A predictive healthcare model using machine learning and psychological factors for medication adherence by Junwu Dong, Minyi Chu, Yirou Xu

“…This study aims to develop a predictive model for medication adherence behaviour (MAB) employing machine learning techniques, addressing the limitations of traditional correlation-based approaches. …”
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Pedestrian-Crossing Detection Enhanced by CyclicGAN-Based Loop Learning and Automatic Labeling by Kuan-Chieh Wang, Chao-Li Meng, Chyi-Ren Dow, Bonnie Lu

“…This study proposes a CyclicGAN-based loop-learning framework, in which the learning process begins with a set of manually annotated images used to train an initial labeling model. …”
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Comparative Evaluation of Ensemble Machine Learning Models for Methane Production from Anaerobic Digestion by Dorijan Radočaj, Mladen Jurišić

“…From the authors’ knowledge based on existing research, present knowledge of their prediction accuracy and utilization in anaerobic digestion modeling relative to individual machine learning methods is incomplete. …”
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In-situ manipulation of wireless link with reinforcement-learning-driven programmable metasurface in indoor environment by Jiawen Xu, Rong Zhang, Jie Ma, Hanting Zhao, Lianlin Li

“…Here, we propose a reinforcement learning (RL) approach to online control of the PM and thus in-situ improve the quality of the underline wireless link. …”
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Machine Learning in Biomedical Informatics: Optimizing Resource Allocation and Energy Efficiency in Public Hospitals by Agostino Marengo, Vito Santamato, Massimo Iacoviello

“…This paper presents a computational framework that combines supervised machine learning and multi-objective optimization to support data-driven decision-making for resource allocation in public healthcare systems. …”
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Integrating Remote Photoplethysmography and Machine Learning on Multimodal Dataset for Noninvasive Heart Rate Monitoring by Rinaldi Anwar Buyung, Alhadi Bustamam, Muhammad Remzy Syah Ramazhan

“…We compare the performance of two machine learning models, support vector regression (SVR) and random forest regression on the multimodal dataset. …”
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Técnicas y Herramientas de Deep Learning para la Predicción Meteorológica Inteligente by Kevin Parimango Gómez, Jose Luis Gutierrez Diaz, Marcelino Torres Villanueva

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Applications of implementation science (IS) in learning health systems (LHS): a scoping review protocol by Carol Mita, Aimee K Huang, Rachel Vanderkruik, Salma Argueta, Stephen J Bartels

“…Introduction Despite advancements in biomedical and healthcare research, the translation of evidence into routine practice within healthcare systems often lags, perpetuating inefficiencies and disparities in care delivery. Learning health systems (LHS), which integrate internal data and external evidence for continuous improvement, hold promise for addressing these gaps. …”
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Comparative analysis of the performance of selected machine learning algorithms depending on the size of the training sample by Kupidura Przemysław, Kępa Agnieszka, Krawczyk Piotr

“…The article presents an analysis of the effectiveness of selected machine learning methods: Random Forest (RF), Extreme Gradient Boosting (XGB), and Support Vector Machine (SVM) in the classification of land use and cover in satellite images. …”
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Estimating NOx Emissions in China via Multisource Satellite Data and Deep Learning Model by Kun Cai, Yanfang Shao, Yinghao Lin, Shenshen Li, Minghu Fan

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LSTM-SAC reinforcement learning based resilient energy trading for networked microgrid system by Desh Deepak Sharma, Ramesh C Bansal

“…On the whole, the present microgrid constitutes numerous actors in highly decentralized environments and liberalized electricity markets. …”
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Predictive modelling of aquaculture water quality using IoT and advanced machine learning algorithms by Md. Abdullah Al Mamun Hridoy, Chiara Bordin, Andleeb Masood, Khalid Masood

“…The parameters monitored include pH, turbidity, temperature, and dissolved oxygen (DO)—critical indicators of aquatic health and fish productivity. Advanced machine learning models, including TensorFlow Neural Networks (TFN) and Aqua Enviro Index (AEI), were applied for predictive analysis. …”
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Source Term Estimation for Puff Releases Using Machine Learning: A Case Study by John Bartzis, Spyros Andronopoulos, Ioannis Sakellaris

“…A more efficient approach is leveraging past and present data using Machine Learning (ML) techniques. …”
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Classifying social and physical pain from multimodal physiological signals using machine learning by Eun-Hye Jang, Young-Ji Eum, Daesub Yoon, Sangwon Byun

“…Abstract Accurate pain assessment is essential for effective management; however, most studies have focused on differentiating pain from non-pain or estimating pain intensity rather than distinguishing between distinct pain types. We present a machine learning method for classifying physical and social pain using physiological signals. …”
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An Explainable Deep Learning Network With Transformer and Custom CNN for Bean Leaf Disease Classification by R. Karthik, R. Aswin, K. S. Geetha, K. Suganthi

“…Prompt disease identification maximizes productivity, but traditional methods need specialized expertise. This research presents an explainable deep learning model that combines the Pyramid Vision Transformer (PVT) and Group Context Aware Depthwise Shuffle Network (GCADSN). …”
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An Intelligent Carbon-Based Prediction of Wastewater Treatment Plants Using Machine Learning Algorithms by Anwer Mustafa Hilal, Maha M. Althobaiti, Taiseer Abdalla Elfadil Eisa, Rana Alabdan, Manar Ahmed Hamza, Abdelwahed Motwakel, Mesfer Al Duhayyim, Noha Negm

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Comparative analysis of machine learning models for detecting water quality anomalies in treatment plants by P. Prabu, Ala Saleh Alluhaidan, Romana Aziz, Shakila Basheer

“…In addition to developing this model, we present a comparative analysis with several existing machine learning models, demonstrating the effectiveness of our approach in detecting water quality anomalies. …”
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Selective Laser Sintering of Polymers: Process Parameters, Machine Learning Approaches, and Future Directions by Hossam M. Yehia, Atef Hamada, Tamer A. Sebaey, Walaa Abd-Elaziem

“…Additionally, the study explores the application of machine learning (ML) techniques—supervised, unsupervised, and reinforcement learning—in optimizing processes, detecting defects, and ensuring quality control within SLS. …”
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Technology-Enhanced Multimodal Learning Analytics in Higher Education: A Systematic Literature Review by Ozan Rasit Yurum

“…Multimodal learning analytics (MMLA) is an emerging field of learning analytics and promises a more comprehensive analysis of the learning process thanks to advances in technological devices and data science. …”
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Development and application of advanced learning models for predicting the land subsidence due to coal mining by Shirin Jahanmiri, Majid Noorian-Bidgoli

“…Abstract Underground coal mining presents significant environmental challenges, particularly land subsidence, which can lead to severe ecological and structural consequences. …”
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