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In incremental record linkage, every inserted record is compared with some existing clusters of records based on its blocking key value. Then, considering similarity, either the record will be put into an existing cluster, or a new cluster will be created for it. Although few papers have presented their solutions for incremental record linkage targeting the linkage quality or efficiency, privacy issue regarding the approach has not yet been discussed. Privacy is a major concern when record linkage is performed for sensitive data, e.g., health records, financial records, etc. In this regard, we have come up with a novel concept privacy-preserving incremental record linkage (PPiRL) which encapsulates privacy-preserving techniques with an incremental record linkage approach. In this chapter, we have proposed an end-to-end framework as our solution for PPiRL. For preserving privacy, we have used two types of privacy techniques namely phonetic encoding and generalization. We have used a recently developed phonetic algorithm “nameGist” to handle text-based features. For generalization, we have used the K-anonymization algorithm for numeric and categorical features. For handling incremental updates and internal linkage, we have used the Naive incremental clustering approach using Hierarchical Agglomerative clustering as the base clustering algorithm. We have performed various experiments to test the privacy and linkage quality of PPiRL. We have compared our work with the existing incremental record linkage framework and also with existing privacy-preserved record linkage techniques. It is apparent from our results that other than a small trade-off in linkage quality, our framework works better as a combined package of privacy and linkage solutions that any existing frameworks do not yet provide.",{"EN":133},"Privacy preserved incremental record linkage",{"VOID":135},"[\"16276120193358365377\"]",{"VOID":137},"Al-Lawati A, Lee D, McDaniel P. Blocking-aware private record linkage. In: Proceedings of the 2nd international workshop on Information quality in information systems. ACM; 2005. p. 59–68\nBachteler T, Schnell R, Reiher J. An empirical comparison of approaches to approximate string matching in private record linkage. In: Proceedings of statistics canada symposium, vol. 2010. Citeseer; 2010\nBatini C, Scannapieco M, et al. Data and information quality. Cham: Springer International Publishing. Google Scholar; 2016.\nBaxter R, Christen P, Churches T, et al. A comparison of fast blocking methods for record linkage. In: ACM SIGKDD, vol. 3. Citeseer; 2003. p. 25–27\nBayardo RJ, Agrawal R. Data privacy through optimal k-anonymization. In: 21st International conference on data engineering (ICDE’05). IEEE; 2005. p. 217–228\nBellahsène Z, Bonifati A, Rahm E. Schema matching and mapping. Springer; 2011.\nBleiholder J, Naumann F. Data fusion. ACM Comput Surv (CSUR). 2009;41(1):1.\nCharikar M, Chekuri C, Feder T, Motwani R. Incremental clustering and dynamic information retrieval. SIAM J Comput. 2004;33(6):1417–40.\nChristen P. A comparison of personal name matching: Techniques and practical issues. In: Sixth IEEE international conference on Data mining workshops, 2006. ICDM Workshops 2006. IEEE; 2006. p. 290–294\nChristen P. Development and user experiences of an open source data cleaning, deduplication and record linkage system. ACM SIGKDD Explorations Newsl. 2009;11(1):39–48.\nChristen P. A survey of indexing techniques for scalable record linkage and deduplication. IEEE Trans Knowl Data Eng. 2012;24(9):1537–55.\nChristen P, Goiser K. Quality and complexity measures for data linkage and deduplication. In: Quality measures in data mining. Springer; 2007. p. 127–151\nChurches T, Christen P. Some methods for blindfolded record linkage. BMC Med Inform Decis Mak. 2004;4(1):9.\nCohen WW, Richman J. Learning to match and cluster large high-dimensional data sets for data integration. In: Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining. ACM; 2002. p. 475–480\nElmagarmid AK, Ipeirotis PG, Verykios VS. Duplicate record detection: a survey. IEEE Trans Knowl Data Eng. 2006;19(1):1–16.\nFellegi IP, Sunter AB. A theory for record linkage. J Am Stat Assoc. 1969;64(328):1183–210.\nFranzak F, Pitta D, Fritsche S. Online relationships and the consumer’s right to privacy. J Consum Mark. 2001;18(7):631–42.\nGruenheid A, Dong XL, Srivastava D. Incremental record linkage. Proc VLDB Endow. 2014;7(9):697–708.\nGu L, Baxter R. Decision models for record linkage. In: Data mining. Springer; 2006. p. 146–160\nHauser V. The hacker’s choice, a very fast network logon cracker which support many different services. 2010\nHernández MA, Stolfo SJ. Real-world data is dirty: data cleansing and the merge\u002Fpurge problem. Data Min Knowl Disc. 1998;2(1):9–37.\nHerzog TN, Scheuren FJ, Winkler WE. Data quality and record linkage techniques. Springer Science & Business Media; 2007.\nHumer C, Finkle J. Your medical record is worth more to hackers than your credit card. Reuters.com US Edition 24 (2014)\nInan A, Kantarcioglu M, Bertino E, Scannapieco M. A hybrid approach to private record linkage. In: IEEE 24th international conference on data engineering, 2008. ICDE 2008. IEEE; 2008. p. 496–505\nKhan SI, Hoque ASL. Privacy and security problems of national health data warehouse: a convenient solution for developing countries. In: 2016 international conference on networking systems and security (NSysS). IEEE; 2016. p. 1–6\nKhan SI, Hoque ASML. An analysis of the problems for health data integration in Bangladesh. In: 2016 International conference on innovations in science, engineering and technology (ICISET). IEEE; 2016. p. 1–4\nKhan SI, Latiful Hoque ASM. Digital health data: a comprehensive review of privacy and security risks and some recommendations. Comput Sci J Mold 2016; 24(2)\nLee YW, Pipino LL, Funk JD, Wang RY. Journey to data quality. The MIT Press; 2009.\nMathieu C, Sankur O, Schudy W. Online correlation clustering. 2010. arXiv preprint arXiv:1001.0920\nMukherjee A, Nath P. A model of trust in online relationship banking. I J Bank Market. 2003;21(1):5–15.\nNarayanan A, Shmatikov V. Fast dictionary attacks on passwords using time-space tradeoff. In: Proceedings of the 12th ACM conference on computer and communications security. 2005. p. 364–372\nDo Nascimento DC, Pires CES, Mestre DG. Heuristic-based approaches for speeding up incremental record linkage. J Syst Softw. 2018;137:335–54.\nNaumann F, Herschel M. An introduction to duplicate detection. Synth Lect Data Manage. 2010;2(1):1–87.\nPaverd A, Martin A, Brown I. Modelling and automatically analysing privacy properties for honest-but-curious adversaries. Tech Rep. 2014\nRahm E, Do HH. Data cleaning: problems and current approaches. IEEE Data Eng Bull. 2000;23(4):3–13.\nSherstobitoff R. Anatomy of a data breach. Inf Security J Glob Perspect. 2008;17(5–6):247–52.\nSteorts RC, Ventura SL, Sadinle M, Fienberg SE. A comparison of blocking methods for record linkage. In: International conference on privacy in statistical databases. Springer; 2014. p. 253–268\nTauer G, Date K, Nagi R, Sudit M. An incremental graph-partitioning algorithm for entity resolution. Inf Fusion. 2019;46:171–83.\nVatsalan D, Christen P. Scalable privacy-preserving record linkage for multiple databases. In: Proceedings of the 23rd ACM international conference on conference on information and knowledge management. 2014. p. 1795–1798\nVatsalan D, Christen P, Verykios VS. A taxonomy of privacy-preserving record linkage techniques. Inf Syst. 2013;38(6):946–69.\nVatsalan D, Sehili Z, Christen P, Rahm E. Privacy-preserving record linkage for big data: current approaches and research challenges. In: Handbook of big data technologies. Springer; 2017. p. 851–895\nVerykios VS, Karakasidis A, Mitrogiannis VK. Privacy preserving record linkage approaches. Int J Data Min Model Manage. 2009;1(2):206–21.\nWeber SC, Lowe H, Das A, Ferris T. A simple heuristic for blindfolded record linkage. J Am Med Inform Assoc. 2012;19(e1):e157–61.\nWhang SE, Garcia-Molina H. Entity resolution with evolving rules. Proc VLDB Endow. 2010;3(1–2):1326–37.\nWhang SE, Garcia-Molina H. Incremental entity resolution on rules and data. VLDB J Int J Very Large Data Bases. 2014;23(1):77–102.\nYakout M, Atallah MJ, Elmagarmid A. Efficient private record linkage. In: IEEE 25th international conference on data engineering, 2009. ICDE’09. IEEE; 2009. p. 1283–1286",{"VOID":139},"10.1186\u002Fs40537-022-00655-7","PUBLICATION","VERIFIED","2024-05-16T12:36:02.891+00:00","Auto Verify","https:\u002F\u002Fjournalofbigdata.springeropen.com\u002Farticles\u002F10.1186\u002Fs40537-022-00655-7",[146,173,186],{"id":147,"sortIndex":21,"researcher":20,"roles":148,"affiliations":150,"properties":168,"displayName":170,"givenName":20,"familyName":20},"ca016784-1875-421b-8a40-e1d2ed5f35b0",[149],"AUTHOR",[151,159],{"id":152,"sortIndex":21,"affiliation":153,"properties":20},"505ae7e9-b13e-46e3-8019-859d9737213f",{"id":152,"createTime":20,"updateTime":20,"relativeEntities":154,"slug":20,"properties":155,"entityType":20,"verifyStatus":20,"verifyTime":20,"verifyNote":20,"languages":20,"translateLanguages":20,"viewCount":20,"url":20,"parentIds":158,"statistic":20},[],{"title":156},{"VI":157},"Department of CSE, International Islamic University Chittagong, Chittagong, 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the past decade, recommendation systems have been one of the most sought after by various researchers. Basket analysis of online systems’ customers and recommending attractive products (movies) to them is very important. Providing an attractive and favorite movie to the customer will increase the sales rate and ultimately improve the system. Various methods have been proposed so far to analyze customer baskets and offer entertaining movies but each of the proposed methods has challenges, such as lack of accuracy and high error of recommendations. In this paper, a link prediction-based method is used to meet the challenges of other methods. The proposed method in this paper consists of four phases: (1) Running the CBRS that in this phase, all users are clustered using Density-based spatial clustering of applications with noise algorithm (DBScan), and classification of new users using Deep Neural Network (DNN) algorithm. (2) Collaborative Recommender System (CRS) Based on Hybrid Similarity Criterion through which similarities are calculated based on a threshold (lambda) between the new user and the users in the selected category. Similarity criteria are determined based on age, gender, and occupation. The collaborative recommender system extracts users who are the most similar to the new user. Then, the higher-rated movie services are suggested to the new user based on the adjacency matrix. (3) Running improved Friendlink algorithm on the dataset to calculate the similarity between users who are connected through the link. (4) This phase is related to the combination of collaborative recommender system’s output and improved Friendlink algorithm. The results show that the Mean Squared Error (MSE) of the proposed model has decreased respectively 8.59%, 8.67%, 8.45% and 8.15% compared to the basic models such as Naive Bayes, multi-attribute decision tree and randomized algorithm. In addition, Mean Absolute Error (MAE) of the proposed method decreased by 4.5% compared to SVD and approximately 4.4% compared to ApproSVD and Root Mean Squared Error (RMSE) of the proposed method decreased by 6.05 % compared to SVD and approximately 6.02 % compared to ApproSVD.",{"EN":280},"A hybrid recommender system based-on link prediction for movie baskets 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Farashah",{"VOID":304},"[\"R40rTOwAAAAJ\"]",{"id":306,"sortIndex":161,"researcher":20,"roles":307,"affiliations":308,"properties":315,"displayName":317,"givenName":20,"familyName":20},"ecd692f5-a38d-4401-a15e-8ae80fc628af",[149],[309],{"id":293,"sortIndex":21,"affiliation":310,"properties":20},{"id":293,"createTime":20,"updateTime":20,"relativeEntities":311,"slug":20,"properties":312,"entityType":20,"verifyStatus":20,"verifyTime":20,"verifyNote":20,"languages":20,"translateLanguages":20,"viewCount":20,"url":20,"parentIds":314,"statistic":20},[],{"title":313},{"EN":298},[],{"title":316},{"VI":317},"Akbar 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Azmi",{"VOID":334},"[\"EFuvUTkAAAAJ\"]",{"id":336,"sortIndex":337,"researcher":20,"roles":338,"affiliations":339,"properties":346,"displayName":348,"givenName":20,"familyName":20},"6d6bf9f9-fc50-4073-9eda-015949f5addd",3,[149],[340],{"id":293,"sortIndex":21,"affiliation":341,"properties":20},{"id":293,"createTime":20,"updateTime":20,"relativeEntities":342,"slug":20,"properties":343,"entityType":20,"verifyStatus":20,"verifyTime":20,"verifyNote":20,"languages":20,"translateLanguages":20,"viewCount":20,"url":20,"parentIds":345,"statistic":20},[],{"title":344},{"EN":298},[],{"title":347},{"VI":348},"Reza Ebrahimzadeh 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M, Gao J, Varlamis I, Tserpes K. Recommender systems for large-scale social networks: a review of challenges and solutions.","https:\u002F\u002Flink.springer.com\u002F10.1007\u002Fs10440-022-00541-7",{"doi":420},"10.1007\u002Fs10440-022-00541-7",{"id":422,"text":423,"url":424,"identifiers":425},"806c68f5-5022-4054-a45e-d3777da82587","Najafabadi MK, Mohamed AH, Mahrin MN. A survey on data mining techniques in recommender systems. Soft Comput. 2019;23(2):627–54.","http:\u002F\u002Flink.springer.com\u002F10.1007\u002Fs00500-017-2918-7",{"doi":426},"10.1007\u002Fs00500-017-2918-7",{"id":428,"text":429,"url":430,"identifiers":431},"fa7c4956-30cd-43fb-9c85-71b1c01410bb","Silveira T, Zhang M, Lin X, Liu Y, Ma S. How good your recommender system is? A survey on evaluations in recommendation. Int J Mach Learn Cybern. 2019;10(5):813–31.","https:\u002F\u002Flink.springer.com\u002Farticle\u002F10.1007\u002Fs13042-017-0762-9",{"doi":432},"10.1007\u002Fs13042-017-0762-9",{"id":434,"text":435,"url":436,"identifiers":437},"ef081516-e3d2-4bc9-b9e8-df96e1789875","Tatiana K, Mikhail M. Market basket analysis of heterogeneous data sources for recommendation system improvement. Procedia Comput Sci. 2018;136:246–54.","https:\u002F\u002Fwww.sciencedirect.com\u002Fscience\u002Farticle\u002Fpii\u002FS1877050918315680",{"doi":438},"10.1016\u002Fj.procs.2018.08.263",{"id":440,"text":441,"url":442,"identifiers":443},"e5f66303-8473-440a-ab50-c24da853e401","Hoang L. HU-FCF++: a novel hybrid method for the new user cold-start problem in recommender systems. Eng Appl Artif Intell. 2015;41:207–22.","https:\u002F\u002Fwww.sciencedirect.com\u002Fscience\u002Farticle\u002Fpii\u002FS0952197615000330",{"doi":444},"10.1016\u002Fj.engappai.2015.02.003",{"id":416,"text":446,"url":418,"identifiers":447},"Pera MS, Ng YK. A group recommender for movies based on content similarity and popularity. Inf Process Manag. 2013;49(3):673–87.",{"doi":420},{"id":416,"text":449,"url":418,"identifiers":450},"Christensen IA, Schiaffino S. Entertainment recommender systems for group of users. Expert Syst Appl. 2011;38(11):14127–35.",{"doi":420},{"id":452,"text":453,"url":454,"identifiers":455},"10999cb3-7961-407b-9bf9-259b57079e30","Camacho LA, Alves-Souza SN. Social network data to alleviate cold-start in recommender system: a systematic review. Inf Process Manag. 2018;54(4):529–44.","https:\u002F\u002Fwww.sciencedirect.com\u002Fscience\u002Farticle\u002Fpii\u002FS0306457317306544",{"doi":456},"10.1016\u002Fj.ipm.2018.03.004",{"id":20,"text":458,"url":20,"identifiers":459},"Van Meteren R, Van Someren M. Using content-based filtering for recommendation. In: Proceedings of the machine learning in the new information age: MLnet\u002FECML2000 Workshop, vol. 30. 2000. p. 47–56.",{},{"id":416,"text":461,"url":418,"identifiers":462},"Basu C, Hirsh H, Cohen W. Recommendation as classification: using social and content-based information in recommendation. In: Aaai\u002Fiaai. 1998. p. 714–20.",{"doi":420},{"id":464,"text":465,"url":466,"identifiers":467},"ef4cfb3c-b8ad-405e-9cdb-bb21d15c2379","Chen J, Wang H, Yan Z. Evolutionary heterogeneous clustering for rating prediction based on user collaborative filtering. Swarm Evol Comput. 2018;1:35–41.","https:\u002F\u002Fwww.sciencedirect.com\u002Fscience\u002Farticle\u002Fpii\u002FS2210650216305648",{"doi":468},"10.1016\u002Fj.swevo.2017.05.008",{"id":416,"text":470,"url":418,"identifiers":471},"Benkhelifa R, Bouhyaoui N, Laallam FZ. A demographic-based approach for improved content categorization in social networking. In: 2018 2nd international conference on natural language and speech processing (ICNLSP). New York: IEEE; 2018. p. 1–5.",{"doi":420},{"id":416,"text":473,"url":418,"identifiers":474},"Nirenburg S, Carbonell J, Tomita M, Goodman K. Machine translation: a knowledge-based approach. Burlington: Morgan Kaufmann Publishers Inc.; 1994.",{"doi":420},{"id":416,"text":476,"url":418,"identifiers":477},"Ha T, Lee S. Item-network-based collaborative filtering: a personalized recommendation method based on a user’s item network. Inf Process Manag. 2017;53(5):1171–84.",{"doi":420},{"id":416,"text":479,"url":418,"identifiers":480},"Sharma L, Gera A. A survey of recommendation system: research challenges. Int J Eng Trends Technol. 2013;4(5):1989–92.",{"doi":420},{"id":416,"text":482,"url":418,"identifiers":483},"Ghazanfar MA, Prugel-Bennett A. A scalable, accurate hybrid recommender system. In: 2010 third international conference on knowledge discovery and data mining. New York: IEEE; 2010. p. 94–8.",{"doi":420},{"id":416,"text":485,"url":418,"identifiers":486},"Kim HN, El-Saddik A, Jo GS. Collaborative error-reflected models for cold-start recommender systems. Decis Support Syst. 2011;51(3):519–31.",{"doi":420},{"id":488,"text":489,"url":490,"identifiers":491},"8dc1737c-678c-4e9e-89ff-0b9a900bb622","Bobadilla J, Ortega F, Hernando A, Bernal J. A collaborative filtering approach to mitigate the new user cold start problem. Knowl Based Syst. 2012;26:225–38.","https:\u002F\u002Fwww.sciencedirect.com\u002Fscience\u002Farticle\u002Fpii\u002FS0950705111001882",{"doi":492},"10.1016\u002Fj.knosys.2011.07.021",{"id":416,"text":494,"url":418,"identifiers":495},"Byström H. Movie recommendations from user ratings.",{"doi":420},{"id":497,"text":498,"url":499,"identifiers":500},"0fc21bc2-94b7-4c05-9f5e-1d976f3bf323","Lika B, Kolomvatsos K, Hadjiefthymiades S. Facing the cold start problem in recommender systems. Expert Syst Appl. 2014;41(4):2065–73.","https:\u002F\u002Flinkinghub.elsevier.com\u002Fretrieve\u002Fpii\u002FS0957417413007240",{"doi":501},"10.1016\u002Fj.eswa.2013.09.005",{"id":503,"text":504,"url":505,"identifiers":506},"03eeaba7-5fb7-45a7-9437-7aa87695ce92","Pereira AL, Hruschka ER. Simultaneous co-clustering and learning to address the cold start problem in recommender systems. Knowl Based Syst. 2015;1:11–9.","https:\u002F\u002Fwww.sciencedirect.com\u002Fscience\u002Farticle\u002Fpii\u002FS0950705115000593",{"doi":507},"10.1016\u002Fj.knosys.2015.02.016",{"id":20,"text":509,"url":510,"identifiers":511},"MovieLens GroupLens. 2015. http:\u002F\u002Fgrouplens.org\u002Fdatasets\u002Fmovielens\u002F2013.","http:\u002F\u002Fgrouplens.org\u002Fdatasets\u002Fmovielens\u002F2013",{},{"id":416,"text":513,"url":418,"identifiers":514},"Kutty S, Chen L, Nayak R. A people-to-people recommendation system using tensor space models. In: Proceedings of the 27th annual ACM symposium on applied computing. 2012. p. 187–92.",{"doi":420},{"id":416,"text":516,"url":418,"identifiers":517},"Lin CH, Chi H. A novel movie recommendation system based on collaborative filtering and neural networks. In: International conference on advanced information networking and applications. Cham: Springer; 2019. p. 895–903.",{"doi":420},{"id":519,"text":520,"url":521,"identifiers":522},"7faf66c5-2660-4f77-9134-d9e357234907","Walek B, Fojtik V. A hybrid recommender system for recommending relevant movies using an expert system. Expert Syst Appl. 2020;13:113452.","https:\u002F\u002Fwww.sciencedirect.com\u002Fscience\u002Farticle\u002Fpii\u002FS0957417420302761",{"doi":523},"10.1016\u002Fj.eswa.2020.113452",{"id":416,"text":525,"url":418,"identifiers":526},"Zhou X, He J, Huang G, Zhang Y. SVD-based incremental approaches for recommender systems. J Comput Syst Sci. 2015;81(4):717–33.",{"doi":420},{"id":528,"createTime":529,"updateTime":530,"relativeEntities":531,"slug":532,"properties":533,"entityType":140,"verifyStatus":141,"verifyTime":544,"verifyNote":143,"languages":20,"translateLanguages":20,"viewCount":21,"primaryUrl":545,"fullTextUrl":20,"authors":546,"publicationType":202,"publisherRelationship":629,"citationCount":20,"citationInfo":20,"publishDate":688,"publishYear":689,"citationAnalyzeStatus":690,"lastCitationAnalyze":691,"indexDatabases":692,"openAccess":20,"references":20,"isForceReanalyzing":269},"373eb62b-7391-4c46-8157-0e136375ee97","2024-01-11T18:10:04.796+00:00","2026-07-27T15:18:53.258+00:00",[],"CNN-IKOA-convolutional-neural-network-with-improved-Kepler-optimization-algorithm-for-image-segmentation-experimental-validation-and-numerical-exploration",{"abstract":534,"title":536,"gsPaper":538,"references":540,"doi":542},{"EN":535},"Chest diseases, especially COVID-19, have quickly spread throughout the world and caused many deaths. Finding a rapid and accurate diagnostic tool was indispensable to combating these diseases. Therefore, scientists have thought of combining chest X-ray (CXR) images with deep learning techniques to rapidly detect people infected with COVID-19 or any other chest disease. Image segmentation as a preprocessing step has an essential role in improving the performance of these deep learning techniques, as it could separate the most relevant features to better train these techniques. Therefore, several approaches were proposed to tackle the image segmentation problem accurately. Among these methods, the multilevel thresholding-based image segmentation methods won significant interest due to their simplicity, accuracy, and relatively low storage requirements. However, with increasing threshold levels, the traditional methods have failed to achieve accurate segmented features in a reasonable amount of time. Therefore, researchers have recently used metaheuristic algorithms to tackle this problem, but the existing algorithms still suffer from slow convergence speed and stagnation into local minima as the number of threshold levels increases. Therefore, this study presents an alternative image segmentation technique based on an enhanced version of the Kepler optimization algorithm (KOA), namely IKOA, to better segment the CXR images at small, medium, and high threshold levels. Ten CXR images are used to assess the performance of IKOA at ten threshold levels (T-5, T-7, T-8, T-10, T-12, T-15, T-18, T-20, T-25, and T-30). To observe its effectiveness, it is compared to several metaheuristic algorithms in terms of several performance indicators. The experimental outcomes disclose the superiority of IKOA over all the compared algorithms. Furthermore, the IKOA-based segmented CXR images at eight different threshold levels are used to train a newly proposed CNN model called CNN-IKOA to find out the effectiveness of the segmentation step. Five performance indicators, namely overall accuracy, precision, recall, F1-score, and specificity, are used to disclose the CNN-IKOA’s effectiveness. CNN-IKOA, according to the experimental outcomes, could achieve outstanding outcomes for the images segmented at T-12, where it could reach 94.88% for overall accuracy, 96.57% for specificity, 95.40% for precision, and 95.40% for recall.",{"EN":537},"CNN-IKOA: convolutional neural network with improved Kepler optimization algorithm for image segmentation: experimental validation and numerical exploration",{"VOID":539},"[]",{"VOID":541},"World Health, O., Coronavirus disease (COVID-19), 12 Oct 2020. 2020.\nKong W-H, et al. SARS-CoV-2 detection in patients with influenza-like illness. Nat Microbiol. 2020;5(5):675–8.\nBassi, P.R.A.S. and R. 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Nutcracker optimizer: a novel nature-inspired metaheuristic algorithm for global optimization and engineering design problems. Knowl-Based Syst. 2023;262: 110248.\nAbdel-Basset M, Chang V, Mohamed R. A novel equilibrium optimization algorithm for multi-thresholding image segmentation problems. Neural Comput Appl. 2021;33:10685–718.\nRao RV, Savsani VJ, Vakharia DP. Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Comput Aided Des. 2011;43(3):303–15.\nCuevas E, Zaldivar D, Pérez-Cisneros M. A novel multi-threshold segmentation approach based on differential evolution optimization. Expert Syst Appl. 2010;37(7):5265–71.\nAhmadianfar I, Bozorg-Haddad O, Chu X. Gradient-based optimizer: a new metaheuristic optimization algorithm. Inf Sci. 2020;540:131–59.\nLam FC, Longnecker MT. A modified Wilcoxon rank sum test for paired data. Biometrika. 1983;70(2):510–3.\nAhsan, M.M., et al. Detection of COVID-19 patients from CT scan and chest X-ray data using modified MobileNetV2 and LIME. MDPI.",{"VOID":543},"10.1186\u002Fs40537-023-00858-6","2024-06-25T01:07:59.006+00:00","https:\u002F\u002Fjournalofbigdata.springeropen.com\u002Farticles\u002F10.1186\u002Fs40537-023-00858-6",[547,562,575,590,614],{"id":548,"sortIndex":21,"researcher":20,"roles":549,"affiliations":550,"properties":559,"displayName":561,"givenName":20,"familyName":20},"c54098c3-800f-4fc7-945e-bfd318c5a7b1",[149],[551],{"id":552,"sortIndex":21,"affiliation":553,"properties":20},"eae32cf4-7eee-49bb-ac68-08c45d2cf940",{"id":552,"createTime":20,"updateTime":20,"relativeEntities":554,"slug":20,"properties":555,"entityType":20,"verifyStatus":20,"verifyTime":20,"verifyNote":20,"languages":20,"translateLanguages":20,"viewCount":20,"url":20,"parentIds":558,"statistic":20},[],{"title":556},{"VI":557},"Faculty of Computers and Informatics, Zagazig University, Zagazig, Egypt",[],{"title":560},{"VI":561},"Mohamed Abdel-Basset",{"id":563,"sortIndex":161,"researcher":20,"roles":564,"affiliations":565,"properties":572,"displayName":574,"givenName":20,"familyName":20},"7362a196-6dc6-4bd8-a581-eab5bf8ee114",[149],[566],{"id":552,"sortIndex":21,"affiliation":567,"properties":20},{"id":552,"createTime":20,"updateTime":20,"relativeEntities":568,"slug":20,"properties":569,"entityType":20,"verifyStatus":20,"verifyTime":20,"verifyNote":20,"languages":20,"translateLanguages":20,"viewCount":20,"url":20,"parentIds":571,"statistic":20},[],{"title":570},{"VI":557},[],{"title":573},{"VI":574},"Reda Mohamed",{"id":576,"sortIndex":188,"researcher":20,"roles":577,"affiliations":578,"properties":587,"displayName":589,"givenName":20,"familyName":20},"fa41c6e8-56d3-4461-8f14-b1113c91d517",[149],[579],{"id":580,"sortIndex":21,"affiliation":581,"properties":20},"0b1fb038-ccc1-4a61-8584-c534631e9f41",{"id":580,"createTime":20,"updateTime":20,"relativeEntities":582,"slug":20,"properties":583,"entityType":20,"verifyStatus":20,"verifyTime":20,"verifyNote":20,"languages":20,"translateLanguages":20,"viewCount":20,"url":20,"parentIds":586,"statistic":20},[],{"title":584},{"VI":585},"Department of Computer Science, College of Computer and Information Sciences, Jouf University, Sakaka, Saudi Arabia",[],{"title":588},{"VI":589},"Ibrahim Alrashdi",{"id":591,"sortIndex":337,"researcher":20,"roles":592,"affiliations":593,"properties":611,"displayName":613,"givenName":20,"familyName":20},"97f9770b-318a-4c3b-943d-5264308072d3",[149],[594,602],{"id":595,"sortIndex":21,"affiliation":596,"properties":20},"bc2f9f1e-2ba3-4174-9cc5-78f95b948541",{"id":595,"createTime":20,"updateTime":20,"relativeEntities":597,"slug":20,"properties":598,"entityType":20,"verifyStatus":20,"verifyTime":20,"verifyNote":20,"languages":20,"translateLanguages":20,"viewCount":20,"url":20,"parentIds":601,"statistic":20},[],{"title":599},{"VI":600},"Department of Computer Science, University of Sharjah, Sharjah, United Arab Emirates",[],{"id":603,"sortIndex":161,"affiliation":604,"properties":610},"3c05de22-fcd1-484c-bf87-3772347d3c91",{"id":603,"createTime":20,"updateTime":20,"relativeEntities":605,"slug":20,"properties":606,"entityType":20,"verifyStatus":20,"verifyTime":20,"verifyNote":20,"languages":20,"translateLanguages":20,"viewCount":20,"url":20,"parentIds":609,"statistic":20},[],{"title":607},{"EN":608},"School of IT and Systems, Faculty of Science and Technology, University of Canberra, Canberra, Australia",[],{},{"title":612},{"VI":613},"Karam M. 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Big data characterized, among others, the data comes from multiple sources, multi-format, comply to 5-V’s in nature (value, volume, velocity, variety, and veracity). Big data also constitutes structured data, semi-structured data, and unstructured-data. These characteristics of big data formed “big data ecosystem” that have various active nodes involved. Regardless such complex characteristics of big data, the studies show that there exists inherent structure that can be very useful to provide meaningful solutions for various problems. One of the problems is anticipating proper action to students’ achievement. It is common practice that lecturer treat his\u002Fher class with “one-size-fits-all” policy and strategy. Whilst, the degree of students’ understanding, due to several factors, may not the same. Furthermore, it is often too late to take action to rescue the student’s achievement in trouble. This study attempted to gather all possible features involved from multiple data sources: national education databases, reports, webpages and so forth. The multiple data sources comprise data on undergraduate students from 13 provinces in Indonesia, including students’ academic histories, demographic profiles and socioeconomic backgrounds and institutional information (i.e. level of accreditation, programmes of study, type of university, geographical location). Gathered data is furthermore preprocessed using various techniques to overcome missing value, data categorisation, data consistency, data quality assurance, to produce relatively clean and sound big dataset. Principal component analysis (PCA) is employed in order to reduce dimensions of big dataset and furthermore use K-Means methods to reveal clusters (inherent structure) that may occur in that big dataset. There are 7 clusters suggested by K-Means analysis: 1. very low-risk students, 2. low-risk students, 3. moderate-risk students, 4. fluctuating-risk students, 5. high risk students, 6. very high-risk students and, 7. fail students. Among the clusters unreveal, (1) a gap between public universities and private universities across the three regions in Indonesia, (2) a gap between STEM and non-STEM programmes of study, (3) a gap between rural versus urban, (4) a gap of accreditation status, (5) a gap of quality human resources distribution, etc. Further study, we will use the characteristics of each cluster to predict students’ achievement based on students’ profiles, and provide solutions and interventions strategies for students to improve their likely success.",{"EN":703},"‘Everything is data’: towards one big data ecosystem using multiple sources of data on higher education in Indonesia",{"VOID":705},"[\"4407524640134237462\"]",{"VOID":707},"Rydning DR-JG-J, others. The digitization of the world from edge to core. Fram. Int. Data Corp. 2018 [cited 2021 Dec 25]. p. 16. https:\u002F\u002Fwww.seagate.com\u002Ffiles\u002Fwww-content\u002Four-story\u002Ftrends\u002Ffiles\u002Fidc-seagate-dataage-whitepaper.pdf\nWu C, Buyya R, Ramamohanarao K. Big data analytics = machine learning + cloud computing. In: Buyya R, Calheiros RN, Dastjerdi AV, editors. Big Data Princ Paradig. Morgan Kaufmann; 2016. p. 1–13.\nRaut RD, Mangla SK, Narwane VS, Dora M, Liu M. Big Data Analytics as a mediator in Lean, Agile, Resilient, and Green (LARG) practices effects on sustainable supply chains. Transp Res Part E Logist Transp Rev. 2021;145:102170. https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.tre.2020.102170.\nAnshari M, Almunawar MN, Lim SA, Al-Mudimigh A. Customer relationship management and big data enabled: Personalization & customization of services. Appl Comput Informatics. 2019;15:94–101. https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.aci.2018.05.004.\nAloqool A, Alharafsheh M, Abdellatif H, Alghasawneh LAS, Al-Gasawneh JA. The mediating role of customer relationship management between e-supply chain management and competitive advantage. Int J Data Netw Sci. 2022;6:263–72. https:\u002F\u002Fdoi.org\u002F10.5267\u002FJ.IJDNS.2021.9.002.\nHasibuan ZA. Towards using universal big data in artificial intelligence research and development to gain meaningful insights and automation systems. Int Work Big Data Inf Secur IWBIS IEEE. 2020;2020:9–15. https:\u002F\u002Fdoi.org\u002F10.1109\u002FIWBIS50925.2020.9255497.\nDash S, Shakyawar SK, Sharma M, Kaushik S. Big data in healthcare: management, analysis and future prospects. J Big Data. 2019;6:54. https:\u002F\u002Fdoi.org\u002F10.1186\u002Fs40537-019-0217-0.\nJamjoom AA. The use of knowledge extraction in predicting customer churn in B2B. J Big Data. 2021;8:110. https:\u002F\u002Fdoi.org\u002F10.1186\u002Fs40537-021-00500-3.\nYunita A, Santoso HB, Hasibuan ZA. Deep learning for predicting students’ academic performance. In: Proc 2019 4th Int Conf Informatics Comput ICIC 2019. 2019. p. 1–6. https:\u002F\u002Fdoi.org\u002F10.1109\u002FICIC47613.2019.8985721.\nPurwoningsih T, Santoso HB, Hasibuan ZA. Online Learners’ Behaviors Detection Using Exploratory Data Analysis and Machine Learning Approach. In: Proc 2019 4th Int Conf Informatics Comput ICIC 2019. 2019. p. 1–8. https:\u002F\u002Fdoi.org\u002F10.1109\u002FICIC47613.2019.8985918.\nWibisono A, Wisesa HA, Rahmadhani ZP, Fahira PK, Mursanto P, Jatmiko W. Traditional food knowledge of Indonesia: a new high-quality food dataset and automatic recognition system. J Big Data. 2020;7:69. https:\u002F\u002Fdoi.org\u002F10.1186\u002Fs40537-020-00342-5.\nKemendagri. 273 Juta Penduduk Indonesia Terupdate Versi Kemendagri. 2022. https:\u002F\u002Fdukcapil.kemendagri.go.id\u002Fberita\u002Fbaca\u002F1032\u002F273-juta-penduduk-indonesia-terupdate-versi-kemendagri#:~:text=Jakarta-KemendagrimelaluiDirektoratJenderal,Indonesiaadalah273.879.750jiwa.\nBPS. Statistical yearbook of Indonesia 2021. Jakarta; 2021. https:\u002F\u002Fwww.bps.go.id\u002Fpublication\u002F2021\u002F02\u002F26\u002F938316574c78772f27e9b477\u002Fstatistik-indonesia-2021.html\nWilantika N, Sensuse DI, Wibisono SB, Putro PL, Damanik A. Grouping of provinces in Indonesia according to digital divide index. 6th Int Conf Inf Commun Technol ICoICT 2018. IEEE. 2018;2018:380–8. https:\u002F\u002Fdoi.org\u002F10.1109\u002FICoICT.2018.8528753.\nYunita A, Santoso HB, Hasibuan ZA. Research review on big data usage for learning analytics and educational data mining: A way forward to develop an intelligent automation system. J Phys Conf Ser. 2021;1898:13. https:\u002F\u002Fdoi.org\u002F10.1088\u002F1742-6596\u002F1898\u002F1\u002F012044.\nBappenas. Bappenas wujudkan transformasi digital melalui satu data Indonesia untuk PEN. 2021. https:\u002F\u002Fwww.bappenas.go.id\u002Fid\u002Fberita\u002Fbappenas-wujudkan-transformasi-digital-melalui-satu-data-indonesia-untuk-pen\nManning P, Van Der Plas F, Soliveres S, Allan E, Maestre FT, Mace G, et al. Redefining ecosystem multifunctionality. Nat Ecol Evol. 2018;2:427–36. https:\u002F\u002Fdoi.org\u002F10.1038\u002Fs41559-017-0461-7.\nOdum EP, Barrett GW. Fundamentals of ecology. New York: Saunders Philadelphia; 1971.\nAnuradha J. A brief introduction on big data 5Vs characteristics and hadoop technology. Procedia Comput Sci. 2015. https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.procs.2015.04.188.\nCui Y, Kara S, Chan KC. Manufacturing big data ecosystem: A systematic literature review. Robot Comput Integr Manuf. 2020;62:101861. https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.rcim.2019.101861.\nPatgiri R, Ahmed A. Big data: The v’s of the game changer paradigm. In: 2016 IEEE 18th Int Conf high Perform Comput Commun IEEE 14th Int Conf smart city; IEEE 2nd Int Conf data Sci Syst. 2016. p. 17–24. https:\u002F\u002Fdoi.org\u002F10.1109\u002FHPCC-SmartCity-DSS.2016.0014\nGkontzis A, Kotsiantis S, Panagiotakopoulos C, Verykios V. A predictive analytics framework as a countermeasure for attrition of students. Interact Learn Environ. Routledge; 2019;1–16.\nLemay DJ, Doleck T. Grade prediction of weekly assignments in MOOCS: mining video-viewing behavior. Educ Inf Technol Springer. 2020;25:1333–42. https:\u002F\u002Fdoi.org\u002F10.1007\u002Fs10639-019-10022-4.\nHuang AYQ, Lu OHT, Huang JCH, Yin CJ, Yang SJH. Predicting students’ academic performance by using educational big data and learning analytics: evaluation of classification methods and learning logs. Interact Learn Environ Routledge. 2020;28:206–30. https:\u002F\u002Fdoi.org\u002F10.1080\u002F10494820.2019.1636086.\nYang SJH, Lu OHT, Huang AYQ, Huang JCH, Ogata H, Lin AJQ. Predicting students’ academic performance using multiple linear regression and principal component analysis. J Inf Process. 2018;26:170–6. https:\u002F\u002Fdoi.org\u002F10.2197\u002Fipsjjip.26.170.\nXi J, Chen Y, Wang G. Design of a personalized massive open online course platform. Int J Emerg Technol Learn. 2018;13:58–70. https:\u002F\u002Fdoi.org\u002F10.3991\u002Fijet.v13i04.8470.\nQu S, Li K, Zhang S, Wang Y. Predicting achievement of students in smart campus. IEEE Access. 2018;6:60264–73. https:\u002F\u002Fdoi.org\u002F10.1109\u002FACCESS.2018.2875742.\nZaki MJ, Meira W Jr, Meira W. Data Mining and Analysis. Cambridge: Cambridge University Press; 2014.\nMalley B, Ramazzotti D, Wu JT. Data pre-processing. Cham: Springer International Publishing; 2016. p. 115–41.\nLavangnananda K, Chattanachot S. Study of discretization methods in classification. 9th Int Conf Knowl Smart Technol Crunching Inf Everything. KST. 2017;2017(2017):50–5. https:\u002F\u002Fdoi.org\u002F10.1109\u002FKST.2017.7886082.\nDimić G, Rančić D, Milentijević I, Spalević P. Improvement of the accuracy of prediction using unsupervised discretization method: Educational data set case study. Teh Vjesn. 2018;25:407–14. https:\u002F\u002Fdoi.org\u002F10.17559\u002FTV-20170220135853.\nTsai CF, Chen YC. The optimal combination of feature selection and data discretization: An empirical study. Inf Sci. 2019;505:282–93. https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.ins.2019.07.091.\nHevner AR, March ST, Park J, Ram S. Design science in information systems research. Mis Q Jstor; 2004. p.75–105.\nHasibuan ZA, Dantes GR. Priority of key success factors (KSFS) on enterprise resource planning (ERP) system implementation life cycle. J Enterp Resour Plan Stud. 2012;2012:1.\nIswari NMS, Budiardjo EK, Hasibuan ZA. E-business applications recommendation for SMES using advanced user-based collaboration filtering. ICIC Express Lett. 2021;15:517–26. https:\u002F\u002Fdoi.org\u002F10.24507\u002Ficicel.15.05.517.\nFatimah YA, Putra POH, Hasibuan ZA. E-business adoption and application portfolio management in remanufacturing small and medium enterprises. In:2016 Int Conf Informatics Comput. 2016. p. 349–54. https:\u002F\u002Fdoi.org\u002F10.1109\u002FIAC.2016.7905743.\nHadi Putra PO, Hasibuan ZA. The relationship between enterprise internationalization and E-business adoption: A perspective of Indonesian SMEs. Int Conf Inf Soc. 2015;2015:122–6. https:\u002F\u002Fdoi.org\u002F10.1109\u002Fi-Society.2015.7366872.\nRiana RA. Middle-Class composition and growth in middle-income countries. Asian Dev. Bank Inst. 2017.\nAnlimachie MA, Avoada C. Socio-economic impact of closing the rural-urban gap in pre-tertiary education in Ghana: context and strategies. Int J Educ Dev. 2020;77:102236. https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.ijedudev.2020.102236.\nLiu J, Peng P, Luo L. The relation between family socioeconomic status and academic achievement in China: a meta-analysis. Educ Psychol Rev. 2020;32:49–76. https:\u002F\u002Fdoi.org\u002F10.1007\u002Fs10648-019-09494-0.\nRahmah A. Digital literacy learning system for Indonesian citizen. In: Procedia Computer Science. New York: Elsevier; 2015. p. 94–101.\nVygotsky LS, Cole M. Mind in society: Development of higher psychological processes. New York: Harvard University Press; 1978.\nAbdi H, Williams LJ. Principal component analysis. Wiley Interdiscip Rev Comput Stat. Wiley Online Library; 2010;2:433–59.\nJollife IT, Cadima J. Principal component analysis: A review and recent developments. Philos Trans R Soc A Math Phys Eng Sci. 2016;374.",{"VOID":709},"10.1186\u002Fs40537-022-00639-7","2024-06-26T15:23:49.900+00:00","https:\u002F\u002Fjournalofbigdata.springeropen.com\u002Farticles\u002F10.1186\u002Fs40537-022-00639-7",[713,738,753],{"id":714,"sortIndex":21,"researcher":20,"roles":715,"affiliations":716,"properties":733,"displayName":735,"givenName":20,"familyName":20},"872aefb4-cb4d-43a2-b679-2f4d98eb7393",[149],[717,725],{"id":718,"sortIndex":21,"affiliation":719,"properties":20},"1abca2d6-b143-4a56-b6cf-30e8fd98de1a",{"id":718,"createTime":20,"updateTime":20,"relativeEntities":720,"slug":20,"properties":721,"entityType":20,"verifyStatus":20,"verifyTime":20,"verifyNote":20,"languages":20,"translateLanguages":20,"viewCount":20,"url":20,"parentIds":724,"statistic":20},[],{"title":722},{"VI":723},"Faculty of Computer Science, Universitas Indonesia, Depok, Indonesia",[],{"id":726,"sortIndex":161,"affiliation":727,"properties":20},"f74423fa-e232-4bde-ac51-9027c1910204",{"id":726,"createTime":20,"updateTime":20,"relativeEntities":728,"slug":20,"properties":729,"entityType":20,"verifyStatus":20,"verifyTime":20,"verifyNote":20,"languages":20,"translateLanguages":20,"viewCount":20,"url":20,"parentIds":732,"statistic":20},[],{"title":730},{"VI":731},"Faculty of Science and Computer Science, Universitas Pertamina, South Jakarta, Indonesia",[],{"title":734,"gsAuthor":736},{"VI":735},"Ariana Yunita",{"VOID":737},"[\"YcHCpq0AAAAJ\"]",{"id":739,"sortIndex":161,"researcher":20,"roles":740,"affiliations":741,"properties":748,"displayName":750,"givenName":20,"familyName":20},"fb42cee4-c4cb-4620-b789-a4591c5e2aa3",[149],[742],{"id":718,"sortIndex":21,"affiliation":743,"properties":20},{"id":718,"createTime":20,"updateTime":20,"relativeEntities":744,"slug":20,"properties":745,"entityType":20,"verifyStatus":20,"verifyTime":20,"verifyNote":20,"languages":20,"translateLanguages":20,"viewCount":20,"url":20,"parentIds":747,"statistic":20},[],{"title":746},{"VI":723},[],{"title":749,"gsAuthor":751},{"VI":750},"Harry B. 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Social media data has grown exponentially and now there is major interest in extracting any useful information from the social media data to apply in various domains. Currently, there are various tools available to analyze the large amounts of social media data. However, these tools do not consider the diversity of the social media data, and treat social media as a uniform data source with similar features. Thus, these tools lack the flexibility to dynamically process and analyze the social media data according to its diverse features. In this paper, we develop a ‘Big Social Data as a Service’ (BSDaaS) composition framework that extracts the data from various social media platforms, and transforms it into useful information. The framework provides a quality model to capture the dynamic features of social media data. In addition, our framework dynamically assesses the quality features of the social media data and composes appropriate services required for various information analyses. We present a social media based sentiment analysis system as a motivating scenario and conduct experiments using real-world datasets to show the efficiency of our approach.",{"EN":845},"Big social data as a service (BSDaaS): a service composition framework for social media analysis",{"VOID":847},"[\"11908694143101702079\"]",{"VOID":849},"Becker D, King T, McMullen B. Big data, big data quality problem. In: IEEE International Conference on Big Data. 2015; pp. 2644–2653.\nAhsaan S, Mourya A. Big data analytics: challenges and technologies. Ann Faculty Eng Hunedoara. 2019;17(4):75–9.\nAbdrabo M, Elmogy M, Eltaweel G, Barakat S. Enhancing big data value using knowledge discovery techniques. Inf Technol Comput Sci. 2016; 1–12.\nTakeshi S, Okazaki M, Matsuo Y. Earthquake shakes twitter users: real-time event detection by social sensors. In: 19th International Conference on World Wide Web, ACM. 2010; pp. 851–860.\nKaplan A, Haenlein M. Users of the world, unite! the challenges and opportunities of social media. Bus Horiz. 2010;53:59–68.\nMusaev A, Wang D, Pu C. Landslide detection service based on composition of physical and social information services. In: IEEE International Conference on Web Services.2014; pp. 97–104.\nEl Alaoui I, Gahi Y. The impact of big data quality on sentiment analysis approaches. Proc Comput Sci. 2019;160:803–10.\nNilashi M, Minaei Bidgoli B, Alrizq M, Alghamdi A, Alsulami A, Samad S, Mohd S. An analytical approach for big social data analysis for customer decision-making in eco-friendly hotels. Expert Syst Appl. 2021; 186.\nSingh T, Kumari M. Burst: real-time events burst detection in social text stream. J Supercomput. 2021;77(10):11228–56.\nAli K, Hamilton M, Thevathayan C, Zhang X. Social information services: a service oriented analysis of social media. In: International Conference on Web Services. 2018; pp. 63–279.\nBebić D, Volarevic M. Do not mess with a meme: the use of viral content in communicating politics. Commun Soc. 2018;31(3):43–56.\nKumar R, Ravi V. A survey on opinion mining and sentiment analysis: tasks, approaches and applications. Knowl-Based Syst. 2015;89:14–46.\nDai S, Gao Q, Fan Z, Kang G. User perceived quality of online social information services: from the perspective of knowledge management. In: IEEE International Conference on Industrial Engineering and Engineering Management. 2007; pp. 482–486.\nAli K, Dong H, Bouguettaya A, Hadjidj R. Sentiment analysis as a service: a social media based sentiment analysis framework. In: International Conference on Web Services.2017; pp. 660–667.\nWan S, Paris C. Improving government services with social media feedback. In: Proceedings of the 19th International Conference on Intelligent User Interfaces.2014; pp. 27–36.\nTinoco F, Hernández G, Zepahua J, Zepahua B, Mazahua L. A brief review on the use of sentiment analysis approaches in social networks. In: International Conference on Software Process Improvement. 2017; pp. 263–273.\nMusaev A, Wang D, Calton P. Litmus: a multi-service composition system for landslide detection. IEEE Trans Serv Comput. 2015;8:715–26.\nThelwall M, Buckley K, Cai D, Kappas A. Sentiment strength detection in short informal text. J Am Soc Inform Sci Technol. 2010;61:2544–58.\nMedhat W, Hassan A, Korashy H. Sentiment analysis algorithms and applications: a survey. Ain Shams Eng J. 2014;5:1093–113.\nCuomo M, Tortora D, Foroudi P, Giordano A, Festa G, Metallo G. Digital transformation and tourist experience co-design: big social data for planning cultural tourism. Technol Forecasting Soc Change. 2021; 162.\nCheung M, Pires G, Rosenberger III P, Leung W, Chang M. The role of social media elements in driving co-creation and engagement. Pacific J Mark Logist. 2021.\nFujiwara T, Müller K, Schwarz C. National bureau of economic research. Pacific J Mark Logist. 2021; 28849.\nZhou X, Chen L. Event detection over twitter social media streams. The VLDB J-Int J Very Large Data Bases. 2014;23(3):381–400.\nKitazawa K, Hale S. Social media and early warning systems for natural disasters: A case study of typhoon etau in Japan. Int J Disaster Risk Reduction. 2021; 51\nBarbara M, Manso M. The role of social media in crisis. In: International Command and Control Research and Technology Symposium. 2012; pp. 19–21.\nFinau G, Tarai J, Varea R, Titifanue J, Kant R, Cox J. Social media and disaster communication: a case study of cyclone Winston. Pac J Rev. 2018;24(1):123–37.\nBoghiu S, Gîfu D. A spatial-temporal model for event detection in social media. Proc Comput Sci. 2020;176:541–50.\nPhengsuwan J, Shah T, Thekkummal N, Wen Z, Sun R, Pullarkatt D, Ranjan R. Use of social media data in disaster management: a survey. Future Internet. 2021;13(2):46.\nWang Z, Ye X. Social media analytics for natural disaster management. Int J Geogr Inf Sci. 2018;32(1):49–72.\nKankanamge N, Yigitcanlar T, Goonetilleke A, Kamruzzaman M. Determining disaster severity through social media analysis: Testing the methodology with south east queensland flood tweets. Int J Disaster Risk Reduction. 2020; 42.\nPatil H, Atique M. Sentiment analysis for social media: a survey. In: 2nd International Conference on Information Science and Security (ICISS).2015; pp. 1–4.\nSerrano-Guerrero J, Olivas J, Romero F, Herrera-Viedma E. Sentiment analysis: a review and comparative analysis of web services. Inf Sci. 2015;311:18–38.\nKeith Norambuena B, Lettura E, Villegas C. Sentiment analysis and opinion mining applied to scientific paper reviews. Intel Data Anal. 2019;23(1):191–214.\nGuellil I, Boukhalfa K. Social big data mining: a survey focused on opinion mining and sentiments analysis. In: 12th International Symposium on Programming and Systems (ISPS). 2015; pp. 1–10.\nBirjali M, Kasri M, Beni-Hssane A. A comprehensive survey on sentiment analysis: approaches, challenges and trends. Knowl-Based Syst. 2021; 226.\nMehta P, Pandya S. A review on sentiment analysis methodologies, practices and applications. Int J Sci Technol Res. 2020;9(2):601–9.\nNiknejad N, Ismail W, Ghani I, Nazari B, Bahari M. Understanding service-oriented architecture (soa): a systematic literature review and directions for further investigation. Inf Syst. 2020; 91.\nHammoudeh M, Epiphaniou G, Belguith S, Unal D, Adebisi B, Baker T, Watters P. A service-oriented approach for sensing in the internet of things: intelligent transportation systems and privacy use cases. IEEE Sens J. 2020;21(14):15753–61.\nHustad E, Olsen D. Creating a sustainable digital infrastructure: the role of service-oriented architecture. Proc Comput Sci. 2021;181:597–604.\nHayyolalam V, Kazem A. A systematic literature review on qos-aware service composition and selection in cloud environment. J Netw Comput Appl. 2018;110:52–74.\nWang J, Yang Y, Wang T, Sherratt R, Zhang J. Big data service architecture: a survey. J Internet Technol. 2020;21(2):393–405.\nSaggi M, Jain S. A survey towards an integration of big data analytics to big insights for value-creation. Inf Process Manage. 2018;54(5):758–90.\nNeves P, Schmerl B, Cámara J, Bernardino J. Big data in cloud computing: features and issues. IoTBD. 2016; 307–314.\nMing Z, Kumar A, Ali M, Chong P. A cloud-based network architecture for big data services. In: 14th International Conference on Pervasive Intelligence and Computing.2016; pp. 654–659.\nVu H, Asal R. A framework for big data as a service. In: IEEE International Conference on Digital Signal Processing (DSP). 2015; pp. 492–496.\nKhan S, Shakil K, Ali S, Alam M. On designing a generic framework for big data-as-a-service. In: 1st International Conference on Advanced Research in Engineering Sciences (ARES), IEEE. 2018; pp. 1–5.\nPersico V, Pescapé A, Picariello A, Sperlí G. Benchmarking big data architectures for social networks data processing using public cloud platforms. Futur Gener Comput Syst. 2018;89:98–109.\nEl Alaoui I, Gahi Y, Messoussi R, Chaabi Y, Todoskoff A, Kobi A. A novel adaptable approach for sentiment analysis on big social data. J Big Data. 2018;5(1):1–18.\nGrossman A, Frieder O. Information retrieval: algorithms and heuristics. Sci Business Media. 2012; 15.\nKevin C, Potdar V, Dillon T. Content quality assessment related frameworks for social media. In: Computational Science and Its Applications-ICCSA.2009; pp. 791–805.\nAggarwal C, Abdelzaher T. Integrating sensors and social networks. Soc Netw Data Anal. 2011; 379–412.\nPotthast M, Stein B, Loose F, Becker S. Information retrieval in the commentsphere. ACM Trans Intel Syst Technol (TIST). 2012;3(4):68.\nSiriweera S, Paik I, Kumara B. Constraint-driven dynamic workflow for automation of big data analytics based on graphplan. In: International Conference on Web Services.2017; pp. 357–364.\nLoria S, Keen P, Honnibal M, Yankovsky R, Karesh D, Dempsey E. 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Recognition based on Gait Model (PRGM) and motion features is are indeed a challenging and novel task due to  their usages and to the critical issues of human pose variation, human body occlusion, camera view variation, etc. In this project, a deep convolution neural network (CNN) was modified and adapted for person recognition with Image Augmentation (IA) technique depending on gait features. Adaptation aims to get best values for CNN parameters to get best CNN model. In Addition to the CNN parameters Adaptation, the design of CNN model itself was adapted to get best model structure; Adaptation in the design was affected the type, the number of layers in CNN and normalization between them. After choosing best parameters and best design, Image augmentation was used to increase the size of train dataset with many copies of the image to boost the number of different images that will be used to train Deep learning algorithms. The tests were achieved using known dataset (Market dataset). The dataset contains sequential pictures of people in different gait status. The image in CNN model as matrix is extracted to many images or matrices by the convolution, so dataset size may be bigger by hundred times to make the problem a big data issue. In this project, results show that adaptation has improved the accuracy of person recognition using gait model comparing to model without adaptation. In addition, dataset contains images of person carrying things. IA technique improved the model to be robust to some variations such as image dimensions (quality and resolution), rotations and carried things by persons. Results for 200 persons recognition, validation accuracy was about 82% without IA and 96.23 with IA. For 800 persons recognition, validation accuracy was 93.62% without IA.",{"EN":987},"Analysis and best parameters selection for person recognition based on gait model using CNN algorithm and image 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Soft and hard biometrics for the authentication of remote people in front and side views. Int J Appl Eng Res. 2016;11(14):8120–7.",{},{"id":416,"text":1108,"url":418,"identifiers":1109},"Sun J, Wang Y, Li J, Wan W, Cheng D, Zhang H. View-invariant gait recognition based on kinect skeleton feature. Multimedia Tools Appl. 2018;77(19):24909–35.",{"doi":420},{"id":416,"text":1111,"url":418,"identifiers":1112},"Zhang Y, Huang Y, Wang L, Yu S. A comprehensive study on gait biometrics using a joint cnn-based method. Pattern Recogn. 2019;93:228–36.",{"doi":420},{"id":416,"text":1114,"url":418,"identifiers":1115},"Strukova O, Shiripova L, Myasnikov E. Gait analysis for person recognition using principal component analysis and support vector machines. CEUR Workshop Proc. 2018;2210:170–6.",{"doi":420},{"id":416,"text":1117,"url":418,"identifiers":1118},"Wang X, Zhao R. Person re-identification: System design and evaluation overview. In: Person Re-Identification, Springer 2014;351–370.",{"doi":420},{"id":416,"text":1120,"url":418,"identifiers":1121},"Hahnel M, Klunder D, Kraiss K-F. Color and texture features for person recognition. In: 2004 IEEE International Joint Conference on Neural Networks (IEEE Cat. No. 04CH37541), IEEE 2004;1:647–652.",{"doi":420},{"id":416,"text":1123,"url":418,"identifiers":1124},"Zou Q, Wang Y, Wang Q, Zhao Y, Li Q. Deep learning-based gait recognition using smartphones in the wild. IEEE Trans Inform Forensics Security. 2020;15:3197–212.",{"doi":420},{"id":416,"text":1126,"url":418,"identifiers":1127},"Charalambous CC, Bharath AA. A data augmentation methodology for training machine\u002Fdeep learning gait recognition algorithms. 2016; arXiv preprint arXiv:1610.07570",{"doi":420},{"id":416,"text":1129,"url":418,"identifiers":1130},"Simhi N, Yovel G. Dissociating identity from gait: A virtual reality study of the role of dynamic identity signatures in person recognition 2019;",{"doi":420},{"id":1132,"text":1133,"url":1134,"identifiers":1135},"764020d2-6910-4bf0-ac99-e6d99353edd5","Elharrouss O, Almaadeed N, Al-Maadeed S, Bouridane A. Gait recognition for person re-identification. J Supercomput. 2020. https:\u002F\u002Fdoi.org\u002F10.1007\u002Fs11227-020-03409-5.","https:\u002F\u002Flink.springer.com\u002Farticle\u002F10.1007\u002Fs11227-020-03409-5",{"doi":1136},"10.1007\u002Fs11227-020-03409-5",{"id":416,"text":1138,"url":418,"identifiers":1139},"Zhang Z, Tran L, Yin X, Atoum Y, Liu X, Wan J, Wang N. Gait recognition via disentangled representation learning. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2019;4710–4719.",{"doi":420},{"id":416,"text":1141,"url":418,"identifiers":1142},"Terrier P. Gait recognition via deep learning of the center-of-pressure trajectory. 2019; arXiv preprint arXiv:1908.04758",{"doi":420},{"id":20,"text":1144,"url":20,"identifiers":1145},"Alotaibi M, Mahmood A. Improved gait recognition based on specialized deep convolutional neural network. Comput Vision Image Understanding. 2017;164:103–10.",{},{"id":416,"text":1147,"url":418,"identifiers":1148},"Nguyen CH, Tran LH, Ho KN. Application of neural network to predict the workability parameters of self-compacting concrete. In: CIGOS 2019, Innovation for Sustainable Infrastructure, Springer, 2020:1161–1166.",{"doi":420},{"id":416,"text":1150,"url":418,"identifiers":1151},"Assad A, Khalaf W, Chouaib I. Radial basis function kalman filter for attitude estimation in gps-denied environment. IET Radar, Sonar & Navigation. 2020;14(5):736–46.",{"doi":420},{"id":416,"text":1153,"url":418,"identifiers":1154},"Krenn M, Zeilinger A. Predicting research trends with semantic and neural networks with an application in quantum physics. Proceedings of the National Academy of Sciences. 2020;.",{"doi":420},{"id":416,"text":1156,"url":418,"identifiers":1157},"De Marsico M, Mecca A. Gait recognition: The wearable solution. In: Human Recognition in Unconstrained Environments, Elsevier 2017:177–195.",{"doi":420},{"id":20,"text":1159,"url":1160,"identifiers":1161},"Kawakami T. Coronavirus gives China more reason to employ biometric tech. Nikkei Asian Review 2020; https:\u002F\u002Fasia.nikkei.com\u002FBusiness\u002FChina-tech\u002FCoronavirus-gives-China-more-reason-to-employ-biometric-tech","https:\u002F\u002Fasia.nikkei.com\u002FBusiness\u002FChina-tech\u002FCoronavirus-gives-China-more-reason-to-employ-biometric-tech",{},{"id":416,"text":1163,"url":418,"identifiers":1164},"Makihara Y, Suzuki A, Muramatsu D, Li X, Yagi Y. Joint intensity and spatial metric learning for robust gait recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2017:5705–5715.",{"doi":420},{"id":416,"text":1166,"url":418,"identifiers":1167},"Shorten C, Khoshgoftaar TM. A survey on image data augmentation for deep learning. J Big Data. 2019;6(1):60.",{"doi":420},{"id":1169,"text":1170,"url":1171,"identifiers":1172},"35f7e55b-e671-4e99-82d2-fc2429dde422","Yamashita R, Nishio M, Do RKG, Togashi K. Convolutional neural networks: an overview and application in radiology. Insights Into Imaging. 2018;9(4):611–29.","https:\u002F\u002Finsightsimaging.springeropen.com\u002Farticles\u002F10.1007\u002Fs13244-018-0639-9",{"doi":1173},"10.1007\u002Fs13244-018-0639-9",{"id":1175,"text":1176,"url":1177,"identifiers":1178},"5698e797-2376-4e32-9ef2-d241860826d1","Gu J, Wang Z, Kuen J, Ma L, Shahroudy A, Shuai B, Liu T, Wang X, Wang G, Cai J, et al. Recent advances in convolutional neural networks. Pattern Recogn. 2018;77:354–77.","https:\u002F\u002Fwww.sciencedirect.com\u002Fscience\u002Farticle\u002Fpii\u002FS0031320317304120",{"doi":1179},"10.1016\u002Fj.patcog.2017.10.013",{"id":416,"text":1181,"url":418,"identifiers":1182},"Castro FM, Marín-Jiménez MJ, Guil N, De La Blanca NP. Automatic learning of gait signatures for people identification. In: International Work-Conference on Artificial Neural Networks, Springer 2017:257–270.",{"doi":420},{"id":416,"text":1184,"url":418,"identifiers":1185},"Shiraga K, Makihara Y, Muramatsu D, Echigo T, Yagi Y. Geinet: View-invariant gait recognition using a convolutional neural network. In: 2016 International Conference on Biometrics (ICB), 2016:1–8. IEEE",{"doi":420},{"id":416,"text":1187,"url":418,"identifiers":1188},"Terrier P. Gait recognition via deep learning of the center-of-pressure trajectory. Appl Sci. 2020;10(3):774.",{"doi":420},{"id":416,"text":1190,"url":418,"identifiers":1191},"Sagawa R, Shiba Y, Hirukawa T, Ono S, Kawasaki H, Furukawa R. Automatic feature extraction using cnn for robust active one-shot scanning. In: 2016 23rd International Conference on Pattern Recognition (ICPR), 2016:234–239. IEEE, New York.",{"doi":420},{"id":416,"text":1193,"url":418,"identifiers":1194},"Li Y, Liu D, Li H, Li L, Li Z, Wu F. Learning a convolutional neural network for image compact-resolution. IEEE Trans Image Processing. 2018;28(3):1092–107.",{"doi":420},{"id":416,"text":1196,"url":418,"identifiers":1197},"Lawrence S, Giles CL, Tsoi AC, Back AD. Face recognition: a convolutional neural-network approach. IEEE Trans Neural Networks. 1997;8(1):98–113.",{"doi":420},{"id":20,"text":1199,"url":20,"identifiers":1200},"Wang Y, Bian Z-P, Hou J, Chau L-P. Convolutional neural networks with dynamic regularization. 2019; arXiv preprint arXiv:1909.11862",{"arxiv":1201},"arXiv:1909.11862",{"id":416,"text":1203,"url":418,"identifiers":1204},"Gal Y, Ghahramani Z. Dropout as a bayesian approximation: Representing model uncertainty in deep learning. In: International Conference on Machine Learning, 2016:1050–1059",{"doi":420},{"id":20,"text":1206,"url":1207,"identifiers":1208},"Market Dataset. https:\u002F\u002Fwww.aitribune.com\u002Fdataset\u002F2018051063","https:\u002F\u002Fwww.aitribune.com\u002Fdataset\u002F2018051063",{},{"id":20,"text":1210,"url":20,"identifiers":1211},"Kingma DP, Ba J. Adam: A Method for Stochastic Optimization 2017; arXiv:1412.6980",{"arxiv":1212},"arXiv:1412.6980",{"id":20,"text":1214,"url":20,"identifiers":1215},"Wu Y, He K. Group normalization. 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Developing an efficient and accurate object-tracking method that can operate in real-time while handling occlusion is essential for various applications, including surveillance, autonomous driving, and robotics. However, relying solely on a single hand-crafted feature results in less robust tracking. As a hand-crafted feature extraction technique, HOG effectively detects edges and contours, which is essential in localizing objects in images. However, it does not capture fine details in object appearance and is sensitive to changes in lighting conditions. On the other hand, the grayscale feature has computational efficiency and robustness to changes in lighting conditions. The deep feature can extract features that express the image in more detail and discriminate between different objects. By fusing different features, the tracking method can overcome the limitations of individual features and capture a complete representation of the object. The deep features can be generated with transfer learning networks. However, selecting the right network is difficult, even in real-time applications. This study integrated the deep feature architecture and hand-crafted features HOG and grayscale in the KCF method to solve this problem. The object images were obtained through at least three convolution blocks of transfer learning architecture, such as Xception, DenseNet, VGG16, and MobileNet. Once the deep feature was extracted, the HOG and grayscale features were computed and combined into a single stack. In the KCF method, the stacked features acquired the actual object location by conveying a maximum response. 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Improved anti-occlusion object tracking algorithm using unscented Rauch-Tung-Striebel smoother and kernel correlation filter. J King Saud Univ Comput Inf Sci. 2022;34:6008–18.",{"doi":420},{"id":1522,"createTime":1523,"updateTime":1524,"relativeEntities":1525,"slug":1526,"properties":1527,"entityType":140,"verifyStatus":141,"verifyTime":1538,"verifyNote":143,"languages":20,"translateLanguages":20,"viewCount":21,"primaryUrl":1539,"fullTextUrl":20,"authors":1540,"publicationType":202,"publisherRelationship":1581,"citationCount":20,"citationInfo":20,"publishDate":1639,"publishYear":264,"citationAnalyzeStatus":19,"lastCitationAnalyze":1524,"indexDatabases":1640,"openAccess":20,"references":20,"isForceReanalyzing":269},"2d3fbd8d-4503-4768-8139-84983567e783","2024-01-01T07:09:08.853+00:00","2026-07-06T10:03:50.474+00:00",[],"Data-analytics-for-crop-management-a-big-data-view",{"abstract":1528,"title":1530,"gsPaper":1532,"references":1534,"doi":1536},{"EN":1529},"Recent advances in Information and Communication Technologies have a significant impact on all sectors of the economy worldwide. Digital Agriculture appeared as a consequence of the democratisation of digital devices and advances in artificial intelligence and data science. Digital agriculture created new processes for making farming more productive and efficient while respecting the environment. Recent and sophisticated digital devices and data science allowed the collection and analysis of vast amounts of agricultural datasets to help farmers, agronomists, and professionals understand better farming tasks and make better decisions. In this paper, we present a systematic review of the application of data mining techniques to digital agriculture. We introduce the crop yield management process and its components while limiting this study to crop yield and monitoring. After identifying the main categories of data mining techniques for crop yield monitoring, we discuss a panoply of existing works on the use of data analytics. This is followed by a general analysis and discussion on the impact of big data on agriculture.",{"EN":1531},"Data analytics for crop management: a big data view",{"VOID":1533},"[\"14152139469023926303\"]",{"VOID":1535},"Abbas F, Afzaal H, Farooque A, Tang S. Crop yield prediction through proximal sensing and machine learning algorithms. Agronomy. 2020. https:\u002F\u002Fdoi.org\u002F10.3390\u002Fagronomy10071046.\nAhmed F, Al-Mamun H, Bari H, Hossain E, Kwan P. Classification of crops and weeds from digital images: a support vector machine approach. Crop Prot. 2012;40:98–104. https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.cropro.2012.04.024.\nAkbarzadeh S, Paap A, Ahderom S, Apopei B, Alameh K. Plant discrimination by support vector machine classifier based on spectral reflectance. 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Precision agriculture: technology and economic perspectives, progress in precision agriculture, chapter 2. Cham: Springer; 2017. p. 21–78. https:\u002F\u002Fdoi.org\u002F10.1007\u002F978-3-319-68715-5.\nBarbedo JA. Impact of dataset size and variety on the effectiveness of deep learning and transfer learning for plant disease classification. Comput Electron Agric. 2018;153:46–53. https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.compag.2018.08.013.\nBehmann J, Mahlein AK, Rumpf T, Romer C, Plumer L. A review of advanced machine learning methods for the detection of biotic stress in precision crop protection. J Precis Agric. 2014;16:239–60. https:\u002F\u002Fdoi.org\u002F10.1007\u002Fs11119-014-9372-7.\nBendre M, Thool R, Thool V. Big data in precision agriculture through ICT: rainfall prediction using neural network approach. In: Satapathy S, Bhatt Y, Joshi A, Mishra D, editors. Proceedings of the International congress on information and communication technology. Singapore: Springer; 2016. p. 165–75.\nBerckmans D. Precision livestock farming technologies for welfare management in intensive livestock systems. Rev Sci. 2014;33:189–96.\nBi L, Hu G, Raza M, Kandel Y, Leandro L, Mueller D. A gated recurrent units (gru)-based model for early detection of soybean sudden death syndrome through time-series satellite imagery. Remote Sens. 2020. https:\u002F\u002Fdoi.org\u002F10.3390\u002Frs12213621.\nBrahimi M, Arsenovic M, Laraba S, Sladojevic S, Boukhalfa K, Moussaoui A. Deep learning for plant diseases: detection and saliency map visualisation. In: Zhou J, Chen F, editors. Human and machine learning. Cham: Springer; 2018. p. 93–117. https:\u002F\u002Fdoi.org\u002F10.1007\u002F978-3-319-90403-0_6.\nBreunig F, Galvao L, Dalagnol R, Dauve C, Parraga A, Santi A, Flora DD, Chen S. Delineation of management zones in agricultural fields using cover-crop biomass estimates from planetscope data. 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Computer Science, University College Dublin, Dublin, Ireland",[],{"title":1577,"gsAuthor":1579},{"VI":1578},"Mohand Tahar 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language processing (NLP) refers to the field of study that focuses on the interactions between human language and computers. It has recently gained much attention for analyzing human language computationally and has spread its applications for various tasks such as machine translation, information extraction, summarization, question answering, and others. With the rapid growth of cloud computing services, merging NLP in the cloud is a significant benefit. It allows researchers to conduct NLP-related experiments on large amounts of data handled by big data techniques while harnessing the cloud’s vast, on-demand computing power. However, it has not sufficiently spread its tools and applications as a service in the cloud and there is little literature available that discusses the scope of interdisciplinary work. NLP, cloud Computing, and big data are vast domains and contain their challenges and potentials. By overcoming those challenges and integrating these fields, great potential for NLP and its applications can be unleashed. 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Big data analytics: a supervised approach for sentiment classification using mahout: an illustration. Int J Appl Eng Res. 2015;10(5):13447–57.\nDean J. Big data, data mining, and machine learning: value creation for business leaders and practitioners. US: Wiley; 2014.\nvan Banerveld M, Le-Khac N-A, Kechadi M-T. Performance evaluation of a natural language processing approach applied in white collar crime investigation. In: International conference on future data and security engineering, 2014;29–43. Springer.\nArtola X, Beloki Z, Soroa A. A stream computing approach towards scalable nlp. In: LREC, 2014;8–13.\nSanchez-Graillet O, Poesio M. Acquiring bayesian networks from text. In: LREC 2004.\nFeldman R, Sanger J. The text mining handbook: advanced approaches in analyzing unstructured data. Cambridge: Cambridge University Press; 2007.\nManning C. Generating typed dependency parses from phrase structure parses 2008.\nTrovati M, Hayes J, Palmieri F, Bessis N. Automated extraction of fragments of bayesian networks from textual sources. Appl Soft Comput. 2017;60:508–19.\nTrovati M, Bessis N, Huber A, Zelenkauskaite A, Asimakopoulou E. Extraction, identification, and ranking of network structures from data sets. In: 2014 Eighth international conference on complex, intelligent and software intensive systems, 2014;331–337. IEEE.\nLiu B. Sentiment analysis and opinion mining. Synth Lect Hum Lang Technol. 2012;5(1):1–167.\nRay J, Trovati M. A survey of topological data analysis (tda) methods implemented in python. In: International conference on intelligent networking and collaborative systems, 2017;594–600. Springer.\nInoubli W, Aridhi S, Mezni H, Maddouri M, Nguifo EM. An experimental survey on big data frameworks. Fut Gener Comput Syst. 2018;86:546–64.\nHutto CJ, Gilbert E. Vader: a parsimonious rule-based model for sentiment analysis of social media text. In: Eighth international AAAI conference on weblogs and social media. 2014.\nStanley KO, Miikkulainen R. Evolving neural networks through augmenting topologies. Evol Comput. 2002;10(2):99–127.\nCrabb ES. “Time for some traffic problems’’: enhancing e-discovery and big data processing tools with linguistic methods for deception detection. J Digit Forens Secur Law. 2014;9(2):14.\nKhan E. Addressing big data problems using semantics and natural language understanding. In: 12th Wseas International Conference on Telecommunications and Informatics (Tele-Info ’13), Baltimore. 2013.\nCambria E, Schuller B, Xia Y, Havasi C. New avenues in opinion mining and sentiment analysis. IEEE Intell Syst. 2013;28(2):15–21.\nPriyanka K, Kulennavar N. A survey on big data analytics in health care. Int J Comput Sci Inf Technol. 2014;5(4):5865–8.\nSocher R. Recursive deep learning for natural language processing and computer vision. PhD thesis, Citeseer. 2014.\nCheptsov A, Tenschert A, Schmidt P, Glimm B, Matthesius M, Liebig T. Introducing a new scalable data-as-a-service cloud platform for enriching traditional text mining techniques by integrating ontology modelling and natural language processing. In: International Conference on Web Information Systems Engineering, 2013;62–74. Springer.\nMladenić D, Grobelnik M. Automatic text analysis by artificial intelligence. Informatica, 2013;37(1).",{"VOID":1656},"10.1186\u002Fs40537-022-00603-5","2024-06-26T22:47:09.754+00:00","https:\u002F\u002Fjournalofbigdata.springeropen.com\u002Farticles\u002F10.1186\u002Fs40537-022-00603-5",[1660,1693,1715],{"id":1661,"sortIndex":21,"researcher":20,"roles":1662,"affiliations":1663,"properties":1690,"displayName":1692,"givenName":20,"familyName":20},"43464ae3-3400-4000-bb6f-c59447ae7a72",[149],[1664,1672,1681],{"id":1665,"sortIndex":21,"affiliation":1666,"properties":20},"62dd9a0f-3934-4f1e-83d8-8f6b75c77de9",{"id":1665,"createTime":20,"updateTime":20,"relativeEntities":1667,"slug":20,"properties":1668,"entityType":20,"verifyStatus":20,"verifyTime":20,"verifyNote":20,"languages":20,"translateLanguages":20,"viewCount":20,"url":20,"parentIds":1671,"statistic":20},[],{"title":1669},{"VI":1670},"Department of Computer Science, University of Beira Interior, Covilha, Portugal",[],{"id":1673,"sortIndex":161,"affiliation":1674,"properties":1680},"2bde60fe-526f-497f-8ff8-cc906244e39f",{"id":1673,"createTime":20,"updateTime":20,"relativeEntities":1675,"slug":20,"properties":1676,"entityType":20,"verifyStatus":20,"verifyTime":20,"verifyNote":20,"languages":20,"translateLanguages":20,"viewCount":20,"url":20,"parentIds":1679,"statistic":20},[],{"title":1677},{"VI":1678},"NOVA Laboratory for Computer Science and Informatics (NOVA LINCS), \nCosta da Caparica, Portugal",[],{},{"id":1682,"sortIndex":188,"affiliation":1683,"properties":1689},"dfcef6f0-a382-4a2c-a2ab-cce7b750cfed",{"id":1682,"createTime":20,"updateTime":20,"relativeEntities":1684,"slug":20,"properties":1685,"entityType":20,"verifyStatus":20,"verifyTime":20,"verifyNote":20,"languages":20,"translateLanguages":20,"viewCount":20,"url":20,"parentIds":1688,"statistic":20},[],{"title":1686},{"VI":1687},"GREYC, Groupe de Recherche en Informatique, Image et Instrumentation de University of Caen Normandie, Caen\n, France",[],{},{"title":1691},{"VI":1692},"Sebastião Pais",{"id":1694,"sortIndex":161,"researcher":20,"roles":1695,"affiliations":1696,"properties":1712,"displayName":1714,"givenName":20,"familyName":20},"445276c3-12ab-4b53-9464-61bd7b26abc2",[149],[1697,1703],{"id":1665,"sortIndex":21,"affiliation":1698,"properties":20},{"id":1665,"createTime":20,"updateTime":20,"relativeEntities":1699,"slug":20,"properties":1700,"entityType":20,"verifyStatus":20,"verifyTime":20,"verifyNote":20,"languages":20,"translateLanguages":20,"viewCount":20,"url":20,"parentIds":1702,"statistic":20},[],{"title":1701},{"VI":1670},[],{"id":1704,"sortIndex":161,"affiliation":1705,"properties":1711},"5dfa8a74-7b4d-4080-8466-27f743d46573",{"id":1704,"createTime":20,"updateTime":20,"relativeEntities":1706,"slug":20,"properties":1707,"entityType":20,"verifyStatus":20,"verifyTime":20,"verifyNote":20,"languages":20,"translateLanguages":20,"viewCount":20,"url":20,"parentIds":1710,"statistic":20},[],{"title":1708},{"VI":1709},"INESC-TEC, Instituto de Engenharia de Sistemas e Computadores (INESC), Porto, Portugal",[],{},{"title":1713},{"VI":1714},"João Cordeiro",{"id":1716,"sortIndex":188,"researcher":20,"roles":1717,"affiliations":1718,"properties":1725,"displayName":1727,"givenName":20,"familyName":20},"d16f8ad4-0780-4aaf-ae0f-a0037493d31c",[149],[1719],{"id":1665,"sortIndex":21,"affiliation":1720,"properties":20},{"id":1665,"createTime":20,"updateTime":20,"relativeEntities":1721,"slug":20,"properties":1722,"entityType":20,"verifyStatus":20,"verifyTime":20,"verifyNote":20,"languages":20,"translateLanguages":20,"viewCount":20,"url":20,"parentIds":1724,"statistic":20},[],{"title":1723},{"VI":1670},[],{"title":1726},{"VI":1727},"M. 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The process discovery algorithms have been developed rapidly to discover several types of relations, i.e., choice relations, non-free choice relations with invisible tasks. Invisible tasks in non-free choice, introduced by \n                \n                  \n                \n                $$\\alpha ^{\\$ }$$\n                \n               method, is a type of relationship that combines the non-free choice and the invisible task. \n                \n                  \n                \n                $$\\alpha ^{\\$ }$$\n                \n               proposed rules of ordering relations of two activities for determining invisible tasks in non-free choice. The event log records sequences of activities, so the rules of \n                \n                  \n                \n                $$\\alpha ^{\\$ }$$\n                \n               check the combination of invisible task within non-free choice. The checking processes are time-consuming and result in high computing times of \n                \n                  \n                \n                $$\\alpha ^{\\$ }$$\n                \n              . This research proposes Graph-based Invisible Task (GIT) method to discover efficiently invisible tasks in non-free choice. GIT method develops sequences of business activities as graphs and determines rules to discover invisible tasks in non-free choice based on relationships of the graphs. The analysis of the graph relationships by rules of GIT is more efficient than the iterative process of checking combined activities by \n                \n                  \n                \n                $$\\alpha ^{\\$ }$$\n                \n              . This research measures the time efficiency of storing the event log and discovering a process model to evaluate GIT algorithm. Graph database gains highest storing computing time of batch event logs; however, this database obtains low storing computing time of streaming event logs. Furthermore, based on an event log with 99 traces, GIT algorithm discovers a process model 42 times faster than α++ and 43 times faster than α$. GIT algorithm can also handle 981 traces, while α++ and α$ has maximum traces at 99 traces. Discovering a process model by GIT algorithm has less time complexity than that by \n                \n                  \n                \n                $$\\alpha ^{\\$ }$$\n                \n              , wherein GIT obtains \n                \n                  \n                \n                $$O(n^{3} )$$\n                \n               and \n                \n                  \n                \n                $$\\alpha ^{\\$ }$$\n                \n               obtains \n                \n                  \n                \n                $$O(n^{4} )$$\n                \n              . Those results of the evaluation show a significant improvement of GIT method in term of time efficiency.",{"EN":1799},"Improving efficiency for discovering business processes containing invisible tasks in non-free choice",{"VOID":1801},"[\"1648228036697611157\"]",{"VOID":1803},"10.1186\u002Fs40537-021-00487-x","2024-05-01T08:42:40.268+00:00","https:\u002F\u002Fjournalofbigdata.springeropen.com\u002Farticles\u002F10.1186\u002Fs40537-021-00487-x",[1807,1824,1839,1852,1865,1889],{"id":1808,"sortIndex":21,"researcher":20,"roles":1809,"affiliations":1810,"properties":1819,"displayName":1821,"givenName":20,"familyName":20},"55a18ebd-7a05-4896-a520-00da0db679d2",[149],[1811],{"id":1812,"sortIndex":21,"affiliation":1813,"properties":20},"e091c757-f61f-41c9-b1fb-5d40f76c6411",{"id":1812,"createTime":20,"updateTime":20,"relativeEntities":1814,"slug":20,"properties":1815,"entityType":20,"verifyStatus":20,"verifyTime":20,"verifyNote":20,"languages":20,"translateLanguages":20,"viewCount":20,"url":20,"parentIds":1818,"statistic":20},[],{"title":1816},{"VI":1817},"Department of Informatics Engineering, Institut Teknologi Sepuluh Nopember, Surabaya, Indonesia",[],{"title":1820,"gsAuthor":1822},{"VI":1821},"Riyanarto Sarno",{"VOID":1823},"[\"QOMOtp0AAAAJ\"]",{"id":1825,"sortIndex":161,"researcher":20,"roles":1826,"affiliations":1827,"properties":1834,"displayName":1836,"givenName":20,"familyName":20},"cbb9a2e4-5e2d-4bad-aa4a-ded1a3956b5f",[149],[1828],{"id":1812,"sortIndex":21,"affiliation":1829,"properties":20},{"id":1812,"createTime":20,"updateTime":20,"relativeEntities":1830,"slug":20,"properties":1831,"entityType":20,"verifyStatus":20,"verifyTime":20,"verifyNote":20,"languages":20,"translateLanguages":20,"viewCount":20,"url":20,"parentIds":1833,"statistic":20},[],{"title":1832},{"VI":1817},[],{"title":1835,"gsAuthor":1837},{"VI":1836},"Kelly Rossa 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