Groundwater Level Forecasting Using Machine Learning: A Case Study of the Baekje Weir in Four Major Rivers Project, South Korea by Sooyeon Yi, G. Mathias Kondolf, Samuel Sandoval Solis, Larry Dale

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Fault Detection in Photovoltaic Systems Using a Machine Learning Approach by Jossias Zwirtes, Fausto Bastos Libano, Luis Alvaro de Lima Silva, and Edison Pignaton de Freitas

“…The proposed fault detection solutions rely on analyzing different algorithms, including Support Vector Machine, Artificial Neural Network, Random Forest, Decision Tree, and Logistic Regression. …”
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Comparison of Satellite-based PM2.5 Estimation from Aerosol Optical Depth and Top-of-atmosphere Reflectance by Heming Bai, Zhi Zheng, Yuanpeng Zhang, He Huang, Li Wang

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House Price Prediction of Real Time Data (DHA Defence) Karachi Using Machine Learning by Lata Bai Gokalani, Bhagwan Das, Dilip Kumar Ramnani, Mahender Kumar, Mazhar Ali Shah

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Joint modelling of longitudinal data: a scoping review of methodology and applications for non-time to event data by Rehema K. Ouko, Mavuto Mukaka, Eric O. Ohuma

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4SQR-Code: A 4-state QR code generation model for increasing data storing capacity in the Digital Twin framework by Ababil Islam Udoy, Muhammad Aminur Rahaman, Md. Jahidul Islam, Anichur Rahman, Zulfiqar Ali, Ghulam Muhammad

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Learning Optimal Dynamic Treatment Regime from Observational Clinical Data through Reinforcement Learning by Seyum Abebe, Irene Poli, Roger D. Jones, Debora Slanzi

“…Our study aims to evaluate the performance and feasibility of such algorithms: tree-based reinforcement learning (T-RL), DTR-Causal Tree (DTR-CT), DTR-Causal Forest (DTR-CF), stochastic tree-based reinforcement learning (SL-RL), and Q-learning with Random Forest. …”
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AI in Medical Questionnaires: Innovations, Diagnosis, and Implications by Xuexing Luo, Yiyuan Li, Jing Xu, Zhong Zheng, Fangtian Ying, Guanghui Huang

“… This systematic review aimed to explore the current applications, potential benefits, and issues of artificial intelligence (AI) in medical questionnaires, focusing on its role in 3 main functions: assessment, development, and prediction. …”
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Predicting Livestock Farmers’ Attitudes towards Improved Sheep Breeds in Ahar City through Data Mining Methods by Jabraeil Vahedi, Masoumeh Niazifar, Mohammad Ghahremanzadeh, Akbar Taghizadeh, Soheila Abachi, Valiollah Palangi, Maximilian Lackner

“…Next, we employed data mining-based methods, including multilayer perceptron neural networks, random forest, and random tree algorithms. These helped identify essential variables affecting ranchers’ attitudes. …”
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Integrated Single-Cell and Spatial Transcriptomic Analysis Identifies ISR-Related Genes Driving Immune Regulation in Parkinson’s Disease by Jiang H, Zhang X, Feng S, Feng W

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Causal Unit Selection using Tractable Arithmetic Circuits by Haiying Huang, Adnan Darwiche

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Interpersonal counselling for adolescent depression delivered by youth mental health workers without core professional training: the ICALM feasibility RCT by Jon Wilson, Viktoria Cestaro, Eirini Charami-Roupa, Timothy Clarke, Aoife Dunne, Brioney Gee, Sharon Jarrett, Thando Katangwe-Chigamba, Andrew Laphan, Susie McIvor, Richard Meiser-Stedman, Jamie Murdoch, Thomas Rhodes, Carys Seeley, Lee Shepstone, David Turner, Paul Wilkinson

“…Progression criteria The primary intended output of the research was the design of a subsequent trial. The following criteria were set out at the beginning of the study to make recommendations regarding the suitability of the proposed design for the full-scale trial: (1) recruitment rate is at least 80% of target, (2) at least 70% of those randomised to receive the intervention attended at least three therapy sessions within the 10-week treatment window, (3) follow-up assessments are completed by at least 80% of participants at 10 weeks and 70% of participants at 23 weeks, (4) at least 80% of IPC treatment sessions reviewed meet treatment fidelity criteria, (5) contamination of the control arm can be sufficiently limited for individual randomisation to be justified and (6) the mean Revised Children’s Anxiety and Depression Scale (RCADS) depression scores of the IPC-A and TAU groups at 10 weeks are indicative of a clinically significant difference in depression (3 points). …”
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Determining the Number of Measurements and Bootstrap Samples Required to Estimate of Long-Term Noise Indicators by Bartłomiej STĘPIEŃ

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Impact of climate change over distribution and potential range of chestnut in the Iberian Peninsula by Pedro Álvarez-Álvarez, Adrián Aviñoa-Arias, Adrián Aviñoa-Arias, Emilio Díaz-Varela, José Vicente López-Bao, José Carlos Pérez-Girón

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High-precision positioning of robotic systems on programme trajectories using satellite navigation measurements by S. V. Sokolov, A. I. Okhotnikov, D. V. Marshakov, I. V. Reshetnikova

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Visual–Inertial Autonomous UAV Navigation in Complex Illumination and Highly Cluttered Under-Canopy Environments by Leyang Zhao, Weixi Wang, Qizhi He, Li Yan, Xiaoming Li

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A Multi-Mode Recognition Method for Broadband Oscillation Based on Compressed Sensing and EEMD by Jinggeng Gao, Honglei Xu, Yong Yang, Haoming Niu, Jinping Liang, Haiying Dong

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Machine Learning-Based Objective Evaluation Model of CTPA Image Quality: A Multi-Center Study by Sun Q, Liu Z, Ding T, Shi C, Hou N, Sun C

“…Feature selection was performed using the Lasso algorithm and Pearson correlation coefficient, and a random forest regression model was constructed. …”
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Investigating the contributory factors influencing speeding behavior among long-haul truck drivers traveling across India: Insights from binary logit and machine learning technique... by Balamurugan Shandhana Rashmi, Sankaran Marisamynathan

“…While conventional statistical methods like binary logit technique lacked prediction capabilities, machine learning (ML) algorithms including decision tree (DT), random forest (RF), adaptive boosting (AdaBoost), and extreme gradient boosting (XGBoost) were employed to model speeding behavior among LHTDs. …”
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Development of a machine learning-based prediction model for extremely rapid decline in estimated glomerular filtration rate in patients with chronic kidney disease: a retrospectiv... by Shingo Fukuma, Yukio Yuzawa, Daijo Inaguma, Hiroki Hayashi, Ryosuke Yanagiya, Akira Koseki, Toshiya Iwamori, Michiharu Kudo

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