A Survey on Machine Learning Approaches for Personalized Coaching with Human Digital Twins by Harald H. Rietdijk, Patricia Conde-Cespedes, Talko B. Dijkhuis, Hilbrand K. E. Oldenhuis, Maria Trocan

“…Preliminary investigations revealed a significant difference in performance, as measured by prediction accuracy and F1-score, between the optimal choice of machine learning algorithms for generalized and personalized processing of the available data. …”
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Traumatic Brain Injury and Artificial Intelligence: Shaping the Future of Neurorehabilitation—A Review by Seun Orenuga, Philip Jordache, Daniel Mirzai, Tyler Monteros, Ernesto Gonzalez, Ahmed Madkoor, Rahim Hirani, Raj K. Tiwari, Mill Etienne

“…This literature review explores the current and potential applications of AI in TBI management, focusing on AI’s role in diagnostic tools, neuroimaging, prognostic modeling, and rehabilitation programs. AI-driven algorithms have demonstrated high accuracy in predicting mortality, functional outcomes, and personalized rehabilitation strategies based on patient data. …”
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Data-Driven Approaches for Estimation of EV Battery SoC and SoH: A Review by Shahid Gulzar Padder, Jayesh Ambulkar, Atul Banotra, Sudhakar Modem, Sidharth Maheshwari, Kolleboyina Jayaramulu, Chinmoy Kundu

“…In this study, machine learning algorithms exhibited substantial enhancements in predicting and maintaining the lifespan of electric vehicle batteries. …”
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Advanced Deep Learning and Machine Learning Techniques for MRI Brain Tumor Analysis: A Review by Rim Missaoui, Wided Hechkel, Wajdi Saadaoui, Abdelhamid Helali, Marco Leo

“…In this study, we focus on current advances in medical imaging, particularly magnetic resonance imaging (MRI), and how machine learning (ML) and deep learning (DL) algorithms might be combined with clinical assessments to improve brain tumor diagnosis. …”
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Do Machine Learning and Business Analytics Approaches Answer the Question of ‘Will Your Kickstarter Project be Successful? by Murat Kılınç, Can Aydın, Çiğdem Tarhan

“…Within the scope of machine learning, various algorithms were applied for success classification and the best results were given by 91% Random Forest, 85% Decision Tree, 84% K-Nearest Neighbors (KNN) algorithms. …”
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Comparative study of different machine learning models in landslide susceptibility assessment: A case study of Conghua District, Guangzhou, China by Ao Zhang, Xin-wen Zhao, Xing-yuezi Zhao, Xiao-zhan Zheng, Min Zeng, Xuan Huang, Pan Wu, Tuo Jiang, Shi-chang Wang, Jun He, Yi-yong Li

“…RF and XGB models were superior to LR and SVM models, indicating that the integrated algorithm has better predictive ability than a single classification algorithm in regional landslide classification problems.©2024 China Geology Editorial Office.…”
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Simulating high-speed solar wind streams from coronal holes using an L5-L1 configuration of Lagrangian points by Tatiana Podladchikova, Astrid M. Veronig, Manuela Temmer, Stefan J. Hofmeister

“…We use these insights to develop a predictive algorithm for HSSs, beginning with an analysis of the separation angle and distances between L5 and L1. …”
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Bridging Behavioral Insights and Automated Trading: An Internet of Behaviors Approach for Enhanced Financial Decision-Making by Imane Moustati, Noreddine Gherabi

“…Effective investment decision-making in today’s volatile financial market demands the integration of advanced predictive analytics, alternative data sources, and behavioral insights. …”
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Identification of Molecular Subtypes and Prognostic Features of Breast Cancer Based on TGF-β Signaling-related Genes by Jia Qu, Mei-Huan Wang, Yue-Hua Gao, Hua-Wei Zhang

“…Methods: The Cancer Genome Atlas (TCGA) database was utilized to develop a predictive model incorporating five TGF-β signaling-related genes (TSRGs). …”
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Estimation of Daily Suspended Sediment Load Using a Novel Hybrid Support Vector Regression Model Incorporated with Observer-Teacher-Learner-Based Optimization Method by Siyamak Doroudi, Ahmad Sharafati, Seyed Hossein Mohajeri

“…Predicting suspended sediment load (SSL) in water resource management requires efficient and reliable predicted models. …”
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Low-Illumination Parking Scenario Detection Based on Image Adaptive Enhancement by Xixi Xu, Meiqi Zhang, Hao Tang, Weiye Xu, Bowen Sun, Zhu’an Zheng

“…Aiming at the problem of easily missed and misdetected parking spaces and obstacles in the automatic parking perception task under low-illumination conditions, this paper proposes a low-illumination parking space and obstacle detection algorithm based on image adaptive enhancement. The algorithm comprises an image adaptive enhancement module, which predicts adaptive parameters using CNN and integrates the low-light image enhancement via illumination map estimation and contrast-limited adaptive histogram equalization algorithms for image processing. …”
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Design and Application of an Energy Management System Based on Artificial Intelligence Technology by Hongye Lin, Xuanying Bai, Chun Li, Shenghan Xu, Haibin Xu, Zne-Jung Lee, Yun Lin, Qunshan Zhou, Jingxun Cai

“…The system adopts sensor technology and data acquisition equipment to monitor various types of energy consumption in buildings in real time, efficiently process and predict these data through machine learning algorithms, and finally visualize the results. …”
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Model Design for Personnel Selection with Data Mining Approach (Case Study: A Commerce Bank of Iran) by Adel Azar, Parviz Ahmadi, Mohammad Vahid Sebt

“…Rules Derivation has been accomplished by the QUEST, CHAID, C5.0 and CART algorithms. The objective and the appropriate algorithms are determined based on seemingly “irrelevant” components, which the Commerce Bank Human Resources management experts described. …”
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Simultaneous Instance and Attribute Selection for Noise Filtering by Yenny Villuendas-Rey, Claudia C. Tusell-Rey, Oscar Camacho-Nieto

“…Removing or reducing noise can help classification algorithms focus on relevant patterns, preventing them from being affected by irrelevant or incorrect information. …”
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Research on Road Traffic Safety Risk Assessment Based on the Data of Radar Video Integrated Sensors by Xiaoyu CAI, Zimu LI, Wufeng QIAO, Xiling CHENG, Bo PENG, Dong ZHANG

“…To accurately prevent and warn of traffic accidents, this article proposes a method for predicting urban road traffic safety risks based on vehicle driving behaviour data and information entropy theory. …”
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High throughput computational screening and interpretable machine learning for iodine capture of metal-organic frameworks by Haoyi Tan, Yukun Teng, Guangcun Shan

“…Subsequently, two machine learning regression algorithms—Random Forest and CatBoost, were employed to predict the iodine adsorption capabilities of MOF materials. …”
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Demographic bias mitigation at test-time using uncertainty estimation and human–machine partnership by Anoop Krishnan Upendran Nair, Ajita Rattani

“…Facial attribute classification algorithms frequently manifest demographic biases by obtaining differential performance across gender and racial groups. …”
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Innovative Solutions for Oil Well Monitoring: Data-Driven Multiphase Virtual Flow Metering Using Ensemble Machine Learning and Historical Field Data by Wael A. Farag, Hussein H. Ismail, Muhammad Nadeem

“…By combining data-driven ensemble machine-learning algorithms and historical oil field portable test reports, this paper proposes a Data-Drive Multiphase Virtual Flow Meter (DD-MVFM) that estimates oil, gas, and water flow rates and provides real-time monitoring, and predicts future production with appropriate accuracy. …”
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The analysis of strategic management decisions and corporate competitiveness based on artificial intelligence by Yang Pu, Hao Li, Wenjia Hou, Xingchen Pan

“…Finally, real-world data are applied to evaluate model performance across indicators such as training time, convergence speed, and prediction effectiveness. The results demonstrate that the hybrid model successfully converges within 150 iterations and exhibits substantial advantages over traditional algorithms, particularly in prediction accuracy for market share (92%), profit growth rate (91%), and customer satisfaction (89%). …”
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Learning the Value of Place: Machine Learning Models for Real Estate Appraisal in Istanbul’s Diverse Urban Landscape by Ahmet Hilmi Erciyes, Toygun Atasoy, Abdurrahman Tursun, Sibel Canaz Sevgen

“…The prediction of real estate values is vital for taxation, transactions, mortgages, and urban policy development. …”
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