Spatiotemporal Analysis of Sea-Surface pH in the Pacific Ocean Based on Interpretable Machine Learning by Minlong Huang, Jin Qi, Can Zhang, Yuanyuan Wang, Yijun Chen, Jian Shao, Sensen Wu

“…Therefore, this study provides a data-driven approach to gain deeper insights into the spatiotemporal patterns of pH and its influencing factors.…”
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Estimating shear strength of dredged soils for marine engineering: experimental investigation and machine learning modeling by Zheng Yao, Kaiwei Xu, Zejin Wang, Haodong Sun, Peng Cui, Peng Cui

“…The motivation behind this hybridization lies in the need to effectively capture nonlinear interactions and latent logical patterns among influencing factors, which are often overlooked by traditional single-algorithm models. …”
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The impact of cultural factors on digital marketing strategies with Machine learning and honey bee Algorithm (HBA) by Muhammad Khan, Masood Ahmad, Rakhmonov Dilshodjon Alidjonovich, Kalonov Mukhiddin Bakhritdinovich, Kurbanbekova Mohichehra Turobjonovna, Imomov Jamshidxon Odilovich

“…Predictive models are applied to understand consumer behavior patterns, and HBA is utilized to optimize key marketing parameters, including content personalization and ad placement. …”
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An Elderly Fall Detection Method Based on Federated Learning and Extreme Learning Machine (Fed-ELM) by Zhigang Yu, Jiahui Liu, Mingchuan Yang, Yanmin Cheng, Jie Hu, Xinchi Li

“…However, there are differences in movement patterns between young and elderly individuals due to bone aging, which leads to the degradation of the algorithm performance in the elderly population. …”
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Machine learning algorithms applied to the diagnosis of COVID-19 based on epidemiological, clinical, and laboratory data by Silvia Elaine Cardozo Macedo, Marina de Borba Oliveira Freire, Oscar Schmitt Kremer, Ricardo Bica Noal, Fabiano Sandrini Moraes, Mauro André Barbosa Cunha

“…Epidemiological, clinical, and laboratory data were processed by machine learning algorithms in order to identify patterns. …”
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A Compact Dual-Band Antenna for Brain–Machine Interface and Skin-Implantable Biotelemetry Applications by Emtiaz Ahmed Mainul, Md. Faruque Hossain

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Optimized machine learning algorithms with SHAP analysis for predicting compressive strength in high-performance concrete by Samuel Olaoluwa Abioye, Yusuf Olawale Babatunde, Oluwafikejimi Abigail Abikoye, Aisha Nene Shaibu, Bailey Jonathan Bankole

“…Validation against experimental data confirmed the reliability of XGBoost and GBR through consistent prediction patterns and close alignment with empirical measurements. …”
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High School and Undergraduate Student Volunteers as an Imperfect Solution to Machine Learning Geoscience Research Needs by Sarah E. Esenther, Neiv Gupta, Chanatip Vongkitbuncha, Mason N. Lee, Laurence C. Smith

“…We describe our experiences working with 20 early‐stage students to build a large training data set digitized from satellite images of meltwater drainage patterns on ice sheets. The intent of this Perspective is to share our experience and lessons learned with other machine learning researchers who, like us, may have minimal experience mentoring young volunteer researchers but may seek such partnerships for the first time in response to their machine learning training data set needs. …”
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Effects of Broken Rotor Bars on Three-Phase Induction Machines and Detection Methods – State of Arts by Boudmane Bilal, Rachid Aziz, Mortabit Idriss, Errifai Nidale

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A Machine Learning Framework for Urban Ventilation Corridor Identification Using LBM and Morphological Indices by Bu Yu, Peng Xie

“…The results show that the proposed method can accurately predict spatial wind speed patterns and identify both primary and secondary ventilation corridors. …”
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MLRec: A Machine Learning-Based Recommendation System for High School Students Context of Bangladesh by Momotaz Begum, Mehedi Hasan Shuvo, Jia Uddin

“…Social media and mobile devices, commonly referred to as socimedevices, have become integral to students’ daily lives, influencing both their academic performance and overall well-being. Depending on usage patterns, these technologies can positively or negatively impact students’ education. …”
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Adaptive optimization of natural coagulants using hybrid machine learning approach for sustainable water treatment by Pallavi Randive, Madhuri S. Bhagat, Mangesh P. Bhorkar, Rajesh M. Bhagat, Shilpa M. Vinchurkar, Sagar Shelare, Shubham Sharma, N. Beemkumar, S. Hemalatha, Parveen Kumar, Ankit Kedia, Ehab El Sayed Massoud, Deepak Gupta, Jasmina Lozanovic

“…Lastly, SOMs and MARS are used to identify pattern recognitions in tracing the crucial interaction among mixing parameters. …”
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A Robust Behavioral Biometrics Framework for Smartphone Authentication via Hybrid Machine Learning and TOPSIS by Moceheb Lazam Shuwandy, Qutaiba Alasad, Maytham M. Hammood, Ayad A. Yass, Salwa Khalid Abdulateef, Rawan A. Alsharida, Sahar Lazim Qaddoori, Saadi Hamad Thalij, Maath Frman, Abdulsalam Hamid Kutaibani, Noor S. Abd

“…In this study, a robust framework for smartphone authentication is presented. Touch dynamic pattern recognitions, including trajectory curvature, touch pressure, acceleration, two-dimensional spatial coordinates, and velocity, have been extracted and assessed as behavioral biometric features. …”
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Investigating Spatial Effects through Machine Learning and Leveraging Explainable AI for Child Malnutrition in Pakistan by Xiaoyi Zhang, Muhammad Usman, Ateeq ur Rehman Irshad, Mudassar Rashid, Amira Khattak

“…The study found a clear pattern of spatial clustering and geographical disparities in childhood stunting, with multidimensional poverty, high climate vulnerability and early marriage worsening childhood stunting. …”
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Machine learning-based identification of efficient and restrictive physiological subphenotypes in acute respiratory distress syndrome by Gabriela Meza-Fuentes, Iris Delgado, Mario Barbé, Ignacio Sánchez-Barraza, Mauricio A. Retamal, René López

“…Subphenotype Efficient (n = 172) was characterized by lower mortality, lower clinical severity and presented a less restrictive pattern with better gas exchange compared to Subphenotype Restrictive (n = 52), which showed the opposite. …”
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Enhancing decision-making on detractor-causing failures: an approach combining data mining and machine learning by Yuri A. V. da Silva, Geraldo Cardoso de Oliveira Neto, Gustavo Lima, Sidnei A. de Araújo, Rodrigo Neri Bueno da Silva, Francisco Elanio Bezerra, Marlene Amorim

“…The proposed approach employs Decision Tree (DT) algorithms to uncover patterns linked to service failures. The results highlight two primary issues: delivery discrepancies and product returns due to dissatisfaction, both of which directly affect customer loyalty. …”
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Assessing the association of multi-environmental chemical exposures on metabolic syndrome: A machine learning approach by Yehoon Jo, Mi-Yeon Shin, Sungkyoon Kim

“…SHapley Additive exPlanations (SHAP) and partial dependence plots (PDP) revealed both linear and nonlinear exposure–response patterns, suggesting threshold effects for key chemicals. …”
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Student dropout prediction through machine learning optimization: insights from moodle log data by Markson Rebelo Marcolino, Thiago Reis Porto, Tiago Thompsen Primo, Rafael Targino, Vinicius Ramos, Emanuel Marques Queiroga, Roberto Munoz, Cristian Cechinel

“…Learning management systems such as Moodle generate extensive datasets reflecting student interactions and enrollment patterns, presenting opportunities for predictive analytics. …”
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Comparative Analysis of Machine Learning Techniques for Fault Diagnosis of Rolling Element Bearing with Wear Defects by Devendra Sahu, Ritesh Kumar Dewangan, Surendra Pal Singh Matharu

“…The model was further refined by extracting 14 types of features from the SNR-enhanced vibration data, presenting a comprehensive depiction of fault patterns and finally, machine learning techniques were applied to categorize faults using the aforementioned datasets, facilitating a comparative analysis of results. …”
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Texas rural land market integration: A causal analysis using machine learning applications by Tian Su, Senarath Dharmasena, David Leatham, Charles Gilliland

“…Using quarterly transactional land value data from 1966 to 2017, this study uses cutting-edge machine learning algorithms and probabilistic graphical models to uncover causal interaction patterns of different land markets in Texas. …”
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