{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,12,31]],"date-time":"2025-12-31T00:37:07Z","timestamp":1767141427289,"version":"build-2238731810"},"reference-count":63,"publisher":"Springer Science and Business Media LLC","issue":"23","license":[{"start":{"date-parts":[[2022,4,17]],"date-time":"2022-04-17T00:00:00Z","timestamp":1650153600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2022,4,17]],"date-time":"2022-04-17T00:00:00Z","timestamp":1650153600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"funder":[{"DOI":"10.13039\/501100009367","name":"Mansoura University","doi-asserted-by":"crossref","id":[{"id":"10.13039\/501100009367","id-type":"DOI","asserted-by":"crossref"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Multimed Tools Appl"],"published-print":{"date-parts":[[2022,9]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:p>This paper is about enhancing the smart grid by proposing a new hybrid feature-selection method called feature selection-based ranking (FSBR). In general, feature selection is to exclude non-promising features out from the collected data at Fog. This could be achieved using filter methods, wrapper methods, or a hybrid. Our proposed method consists of two phases: filter and wrapper phases. In the filter phase, the whole data go through different ranking techniques (i.e., relative weight ranking, effectiveness ranking, and information gain ranking) The results of these ranks are sent to a fuzzy inference engine to generate the final ranks. In the wrapper phase, data is being selected based on the final ranks and passed on three different classifiers (i.e., Naive Bayes, Support Vector Machine, and neural network) to select the best set of the features based on the performance of the classifiers. This process can enhance the smart grid by reducing the amount of data being sent to the cloud, decreasing computation time, and decreasing data complexity. Thus, the FSBR methodology enables the user load forecasting (ULF) to take a fast decision, the fast reaction in short-term load forecasting, and to provide a high prediction accuracy. The authors explain the suggested approach via numerical examples. Two datasets are used in the applied experiments. The first dataset reported that the proposed method was compared with six other methods, and the proposed method was represented the best accuracy of 91%. The second data set, the generalization data set, reported 90% accuracy of the proposed method compared to fourteen different methods.<\/jats:p>","DOI":"10.1007\/s11042-022-12987-w","type":"journal-article","created":{"date-parts":[[2022,4,17]],"date-time":"2022-04-17T11:02:48Z","timestamp":1650193368000},"page":"33017-33049","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":6,"title":["Enhancing the performance of smart electrical grids using data mining and fuzzy inference engine"],"prefix":"10.1007","volume":"81","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-8137-7752","authenticated-orcid":false,"given":"Rana Mohamed","family":"El-Balka","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ahmed I.","family":"Saleh","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ahmed A.","family":"Abdullah","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Noha","family":"Sakr","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2022,4,17]]},"reference":[{"key":"12987_CR1","doi-asserted-by":"crossref","unstructured":"Abualigah L, Dulaimi AJ (2021) A novel feature selection method for data mining tasks using hybrid sine cosine algorithm and genetic algorithm. Clust Comput:1\u201316","DOI":"10.1007\/s10586-021-03254-y"},{"issue":"4","key":"12987_CR2","doi-asserted-by":"publisher","first-page":"1756","DOI":"10.3390\/app5041756","volume":"5","author":"A Ahmad","year":"2015","unstructured":"Ahmad A, Javaid N, Alrajeh N, Khan Z, Qasim U, Khan A (2015) A modified feature selection and artificial neural network-based day-ahead load forecasting model for a smart grid. Appl Sci 5(4):1756\u20131772","journal-title":"Appl Sci"},{"key":"12987_CR3","doi-asserted-by":"publisher","first-page":"27518","DOI":"10.1109\/ACCESS.2018.2835527","volume":"6","author":"S Ahmed","year":"2018","unstructured":"Ahmed S, Lee Y, Hyun SH, Koo I (2018) Feature selection\u2013based detection of covert cyber deception assaults in smart grid communications networks using machine learning. IEEE Access 6:27518\u201327529","journal-title":"IEEE Access"},{"issue":"1","key":"12987_CR4","doi-asserted-by":"publisher","first-page":"38","DOI":"10.3390\/info11010038","volume":"11","author":"MR Alhamidi","year":"2020","unstructured":"Alhamidi MR, Jatmiko W (2020) Optimal feature aggregation and combination for two-dimensional ensemble feature selection. Information 11(1):38","journal-title":"Information"},{"key":"12987_CR5","doi-asserted-by":"crossref","unstructured":"Ali SH, et al (2020)\u00a0A Gen-Fuzzy Based Strategy (GFBS) for Web Service Classification. Wire Person Commun 113(4):1917\u20131953","DOI":"10.1007\/s11277-020-07300-7"},{"key":"12987_CR6","doi-asserted-by":"publisher","first-page":"579","DOI":"10.1016\/j.future.2019.02.012","volume":"96","author":"F Al-Turjman","year":"2019","unstructured":"Al-Turjman F, Abujubbeh M (2019) IoT-enabled smart grid via SM: an overview. Futur Gener Comput Syst 96:579\u2013590","journal-title":"Futur Gener Comput Syst"},{"issue":"2","key":"12987_CR7","doi-asserted-by":"publisher","first-page":"225","DOI":"10.1016\/j.jksuci.2018.05.010","volume":"32","author":"S Bahassine","year":"2020","unstructured":"Bahassine S, Madani A, al-Sarem M, Kissi M (2020) Feature selection using an improved chi-square for Arabic text classification. Journal of King Saud University-Computer and Information Sciences 32(2):225\u20132, 231","journal-title":"Journal of King Saud University-Computer and Information Sciences"},{"key":"12987_CR8","doi-asserted-by":"publisher","first-page":"71","DOI":"10.1016\/j.pmcj.2018.12.007","volume":"52","author":"P Bellavista","year":"2019","unstructured":"Bellavista P, Berrocal J, Corradi A, Das SK, Foschini L, Zanni A (2019) A survey on fog computing for the internet of things. Pervasive and mobile computing 52:71\u201399","journal-title":"Pervasive and mobile computing"},{"key":"12987_CR9","doi-asserted-by":"publisher","first-page":"77","DOI":"10.1016\/j.patrec.2018.04.007","volume":"121","author":"ND Cilia","year":"2019","unstructured":"Cilia ND, de Stefano C, Fontanella F, Scotto di Freca A (2019) A ranking-based feature selection approach for handwritten character recognition. Pattern Recogn Lett 121:77\u201386","journal-title":"Pattern Recogn Lett"},{"key":"12987_CR10","doi-asserted-by":"publisher","first-page":"114312","DOI":"10.1016\/j.eswa.2020.114312","volume":"168","author":"NL da Costa","year":"2021","unstructured":"da Costa NL et al (2021) Evaluation of feature selection methods based on artificial neural network weights. Expert Syst Appl 168:114312","journal-title":"Expert Syst Appl"},{"issue":"10","key":"12987_CR11","doi-asserted-by":"publisher","first-page":"4151","DOI":"10.1007\/s12652-017-0659-1","volume":"10","author":"A Darwish","year":"2019","unstructured":"Darwish A, Hassanien AE, Elhoseny M, Sangaiah AK, Muhammad K (2019) The impact of the hybrid platform of internet of things and cloud computing on healthcare systems: opportunities, challenges, and open problems. J Ambient Intell Humaniz Comput 10(10):4151\u20134166","journal-title":"J Ambient Intell Humaniz Comput"},{"issue":"8","key":"12987_CR12","doi-asserted-by":"publisher","first-page":"112","DOI":"10.1109\/MC.2016.245","volume":"49","author":"AV Dastjerdi","year":"2016","unstructured":"Dastjerdi AV, Buyya R (2016) Fog computing: helping the internet of things realize its potential. Computer 49(8):112\u2013116","journal-title":"Computer"},{"issue":"3","key":"12987_CR13","doi-asserted-by":"publisher","first-page":"3797","DOI":"10.1007\/s11042-018-6083-5","volume":"78","author":"X Deng","year":"2019","unstructured":"Deng X, Li Y, Weng J, Zhang J (2019) Feature selection for text classification: a review. Multimed Tools Appl 78(3):3797\u20133816","journal-title":"Multimed Tools Appl"},{"key":"12987_CR14","doi-asserted-by":"publisher","first-page":"2589","DOI":"10.1016\/j.renene.2019.08.092","volume":"146","author":"G Dileep","year":"2020","unstructured":"Dileep G (2020) A survey on smart grid technologies and applications. Renew Energy 146:2589\u20132625","journal-title":"Renew Energy"},{"key":"12987_CR15","unstructured":"European Network on Intelligent TEchnologies for Smart Adaptive Systems (n.d.) https:\/\/www.eunite.org\/. The competition page is:\u00a0http:\/\/neuron.tuke.sk\/competition\/.\u00a0Accessed 27 June 2021"},{"key":"12987_CR16","doi-asserted-by":"publisher","first-page":"30","DOI":"10.1016\/j.patrec.2018.08.029","volume":"132","author":"J Gan","year":"2020","unstructured":"Gan J, Wen G, Yu H, Zheng W, Lei C (2020) Supervised feature selection by self-paced learning regression. Pattern Recogn Lett 132:30\u201337","journal-title":"Pattern Recogn Lett"},{"key":"12987_CR17","doi-asserted-by":"crossref","unstructured":"Ghobaei-Arani M et al (2019) Resource management approaches in fog computing: a comprehensive review. Journal of Grid Computing:1\u201342","DOI":"10.1007\/s10723-019-09491-1"},{"key":"12987_CR18","doi-asserted-by":"publisher","first-page":"19304","DOI":"10.1109\/ACCESS.2021.3053759","volume":"9","author":"P Ghosh","year":"2021","unstructured":"Ghosh P, Azam S, Jonkman M, Karim A, Shamrat FMJM, Ignatious E, Shultana S, Beeravolu AR, de Boer F (2021) Efficient prediction of cardiovascular disease using machine learning algorithms with relief and LASSO feature selection techniques. IEEE Access 9:19304\u201319326","journal-title":"IEEE Access"},{"key":"12987_CR19","doi-asserted-by":"publisher","first-page":"21","DOI":"10.1016\/j.erss.2014.04.008","volume":"2","author":"M Goulden","year":"2014","unstructured":"Goulden M, Bedwell B, Rennick-Egglestone S, Rodden T, Spence A (2014) Smart grids, smart users? The role of the user in demand side management. Energy Res Soc Sci 2:21\u201329","journal-title":"Energy Res Soc Sci"},{"key":"12987_CR20","doi-asserted-by":"publisher","first-page":"96210","DOI":"10.1109\/ACCESS.2020.2985732","volume":"8","author":"G Hafeez","year":"2020","unstructured":"Hafeez G, Alimgeer KS, Qazi AB, Khan I, Usman M, Khan FA, Wadud Z (2020) A hybrid approach for energy consumption forecasting with a new feature engineering and optimization framework in smart grid. IEEE Access 8:96210\u201396226","journal-title":"IEEE Access"},{"key":"12987_CR21","doi-asserted-by":"publisher","first-page":"23","DOI":"10.1016\/j.isprsjprs.2017.11.004","volume":"145","author":"W Han","year":"2018","unstructured":"Han W, Feng R, Wang L, Cheng Y (2018) A semi-supervised generative framework with deep learning features for high-resolution remote sensing image scene classification. ISPRS J Photogramm Remote Sens 145:23\u201343","journal-title":"ISPRS J Photogramm Remote Sens"},{"issue":"13","key":"12987_CR22","doi-asserted-by":"publisher","first-page":"5233","DOI":"10.1007\/s00500-018-3545-7","volume":"23","author":"E Hancer","year":"2019","unstructured":"Hancer E (2019) Differential evolution for feature selection: a fuzzy wrapper\u2013filter approach. Soft Comput 23(13):5233\u20135248","journal-title":"Soft Comput"},{"key":"12987_CR23","unstructured":"Handwritten Digits USPS dataset (n.d.) https:\/\/www.csie.ntu.edu.tw\/~cjlin\/libsvmtools\/datasets\/multiclass.html#usps. Accessed on 5 August 2021."},{"key":"12987_CR24","doi-asserted-by":"publisher","first-page":"106202","DOI":"10.1016\/j.knosys.2020.106202","volume":"204","author":"Y Huang","year":"2020","unstructured":"Huang Y, Jin W, Yu Z, Li B (2020) Supervised feature selection through deep neural networks with pairwise connected structure. Knowl-Based Syst 204:106202","journal-title":"Knowl-Based Syst"},{"issue":"2","key":"12987_CR25","doi-asserted-by":"publisher","first-page":"1433","DOI":"10.1007\/s11276-019-02208-y","volume":"26","author":"G Javadzadeh","year":"2020","unstructured":"Javadzadeh G, Rahmani AM (2020) Fog computing applications in smart cities: a systematic survey. Wirel Netw 26(2):1433\u20131457","journal-title":"Wirel Netw"},{"issue":"3","key":"12987_CR26","doi-asserted-by":"publisher","first-page":"2886","DOI":"10.1109\/COMST.2019.2899354","volume":"21","author":"P Kumar","year":"2019","unstructured":"Kumar P, Lin Y, Bai G, Paverd A, Dong JS, Martin A (2019) Smart grid metering networks: a survey on security, privacy and open research issues. IEEE Communications Surveys & Tutorials 21(3):2886\u20132927","journal-title":"IEEE Communications Surveys & Tutorials"},{"key":"12987_CR27","doi-asserted-by":"publisher","first-page":"107663","DOI":"10.1016\/j.patcog.2020.107663","volume":"111","author":"H Lim","year":"2021","unstructured":"Lim H, Kim D-W (2021) Pairwise dependence-based unsupervised feature selection. Pattern Recogn 111:107663","journal-title":"Pattern Recogn"},{"issue":"3","key":"12987_CR28","doi-asserted-by":"publisher","first-page":"703","DOI":"10.1109\/JAS.2019.1911447","volume":"6","author":"H Liu","year":"2019","unstructured":"Liu H, Zhou MC, Liu Q (2019) An embedded feature selection method for imbalanced data classification. IEEE\/CAA Journal of Automatica Sinica 6(3):703\u2013715","journal-title":"IEEE\/CAA Journal of Automatica Sinica"},{"issue":"15","key":"12987_CR29","doi-asserted-by":"publisher","first-page":"6249","DOI":"10.1007\/s00500-018-3282-y","volume":"23","author":"MM Mafarja","year":"2019","unstructured":"Mafarja MM, Mirjalili S (2019) Hybrid binary ant lion optimizer with rough set and approximate entropy reducts for feature selection. Soft Comput 23(15):6249\u20136265","journal-title":"Soft Comput"},{"key":"12987_CR30","doi-asserted-by":"publisher","first-page":"267","DOI":"10.1016\/j.eswa.2018.09.015","volume":"117","author":"M Mafarja","year":"2019","unstructured":"Mafarja M, Aljarah I, Faris H, Hammouri AI, al-Zoubi A\u2019M, Mirjalili S (2019) Binary grasshopper optimisation algorithm approaches for feature selection problems. Expert Syst Appl 117:267\u2013286","journal-title":"Expert Syst Appl"},{"issue":"1","key":"12987_CR31","doi-asserted-by":"publisher","first-page":"170","DOI":"10.1186\/s12859-019-2754-0","volume":"20","author":"Y Masoudi-Sobhanzadeh","year":"2019","unstructured":"Masoudi-Sobhanzadeh Y, Motieghader H, Masoudi-Nejad A (2019) FeatureSelect: a software for feature selection based on machine learning approaches. BMC bioinformatics 20(1):170","journal-title":"BMC bioinformatics"},{"issue":"1","key":"12987_CR32","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1016\/j.icte.2017.12.005","volume":"5","author":"K Mekki","year":"2019","unstructured":"Mekki K, Bajic E, Chaxel F, Meyer F (2019) A comparative study of LPWAN technologies for large-scale IoT deployment. ICT express 5(1):1\u20137","journal-title":"ICT express"},{"issue":"3","key":"12987_CR33","doi-asserted-by":"publisher","first-page":"1826","DOI":"10.1109\/COMST.2018.2814571","volume":"20","author":"M Mukherjee","year":"2018","unstructured":"Mukherjee M, Shu L, Wang D (2018) Survey of fog computing: fundamental, network applications, and research challenges. IEEE Communications Surveys & Tutorials 20(3):1826\u20131857","journal-title":"IEEE Communications Surveys & Tutorials"},{"key":"12987_CR34","doi-asserted-by":"publisher","first-page":"113103","DOI":"10.1016\/j.eswa.2019.113103","volume":"145","author":"N Neggaz","year":"2020","unstructured":"Neggaz N, Ewees AA, Elaziz MA, Mafarja M (2020) Boosting salp swarm algorithm by sine cosine algorithm and disrupt operator for feature selection. Expert Syst Appl 145:113103","journal-title":"Expert Syst Appl"},{"issue":"5","key":"12987_CR35","doi-asserted-by":"publisher","DOI":"10.1088\/1748-9326\/abeeb1","volume":"16","author":"W-J Niu","year":"2021","unstructured":"Niu W-J et al (2021) Short-term electricity load time series prediction by machine learning model via feature selection and parameter optimization using hybrid cooperation search algorithm. Environ Res Lett 16(5):055032","journal-title":"Environ Res Lett"},{"issue":"4","key":"12987_CR36","doi-asserted-by":"publisher","first-page":"956","DOI":"10.1007\/s11036-017-0961-3","volume":"23","author":"M Ozger","year":"2018","unstructured":"Ozger M, Cetinkaya O, Akan OB (2018) Energy harvesting cognitive radio networking for IoT-enabled smart grid. Mobile Networks and Applications 23(4):956\u2013966","journal-title":"Mobile Networks and Applications"},{"issue":"2","key":"12987_CR37","doi-asserted-by":"publisher","first-page":"2647","DOI":"10.32604\/cmc.2021.015026","volume":"67","author":"SK Pande","year":"2021","unstructured":"Pande SK et al (2021) A resource management algorithm for virtual machine migration in vehicular cloud computing. Computers, Materials & Continua 67(2):2647\u20132663","journal-title":"Computers, Materials & Continua"},{"issue":"1","key":"12987_CR38","doi-asserted-by":"publisher","first-page":"77","DOI":"10.1016\/j.ptlrs.2020.10.001","volume":"6","author":"E Priyanka","year":"2021","unstructured":"Priyanka E et al (2021) Review analysis on cloud computing based smart grid technology in the oil pipeline sensor network system. Petroleum Research 6(1):77\u201390","journal-title":"Petroleum Research"},{"issue":"1","key":"12987_CR39","doi-asserted-by":"publisher","first-page":"241","DOI":"10.1007\/s10586-018-2848-x","volume":"22","author":"AH Rabie","year":"2019","unstructured":"Rabie AH, Ali SH, Ali HA, Saleh AI (2019) A fog based load forecasting strategy for smart grids using big electrical data. Clust Comput 22(1):241\u2013270","journal-title":"Clust Comput"},{"key":"12987_CR40","doi-asserted-by":"crossref","unstructured":"Rai S, De M (2021) Analysis of classical and machine learning based short-term and mid-term load forecasting for smart grid.\u00a0Int J Sustain Energy\u00a040(9):821\u2013839","DOI":"10.1080\/14786451.2021.1873339"},{"issue":"3","key":"12987_CR41","doi-asserted-by":"publisher","first-page":"2637","DOI":"10.1109\/COMST.2019.2908266","volume":"21","author":"MH Rehmani","year":"2019","unstructured":"Rehmani MH, Davy A, Jennings B, Assi C (2019) Software defined networks-based smart grid communication: a comprehensive survey. IEEE Communications Surveys & Tutorials 21(3):2637\u20132670","journal-title":"IEEE Communications Surveys & Tutorials"},{"issue":"2","key":"12987_CR42","doi-asserted-by":"publisher","first-page":"162","DOI":"10.1080\/00051144.2019.1602293","volume":"60","author":"D\u00d6 \u015eahin","year":"2019","unstructured":"\u015eahin D\u00d6, K\u0131l\u0131\u00e7 E (2019) Two new feature selection metrics for text classification. Automatika: \u010dasopis za automatiku, mjerenje, elektroniku, ra\u010dunarstvo i komunikacije 60(2):162\u2013171","journal-title":"Automatika: \u010dasopis za automatiku, mjerenje, elektroniku, ra\u010dunarstvo i komunikacije"},{"key":"12987_CR43","doi-asserted-by":"publisher","first-page":"192","DOI":"10.1016\/j.knosys.2014.12.002","volume":"75","author":"AI Saleh","year":"2015","unstructured":"Saleh AI, el Desouky AI, Ali SH (2015) Promoting the performance of vertical recommendation systems by applying new classification techniques. Knowl-Based Syst 75:192\u2013223","journal-title":"Knowl-Based Syst"},{"issue":"1","key":"12987_CR44","doi-asserted-by":"publisher","first-page":"171","DOI":"10.1007\/s00521-017-2988-6","volume":"31","author":"GI Sayed","year":"2019","unstructured":"Sayed GI, Hassanien AE, Azar AT (2019) Feature selection via a novel chaotic crow search algorithm. Neural Comput & Applic 31(1):171\u2013188","journal-title":"Neural Comput & Applic"},{"key":"12987_CR45","doi-asserted-by":"publisher","first-page":"106270","DOI":"10.1016\/j.knosys.2020.106270","volume":"205","author":"WM Shaban","year":"2020","unstructured":"Shaban WM, Rabie AH, Saleh AI, Abo-Elsoud MA (2020) A new COVID-19 patients detection strategy (CPDS) based on hybrid feature selection and enhanced KNN classifier. Knowl-Based Syst 205:106270","journal-title":"Knowl-Based Syst"},{"issue":"4","key":"12987_CR46","doi-asserted-by":"publisher","first-page":"881","DOI":"10.3390\/en14040881","volume":"14","author":"K Shahzad","year":"2021","unstructured":"Shahzad K, Iqbal S, Mukhtar H (2021) Optimal fuzzy energy trading system in a fog-enabled smart grid. Energies 14(4):881","journal-title":"Energies"},{"key":"12987_CR47","doi-asserted-by":"crossref","unstructured":"Singer G, et al (2020) A weighted information-gain measure for ordinal classification trees. Expert Systems with Applications 152:113375","DOI":"10.1016\/j.eswa.2020.113375"},{"issue":"4","key":"12987_CR48","doi-asserted-by":"publisher","first-page":"2070","DOI":"10.1007\/s11227-018-2701-2","volume":"75","author":"SP Singh","year":"2019","unstructured":"Singh SP, Nayyar A, Kumar R, Sharma A (2019) Fog computing: from architecture to edge computing and big data processing. J Supercomput 75(4):2070\u20132105","journal-title":"J Supercomput"},{"key":"12987_CR49","doi-asserted-by":"publisher","first-page":"132911","DOI":"10.1109\/ACCESS.2020.3009843","volume":"8","author":"D Stiawan","year":"2020","unstructured":"Stiawan D et al (2020) CICIDS-2017 dataset feature analysis with information gain for anomaly detection. IEEE Access 8:132911\u2013132921","journal-title":"IEEE Access"},{"key":"12987_CR50","doi-asserted-by":"publisher","first-page":"48","DOI":"10.1016\/j.neucom.2020.02.098","volume":"399","author":"B Tang","year":"2020","unstructured":"Tang B, Zhang L (2020) Local preserving logistic I-relief for semi-supervised feature selection. Neurocomputing 399:48\u201364","journal-title":"Neurocomputing"},{"key":"12987_CR51","doi-asserted-by":"publisher","first-page":"100653","DOI":"10.1016\/j.seta.2020.100653","volume":"38","author":"RJ Tom","year":"2020","unstructured":"Tom RJ, Sankaranarayanan S, Rodrigues JJPC (2020) Agent negotiation in an IoT-fog based power distribution system for demand reduction. Sustainable Energy Technologies and Assessments 38:100653","journal-title":"Sustainable Energy Technologies and Assessments"},{"issue":"6","key":"12987_CR52","doi-asserted-by":"publisher","first-page":"70","DOI":"10.1109\/MWC.2016.1400377RP","volume":"23","author":"W Tushar","year":"2016","unstructured":"Tushar W, Yuen C, Chai B, Huang S, Wood KL, Kerk SG, Yang Z (2016) Smart grid testbed for demand focused energy management in end user environments. IEEE Wirel Commun 23(6):70\u201380","journal-title":"IEEE Wirel Commun"},{"key":"12987_CR53","doi-asserted-by":"crossref","unstructured":"Verma AK et al (2020) Skin disease prediction using ensemble methods and a new hybrid feature selection technique. Iran Journal of Computer Science:1\u201310","DOI":"10.1007\/s42044-020-00058-y"},{"issue":"5","key":"12987_CR54","doi-asserted-by":"publisher","first-page":"3601","DOI":"10.1109\/TGRS.2019.2958812","volume":"58","author":"Y Wan","year":"2020","unstructured":"Wan Y, Ma A, Zhong Y, Hu X, Zhang L (2020) Multiobjective hyperspectral feature selection based on discrete sine cosine algorithm. IEEE Trans Geosci Remote Sens 58(5):3601\u20133618","journal-title":"IEEE Trans Geosci Remote Sens"},{"key":"12987_CR55","doi-asserted-by":"publisher","first-page":"151525","DOI":"10.1109\/ACCESS.2019.2948095","volume":"7","author":"X Wang","year":"2019","unstructured":"Wang X, Guo B, Shen Y, Zhou C, Duan X (2019) Input feature selection method based on feature set equivalence and mutual information gain maximization. IEEE Access 7:151525\u2013151538","journal-title":"IEEE Access"},{"key":"12987_CR56","doi-asserted-by":"publisher","first-page":"106337","DOI":"10.1016\/j.asoc.2020.106337","volume":"93","author":"G Wei","year":"2020","unstructured":"Wei G, Zhao J, Feng Y, He A, Yu J (2020) A novel hybrid feature selection method based on dynamic feature importance. Appl Soft Comput 93:106337","journal-title":"Appl Soft Comput"},{"key":"12987_CR57","doi-asserted-by":"publisher","first-page":"205","DOI":"10.1016\/j.rser.2017.01.064","volume":"72","author":"Y Yolda\u015f","year":"2017","unstructured":"Yolda\u015f Y, \u00d6nen A, Muyeen SM, Vasilakos AV, Alan \u0130 (2017) Enhancing smart grid with microgrids: challenges and opportunities. Renew Sust Energ Rev 72:205\u2013214","journal-title":"Renew Sust Energ Rev"},{"issue":"11","key":"12987_CR58","doi-asserted-by":"publisher","first-page":"6199","DOI":"10.3390\/su13116199","volume":"13","author":"A Yousaf","year":"2021","unstructured":"Yousaf A, Asif RM, Shakir M, Rehman AU, S. Adrees M (2021) An improved residential electricity load forecasting using a machine-learning-based feature selection approach and a proposed integration strategy. Sustainability 13(11):6199","journal-title":"Sustainability"},{"key":"12987_CR59","doi-asserted-by":"publisher","first-page":"107297","DOI":"10.1016\/j.knosys.2021.107297","volume":"228","author":"Z Zhang","year":"2021","unstructured":"Zhang Z, Hong W-C (2021) Application of variational mode decomposition and chaotic grey wolf optimizer with support vector regression for forecasting electric loads. Knowl-Based Syst 228:107297","journal-title":"Knowl-Based Syst"},{"key":"12987_CR60","doi-asserted-by":"publisher","first-page":"2426","DOI":"10.1016\/j.neucom.2017.11.016","volume":"275","author":"X Zhang","year":"2018","unstructured":"Zhang X, Zhang Q, Chen M, Sun Y, Qin X, Li H (2018) A two-stage feature selection and intelligent fault diagnosis method for rotating machinery using hybrid filter and wrapper method. Neurocomputing 275:2426\u20132439","journal-title":"Neurocomputing"},{"key":"12987_CR61","doi-asserted-by":"publisher","first-page":"105417","DOI":"10.1016\/j.knosys.2019.105417","volume":"193","author":"P Zhou","year":"2020","unstructured":"Zhou P, Chen J, Fan M, du L, Shen YD, Li X (2020) Unsupervised feature selection for balanced clustering. Knowl-Based Syst 193:105417","journal-title":"Knowl-Based Syst"},{"key":"12987_CR62","doi-asserted-by":"publisher","first-page":"113842","DOI":"10.1016\/j.eswa.2020.113842","volume":"164","author":"H Zhou","year":"2021","unstructured":"Zhou H, Zhang JW, Zhou YQ, Guo XJ, Ma YM (2021) A feature selection algorithm of decision tree based on feature weight. Expert Syst Appl 164:113842","journal-title":"Expert Syst Appl"},{"key":"12987_CR63","doi-asserted-by":"publisher","first-page":"89","DOI":"10.1016\/j.patrec.2017.08.018","volume":"109","author":"Y Zhu","year":"2018","unstructured":"Zhu Y, Zhang X, Hu R, Wen G (2018) Adaptive structure learning for low-rank supervised feature selection. Pattern Recogn Lett 109:89\u201396","journal-title":"Pattern Recogn Lett"}],"updated-by":[{"DOI":"10.1007\/s11042-022-14280-2","type":"correction","label":"Correction","source":"publisher","updated":{"date-parts":[[2022,12,13]],"date-time":"2022-12-13T00:00:00Z","timestamp":1670889600000}}],"container-title":["Multimedia Tools and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11042-022-12987-w.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s11042-022-12987-w\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11042-022-12987-w.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,12,14]],"date-time":"2022-12-14T03:08:28Z","timestamp":1670987308000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s11042-022-12987-w"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,4,17]]},"references-count":63,"journal-issue":{"issue":"23","published-print":{"date-parts":[[2022,9]]}},"alternative-id":["12987"],"URL":"https:\/\/doi.org\/10.1007\/s11042-022-12987-w","relation":{},"ISSN":["1380-7501","1573-7721"],"issn-type":[{"value":"1380-7501","type":"print"},{"value":"1573-7721","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,4,17]]},"assertion":[{"value":"3 September 2021","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"13 January 2022","order":2,"name":"revised","label":"Revised","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"27 March 2022","order":3,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"17 April 2022","order":4,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"13 December 2022","order":5,"name":"change_date","label":"Change Date","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"Correction","order":6,"name":"change_type","label":"Change Type","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"A Correction to this paper has been published:","order":7,"name":"change_details","label":"Change Details","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"https:\/\/doi.org\/10.1007\/s11042-022-14280-2","URL":"https:\/\/doi.org\/10.1007\/s11042-022-14280-2","order":8,"name":"change_details","label":"Change Details","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"We wish to confirm that, there are no known conflicts of interest associated with this publication and there has been no significant financial support for this work that could have influenced its outcome.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}},{"value":"We confirm that, we have given due consideration to the protection of intellectual property associated with this work and that there are no impediments to publication, including the timing of publication, concerning intellectual property. In so doing we confirm that we have followed the regulations of our institutions concerning intellectual property.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Intellectual property"}}]}}