LEADER 07749nam 22007215 450 001 996543168203316 005 20230808014301.0 010 $a83-67405-23-4 035 $a(CKB)27977600100041 035 $a(DE-B1597)652073 035 $a(DE-B1597)9788367405232 035 $a(EXLCZ)9927977600100041 100 $a20230808h20232023 fg 101 0 $aeng 135 $aur||||||||||| 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 00$a3RD INTERNATIONAL CONFERENCE ON BIG DATA AND MACHINE LEARNING (BML'22) $e23-24 May, 2022 Istanbul, Turkey (Hybrid) 210 1$aWarsaw ;$aBerlin : $cSciendo, $d[2023] 210 4$dİ2023 215 $a1 online resource (153 p.) 327 $tFrontmatter -- $tContents -- $tAutomatic Filtering of LiDAR Building Point Cloud Using Multilayer Perceptron Neuron Network -- $tThe Profitable Gates Location on the Highway of the Dynamic Wireless Charging Transmitter for the Electric Vehicles -- $tMetadata modeling for data lakes based on dynamic ontologies -- $tA Novel hybrid model for the detection of drivers' drowsiness based on EEG signals and personalized extra-trees classification -- $tOptimal power flow using Machine Learning: A survey -- $tCartography for the training of Deep Learning algorithms on fire forest risk areas in the TAZEKKA park of MOROCCO: Geostatistical approach -- $tWaste collection by compartmentalized vehicles in smart cities -- $tBrain Tumor Semantic Segmentation Using Convolutional Neural Networks in 2D MRI Images -- $tThe Truncated M-Fractional Ordinary Differential Equations: Lie Symmetry Analysis -- $tA Hybrid Machine Learning-Based Malware Detection Model -- $tHigh efficacy of handling imbalanced data to predict bankruptcy -- $tObjective weighting methods combined with TOPSIS and their effects on ranking nodes in social networks -- $tBig Data: Concept and Security and Privacy Challenges -- $tBig Data in Precision Agriculture: A comprehensive Review -- $tAssessment in e-learning systems Feedback on the adoption of peer assessment for the correction of continuous control in algorithmic and programming -- $tExploring Big Data Analytics: Challenges and Infrastructures -- $tLSTM Recurrent Neural Network for Daily Forecast of PM10 Concentrations in Tangier -- $tMetadata modeling for data lakes based on dynamic ontologies -- $tOptimal power flow using Machine Learning: A survey -- $tWaste collection by compartmentalized vehicles in smart cities 330 $aThe 3rd edition of the International Conference on Big Data and Machine Learning (BML?22) is a major gathering of researchers, engineers, and practitioners within the fields of Big Data, Machine Learning, and Artificial Intelligence, from both theoretical and practical perspectives. We strongly encourage contributions that describe real-world problems, interdisciplinary research, and experimental and/or theoretical studies that yield new insights to advance Big Data and Machine Learning. Submitted papers should be original and relevant to any of the areas listed below. All accepted papers will be presented at the conference by one of the authors and published in the proceedings with an ISBN. The acceptance will be based on their quality, relevance, and originality. Presentation options include both oral and poster sessions. We have planned special sessions for case studies and commercial presentations, as well as tutorials focusing on technical or scientific matters. Companies interested in presenting their products or methodologies, as well as researchers wishing to demonstrate a demo or present a tutorial are encouraged to contact the conference secretariat. We cordially invite all speakers and participants to present their research and/or experiences at BML?22concerning the subjects listed below. While each of the main topic areas is further elaborated below, the sub-topics list provided is not exhaustive. Authors may address one or more of the listed sub-topics, while also being free to explore additional ones that align with the following main topic areas: BIG DATA MACHINE LEARNING ARTIFICIAL INTELLIGENCE Registration Fees Open Access Licence These conference proceedings provide immediate open access to its content under the CC-BY https://creativecommons.org/licenses/by/4.0/. Authors who will publish with these proceedings retain all copyrights and agree to the terms of the above-mentioned CC-BY 4.0 licence Open Access Statement This conference proceedings is an Open Access proceedings that allows a free unlimited access to all its contents without any restrictions upon publication to all users. 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