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J. Distrib. Sens. Netw., 2013\nArami, 2013, Accurate measurement of concurrent flexion–extension and internal–external rotations in smart knee prostheses, IEEE Trans. Biomed. Eng., 60, 2504, 10.1109\u002FTBME.2013.2259489\nArmbrust, 2009\nAtzori, 2010, The internet of things: a survey, Comput. Netw., 54, 2787, 10.1016\u002Fj.comnet.2010.05.010\nAvrunin, 2012, Smart checklists for human-intensive medical systems, 1\nAyan Banerjee, Sandeep K.S. Gupta, Georgios Fainekos, Georgios Varsamopoulos. Towards modeling and analysis of cyber-physical medical systems, 2011.\nBizer, 2012, The meaningful use of big data: four perspectives–four challenges, ACM SIGMOD Rec., 40, 56, 10.1145\u002F2094114.2094129\nBrownstein, 2009, Digital disease detection, harnessing the web for public health surveillance, N. Engl. J. Med., 360, 2153, 10.1056\u002FNEJMp0900702\nBurke, 2013\nCaballero, 2003, Pathways: a school-based, randomized controlled trial for the prevention of obesity in American Indian schoolchildren, Am. J. Clin. Nutr., 78, 1030, 10.1093\u002Fajcn\u002F78.5.1030\nCallisaya, 2010, Ageing and gait variability, a population-based study of older people, Age Ageing, 39, 191, 10.1093\u002Fageing\u002Fafp250\nCarneiro, 2009, Google trends: a web-based tool for real-time surveillance of disease outbreaks, Clin. Infect. Dis., 49, 1557, 10.1086\u002F630200\nDean, 2008, Mapreduce: simplified data processing on large clusters, Commun. ACM, 51, 107, 10.1145\u002F1327452.1327492\nDon, 2013, Medical cyber physical systems and bigdata platforms\nDrouin, 2008, Health care costs: a market-based view, McKinsey Q.\nErl, 2013\nGellersen, 2002, Multi-sensor context-awareness in mobile devices and smart artifacts, Mob. Netw. Appl., 7, 341, 10.1023\u002FA:1016587515822\nGoodchild, 2007, Citizens as sensors: the world of volunteered geography, GeoJournal, 69, 211, 10.1007\u002Fs10708-007-9111-y\nChristian Hagist, Laurence J. Kotlikoff, Health care spending: what the future will look like, 2006.\nAhsanul Haque, 2014, Review of cyber-physical system in healthcare, Int. J. Distrib. Sens. Netw., 2014\nLee, 2012, Effect of physical inactivity on major non-communicable diseases worldwide: an analysis of burden of disease and life expectancy, Lancet, 380, 219, 10.1016\u002FS0140-6736(12)61031-9\nInsup, 2012, Challenges and research directions in medical cyber physical systems, Proc. IEEE, 100, 75, 10.1109\u002FJPROC.2011.2165270\nJencks, 2009, Rehospitalizations among patients in the medicare fee-for-service program, N. Engl. J. Med., 360, 1418, 10.1056\u002FNEJMsa0803563\nKailanto, 2008, Mobile ECG measurement and analysis system using mobile phone as the base station, 12\nKang, 2009\nKayyali, 2013\nKonstantas, 2003, Continuous monitoring of vital constants for mobile users: the MobiHealth approach, 3728\nKopetz, 2011, Internet of things, 307\nKraska, 2013, MLbase: a distributed machine-learning system\nKyoung-Don, 2004, Managing deadline miss ratio and sensor data freshness in real-time databases, IEEE Trans. Knowl. Data Eng., 16, 1200, 10.1109\u002FTKDE.2004.61\nLee, 2008, Cyber physical systems: design challenges, 363\nLee, 2012, Challenges and research directions in medical cyber-physical systems, Proc. IEEE, 100, 75, 10.1109\u002FJPROC.2011.2165270\nLiang, 2012, Review of cyber-physical system architecture, 25\nMalewicz, 2010, Pregel: a system for large-scale graph processing, 135\nManyika, 2011\nMarx, 2013, Biology: the big challenges of big data, Nature, 498, 255, 10.1038\u002F498255a\nMassé, 2013, Miniaturized wireless ECG monitor for real-time detection of epileptic seizures, ACM Trans. Embed. Comput. Syst., 12, 102, 10.1145\u002F2485984.2485990\nMcCaffrey, 2008, Swallowable-capsule technology, IEEE Pervasive Comput., 7, 23, 10.1109\u002FMPRV.2008.17\nMcGrath, 2013\nMcLachlan, 2011, A new fluorescence complementation biosensor for detection of estrogenic compounds, Biotechnol. Bioeng., 108, 2794, 10.1002\u002Fbit.23254\nMelnik, 2010, Dremel: interactive analysis of web-scale datasets, Proc. VLDB Endow., 3, 330, 10.14778\u002F1920841.1920886\nMendez, 2012, Design of cyber-physical interface for automated vital signs reading in electronic medical records systems, 1\nMeng, 2012, Compressed sensing photoacoustic tomography in vivo in time and frequency domains, 717\nMiller, 1982, Pediatric counseling and subsequent use of smoke detectors, Am. J. Publ. Health, 72, 392, 10.2105\u002FAJPH.72.4.392\nMurdoch, 2013, The inevitable application of big data to health care, JAMA, 309, 1351, 10.1001\u002Fjama.2013.393\nNing, 2013\nPark, 2003, Enhancing the quality of life through wearable technology, IEEE Eng. Med. Biol. Mag., 22, 41, 10.1109\u002FMEMB.2003.1213625\nPavlo, 2009, A comparison of approaches to large-scale data analysis, 165\nPlummer, 2008, Cloud computing: defining and describing an emerging phenomenon, Gartner, 17\nProvost, 2013\nM. Quin, Predicting vital signs, 2014.\nRaghupathi, 2014, Big data analytics in healthcare: promise and potential, Health Inf. Sci. Syst., 2, 3, 10.1186\u002F2047-2501-2-3\nSherif Sakr, 2014\nSherif Sakr, 2011, A survey of large scale data management approaches in cloud environments, IEEE Commun. Surv. Tutor., 13, 311, 10.1109\u002FSURV.2011.032211.00087\nSherif Sakr, 2013, The family of mapreduce and large-scale data processing systems, ACM Comput. Surv., 46, 11\nSheth, 2013, Physical-cyber-social computing: an early 21st century approach, IEEE Intell. Syst., 28, 78, 10.1109\u002FMIS.2013.20\nVictor Shnayder, Bor-rong Chen, Konrad Lorincz, Thaddeus R.F. Fulford Jones, Matt Welsh, Sensor networks for medical care, 2005.\nStaten, 2008, Is cloud computing ready for the enterprise, Forrester Res., 7\nTilak, 2013, Real-world deployments of participatory sensing applications: current trends and future directions, ISRN Sens. Netw., 2013\nUzor, 2013, Exploring & designing tools to enhance falls rehabilitation in the home, 1233\nVaquero, 2008, A break in the clouds: towards a cloud definition, Comput. Commun. Rev., 39, 50, 10.1145\u002F1496091.1496100\nVegoda, 2002, Introducing the IHE (integrating the healthcare enterprise) concept, J. Healthc. Inf. Manag., 16, 22\nVenkatasubramanian, 2012, Cyber physical security solutions for pervasive health monitoring systems\nWang, 2011, A secured health care application architecture for cyber-physical systems, Control Eng. Appl. Inform., 13, 101\nWang\nWeber, 2010\nWeigel, 2002\nWood, 2008, Context-aware wireless sensor networks for assisted living and residential monitoring, IEEE Netw., 22, 26, 10.1109\u002FMNET.2008.4579768\nWu, 2011, From wireless sensor networks towards cyber physical systems, Pervasive Mob. Comput., 7, 397, 10.1016\u002Fj.pmcj.2011.03.003\nYang, 2013, Design and evaluation of a portable optical-based biosensor for testing whole blood prothrombin time, Talanta, 116, 704, 10.1016\u002Fj.talanta.2013.07.064\nYang, 2014, 59\nYuriyama, 2010, Sensor-cloud infrastructure – physical sensor management with virtualized sensors on cloud computing, 1\nZhang, 2015, In-memory big data management and processing: a survey, IEEE Trans. Knowl. Data Eng., 27, 1920, 10.1109\u002FTKDE.2015.2427795\nZhaoyang, 2013, Interference mitigation for cyber-physical wireless body area network system using social networks, IEEE Trans. Emerg. Top. 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Lang. Eng., 11, 207, 10.1017\u002FS135132490400364X\nBrown, 1992, Class-based n-gram models of natural language, Comput. Linguist., 18, 467\nHuffman, 1995, Learning information extraction patterns from examples, vol. 1040, 246\nCaliff, 2003, Bottom-up relational learning of pattern matching rules for information extraction, J. Mach. Learn. 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2002, Community structure in social and biological networks, Proc. Natl. Acad. Sci., 99, 7821, 10.1073\u002Fpnas.122653799\nBarabasi, 2004, Network biology: understanding the cell's functional organization, Nat. Rev. Genet., 5, 101, 10.1038\u002Fnrg1272\nFortunato, 2010, Community detection in graphs, Phys. Rep., 486, 75, 10.1016\u002Fj.physrep.2009.11.002\nBlondel, 2008, Fast unfolding of communities in large networks, J. Stat. Mech. Theory Exp., 2008, 10.1088\u002F1742-5468\u002F2008\u002F10\u002FP10008\nOliveira, 2017, High quality multi-core multi-level community detection algorithm, Int. J. Comput. Sci. Eng., 15, 311, 10.1504\u002FIJCSE.2017.087399\nRaghavan, 2007, Near linear time algorithm to detect community structures in large-scale networks, Phys. Rev. E, 76, 10.1103\u002FPhysRevE.76.036106\nKarypis, 1998, A fast and high quality multilevel scheme for partitioning irregular graphs, SIAM J. Sci. Comput., 20, 359, 10.1137\u002FS1064827595287997\nKarypis, 1996, Parallel multilevel graph partitioning, 314\nKirmani, 2013, Scalable parallel graph partitioning, 51\nMeyerhenke, 2015, Parallel graph partitioning for complex networks, 1055\nNewman, 2004, Finding and evaluating community structure in networks, Phys. Rev. E, 69, 10.1103\u002FPhysRevE.69.026113\nRadicchi, 2004, Defining and identifying communities in networks, Proc. Natl. Acad. Sci. USA, 101, 2658, 10.1073\u002Fpnas.0400054101\nFortunato, 2004, Method to find community structures based on information centrality, Phys. Rev. E, 70, 10.1103\u002FPhysRevE.70.056104\nNewman, 2004, Fast algorithm for detecting community structure in networks, Phys. Rev. E, 69, 10.1103\u002FPhysRevE.69.066133\nVieira, 2014, Modularity based hierarchical community detection in networks, 146\nDe Meo, 2011, Generalized Louvain method for community detection in large networks, 88\nHashimoto, 2012, Social media analysis—determining the number of topic clusters from buzz marketing site, Int. J. Comput. Sci. Eng., 7, 65, 10.1504\u002FIJCSE.2012.046181\nOliveira, 2016, Identification and prediction of functional protein modules using a bi-level community detection algorithm, Int. J. Bioinform. Res. Appl., 12, 129, 10.1504\u002FIJBRA.2016.077124\nLiu, 2013, Selection of canonical images of travel attractions using image clustering and aesthetics analysis, Int. J. Comput. Sci. Eng., 8, 324, 10.1504\u002FIJCSE.2013.057297\nPons, 2005, Computing communities in large networks using random walks, 284\nRosvall, 2008, Maps of random walks on complex networks reveal community structure, Proc. Natl. Acad. Sci., 105, 1118, 10.1073\u002Fpnas.0706851105\nPandey, 2012, A framework for interest-based community evolution and sharing of latent knowledge, Int. J. Grid Util. Comput., 3, 200, 10.1504\u002FIJGUC.2012.047771\nRathnayaka, 2014, Formation of virtual community groups to manage prosumers in smart grids, Int. J. Grid Util. Comput., 6, 47, 10.1504\u002FIJGUC.2015.066396\nLancichinetti, 2009, Community detection algorithms: a comparative analysis, Phys. Rev. E, 80, 10.1103\u002FPhysRevE.80.056117\nNadakuditi, 2012, Graph spectra and the detectability of community structure in networks, Phys. Rev. Lett., 108, 10.1103\u002FPhysRevLett.108.188701\nRadicchi, 2014, A paradox in community detection, Europhys. Lett., 106, 10.1209\u002F0295-5075\u002F106\u002F38001\nPrat-Pérez, 2014, High quality, scalable and parallel community detection for large real graphs, 225\nPrat-Pérez, 2012, Shaping communities out of triangles, 1677\nRytsareva, 2014, Parallel algorithms for clustering biological graphs on distributed and shared memory architectures, Int. J. High. Perform. Comput. Networking, 7, 241, 10.1504\u002FIJHPCN.2014.062724\nSoman, 2011, Fast community detection algorithm with GPUs and multicore architectures, 568\nLu, 2015, Parallel heuristics for scalable community detection, Parallel Comput., 47, 19, 10.1016\u002Fj.parco.2015.03.003\nBae, 2013, Scalable flow-based community detection for large-scale network analysis, 303\nWickramaarachchi, 2014, Fast parallel algorithm for unfolding of communities in large graphs, 1\nMoon, 2014, Scalable community detection from networks by computing edge betweenness on mapreduce, 145\nJancura, 2012, Deen: a simple and fast algorithm for network community detection, 150\nAvery, 2011, Giraph: large-scale graph processing infrastructure on Hadoop\nKwak, 2010, What is twitter, a social network or a news media?, 591\nBoldi, 2004, Ubicrawler: a scalable fully distributed web crawler, Softw. Pract. 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Knowl. Discov., 31, 606, 10.1007\u002Fs10618-016-0483-9\nAbanda, 2019, A review on distance based time series classification, Data Min. Knowl. Discov., 33, 378, 10.1007\u002Fs10618-018-0596-4\nParmezan, 2022, Time series prediction via similarity search: exploring invariances, distance measures and ensemble functions, IEEE Access, 10, 78022, 10.1109\u002FACCESS.2022.3192849\nParmezan, 2019, Evaluation of statistical and machine learning models for time series prediction: identifying the state-of-the-art and the best conditions for the use of each model, Inf. Sci., 484, 302, 10.1016\u002Fj.ins.2019.01.076\nLiao, 2005, Clustering of time series data—a survey, Pattern Recognit., 38, 1857, 10.1016\u002Fj.patcog.2005.01.025\nMueen, 2014, Time series motif discovery: dimensions and applications, Wiley Interdiscip. Rev. Data Min. Knowl. Discov., 4, 152, 10.1002\u002Fwidm.1119\nBlázquez-García, 2021, A review on outlier\u002Fanomaly detection in time series data, ACM Comput. Surv., 54, 1, 10.1145\u002F3444690\nFreeman, 2021, Experimental comparison and survey of twelve time series anomaly detection algorithms, J. Artif. Intell. Res., 72, 849, 10.1613\u002Fjair.1.12698\nBatista, 2014, Cid: an efficient complexity-invariant distance for time series, Data Min. Knowl. Discov., 28, 634, 10.1007\u002Fs10618-013-0312-3\nSlijepcevic, 2021, Explaining machine learning models for clinical gait analysis, ACM Trans. Comput. Healthc., 3, 1, 10.1145\u002F3474121\nYamada, 1991, Phonetic typewriter based on phoneme source modeling, 169\nAlpaydin, 1996, Comparison of statistical and neural classifiers and their applications to optical character recognition and speech classification, 61\nDing, 2008, Querying and mining of time series data: experimental comparison of representations and distance measures, 1542\nMueen, 2011, Logical-shapelets: an expressive primitive for time series classification, 1154\nRakthanmanon, 2013, Fast shapelets: a scalable algorithm for discovering time series shapelets, 668\nEuachongprasit, 2008, Accurate and efficient retrieval of multimedia time series data under uniform scaling and time warping, 100\nRakthanmanon, 2012, Searching and mining trillions of time series subsequences under dynamic time warping, 262\nLi, 2012, Visualizing variable-length time series motifs, 895\nRakthanmanon, 2013, Addressing big data time series: mining trillions of time series subsequences under dynamic time warping, ACM Trans. Knowl. Discov. Data, 7, 1, 10.1145\u002F2500489\nPaparrizos, 2015, k-shape: efficient and accurate clustering of time series, 1855\nParmezan, 2015, A study of the use of complexity measures in the similarity search process adopted by knn algorithm for time series prediction, 45\nYeh, 2016, Matrix profile i: all pairs similarity joins for time series: a unifying view that includes motifs, discords and shapelets, 1317\nSchäfer, 2016, Scalable time series classification, Data Min. Knowl. Discov., 30, 1273, 10.1007\u002Fs10618-015-0441-y\nWang, 2017, Time series classification from scratch with deep neural networks: a strong baseline, 1578\nGogolou, 2018, Comparing similarity perception in time series visualizations, IEEE Trans. Vis. Comput. Graph., 25, 523, 10.1109\u002FTVCG.2018.2865077\nValovage, 2018, Enhancing machine learning classification for electrical time series applications, 8042\nKarim, 2019, Insights into lstm fully convolutional networks for time series classification, IEEE Access, 7, 67718, 10.1109\u002FACCESS.2019.2916828\nChen, 2022, Learning-based shapelets discovery by feature selection for time series classification, Appl. Intell., 1\nLiu, 2022, 1d convolutional neural networks for chart pattern classification in financial time series, J. Supercomput., 1, 10.1007\u002Fs11227-021-03859-5\nXiao-Xia, 2022, Time series based data explorer and stream analysis for anomaly prediction, Wirel. Commun. Mob. Comput.\nZanella, 2022, Ts-dense: time series data augmentation by subclass clustering, 1\nBountrogiannis, 2022, Distribution agnostic symbolic representations for time series dimensionality reduction and online anomaly detection, IEEE Trans. Knowl. Data Eng., 35, 5752\nKeogh, 2002, On the need for time series data mining benchmarks: a survey and empirical demonstration, 102\nPanigrahi, 2013, Effect of normalization techniques on univariate time series forecasting using evolutionary higher order neural network, Int. J. Eng. Adv. Technol., 3, 280\nBhanja, 2019, Impact of data normalization on deep neural network for time series forecasting, 27\nAl-Ghamdi, 2021, Evaluation of artificial neural networks performance using various normalization methods for water demand forecasting, 1\nHöppner, 2014, Less is more: similarity of time series under linear transformations, 560\nLines, 2015, Time series classification with ensembles of elastic distance measures, Data Min. Knowl. Discov., 29, 565, 10.1007\u002Fs10618-014-0361-2\nIsmail Fawaz, 2019, Deep learning for time series classification: a review, Data Min. Knowl. Discov., 33, 917, 10.1007\u002Fs10618-019-00619-1\nJiang, 2020, Time series classification: nearest neighbor versus deep learning models, SN Appl. Sci., 2, 1, 10.1007\u002Fs42452-020-2506-9\nBerndt, 1994, Using dynamic time warping to find patterns in time series, 359\nVlachos, 2003, Indexing multi-dimensional time-series with support for multiple distance measures, 216\nChen, 2005, Robust and fast similarity search for moving object trajectories, 491\nMarteau, 2009, Time warp edit distance with stiffness adjustment for time series matching, IEEE Trans. Pattern Anal. Mach. Intell., 31, 306, 10.1109\u002FTPAMI.2008.76\nGiusti, 2013, An empirical comparison of dissimilarity measures for time series classification, 82\nHe, 2016, Deep residual learning for image recognition, 770\nZhong, 2022, Combining filtering and cross-correlation efficiently for streaming time series, ACM Trans. Knowl. Discov. Data, 16, 1, 10.1145\u002F3502738\nIoffe, 2015, Batch normalization: accelerating deep network training by reducing internal covariate shift, 448\nKvalheim, 1994, Preprocessing of analytical profiles in the presence of homoscedastic or heteroscedastic noise, Anal. Chem., 66, 43, 10.1021\u002Fac00073a010\nSingh, 2020, Investigating the impact of data normalization on classification performance, Appl. Soft Comput., 97, 10.1016\u002Fj.asoc.2019.105524\nLatha, 2011, Efficient approach to normalization of multimodal biometric scores, Int. J. Comput. Appl., 32, 57\nHan, 2022\nBolstad, 2003, A comparison of normalization methods for high density oligonucleotide array data based on variance and bias, Bioinformatics, 19, 185, 10.1093\u002Fbioinformatics\u002F19.2.185\nYeo, 2000, A new family of power transformations to improve normality or symmetry, Biometrika, 87, 954, 10.1093\u002Fbiomet\u002F87.4.954\nLima\nDau\nDau, 2019, The ucr time series archive, IEEE\u002FCAA J. Autom. Sin., 6, 1293, 10.1109\u002FJAS.2019.1911747\nGuyon, 2007, Competitive baseline methods set new standards for the nips 2003 feature selection benchmark, Pattern Recognit. Lett., 28, 1438, 10.1016\u002Fj.patrec.2007.02.014\nSouza, 2013, Classification of data streams applied to insect recognition: initial results, 76\nSilva, 2015, Exploring low cost laser sensors to identify flying insect species, J. Intell. Robot. Syst., 80, 313, 10.1007\u002Fs10846-014-0168-9\nJiménez, 2019, Prediction of mosquito species and population age structure using mid-infrared spectroscopy and supervised machine learning, Wellcome Open Res., 4\nRossi, 2015, The network data repository with interactive graph analytics and visualization\nSouza, 2021, Efficient unsupervised drift detector for fast and high-dimensional data streams, Knowl. Inf. Syst., 63, 1497, 10.1007\u002Fs10115-021-01564-6\nWolpert, 1996, The lack of a priori distinctions between learning algorithms, Neural Comput., 8, 1341, 10.1162\u002Fneco.1996.8.7.1341\nLubba, 2019, catch22: canonical time-series characteristics: selected through highly comparative time-series analysis, Data Min. Knowl. Discov., 33, 1821, 10.1007\u002Fs10618-019-00647-x\nLarge, 2019, On time series classification with dictionary-based classifiers, Intell. Data Anal., 23, 1073, 10.3233\u002FIDA-184333\nBaydogan, 2013, A bag-of-features framework to classify time series, IEEE Trans. Pattern Anal. Mach. 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Integr. Bus. Econ. Res., 6, 271\nAhmad, 2020, A machine-learning based ConvLSTM architecture for NDVI forecasting, Int. Trans. Oper. Res., 10.1111\u002Fitor.12887\nAlhussein, 2020, Hybrid CNN-LSTM model for short-term individual household load forecasting, IEEE Access, 8, 180544, 10.1109\u002FACCESS.2020.3028281\nAshraf, 2019, Application of deep convolutional neural networks and smartphone sensors for indoor localization, Appl. Sci., 9, 2337, 10.3390\u002Fapp9112337\nAzzah, 2021, Resolving energy consumption issues and spectrum allocation for future broadband networks, IEEE Access, 9, 166071, 10.1109\u002FACCESS.2021.3135934\nBeckel, 2014, Revealing household characteristics from smart meter data, Energy, 78, 397, 10.1016\u002Fj.energy.2014.10.025\nBedi, 2020, Development of an IoT driven building environment for prediction of electric energy consumption, IEEE Int. Things J., 10.1109\u002FJIOT.2020.2975847\nBengio, 2013, Representation learning: a review and new perspectives, IEEE Trans. Pattern Anal. Mach. Intell., 35, 1798, 10.1109\u002FTPAMI.2013.50\nBogomolov, 2016, Energy consumption prediction using people dynamics derived from cellular network data, EPJ Data Sci., 5, 10.1140\u002Fepjds\u002Fs13688-016-0075-3\nBrown, 2017, Occupancy based household energy disaggregation using ultra wideband radar and electrical signature profiles, Energy Build., 141, 134, 10.1016\u002Fj.enbuild.2017.02.004\nB.P. Center, Annual energy outlook 2020, 2020.\nChai, 2014, Root mean square error (RMSE) or mean absolute error (MAE)? – Arguments against avoiding RMSE in the literature, Geosci. Model Dev., 7, 1247, 10.5194\u002Fgmd-7-1247-2014\nChakhchoukh, 2010, Electric load forecasting based on statistical robust methods, IEEE Trans. Power Syst., 26, 982, 10.1109\u002FTPWRS.2010.2080325\nChen, 2017, Short-term electrical load forecasting using the support vector regression (SVR) model to calculate the demand response baseline for office buildings, Appl. Energy, 195, 659, 10.1016\u002Fj.apenergy.2017.03.034\nChui, 2020, Predicting students' performance with school and family tutoring using generative adversarial network-based deep support vector machine, IEEE Access, 8, 86745, 10.1109\u002FACCESS.2020.2992869\nDivina, 2020, Hybridizing deep learning and neuroevolution: application to the Spanish short-term electric energy consumption forecasting, Appl. Sci., 10, 5487, 10.3390\u002Fapp10165487\nGreff, 2016, LSTM: a search space odyssey, IEEE Trans. Neural Netw. Learn. Syst., 28, 2222, 10.1109\u002FTNNLS.2016.2582924\nGrzegorowski, 2021, Cost optimization for big data workloads based on dynamic scheduling and cluster-size tuning, Big Data Res., 25, 10.1016\u002Fj.bdr.2021.100203\nGupta, 2018, An overview of internet of things (IoT): architectural aspects, challenges, and protocols, Concurr. Comput., 32\nHammad, 2022, Deep learning models for arrhythmia detection in IoT healthcare applications, Comput. Electr. Eng., 100, 10.1016\u002Fj.compeleceng.2022.108011\nHe, 2015, Convolutional neural networks at constrained time cost, 5353\nHebrail, 2012, Individual household electric power consumption data set\nHochreiter, 1997, Long short-term memory, Neural Comput., 9, 1735, 10.1162\u002Fneco.1997.9.8.1735\nHuang, 2003, Short-term load forecasting via ARMA model identification including non-Gaussian process considerations, IEEE Trans. Power Syst., 18, 673, 10.1109\u002FTPWRS.2003.811010\nHyeon, 2020, Deep learning-based household electric energy consumption forecasting, J. Eng., 2020, 639, 10.1049\u002Fjoe.2019.1219\nIbrahim, 2008, Energy storage systems—characteristics and comparisons, Renew. Sustain. Energy Rev., 12, 1221, 10.1016\u002Fj.rser.2007.01.023\nJozefowicz, 2015, An empirical exploration of recurrent network architectures, 2342\nKaggle, 2019, EEML 2019 – electricity prediction.\nKetkar, 2017, Convolutional neural networks, 63\nKim, 2018, Web traffic anomaly detection using C-LSTM neural networks, Expert Syst. Appl., 106, 66, 10.1016\u002Fj.eswa.2018.04.004\nKim, 2019, Predicting residential energy consumption using CNN-LSTM neural networks, Energy, 182, 72, 10.1016\u002Fj.energy.2019.05.230\nKiprijanovska, 2020, HousEEC: day-ahead household electrical energy consumption forecasting using deep learning, Energies, 13, 2672, 10.3390\u002Fen13102672\nKong, 2017, Short-term residential load forecasting based on resident behaviour learning, IEEE Trans. Power Syst., 33, 1087, 10.1109\u002FTPWRS.2017.2688178\nLe, 2020, Multiple electric energy consumption forecasting using a cluster-based strategy for transfer learning in smart building, Sensors, 20, 2668, 10.3390\u002Fs20092668\nLee, 2011, Forecasting time series using a methodology based on autoregressive integrated moving average and genetic programming, Knowl.-Based Syst., 24, 66, 10.1016\u002Fj.knosys.2010.07.006\nLi, 2017, Building energy consumption prediction: an extreme deep learning approach, Energies, 10, 1525, 10.3390\u002Fen10101525\nLi, 2019, A novel CNN based security guaranteed image watermarking generation scenario for smart city applications, Inf. Sci., 479, 432, 10.1016\u002Fj.ins.2018.02.060\nLiang, 2020, Household power consumption prediction method based on selective ensemble learning, IEEE Access, 8, 95657, 10.1109\u002FACCESS.2020.2996260\nMocanu, 2016, Deep learning for estimating building energy consumption, Sustain. Energy Grids Netw., 6, 91, 10.1016\u002Fj.segan.2016.02.005\nNejat, 2015, A global review of energy consumption, CO2 emissions and policy in the residential sector (with an overview of the top ten CO2 emitting countries), Renew. Sustain. Energy Rev., 43, 843, 10.1016\u002Fj.rser.2014.11.066\nPapalexopoulos, 1990, A regression-based approach to short-term system load forecasting, IEEE Trans. Power Syst., 5, 1535, 10.1109\u002F59.99410\nPavlicko, 2022, Forecasting of electrical energy consumption in Slovakia, Mathematics, 10, 577, 10.3390\u002Fmath10040577\nRustam, 2021, Wireless capsule endoscopy bleeding images classification using CNN based model, IEEE Access, 9, 33675, 10.1109\u002FACCESS.2021.3061592\nSadiq, 2020, Aggression detection through deep neural model on Twitter, Future Gener. Comput. Syst., 114, 120, 10.1016\u002Fj.future.2020.07.050\nSeddik, 2022, Ai-enabled digital forgery analysis and crucial interactions monitoring in smart communities, Technol. Forecast. Soc. Change, 177\nShi, 2017, Deep learning for household load forecasting—a novel pooling deep RNN, IEEE Trans. Smart Grid, 9, 5271, 10.1109\u002FTSG.2017.2686012\nSiddiqa, 2018, Social internet of vehicles: complexity, adaptivity, issues and beyond, IEEE Access, 6, 62089, 10.1109\u002FACCESS.2018.2872928\nSomu, 2020, A hybrid model for building energy consumption forecasting using long short term memory networks, Appl. Energy, 261, 10.1016\u002Fj.apenergy.2019.114131\nSpandagos, 2017, Equivalent full-load hours for assessing climate change impact on building cooling and heating energy consumption in large Asian cities, Appl. Energy, 189, 352, 10.1016\u002Fj.apenergy.2016.12.039\nTakeda, 2016, Using the ensemble Kalman filter for electricity load forecasting and analysis, Energy, 104, 184, 10.1016\u002Fj.energy.2016.03.070\nTeeraratkul, 2017, Shape-based approach to household electric load curve clustering and prediction, IEEE Trans. Smart Grid, 9, 5196, 10.1109\u002FTSG.2017.2683461\nUllah, 2020, Deep learning assisted buildings energy consumption profiling using smart meter data, Sensors, 20, 873, 10.3390\u002Fs20030873\nUmer, 2022, COVINet: a convolutional neural network approach for predicting COVID-19 from chest X-ray images, J. Ambient Intell. Humaniz. Comput., 13, 535, 10.1007\u002Fs12652-021-02917-3\nUmer, 2020, A novel stacked CNN for malarial parasite detection in thin blood smear images, IEEE Access, 8, 93782, 10.1109\u002FACCESS.2020.2994810\nUsha Rani, 2022, An improvement of yield production rate for crops by predicting disease rate using intelligent decision systems, Int. J. Soft. Sci. Comput. Intell., 14, 1, 10.4018\u002FIJSSCI.291714\nVazquez, 2017, Assessment of an adaptive load forecasting methodology in a smart grid demonstration project, Energies, 10, 190, 10.3390\u002Fen10020190\nWang, 2018, DeepSTCL: a deep spatio-temporal ConvLSTM for travel demand prediction, 1\nWang, 2022, PCNNCEC: efficient and privacy-preserving convolutional neural network inference based on cloud-edge-client collaboration, IEEE Trans. Netw. Sci. Eng.\nWang, 2018, Random forest based hourly building energy prediction, Energy Build., 171, 11, 10.1016\u002Fj.enbuild.2018.04.008\nYan, 2018, Multi-step short-term power consumption forecasting with a hybrid deep learning strategy, Energies, 11, 3089, 10.3390\u002Fen11113089\nZapata-Impata, 2019, Learning spatio temporal tactile features with a ConvLSTM for the direction of slip detection, Sensors, 19, 523, 10.3390\u002Fs19030523\nZhang, 2020, Incorporating phase-encoded spectrum masking into speaker-independent monaural source separation, Big Data Res., 22, 10.1016\u002Fj.bdr.2020.100158\nZhao, 2017, Energy consumption in machining: classification, prediction, and reduction strategy, Energy, 133, 142, 10.1016\u002Fj.energy.2017.05.110\nZhou\nZhou, 2016, Understanding household energy consumption behavior: the contribution of energy big data analytics, Renew. Sustain. 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2015, On construction of China's space information network, Geomat. Inf. Sci. Wuhan Univ., 40, 711\nLee, 2015, Geospatial Big Data: challenges and opportunities, Big Data Res., 2, 74, 10.1016\u002Fj.bdr.2015.01.003\nLovelly, 2014, A framework to analyze processor architectures for next-generation on-board space computing, 1\nRho, 2018, Social Internet of Things: applications, architectures and protocols, Future Gener. Comput. Syst., 82, 667, 10.1016\u002Fj.future.2018.01.035\nAfzal, 2019, Enabling IoT platforms for social IoT applications: vision, feature mapping, and challenges, Future Gener. Comput. Syst., 92, 718, 10.1016\u002Fj.future.2017.12.002\nNeilson, 2019, Systematic review of the literature on Big Data in the transportation domain: concepts and applications, Big Data Res., 17, 35, 10.1016\u002Fj.bdr.2019.03.001\nBehera, 2022, Vegetation extraction from uav-based aerial images through deep learning, Comput. Electron. Agric., 198, 10.1016\u002Fj.compag.2022.107094\nAl-Jarrah, 2015, Efficient machine learning for Big Data: a review, Big Data Res., 2, 87, 10.1016\u002Fj.bdr.2015.04.001\nKrizhevsky, 2017, ImageNet classification with deep convolutional neural networks, Commun. ACM, 60, 84, 10.1145\u002F3065386\nSimonyan, 2015, Very deep convolutional networks for large-scale image recognition\nHe, 2016, Deep residual learning for image recognition, 770\nGirshick, 2016, 1440\nRedmon, 2016, You only look once: unified, real-time object detection, 779\nWang, 2015, Visual tracking with fully convolutional networks, 3119\nShelhamer, 2017, Fully convolutional networks for semantic segmentation, IEEE Trans. Image Process., 39, 640\nSzegedy, 2017, Inception-v4, inception-ResNet and the impact of residual connections on learning, 4278\nMyint, 2011, Per-pixel vs. object-based classification of urban land cover extraction using high spatial resolution imagery, Remote Sens. Environ., 115, 1145, 10.1016\u002Fj.rse.2010.12.017\nFukushima, 1982, Neocognitron: a self-organizing neural network model for a mechanism of visual pattern recognition, 267\nLeCun, 1989, Backpropagation applied to handwritten zip code recognition, Neural Comput., 1, 541, 10.1162\u002Fneco.1989.1.4.541\nBacco, 2019, IoT applications and services in space information networks, IEEE Wirel. Commun., 26, 31, 10.1109\u002FMWC.2019.1800297\nWei, 2019, Application of edge intelligent computing in satellite Internet of things, 85\nCao, 2019, Space-based cloud-fog computing architecture and its applications, 166\nWang, 2019, Satellite edge computing for the Internet of things in aerospace, Sensors, 19, 10.3390\u002Fs19204375\nJan, 2019, Deep learning in big data analytics: a comparative study, Comput. Electr. Eng., 75, 275, 10.1016\u002Fj.compeleceng.2017.12.009\nAl-Turjman, 2019, 5G-enabled devices and smart-spaces in social-IoT: an overview, Future Gener. Comput. Syst., 92, 732, 10.1016\u002Fj.future.2017.11.035\nRoopa, 2020, Dynamic management of traffic signals through social IoT, Proc. Comput. Sci., 171, 1908, 10.1016\u002Fj.procs.2020.04.204\nBehera, 2021, Aerial data aiding smart societal reformation: current applications and path ahead, IEEE IT Prof., 23, 82, 10.1109\u002FMITP.2020.3020433\nSwain, 2021, METO: matching-theory-based efficient task offloading in IoT-Fog interconnection networks, IEEE Int. Things J., 8, 12705, 10.1109\u002FJIOT.2020.3025631\nHedman, 2010, Road network extraction in VHR SAR images of urban and suburban areas by means of class-aided feature-level fusion, IEEE Trans. Geosci. Remote Sens., 48, 1294, 10.1109\u002FTGRS.2009.2025123\nLi, 2015, Superpixel segmentation using linear spectral clustering, 1356\nDavis, 1975, Region extraction by averaging and thresholding, IEEE Trans. Syst. Man Cybern., 383, 10.1109\u002FTSMC.1975.5408419\nSaati, 2015, A method for automatic road extraction of high resolution SAR imagery, J. Indian Soc. Remote, 43, 697, 10.1007\u002Fs12524-015-0454-4\nZhong, 2015, Scene classification based on the multifeature fusion probabilistic topic model for high spatial resolution remote sensing imagery, IEEE Trans. Geosci. Remote Sens., 53, 6207, 10.1109\u002FTGRS.2015.2435801\nMaboudi, 2018, Integrating fuzzy object based image analysis and ant colony optimization for road extraction from remotely sensed images, ISPRS J. Photogramm. Remote Sens., 138, 151, 10.1016\u002Fj.isprsjprs.2017.11.014\nMnih, 2013\nCai, 2018, Joint feature network for bridge segmentation in remote sensing images, 2515\nSaito, 2015, Building and Road Detection from Large Aerial Imagery, vol. 9405, 153\nAlshehhi, 2017, Simultaneous extraction of roads and buildings in remote sensing imagery with convolutional neural networks, ISPRS J. Photogramm. Remote Sens., 130, 139, 10.1016\u002Fj.isprsjprs.2017.05.002\nMaggiori, 2016, Fully convolutional neural networks for remote sensing image classification, 5071\nBadrinarayanan, 2017, SegNet: a deep convolutional encoder-decoder architecture for image segmentation, IEEE Trans. Image Process., 39, 2481\nCheng, 2017, Automatic road detection and centerline extraction via cascaded end-to-end convolutional neural network, IEEE Trans. Geosci. Remote Sens., 55, 3322, 10.1109\u002FTGRS.2017.2669341\nLi, 2021, Automatic road extraction from remote sensing imagery using ensemble learning and postprocessing, IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens., 14, 10535, 10.1109\u002FJSTARS.2021.3094673\nBehera, 2022, The NITRDrone dataset to address the challenges for road extraction from aerial images, J. Signal Process. Syst.\nWu, 2012, Superpixel-based unsupervised change detection using multi-dimensional change vector analysis and SVM-based classification, 257\nAudebert, 2016, How useful is region-based classification of remote sensing images in a deep learning framework?, 5091\nZhao, 2017, Superpixel-based multiple local CNN for panchromatic and multispectral image classification, IEEE Trans. Geosci. Remote Sens., 55, 4141, 10.1109\u002FTGRS.2017.2689018\nAli, 2017, A new proposed the Internet of Things (IoT) virtualization framework based on sensor-as-a-service concept, Wirel. Pers. Commun., 97, 1419, 10.1007\u002Fs11277-017-4580-x\nAchanta, 2010\nAchanta, 2012, SLIC superpixels compared to state-of-the-art superpixel methods, IEEE Trans. Image Process., 34, 2274\nLeCun, 1998, Gradient-based learning applied to document recognition, Proc. IEEE, 86, 2278, 10.1109\u002F5.726791\nRonneberger, 2015, U-Net: convolutional networks for biomedical image segmentation, 234\nDemir, 2018, DeepGlobe 2018: a challenge to parse the Earth through satellite images\nDeng, 2009, ImageNet: a large-scale hierarchical image database, 248\nHu, 2018, Squeeze-and-excitation networks, 7132\nHuang, 2017, Densely connected convolutional networks, 4700\nPan, 2009, A survey on transfer learning, IEEE Trans. Knowl. Data Eng., 22, 1345, 10.1109\u002FTKDE.2009.191\nKingma, 2015, Adam: a method for stochastic optimization\nA.F. 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2003, Experiments on graph clustering algorithms, 568\nLeskovec, 2010, Empirical comparison of algorithms for network community detection, 631\nBlondel, 2008, Fast unfolding of communities in large networks, J. Stat. Mech. Theory Exp., 2008, 10.1088\u002F1742-5468\u002F2008\u002F10\u002FP10008\nGirvan, 2002, Community structure in social and biological networks, Proc. Natl. Acad. Sci. USA, 99, 7821, 10.1073\u002Fpnas.122653799\nHawick, 2011, Applying enumerative, spectral and hybrid graph analyses to biological network data, 89\nHawick, 2012, Betweenness centrality metrics for assessing electrical power network robustness against fragmentation and node failure, 186\nChen, 2010, Attack structural vulnerability of power grids: a hybrid approach based on complex networks, Physica A, 389, 595, 10.1016\u002Fj.physa.2009.09.039\nHawick, 2012, Water distribution network robustness and fragmentation using graph metrics, 304\nHawick, 2016\nSchaeffer, 2007, Graph clustering, Comput. Sci. Rev., 1, 27, 10.1016\u002Fj.cosrev.2007.05.001\nPizzuti, 2014, Algorithms and tools for protein–protein interaction networks clustering, with a special focus on population-based stochastic methods, Bioinformatics, 30, 1343, 10.1093\u002Fbioinformatics\u002Fbtu034\nLeskovec, 2008, Statistical properties of community structure in large social and information networks, 695\nLeskovec, 2009, Community structure in large networks: natural cluster sizes and the absence of large well-defined clusters, Internet Math., 6, 29, 10.1080\u002F15427951.2009.10129177\nPizzuti, 2018, Evolutionary computation for community detection in networks: a review, IEEE Trans. Evol. Comput., 10.1109\u002FTEVC.2017.2737600\nBackstrom, 2006, Group formation in large social networks: membership, growth, and evolution, 44\nPapadopoulos, 2012, Community detection in social media, Data Min. Knowl. Discov., 24, 515, 10.1007\u002Fs10618-011-0224-z\nMetwally, 2007, Detectives: detecting coalition hit inflation attacks in advertising networks streams, 241\nBocewicz, 2017, Reduction of congestion in transport networks with a fractal structure, 189\nKhandekar, 2009, 308\nArias-Castro, 2012, The normalized graph cut and Cheeger constant: from discrete to continuous, Adv. Appl. Probab., 44, 907, 10.1239\u002Faap\u002F1354716583\nŠíma, 2006, On the NP-completeness of some graph cluster measures, 530\nChawla, 2006, On the hardness of approximating multicut and sparsest-cut, Comput. Complex., 15, 94, 10.1007\u002Fs00037-006-0210-9\nChalupa\nTarjan, 1974, A note on finding the bridges of a graph, Inf. Process. Lett., 2, 160, 10.1016\u002F0020-0190(74)90003-9\nVan Laarhoven, 2016, Local network community detection with continuous optimization of conductance and weighted kernel k-means, J. Mach. Learn. Res., 17, 1\nFortunato, 2010, Community detection in graphs, Phys. Rep., 486, 75, 10.1016\u002Fj.physrep.2009.11.002\nChalupa, 2013, Metaheuristically optimized multicriteria clustering for medium-scale networks, vol. 188, 337\nChen, 2013, Second order partial derivatives for NK-landscapes, 503\nTintos, 2015, Partition crossover for pseudo-Boolean optimization, 137\nChicano, 2014, Efficient identification of improving moves in a ball for pseudo-boolean problems, 437\nWhitley, 2012, Constant time steepest descent local search with lookahead for NK-landscapes and MAX-kSAT, 1357\nPizzuti, 2012, A multiobjective genetic algorithm to find communities in complex networks, IEEE Trans. Evol. Comput., 16, 418, 10.1109\u002FTEVC.2011.2161090\nBenlic, 2011, A multilevel memetic approach for improving graph k-partitions, IEEE Trans. Evol. Comput., 15, 624, 10.1109\u002FTEVC.2011.2136346\nSyswerda, 1991, A study of reproduction in generational and steady state genetic algorithms, Found. Genet. Algorithms, 2, 94\nTakac, 2012, Data analysis in public social networks, 1\nJ. Leskovec, A. Krevl, SNAP Datasets: Stanford large network dataset collection.\nSalwinski, 2004, The database of interacting proteins: 2004 update, Nucleic Acids Res., 32, D449, 10.1093\u002Fnar\u002Fgkh086\nXenarios, 2001, DIP: the Database of Interacting Proteins: 2001 update, Nucleic Acids Res., 29, 239, 10.1093\u002Fnar\u002F29.1.239\nXenarios, 2000, DIP: the Database of Interacting Proteins, Nucleic Acids Res., 28, 289, 10.1093\u002Fnar\u002F28.1.289\nXenarios, 2002, DIP, the Database of Interacting Proteins: a research tool for studying cellular networks of protein interactions, Nucleic Acids Res., 30, 303, 10.1093\u002Fnar\u002F30.1.303\nNewman, 2006, Finding community structure in networks using the eigenvectors of matrices, Phys. Rev. E, 74\nKnuth, 1993\nWatts, 1998, Collective dynamics of “small-world” networks, Nature, 393, 440, 10.1038\u002F30918\nLusseau, 2003, The bottlenose dolphin community of doubtful sound features a large proportion of long-lasting associations, Behav. Ecol. Sociobiol., 54, 396, 10.1007\u002Fs00265-003-0651-y\nChalupa, 2017, Computational methods for finding long simple cycles in complex networks, Knowl.-Based Syst., 125, 96, 10.1016\u002Fj.knosys.2017.03.022\nChalupa, 2017, Mining k-reachable sets in real-world networks using domination in shortcut graphs, J. Comput. Sci., 22, 1, 10.1016\u002Fj.jocs.2017.07.012\nBarabási, 1999, Emergence of scaling in random networks, Science, 286, 509, 10.1126\u002Fscience.286.5439.509\nAlbert, 2002, Statistical mechanics of complex networks, Rev. Mod. Phys., 74, 47, 10.1103\u002FRevModPhys.74.47\nZachary, 1977, An information flow model for conflict and fission in small groups, J. Anthropol. 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VLDB Endow., 9, 636, 10.14778\u002F2947618.2947620\nVernica, 2010, Efficient parallel set-similarity joins using MapReduce, 6\nDeng, 2018, Overlap set similarity joins with theoretical guarantees, 905\nWang, 2012, Can we beat the prefix filtering?: an adaptive framework for similarity join and search, 85\nWang, 2019, Leveraging set relations in exact and dynamic set similarity join, VLDB J., 28, 267, 10.1007\u002Fs00778-018-0529-2\nMa, 2019, Similarity histogram estimation based top-k similarity join algorithm on high-dimensional data, vol. 11817\nZhou, 2018, A generic inverted index framework for similarity search on the GPU\nSandes, 2017\nLi, 2018, A GPU accelerated update efficient index for kNN queries in road networks\nKruliš, 2015, Optimizing sorting and top-k selection steps in permutation based indexing on GPUs\nWang, 2017\nGowanlock, 2016, Distance threshold similarity searches: efficient trajectory indexing on the GPU, IEEE Trans. Parallel Distrib. Syst., 27, 2533, 10.1109\u002FTPDS.2015.2500896\nPapenbrock, 2015, Progressive duplicate detection, IEEE Trans. Knowl. Data Eng., 27, 1316, 10.1109\u002FTKDE.2014.2359666\nWhang, 2013, Pay-as-you-go entity resolution, IEEE Trans. Knowl. Data Eng., 25, 1111, 10.1109\u002FTKDE.2012.43\nSimonini, 2019, Schema-agnostic progressive entity resolution, IEEE Trans. Knowl. Data Eng., 31, 1208, 10.1109\u002FTKDE.2018.2852763\nCai, 2020, Target-aware holistic influence maximization in spatial social networks, IEEE Trans. Knowl. Data Eng. early access, 10.1109\u002FTKDE.2020.3003047\nHernández, 1995, The merge\u002Fpurge problem for large databases, 127\nBloom, 1970, Space\u002Ftime tradeoffs in hash coding with allowable errors, Commun. ACM, 13, 422, 10.1145\u002F362686.362692\nChristen, 2012, A survey of indexing techniques for scalable set linkage and deduplication, IEEE Trans. Knowl. Data Eng., 24, 1537, 10.1109\u002FTKDE.2011.127\nNvidia, 2017\nYu, 2020, An approach for progressive set similarity join with GPU accelerating, 155\nZhao, 2021, Deep Attributed Network Representation Learning of Complex Coupling and Interaction, Knowl.-Based Syst., 212\nWang, 2020, Distributed Pregel-Based Provenance-Aware Regular Path Query Processing on RDF Knowledge Graphs, World Wide Web J., 23, 1465, 10.1007\u002Fs11280-019-00739-0",{"EN":888},"Accelerating Progressive Set Similarity Join with the CPU-GPU 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Commun. ACM, 57, 78, 10.1145\u002F2500873\nBertot, 2013, Big data and e-government: issues, policies, and recommendations\nNakazato, 2014, Influencing driver behavior through future expressway traffic predictions\nChen, 2013, DiabeticLink: a health big data system for patient empowerment and personalized healthcare\nBillen, 2013, A mobile sensor data acquisition and evaluation framework for crowd sourcing data\nDai, 2010, Mobile phone based drunk driving detection\nEriksson, 2008, The pothole patrol: using a mobile sensor network for road surface monitoring\nYang, 2007, Map–reduce–merge: simplified relational data processing on large clusters\nVieira, 2013, Evaluating mapreduce for profiling application traffic\nLee, 2013, Toward scalable internet traffic measurement and analysis with Hadoop, 5\nAljarah, 2013, Towards a scalable intrusion detection system based on parallel PSO clustering using mapreduce\nLee, 2011, Detecting DDoS attacks with Hadoop\nNarang, 2014, HaDeS: a Hadoop-based framework for detection of peer-to-peer botnets\nDai, 2012, Bioinformatics clouds for big data manipulation, Biol. Direct, 7, 10.1186\u002F1745-6150-7-43\nBryant\nMayer-Schönberger, 2013\nBoyd, 2012, Critical questions for big data: provocations for a cultural, technological, and scholarly phenomenon, Inf. Commun. Soc., 15, 662, 10.1080\u002F1369118X.2012.678878\nYiu, 2012, The big data opportunity: making government faster, smarter and more personal\nManovich\nChen, 2012, Business intelligence and analytics: from big data to big impact, Manag. Inf. Syst. Q., 36, 1165, 10.2307\u002F41703503\nZhao, 2014, A security framework in G-Hadoop for big data computing across distributed cloud data centres, J. Comput. Syst. Sci., 80, 994, 10.1016\u002Fj.jcss.2014.02.006\nBloedorn, 2001\nFrancois, 2011, Botcloud: detecting botnets using mapreduce\nKoufakou, 2008, Fast parallel outlier detection for categorical datasets using MapReduce\nKumar, 2010, DEDUCE: at the intersection of MapReduce and stream processing\nXiao, 2011, Accountable MapReduce in cloud computing\nHoltz, 2011, Building scalable distributed intrusion detection systems based on the mapreduce framework, vol. 1\nChoi, 2014, Detecting web based DDoS attack using MapReduce operations in cloud computing environment, J. Internet Serv. Inf. Secur., 3, 28\nCárdenas, 2013, Big data analytics for security\nHampton, 2013, Big data and the future of ecology\nKaisler, 2013, Big data: issues and challenges moving forward\nCorchado, 2012, Neural visualization of network traffic data for intrusion detection\nDemchenko, 2012, Addressing big data challenges for scientific data infrastructure\nWei Yu, Guobin Xu, Khanh D. Pham, Erik P. Blasch, Genshe Chen, Dan Shen, Paul Moulema, A framework for cyber–physical system security situation awareness, Foundational Methods for Cyber–Physical Systems, 2015, in press.\nGe, 2015, Towards MapReduce Based Machine Learning Techniques for Processing Massive Network Threat Monitoring Data\nXu, 2015, A cloud computing based system for network security management, Int. J. Parallel Emerg. Distrib. Syst., 30, 29, 10.1080\u002F17445760.2014.925110\nYu, 2013, A cloud computing based architecture for cyber security situation awareness\nHadoop\nSpark\nAmir, 2007, A k-mean clustering algorithm for mixed numeric and categorical data\nCAIDA Data\ntroyhunt\nJuuso, 2013, Proactive cyber defense: understanding and testing for advanced persistent threats (APTs)\nSYSSTAT\nYang, 2014, On false data-injection attacks against power system state estimation: modeling and countermeasures, IEEE Trans. Parallel Distrib. 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2010, Cancer statistics, CA Cancer J. Clin., 60, 277, 10.3322\u002Fcaac.20073\nRios Ataxca, 2018, A passive state simulation of an anal sphincter using simmechanics, J. Mech. Med. Biol., 18, 10.1142\u002FS0219519418500598\nSiegel, 2018, Cancer statistics, CA Cancer J. Clin., 68, 7, 10.3322\u002Fcaac.21442\nOser, 2015, Transformation from non-small-cell lung cancer to small-cell lung cancer: molecular drivers and cells of origin, Lancet Oncol., 16, e165, 10.1016\u002FS1470-2045(14)71180-5\nJohnson, 1996, Demographics of brain metastasis, Neurosurg. Clin. North. Am., 7, 337, 10.1016\u002FS1042-3680(18)30365-6\nZappa, 2016, Non-small cell lung cancer: current treatment and future advances, Transl. Lung Cancer Res., 5, 288, 10.21037\u002Ftlcr.2016.06.07\nGrossman, 2021, Differentiating small-cell lung cancer from non-small-cell lung cancer brain metastases based on MRI using efficientnet and transfer learning approach, Technol. Cancer Res. Treat., 20, 10.1177\u002F15330338211004919\nAdelstein, 1986, Mixed small cell and non-small cell lung cancer, Chest, 89, 699, 10.1378\u002Fchest.89.5.699\nOchiai, 2015, Comparison of therapeutic results from radiofrequency ablation and stereotactic body radiotherapy in solitary lung tumors measuring 5 cm or smaller, Int. J. Clin. Oncol., 20, 499, 10.1007\u002Fs10147-014-0741-z\nChi, 2010, Treatment of brain metastasis from lung cancer, Cancers (Basel), 2, 2100, 10.3390\u002Fcancers2042100\nZheng, 2016, Classification and pathology of lung cancer, Surg. Oncol. Clin. N. Am., 25, 447, 10.1016\u002Fj.soc.2016.02.003\nKriegsmann, 2020, Deep learning for the classification of small-cell and non-small-cell lung cancer, Cancers (Basel), 12, 1604, 10.3390\u002Fcancers12061604\nTeramoto, 2017, Automated classification of lung cancer types from cytological images using deep convolutional neural networks, BioMed Res. Int., 2017, 1, 10.1155\u002F2017\u002F4067832\nWang, 2020, Classification of pathological types of lung cancer from CT images by deep residual neural networks with transfer learning strategy, Open Med., 15, 190, 10.1515\u002Fmed-2020-0028\nPark, 2012, Development and validation of a prognostic gene-expression signature for lung adenocarcinoma, PLoS One, 7\nPotti, 2006, A genomic strategy to refine prognosis in early-stage non–small-cell lung cancer, N. Engl. J. Med., 355, 570, 10.1056\u002FNEJMoa060467\nZhang, 2016, A nomogram to predict brain metastases of resected non-small cell lung cancer patients, Ann. Surg. Oncol., 23, 3033, 10.1245\u002Fs10434-016-5206-3\nFernandes, 2009, Expression profiles of thioredoxin family proteins in human lung cancer tissue: correlation with proliferation and differentiation, Histopathology, 55, 313, 10.1111\u002Fj.1365-2559.2009.03381.x\nLi, 2015, An array-based approach to determine different subtype and differentiation of non-small cell lung cancer, Theranostics, 5, 62, 10.7150\u002Fthno.10145\nCalbo, 2011, A functional role for tumor cell heterogeneity in a mouse model of small cell lung cancer, Cancer Cell, 19, 244, 10.1016\u002Fj.ccr.2010.12.021\nKrishnaiah, 2013, Diagnosis of lung cancer prediction system using data mining classification techniques, Int. J. Comput. Sci. Inf. Technol., 4, 39\nSilvestri, 2007, 178S\nTravis, 2011, International Association for the Study of Lung Cancer\u002FAmerican Thoracic Society\u002FEuropean Respiratory Society: International Multidisciplinary Classification of Lung Adenocarcinoma, Proc. Am. Thorac Soc., 8, 381, 10.1513\u002Fpats.201107-042ST\nLoo, 2010, Subtyping of undifferentiated non-small cell carcinomas in bronchial biopsy specimens, J. Thorac. Oncol., 5, 442, 10.1097\u002FJTO.0b013e3181d40fac\nNicholson, 2010, Refining the diagnosis and EGFR status of non-small cell lung carcinoma in biopsy and cytologic material, using a panel of mucin staining, TTF-1, cytokeratin 5\u002F6, and P63, and EGFR mutation analysis, J. Thorac. Oncol., 5, 436, 10.1097\u002FJTO.0b013e3181c6ed9b\nJiang, 2017, A novel pixel value space statistics map of the pulmonary nodule for classification in computerized tomography images, 556\nWu, 2013, Can diffusion-weighted imaging be used as a reliable sequence in the detection of malignant pulmonary nodules and masses?, Magn. Reson. Imaging, 31, 235, 10.1016\u002Fj.mri.2012.07.009\nKhalil, 2020, A new expert system in prediction of lung cancer disease based on fuzzy soft sets, Soft Comput., 24, 14179, 10.1007\u002Fs00500-020-04787-x\nLee, 2001, Automated detection of pulmonary nodules in helical CT images based on an improved template-matching technique, IEEE Trans. Med. Imaging, 20, 595, 10.1109\u002F42.932744\nRathore, 2014, Ensemble classification of colon biopsy images based on information rich hybrid features, Comput. Biol. Med., 47, 76, 10.1016\u002Fj.compbiomed.2013.12.010\nRathore, 2013, A recent survey on colon cancer detection techniques, IEEE\u002FACM Trans. Comput. Biol. Bioinform., 10, 545, 10.1109\u002FTCBB.2013.84\nRathore, 2012, Capture largest included circles: an approach for counting red blood cells, vol. 281, 373\nRathore, 2015, Automated colon cancer detection using hybrid of novel geometric features and some traditional features, Comput. Biol. Med., 65, 279, 10.1016\u002Fj.compbiomed.2015.03.004\nHussain, 2019, Detecting brain tumor using machine learning techniques based on different features extracting strategies, Curr. Med. Imaging, 14, 595, 10.2174\u002F1573405614666180718123533\nHussain, 2018, Automated breast cancer detection using machine learning techniques by extracting different feature extracting strategies, 327\nHussain, 2018, Prostate cancer detection using machine learning techniques by employing combination of features extracting strategies, Cancer Biomark., 21, 393, 10.3233\u002FCBM-170643\nFenton, 2006, The lung cancer alliance, J. Oncol. Pract., 2, 306, 10.1200\u002Fjop.2006.2.6.306\nTiwari, 2016, Brightness preserving contrast enhancement of medical images using adaptive gamma correction and homomorphic filtering, 1\nFarid, 2001, Blind inverse gamma correction, IEEE Trans. Image Process., 10, 1428, 10.1109\u002F83.951529\nBhandari, 2016, Dark satellite image enhancement using knee transfer function and gamma correction based on DWT–SVD, vol. 27, 453\nNgo, 2021, Taylor-series-based reconfigurability of gamma correction in hardware designs, Electronics, 10, 1959, 10.3390\u002Felectronics10161959\nHussain, 2019, Automated lung cancer detection based on multimodal features extracting strategy using machine learning techniques, 134\nHussain, 2019, Analyzing the dynamics of lung cancer imaging data using refined fuzzy entropy methods by extracting different features, IEEE Access, 7, 64704, 10.1109\u002FACCESS.2019.2917303\nZhou, 2018, Radiomics in brain tumor: image assessment, quantitative feature descriptors, and machine-learning approaches, Am. J. Neuroradiol., 39, 208, 10.3174\u002Fajnr.A5391\nGoh, 2011, Assessment of response to tyrosine kinase inhibitors in metastatic renal cell cancer: CT texture as a predictive biomarker, Radiology, 261, 165, 10.1148\u002Fradiol.11110264\nGiraud, 2019, Radiomics and machine learning for radiotherapy in head and neck cancers, Front. Oncol., 9\nNioche, 2018, LIFEx: a freeware for radiomic feature calculation in multimodality imaging to accelerate advances in the characterization of tumor heterogeneity, Cancer Res., 78, 4786, 10.1158\u002F0008-5472.CAN-18-0125\nWeninger, 2019, Robustness of radiomics for survival prediction of brain tumor patients depending on resection status, Front. Comput. Neurosci., 13, 10.3389\u002Ffncom.2019.00073\nLohmann, 2021, Radiomics in neuro-oncology: basics, workflow, and applications, Methods, 188, 112, 10.1016\u002Fj.ymeth.2020.06.003\nde Leon, 2019, Radiomics in kidney cancer: MR imaging, Magn. Reson. Imaging Clin. N. Am., 27, 1, 10.1016\u002Fj.mric.2018.08.005\nKalkhaire, 2017, Remote detection of photoplethysmographic signal and SVM based classification, 128\nTariq, 2019, Breast cancer classification using global discriminate features in mammographic images, Int. J. Adv. Comput. Sci. Appl., 10, 381\nRaghtate, 2015, Comparison of classification methods with second order statistical analysis and wavelet transform for texture image classification, 312\nJain, 2019\nThibault, 2013, Shape and texture indexes application to cell nuclei classification, Int. J. Pattern Recognit. Artif. Intell., 27, 10.1142\u002FS0218001413570024\nChu, 1990, Use of gray value distribution of run lengths for texture analysis, Pattern Recognit. Lett., 11, 415, 10.1016\u002F0167-8655(90)90112-F\nKairuddin, 2017, Texture feature analysis for different resolution level of kidney ultrasound images, IOP Conf. Ser., Mater. Sci. Eng., 226, 10.1088\u002F1757-899X\u002F226\u002F1\u002F012136\nWang, 2010, A comparative study of filter-based feature ranking techniques, 43\nShakir, 2019, Radiomics based likelihood functions for cancer diagnosis, Sci. Rep., 9, 9501, 10.1038\u002Fs41598-019-45053-x\nWu, 2016, Exploratory study to identify radiomics classifiers for lung cancer histology, Front. Oncol., 6, 187, 10.3389\u002Ffonc.2016.00071\nYu, 2019, A Matlab toolbox for feature importance ranking, 1\nTeng, 2019, Unsupervised feature selection with adaptive residual preserving, Neurocomputing, 367, 259, 10.1016\u002Fj.neucom.2019.05.097\nSaeys, 2007, A review of feature selection techniques in bioinformatics, Bioinformatics, 23, 2507, 10.1093\u002Fbioinformatics\u002Fbtm344\nSolorio-Fernández, 2020, A review of unsupervised feature selection methods, Artif. Intell. Rev., 53, 907, 10.1007\u002Fs10462-019-09682-y\nVenkatesh, 2019, A review of feature selection and its methods, Cybern. Inf. Technol., 19, 3\nGu\nRoffo, 2017, Infinite latent feature selection: a probabilistic latent graph-based ranking approach, 1407\nChien, 2018, Applying Gini coefficient to evaluate the author research domains associated with the ordering of author names, Medicine, 97, 10.1097\u002FMD.0000000000012418\nZhao, 2007, Spectral feature selection for supervised and unsupervised learning, 1151\nRoffo, 2020, Infinite feature selection: a graph-based feature filtering approach, IEEE Trans. Pattern Anal. Mach. Intell., 1\nHou, 2014, Joint embedding learning and sparse regression: a framework for unsupervised feature selection, IEEE Trans. Cybern., 44, 793, 10.1109\u002FTCYB.2013.2272642\nLind, 1993, The continuity principle in psychological research: an introduction to robust statistics, Can. J. Psychol., 34, 407, 10.1037\u002Fh0078861\nLi, 2018, Feature selection, ACM Comput. Surv., 50, 1, 10.1145\u002F3136625\nZeng, 2011, Feature selection and kernel learning for local learning-based clustering, IEEE Trans. Pattern Anal. Mach. Intell., 33, 1532, 10.1109\u002FTPAMI.2010.215\nCai, 2010, Unsupervised feature selection for multi-cluster data, 333\nKim, 2015, T test as a parametric statistic, Korean J. Anesthesiol., 68, 540, 10.4097\u002Fkjae.2015.68.6.540\nHeyer, 1982, 142\nHoeffding, 1994, 409\nBradley, 1997, The use of the area under the ROC curve in the evaluation of machine learning algorithms, Pattern Recognit., 30, 1145, 10.1016\u002FS0031-3203(96)00142-2\nWilcoxon, 1992, Individual comparisons by ranking methods, Biom. Bull., 196\nKononenko, 1995, Induction of decision trees using relieff, 199\nTibshirani, 1996, Regression shrinkage and selection via the Lasso, J. R. Stat. Soc. B, 58, 267\nHu, 2013, Minimum–maximum local structure information for feature selection, Pattern Recognit. Lett., 34, 527, 10.1016\u002Fj.patrec.2012.11.012\nLi, 2014, Clustering-guided sparse structural learning for unsupervised feature selection, IEEE Trans. Knowl. Data Eng., 26, 2138, 10.1109\u002FTKDE.2013.65\nGuo, 2017, Unsupervised feature selection with ordinal locality, 1213\nHappy, 2017, An effective feature selection method based on pair-wise feature proximity for high dimensional low sample size data, 1574\nShi, 2014, Robust spectral learning for unsupervised feature selection, 977\nGravier, 2000, A Markov random field model for automatic speech recognition, 254\nVapnik, 1999, An overview of statistical learning theory, IEEE Trans. Neural Netw., 10, 988, 10.1109\u002F72.788640\nToccaceli, 2017, Combination of conformal predictors for classification, vol. 60, 39\nSubasi, 2013, Classification of EMG signals using PSO optimized SVM for diagnosis of neuromuscular disorders, Comput. Biol. Med., 43, 576, 10.1016\u002Fj.compbiomed.2013.01.020\nDobrowolski, 2012, Multiresolution MUAPs decomposition and SVM-based analysis in the classification of neuromuscular disorders, Comput. Methods Programs Biomed., 107, 393, 10.1016\u002Fj.cmpb.2010.12.006\nList, 1997, Characterization of bovine endothelial nitric oxide synthase as a homodimer with down-regulated uncoupled NADPH oxidase activity: tetrahydrobiopterin binding kinetics and role of haem in dimerization, Biochem. J., 323, 159, 10.1042\u002Fbj3230159\nSmith, 2015, Conformal anomaly detection of trajectories with a multi-class hierarchy, 281\nAitkenhead, 2008, A co-evolving decision tree classification method, Expert Syst. Appl., 34, 18, 10.1016\u002Fj.eswa.2006.08.008\nHussain, 2018, Detecting epileptic seizure with different feature extracting strategies using robust machine learning classification techniques by applying advance parameter optimization approach, Cogn. Neurodyn., 12, 271, 10.1007\u002Fs11571-018-9477-1\nRissanen, 1996, Fisher information and stochastic complexity, IEEE Trans. Inf. Theory, 42, 40, 10.1109\u002F18.481776\nZaidi, 2012, Bayesian reliability models of Weibull systems: state of the art, Int. J. Appl. Math. Comput. Sci., 22, 585, 10.2478\u002Fv10006-012-0045-2\nYang, 2012, Content-based retrieval of brain tumor in contrast-enhanced MRI images using tumor margin information and learned distance metric, Med. Phys., 39, 6929, 10.1118\u002F1.4754305\nHuang, 2012, Retrieval of brain tumors with region-specific bag-of-visual-words representations in contrast-enhanced MRI images, Comput. Math. Methods Med., 2012, 1\nHuang, 2014, Content-based image retrieval using spatial layout information in brain tumor T1-weighted contrast-enhanced MR images, PLoS One, 9\nCheng, 2016, Retrieval of brain tumors by adaptive spatial pooling and Fisher vector representation, PLoS One, 11\nHajian-Tilaki, 2013, Summary for policymakers, 1\nKashyap, 2015, Breast cancer detection in digital mammograms, IEEE Int Conf Imaging Syst Tech, 6\nKanakatte, 2008, Pulmonary tumor volume detection from positron emission tomography images, 213\nLiu, 2010, A method of pulmonary nodule detection utilizing multiple support vector machines\nParveen, 2013, Detection of lung cancer nodules using automatic region growing method, 1\nTurkki, 2015, Assessment of tumour viability in human lung cancer xenografts with texture-based image analysis, J. Clin. Pathol., 68, 614, 10.1136\u002Fjclinpath-2015-202888\nDennie, 2016, Role of quantitative computed tomography texture analysis in the differentiation of primary lung cancer and granulomatous nodules, Quant. Imaging Med. Surg., 6, 6\nRoth, 2015, 1\nShaffie, 2018, A novel autoencoder-based diagnostic system for early assessment of lung cancer, 1393\nChen Y-J, 2015, Computer-aided classification of lung nodules on computed tomography images via deep learning technique, OncoTargets Ther., 2015, 10.2147\u002FOTT.S80733\nKrewer, 2013, Effect of texture features in computer aided diagnosis of pulmonary nodules in low-dose computed tomography, 3887\nL, 2019, Optimal deep learning model for classification of lung cancer on CT images, Future Gener. Comput. Syst., 92, 374, 10.1016\u002Fj.future.2018.10.009\nSingh, 2019, Performance analysis of various machine learning-based approaches for detection and classification of lung cancer in humans, Neural Comput. 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