003. Lecture Notes in Computer Science. Berlin: Springer; 2003. p. 173–91.\nSaba T, Sadad T, Rehman A, Mehmood Z, Javaid Q. Intrusion detection system through advanced machine learning for the Internet of things networks. IT prof. 2021;23(2):58–64.\nBuczak AL, Guven E. A survey of data mining and machine learning methods for cyber security intrusion detection. IEEE Commun Surv Tutor. 2015;18(2):1153–76.\nPacheco, Jesus, Hariri Salim. “IoT security framework for smart cyber infrastructures.” 2016 IEEE 1st International Workshops on Foundations and Applications of Self Systems (FAS W). IEEE, 2016.\nKhraisat A, Gondal I, Vamplew P. An Anomaly Intrusion Detection System Using C5 Decision Tree Classifier. Cham: Springer International Publishing; 2018. p. 149–55.\nChauhan H, Kumar V, Pundir S, Pilli ES. “A Comparative Study of Classification Techniques for Intrusion Detection,.” International Symposium on Computational and Business Intelligence. New Delhi. 2013;2013:40–3.\nKhraisat A, Gondal I, Vamplew P, Kamruzzaman J. Survey of intrusion detection systems: techniques, datasets and challenges. Cybersecurity. 2019;2(1):1–22.\nLiao H-J, Richard Lin C-H, Lin Y-C, Tung K-Y. Intrusion detection system: a comprehensive review. J Netw Comput Appl. 2013;36(1):16–24.\nFadlullah Zubair Md, et al. State-of-the-art deep learning: Evolving machine intelligence toward tomorrow’s intelligent network traffic control systems’’. IEEE Commun Surv Tutorials. 2017;19(4):2432–55.\nSAADI C, CHAOUI H. Proposed security by IDSAM in the Android system. In 2019 5th International Conference on Optimization and Applications (lCOA) (pp. 1-7). IEEE. 2019, April.\nTong Z, Ying H. Application of frequent item set mining algorithm in IDS based on Hadoop framework. In 2018 Chinese Control And Decision Conference (CCDC) (pp. 1908-1911). IEEE. 2018, June\nBakhtiar FA, Pramukantoro ES, Nihri H. A Lightweight IDS Based on J48 Algorithm for Detecting DoS Attacks on loT Middleware. In 2019 IEEE 1st Global Conference on Life Sciences and Technologies (LifeTech) (pp. 41-42). IEEE. 2019, March\nSonali R, Saxen A, Manoria M. Intrusion detection system on KDDcup99 dataset: A survey IJCSIT. Int J Comput Sci Inform technol. 2015;6(4):3345–8.\nThomas R, Pavithra, D. A survey of intrusion detection models based on NSL-KDD data set. In: 2018 Fifth HCT Information Technology Trends (ITT); 2018, p. 286–91.\nDivekar Abhishek, Parekh Meet, Savla Vaibhav, Mishra Rudra, Shirole Mahesh. Benchmarking datasets for Anomaly-based network intrusion detection: KDD CUP 99 alternatives, arXiv:1811.0537v1 [cs.LG] Nov 2018.\nIeracitano C, Adeel A, Morabito FC, Hussain A. A novel statistical analysis and autoencoder driven intelligent intrusion detection approach. Neurocomputing. 2020;387:51–62.\nBakhtiar FA, Pramukantoro ES, Nihri H. A Lightweight IDS Based on J48 Algorithm for Detecting DoS Attacks on loT Middleware. In 2019 IEEE 1st Global Conference on Life Sciences and Technologies (LifeTech) (pp. 41-42). IEEE. 2019 March\nAbaas Hassan Asma, Sheta Alaa F, Wahbi Talaat M. Intrusion detection system using Weka Data Mining tool. Int J Sci Res. 2017;6(9):1091703.\nSharafaldin Iman, Lashkari Arash Habibi, Hakak Saqib, Ghorbani Ali A. “Developing Realistic Distributed Denial of Service (DDoS) Attack Dataset and Taxonomy,” IEEE 53rd International Carnahan Conference on Security Technology, Chennai, India, 2019\nAnish Halimaa A, DR. K. Sundarakantham, Machine learning based intrusion detection system, 978-1-5386-9439-8\u002F19, 2019 IEEE, proceeding of 3rd ICOEI 2019.\nSaheed Yakub Kayode, Abiodum Aremu Irdis, Misra Sanjay, Holone Monica Kristiansen, Colomo-Palacios Ricardo. A machine learning-based intrusion detection for detecting internet of things network attacks, by Elsevier BV on behalf of faculty of engineering, Alexandria University, https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.aej.2022.02.063.\nMoustafa N, Slay J. “UNSW-NBI5: a comprehensive data set for network intrusion detection systems (UNSW-NBI5 network data set),”. Military Communications and Information Systems Conference (MilClS). Canberra, ACT. 2015;2015:1–6.\nBiesiada J, Duch W. Feature selection for high-dimensional data—a Pearson redundancy based filter. In: Computer recognition systems 2. Berlin, Heidelberg: Springer; 2007. p. 242–9.\nChebrolu S, Abraham A, Thomas JP. Feature deduction and ensemble design of intrusion detection systems. Comput Secur. 2005;24(4):295–307.\nSuresh M, Anitha R. “Evaluating machine learning algorithms for detecting DDoS attacks,” in International Conference on Network Security and Applications, 2011, pp. 441-452: Springer.\nYassin W, Udzir NI, Muda Z, Sulaiman MN. “Anomaly-based intrusion detection through k-means clustering and naives bayes classification,” in Proc. 4th Int. Conf. Comput. Informatics, ICOCI, 2013, no. 49, pp. 298-303.\nFerrag MA, Maglaras L, Ahmim A, Derdour M, Janicke H. RDTIDS: Rules and Decision Tree-Based Intrusion Detection System for Internet-of-Things Networks. Future Int. 2020;12(44):1–14.\nSharafaldin I, Gharib A, Lashkari AH, Ghorbani AA. Towards a reliable intrusion detection benchmark dataset. Softw Netw. 2017;1(1):177–200.\nKunhare N, Tiwari R, Dhar J. Particle swarm optimization and feature selection for intrusion detection system. Sadhana. 2020;45(109):1–14.\nYin C, Zhu Y, Fei J, He X. A deep learning approach for intrusion detection using recurrent neural networks. IEEE Access. 2017;5:21954–61.\nNaseer S, Saleem Y, Khalid S, et al. Enhanced network anomaly detection based on deep neural networks. IEEE Access. 2018;6:48231–46.\nXiao Y, Xing C, Zhang T, Zhao Z. An intrusion detection model based on feature reduction and convolutional neural networks. IEEE Access. 2019;7:42210–422019.\nJiang K, Wang W, Wang A. Network intrusion WH. Detection combined hybrid sampling with deep hierarchical network. IEEE Access. 2020;8:32464–746.\nChawla Nitesh V, et al. SMOTE: synthetic minority over-sampling technique. J Artif Intel Res. 2002;16:321–57.\nAwajan Albara. A Novel Deep Learning-Based Intrusion Detection System for IoT Networks. Computers. 2023;12(2):34.\nRayeesa Malik, Yashwant Singh, Ahmad Sheikh Zakir, Pooja Anand, Kumar Singh Pradeep, Chekole Workneh Tewabe. An Improved Deep Belief Network IDS on IoT-Based Network for Traffic Systems. J Adv Transp. 2022. https:\u002F\u002Fdoi.org\u002F10.1155\u002F2022\u002F7892130.\nAlqahtani AS. FSO-LSTM IDS: hybrid optimized and ensembled deep-learning network-based intrusion detection system for smart networks. J Supercomput. 2022;78:9438–55. https:\u002F\u002Fdoi.org\u002F10.1007\u002Fs11227-021-04285-3.\nSharma Bhawana, et al. Anomaly based network intrusion detection for IoT attacks using deep learning technique. Comput Electr Eng. 2023;107: 108626.\nTavallaee M, Bagheri E, Lu W, Ghorbani AA. A detailed analysis of the KDD CUP 99 data set. In 2009 IEEE symposium on computational intelligence for security and defense applications. IEEE; 2009. p. 1–6.\nhttps:\u002F\u002Fwww.unb.ca\u002Fcic\u002Fdatasets\u002Fnsl.html. Accessed 20 Apr 2023.\nSharafaldin Iman, Lashkari Arash Habibi, Ghorbani Ali A. “ Toward Generating a New Intrusion Detection Dataset and Intrusion Traffic Characterization,” 4th International Conference on Information Systems Security and Privacy (ICISSP), Portugal, January 2018\nhttps:\u002F\u002Fwww.unb.ca\u002Fcic\u002Fdatasets\u002Fids-2017.html. Accessed 20 Apr 2023.\nKang H, Ahn DH, Lee GM, Yoo JD, Park KH, Kim HK. “IoT Network Intrusion Dataset,” IEEE DataPort, 2019. [Online]. Available: https:\u002F\u002Fieeedataport.org\u002Fopen-access\u002Fiot-network-intrusion-dataset. Accessed 2020.\nJingyi Su, He Shan. Yan Wu. Features selection and prediction for IOT attacks, high conference computing. 2022;2: 100047. https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.hcc.2021.100047.\nhttps:\u002F\u002Fgithub.com\u002Fabhinav-bhardwaj\u002FNetwork-Intrusion-Detection-Using-Machine-Learning\u002Fblob\u002Fmaster\u002FREADME.md. Accessed Apr 2022.\nhttps:\u002F\u002Fieee-dataport.org\u002Fopen-access\u002Fiot-network-intrusion-dataset. Accessed 20 Apr 2023.\nAlhowaide A, Alsmadi I, Tang J. “PCA, Random-Forest and Pearson Correlation for Dimensionality Reduction in IoT IDS,”. IEEE International IOT, Electronics and Mechatronics Conference (IEMTRONICS). Vancouver, BC, Canada. 2020;2020:1–6. https:\u002F\u002Fdoi.org\u002F10.1109\u002FIEMTRONICS51293.2020.9216388.\nZhou H, Deng Z, Xia Y, Fu M. A new sampling method in particle filter based on Pearson correlation coefficient. Neurocomputing. 2016;216:208–15. https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.neucom.2016.07.036.\nMukaka MM. Statistics Corner: A guide to the appropriate use of Correlation coefficient in medical research. Malawi Med J. 2012;24:69–71.\nHall M. “CorrelationAttributeEval.” [Online]. Available: http:\u002F\u002Fweka.sourceforge.net\u002Fdoc.dev\u002Fweka\u002FattributeSelection\u002FCorrelation AttributeEval.html. Accessed 06 Nov 2019\nSugianela Y, Ahmad T. “Pearson Correlation Attribute Evaluation-based Feature Selection for Intrusion Detection System,” 2020 International Conference on Smart Technology and Applications (ICoSTA), Surabaya, Indonesia, 2020, pp. 1-5, https:\u002F\u002Fdoi.org\u002F10.1109\u002FICoSTA48221.2020.1570613717.\nMukaka MM. Statistics Corner: A guide to appropriate use of Correlation coefficient in medical research. Malawi Med J. 2012;24:69–71.\nhttps:\u002F\u002Fwww.sas.com\u002Fen_gb\u002Finsights\u002Farticles\u002Fanalytics\u002Fmachine-learning-algorithms.html. Accessed Jan 2023.\nKhatib Amine, Hamlich Mohamed, Hamad Denis. Machine learning based intrusion detection for cyber-security in IoT networks, https:\u002F\u002Fdoi.org\u002F10.1051\u002Fe3sconf\u002F202129701057, E3S Web of conferences 297, 01057(2021), ICCSRE2021.\nBolón-Canedo V, Alonso-Betanzos A. Ensembles for feature selection: a review and future trends. Inf Fus. 2019;52:1–12.\nChelvan M, Perumal K. On Feature Selection Algorithms and Feature Selection Stability Measures: A Comparative Analysis. Int J Comput Sci Inf Technol. 2017;9(3):159–68.\nBeheshti I, Demirel H, Farokhian F, Yang C. Structural MRI-based detection of Alzheimer’s. Comput Methods Programs Biomed. 2016;137(177):193.\nAlaiz-Rodriguez R, Parnell AC. An information theoretic approach to quantify the stability of feature selection and ranking algorithms. Knowl Based Syst. 2020;195: 105745.",{"EN":1065,"VI":1066},"Internet-of-Things (IoT) connects various physical objects through the Internet and it has a wide application, such as in transportation, military, healthcare, agriculture, and many more. Those applications are increasingly popular because they address real-time problems. In contrast, the use of transmission and communication protocols has raised serious security concerns for IoT devices, and traditional methods such as signature and rule-based methods are inefficient for securing these devices. Hence, identifying network traffic behavior and mitigating cyber attacks are important in IoT to provide guaranteed network security. Therefore, we develop an Intrusion Detection System (IDS) based on a deep learning model called Pearson-Correlation Coefficient - Convolutional Neural Networks (PCC-CNN) to detect network anomalies. The PCC-CNN model combines the important features obtained from the linear-based extractions followed by the Convolutional Neural Network. It performs a binary classification for anomaly detection and also a multiclass classification for various types of attacks. The model is evaluated on three publicly available datasets: NSL-KDD, CICIDS-2017, and IOTID20. We first train and test five different (Logistic Regression, Linear Discriminant Analysis, K Nearest Neighbour, Classification and Regression Tree,& Support Vector Machine) PCC-based Machine Learning models to evaluate the model performance. We achieve the best similar accuracy from the KNN and CART model of 98%, 99%, and 98%, respectively, on the three datasets. On the other hand, we achieve a promising performance with a better detection accuracy of 99.89% and with a low misclassification rate of 0.001 with our proposed PCC-CNN model. The integrated model is promising, with a misclassification rate (or False alarm rate) of 0.02, 0.02, and 0.00 with Binary and Multiclass intrusion detection classifiers. Finally, we compare and discuss our PCC-CNN model in comparison to five traditional PCC-ML models. Our proposed Deep Learning (DL)-based IDS outperforms traditional methods.","Internet vạn vật (IoT) kết nối nhiều đối tượng vật lý khác nhau qua Internet và nó có ứng dụng rộng rãi, chẳng hạn như trong giao thông, quân sự, y tế, nông nghiệp và nhiều lĩnh vực khác. Những ứng dụng này ngày càng trở nên phổ biến vì chúng giải quyết các vấn đề theo thời gian thực. Ngược lại, việc sử dụng các giao thức truyền tải và giao tiếp đã đặt ra những lo ngại nghiêm trọng về an ninh cho các thiết bị IoT, và các phương pháp truyền thống như chữ ký và phương pháp dựa trên quy tắc không hiệu quả trong việc bảo vệ các thiết bị này. Do đó, việc xác định hành vi lưu lượng mạng và giảm thiểu các cuộc tấn công mạng là rất quan trọng trong IoT để đảm bảo an ninh mạng. Vì vậy, chúng tôi phát triển một Hệ thống Phát hiện Xâm nhập (IDS) dựa trên một mô hình học sâu có tên là Hệ số Tương quan Pearson - Mạng nơ-ron Tích chập (PCC-CNN) để phát hiện các bất thường trong mạng. Mô hình PCC-CNN kết hợp các đặc trưng quan trọng thu được từ các phương pháp trích xuất dựa trên tuyến tính và sau đó là Mạng nơ-ron Tích chập. Nó thực hiện phân loại nhị phân cho việc phát hiện bất thường và cũng thực hiện phân loại đa lớp cho nhiều loại tấn công khác nhau. Mô hình được đánh giá trên ba tập dữ liệu công khai: NSL-KDD, CICIDS-2017 và IOTID20. Chúng tôi trước tiên đã huấn luyện và kiểm tra năm mô hình Machine Learning dựa trên PCC khác nhau (Phân tích Hồi quy Logistic, Phân tích Phân biệt Tuyến tính, K Láng giềng Gần nhất, Cây Phân loại và Hồi quy, & Máy Vector Hỗ trợ) để đánh giá hiệu suất của mô hình. Chúng tôi đạt được độ chính xác tương tự tốt nhất từ các mô hình KNN và CART là 98%, 99% và 98%, tương ứng, trên ba tập dữ liệu. Mặt khác, chúng tôi đạt được hiệu suất hứa hẹn với độ chính xác phát hiện tốt hơn là 99,89% và tỷ lệ phân loại sai thấp là 0,001 với mô hình PCC-CNN mà chúng tôi đề xuất. Mô hình tích hợp hứa hẹn, với tỷ lệ phân loại sai (hoặc tỷ lệ báo động giả) là 0,02, 0,02 và 0,00 với các bộ phân loại phát hiện xâm nhập nhị phân và đa lớp. Cuối cùng, chúng tôi so sánh và thảo luận về mô hình PCC-CNN của chúng tôi so với năm mô hình PCC-ML truyền thống. Hệ thống IDS dựa trên Học sâu (DL) mà chúng tôi đề xuất vượt trội hơn so với các phương pháp truyền thống.",{"EN":1068,"VI":1069},"Anomaly-based intrusion detection system for IoT application","Hệ thống phát hiện xâm nhập dựa trên bất thường cho ứng dụng IoT",{"VOID":1071},"10.1007\u002Fs43926-023-00034-5",{"VI":1073},"Internet-of-Things, xâm nhập mạng, phát hiện bất thường, học sâu, Hệ số Tương quan Pearson, Mạng nơ-ron Tích chập","2025-02-06T06:11:55.663+00:00",[1076],"VI","https:\u002F\u002Flink.springer.com\u002Farticle\u002F10.1007\u002Fs43926-023-00034-5",[1079,1094,1109,1121],{"id":1080,"sortIndex":110,"researcher":20,"roles":1081,"affiliations":1082,"properties":1091},"fadab06b-7887-4b15-abf1-1c4f2e3720d4",[79],[1083],{"id":20,"sortIndex":21,"affiliation":1084,"properties":20},{"id":1085,"createTime":1086,"updateTime":1086,"relativeEntities":1087,"slug":20,"properties":1088,"entityType":91,"verifyStatus":19,"verifyTime":20,"verifyNote":20,"syncStatus":19,"languages":20,"translateLanguages":20,"viewCount":21},"76d84780-ab2f-494d-9217-4a7be83c7cdd","2024-01-13T00:32:31.476+00:00",[],{"title":1089},{"VI":1090},"Computer Science, North Carolina A&T State University, Greensboro, USA",{"title":1092},{"VI":1093},"Kaushik Roy",{"id":1095,"sortIndex":97,"researcher":20,"roles":1096,"affiliations":1097,"properties":1106},"3bd2d41d-a3b1-4a7c-a286-d06e8b813c83",[79],[1098],{"id":20,"sortIndex":21,"affiliation":1099,"properties":20},{"id":1100,"createTime":1101,"updateTime":1101,"relativeEntities":1102,"slug":20,"properties":1103,"entityType":91,"verifyStatus":19,"verifyTime":20,"verifyNote":20,"syncStatus":19,"languages":20,"translateLanguages":20,"viewCount":21},"1851bc98-8be0-4183-886e-bb7eebe5a0ce","2024-01-19T10:17:16.989+00:00",[],{"title":1104},{"VI":1105},"Electrical & Computer Engineering, North Carolina A &T State University, Greensboro, USA",{"title":1107},{"VI":1108},"John Kelly",{"id":1110,"sortIndex":21,"researcher":20,"roles":1111,"affiliations":1112,"properties":1118},"ea91c9d8-cfae-4e9c-9e66-c2dc9c4f7bc6",[79],[1113],{"id":20,"sortIndex":21,"affiliation":1114,"properties":20},{"id":1100,"createTime":1101,"updateTime":1101,"relativeEntities":1115,"slug":20,"properties":1116,"entityType":91,"verifyStatus":19,"verifyTime":20,"verifyNote":20,"syncStatus":19,"languages":20,"translateLanguages":20,"viewCount":21},[],{"title":1117},{"VI":1105},{"title":1119},{"VI":1120},"Mansi Bhavsar",{"id":1122,"sortIndex":185,"researcher":20,"roles":1123,"affiliations":1124,"properties":1130},"fbe22247-8b62-420e-b9fe-565e883d0c84",[79],[1125],{"id":20,"sortIndex":21,"affiliation":1126,"properties":20},{"id":1085,"createTime":1086,"updateTime":1086,"relativeEntities":1127,"slug":20,"properties":1128,"entityType":91,"verifyStatus":19,"verifyTime":20,"verifyNote":20,"syncStatus":19,"languages":20,"translateLanguages":20,"viewCount":21},[],{"title":1129},{"VI":1090},{"title":1131},{"VI":1132},"Odeyomi Olusola",{"url":1077,"publisher":1134,"properties":1154},{"id":6,"createTime":7,"updateTime":8,"relativeEntities":1135,"slug":10,"properties":1136,"entityType":18,"verifyStatus":19,"verifyTime":20,"verifyNote":20,"syncStatus":19,"languages":20,"translateLanguages":20,"viewCount":21,"subjectFields":1140,"manageAffiliations":1141,"indexDatabases":1142,"url":20,"thumbnailPath":20,"statistic":1149,"gsStatistic":20,"type":51,"analyzePriority":20},[],{"issn":1137,"title":1138,"url":1139},{"VOID":13},{"EN":15},{"VOID":17},[],[],[1143],{"id":26,"indexDatabase":1144,"url":39,"indexYears":40,"academicFieldIds":20,"indexDatabaseRanking":41},{"id":28,"createTime":29,"updateTime":30,"relativeEntities":1145,"label":1146,"description":1147,"key":36,"publicationTags":1148,"standard":20},[],{"EN":33,"VI":33},{"EN":33,"VI":35},[38],{"impactFactor":21,"impactFactorByYear":1150,"i10Index":21,"i10IndexLast5Year":21,"totalPublication":44,"totalPublicationByYear":1151,"totalCitation":21,"totalCitationByYear":1152,"totalCitationPerPublication":21,"totalCitationPerPublicationByYear":1153,"hindexLast5Year":21,"hindex":21},{},{"2021":46,"2022":47,"2023":48},{},{},{"volume":1155,"pages":1156},{"VOID":145},{"VOID":1157},"1-23","2023-05-30",{"id":1160,"createTime":1161,"updateTime":1162,"relativeEntities":1163,"slug":1164,"properties":1165,"entityType":71,"verifyStatus":72,"verifyTime":1162,"verifyNote":73,"syncStatus":19,"languages":1175,"translateLanguages":20,"viewCount":21,"primaryUrl":1176,"fullTextUrl":20,"authors":1177,"publicationType":121,"publisherRelationship":1198,"citationCount":110,"citationInfo":1219,"publishDate":20,"publishYear":20,"citationAnalyzeStatus":367,"lastCitationAnalyze":1221,"indexDatabases":20,"openAccess":20,"references":1222,"isForceReanalyzing":150},"c3b3f731-3e33-4e94-835d-945f04a88af6","2024-04-11T16:35:36.565+00:00","2024-12-05T20:08:59.060+00:00",[],"Detection-of-road-traffic-anomalies-based-on-computational-data-science",{"keywords":1166,"openalex":1167,"abstract":1169,"title":1171,"doi":1173},{},{"VOID":1168},"W4306888805",{"EN":1170},"\u003Cjats:title>Abstract\u003C\u002Fjats:title>\u003Cjats:p>The development of 5G has enabled the autonomous vehicles (AVs) to have full control over all functions. The AV acts autonomously and collects travel data based on various smart devices and sensors, with the goal of enabling it to operate under its own power. However, the collected data is affected by several sources that degrade the forecasting accuracy. To manage large amounts of traffic data in different formats, a computational data science approach (CDS) is proposed. The computational data science scheme introduced to detect anomalies in traffic data that negatively affect traffic efficiency. The combination of data science and advanced artificial intelligence techniques, such as deep leaning provides higher degree of data anomalies detection which leads to reduce traffic congestion and vehicular queuing. The main contribution of the CDS approach is summarized in detection of the factors that caused data anomalies early to avoid long-term traffic congestions. Moreover, CDS indicated a promoting results in various road traffic scenarios.\u003C\u002Fjats:p>",{"EN":1172},"Detection of road traffic anomalies based on computational data science",{"VOID":1174},"10.1007\u002Fs43926-022-00025-y",[263],"https:\u002F\u002Flink.springer.com\u002F10.1007\u002Fs43926-022-00025-y",[1178],{"id":1179,"sortIndex":21,"researcher":20,"roles":1180,"affiliations":1181,"properties":1191},"51280268-736c-4228-bf97-3622eaddf0b5",[],[1182],{"id":20,"sortIndex":21,"affiliation":1183,"properties":20},{"id":1184,"createTime":1185,"updateTime":1185,"relativeEntities":1186,"slug":1187,"properties":1188,"entityType":91,"verifyStatus":19,"verifyTime":20,"verifyNote":20,"syncStatus":19,"languages":20,"translateLanguages":20,"viewCount":21},"24b11856-a07e-4e85-99c6-ec8ff6716dee","2024-04-11T16:35:36.571+00:00",[],"Computer-Science-Department-Al-Qasemi-Academic-College-Baqa-Al-Gharbiah-Israel",{"title":1189},{"EN":1190},"Computer Science Department, Al Qasemi Academic College, Baqa Al Gharbiah, Israel",{"openalex":1192,"orcid":1194,"title":1196},{"VOID":1193},"A5066121620",{"VOID":1195},"https:\u002F\u002Forcid.org\u002F0000-0002-8609-3935",{"EN":1197},"Jamal Raiyn",{"url":20,"publisher":1199,"properties":20},{"id":6,"createTime":7,"updateTime":8,"relativeEntities":1200,"slug":10,"properties":1201,"entityType":18,"verifyStatus":19,"verifyTime":20,"verifyNote":20,"syncStatus":19,"languages":20,"translateLanguages":20,"viewCount":21,"subjectFields":1205,"manageAffiliations":1206,"indexDatabases":1207,"url":20,"thumbnailPath":20,"statistic":1214,"gsStatistic":20,"type":51,"analyzePriority":20},[],{"issn":1202,"title":1203,"url":1204},{"VOID":13},{"EN":15},{"VOID":17},[],[],[1208],{"id":26,"indexDatabase":1209,"url":39,"indexYears":40,"academicFieldIds":20,"indexDatabaseRanking":41},{"id":28,"createTime":29,"updateTime":30,"relativeEntities":1210,"label":1211,"description":1212,"key":36,"publicationTags":1213,"standard":20},[],{"EN":33,"VI":33},{"EN":33,"VI":35},[38],{"impactFactor":21,"impactFactorByYear":1215,"i10Index":21,"i10IndexLast5Year":21,"totalPublication":44,"totalPublicationByYear":1216,"totalCitation":21,"totalCitationByYear":1217,"totalCitationPerPublication":21,"totalCitationPerPublicationByYear":1218,"hindexLast5Year":21,"hindex":21},{},{"2021":46,"2022":47,"2023":48},{},{},{"total":110,"publishYear":20,"statisticByYear":1220},{"2023":110},"2024-04-11T19:20:43.572+00:00",[1223,1227,1230,1234,1238,1242,1245,1249,1252,1256,1260,1264,1268,1272,1276,1279,1282,1285,1289,1292,1295,1299,1302,1306,1310,1313,1317,1321,1325,1329,1332,1336,1340,1344,1348,1352,1356,1360,1364,1368,1372,1376,1380,1384,1388,1391,1395,1399,1402,1406,1410,1414,1418,1421,1424,1427,1431,1434,1437,1441],{"id":20,"text":1224,"url":20,"identifiers":1225},"Ancans G, Bobrovsa V, Ancansb A, Kalibatiene D. Spectrum. Considerations for 5G mobile communication systems. Procedia Comput Sci. 2017;104:509–16.",{"doi":1226},"10.1016\u002Fj.procs.2017.01.166",{"id":20,"text":1228,"url":20,"identifiers":1229},"Aqib M, Mehmood R, Alzahrani A, Katib I, Albeshri A. A deep learning model to predict vehicles occupancy on freeways for traffic management. Int J Comput Sci Netw Secu. 2018;18(12):1–8.",{},{"id":20,"text":1231,"url":20,"identifiers":1232},"Alrajhi M, Kamel M. A deep-learning model for predicting and visualizing the risk of road traffic accidents in Saudi Arabia: a tutorial approach. IJACSA. 2019;10(11):475–83.",{"doi":1233},"10.14569\u002FIJACSA.2019.0101166",{"id":20,"text":1235,"url":20,"identifiers":1236},"Dey KC, et al. Vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I) communication in a heterogeneous wireless network—Performance evaluation. Transp Res Part C. 2016;68:168–84.",{"doi":1237},"10.1016\u002Fj.trc.2016.03.008",{"id":20,"text":1239,"url":20,"identifiers":1240},"Berman SD, Buczak LA, Chavis SJ, Corbett LC. A survey of deep learning methods for cyber security. Information. 2019;10:122.",{"doi":1241},"10.3390\u002Finfo10040122",{"id":20,"text":1243,"url":20,"identifiers":1244},"Qin H, Yan M, Ji H. Application of Controller Area Network (CAN) bus anomaly detection based on time series prediction. Veh Commun. 2021;27:100291.",{},{"id":20,"text":1246,"url":20,"identifiers":1247},"Brandl O. V2X traffic management. Elektrotech Inftech. 2016;133(7):353–5.",{"doi":1248},"10.1007\u002Fs00502-016-0434-6",{"id":20,"text":1250,"url":20,"identifiers":1251},"Borzacchielo TM. The use of data from mobile phone networks for transportation applications. In: TRB 2010 Annual Meeting. 2010.",{},{"id":20,"text":1253,"url":20,"identifiers":1254},"Chen C, Liu Y, Sun X, Cairano-Gilfedder DC, Titmus S. Automobile maintenance prediction using deep learning with GIS data. Procedia CIRP. 2019;81:447–52.",{"doi":1255},"10.1016\u002Fj.procir.2019.03.077",{"id":20,"text":1257,"url":20,"identifiers":1258},"Liang F, Yu A, Hatcher GW, Yu W, Lu C. Deep leaning-based power usage forecast modeling and evaluation. Procedia Comput Sci. 2019;154:102–8.",{"doi":1259},"10.1016\u002Fj.procs.2019.06.016",{"id":20,"text":1261,"url":20,"identifiers":1262},"Polson GN, Sokolov OV. Deep learning for short-term traffic flow prediction. Transp Res Part C. 2017;79:1–17.",{"doi":1263},"10.1016\u002Fj.trc.2017.02.024",{"id":20,"text":1265,"url":20,"identifiers":1266},"Nguyen H, Kieu L-M, Wen T, Cai C. Deep learning methods in transportation domain: a review. IET Intell Transp Syst. 2018;12(9):998–1004.",{"doi":1267},"10.1049\u002Fiet-its.2018.0064",{"id":20,"text":1269,"url":20,"identifiers":1270},"Suhao L, Jinzhao L, Guoquan L, Tong B, Huiqian W, Yu P. Vehicle type detection based on deep learning in traffic scene. Procedia Comput Sci. 2018;131:564–72.",{"doi":1271},"10.1016\u002Fj.procs.2018.04.281",{"id":20,"text":1273,"url":20,"identifiers":1274},"Yan M, Li M, He H, Peng J. Deep learning for vehicle speed prediction. Energy Procedia. 2018;152:618–23.",{"doi":1275},"10.1016\u002Fj.egypro.2018.09.220",{"id":20,"text":1277,"url":20,"identifiers":1278},"Raiyn J. Speed adaptation in urban road network management. Transp Telecommun. 2016;17(2):11–121.",{},{"id":20,"text":1280,"url":20,"identifiers":1281},"Jan B, et al. Deep learning in big data analytics: a comparative study. Comput Electr Eng. 2017;75:1–13.",{},{"id":20,"text":1283,"url":20,"identifiers":1284},"Jawhar I, Mohamed N, Usmani H. An overview of inter-vehicular communication systems, protocols and middleware. J Netw. 2013;8(12):2749–61.",{},{"id":20,"text":1286,"url":20,"identifiers":1287},"Park CR, Homg JE. Urban traffic accident risk prediction for knowledge-based mobile multimedia service. Pers Ubiquit Comput. 2020. https:\u002F\u002Fdoi.org\u002F10.1007\u002Fs00779-020-01442-y.",{"doi":1288},"10.1007\u002Fs00779-020-01442-y",{"id":20,"text":1290,"url":20,"identifiers":1291},"Raiyn J. Road traffic congestion management based on search allocation approach. Transp Telecommun. 2017;18(1):25–33.",{},{"id":20,"text":1293,"url":20,"identifiers":1294},"Raiyn J. Developing vehicle locations strategy on urban road. Transp Telecommun. 2017;18(4):253–62.",{},{"id":20,"text":1296,"url":20,"identifiers":1297},"Ramm K, Schwieger V. Mobile positioning for traffic state acquisition. J Location Serv. 2007;1(2):133–44.",{"doi":1298},"10.1080\u002F17489720701779651",{"id":20,"text":1300,"url":20,"identifiers":1301},"Lv Y, Tang S. Real-time highway traffic accident prediction based on the K-nearest neighbor method. International conference on measuring technology and mechatronics automation. IEEE: Piscataway; 2010.",{},{"id":20,"text":1303,"url":20,"identifiers":1304},"Xiaoqiang Z, Ruimin L, Xinxin Y. Incident duration model on urban freeways based on classification and regression tree. In: 2nd international conference on intelligent computation technology and automation, TRB annual meeting. 2010; 2: 526–528.",{"doi":1305},"10.1109\u002FICICTA.2009.616",{"id":20,"text":1307,"url":20,"identifiers":1308},"Wang J, Cehn R, He Z. Traffic speed prediction for urban transportation network: a path based deep learning approach. Transp Res Part C. 2019;100:372–85.",{"doi":1309},"10.1016\u002Fj.trc.2019.02.002",{"id":20,"text":1311,"url":20,"identifiers":1312},"Wang Z, Murray-Tuite P. Modeling incident-related traffic and estimating travel time with a cellular automaton model. In: proceedings of transportation research board’s 89th annual meeting CD-ROM. DC; 10–14 Jan 2010.",{},{"id":20,"text":1314,"url":20,"identifiers":1315},"Chrobok R, Kaumann O, Wahle J, Schreckenberg M. Different methods of traffic forecast based on real data. Eur J Oper Res. 2004;15:558–68.",{"doi":1316},"10.1016\u002Fj.ejor.2003.08.005",{"id":20,"text":1318,"url":20,"identifiers":1319},"Mishra A, Cohen A, Reichherzer T, Wilde T. Detection of data anomalies at the edge of pervasive IoT Systems. Computing. 2021. https:\u002F\u002Fdoi.org\u002F10.1007\u002Fs00607-021-00927-9.",{"doi":1320},"10.1007\u002Fs00607-021-00927-9",{"id":20,"text":1322,"url":20,"identifiers":1323},"Chang W, Jung BC. Optimal transmission strategy without capacity loss at a primary user in cognitive radio networks over inter-symbol interference channels. IEEE Commun Lett. 2014;18(3):411–4.",{"doi":1324},"10.1109\u002FLCOMM.2013.123013.132487",{"id":20,"text":1326,"url":20,"identifiers":1327},"Li J, Li S, Zhao F, Du R. Co-channel interference modeling in cognitive wireless networks. IEEE Trans Commun. 2014;62(9):3113–27.",{"doi":1328},"10.1109\u002FTCOMM.2014.2341628",{"id":20,"text":1330,"url":20,"identifiers":1331},"Adnan A, Nordina S, Bahruddinb MAB, Alic M. How trust can drive forward the user acceptance to the technology? In-vehicle technology for autonomous vehicle. Transp Res Part A. 2018;118:819–36.",{},{"id":20,"text":1333,"url":20,"identifiers":1334},"Martinez-Diaz M, Soriguera F. Autonomous vehicles: theoretical and practical challenges. Transp Res Procedia. 2018;33:275–82.",{"doi":1335},"10.1016\u002Fj.trpro.2018.10.103",{"id":20,"text":1337,"url":20,"identifiers":1338},"Xiang W, Huang T, Wan W. Machine learning based optimization for vehicle-to-infrastructure communications. Futur Gener Comput Syst. 2019;94:488–95.",{"doi":1339},"10.1016\u002Fj.future.2018.10.047",{"id":20,"text":1341,"url":20,"identifiers":1342},"Ndashimye E, Ray KS, Sarkar N, Gutiérrez AI. Vehicle-to-infrastructure communication over multi-tier heterogeneous networks: a survey. Comput Netw. 2017;112:144–66.",{"doi":1343},"10.1016\u002Fj.comnet.2016.11.008",{"id":20,"text":1345,"url":20,"identifiers":1346},"Silva N, Shah V, Soares J, Rodrigues H. Road anomalies detection system evaluation. Sensors. 2018;18:1–20.",{"doi":1347},"10.3390\u002Fs18071984",{"id":20,"text":1349,"url":20,"identifiers":1350},"Wang T, Zhao J, Li P. An extended car-following model at unsignalized intersections under V2V communication environment. PLoS ONE. 2018;13(2):e0192787.",{"doi":1351},"10.1371\u002Fjournal.pone.0192787",{"id":20,"text":1353,"url":20,"identifiers":1354},"Sepulcre M, Gozalvez J. Context-aware heterogeneous V2X communications for connected vehicles. Comput Netw. 2018;136:13–21.",{"doi":1355},"10.1016\u002Fj.comnet.2018.02.024",{"id":20,"text":1357,"url":20,"identifiers":1358},"Choudhury A, Maszczyk T, Math CB, Li H, Dauwels J. An integrated simulation environment for testing V2X protocols and applications. Procedia Comput Sci. 2016;80:2042–52.",{"doi":1359},"10.1016\u002Fj.procs.2016.05.524",{"id":20,"text":1361,"url":20,"identifiers":1362},"Weiß C. V2X communication in Europe—from research projects towards standardization and field testing of vehicle communication technology. Comput Netw. 2011;55:3103–19.",{"doi":1363},"10.1016\u002Fj.comnet.2011.03.016",{"id":20,"text":1365,"url":20,"identifiers":1366},"Jin Q, Wu G, Boriboonsomsin K, Barth M. Platoon-based multi-agent intersection management for connected vehicles. In: 16th international IEEE conference on intelligent transportation systems. 2013.",{"doi":1367},"10.1109\u002FITSC.2013.6728436",{"id":20,"text":1369,"url":20,"identifiers":1370},"Gora P, Rüb I. Traffic models for self-driving connected cars. Transp Res Procedia. 2016;14:2207–16.",{"doi":1371},"10.1016\u002Fj.trpro.2016.05.236",{"id":20,"text":1373,"url":20,"identifiers":1374},"Li J, Dridi M, El-Moudni A. A cooperative traffic control of vehicle-intersection, (CTCVI) for the reduction of traffic delays and fuel consumption. Sensors. 2016;16:1–20.",{"doi":1375},"10.3390\u002Fs16122175",{"id":20,"text":1377,"url":20,"identifiers":1378},"Bergenhem C, Hedin E, Skarin D. Vehicle-to-vehicle communication for a platooning system. Procedia Soc Behav Sci. 2012;48:1222–33.",{"doi":1379},"10.1016\u002Fj.sbspro.2012.06.1098",{"id":20,"text":1381,"url":20,"identifiers":1382},"Jiang T, Chen H-H, Wu H-C, Yi Y. Channel modeling and inter-carrier interference analysis for V2V communication systems in frequency-dispersive channels. Mobile Netw Appl. 2010;14:4–12.",{"doi":1383},"10.1007\u002Fs11036-009-0177-2",{"id":20,"text":1385,"url":20,"identifiers":1386},"Sun W, Ström EG, Brännström F, Sou KC, Sui Y. Radio resource management for D2D-Based V2V communication. IEEE Trans Veh Technol. 2016;65(8):6636–50.",{"doi":1387},"10.1109\u002FTVT.2015.2479248",{"id":20,"text":1389,"url":20,"identifiers":1390},"Xinran L, Xingwu L, Yuanhong W, Juhua P, Xiangliang Z. Detecting anomaly in traffic flow from road similarity analysis. Lecture notes in computer science. Berlin: Springer Nature; 2016. p. 92–104.",{},{"id":20,"text":1392,"url":20,"identifiers":1393},"Du LL, Dao H, Li X-X. Information dissemination delay in vehicle-to-vehicle communication networks in a traffic stream. IEEE Trans Intell Transp Syst. 2015;16(1):66–80.",{"doi":1394},"10.1109\u002FTITS.2014.2326331",{"id":20,"text":1396,"url":20,"identifiers":1397},"Jing Bai J, Chen Y. A deep neural network based on classification of traffic volume for short-term forecasting, Hindawi. Math Probl Eng. 2019. https:\u002F\u002Fdoi.org\u002F10.1155\u002F2019\u002F6318094.",{"doi":1398},"10.1155\u002F2019\u002F6318094",{"id":20,"text":1400,"url":20,"identifiers":1401},"Nam VH, Dang HN. An improvement of traffic incident recognition by deep convolutional neural network. Int J Innov Technol Explor Eng (IJITEE). 2018;8(1):10–4.",{},{"id":20,"text":1403,"url":20,"identifiers":1404},"Li R, Pereira FC, Ben-Akiva ME. Overview of traffic incident duration analysis and prediction. Eur Transp Res Rev. 2018;10(22):1–13. https:\u002F\u002Fdoi.org\u002F10.1186\u002Fs12544-018-0300-1.",{"doi":1405},"10.1186\u002Fs12544-018-0300-1",{"id":20,"text":1407,"url":20,"identifiers":1408},"Lu N, Cheng N, Zhang N, Shen X, Mark JW. Connected vehicles: solutions and challenges. IEEE Internet Things J. 2014;1(4):289–99.",{"doi":1409},"10.1109\u002FJIOT.2014.2327587",{"id":20,"text":1411,"url":20,"identifiers":1412},"Iliopoulou C, Kepaptsoglou K. Combining ITS and optimization in public transportation planning: state of the art and future research paths. Eur Transp Res Rev. 2019;11(27):1–16. https:\u002F\u002Fdoi.org\u002F10.1186\u002Fs12544-019-0365-5.",{"doi":1413},"10.1186\u002Fs12544-019-0365-5",{"id":20,"text":1415,"url":20,"identifiers":1416},"Zhanga Q, Yang TL, Chenc Z, Li P. A survey on deep learning for big data. Inf Fus. 2018;42:146–57. https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.inffus.2017.10.006.",{"doi":1417},"10.1016\u002Fj.inffus.2017.10.006",{"id":20,"text":1419,"url":20,"identifiers":1420},"Raiyn J. Road traffic anomaly detection based on deep learning technology. In: 7th international conference on vehicle technology and intelligent transport systems (VEHITS). Apr 2021.",{},{"id":20,"text":1422,"url":20,"identifiers":1423},"Wooldrige M, Jennings RN. Intelligent agents: theory and practice. Cambridge: Cambridge University Press; 2009.",{},{"id":20,"text":1425,"url":20,"identifiers":1426},"Russel S, Norvig P. Artificial intelligence, a modern approach. 4th ed. London: Pearson Education Limited; 2021.",{},{"id":20,"text":1428,"url":20,"identifiers":1429},"Raiyn J. Classification of road traffic anomaly based on travel data analysis. Int Rev Civ Eng (IRECE). 2021. https:\u002F\u002Fdoi.org\u002F10.15866\u002Firece.v12i6.20530.",{"doi":1430},"10.15866\u002Firece.v12i6.20530",{"id":20,"text":1432,"url":20,"identifiers":1433},"Santhosh KK, Dogra DP, Roy PP. Anomaly detection in road traffic using visual surveillance: a survey. ACM Comput Surv. 2020;6(53):1–26.",{},{"id":20,"text":1435,"url":20,"identifiers":1436},"Dogra DP, Roy PP, Mitra A. Video trajectory classification and anomaly detection using hybrid CNN-VAE Architثcture. IEEE transactions on Intelligent Transportation Systems, 2021.",{},{"id":20,"text":1438,"url":20,"identifiers":1439},"Santhosh KK, Mohapatra S, Debi MS, Dogra DP, Roy PP, Kim Mitra GB. Computer vision-guided intelligent traffic signaling for isolated intersections. Expert Syst With Appl. 2019;134:267–78.",{"doi":1440},"10.1016\u002Fj.eswa.2019.05.049",{"id":20,"text":1442,"url":20,"identifiers":1443},"Santhosh KK, Dogra DP, Roy PP. Queuing theory guided intelligent traffic scheduling through video analysis using Dirichlet process mixture model. Expert Syst With Appl. 2019;118:169–81.",{"doi":1444},"10.1016\u002Fj.eswa.2018.09.057",{"id":1446,"createTime":1447,"updateTime":1448,"relativeEntities":1449,"slug":1450,"properties":1451,"entityType":71,"verifyStatus":72,"verifyTime":1448,"verifyNote":73,"syncStatus":19,"languages":20,"translateLanguages":20,"viewCount":21,"primaryUrl":1460,"fullTextUrl":20,"authors":1461,"publicationType":121,"publisherRelationship":1583,"citationCount":20,"citationInfo":20,"publishDate":1609,"publishYear":1610,"citationAnalyzeStatus":19,"lastCitationAnalyze":20,"indexDatabases":20,"openAccess":20,"references":20,"isForceReanalyzing":150},"029e80cb-2d5b-4bf7-9ae3-192f5e69b803","2023-11-28T13:29:46.530+00:00","2025-01-13T19:45:08.528+00:00",[],"Evaluating-student-levelling-based-on-machine-learning-model-s-performance",{"references":1452,"abstract":1454,"title":1456,"doi":1458},{"VOID":1453},"Albreiki B, Zaki N, Alashwal H. A systematic literature review of student’ performance prediction using machine learning techniques. Educ Sci. 2021;11(9):552. https:\u002F\u002Fdoi.org\u002F10.3390\u002Feducsci11090552.\nLee MW, Chen SY, Chrysostomou K, Liu X. Mining students’ behavior in web-based learning programs. Exp Syst Appl. 2009;36(2):3459–64. https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.eswa.2008.02.054.\nAL, Wiener M. Classification and regression by randomForest. R News 2. 2003;3:18–22.\nGhareeb S, Hussain A, Khan W, Al-Jumeily D, Baker T, Al-Jumeily R. Dataset of student level prediction in UAE. Data Brief. 2021;35: 106908. https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.dib.2021.106908.\nKučak D, Juričić V, Đambić G. Machine learning in education—a survey of current research trends. Ann DAAAM Proc Int DAAAM Symp. 2018;29(1):0406–10. https:\u002F\u002Fdoi.org\u002F10.2507\u002F29th.daaam.proceedings.059.\nGhareeb AS, Al-jumeily R, Baker T. A machine learning based framework for education levelling in multicultural countries: UAE as a case study. 2020;14(3).\nMasci C, Johnes G, Agasisti T. Student and school performance across countries: a machine learning approach. Eur J Oper Res. 2018;269(3):1072–85. https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.ejor.2018.02.031.\nAl-Shabandar R, Hussain A, Laws A, Keight R, Lunn J, Radi N. Machine learning approaches to predict learning outcomes in Massive open online courses. Proc Int Jt Conf Neural Netw. 2017;713–720:2017. https:\u002F\u002Fdoi.org\u002F10.1109\u002FIJCNN.2017.7965922.\nHsia TC, Shie AJ, Chen LC. Course planning of extension education to meet market demand by using data mining techniques—an example of Chinkuo technology university in Taiwan. Expert Syst Appl. 2008;34(1):596–602. https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.eswa.2006.09.025.\nLykourentzou I, Giannoukos I, Nikolopoulos V, Mpardis G, Loumos V. Dropout prediction in e-learning courses through the combination of machine learning techniques. Comput Educ. 2009;53(3):950–65. https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.compedu.2009.05.010.\nNath V, Levinson SE. Machine learning. 2014. https:\u002F\u002Fdoi.org\u002F10.1007\u002F978-3-319-05606-7_6.\nTong JC. Cross-validation. Encyclopedia of Systems Biology. 2013;508–508. https:\u002F\u002Fdoi.org\u002F10.1007\u002F978-1-4419-9863-7_941.\nBertsekas DP. Dynamic programming and optimal control. 4th edn. 2012.\nCelik AN. A techno-economic analysis of wind energy in Southern Turkey. Int J Green Energy. 2007;4(3):233–47. https:\u002F\u002Fdoi.org\u002F10.1080\u002F15435070701338358.\nZhuang X, Zhang W, Wu Y, Zhao Z. Comprehensive prediction method for die-roll height of fine-blanking components. Int J Adv Manuf Technol. 2018;98(9–12):2819–29. https:\u002F\u002Fdoi.org\u002F10.1007\u002Fs00170-018-2430-y.\nAgatonovic-Kustrin S, Beresford R. Basic concepts of artificial neural network (ANN) modeling and its application in pharmaceutical research. J Pharm Biomed Anal. 2000;22(5):717–27. https:\u002F\u002Fdoi.org\u002F10.1016\u002FS0731-7085(99)00272-1.\nHaghbakhsh R, Adib H, Keshavarz P, Koolivand M, Keshtkari S. Development of an artificial neural network model for the prediction of hydrocarbon density at high-pressure, high-temperature conditions. Thermochim Acta. 2013;551:124–30. https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.tca.2012.10.022.\nJain A, Solanki S. An efficient approach for multiclass student performance prediction based upon machine learning. 2019;1457–1462.\nKotsiantis SB. Use of machine learning techniques for educational proposes: a decision support system for forecasting students’ grades. Artif Intell Rev. 2012;37(4):331–44. https:\u002F\u002Fdoi.org\u002F10.1007\u002Fs10462-011-9234-x.\nKhalaf M, et al. Machine learning approaches to the application of disease modifying therapy for sickle cell using classification models. Neurocomputing. 2017;228:154–64. https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.neucom.2016.10.043.\nTing SL, Ip WH, Tsang AHC. Is Naïve Bayes a Good Classifier for Document Classification? 2011. [Online]. Available: https:\u002F\u002Fwww.researchgate.net\u002Fpublication\u002F266463703.\nSu RKRB. Linear feature extraction and description. 1980.\nThomas Rincy N, Gupta R. An efficient feature subset selection approach for machine learning. Multimed Tools Appl. 2021;80(8):12737–830. https:\u002F\u002Fdoi.org\u002F10.1007\u002Fs11042-020-10011-7.\nDietterich T. Overfitting and Undercomputing in Machine Learning.\nHall MA. Correlation-based feature selection for machine learning. 1999.\nVafaie H, De Jong K. Genetic algorithms as a tool for feature selection in machine learning. In: Proceedings International Conference on Tools with Artificial Intelligence, ICTAI. pp. 200–203, 1992, https:\u002F\u002Fdoi.org\u002F10.1109\u002FTAI.1992.246402.\nRamaswami M, Bhaskaran R. A study on feature selection techniques in educational data mining. 2009;1(1): 7–11. [Online]. Available: http:\u002F\u002Farxiv.org\u002Fabs\u002F0912.3924.\nJović A, Brkić K, Bogunović N. A review of feature selection methods with applications. In: 2015 38th International Convention on Information and Communication Technology, Electronics and Microelectronics, MIPRO 2015 Proceedings, 2015, pp. 1200–1205. https:\u002F\u002Fdoi.org\u002F10.1109\u002FMIPRO.2015.7160458.\nLuo S. Data mining of many-attribute data: investigating the interaction between feature selection strategy and statistical features of datasets.\nBreiman L. Randon Forests. Machinelearning202.Pbworks.Com, pp. 1–35, 1999, [Online]. Available: http:\u002F\u002Fmachinelearning202.pbworks.com\u002Fw\u002Ffile\u002Ffetch\u002F60606349\u002Fbreiman_randomforests.pdf.\nBelayneh A, Adamowski J, Khalil B, Quilty J. Coupling machine learning methods with wavelet transforms and the bootstrap and boosting ensemble approaches for drought prediction. Atmos Res. 2016;172–173:37–47. https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.atmosres.2015.12.017.\nSchapire RE. The strength of weak learnability. Mach Learn. 1990;227:197–227.\nAguirre-Gutiérrez J, Seijmonsbergen AC, Duivenvoorden JF. Optimizing land cover classification accuracy for change detection, a combined pixel-based and object-based approach in a mountainous area in Mexico. Appl Geogr. 2012;34:29–37. https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.apgeog.2011.10.010.\nKo BC, Kim HH, Nam JY. Classification of Potential Water Bodies Using Landsat 8 OLI and a Combination of Two Boosted Random Forest Classifiers. 2015; 13763–13777. https:\u002F\u002Fdoi.org\u002F10.3390\u002Fs150613763.\nSikder MF, Uddin MJ, Halder S. Predicting students yearly performance using neural network: a case study of BSMRSTU. In: 2016 5th International Conference on Informatics, Electronics and Vision, ICIEV 2016, Nov. 2016, pp. 524–529. https:\u002F\u002Fdoi.org\u002F10.1109\u002FICIEV.2016.7760058.",{"EN":1455},"In this paper, a novel application of machine learning algorithms is presented for student levelling. In multicultural countries such as UAE, there are various education curriculums where the sector of private schools and quality assurance is supervising various private schools for many nationalities. As there are various education curriculums in United Arab Emirates, specifically Abu Dhabi, to meet expats’ needs, there are different requirements for registration and success. In addition, there are different age groups for starting education in each curriculum. Every curriculum follows different education methods such as assessment techniques, reassessment rules, and exam boards. Currently, students who transfer to other curriculums are not correctly placed to their appropriate year group as a result of the start and end dates of each academic year as well as due to their date of birth, in which students who are either younger or older for that year group can create gaps in their learning and performance. In addition, pupils’ academic journeys are not stored which create a gap for the schools to track their learning process. In this paper, we propose a computational framework applicable in multicultural countries such as United Arab Emirates in which multi-education systems are implemented. Machine Learning are used to provide the appropriate student’ level aiding schools to provide a smooth transition when assigning students to their year groups and provide levelling and differentiation information of pupils for a smooth transition between one education curriculums to another, in which retrieval of their progress is possible. For classification and discriminant analysis of pupils levelling, three machine learning classifiers are utilised including random forest classifier, Artificial Neural Network, and combined classifiers. The simulation results indicated that the proposed machine learning classifiers generated effective performance in terms of accuracy.",{"EN":1457},"Evaluating student levelling based on machine learning model’s performance",{"VOID":1459},"10.1007\u002Fs43926-022-00023-0","https:\u002F\u002Flink.springer.com\u002F10.1007\u002Fs43926-022-00023-0",[1462,1478,1495,1519,1531,1543,1555,1567],{"id":1463,"sortIndex":649,"researcher":20,"roles":1464,"affiliations":1465,"properties":1475},"4663b7b5-4636-458a-b0c1-8d9ede266d76",[79],[1466],{"id":20,"sortIndex":21,"affiliation":1467,"properties":20},{"id":1468,"createTime":1469,"updateTime":1469,"relativeEntities":1470,"slug":1471,"properties":1472,"entityType":91,"verifyStatus":19,"verifyTime":20,"verifyNote":20,"syncStatus":19,"languages":20,"translateLanguages":20,"viewCount":21},"c1a44578-89ba-41b5-8bf6-3a388c51b958","2024-04-16T03:23:48.485+00:00",[],"Faculty-of-Engineering-and-Technology-Liverpool-John-Moores-University-Liverpool-UK",{"title":1473},{"EN":1474},"Faculty of Engineering and Technology, Liverpool John Moores University, Liverpool, UK",{"title":1476},{"VI":1477},"Rawaa Al-Jumeily",{"id":1479,"sortIndex":550,"researcher":20,"roles":1480,"affiliations":1481,"properties":1492},"0be91c44-9a9e-4dd8-a966-b22685369a45",[79],[1482],{"id":20,"sortIndex":21,"affiliation":1483,"properties":20},{"id":1484,"createTime":1485,"updateTime":1486,"relativeEntities":1487,"slug":1488,"properties":1489,"entityType":91,"verifyStatus":19,"verifyTime":20,"verifyNote":20,"syncStatus":19,"languages":20,"translateLanguages":20,"viewCount":21},"857992c2-5f53-4179-9e7d-961d938a46d6","2024-01-08T21:34:15.986+00:00","2025-01-25T03:41:52.346+00:00",[],"College-of-Engineering-University-of-Sharjah-Sharjah-United-Arab-Emirates",{"title":1490},{"VI":1491},"College of Engineering, University of Sharjah, Sharjah, United Arab Emirates",{"title":1493},{"VI":1494},"Ahmed Al Shammaa",{"id":1496,"sortIndex":110,"researcher":20,"roles":1497,"affiliations":1498,"properties":1516},"7d4ecf69-ece6-44b4-a70a-d2e8c109273c",[79],[1499,1511],{"id":1500,"sortIndex":110,"affiliation":1501,"properties":1510},"a7ac972c-d2c7-4c19-af3e-317b75f260a9",{"id":1502,"createTime":1503,"updateTime":1504,"relativeEntities":1505,"slug":1506,"properties":1507,"entityType":91,"verifyStatus":19,"verifyTime":20,"verifyNote":20,"syncStatus":19,"languages":20,"translateLanguages":20,"viewCount":21},"de066909-0ee7-4f79-bc40-fba3ed698db9","2024-04-07T13:57:01.760+00:00","2024-08-27T00:36:04.299+00:00",[],"-College-of-computing-and-informatics-University-of-Sharjah-Sharjah-United-Arab-Emirates",{"title":1508},{"VI":1509}," College of computing and informatics, University of Sharjah, Sharjah, United Arab Emirates",{},{"id":20,"sortIndex":21,"affiliation":1512,"properties":20},{"id":1468,"createTime":1469,"updateTime":1469,"relativeEntities":1513,"slug":1471,"properties":1514,"entityType":91,"verifyStatus":19,"verifyTime":20,"verifyNote":20,"syncStatus":19,"languages":20,"translateLanguages":20,"viewCount":21},[],{"title":1515},{"EN":1474},{"title":1517},{"VI":1518},"Abir Jaafar Hussain",{"id":1520,"sortIndex":47,"researcher":20,"roles":1521,"affiliations":1522,"properties":1528},"943aaf85-8bc9-42d6-87c0-8558202de647",[79],[1523],{"id":20,"sortIndex":21,"affiliation":1524,"properties":20},{"id":1502,"createTime":1503,"updateTime":1504,"relativeEntities":1525,"slug":1506,"properties":1526,"entityType":91,"verifyStatus":19,"verifyTime":20,"verifyNote":20,"syncStatus":19,"languages":20,"translateLanguages":20,"viewCount":21},[],{"title":1527},{"VI":1509},{"title":1529},{"VI":1530},"Thar Baker",{"id":1532,"sortIndex":21,"researcher":20,"roles":1533,"affiliations":1534,"properties":1540},"a4fa17c5-31fd-4016-a6e9-2a3d1904540d",[79],[1535],{"id":20,"sortIndex":21,"affiliation":1536,"properties":20},{"id":1468,"createTime":1469,"updateTime":1469,"relativeEntities":1537,"slug":1471,"properties":1538,"entityType":91,"verifyStatus":19,"verifyTime":20,"verifyNote":20,"syncStatus":19,"languages":20,"translateLanguages":20,"viewCount":21},[],{"title":1539},{"EN":1474},{"title":1541},{"VI":1542},"Shatha Ghareeb",{"id":1544,"sortIndex":97,"researcher":20,"roles":1545,"affiliations":1546,"properties":1552},"a3e514b3-5e77-4014-83c9-2dab3c946548",[79],[1547],{"id":20,"sortIndex":21,"affiliation":1548,"properties":20},{"id":1468,"createTime":1469,"updateTime":1469,"relativeEntities":1549,"slug":1471,"properties":1550,"entityType":91,"verifyStatus":19,"verifyTime":20,"verifyNote":20,"syncStatus":19,"languages":20,"translateLanguages":20,"viewCount":21},[],{"title":1551},{"EN":1474},{"title":1553},{"VI":1554},"Dhiya Al-Jumeily",{"id":1556,"sortIndex":185,"researcher":20,"roles":1557,"affiliations":1558,"properties":1564},"2e85e009-fb3b-446f-b26a-634a8b4cbe3f",[79],[1559],{"id":20,"sortIndex":21,"affiliation":1560,"properties":20},{"id":1468,"createTime":1469,"updateTime":1469,"relativeEntities":1561,"slug":1471,"properties":1562,"entityType":91,"verifyStatus":19,"verifyTime":20,"verifyNote":20,"syncStatus":19,"languages":20,"translateLanguages":20,"viewCount":21},[],{"title":1563},{"EN":1474},{"title":1565},{"VI":1566},"Wasiq Khan",{"id":1568,"sortIndex":364,"researcher":20,"roles":1569,"affiliations":1570,"properties":1580},"1fe4504d-4b59-4784-8c29-de5e503ab1dd",[79],[1571],{"id":20,"sortIndex":21,"affiliation":1572,"properties":20},{"id":1573,"createTime":1574,"updateTime":1574,"relativeEntities":1575,"slug":1576,"properties":1577,"entityType":91,"verifyStatus":19,"verifyTime":20,"verifyNote":20,"syncStatus":19,"languages":20,"translateLanguages":20,"viewCount":21},"8d3da592-e246-4970-ba46-f74f5214f1fd","2023-11-28T13:29:46.603+00:00",[],"Department-of-Computer-Science-Al-Maarif-University-College-Ramadi-Iraq",{"title":1578},{"VI":1579},"Department of Computer Science, Al-Maarif University College, Ramadi, Iraq",{"title":1581},{"VI":1582},"Mohammed Khalaf",{"url":1460,"publisher":1584,"properties":1604},{"id":6,"createTime":7,"updateTime":8,"relativeEntities":1585,"slug":10,"properties":1586,"entityType":18,"verifyStatus":19,"verifyTime":20,"verifyNote":20,"syncStatus":19,"languages":20,"translateLanguages":20,"viewCount":21,"subjectFields":1590,"manageAffiliations":1591,"indexDatabases":1592,"url":20,"thumbnailPath":20,"statistic":1599,"gsStatistic":20,"type":51,"analyzePriority":20},[],{"issn":1587,"title":1588,"url":1589},{"VOID":13},{"EN":15},{"VOID":17},[],[],[1593],{"id":26,"indexDatabase":1594,"url":39,"indexYears":40,"academicFieldIds":20,"indexDatabaseRanking":41},{"id":28,"createTime":29,"updateTime":30,"relativeEntities":1595,"label":1596,"description":1597,"key":36,"publicationTags":1598,"standard":20},[],{"EN":33,"VI":33},{"EN":33,"VI":35},[38],{"impactFactor":21,"impactFactorByYear":1600,"i10Index":21,"i10IndexLast5Year":21,"totalPublication":44,"totalPublicationByYear":1601,"totalCitation":21,"totalCitationByYear":1602,"totalCitationPerPublication":21,"totalCitationPerPublicationByYear":1603,"hindexLast5Year":21,"hindex":21},{},{"2021":46,"2022":47,"2023":48},{},{},{"volume":1605,"pages":1607},{"VOID":1606},"2",{"VOID":1608},"1-25","2022-05-30",2022,{"id":1612,"createTime":1613,"updateTime":1614,"relativeEntities":1615,"slug":1616,"properties":1617,"entityType":71,"verifyStatus":72,"verifyTime":1614,"verifyNote":73,"syncStatus":19,"languages":20,"translateLanguages":20,"viewCount":21,"primaryUrl":1626,"fullTextUrl":20,"authors":1627,"publicationType":121,"publisherRelationship":1657,"citationCount":20,"citationInfo":20,"publishDate":1682,"publishYear":1610,"citationAnalyzeStatus":19,"lastCitationAnalyze":20,"indexDatabases":20,"openAccess":20,"references":20,"isForceReanalyzing":150},"0d4549ce-3269-4838-a299-bad58a413fe3","2023-12-29T12:51:17.274+00:00","2025-01-26T19:42:45.118+00:00",[],"A-framework-for-evaluating-security-risk-in-system-design",{"references":1618,"abstract":1620,"title":1622,"doi":1624},{"VOID":1619},"Alfakeeh AS, Almalawi A, Alsolami FJ, Abushark YB, Khan AI, Bahaddad AAS, Agrawal A, Kumar R, Khan RA. Hesitant fuzzy-sets based decision-making model for security risk assessment. CMC-Comput Mater Contin. 2022;70:2297–317.\nAmoore L. Security and the incalculable. Secur Dialogue. 2014;45(5):423–39.\nBandi C, Salehi S, Hassan R, PD SM, Homayoun H, Rafatirad S. Ontology-driven framework for trend analysis of vulnerabilities and impacts in IoT hardware. In 2021 IEEE 15th international conference on semantic computing (ICSC), IEEE, 2021;211–4.\nBellay J, Forte D, Martin R, Taylor C. Hardware vulnerability description, sharing and reporting: challenges and opportunities. GOMACTech. 2021.\nCybersecurity and infrastructure security agency. ICS advisory (ICSA-14-202-01A). https:\u002F\u002Fwww.cisa.gov\u002Fuscert\u002Fics\u002Fadvisories\u002FICSA-14-202-01A. Accessed 21 June 2022.\nde Gusmão APH, Silva MM, Poleto T, e Silva LC, Costa APCS. Cybersecurity risk analysis model using fault tree analysis and fuzzy decision theory. Int J Inform Manag. 2018;43:248–60.\nEvaluators IS. Securing hospitals: a research student and blueprint. 2017. https:\u002F\u002Fwww.ise.io\u002Fwp-content\u002Fuploads\u002F2017\u002F07\u002Fsecuring_hospitals.pdf. Accessed 27 Apr 2022.\nFeiler PH, Gluch DP, Hudak JJ. The architecture analysis & design language (AADL): an introduction. Technical report, Carnegie-Mellon Univ Pittsburgh PA Software Engineering Inst. 2006.\nFerrante A, Milosevic J, JanjuJanjus̆evic̀ M. A security-enhanced design methodology for embedded systems. In proceedings of international conference on security and cryptography (SECRYPT). 2013:39–50.\nGreen B, Chen Y. Algorithmic risk assessments can alter human decision-making processes in high-stakes government contexts. Proc ACM Hum Comput Interact. 2021;5(CSCW2):1–33.\nHuff P, McClanahan K, Le T, Li Q. A recommender system for tracking vulnerabilities. In the 16th international conference on availability, reliability and security. 2021:1–7.\nIBM. Cost of a data breach report. 2022. https:\u002F\u002Fwww.ibm.com\u002Fsecurity\u002Fdata-breach. Accessed 27 Apr 2022.\nINTEL Corporation. INTEL-SA-00127. https:\u002F\u002Fwww.intel.com\u002Fcontent\u002Fwww\u002Fus\u002Fen\u002Fsecurity-center\u002Fadvisory\u002Fintel-sa-00127.html. Accessed 21 June 2022.\nKalinin M, Krundyshev V, Zegzhda P. Cybersecurity risk assessment in smart city infrastructures. Machines. 2021;9(4):78.\nLenovo Corporation. Lenovo security advisory LEN-23611. https:\u002F\u002Fsupport.lenovo.com\u002Fus\u002Fen\u002Fproduct_security\u002Fps500204-intel-dci-policy-update. Accessed 21 June 2022.\nMauw S, Oostdijk M. Foundations of attack trees. In international conference on information security and cryptology. Springer. 2005:186–98.\nMichalec O, Milyaeva S, Rashid A. When the future meets the past: can safety and cyber security coexist in modern critical infrastructures? Big Data Soc. 2022;9(1):20539517221108370.\nMITRE Group. VulDB added as CVE numbering authority (CNA). https:\u002F\u002Fwww.cve.org\u002FMedia\u002FNews\u002Fitem\u002Fnews\u002F2021\u002F12\u002F21\u002FVulDB-Added-as-CVE-Numbering. Accessed 21 June 2022.\nMukhopadhyay A, Chatterjee S, Saha D, Mahanti A, Sadhukhan SK. Cyber-risk decision models: to insure it or not? Decis Support Syst. 2013;56:11–26.\nNational Institute of Standards and Technology. NVD data feeds. 2022. https:\u002F\u002Fnvd.nist.gov\u002Fvuln\u002Fdata-feeds#JSON_FEED. Accessed 24 Oct 2022.\nOleumTech. Sx1000-cc2. https:\u002F\u002Fshop.oleumtech.com\u002Fproducts\u002Fsx1000-cc2.\nOsborne L, Brummond J, Hart R, Zarean M, Conger S. Clarus: concept of operations. Technical report FHWA-JPO-05-072, Federal Highway Administration, US Department of Transportation, October 2005.\nPrinetto P, Roascio G. Hardware security, vulnerabilities, and attacks: a comprehensive taxonomy. In ITASEC, 2020:177–89.\nShreeve B, Hallett J, Edwards M, Anthonysamy P, Frey S, Rashid A. “So if mr blue head here clicks the link\\(\\ldots\\)” risk thinking in cyber security decision making. ACM Trans Priv Secur (TOPS). 2020;24(1):1–29.\nSlupska J, Dawson Duckworth SD, Ma L, Neff G. Participatory threat modelling: exploring paths to reconfigure cybersecurity. In extended abstracts of the 2021 CHI conference on human factors in computing systems. 2021:1–6.\nSmith A. The wealth of nations, vol. 11937. New York: Random House; 1776.\nSonnenreich W, Albanese J, Stout B. Return on security investment (ROSI)—a practical quantitative model. J Res Pract Inf Technol. 2006;38(1):45–56.\nStack B. Here’s how much your personal information is selling for on the dark web. 2017. https:\u002F\u002Fwww.experian.com\u002Fblogs\u002Fask-experian\u002Fheres-how-much-your-personal-information-is-selling-for-on-the-dark-web\u002F. Accessed 27 Apr 2022.\nTatham M. Identity theft statistics. 2018. https:\u002F\u002Fwww.experian.com\u002Fblogs\u002Fask-experian\u002Fidentity-theft-statistics\u002F. Accessed 27 Apr 2022.\nTechRadar Group. AMD admits Zen 3 processors are vulnerable to Spectre-like side-channel attack. https:\u002F\u002Fwww.techradar.com\u002Fnews\u002Famd-admits-zen-3-processors-are-vulnerable-to-spectre-like-side-channel-attack. Accessed 21 June 2022.\nTehranipoor F, Karimian N, Wortman PA, Chandy JA. Low-cost authentication paradigm for consumer electronics within the internet of wearable fitness tracking applications. In 2018 IEEE international conference on consumer electronics (ICCE), IEEE. 2018:1–6.\nTsaregorodtsev AV, Kravets OJ, Choporov ON, Zelenina AN. Information security risk estimation for cloud infrastructure. Int J Inform Technol Secur. 2018;11:91.\nVerizon. Data breach investigations report. 2022. https:\u002F\u002Fwww.verizon.com\u002Fbusiness\u002Fresources\u002Freports\u002Fdbir\u002F. Accessed 27 Apr 2022.\nVulDB Group. VulDB. https:\u002F\u002Fvuldb.com\u002F. Accessed 21 June 2022.\nWortman P, Chandy J. Translation of AADL model to security attack tree (TAMSAT) to SMART evaluation of monetary security risk. Inform Secur J A Glob Perspect. 2022;5:1–7.\nWortman P, Yan W, Chandy J, Tehranipoor F. P2m-based security model: security enhancement using combined PUF and PRNG models for authenticating consumer electronic devices. IET Comput Digit Techn. 2018;12(6):289–96.\nWortman PA, Chandy JA. Smart: security model adversarial risk-based tool for systems security design evaluation. J Cybersecur. 2020;6(1):tyaa003.\nWortman PA, Tehranipoor F, Chandy JA. An adversarial risk-based approach for network architecture security modeling and design. In 2018 international conference on cyber security and protection of digital services (cyber security). IEEE. 2018;1–8.\nWortman PA, Tehranipoor F, Chandy JA. Exploring the coverage of existing hardware vulnerabilities in community standards. In silicon valley cybersecurity conference, Springer. 2020;87–97.\nWortman PA, Tehranipoor F, Karimian N, Chandy JA. Proposing a modeling framework for minimizing security vulnerabilities in iot systems in the healthcare domain. In 2017 IEEE EMBS international conference on biomedical & health informatics (BHI). IEEE. 2017;185–8.",{"EN":1621},"Design and development of ubiquitous computer network systems has become increasingly difficult as technology continues to grow. From the introduction of new technologies to the discovery of existing threats, weaknesses, and vulnerabilities there is a constantly changing landscape of potential risks and rewards. The cyber security community, and industry at large, is learning to account for these increasing threats by including protections and mitigations from the beginning of the design V process. However, issues still come from limitations in time for thoroughly exploring a potential design space and the knowledge base required to easily account for potential vulnerabilities in each. To address this problem we propose the G-T-S framework, which is an automated tool that allows a user to provide a set of inputs relating to the desired design space and returns a monetary security risk evaluation of each. This methodology first generates a series of potential designs, then dissects their contents to associate possible vulnerabilities to device elements, and finally evaluates the security risk poised to a central asset of importance. We exemplify the tools, provide methodologies for required background research, and discuss the results in evaluating a series of IoT Home models using the GTS framework. Through implementation of our framework we simplify the information an individual will require to begin the design process, lower the bar for entry to perform evaluating security risk, and present the risk as an easily understood monetary metric.",{"EN":1623},"A framework for evaluating security risk in system design",{"VOID":1625},"10.1007\u002Fs43926-022-00027-w","https:\u002F\u002Flink.springer.com\u002Farticle\u002F10.1007\u002Fs43926-022-00027-w",[1628,1645],{"id":1629,"sortIndex":110,"researcher":20,"roles":1630,"affiliations":1631,"properties":1642},"b2cd00c2-7c29-4f48-9e45-02da68779511",[79],[1632],{"id":20,"sortIndex":21,"affiliation":1633,"properties":20},{"id":1634,"createTime":1635,"updateTime":1636,"relativeEntities":1637,"slug":1638,"properties":1639,"entityType":91,"verifyStatus":19,"verifyTime":20,"verifyNote":20,"syncStatus":19,"languages":20,"translateLanguages":20,"viewCount":21},"b31e9a55-20fe-44e8-9860-8d86ceb5137f","2024-04-15T18:50:21.295+00:00","2024-10-11T12:45:28.274+00:00",[],"-University-of-Connecticut-Storrs-USA-",{"title":1640},{"EN":1641},"[University of Connecticut, Storrs, USA]",{"title":1643},{"VI":1644},"John A. Chandy",{"id":1646,"sortIndex":21,"researcher":20,"roles":1647,"affiliations":1648,"properties":1654},"eeb75bd6-2bed-411c-8dae-bab85797adef",[79],[1649],{"id":20,"sortIndex":21,"affiliation":1650,"properties":20},{"id":1634,"createTime":1635,"updateTime":1636,"relativeEntities":1651,"slug":1638,"properties":1652,"entityType":91,"verifyStatus":19,"verifyTime":20,"verifyNote":20,"syncStatus":19,"languages":20,"translateLanguages":20,"viewCount":21},[],{"title":1653},{"EN":1641},{"title":1655},{"VI":1656},"Paul A. Wortman",{"url":1626,"publisher":1658,"properties":1678},{"id":6,"createTime":7,"updateTime":8,"relativeEntities":1659,"slug":10,"properties":1660,"entityType":18,"verifyStatus":19,"verifyTime":20,"verifyNote":20,"syncStatus":19,"languages":20,"translateLanguages":20,"viewCount":21,"subjectFields":1664,"manageAffiliations":1665,"indexDatabases":1666,"url":20,"thumbnailPath":20,"statistic":1673,"gsStatistic":20,"type":51,"analyzePriority":20},[],{"issn":1661,"title":1662,"url":1663},{"VOID":13},{"EN":15},{"VOID":17},[],[],[1667],{"id":26,"indexDatabase":1668,"url":39,"indexYears":40,"academicFieldIds":20,"indexDatabaseRanking":41},{"id":28,"createTime":29,"updateTime":30,"relativeEntities":1669,"label":1670,"description":1671,"key":36,"publicationTags":1672,"standard":20},[],{"EN":33,"VI":33},{"EN":33,"VI":35},[38],{"impactFactor":21,"impactFactorByYear":1674,"i10Index":21,"i10IndexLast5Year":21,"totalPublication":44,"totalPublicationByYear":1675,"totalCitation":21,"totalCitationByYear":1676,"totalCitationPerPublication":21,"totalCitationPerPublicationByYear":1677,"hindexLast5Year":21,"hindex":21},{},{"2021":46,"2022":47,"2023":48},{},{},{"volume":1679,"pages":1680},{"VOID":1606},{"VOID":1681},"1-30","2022-12-05"]