Exploring emotions and perspectives of families with autism spectrum disorder individuals on sports participation: a case study on Weibo using machine learning method by Shan He, Fengshi Jing, Hao Ren, Yanning Xiao, Xia Sebastiane Chen, Ruihong Tang, Yue Qiu, Junwei Zeng, Tianyu Gao, Junhao Huang

“…Objective This study investigates family sentiments and attitudes toward sports participation by ASD individuals, leveraging longitudinal data from Sina Weibo and machine learning–based sentiment analysis. …”
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Artificial intelligence in breast cancer survival prediction: a comprehensive systematic review and meta-analysis by Zohreh Javanmard, Saba Zarean Shahraki, Kosar Safari, Abbas Omidi, Sadaf Raoufi, Mahsa Rajabi, Mohammad Esmaeil Akbari, Mehrad Aria

“…Artificial Intelligence (AI) and Machine Learning (ML) algorithms offer promising solutions for automated survival prediction, driving this study’s systematic review and meta-analysis.MethodsThree online databases (Web of Science, PubMed, and Scopus) were comprehensively searched (January 2016-August 2023) using key terms (“Breast Cancer”, “Survival Prediction”, and “Machine Learning”) and their synonyms. …”
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Estimation of Hydroelectric Power Generation Forecasting and Analysis of Climate Factors with Deep Learning Methods: A Case Study in Yozgat Province in Turkey by Feyza Nur Çakıcı, Suleyman Sungur Tezcan, Hıdır Düzkaya

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An Explainable Machine Learning Approach for IoT-Supported Shaft Power Estimation and Performance Analysis for Marine Vessels by Yiannis Kiouvrekis, Katerina Gkirtzou, Sotiris Zikas, Dimitris Kalatzis, Theodor Panagiotakopoulos, Zoran Lajic, Dimitris Papathanasiou, Ioannis Filippopoulos

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STPam: Software for Intelligently Analyzing and Mining Spatiotemporal Processes Based on Multi-Source Big Data by Rongjun Xiong, Zeqiang Chen, Huiwen Pan, Dongyang Liu, Aiguo Sun, Nengcheng Chen

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Temporal scalability analysis of spectral remote sensing and Machine Learning-Based models for forest mapping in the tropical region of Nigeria by K. Pawłuszek-Filipiak, D. Włodarczyk, K. Stuchły, F.E. Ikuemonisan

“…The integration of machine learning (ML) algorithms with remote sensing (RS) data offers significant potential for enhancing dynamic forest characterization and change detection through high temporal density mapping. …”
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From data to nutrition: the impact of computing infrastructure and artificial intelligence by Pierpaolo Di Bitonto, Michele Magarelli, Pierfrancesco Novielli, Donato Romano, Domenico Diacono, Lorenzo de Trizio, Angelo Mariano, Claudia Zoani, Riccardo Ferrero, Alessandra Manzin, Maria De Angelis, Roberto Bellotti, Sabina Tangaro

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Machine Learning-Based Detection of Archeological Sites Using Satellite and Meteorological Data: A Case Study of Funnel Beaker Culture Tombs in Poland by Krystian Kozioł, Natalia Borowiec, Urszula Marmol, Mateusz Rzeszutek, Celso Augusto Guimarães Santos, Jerzy Czerniec

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A Categorical Confirmatory Factor Analysis for Validating the Turkish Version of the Self-Directed Online Learning Scale (SDOLS-T) by Hongwei Yang, Müslim Alanoğlu, Songül Karabatak, Jian Su, Kelly D. Bradley

Subjects: “…self-directed learning…”
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A Data-Driven Approach for Automatic Aircraft Engine Borescope Inspection Defect Detection Using Computer Vision and Deep Learning by Thibaud Schaller, Jun Li, Karl W. Jenkins

“…This paper presents a data-driven deep learning framework capable of automatically detecting defects on reactor blades. …”
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A Novel Spatio–Temporal Deep Learning Vehicle Turns Detection Scheme Using GPS-Only Data by Mussadiq Abdul Rahim, Sultan Daud Khan, Salabat Khan, Muhammad Rashid, Rafi Ullah, Hanan Tariq, Stanislaw Czapp

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Leveraging autoencoder models and data augmentation to uncover transcriptomic diversity of gingival keratinocytes in single cell analysis by Pradeep Kumar Yadalam, Prabhu Manickam Natarajan, Carlos M. Ardila

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An Enhanced Gas Sensor Data Classification Method Using Principal Component Analysis and Synthetic Minority Over-Sampling Technique Algorithms by Xianzhang Zeng, Muhammad Shahzeb, Xin Cheng, Qiang Shen, Hongyang Xiao, Cao Xia, Yuanlin Xia, Yubo Huang, Jingfei Xu, Zhuqing Wang

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COMPARATIVE STUDY OF SURVIVAL SUPPORT VECTOR MACHINE AND RANDOM SURVIVAL FOREST IN SURVIVAL DATA by Ni Gusti Ayu Putu Puteri Suantari, Anwar Fitrianto, Bagus Sartono

“…Survival analysis is a statistical procedure in analyzing data with the response variable is time until an event occurs (time-to-event). …”
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An EWS-LSTM-Based Deep Learning Early Warning System for Industrial Machine Fault Prediction by Fabio Cassano, Anna Maria Crespino, Mariangela Lazoi, Giorgia Specchia, Alessandra Spennato

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A Novel Deep Learning-Based Data Analysis Model for Solar Photovoltaic Power Generation and Electrical Consumption Forecasting in the Smart Power Grid by Camille Franklin Mbey, Felix Ghislain Yem Souhe, Vinny Junior Foba Kakeu, Alexandre Teplaira Boum

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A comparative performance analysis of machine learning models for compressive strength prediction in fly ash-based geopolymers concrete using reference data by Muhammad Kashif Anwar, Muhammad Ahmed Qurashi, Xingyi Zhu, Syyed Adnan Raheel Shah, Muhammad Usman Siddiq

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Future groundwater drought analysis under data scarcity using MedCORDEX regional climatic models and machine learning: The case of the Haouz Aquifer by El Bouazzaoui Imane, Ait Elbaz Aicha, Ait Brahim Yassine, Machay Hicham, Bougadir Blaid

“…The comparative analysis of the machine learning models shows that Random Forest stands out for its superior predictive performance, influenced by annual trends and long-term climatic indices, with significant contributions from geographical variables. …”
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Forecasting birth trends in Ethiopia using time-series and machine-learning models: a secondary data analysis of EDHS surveys (2000–2019) by Daniel Adane, Assefa Andargie Kassa, Berhanu Abebaw Mekonnen, Rahel Mulatie Anteneh, Chalachew Yenew, Meron Asmamaw Alemayehu, Tilahun Degu Tsega, Sintayehu Simie Tsega, Almaw Genet Yeshiwas, Abathun Temesegen, Habitamu Mekonen, Amare Genetu Ejigu, Abraham Teym, Gashaw Melkie Bayeh, Getaneh Atikilit, Tesfaneh Shimels, Wolde Melese Ayele, Ahmed Fentaw Ahmed, Birhanemaskal Malkamu, Wondimnew Desalegn Addis, Getasew Yirdaw, Chalachew Abiyu Ayalew, Kalaab Esubalew, Zeamanuel Anteneh Yigzaw

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Data-driven analysis in the selective oligomerization of long-chain linear alpha olefin on zeolite catalysts: A machine learning-based parameter study by Sung Woo Lee, Marcel Jonathan Hidajat, Seung Hyeok Cha, Gwang-Nam Yun, Dong Won Hwang

“…While the SHAP method is a well-validated conventional approach, it has limitations due to its high data requirements. Therefore, the SISSO method was applied, as it requires fewer data points and offers a transparent computational process. …”
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