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IOT systems and interoperability. 2023. https:\u002F\u002Fdoi.org\u002Fhttps:\u002F\u002Fwww.trialog.com\u002Fen\u002Fiot-systems-and-interoperability\u002F. Accessed 19 July 2023.\nGonzalez-Usach R, Palau CE, Julian M, Belsa A, Llorente MA, Montesinos M, Ganzha M, Wasielewska K, Sala P. Next generation internet of things–distributed intelligence at the edge and human- machine interactions. River Publishers, 2022; p. 139–73.\nPetrasch RJ, Petrasch RR. Data integration and interoperability: towards a model-driven and pattern-oriented approach. Modelling. 2022;3(1):105–26.\nBalakrishna S, Thirumaran M, Solanki V. IoT sensor data integration in healthcare using semantics and machine learning approaches, pp. 275–300. 2019. https:\u002F\u002Fdoi.org\u002F10.1007\u002F978-3-030-23983-1_11\nMehta Y. Data integration in the Internet of Things (IoT) ecosystem | IoT Now News & Reports. 2022. https:\u002F\u002Fwww.iot-now.com\u002F2022\u002F02\u002F23\u002F119714-data-integration-in-the-internet-of-things-iot-ecosystem\u002F. Accessed 19 July 2023.\nWhat is interoperability in healthcare? 2023. https:\u002F\u002Fdoi.org\u002Fhttps:\u002F\u002Fwww.ibm.com\u002Fin-en\u002Ftopics\u002Finteroperability-in-healthcare. Accessed 19 July 2023.\nPhan L-A, Kim T. Breaking down the compatibility problem in smart homes: a dynamically updatable gateway platform. Sensors. 2020;20:2783. https:\u002F\u002Fdoi.org\u002F10.3390\u002Fs20102783.\nInformatica: What is data integration. 2023. https:\u002F\u002Fwww.informatica.com\u002Fresources\u002Farticles\u002Fwhat-is-data-integration.html. Accessed 19 July 2023.\nK2View: data integration tools | K2View. https:\u002F\u002Fdoi.org\u002Fhttps:\u002F\u002Fwww.k2view.com\u002Fplatform\u002Fdata-integration-tools\u002F. Accessed 19 July 2023.\nGartner I. K2View data product platform review in data integration tools. 2022. https:\u002F\u002Fwww.gartner.com\u002Freviews\u002Fmarket\u002Fdata-integration-tools\u002Fvendor\u002Fk2view\u002Fproduct\u002Fk2view-data-product-platform\u002Freview\u002Fview\u002F4098826. Accessed 19 July 2023.\nVarma O. What is data transformation?: A comprehensive guide 101. 2020. https:\u002F\u002Fhevodata.com\u002Flearn\u002Fwhat-is-data-transformation\u002F. Accessed 19 July 2023.\nIBM App Connect Enterprise introduction. IBM. 2023. https:\u002F\u002Fwww.ibm.com\u002Fdocs\u002Fen\u002Fapp-connect\u002F11.0.0?topic=overview-app-connect-enterprise-introduction. Accessed 19 July 2023.\nIBM App Connect Enterprise software. 2023. https:\u002F\u002Fwww.ibm.com\u002Fdocs\u002Fen\u002Fapp-connect\u002F11.0.0?topic=app-connect-enterprise-software. Accessed 19 July 2023.\nBacklund N. Implementing amazon web services integration connector with IBM app connect enterprise. 2023. https:\u002F\u002Fdoi.org\u002Fhttps:\u002F\u002Furn.fi\u002FURN:NBN:fi:amk-2020100520985. Accessed 19 July 2023.\nIntegrating ERP and CRM Applications with IBM WebSphere Cast Iron | IBM Redbooks. 2013. https:\u002F\u002Fwww.redbooks.ibm.com\u002FRedbooks.nsf\u002FRedbookAbstracts\u002Ftips0961.html?Open. Accessed 19 July 2023.\nCloud data mashups using IBM App Connect. IBM. 2023. https:\u002F\u002Fwww.ibm.com\u002Fdocs\u002Fen\u002Fapp-connect\u002Fcloud?topic=cases-cloud-data-mashups-using-app-connect. Accessed 19 July 2023.\nCloud data migration using IBM App Connect. IBM. 2023. https:\u002F\u002Fwww.ibm.com\u002Fdocs\u002Fen\u002Fapp-connect\u002Fcloud?topic=cases-cloud-data-migration-using-app-connect. Accessed 19 July 2023.\nCloud data synchronization using IBM App Connect. IBM. 2023. https:\u002F\u002Fwww.ibm.com\u002Fdocs\u002Fen\u002Fapp-connect\u002Fcloud?topic=cases-cloud-data-synchronization-using-app-connect. Accessed 19 July 2023.\nOData APIs and connectivity using IBM App Connect. 2023. https:\u002F\u002Fwww.ibm.com\u002Fdocs\u002Fen\u002Fapp-connect\u002Fcloud?topic=cases-odata-apis-connectivity-using-app-connect. Accessed 19 July 2023.\nGonzalez-Usach R, Julian M, Esteve M, Palau C. Federation of aal & aha systems through semantically interoperable framework. In: 2021 IEEE International Conference on Communications Workshops (ICC Workshops); 2021, p. 1–6. https:\u002F\u002Fdoi.org\u002F10.1109\u002FICCWorkshops50388.2021.9473503\nJaleel A, Mahmood T, Hassan MA, Bano G, Khurshid SK. Towards medical data interoperability through collaboration of healthcare devices. IEEE Access. 2020;8:132302–19.\nAhmed A, Kleiner M, Roucoules L. Model-based interoperability IOT hub for the supervision of smart gas distribution networks. IEEE Syst J. 2018;13(2):1526–33.\nModoni GE, Caldarola EG, Mincuzzi N, Sacco M, Wasielewska K, Szmeja P, Ganzha M, Paprzycki M, Pawłowski W. Integrating IOT platforms using the inter-IOT approach: a case study of the casaware project. J Ambient Intell Smart Environ. 2020;12(6):457–74.\nSingh M, Wu W, Rizou S, Vakaj E. Data information interoperability model for IOT-enabled smart water networks. In: 2022 IEEE 16th International Conference on Semantic Computing (ICSC), IEEE; 2022, p. 179–186.\nBallard C, Bhat V, Choudhary S, Ravindranath R, Ruiz EA, Titus A. InfoSphere datastage for enterprise XML data integration. 2012. https:\u002F\u002Fwww.redbooks.ibm.com\u002Fredbooks\u002Fpdfs\u002Fsg247987.pdf.\nDerhamy H, Eliasson J, Delsing J, Priller P. A survey of commercial frameworks for the internet of things. In: 2015 IEEE 20th Conference on Emerging Technologies & Factory Automation (etfa), IEEE; 2015, p. 1–8.\nJSON-LD 1.1. 2020. https:\u002F\u002Fwww.w3.org\u002FTR\u002Fjson-ld11\u002F. Accessed 19 July 2023.\nProtocol Buffers. 2023. https:\u002F\u002Fprotobuf.dev\u002F. Accessed 19 July 2023.\nShafranovich Y. Common format and MIME type for comma-separated values (CSV) files. RFC Editor. 2005. https:\u002F\u002Fdoi.org\u002F10.17487\u002FRFC4180. https:\u002F\u002Fwww.rfc-editor.org\u002Finfo\u002Frfc4180\nBray T. The JavaScript Object Notation (JSON) Data Interchange Format. RFC Editor. 2017. https:\u002F\u002Fdoi.org\u002F10.17487\u002FRFC8259. https:\u002F\u002Fwww.rfc-editor.org\u002Finfo\u002Frfc8259\nMoriarty K, Farrell S. Deprecating TLS 1.0 and TLS 1.1. RFC Editor. 2021. https:\u002F\u002Fdoi.org\u002F10.17487\u002FRFC8996. https:\u002F\u002Fwww.rfc-editor.org\u002Finfo\u002Frfc8996\nLindsey H. What are vital signs, and what can they tell us about our health?. 2022. https:\u002F\u002Fwww.healthline.com\u002Fhealth\u002Fwhat-are-vital-signs. Accessed 19 July 2023.\nDiGiacinto J, Seladi-Schulman J. Normal vs. dangerous heart rate: How to tell the difference. Heathline. 2022. https:\u002F\u002Fdoi.org\u002Fhttps:\u002F\u002Fwww.healthline.com\u002Fhealth\u002Fdangerous-heart-rate. Accessed 19 July 2023.\nSharma S, Hashmi MF. Hypotension. [Updated 2022 Feb 16]. 2022. https:\u002F\u002Fwww.ncbi.nlm.nih.gov\u002Fbooks\u002FNBK499961\u002F. Accessed 19 July 2023.\nWalker HK, Hall WD, Hurst JW. Clinical methods: the history, physical, and laboratory examinations,chapter—218. 1990. https:\u002F\u002Fdoi.org\u002Fhttps:\u002F\u002Fwww.ncbi.nlm.nih.gov\u002Fbooks\u002FNBK331\u002F.\nShaikh J. MD: what are blood oxygen levels by age? Chart, normal, high & low. 2022. Accessed 19 July 2023.\nHolland K. Is my blood oxygen level normal?. 2022. https:\u002F\u002Fwww.healthline.com\u002Fhealth\u002Fnormal-blood-oxygen-level. Accessed 19 July 2023.\nwhat is the ideal room temperature?. 2022. https:\u002F\u002Fwww.vaillant.co.uk\u002Fhomeowners\u002Fadvice-and-knowledge\u002Fwhat-is-the-ideal-room-temperature-1769698.html. Accessed 19 July 2023.\nBannister M. How humidity damages home. Airthings. 2021. https:\u002F\u002Fwww.airthings.com\u002Fresources\u002Fhome-humidity-damage. Accessed 19 July 2023.\nSinaga KP, Yang M-S. Unsupervised k-means clustering algorithm. IEEE Access. 2020;8:80716–27. https:\u002F\u002Fdoi.org\u002F10.1109\u002FACCESS.2020.2988796.\nMüllner D. Modern hierarchical, agglomerative clustering algorithms. 2011. arXiv:1109.2378.\nSharma P. What is hierarchical clustering in python Analytics Vidhya (2023). https:\u002F\u002Fdoi.org\u002Fhttps:\u002F\u002Fwww.analyticsvidhya.com\u002Fblog\u002F2019\u002F05\u002Fbeginners-guide-hierarchical-clustering\u002F. Accessed 19 July 2023.\nCervantes J, Garcia-Lamont F, Rodríguez-Mazahua L, Lopez A. A comprehensive survey on support vector machine classification: applications, challenges and trends. Neurocomputing. 2020;408:189–215.\nCunningham P, Delany SJ. k-nearest neighbour classifiers—a tutorial. ACM Comput Surv (CSUR). 2021;54(6):1–25.\nCharbuty B, Abdulazeez A. Classification based on decision tree algorithm for machine learning. J Appl Sci Technol Trends. 2021;2(01):20–8.\nBiau G, Scornet E. A random forest guided tour. Test. 2016;25:197–227.\nCutler A, Cutler DR, Stevens JR. Random forests. In: Ensemble machine learning: methods and applications. 2012. p. 157–75.\nBi Z-j, Han Y-q, Huang C-q, Wang M. Gaussian naive bayesian data classification model based on clustering algorithm. In: 2019 International Conference on Modeling, Analysis, Simulation Technologies and Applications (MASTA 2019). Atlantis Press; 2019, p. 396–400.\nGuest_blog: Introduction to XGBOOST Algorithm in Machine Learning. Analytics Vidhya. 2023. https:\u002F\u002Fdoi.org\u002Fhttps:\u002F\u002Fwww.analyticsvidhya.com\u002Fblog\u002F2018\u002F09\u002Fan-end-to-end-guide-to-understand-the-math-behind-xgboost\u002F. Accessed 19 July 2023",{"EN":149},"Data interoperability is a crucial requirement in IoT to improve services and enhance business opportunities and innovation. Integrating synergetic applications with heterogeneous data formats is a critical issue that needs to be addressed to achieve interoperability. The use cases indicate IBM ACE is promising in resolving integration issues among on-premises and cloud applications. Further, many efforts are observed to address the interoperability issue apart from the IBM ACE approach. However, they are complex, restricted to few data formats, and use proprietary solutions. To address these above-mentioned issues, this paper proposes the Integration of Synergetic IoT applications with Heterogeneous format data for Interoperability using IBM ACE (ISHII). Further, an intelligence-based data recognition module in the proposed ISHII is trained with standard features defined in RFC 7111, 8259, 8996, JSON-LD of W3C, and Google’s Protobuf. Subsequently, recognized heterogeneous format data are integrated and translated to interoperable format using Data Format Description Language (DFDL) with Extended SQL codes on IBM ACE. Finally, the performance of ISHII has been evaluated with synthetically generated patient monitoring and room ambiance datasets with reference to accuracy, time required for integration, and translation efficiency.",{"EN":151},"Integration of Synergetic IoT Applications with Heterogeneous Format Data for Interoperability Using IBM ACE",{"VOID":153},"10.1007\u002Fs42979-023-02279-x","PUBLICATION","VERIFIED","2025-01-17T23:59:56.028+00:00","Auto Verify","https:\u002F\u002Flink.springer.com\u002Farticle\u002F10.1007\u002Fs42979-023-02279-x",[160,177],{"id":161,"sortIndex":162,"researcher":20,"roles":163,"affiliations":165,"properties":174},"f3e82cd0-58b9-4b13-94a1-2e2d1e5df1d5",1,[164],"AUTHOR",[166],{"id":20,"sortIndex":21,"affiliation":167,"properties":20},{"id":168,"createTime":169,"updateTime":169,"relativeEntities":170,"slug":20,"properties":171,"entityType":80,"verifyStatus":19,"verifyTime":20,"verifyNote":20,"syncStatus":19,"languages":20,"translateLanguages":20,"viewCount":21},"495686db-2fef-4401-8f75-cb19e1ce9e31","2023-12-06T13:01:19.856+00:00",[],{"title":172},{"VI":173},"Department of Computer Science and Engineering, National Institute of Technology Karnataka, Mangalore, India",{"title":175},{"VI":176},"B. 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In: 24th international conference on enterprise information systems, vol 1. 2022. p. 267–272.\nTapsai C, Meesad P. Natural language interface to database for data retrieval and processing. Appl Sci Eng Prog. 2020;14:435–46.\nXiang S, Tang J, Yang L, Guo Y, Zhao Z, Zhang W. Deep learning-enabled real-time personal handwriting electronic skin with dynamic thermoregulating ability. Npj Flex Electron. 2022;6(1):59.\nLuo Z, Zhang J, Fei J, Ke S. Deep learning modeling m6a deposition reveals the importance of downstream cis-element sequences. Nat Commun. 2022;13(1):2720.\nMaliyaem M, Tuan NM, Lockhart D, Muenthong S. A study of using machine learning in predicting covid-19 cases. NCloud Computing and Data Science. 2022. p. 54–61.\nMinh TN, Meesad P, Ha HCN. English–Vietnamese machine translation using deep learning. Recent Adv Inf Commun Technol. 2021;2021(251):99–107.\nChotirat S, Meesad P. Natural language processing with “more than words—BERT’’. Recent Adv Inf Commun Technol. 2021;2021(251):108–16. https:\u002F\u002Fdoi.org\u002F10.1007\u002F978-3-030-79757-7_11.\nCalin O. Deep learning architectures: a mathematical approach. Berlin: Springer; 2020.\nXu S, Li J, Liu K, Wu L. A parallel gru recurrent network model and its application to multi-channel time-varying signal classification. IEEE Access. 2019;7:118739–48. https:\u002F\u002Fdoi.org\u002F10.1109\u002FACCESS.2019.2936516.\nVaswani A, Shazeer N, Parmar N, Uszkoreit J, Jones L, Gomez AN, Kaiser L, Polosukhin I. Attention is all you need. In: Proceedings of the 31st international conference on neural information processing systems. 2017.\nAggarwal A, Chauhan A, Kumar D, Mittal M, Verma S, Devlin J. Classification of fake news by fine-tuning deep bidirectional transformers based language model. European Alliance for Innovation (EAI). 2020.\nKrishnan S, Magalingam P, Ibrahim R. Hybrid deep learning model using recurrent neural network and gated recurrent unit for heart disease prediction. Int J Electr Comput Eng. 2021;11(6):5467.\nAdamopoulou E, Moussiades L. An overview of chatbot technology. In: IFIP international conference on artificial intelligence applications and innovations, vol 584. 2020. p. 373–383. https:\u002F\u002Fdoi.org\u002F10.1007\u002F978-3-030-49186-4_31.\nLu Z, Chen Y. User evaluation sentiment analysis model based on machine learning. In: 2nd international conference on consumer electronics and computer engineering (ICCECE). 2022. p. 461–464.\nMeesad Phayung. Thai fake news detection based on information retrieval, natural language processing and machine learning. SN Comput Sci. 2021;2(6):425.\nChotirat S, Meesad P. Part-of-speech tagging enhancement to natural language processing for thai wh-question classification with deep learning. Heliyon. 2021;7(10):08216.\nDutta N, Kaliannan P, Shanmugam P. Application of machine learning for inter turn fault detection in pumping system. Sci Rep. 2022;12:12960.\nPopel M, Tomkova M, Tomek J, Kaiser Ł, Uszkoreit J, Bojar O, Žabokrtský Z,. Transforming machine translation: a deep learning system reaches news translation quality comparable to human professionals. Nat Commun. 2020;11:4381.\nJavidi H, Mariam A, Khademi G, Zabor EC, Zhao R, Radivoyevitch T, Rotroff DM. Identification of robust deep neural network models of longitudinal clinical measurements. Npj Digit Med. 2022;5:106.\nRenaud N, Geng C, Georgievska S, Ambrosetti F, Ridder L, Marzella DF, Réau MF, Bonvin AMJJ, Xue LC. Deeprank: a deep learning framework for data mining 3d protein–protein interfaces. Nat Commun. 2021;12:7068.\nChen W, Zhichen Y, Simon F. How to build a chatbot : chatbot framework and its capabilities. In: 10th international conference on machine learning and computing. 2018. p. 369–373.\nPlunza RA, Zhoua Y, Vintimillab MIC, Mckeownc K, Yud T, Uguccionia L, Sutto MP. Twitter sentiment in New York city parks as measure of well-being. Sci Direct. 2019;189:235–46.\nNgoc PV, Ngoc CVT, Ngoc TVT, Nguyen DD, Doan DKL. A valence-totaling model for Vietnamese sentiment classification. Evol Syst. 2019;10(3):453–99.\nAmaar A, Aljedaani W, Rustam F, Ullah S, Rupapara V, Ludi S. Detection of fake job postings by utilizing machine learning and natural language processing approaches. Neural Process Lett. 2022;54(3):2219–47.",{"EN":230},"Recently, artificial intelligence-based machine translation has been much improved over traditional methods. A machine translator is very useful for translating text or speech from one language to another. Machine translators have replaced the word mechanism in one language for words in another with verbatim translations. However, a good translation should be employed as both a sentence and a word that has a completed meaning in accordance with the context of the relevant sentence. In this paper, we studied English–Vietnamese translation using deep learning methods including recurrent neural network, long short-term memory, gated recurrent units, attention, and transformer. The deep learning-based machine translators were compared based on the test accuracy of the result translation. It was found that the best deep learning-based machine translator model was the Attention mechanism, and the Transformer yielded the second rank.",{"EN":232},"English–Vietnamese Machine Translation Using Deep Learning for Chatbot Applications",{"VOID":234},"10.1007\u002Fs42979-023-02339-2","https:\u002F\u002Flink.springer.com\u002Farticle\u002F10.1007\u002Fs42979-023-02339-2",[237,253,268],{"id":238,"sortIndex":239,"researcher":20,"roles":240,"affiliations":241,"properties":250},"f6663a25-0e46-4d5d-9271-37680f7b04bd",2,[164],[242],{"id":20,"sortIndex":21,"affiliation":243,"properties":20},{"id":244,"createTime":245,"updateTime":245,"relativeEntities":246,"slug":20,"properties":247,"entityType":80,"verifyStatus":19,"verifyTime":20,"verifyNote":20,"syncStatus":19,"languages":20,"translateLanguages":20,"viewCount":21},"a1dd5c5e-7261-4206-bb24-6717cb94eaa4","2024-01-27T17:28:07.071+00:00",[],{"title":248},{"VI":249},"Software Development Centre, University of Danang, Le Duan, Hai Chau, Danang, Vietnam",{"title":251},{"VI":252},"Ha Huy Cuong Nguyen",{"id":254,"sortIndex":162,"researcher":20,"roles":255,"affiliations":256,"properties":265},"c7d0ea3f-9fc8-43fa-a414-8b1736879592",[164],[257],{"id":20,"sortIndex":21,"affiliation":258,"properties":20},{"id":259,"createTime":260,"updateTime":260,"relativeEntities":261,"slug":20,"properties":262,"entityType":80,"verifyStatus":19,"verifyTime":20,"verifyNote":20,"syncStatus":19,"languages":20,"translateLanguages":20,"viewCount":21},"5989d3cd-1aa9-4f6d-bebf-a74e0071e22b","2024-01-27T17:28:07.058+00:00",[],{"title":263},{"VI":264},"Information Technology and Management Department, King Mongkut’s University of Technology North Bangkok, Pracharat, Bangsue, Bangkok, Thailand",{"title":266},{"VI":267},"Phayung Meesad",{"id":269,"sortIndex":21,"researcher":20,"roles":270,"affiliations":271,"properties":280},"fc721d64-8bff-4b3c-b9ae-46a3546f6171",[164],[272],{"id":20,"sortIndex":21,"affiliation":273,"properties":20},{"id":274,"createTime":275,"updateTime":275,"relativeEntities":276,"slug":20,"properties":277,"entityType":80,"verifyStatus":19,"verifyTime":20,"verifyNote":20,"syncStatus":19,"languages":20,"translateLanguages":20,"viewCount":21},"eaf90682-4cfa-4707-ba68-089eb9ffe408","2024-01-27T17:28:07.042+00:00",[],{"title":278},{"VI":279},"Mathematics Department, King Mongkut’s University of Technology North Bangkok, Pracharat, Bangsue, Bangkok, Thailand",{"title":281},{"VI":282},"Nguyen Minh Tuan",{"url":235,"publisher":284,"properties":305},{"id":6,"createTime":7,"updateTime":8,"relativeEntities":285,"slug":10,"properties":286,"entityType":18,"verifyStatus":19,"verifyTime":20,"verifyNote":20,"syncStatus":19,"languages":20,"translateLanguages":20,"viewCount":21,"subjectFields":290,"manageAffiliations":291,"indexDatabases":292,"url":107,"thumbnailPath":20,"statistic":300,"gsStatistic":20,"type":134,"analyzePriority":20},[],{"issn":287,"eissn":288,"title":289},{"VOID":13},{"VOID":15},{"EN":17},[],[],[293],{"id":84,"indexDatabase":294,"url":97,"indexYears":98,"academicFieldIds":299,"indexDatabaseRanking":106},{"id":86,"createTime":87,"updateTime":88,"relativeEntities":295,"label":296,"description":297,"key":94,"publicationTags":298,"standard":20},[],{"EN":91,"VI":91},{"EN":91,"VI":93},[96],[100,101,102,103,104,105],{"impactFactor":21,"impactFactorByYear":301,"i10Index":113,"i10IndexLast5Year":114,"totalPublication":115,"totalPublicationByYear":302,"totalCitation":123,"totalCitationByYear":303,"totalCitationPerPublication":128,"totalCitationPerPublicationByYear":304,"hindexLast5Year":133,"hindex":133},{"2021":110,"2022":111,"2023":112},{"2019":117,"2020":118,"2021":119,"2022":120,"2023":121,"2024":122},{"2020":125,"2021":126,"2022":127},{"2020":130,"2021":131,"2022":132},{"volume":306,"pages":307},{"VOID":214},{"VOID":308},"1-10",{"id":310,"createTime":311,"updateTime":312,"relativeEntities":313,"slug":314,"properties":315,"entityType":154,"verifyStatus":155,"verifyTime":312,"verifyNote":157,"syncStatus":19,"languages":20,"translateLanguages":20,"viewCount":21,"primaryUrl":324,"fullTextUrl":20,"authors":325,"publicationType":189,"publisherRelationship":375,"citationCount":20,"citationInfo":20,"publishDate":401,"publishYear":218,"citationAnalyzeStatus":19,"lastCitationAnalyze":20,"indexDatabases":20,"openAccess":20,"references":20,"isForceReanalyzing":219},"16b7fbf9-709c-4987-b08d-1df844f33bb7","2024-01-08T23:31:51.477+00:00","2025-02-18T23:58:13.360+00:00",[],"Efficient-Neuroimaging-Data-Security-and-Encryption-Using-Pixel-Based-Homomorphic-Residue-Number-System",{"references":316,"abstract":318,"title":320,"doi":322},{"VOID":317},"Lundervold AS, Lundervold A. An overview of deep learning in medical imaging focusing on MRI. Z Med Phys. 2019;29:102–27.\nPłoński P, et al. Multi-parameter machine learning approach to the neuroanatomical basis of developmental dyslexia. Hum Brain Mapp. 2017;38:900–8.\nShen D, Wu G, Suk H. Deep learning in medical image analysis. Annu Rev Biomed Eng. 2017;19:221–48.\nUsman OL, Muniyandi RC. CryptoDL: predicting dyslexia biomarkers from encrypted neuroimaging dataset using energy-efficient residue number system and deep convolutional neural network. Symmetry (Basel). 2020;12:1–24.\nUsman OL, Muniyandi RC, Omar K, Mohamad M. Advance machine learning methods for dyslexia biomarker detection: a review of implementation details and challenges. IEEE Access. 2021;9:36879–97.\nHasan MK, et al. Review on cyber-physical and cyber-security system in smart grid: standards, protocols, constraints, and recommendations. J Netw Comput Appl. 2023;209:1–23.\nAlex S, Dhanaraj KJ, Deepthi PP. Private decision tree-based disease detection with energy-efficiency at resource-constrained medical user in mobile healthcare network. IEEE Access. 2022. https:\u002F\u002Fdoi.org\u002F10.1109\u002FACCESS.2022.3149771.\nKwabena OA, Qin Z, Zhuang T, Qin Z. MSCryptoNet: multi-scheme privacy-preserving deep learning in cloud computing. IEEE Access. 2019;7:29344–54.\nBoulemtafes A, Derhab A, Challal Y. A review of privacy-preserving techniques for deep learning. Neurocomputing. 2020;384:21–45.\nUsman OL, Muniyandi RC, Omar K, Mohamad M. Privacy-Preserving Classification Method for Neural-Biomarkers using Homomorphic Residue Number System CNN: HoRNS-CNN. in 2022 International Conference on Business Analytics for Technology and Security, ICBATS 2022 (IEEE, 2022). doi: https:\u002F\u002Fdoi.org\u002F10.1109\u002FICBATS54253.2022.9759007.\nGatta MT, Al-Latief STA. Medical image security using modified chaos-based cryptography approach. J Phys Conf Ser. 2018;1003:1–6.\nKoppu S, Viswanatham VM. A fast enhanced secure image chaotic cryptosystem based on hybrid chaotic magic transform. Model Simul Eng. 2017;2017:1–13.\nMaekawa T, Kawamura A, Kinoshita Y, Kiya H. Privacy-Preserving SVM Computing in the Encrypted Domain. in Proceedings of the 2018 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA ASC 2018, 897–902. 2018. doi:https:\u002F\u002Fdoi.org\u002F10.23919\u002FAPSIPA.2018.8659529.\nChuman T, Sirichotedumrong W, Kiya H. Encryption-then-compression systems using grayscale-based image encryption for JPEG images. IEEE Trans Inf Forensics Secur. 2019;14:1515–25.\nDowlin N, et al. Cryptonets: Applying neural networks to encrypted data with high throughput and accuracy. 33rd Int. Conf. Mach. Learn. ICML 2016 1, 342–351. 2016.\nAl Badawi A, et al. The AlexNet moment for homomorphic encryption: HCNN, the first homomorphic CNN on encrypted data with GPUs. IEEE Trans Emerg Top Comput. 2021;9:1–13.\nSong BK, Yoo JS, Hong M, Yoon JW. A bitwise design and implementation for privacy-preserving data mining: from atomic operations to advanced algorithms. Secur Commun Netw. 2019. https:\u002F\u002Fdoi.org\u002F10.1155\u002F2019\u002F3648671.\nSirichotedumrong W, Maekawa T, Kinoshita Y, Kiya H. Privacy-Preserving Deep Neural Networks with Pixel-Based Image Encryption Considering Data Augmentation in the Encrypted Domain. in Proceedings - International Conference on Image Processing, ICIP vols 2019-Septe 2019; 674–678.\nMuhammed KJ, Isiaka RM, Asaju-Gbolagade AW, Adewole KS, Gbolagade KA. Improved cloud-based N-primes model for symmetric-based fully homomorphic encryption using residue number system. In: Chiroma H, Abdulhamid SM, Fournier-Viger P, Garcia NM, editors. Machine learning and data mining for emerging trend in cyber dynamics. Springer; 2021. p. 197–216. https:\u002F\u002Fdoi.org\u002F10.1007\u002F978-3-030-66288-2_8.\nUsman OL, Olusanya OO, Adedeji OB, Rufai KI. Modelling a secure digital image cryptosystem using the traditional moduli set. TASUED J Pure Appl Sci. 2018;1:197–207.\nAlhassan S, Gbolagade KA. Enhancement of the security of a digital image using the moduli set. Int J Adv Res Comput Eng Technol. 2013;2:2223–9.\nNavin AH, Oskuei AR, Khashandarag AS, Mirnia MA. Novel Approach Cryptography by using Residue Number System. in ICCIT, 6th International Conference on Computer Science and Convergence Information Technology IEEE 2011; 636–639.\nMohan PVA. Residue Number Systems: Theory and Applications. in Residue Number Systems: Theory and Applications 1–7 (Birkhauser, 2016). doi: https:\u002F\u002Fdoi.org\u002F10.1007\u002F978-3-319-41385-3.\nOmondi A, Premkumar B. RESIDUE NUMBER SYSTEMS: theory and implementation. Imperial College Press; 2007.\nMohan PVA. RNS-to-binary converter for a new three-moduli set {2n+1-1; 2n; 2n–1}. IEEE Trans Circuits Syst Express Briefs. 2007;54:775–9.\nGomathisankaran M, Namuduri K, Tyagi A. HORNS: a semi-perfectly secret homomorphic encryption system. Am J Sci Eng. 2013;2:17–23.\nPrasanthi BG, Smitha. Security issues and comparison of existing algorithms in cloud to support multi-cloud. Adarsh J Inf Technol. 2017;6:33–6.\nGentry C, Halevi S. Implementing Gentry’s Fully-Homomorphic Encryption Scheme. 2011; 1–29.\nGentry C. Computing arbitrary functions of encrypted data. Commun ACM. 2010;53:97–105.\nBos JW, Lauter K, Loftus J, Naehrig M. Improved security for a ring-based fully homomorphic encryption scheme. in Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (ed. Stam, M.) vol. 8308 LNCS 45–64 (Springer, Berlin, Heidelberg, 2013).\nGentry C. A fully homomorphic encryption scheme. Standford University; 2009.\nAbdul-mumin S, Gbolagade KA. An improved residue number system based RSA cryptosystem. Int J Emerg Technol Comput Appl Sci. 2017;20:70–4.\nUsman OL, Muniyandi RC. A framework for a secure brain image classification using deep learning and residue number system. TEST Eng Manag. 2020;83:6323–30.\nYoussef MI, Emam AE, Saafan SM, Elghany MABD. Secured image encryption scheme using both residue number system and DNA sequence. Online J Electron Electr Eng. 2013;6:656–64.\nTaylor FJ. Residue arithmetic a tutorial with examples. Computer (Long Beach Calif). 1984;17:50–62.\nYounes D, Steffan P. A Comparative Study on Different Moduli Sets in Residue Number System. in 2012 International Conference on Computer Systems and Industrial Informatics 1–6 (IEEE, 2012). doi: https:\u002F\u002Fdoi.org\u002F10.1109\u002FICCSII.2012.6454344.\nKabra NK, Patel ZM. A radix-8 modulo 2n multiplier using area and power-optimized hard multiple generator. IET Comput Digit Tech. 2021;15:36–55.\nRamya M, Chang C. Hard multiple generator for higher radix modulo 2n-1 multiplication. in 12th International Symposium on Integrated Circuits 2009; 546–549.\nSkavantzos, A. Efficient residue to weighted converter for a new Residue Number System. in Proceedings of the IEEE Great Lakes Symposium on VLSI (1998). doi:https:\u002F\u002Fdoi.org\u002F10.1109\u002FGLSV.1998.665223\nGbolagade, K. A. & Cotofana, S. D. An O(n) Residue Number System to Mixed Radix Conversion Technique. in IEEE Conference on Very Large Scale Integration 2009; 521–524. doi:https:\u002F\u002Fdoi.org\u002F10.1109\u002FISCAS.2009.5117800.\nSzabo NS, Tanaka RI. Residue arithmetic and its application to computer technology. McGraw-Hill Book Co.; 1967.",{"EN":319},"In recent times, there has been an increasing attention in designing a homomorphic privacy-preserving classification method for neuro-images based on the residue number system (RNS) and deep CNN models. This article presents the RNS homomorphic encryption system for neuro-images and evaluates its security efficiency with respect to moduli set \n                \n                  \n                \n                $$\\left\\{{2}^{n}-1, {2}^{n}, {2}^{n+1}-1\\right\\}$$\n                \n              . The efficiency of the proposed system is evaluated through the application of three metrics, namely visual inspection, encoding analysis, and security analysis. The analysis demonstrates that the proposed RNS scheme is a fully homomorphic encryption (FHE) scheme that can encrypt and decrypt neuroimages without compromising any essential neural biomarker features. Additionally, the scheme is robust against statistical attacks like histogram, brute force, correlation coefficient, and key sensitivity. Thus, the proposed RNS-FHE scheme can be utilized for any neuroimaging dataset and is appropriate for the design of homomorphic privacy-preserving methods when compared to the current state-of-the-art. In summary, the contribution and novelty of this study is the development of a systematic RNS-FHE privacy-preserving approach for efficient neuroimaging dataset homomorphic encryption.",{"EN":321},"Efficient Neuroimaging Data Security and Encryption Using Pixel-Based Homomorphic Residue Number System",{"VOID":323},"10.1007\u002Fs42979-023-02297-9","https:\u002F\u002Flink.springer.com\u002Farticle\u002F10.1007\u002Fs42979-023-02297-9",[326,341,363],{"id":327,"sortIndex":239,"researcher":20,"roles":328,"affiliations":329,"properties":338},"c8711fbb-db0f-4fad-8479-a3118e52917e",[164],[330],{"id":20,"sortIndex":21,"affiliation":331,"properties":20},{"id":332,"createTime":333,"updateTime":333,"relativeEntities":334,"slug":20,"properties":335,"entityType":80,"verifyStatus":19,"verifyTime":20,"verifyNote":20,"syncStatus":19,"languages":20,"translateLanguages":20,"viewCount":21},"6c562909-3c55-4975-a76d-3bf0cdeb7f8d","2024-01-08T23:31:51.492+00:00",[],{"title":336},{"VI":337},"Department of Computer Science, Tai Solarin University of Education, Ijagun, Ijebu-Ode, Nigeria",{"title":339},{"VI":340},"Morufat Adebola Usman",{"id":342,"sortIndex":21,"researcher":20,"roles":343,"affiliations":344,"properties":360},"58361850-b275-470d-8657-ebd3625f1f2e",[164],[345,353],{"id":20,"sortIndex":21,"affiliation":346,"properties":20},{"id":347,"createTime":348,"updateTime":348,"relativeEntities":349,"slug":20,"properties":350,"entityType":80,"verifyStatus":19,"verifyTime":20,"verifyNote":20,"syncStatus":19,"languages":20,"translateLanguages":20,"viewCount":21},"8ae84443-469a-407e-b7f8-1ec974d635f6","2024-01-08T23:31:51.503+00:00",[],{"title":351},{"VI":352},"Research Centre for Cyber Security, Faculty of Information Science and Technology, Universiti Kebangsaan Malaysia, UKM Bangi, Malaysia",{"id":354,"sortIndex":162,"affiliation":355,"properties":359},"d4be5aa6-4c28-4d0c-99be-db9ba1d21d89",{"id":332,"createTime":333,"updateTime":333,"relativeEntities":356,"slug":20,"properties":357,"entityType":80,"verifyStatus":19,"verifyTime":20,"verifyNote":20,"syncStatus":19,"languages":20,"translateLanguages":20,"viewCount":21},[],{"title":358},{"VI":337},{},{"title":361},{"VI":362},"Lateef Opeyemi Usman",{"id":364,"sortIndex":162,"researcher":20,"roles":365,"affiliations":366,"properties":372},"3bafd30c-8874-4879-9762-668193b87faf",[164],[367],{"id":20,"sortIndex":21,"affiliation":368,"properties":20},{"id":347,"createTime":348,"updateTime":348,"relativeEntities":369,"slug":20,"properties":370,"entityType":80,"verifyStatus":19,"verifyTime":20,"verifyNote":20,"syncStatus":19,"languages":20,"translateLanguages":20,"viewCount":21},[],{"title":371},{"VI":352},{"title":373},{"VI":374},"Ravie Chandren Muniyandi",{"url":324,"publisher":376,"properties":397},{"id":6,"createTime":7,"updateTime":8,"relativeEntities":377,"slug":10,"properties":378,"entityType":18,"verifyStatus":19,"verifyTime":20,"verifyNote":20,"syncStatus":19,"languages":20,"translateLanguages":20,"viewCount":21,"subjectFields":382,"manageAffiliations":383,"indexDatabases":384,"url":107,"thumbnailPath":20,"statistic":392,"gsStatistic":20,"type":134,"analyzePriority":20},[],{"issn":379,"eissn":380,"title":381},{"VOID":13},{"VOID":15},{"EN":17},[],[],[385],{"id":84,"indexDatabase":386,"url":97,"indexYears":98,"academicFieldIds":391,"indexDatabaseRanking":106},{"id":86,"createTime":87,"updateTime":88,"relativeEntities":387,"label":388,"description":389,"key":94,"publicationTags":390,"standard":20},[],{"EN":91,"VI":91},{"EN":91,"VI":93},[96],[100,101,102,103,104,105],{"impactFactor":21,"impactFactorByYear":393,"i10Index":113,"i10IndexLast5Year":114,"totalPublication":115,"totalPublicationByYear":394,"totalCitation":123,"totalCitationByYear":395,"totalCitationPerPublication":128,"totalCitationPerPublicationByYear":396,"hindexLast5Year":133,"hindex":133},{"2021":110,"2022":111,"2023":112},{"2019":117,"2020":118,"2021":119,"2022":120,"2023":121,"2024":122},{"2020":125,"2021":126,"2022":127},{"2020":130,"2021":131,"2022":132},{"volume":398,"pages":400},{"VOID":399},"4",{"VOID":308},"2023-10-31",{"id":403,"createTime":404,"updateTime":405,"relativeEntities":406,"slug":407,"properties":408,"entityType":154,"verifyStatus":155,"verifyTime":417,"verifyNote":157,"syncStatus":19,"languages":20,"translateLanguages":20,"viewCount":21,"primaryUrl":418,"fullTextUrl":20,"authors":419,"publicationType":189,"publisherRelationship":489,"citationCount":20,"citationInfo":20,"publishDate":516,"publishYear":517,"citationAnalyzeStatus":19,"lastCitationAnalyze":20,"indexDatabases":20,"openAccess":20,"references":20,"isForceReanalyzing":219},"aac01252-7834-4df9-8245-b508884a010c","2023-11-27T13:37:27.639+00:00","2024-10-08T23:57:39.243+00:00",[],"Group-Anomaly-Detection-Past-Notions-Present-Insights-and-Future-Prospects",{"references":409,"abstract":411,"title":413,"doi":415},{"VOID":410},"Muandet K, Schölkopf B. One-class support measure machines for group anomaly detection. 2013. arXiv preprint arXiv:1303.0309.\nTong H, Papadimitriou S, Sun J, Yu PS, Faloutsos C. Colibri: fast mining of large static and dynamic graphs. In: Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining, p. 686–94. ACM; 2008.\nKuppa A, Grzonkowski S, Asghar MR, Le-Khac NA. Finding rats in cats: detecting stealthy attacks using group anomaly detection. In: 2019 18th IEEE International Conference on Trust, Security and Privacy in Computing and Communications\u002F13th IEEE International Conference on Big Data Science and Engineering (TrustCom\u002FBigDataSE), p. 442–449. IEEE; 2019.\nHe Z, Xu X, Deng S. Discovering cluster-based local outliers. Pattern Recogn Lett. 2003;24(9):1641–50.\nEberle W, Holder L, Massengill B. Graph-based anomaly detection applied to homeland security cargo screening. 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Almost all existing anomaly detection techniques have some limitations and do not focus specifically on detecting anomalous groups. Anomaly detection is also a crucial problem in processing large-scale datasets when our goal is to find abnormal values or unusual events. The authors decided to survey existing group anomaly detection techniques because there is a need to consider group anomalies for mitigation of risks, prevention of malicious collaborative activities, and other interesting explanatory insights by identifying groups that are not consistent with regular group patterns. In this research, we bifurcated group anomaly detection techniques into activity-based and graph-based methods. The graphical methodologies are then further classified under static versus dynamic and attributed versus plain graph methods. We have also listed the datasets used in various studies to detect group anomalies along with detected anomalies and the various performance measures used to validate the results. Towards the end, we have provided various applications of group anomaly detection and the research challenges that group anomaly detection presents to the scientific community and enlisted some of the future trends for this particular research area.",{"EN":414},"Group Anomaly Detection: Past Notions, Present Insights, and Future Prospects",{"VOID":416},"10.1007\u002Fs42979-021-00603-x","2024-10-08T23:57:39.242+00:00","https:\u002F\u002Flink.springer.com\u002F10.1007\u002Fs42979-021-00603-x",[420,448,465,477],{"id":421,"sortIndex":162,"researcher":20,"roles":422,"affiliations":423,"properties":445},"44d61dd1-3e76-426d-bf40-2fb530448085",[164],[424,434],{"id":20,"sortIndex":21,"affiliation":425,"properties":20},{"id":426,"createTime":427,"updateTime":428,"relativeEntities":429,"slug":430,"properties":431,"entityType":80,"verifyStatus":19,"verifyTime":20,"verifyNote":20,"syncStatus":19,"languages":20,"translateLanguages":20,"viewCount":21},"06d49fad-079b-4dac-aa12-af5169c7d1ad","2024-01-14T04:10:23.730+00:00","2025-06-11T19:53:18.311+00:00",[],"Department-of-Computer-Science-and-Software-Engineering-International-Islamic-University-Islamabad-Pakistan",{"title":432},{"VI":433},"Department of Computer Science and Software Engineering, International Islamic University, Islamabad, Pakistan",{"id":435,"sortIndex":162,"affiliation":436,"properties":444},"a5b7c143-625c-46af-a187-d642fa0ba147",{"id":437,"createTime":438,"updateTime":438,"relativeEntities":439,"slug":440,"properties":441,"entityType":80,"verifyStatus":19,"verifyTime":20,"verifyNote":20,"syncStatus":19,"languages":20,"translateLanguages":20,"viewCount":21},"454ff509-9a6f-4cb6-b074-bebfa99a1f16","2023-11-27T13:37:27.678+00:00",[],"Department-of-Computer-Science-and-Artificial-Intelligence-College-of-Computer-Science-and-Engineering-Jeddah-University-Jeddah-Saudi-Arabia",{"title":442},{"VI":443},"Department of Computer Science and Artificial Intelligence, College of Computer Science and Engineering, Jeddah University, Jeddah, Saudi Arabia",{},{"title":446},{"VI":447},"Ali 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Feroze",{"url":418,"publisher":490,"properties":511},{"id":6,"createTime":7,"updateTime":8,"relativeEntities":491,"slug":10,"properties":492,"entityType":18,"verifyStatus":19,"verifyTime":20,"verifyNote":20,"syncStatus":19,"languages":20,"translateLanguages":20,"viewCount":21,"subjectFields":496,"manageAffiliations":497,"indexDatabases":498,"url":107,"thumbnailPath":20,"statistic":506,"gsStatistic":20,"type":134,"analyzePriority":20},[],{"issn":493,"eissn":494,"title":495},{"VOID":13},{"VOID":15},{"EN":17},[],[],[499],{"id":84,"indexDatabase":500,"url":97,"indexYears":98,"academicFieldIds":505,"indexDatabaseRanking":106},{"id":86,"createTime":87,"updateTime":88,"relativeEntities":501,"label":502,"description":503,"key":94,"publicationTags":504,"standard":20},[],{"EN":91,"VI":91},{"EN":91,"VI":93},[96],[100,101,102,103,104,105],{"impactFactor":21,"impactFactorByYear":507,"i10Index":113,"i10IndexLast5Year":114,"totalPublication":115,"totalPublicationByYear":508,"totalCitation":123,"totalCitationByYear":509,"totalCitationPerPublication":128,"totalCitationPerPublicationByYear":510,"hindexLast5Year":133,"hindex":133},{"2021":110,"2022":111,"2023":112},{"2019":117,"2020":118,"2021":119,"2022":120,"2023":121,"2024":122},{"2020":125,"2021":126,"2022":127},{"2020":130,"2021":131,"2022":132},{"volume":512,"pages":514},{"VOID":513},"2",{"VOID":515},"1-27","2021-04-16",2021,{"id":519,"createTime":520,"updateTime":521,"relativeEntities":522,"slug":523,"properties":524,"entityType":154,"verifyStatus":155,"verifyTime":521,"verifyNote":157,"syncStatus":19,"languages":20,"translateLanguages":20,"viewCount":21,"primaryUrl":533,"fullTextUrl":20,"authors":534,"publicationType":189,"publisherRelationship":577,"citationCount":20,"citationInfo":20,"publishDate":603,"publishYear":218,"citationAnalyzeStatus":19,"lastCitationAnalyze":20,"indexDatabases":20,"openAccess":20,"references":20,"isForceReanalyzing":219},"228cd482-5a3e-4355-9634-7609d0772b50","2024-01-14T22:36:47.302+00:00","2025-01-05T23:55:47.288+00:00",[],"Effective-Stock-Market-Pricing-Prediction-Using-Long-Short-Term-Memory-Upgraded-Model-LSTM-UP-on-Evolving-Data-Sets",{"references":525,"abstract":527,"title":529,"doi":531},{"VOID":526},"Refenes AN, Zapranis A, Francis G. 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Neural Comput Appli. 2019;32:9713.\nYahya Eru Cakra BDT (2016) Stock Price Prediction using Linear Regression based on Sentiment Analysis, International Conference on Advanced Computer Science and Information Systems, p. 147–154,\nFischer T, Krauss C. Deep learning with long short-term memory networks for financial market predictions. Euro J Operat Res. 2018;270:654–69.\nLiu J et al. “Attention-Based Event Relevance Model for Stock Price Movement Prediction in China”, Conference on Knowledge Graph and Semantic Computing (2017)\nSaud AS, Shakya S. Analysis of look back period for stock price prediction with RNN variants: A case study on banking sector of NEPSE. Procedia Comp Sci. 2020;167:788–98.\nShahi TB, Shrestha A, Neupane A, Guo W (2020) “Stock Price Forecasting with Deep Learning: A Comparative Study”, Multidisciplinary Digital Publishing Institute (MDPI)\nPai P-F, Lin C-S. A hybrid ARIMA and support vector machines model in stock price forecasting. Int J Manag Sci. 2005;33:497–505.\nCheong Fung GP, Xu Yu J, Lam W (2002) \"News sensitive stock trends prediction,\" In: 6th Pacific-Asia Knowledge Discovery in Data Mining, Beijing\nLavrenko V , Schmill M, Lawrie D, Ogilvie P, Jensen D, Allan J (2000) \"Mining of concurrent text and time series.\" In: Workshop of 6th International Conference on Knowledge Discovery and Data Mining\nLi J, Bu H, Wu J. Sentiment-aware stock market prediction: a deep learning method. In Proceedings. Int Conf Ser Syst Ser Manag Dalian China. 2017;16:1–6.",{"EN":528},"Making reliable stock market forecasts is a difficult real-world economics problem. A stock's unpredictable and chaotic nature makes it difficult to predict its future worth. To overcome the limitations of existing models in handling the non-stationary and non-linear characteristics of high-frequency financial time series data, this study proposes a Wavelet transform-based data preprocessing and the development of an LSTM-upgraded model (LSTM-UP) that incorporates human sentiment for predicting stock price. Features are extracted and trained using a Wavelet transform, long short-term memory, and an upgraded mechanism applied to financial time series. To investigate the role of human emotion, we added a sentiment polarity score to the raw data. Using the ADBL, NIB, NABIL, and SCB stock datasets, the suggested model is evaluated and compared to LSTM and GRU. Models' efficacy can be compared using metrics like root-mean-square error, mean absolute error, R2, and MDA. The Root-Mean-Squared Error (RMSE) and the Mean Absolute Error (MAE) were both less than 3.5 whereas R2 and MDA were both greater than 0.95 across all stock datasets used in the experiments. Adding human judgment to the model, as suggested, makes it superior than similar ones.",{"EN":530},"Effective Stock Market Pricing Prediction Using Long Short Term Memory-Upgraded Model (LSTM-UP) on Evolving Data Sets",{"VOID":532},"10.1007\u002Fs42979-023-02100-9","https:\u002F\u002Flink.springer.com\u002Farticle\u002F10.1007\u002Fs42979-023-02100-9",[535,550,562],{"id":536,"sortIndex":21,"researcher":20,"roles":537,"affiliations":538,"properties":547},"3f39cbea-b808-46f9-9d2c-93bc0d9db8ba",[164],[539],{"id":20,"sortIndex":21,"affiliation":540,"properties":20},{"id":541,"createTime":542,"updateTime":542,"relativeEntities":543,"slug":20,"properties":544,"entityType":80,"verifyStatus":19,"verifyTime":20,"verifyNote":20,"syncStatus":19,"languages":20,"translateLanguages":20,"viewCount":21},"ed81fcc8-e72b-46a8-ab88-e7d5a8ba8b2f","2024-01-14T22:36:47.341+00:00",[],{"title":545},{"VI":546},"Department of CSE, Bharath Institute of Higher Education and Research (BIHER), Chennai, India",{"title":548},{"VI":549},"S. Balamohan",{"id":551,"sortIndex":239,"researcher":20,"roles":552,"affiliations":553,"properties":559},"9185f3be-7157-4ff3-8a71-3057876d9637",[164],[554],{"id":20,"sortIndex":21,"affiliation":555,"properties":20},{"id":541,"createTime":542,"updateTime":542,"relativeEntities":556,"slug":20,"properties":557,"entityType":80,"verifyStatus":19,"verifyTime":20,"verifyNote":20,"syncStatus":19,"languages":20,"translateLanguages":20,"viewCount":21},[],{"title":558},{"VI":546},{"title":560},{"VI":561},"K. Sivaraman",{"id":563,"sortIndex":162,"researcher":20,"roles":564,"affiliations":565,"properties":574},"2a10b484-736c-4537-b783-33a5b590c7a2",[164],[566],{"id":20,"sortIndex":21,"affiliation":567,"properties":20},{"id":568,"createTime":569,"updateTime":569,"relativeEntities":570,"slug":20,"properties":571,"entityType":80,"verifyStatus":19,"verifyTime":20,"verifyNote":20,"syncStatus":19,"languages":20,"translateLanguages":20,"viewCount":21},"2cc14efd-b885-4165-85bd-15f9dd835dbc","2024-01-14T22:36:47.331+00:00",[],{"title":572},{"VI":573},"Department of IT, Bharath Institute of Higher Education and Research (BIHER), Chennai, India",{"title":575},{"VI":576},"V. Khanaa",{"url":533,"publisher":578,"properties":599},{"id":6,"createTime":7,"updateTime":8,"relativeEntities":579,"slug":10,"properties":580,"entityType":18,"verifyStatus":19,"verifyTime":20,"verifyNote":20,"syncStatus":19,"languages":20,"translateLanguages":20,"viewCount":21,"subjectFields":584,"manageAffiliations":585,"indexDatabases":586,"url":107,"thumbnailPath":20,"statistic":594,"gsStatistic":20,"type":134,"analyzePriority":20},[],{"issn":581,"eissn":582,"title":583},{"VOID":13},{"VOID":15},{"EN":17},[],[],[587],{"id":84,"indexDatabase":588,"url":97,"indexYears":98,"academicFieldIds":593,"indexDatabaseRanking":106},{"id":86,"createTime":87,"updateTime":88,"relativeEntities":589,"label":590,"description":591,"key":94,"publicationTags":592,"standard":20},[],{"EN":91,"VI":91},{"EN":91,"VI":93},[96],[100,101,102,103,104,105],{"impactFactor":21,"impactFactorByYear":595,"i10Index":113,"i10IndexLast5Year":114,"totalPublication":115,"totalPublicationByYear":596,"totalCitation":123,"totalCitationByYear":597,"totalCitationPerPublication":128,"totalCitationPerPublicationByYear":598,"hindexLast5Year":133,"hindex":133},{"2021":110,"2022":111,"2023":112},{"2019":117,"2020":118,"2021":119,"2022":120,"2023":121,"2024":122},{"2020":125,"2021":126,"2022":127},{"2020":130,"2021":131,"2022":132},{"volume":600,"pages":601},{"VOID":399},{"VOID":602},"1-11","2023-08-29",{"id":605,"createTime":606,"updateTime":607,"relativeEntities":608,"slug":609,"properties":610,"entityType":154,"verifyStatus":155,"verifyTime":607,"verifyNote":157,"syncStatus":19,"languages":20,"translateLanguages":20,"viewCount":21,"primaryUrl":619,"fullTextUrl":20,"authors":620,"publicationType":189,"publisherRelationship":676,"citationCount":20,"citationInfo":20,"publishDate":703,"publishYear":704,"citationAnalyzeStatus":19,"lastCitationAnalyze":20,"indexDatabases":20,"openAccess":20,"references":20,"isForceReanalyzing":219},"68d8b368-4bd3-4031-aba8-3e68252ed5e6","2023-12-26T03:21:00.484+00:00","2025-02-10T23:54:53.818+00:00",[],"Contrastive-Metric-Learning-for-Lithium-Super-ionic-Conductor-Screening",{"references":611,"abstract":613,"title":615,"doi":617},{"VOID":612},"Ahmad Z, Xie T, Maheshwari C, Grossman JC, Viswanathan V. Machine learning enabled computational screening of inorganic solid electrolytes for suppression of dendrite formation in lithium metal anodes. ACS Cent Sci. 2018;4(8):996–1006.\nXie T, Grossman JC. Crystal graph convolutional neural networks for an accurate and interpretable prediction of material properties. Phys Rev Lett. 2018;120(14):145301.\nAhmad Z, Viswanathan V. Stability of electrodeposition at solid-solid interfaces and implications for metal anodes. Phys Rev Lett. 2017;119(5):056003.\nSendek AD, Yang Q, Cubuk ED, Duerloo K-AN, Cui Y, Reed EJ. Holistic computational structure screening of more than 12000 candidates for solid lithium-ion conductor materials. Energy Environ Sci. 2017;10(1):306–20.\nCubuk ED, Sendek AD, Reed EJ. Screening billions of candidates for solid lithium-ion conductors: a transfer learning approach for small data. J Chem Phys. 2019;150(21):214701.\nZhang B, Zhou M, Jianzhong W, Gao F. Predicting the materials properties using a 3D graph neural network with invariant representation. IEEE Access. 2022;10(10):62440–9.\nGou J, Lan D, Zhang Y, Xiong T. A new distance-weighted k-nearest neighbor classifier. J Inf Comput Sci. 2012;9(6):1429–36.\nSchmidt J, Marques MRG, Botti S, Marques MAL. Recent advances and applications of machine learning in solid-state materials science. npj Comput Mater. 2019;5(1):1–36.\nRupp M, Tkatchenko A, Müller K-R, Von Lilienfeld OA. Fast and accurate modeling of molecular atomization energies with machine learning. Phys Rev Lett. 2012;108(5):058301.\nFaber F, Lindmaa A, von Lilienfeld OA, Armiento R. Crystal structure representations for machine learning models of formation energies. Int J Quantum Chem. 2015;115(16):1094–101.\nSchütt KT, Glawe H, Brockherde F, Sanna A, Müller K-R, Gross EKU. How to represent crystal structures for machine learning: towards fast prediction of electronic properties. Phys Rev B. 2014;89(20): 205118.\nBartók AP, Kondor R, Csányi G. On representing chemical environments. Phys Rev B. 2013;87(18): 184115.\nBehler J, Parrinello M. Generalized neural-network representation of high-dimensional potential-energy surfaces. Phys Rev Lett. 2007;98(14): 146401.\nArtrith N, Urban A. An implementation of artificial neural-network potentials for atomistic materials simulations: performance for tio2. Comput Mater Sci. 2016;114:135–50.\nBehler J. Perspective: Machine learning potentials for atomistic simulations. J Chem Phys. 2016;145(17): 170901.\nSeko A, Takahashi A, Tanaka I. Sparse representation for a potential energy surface. Phys Rev B. 2014;90(2): 024101.\nChen C, Ye W, Zuo Y, Zheng C, Ong SP. Graph networks as a universal machine learning framework for molecules and crystals. Chem Mater. 2019;31(9):3564–72.\nSchütt KT, Arbabzadah F, Chmiela S, Müller KR, Tkatchenko A. Quantum-chemical insights from deep tensor neural networks. Nat Commun. 2017;8(1):1–8.\nKearnes S, McCloskey K, Berndl M, Pande V, Riley P. Molecular graph convolutions: moving beyond fingerprints. J Comput Aided Mol Des. 2016;30(8):595–608.\nDuvenaud D, Maclaurin D, Aguilera-Iparraguirre J, Gómez-Bombarelli R, Hirzel T, Aspuru-Guzik A, Adams RP. Convolutional networks on graphs for learning molecular fingerprints. 2015. arXiv preprint arXiv:1509.09292.\nSchütt KT, Sauceda HE, Kindermans P-J, Tkatchenko A, Müller K-R. Schnet—a deep learning architecture for molecules and materials. J Chem Phys. 2018;148(24): 241722.\nKulis B. Metric learning: a survey. Found Trends Mach Learn. 2012;5(4):287–364.\nYang L, Jin R. Distance metric learning: a comprehensive survey. Mich State Univ. 2006;2(2):4.\nBellet A, Habrard A, Sebban M. A survey on metric learning for feature vectors and structured data. 2013. arXiv preprint arXiv:1306.6709.\nYang J, Zhang D, Frangi AF, Yang J. Two-dimensional pca: a new approach to appearance-based face representation and recognition. IEEE Trans Pattern Anal Mach Intell. 2004;26(1):131–7.\nSaul LK, Roweis ST. Think globally, fit locally: unsupervised learning of low dimensional manifolds. In: Departmental Papers (CIS), 2003; p. 12.\nWang L. Support vector machines: theory and applications, vol. 177. Berlin: Springer Science & Business Media; 2005.\nYin X, Chen S, Enliang H, Zhang D. Semi-supervised clustering with metric learning: an adaptive kernel method. Pattern Recogn. 2010;43(4):1320–33.\nChopra S, Hadsell R, LeCun T. Learning a similarity metric discriminatively, with application to face verification. In: 2005 IEEE Computer Society Conference on computer vision and pattern recognition (CVPR’05), 2015; volume 1, pages 539–546.\nTaigman Y, Yang M, Ranzato M, Wolf L. Deepface: closing the gap to human-level performance in face verification. In: 2014 Proceedings of the IEEE conference on computer vision and pattern recognition, vol 1, 2014. pp 1701–8.\nDey S, Dutta A, Ignacio Toledo J, Ghosh SK, Lladós J, Pal U. Signet: convolutional Siamese network for writer independent offline signature verification. 2017. arXiv preprint arXiv:1707.02131.\nBertinetto L, Valmadre J, Henriques JF, Vedaldi A, Torr PHS. Fully-convolutional Siamese networks for object tracking. In: Hua G, Jégou H, editors. 2016 European conference on computer vision. Cham: Springer; 2016. pp. 850–65.\nKoch G. Siamese neural networks for one-shot image recognition. Dissertation, University of Toronto. 2015.\nHadsell R, Chopra S, LeCun Y. Dimensionality reduction by learning an invariant mapping. In: 2006 IEEE Computer Society Conference on computer vision and pattern recognition (CVPR’06), 2006; volume 2, pages 1735–1742. IEEE.\nHe X, Bai Q, Liu Y, Nolan AM, Ling C, Mo Y. Crystal structural framework of lithium super-ionic conductors. Adv Energy Mater. 2019;9(43):1902078.",{"EN":614},"High-performance Li-ion battery significantly impacts modern society, and materials with high conductivity play critical roles in battery development. Machine learning (ML) technologies have rapidly changed the field in recent years. However, it is still challenging to predict the high conductors directly due to the lack of validated conductor samples. This paper presents a succinct but effective metric-learning framework for high conductor screening. The material structures are mapped to an optimized feature space using a Siamese network, and an instance-based method is used to classify the input sample. The experiments demonstrate that the proposed method could effectively extract knowledge from imbalanced data and has good performance and generalization ability.",{"EN":616},"Contrastive Metric Learning for Lithium Super-ionic Conductor Screening",{"VOID":618},"10.1007\u002Fs42979-022-01370-z","https:\u002F\u002Flink.springer.com\u002Farticle\u002F10.1007\u002Fs42979-022-01370-z",[621,636,651],{"id":622,"sortIndex":162,"researcher":20,"roles":623,"affiliations":624,"properties":633},"98f1ea44-6dc8-4a28-a662-468927c29eb6",[164],[625],{"id":20,"sortIndex":21,"affiliation":626,"properties":20},{"id":627,"createTime":628,"updateTime":628,"relativeEntities":629,"slug":20,"properties":630,"entityType":80,"verifyStatus":19,"verifyTime":20,"verifyNote":20,"syncStatus":19,"languages":20,"translateLanguages":20,"viewCount":21},"44e25f00-a4c6-4cc7-b22e-e9ed13d022d6","2024-02-06T02:53:00.764+00:00",[],{"title":631},{"VI":632},"Department of Materials Science and Engineering, University of Maryland, College Park, USA",{"title":634},{"VI":635},"Shuo Wang",{"id":637,"sortIndex":239,"researcher":20,"roles":638,"affiliations":639,"properties":648},"2c527ae9-c190-4b4c-8bf2-9f4dabddbff0",[164],[640],{"id":20,"sortIndex":21,"affiliation":641,"properties":20},{"id":642,"createTime":643,"updateTime":643,"relativeEntities":644,"slug":20,"properties":645,"entityType":80,"verifyStatus":19,"verifyTime":20,"verifyNote":20,"syncStatus":19,"languages":20,"translateLanguages":20,"viewCount":21},"1c1014be-4999-4b13-8072-89068d938f08","2023-12-26T03:21:00.588+00:00",[],{"title":646},{"VI":647},"Department of Mathematics and Statistical Science, University of Idaho, Moscow, USA",{"title":649},{"VI":650},"Fuchang 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USA",{},{"id":20,"sortIndex":21,"affiliation":666,"properties":20},{"id":667,"createTime":668,"updateTime":668,"relativeEntities":669,"slug":20,"properties":670,"entityType":80,"verifyStatus":19,"verifyTime":20,"verifyNote":20,"syncStatus":19,"languages":20,"translateLanguages":20,"viewCount":21},"2b21f0da-b52c-4989-941e-aefcaf97a511","2023-12-26T03:21:00.515+00:00",[],{"title":671},{"VI":672},"Institute for Modeling Collaboration and Innovation, University of Idaho, Moscow, USA",{"title":674},{"VI":675},"Boyu 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College admissions and the stability of marriage. Am Math Mon. 1962;69:9–15.",{"doi":797},"10.1080\u002F00029890.1962.11989827",{"id":20,"text":799,"url":20,"identifiers":800},"Abdollahpouri H, Adomavicius G, Burke R, Guy I, Jannach D, Kamishima T, Krasnodebski J, Pizzato L. Multistakeholder recommendation: survey and research directions. User Model User Adapt Interact. 2020;30(1):127–58. https:\u002F\u002Fdoi.org\u002F10.1007\u002Fs11257-019-09256-1.",{"doi":801},"10.1007\u002Fs11257-019-09256-1",{"id":20,"text":803,"url":20,"identifiers":804},"Golle, P. A private stable matching algorithm. In: Proceedings of the 10th international conference on financial cryptography and data security. FC’06, pp. 65–80. Springer, Berlin, Heidelberg 2006. https:\u002F\u002Fdoi.org\u002F10.1007\u002F11889663_5.",{"doi":805},"10.1007\u002F11889663_5",{"id":20,"text":807,"url":20,"identifiers":808},"Nakamura T, Okada H, Fukushima K, Isohara T. Achieving private verification in multi-stakeholder environment and application to stable matching. In: ICEIS (1). 2023. pp. 768–775.",{"doi":809},"10.5220\u002F0011995800003467",{"id":20,"text":811,"url":20,"identifiers":812},"Acar A, Aksu H, Uluagac AS, Conti M. A survey on homomorphic encryption schemes: theory and implementation. ACM Comput Surv (Csur). 2018;51(4):1–35.",{"doi":813},"10.1145\u002F3214303",{"id":20,"text":815,"url":20,"identifiers":816},"Gentry C. A fully homomorphic encryption scheme. Stanford: Stanford University; 2009.",{},{"id":20,"text":818,"url":20,"identifiers":819},"Costache A, Nürnberger L, Player R. Optimisations and tradeoffs for HElib. In: Cryptographer’s track at the RSA conference, Springer; 2023. pp. 29–53.",{"doi":820},"10.1007\u002F978-3-031-30872-7_2",{"id":20,"text":822,"url":20,"identifiers":823},"Brakerski Z, Gentry C, Vaikuntanathan V. (Leveled) fully homomorphic encryption without bootstrapping. 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IEICE Trans Fundam Electron Commun Comput Sci. 2020;103(10):1134–41.",{"doi":910},"10.1587\u002Ftransfun.2019DMI0001",{"id":912,"createTime":913,"updateTime":914,"relativeEntities":915,"slug":916,"properties":917,"entityType":154,"verifyStatus":155,"verifyTime":914,"verifyNote":157,"syncStatus":19,"languages":20,"translateLanguages":20,"viewCount":21,"primaryUrl":926,"fullTextUrl":20,"authors":927,"publicationType":189,"publisherRelationship":967,"citationCount":20,"citationInfo":20,"publishDate":992,"publishYear":704,"citationAnalyzeStatus":19,"lastCitationAnalyze":20,"indexDatabases":20,"openAccess":20,"references":20,"isForceReanalyzing":219},"79d9c1fb-de94-4cae-8bd0-98eee63edc67","2023-12-28T09:51:25.244+00:00","2024-12-19T23:50:39.819+00:00",[],"Real-Time-Object-Detection-Based-on-YOLO-v2-for-Tiny-Vehicle-Object",{"references":918,"abstract":920,"title":922,"doi":924},{"VOID":919},"Sotelo MÁ, García MÁ, Flores R. 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J Chongqing Univ. 2017;40(7):32–6.\nZhang Qi Hu, Guangdi LY, Xin Z. Binocular vision vehicle detection method on improved Fast-RCNN. J Appl Opt. 2018;39(06):75–81.\nGao Z, Li S, Chen J, Li Z. Pedestrian detection method based on YOLO network. Comput Eng. 2018;44(5):215–9, 226.\nTao J, Wang H, Zhang X, Li X, Yang H. An object detection system based on YOLO in traffic scene. 2017 6th International Conference on Computer Science and Network Technology (ICCSNT). IEEE. 2017.\nRen S, He K, Girshick R, Sun J. Faster r-cnn: towards real-time object detection with region proposal networks. IEEE Trans Pattern Anal Mach Intell. 2015;39(6):1137–49.\nRedmon J, Divvala S, Girshick R, Farhadi A. You only look once: unified, real-time object detection. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR). 2016, pp. 779–788.\nRedmon J, Farhadi A. IEEE 2017 IEEE conference on computer vision and pattern recognition (cvpr)—honolulu, hi (2017.7.21–2017.7.26) 2017 IEEE conference on computer vision and pattern recognition (cvpr)—yolo9000: better, faster, stronger. 2017; 6517–6525.\nHe K, Zhang X, Ren S, Sun J. Deep residual learning for image recognition. In: Proceedings of the IEEE conference on computer vision and pattern recognition. 2016, pp. 770–778.\nRedmon J, Divvala S, Girshick R, Farhadi A. You only look once: unified, real-time object detection. In Proceedings of the 2016 IEEE conference on computer vision and pattern recognition (CVPR), Las Vegas, NV, USA. 27–30 June 2016;779–788.\nRedmon J, Farhadi A, YOLO9000: Better, faster, stronger. In Proceedings of the 2017 IEEE conference on computer vision and pattern recognition (CVPR), Honolulu, HI, USA, 21–26 July 2017; pp. 6517–6525.\nEveringham M, Winn J. The pascal visual object classes challenge 2007 (voc2007) development kit. Int J Comput Vision. 2006;111(1):98–136.",{"EN":921},"Object detection plays an essential role in automatic driving system (ADS) and driver assistance system (DAS). However, existing real-time detection models for tiny vehicle objects have the problems of low precision and poor performance. To solve these issues, a novel real-time object detection model based on You Only Look Once Version 2 (YOLO-v2) deep learning framework is proposed for tiny vehicle objects, called Optimized You Only Look Once Version 2 (O-YOLO-v2). In the proposed model, a new architecture is introduced by adding the convolution layers at different locations to enhance the feature extraction ability of network. At the same time, the residual modules are added to solve the problem of gradient disappearance or dispersion caused by the increase of network depth. Furthermore, the low-level features and high-level features of the network are combined to improve the detection accuracy of tiny vehicle objects. Experimental results on the KITTI dataset show that the model not only improves the accuracy of tiny vehicle object detection but also improves the accuracy of vehicle detection (the accuracy reaches 94%) without decreasing the detection speed.",{"EN":923},"Real-Time Object Detection Based on YOLO-v2 for Tiny Vehicle Object",{"VOID":925},"10.1007\u002Fs42979-022-01229-3","https:\u002F\u002Flink.springer.com\u002Farticle\u002F10.1007\u002Fs42979-022-01229-3",[928,943,955],{"id":929,"sortIndex":239,"researcher":20,"roles":930,"affiliations":931,"properties":940},"2b33e00b-0c45-4c7f-8df9-9b6f1e858bef",[164],[932],{"id":20,"sortIndex":21,"affiliation":933,"properties":20},{"id":934,"createTime":935,"updateTime":935,"relativeEntities":936,"slug":20,"properties":937,"entityType":80,"verifyStatus":19,"verifyTime":20,"verifyNote":20,"syncStatus":19,"languages":20,"translateLanguages":20,"viewCount":21},"e6d00a5c-0265-4701-b295-76a011b177c6","2023-12-28T22:04:32.910+00:00",[],{"title":938},{"VI":939},"Taiyuan University of Technology, Taiyuan, People’s Republic of China",{"title":941},{"VI":942},"Xiaohong Han",{"id":944,"sortIndex":162,"researcher":20,"roles":945,"affiliations":946,"properties":952},"4bd6b5cb-4fbf-40be-a712-c1d536a00ee0",[164],[947],{"id":20,"sortIndex":21,"affiliation":948,"properties":20},{"id":934,"createTime":935,"updateTime":935,"relativeEntities":949,"slug":20,"properties":950,"entityType":80,"verifyStatus":19,"verifyTime":20,"verifyNote":20,"syncStatus":19,"languages":20,"translateLanguages":20,"viewCount":21},[],{"title":951},{"VI":939},{"title":953},{"VI":954},"Kaiyuan Wang",{"id":956,"sortIndex":21,"researcher":20,"roles":957,"affiliations":958,"properties":964},"329e515d-9f8b-4985-ae77-28f93e6751ed",[164],[959],{"id":20,"sortIndex":21,"affiliation":960,"properties":20},{"id":934,"createTime":935,"updateTime":935,"relativeEntities":961,"slug":20,"properties":962,"entityType":80,"verifyStatus":19,"verifyTime":20,"verifyNote":20,"syncStatus":19,"languages":20,"translateLanguages":20,"viewCount":21},[],{"title":963},{"VI":939},{"title":965},{"VI":966},"Pengju Deng",{"url":926,"publisher":968,"properties":989},{"id":6,"createTime":7,"updateTime":8,"relativeEntities":969,"slug":10,"properties":970,"entityType":18,"verifyStatus":19,"verifyTime":20,"verifyNote":20,"syncStatus":19,"languages":20,"translateLanguages":20,"viewCount":21,"subjectFields":974,"manageAffiliations":975,"indexDatabases":976,"url":107,"thumbnailPath":20,"statistic":984,"gsStatistic":20,"type":134,"analyzePriority":20},[],{"issn":971,"eissn":972,"title":973},{"VOID":13},{"VOID":15},{"EN":17},[],[],[977],{"id":84,"indexDatabase":978,"url":97,"indexYears":98,"academicFieldIds":983,"indexDatabaseRanking":106},{"id":86,"createTime":87,"updateTime":88,"relativeEntities":979,"label":980,"description":981,"key":94,"publicationTags":982,"standard":20},[],{"EN":91,"VI":91},{"EN":91,"VI":93},[96],[100,101,102,103,104,105],{"impactFactor":21,"impactFactorByYear":985,"i10Index":113,"i10IndexLast5Year":114,"totalPublication":115,"totalPublicationByYear":986,"totalCitation":123,"totalCitationByYear":987,"totalCitationPerPublication":128,"totalCitationPerPublicationByYear":988,"hindexLast5Year":133,"hindex":133},{"2021":110,"2022":111,"2023":112},{"2019":117,"2020":118,"2021":119,"2022":120,"2023":121,"2024":122},{"2020":125,"2021":126,"2022":127},{"2020":130,"2021":131,"2022":132},{"volume":990,"pages":991},{"VOID":700},{"VOID":308},"2022-06-11",{"id":994,"createTime":995,"updateTime":995,"relativeEntities":996,"slug":997,"properties":998,"entityType":154,"verifyStatus":19,"verifyTime":20,"verifyNote":20,"syncStatus":19,"languages":20,"translateLanguages":20,"viewCount":21,"primaryUrl":1007,"fullTextUrl":20,"authors":1008,"publicationType":189,"publisherRelationship":1041,"citationCount":20,"citationInfo":20,"publishDate":1067,"publishYear":517,"citationAnalyzeStatus":19,"lastCitationAnalyze":20,"indexDatabases":20,"openAccess":20,"references":20,"isForceReanalyzing":219},"e283d80e-245f-490b-bf6c-f9932c43471b","2023-11-12T23:50:06.995+00:00",[],"Seeding-Grammars-in-Grammatical-Evolution-to-Improve-Search-Based-Software-Testing",{"references":999,"abstract":1001,"title":1003,"doi":1005},{"VOID":1000},"Afzal W, Torkar R, Feldt R. 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Grammatical evolution for the multi-objective integration and test order problem. In: Proceedings of the Genetic and Evolutionary Computation Conference 2016. ACM; 2016. p. 1069–76.\nMcDermott J, White DR, Luke S, Manzoni L, Castelli M, Vanneschi L, Jaskowski W, Krawiec K, Harper R, De Jong K, et al. Genetic programming needs better benchmarks. In: Proceedings of the 14th Annual Conference on Genetic and Evolutionary Computation, p. 791–8, 2012.\nMcMinn P. Search-based software test data generation: a survey. Softw Test Verif Reliab. 2004;14(2):105–56.\nMcMinn P, Stevenson M, Harman M. Reducing qualitative human oracle costs associated with automatically generated test data. In: Proceedings of the First International Workshop on Software Test Output Validation. ACM; 2010. p. 1–4.\nMichael CC, McGraw G, Schatz MA. Generating software test data by evolution. IEEE Trans Softw Eng. 2001;12:1085–110.\nMiller J, Reformat M, Zhang H. Automatic test data generation using genetic algorithm and program dependence graphs. Inf Softw Technol. 2006;48(7):586–605.\nMiller W, Spooner DL. Automatic generation of floating-point test data. IEEE Trans Softw Eng. 1976;3:223–6.\nMyers GJ, Badgett T, Thomas TM, Sandler C. The art of software testing, vol. 2. New York: Wiley Online Library; 2004.\nOffutt AJ, Jin Z, Pan J. The dynamic domain reduction procedure for test data generation. Softw Pract Exp. 1999;29(2):167–93.\nO’Neill M, Ryan C. Grammatical evolution. IEEE Trans Evol Comput. 2001;5(4):349–58.\nPanichella A, Kifetew FM, Tonella P. Reformulating branch coverage as a many-objective optimization problem. In: 2015 IEEE 8th International Conference on Software Testing, Verification and Validation (ICST). IEEE; 2015. p. 1–10.\nPargas RP, Harrold MJ, Peck RR. Test-data generation using genetic algorithms. Softw Test Verif Reliab. 1999;9(4):263–82.\nPatten JV, Ryan C. Procedural content generation for games using grammatical evolution and attribute grammars. 2014.\nRojas JM, Fraser G, Arcuri A. Seeding strategies in search-based unit test generation. Softw Test Verif Reliab. 2016;26(5):366–401.\nRyan C, Collins JJ, Neill MO. Grammatical evolution: Evolving programs for an arbitrary language. In: European Conference on Genetic Programming. Springer; 1998. p. 83–96.\nSauder RL. A general test data generator for cobol. In: Proceedings of the May 1–3, 1962, Spring Joint Computer Conference, p. 317–23, 1962.\nSparks S, Embleton S, Cunningham R, Zou C. Automated vulnerability analysis: leveraging control flow for evolutionary input crafting. In: Twenty-Third Annual Computer Security Applications Conference (ACSAC 2007). IEEE; 2007. p. 477–86.\nTlili M, Wappler S, Sthamer H. Improving evolutionary real-time testing. In: Proceedings of the 8th Annual Conference on Genetic and Evolutionary Computation. ACM; 2006. p. 1917–24.\nTracey N, Clark J, Mander K, McDermid J. An automated framework for structural test-data generation. In: ASE. IEEE; 1998. p. 285.\nWegener J, Baresel A, Sthamer H. Evolutionary test environment for automatic structural testing. Inf Softw Technol. 2001;43(14):841–54.\nXanthakis S, Ellis C, Skourlas C, Le Gall A, Katsikas S, Karapoulios K. Application of genetic algorithms to software testing. In: Proceedings of the 5th International Conference on Software Engineering and Applications, p. 625–36, 1992.\nZhu Z, Jiao L, Xu X. Combining search-based testing and dynamic symbolic execution by evolvability metric. In: 2018 IEEE International Conference on Software Maintenance and Evolution (ICSME). IEEE; 2018. p. 59–68.",{"EN":1002},"Heuristic-based optimization techniques have been increasingly used to automate different types of code coverage analysis. Several studies suggest that interdependencies (in the form of comparisons) may exist between the condition constructs, of variables and constant values, in the branching conditions of real-world programs, e.g. (\n                \n                  \n                \n                $$i \\le 100$$\n                \n              ) or (\n                \n                  \n                \n                $$i==j$$\n                \n              ), etc. In this work, by interdependencies we refer to the situations where, to satisfy a branching condition, there must be a certain relationship between the values of some specific condition constructs (which may or may not be a part of the respective condition predicates). For example, the values of variables i and j must be equal to satisfy the condition of (\n                \n                  \n                \n                $$i==j$$\n                \n              ), and the value of variable k must be equal to 100 for the satisfaction of the condition of (\n                \n                  \n                \n                $$k==100$$\n                \n              ). To date, only the Ariadne, a Grammatical Evolution (GE)-based system, exploits these interdependencies between input variables (e.g. of the form (\n                \n                  \n                \n                $$i \\le j$$\n                \n              ) or (\n                \n                  \n                \n                $$i==j$$\n                \n              ), etc.) to efficiently generate test data. Ariadne employs a simple attribute grammar to exploit these dependencies, which enables it to evolve complex test data, and has been compared favourably to other well-known techniques in the literature. However, Ariadne does not benefit from interdependencies involving constants, e.g. (\n                \n                  \n                \n                $$i \\le 100$$\n                \n              ) or (\n                \n                  \n                \n                $$j==500$$\n                \n              ), etc., due to the difficulty in evolving precise values, and these are equally important constructs of condition predicates. Furthermore, constant creation in GE can be difficult, particularly with high precision. We propose to seed the grammar with constants extracted from the source code of the program under test to enhance and extend Ariadne’s capability to exploit richer types of dependencies (involving all combinations of both variables and constant values). We compared our results with the original system of Ariadne against a large set of benchmark problems which include 10 numeric programs in addition to the ones originally used for Ariadne. Our results demonstrate that the seeding strategy not only dramatically improves the generality of the system, as it improves the code coverage (effectiveness) by impressive margins, but it also reduces the search budgets (efficiency) often up to an order of magnitude. Moreover, we also performed a rigorous analysis to investigate the scalability of our improved Ariadne, showing that it stays highly scalable when compared to both the original system of Ariadne and GA-based test data generation approach.",{"EN":1004},"Seeding Grammars in Grammatical Evolution to Improve Search-Based Software Testing",{"VOID":1006},"10.1007\u002Fs42979-021-00631-7","https:\u002F\u002Flink.springer.com\u002F10.1007\u002Fs42979-021-00631-7",[1009,1026],{"id":1010,"sortIndex":21,"researcher":20,"roles":1011,"affiliations":1012,"properties":1023},"5507ebd1-6d40-4a23-bb2c-66a0818f9e62",[164],[1013],{"id":20,"sortIndex":21,"affiliation":1014,"properties":20},{"id":1015,"createTime":1016,"updateTime":1017,"relativeEntities":1018,"slug":1019,"properties":1020,"entityType":80,"verifyStatus":19,"verifyTime":20,"verifyNote":20,"syncStatus":19,"languages":20,"translateLanguages":20,"viewCount":21},"90f16339-78be-4fe6-813b-7399db7d3853","2023-11-12T23:50:12.726+00:00","2024-02-19T17:27:50.521+00:00",[],"Biocomputing-and-Developmental-Systems-Group-Department-of-Computer-Science-and-Information-Systems-Lero-Science-Foundation-Ireland-Research-Centre-for-Software-University-of-Limerick-Limerick-Ireland",{"title":1021},{"VI":1022},"Biocomputing and Developmental Systems Group, Department of Computer Science and Information Systems, Lero – Science Foundation Ireland Research Centre for Software, University of Limerick, Limerick, Ireland",{"title":1024},{"VI":1025},"Muhammad Sheraz Anjum",{"id":1027,"sortIndex":21,"researcher":20,"roles":1028,"affiliations":1029,"properties":1038},"49d6092d-4f22-48a4-9dea-313390f6549e",[164],[1030],{"id":1031,"sortIndex":21,"affiliation":1032,"properties":1036},"9b4dec14-9ed9-41b6-bc37-1ca58b2a9a75",{"id":1015,"createTime":1016,"updateTime":1017,"relativeEntities":1033,"slug":1019,"properties":1034,"entityType":80,"verifyStatus":19,"verifyTime":20,"verifyNote":20,"syncStatus":19,"languages":20,"translateLanguages":20,"viewCount":21},[],{"title":1035},{"VI":1022},{"title":1037},{"VI":1022},{"title":1039},{"VI":1040},"Conor Ryan",{"url":1007,"publisher":1042,"properties":1063},{"id":6,"createTime":7,"updateTime":8,"relativeEntities":1043,"slug":10,"properties":1044,"entityType":18,"verifyStatus":19,"verifyTime":20,"verifyNote":20,"syncStatus":19,"languages":20,"translateLanguages":20,"viewCount":21,"subjectFields":1048,"manageAffiliations":1049,"indexDatabases":1050,"url":107,"thumbnailPath":20,"statistic":1058,"gsStatistic":20,"type":134,"analyzePriority":20},[],{"issn":1045,"eissn":1046,"title":1047},{"VOID":13},{"VOID":15},{"EN":17},[],[],[1051],{"id":84,"indexDatabase":1052,"url":97,"indexYears":98,"academicFieldIds":1057,"indexDatabaseRanking":106},{"id":86,"createTime":87,"updateTime":88,"relativeEntities":1053,"label":1054,"description":1055,"key":94,"publicationTags":1056,"standard":20},[],{"EN":91,"VI":91},{"EN":91,"VI":93},[96],[100,101,102,103,104,105],{"impactFactor":21,"impactFactorByYear":1059,"i10Index":113,"i10IndexLast5Year":114,"totalPublication":115,"totalPublicationByYear":1060,"totalCitation":123,"totalCitationByYear":1061,"totalCitationPerPublication":128,"totalCitationPerPublicationByYear":1062,"hindexLast5Year":133,"hindex":133},{"2021":110,"2022":111,"2023":112},{"2019":117,"2020":118,"2021":119,"2022":120,"2023":121,"2024":122},{"2020":125,"2021":126,"2022":127},{"2020":130,"2021":131,"2022":132},{"volume":1064,"pages":1065},{"VOID":513},{"VOID":1066},"1-19","2021-05-19",{"id":1069,"createTime":1070,"updateTime":1071,"relativeEntities":1072,"slug":1073,"properties":1074,"entityType":154,"verifyStatus":155,"verifyTime":1071,"verifyNote":157,"syncStatus":19,"languages":20,"translateLanguages":20,"viewCount":21,"primaryUrl":1083,"fullTextUrl":20,"authors":1084,"publicationType":189,"publisherRelationship":1115,"citationCount":20,"citationInfo":20,"publishDate":1141,"publishYear":1142,"citationAnalyzeStatus":19,"lastCitationAnalyze":20,"indexDatabases":20,"openAccess":20,"references":20,"isForceReanalyzing":219},"0ac9b29c-4241-4c50-abaa-a949719e26e4","2024-01-08T19:24:20.605+00:00","2025-02-10T23:48:38.580+00:00",[],"Cognitive-Reading-and-Character-Recognition-in-Image-Processing-Techniques",{"references":1075,"abstract":1077,"title":1079,"doi":1081},{"VOID":1076},"Filip L, Wallberg O. Natural image distortions and optical character recognition accuracy. Degree project, KTH Royal Institute of Technology, CSC, KTH20160511; 2016. pp. 21–5.\nAnthony L, Yang J, Koedinger KR. A paradigm for handwriting-based intelligent tutors. Int J Hum Comput Stud. 2012;70(11):866–87.\nBai J, Chen Z, Feng B, Xu B. “Chinese Image Character Recognition Using DNN and Machine Simulated Training Samples,” Lecture Notes in Computer Science, pp. 209–216, 2014.\nOh H-S, Jung Y. Cluster-based query expansion using external collections in medical information retrieval. J Biomed Inform. 2015;58:70–9.\nAngadi SA, Angadi SH. Structural features for recognition of hand written Kannada character based on SVM. Int J Comput Sci, Eng Inform Technol. 2015;5(2):25–32.\nShah KR, Badgujar DD. “Devnagari handwritten character recognition (DHCR) for ancient documents: a review,” 2013 IEEE Conference on Information and Communication Technologies. 2013.\nDongre VJ, Mankar VH, Suganya G. A review of research on Devnagari character Recognition. Int J Comput Sci, Eng Inform Appl. 2010;12(2):8–15.\nGoyal G, Dutta M. Experimental approach for performance analysis of thinning algorithms for offline handwritten devnagri numerals. Indian J Sci Technol. 2016;9(30):34–9.\nSharma KS, Karwankar AR, Bhalchandra AS. Devnagari character recognition using self-organizing maps, 2010 International Conference on Computer and Communication Technologies. 2010.\nElleuch M, Maalej R, Kherallah M. A new design based-SVM of the CNN classifier architecture with dropout for offline Arabic handwritten recognition. Proced Comput Sci. 2016;80:1712–23.\nKherallah M, Elleuch M, Tagougui N. A novel architecture of CNN based on SVM classifier for recognizing Arabic handwritten script. Int J Intell Syst Technol Appl. 2016;15(4):323.\nSrikantan G, Lee DS, Favata JT. “Comparison of normalization methods for character recognition,” In: Proceedings of 3rd International Conference on Document Analysis and Recognition.\nFrankish C, Hull R, Morgan P. “Recognition accuracy and user acceptance of pen interfaces,” In: Proceedings of the SIGCHI conference on Human factors in computing systems—CHI’95, 1995.\nZhu B, Nakagaw M. Online handwritten Chinese\u002FJapanese character recognition, Advances in Character Recognition. 2012.\nLehal GS, Bhatt N. “A Recognition System for Devnagari and English Handwritten Numerals,” Lecture Notes in Computer Science.2000. pp. 442–449.\nKumar M. PCA-based offline handwritten character recognition system. Smart Comput Rev. 2013;3(5):346–57.\nWong C. Hand-written Chinese character recognition by hidden Markov models and radical partition.\nKuhnke K, Simoncini L, Kovacs-V. A system for machine-written and hand-written character distinction. In Proceedings of 3rd International Conference on Document Analysis and Recognition\nJoseph AD, Laskov P, Roli F., Tygar JD, Nelson B. Machine learning methods for computer security (Dagstuhl Perspectives Workshop 12371). In: Dagstuhl Manifestos. Schloss Dagstuhl-Leibniz-Zentrum fuer Informatik. 2013; Vol. 3. pp. 1–30.\nDave N. Segmentation methods for hand written character recognition. Int J Signal Process Image Process Pattern Recognit. 2015;8(4):155–64.\nKuhnke K, Simoncini L, Kovacs-V. A system for machine-written and hand-written character distinction. In: Proceedings of 3rd International Conference on Document Analysis and Recognition.\nAnjurn Ali MA. Language independent optical character recognition for hand written text, 8th International Multitopic Conference. 2004. Proceedings of INMIC 2004.\nBorgo R, Kehrer J, Chung DH, Maguire E, Laramee RS, Hauser H, Ward M, Chen M. Glyph-based visualization: foundations, design guidelines, techniques and applications. In: Eurographics State of the Art Reports. 2013. pp. 39–63.\nChacko AMM, Dhanya PM. Handwritten character recognition in malayalam scripts-a review. Int J Artif Intell Appl (IJAIA). 2014;5(1):6–9.\nMukarambi G, Dhandra BV, Hangarge M. A zone based character recognition engine for kannada and english scripts. Procedia Eng. 2012;38:3292–9.\nLiu CL, Yin F, Wang DH, Wang QF. CASIA online and offline Chinese handwriting databases. In: Document Analysis and Recognition (ICDAR), 2011 International Conference on. IEEE. 2011; pp. 37–41.\nJagtap NI, Kulkarni PS. Off-line Handwritten Devnagri Special Character Recognition Using Neural Network. 2016; pp. 881–6.\nSaharan P, Malhotra R. Handwritten devanagari character recognition system using neural network. J Netw Commun Emerg Technol (JNCET). 2016. pp. 1–4.\nKumar RD. Offline sanskirthandwritten character recognition framework based on multi layerfeed forward network with intelligent character recognition. Asian J Inform Technol. 2016;15(11):1678–85.",{"EN":1078},"The trend of researches in cognitive reading has become so popular in programming area in the field of computer science from late 1990s when scientists and researchers show more interests in computational approaches are complex in nature to derive from a known algorithm of solution. For instance, in the research areas of biology, medicine and human management sciences there are various problems where we need cognitive reading to deliver a complex and in-exact solution when there is no polynomial time to arrive at an exact solution. This article explains some of the methods in cognitive reading in image processing for character recognition and briefly discusses the steps involved in the process of character recognition in image processing.",{"EN":1080},"Cognitive Reading and Character Recognition in Image Processing Techniques",{"VOID":1082},"10.1007\u002Fs42979-020-00142-x","https:\u002F\u002Flink.springer.com\u002Farticle\u002F10.1007\u002Fs42979-020-00142-x",[1085,1100],{"id":1086,"sortIndex":21,"researcher":20,"roles":1087,"affiliations":1088,"properties":1097},"3a62d1e6-95d1-4790-adfe-d740d2d80969",[164],[1089],{"id":20,"sortIndex":21,"affiliation":1090,"properties":20},{"id":1091,"createTime":1092,"updateTime":1092,"relativeEntities":1093,"slug":20,"properties":1094,"entityType":80,"verifyStatus":19,"verifyTime":20,"verifyNote":20,"syncStatus":19,"languages":20,"translateLanguages":20,"viewCount":21},"9be0dfe5-348d-4ae0-90a9-a2b9bcc890b8","2024-01-16T13:32:25.840+00:00",[],{"title":1095},{"VI":1096},"Wipro Technologies, Bengaluru, India",{"title":1098},{"VI":1099},"Magesh Kasthuri",{"id":1101,"sortIndex":162,"researcher":20,"roles":1102,"affiliations":1103,"properties":1112},"9fe4488b-3fe8-4468-8de7-20e0a68bfde2",[164],[1104],{"id":20,"sortIndex":21,"affiliation":1105,"properties":20},{"id":1106,"createTime":1107,"updateTime":1107,"relativeEntities":1108,"slug":20,"properties":1109,"entityType":80,"verifyStatus":19,"verifyTime":20,"verifyNote":20,"syncStatus":19,"languages":20,"translateLanguages":20,"viewCount":21},"e4f03175-7d08-411e-832c-a1d9d36ede99","2024-01-08T19:24:20.628+00:00",[],{"title":1110},{"VI":1111},"Nizwa College of Technology, Nizwa, Oman",{"title":1113},{"VI":1114},"Venkatasubramanian Sivaprasatham",{"url":1083,"publisher":1116,"properties":1137},{"id":6,"createTime":7,"updateTime":8,"relativeEntities":1117,"slug":10,"properties":1118,"entityType":18,"verifyStatus":19,"verifyTime":20,"verifyNote":20,"syncStatus":19,"languages":20,"translateLanguages":20,"viewCount":21,"subjectFields":1122,"manageAffiliations":1123,"indexDatabases":1124,"url":107,"thumbnailPath":20,"statistic":1132,"gsStatistic":20,"type":134,"analyzePriority":20},[],{"issn":1119,"eissn":1120,"title":1121},{"VOID":13},{"VOID":15},{"EN":17},[],[],[1125],{"id":84,"indexDatabase":1126,"url":97,"indexYears":98,"academicFieldIds":1131,"indexDatabaseRanking":106},{"id":86,"createTime":87,"updateTime":88,"relativeEntities":1127,"label":1128,"description":1129,"key":94,"publicationTags":1130,"standard":20},[],{"EN":91,"VI":91},{"EN":91,"VI":93},[96],[100,101,102,103,104,105],{"impactFactor":21,"impactFactorByYear":1133,"i10Index":113,"i10IndexLast5Year":114,"totalPublication":115,"totalPublicationByYear":1134,"totalCitation":123,"totalCitationByYear":1135,"totalCitationPerPublication":128,"totalCitationPerPublicationByYear":1136,"hindexLast5Year":133,"hindex":133},{"2021":110,"2022":111,"2023":112},{"2019":117,"2020":118,"2021":119,"2022":120,"2023":121,"2024":122},{"2020":125,"2021":126,"2022":127},{"2020":130,"2021":131,"2022":132},{"volume":1138,"pages":1140},{"VOID":1139},"1",{"VOID":602},"2020-04-23",2020]