Comprehensive Performance Comparison of Signal Processing Features in Machine Learning Classification of Alcohol Intoxication on Small Gait Datasets by Muxi Qi, Samuel Chibuoyim Uche, Emmanuel Agu

“…Convenient, unobtrusive intoxication detection methods using equipment already owned by users are desirable. Recent research has explored machine learning-based approaches using smartphone accelerometers to classify intoxicated gait patterns. …”
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Informational Approaches in Modelling Social and Economic Relations: Study on Migration and Access to Services in the European Union by Florentina-Loredana Dragomir-Constantin, Camelia Madalina Beldiman, Monica Laura Zlati

“…The applied methodology includes attribute distribution analysis, identification of hidden patterns through clustering algorithms (K-Means and Expectation-Maximisation) and training of classifiers using regression decision trees with linear leaf models (M5P) corresponding to interdependent data processing and integration modules, exploratory analysis module, machine learning and decision-making modules, oriented to support public policies through explainable scenarios and predictive-evaluative structures. …”
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A Flexible Constrained ICA Approach for Multisubject fMRI Analysis by Hanlu Yang, Trung Vu, Ehsan Ahmed Dhrubo, Vince D. Calhoun, Tülay Adali

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Optimizing solar maximum power point tracking with adaptive PSO: A comparative analysis of inertia weight and acceleration coefficient strategies by Denesh Sooriamoorthy, Aaruththiran Manoharan, Siva Kumar Sivanesan, Soon Kian Lun, Alexander Chee Hon Cheong, Sathish Kumar Selva Perumal

“…The individual and combined performance of adaptive w, c1 and c2 are evaluated, especially with small and narrow w operational range studied as it contributes to high convergence, especially under fast-changing shading patterns. The results demonstrate that linear adaptive w combined with trigonometric adaptive c1 and c2 consistently achieves high tracking accuracy (99.4%) with minimal steady-state oscillations and faster convergence times (average 0.0642 s), outperforming conventional PSO and P&O algorithms. …”
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Bridging Satellite Productivity and Global Biodiversity: Unveiling Insights through Dynamic Habitat Indices by Kedi Liu, Chunyan Cao, Sicong Gao, Wei Yang, Xuanlong Ma

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Predicting and Preventing School Dropout with Business Intelligence: Insights from a Systematic Review by Diana-Margarita Córdova-Esparza, Juan Terven, Julio-Alejandro Romero-González, Karen-Edith Córdova-Esparza, Rocio-Edith López-Martínez, Teresa García-Ramírez, Ricardo Chaparro-Sánchez

“…The results highlight a wide range of predictive tools and methodologies, notably data visualization platforms (e.g., Power BI) and algorithms like decision trees, Random Forest, and logistic regression, demonstrating effectiveness in identifying dropout patterns and at-risk students. …”
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A Data-Driven Approach for Predicting Remaining Useful Life of Semiconductor Devices Based on Machine Learning and Synthetic Data Generation: A Review and Case Study on SiC MOSFETs by Yarens J. Yarenscruz, Fernando Castano, Alberto Villalonga, Madhav Mishra, Rodolfo E. Haber

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A novel anthropometric method to accurately evaluate tissue deformation by Chongyang Ye, Xiaolu Li, Haiyan Song, Yu Shi, Ruixin Liang, Jun Zhang, Ka Po Lee, Zhaolong Chen, Beibei Zhou, Raymond Kai-Yu Tong, Kit-Lun Yick, Sun-Pui Ng, Joanne Yip

“…Moreover, a novel anthropometric method based on image recognition algorithms that systematically measures and evaluates tissue deformation while minimizing the impact of the effects of motion is proposed. …”
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Data-driven EV charging infrastructure with uncertainty based on a spatial–temporal flow-driven (STFD) models considering batteries by Talal Alharbi, Ahmed Abdalrahman, Mostafa H. Mostafa

“…Incorporating energy storage systems (ESS) can help address these challenges by improving overall system efficiency. This research proposes a comprehensive planning methodology that optimizes the placement of EV charging stations by considering traffic flow patterns over space and time. …”
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Identification of gene signatures and potential pharmaceutical candidates linked to COVID-19-related depression based on gene expression profiles by Shaojun Chen, Yiyuan Luo, Lihua Zhang

“…The underlying mechanisms responsible for the coexistence of COVID-19 and depression remain unclear, and more research is needed to find hub genes and effective therapies. …”
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Mapping the use of artificial intelligence in medical education: a scoping review by Erwin Hernando Hernández Rincón, Daniel Jimenez, Lizeth Alexandra Chavarro Aguilar, Juan Miguel Pérez Flórez, Álvaro Enrique Romero Tapia, Claudia Liliana Jaimes Peñuela

“…Data were analyzed using thematic analysis to identify patterns in AI curriculum development and implementation. …”
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PlantGaussian: Exploring 3D Gaussian splatting for cross-time, cross-scene, and realistic 3D plant visualization and beyond by Peng Shen, Xueyao Jing, Wenzhe Deng, Hanyue Jia, Tingting Wu

“…Observing plants across time and diverse scenes is critical in uncovering plant growth patterns. Classic methods often struggle to observe or measure plants against complex backgrounds and at different growth stages. …”
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A machine learning model for early detection of sexually transmitted infections by Juma Shija, Judith Leo, Elizabeth Mkoba

“…Also, it can provide insights into infection patterns, allowing practitioners to adapt their responses. …”
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Digital Academic Leadership in Higher Education Institutions: A Bibliometric Review Based on CiteSpace by Olaniyi Joshua Olabiyi, Carl Jansen van Vuuren, Marieta Du Plessis, Yujie Xue, Chang Zhu

“…This was the result of a multi-step refinement process using CiteSpace’s default thresholds and clustering algorithms to detect the most influential nodes based on centrality, citation burst, and network clustering. …”
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Machine learning-based prediction of scale formation in produced water as a tool for environmental monitoring by Arash Tayyebi, Ali Alshami, Erfan Tayyebi, Ademola Owoade, MusabbirJahan Talukder, Nadhem Ismail, Zeinab Rabiei, Xue Yu, Glavic Tikeri

“…Machine learning (ML) as a data-driven method is a powerful tool for uncovering hidden patterns in experimental data necessary for decision-making on scale formation predictions by analyzing the complex relationships between mainly the water chemistry and the pH. …”
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Scalable Clustering of Complex ECG Health Data: Big Data Clustering Analysis with UMAP and HDBSCAN by Vladislav Kaverinskiy, Illya Chaikovsky, Anton Mnevets, Tatiana Ryzhenko, Mykhailo Bocharov, Kyrylo Malakhov

“…The focus is on identifying patterns that correlate with cardiac health risks, potentially aiding in early detection and personalized care. …”
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Development and validation of machine learning-based diagnostic models using blood transcriptomics for early childhood diabetes prediction by Xin Huang, Xin Huang, Di Ouyang, Weiming Xie, Huawei Zhuang, Siyu Gao, Pan Liu, Lizhong Guo

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Deep learning approach with ConvNeXt-SE-attn model for in vitro oral squamous cell carcinoma and chemotherapy analysis by Abhay Nath, Om Roy, Priyanka Silveri, Sanskruti Patel

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Neural Networks vs. Regression: A Comparative Analysis in Medical Data Processing by Minodora ANDOR, Gheorghe Ioan MIHALAŞ

“… Background and Aim: The increasing adoption of artificial intelligence (AI) in medical research offered alternative methods for medical data processing. …”
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Exploration of Epigenetic Mechanisms and Biomarkers Among Patients with Very-Late-Onset Schizophrenia-Like Psychosis by Gan Y, Yue W, Sun J, Yang D, Fang C, Zhou Z, Yin J, Zhou H

“…Yansha Gan,1,* Weihua Yue,2,* JiaoJiao Sun,1 DanTing Yang,1 ChunXia Fang,1 Zhenhe Zhou,1 JiaJun Yin,1 Hongliang Zhou3 1The Affiliated Mental Health Center of Jiangnan University, Wuxi, Jiangsu, 214151, People’s Republic of China; 2National Clinical Research Center for Mental Disorders, Peking University Sixth Hospital, Beijing, 100191, People’s Republic of China; 3Department of Psychology, The Affiliated Hospital of Jiangnan University, Wuxi City, Jiangsu, 214100, People’s Republic of China*These authors contributed equally to this workCorrespondence: JiaJun Yin, The Affiliated Mental Health Center of Jiangnan University, Wuxi, Jiangsu, 214151, People’s Republic of China, Email yinjiajun@jiangnan.edu.cn Hongliang Zhou, Department of Psychology, The Affiliated Hospital of Jiangnan University, No. 200, Huihe Road, Binhu District, Wuxi City, Jiangsu Province, People’s Republic of China, Email Hongliangzh2022@hotmail.comObjective: This study aimed to identify DNA methylation patterns associated with Very Late-Onset Schizophrenia-like Psychosis (VLOSLP) and to develop methylation-based biomarkers that differentiate VLOSLP from Schizophrenia (SCZ) and Alzheimer’s Disease (AD).Methods: We analyzed methylation microarray datasets (n = 1218) from SCZ and AD patients obtained from the GEO database. …”
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