Machine Learning Models for Artist Classification of Cultural Heritage Sketches by Gianina Chirosca, Roxana Rădvan, Silviu Mușat, Matei Pop, Alecsandru Chirosca

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Assessing customer-side demand response identification for optimal scheduling considering satisfaction level for microgrids by Xidong Zheng, Feifei Bai, Tao Jin

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Optimizing ternary hybrid nanofluids using neural networks, gene expression programming, and multi-objective particle swarm optimization: a computational intelligence strategy by Tao Hai, Ali Basem, As’ad Alizadeh, Pradeep Kumar Singh, Husam Rajab, Chemseddine Maatki, Nidhal Becheikh, Lioua Kolsi, Narinderjit Singh Sawaran Singh, H. Maleki

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Machine learning helps reveal key factors affecting tire wear particulate matter emissions by Zhenyu Jia, Jiawei Yin, Tiange Fang, Zhiwen Jiang, Chongzhi Zhong, Zeping Cao, Lin Wu, Ning Wei, Zhengyu Men, Lei Yang, Qijun Zhang, Hongjun Mao

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Automated Defect Detection through Flaw Grading in Non-Destructive Testing Digital X-ray Radiography by Bata Hena, Gabriel Ramos, Clemente Ibarra-Castanedo, Xavier Maldague

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Advanced Data Framework for Sleep Medicine Applications: Machine Learning-Based Detection of Sleep Apnea Events by Kristina Zovko, Yann Sadowski, Toni Perković, Petar Šolić, Ivana Pavlinac Dodig, Renata Pecotić, Zoran Đogaš

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Architecture of a recommendation service for choosing training areas at a higher education institution by applicants with the method of collaborative filtering of machine learning by A. M. Prokhorova

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Predicting largest expected aftershock ground motions using automated machine learning (AutoML)-based scheme by Xiaohui Yu, Meng Wang, Chaolie Ning, Kun Ji

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Assessing and predicting Bojiang lake area and LULC changes from 2000 to 2045 by Muhammad Naeem, Yongqiang Zhang, Xiaoqiang Tian, Ping Miao, Congcong Li, Zhenwu Xu, Longhao Wang, Faisal Mumtaz, Zixuan Tang, Shaoyang He

Subjects: “…Machine Learning…”
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Review on the Application of Remote Sensing Data and Machine Learning to the Estimation of Anthropogenic Heat Emissions by Lingyun Feng, Danyang Ma, Min Xie, Mengzhu Xi

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Enhanced early prediction of Li-ion battery degradation using multicycle features and an ensemble deep learning model by Meilia Safitri, Teguh Bharata Adji, Adha Imam Cahyadi

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Enhancing the mechanical properties’ performances coconut fiber and CDW composite in paver block: multiple AI techniques with a Performance analysis by G. Uday Kiran, G. Nakkeeran, Dipankar Roy, Sumant Nivarutti Shinde, George Uwadiegwu Alaneme

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PUPIL DIAMETER AND MACHINE LEARNING FOR DEPRESSION DETECTION: A COMPARATIVE STUDY WITH DEEP LEARNING MODELS by Islam MOHAMED, Mohamed EL-WAKAD, Khaled ABBAS, Mohamed ABOAMER, Nader A. Rahman MOHAMED

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Big Data Analysis Using Apache Spark MLlib and Hadoop HDFS with Scala and Java by Hoger Khayrolla Omar, Alaa Khalil Jumaa

Subjects: “…Keywords: Big data, Data analysis, Apache Spark, Hadoop HDFS, Machine learning, Spark MLlib, Resilient Distributed Datasets(RDD).…”
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Improving catalysts and operating conditions using machine learning in Fischer-Tropsch synthesis of jet fuels (C8-C16) by Parisa Shafiee, Bogdan Dorneanu, Harvey Arellano-Garcia

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Electrogastrogram-based detection of cybersickness with the application of wavelet transformation and machine learning: A case study by Ilija V. Tanasković, Nenad B. Popović, Jaka J. Sodnik, Sašo J. Tomažič, Nadica S. Miljković

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Auditory steady state response can predict declining EF in healthy elderly individuals by Xiaopeng Mao, Xiaopeng Mao, Nelly Shenton, Sadasivan Puthusserypady, Martin Johannes Lauritzen, Krisztina Benedek

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Improvement in Automated Diagnosis of Soft Tissues Tumors Using Machine Learning by El Arbi Abdellaoui Alaoui, Stéphane Cédric Koumetio Tekouabou, Sri Hartini, Zuherman Rustam, Hassan Silkan, Said Agoujil

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An endoscopic ultrasound-based interpretable deep learning model and nomogram for distinguishing pancreatic neuroendocrine tumors from pancreatic cancer by Nan Yi, Shuangyang Mo, Yan Zhang, Qi Jiang, Yingwei Wang, Cheng Huang, Shanyu Qin, Haixing Jiang

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A Multispectral Automated Transfer Technique (MATT) for Machine-Driven Image Labeling Utilizing the Segment Anything Model (SAM) by James E. Gallagher, Aryav Gogia, Edward J. Oughton

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