Improved empirical wavelet transform combined with particle swarm optimization-support vector machine for EEG-based depression recognition by Yongxin Wang, Longqi Xu, Hongxu Qian, Haijun Lin, Xuhui Zhang

“…In this paper, we propose an improved method for the early objective diagnosis of depression utilizing an empirical wavelet transform (EWT) technique enhanced by a particle swarm optimization-support vector machine (PSO-SVM) algorithm. …”
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ECG Signal Analysis for Detection and Diagnosis of Post-Traumatic Stress Disorder: Leveraging Deep Learning and Machine Learning Techniques by Parisa Ebrahimpour Moghaddam Tasouj, Gökhan Soysal, Osman Eroğul, Sinan Yetkin

“…This study aims to develop and evaluate an artificial intelligence-based classification system using electrocardiogram (ECG) signals for the detection of PTSD. <b>Methods:</b> Raw ECG signals were transformed into time–frequency images using Continuous Wavelet Transform (CWT) to generate 2D scalogram representations. …”
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Predictions of Multilevel Linguistic Features to Readability of Hong Kong Primary School Textbooks: A Machine Learning Based Exploration by Zhengye Xu, Yixun Li, Duo Liu

“…It is also the first readability formula developed in Hong Kong. Method: The corpus comprised 723 texts from 72 Chinese language arts textbooks used in public primary schools. …”
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Machine-Learning-Based Depression Detection Model from Electroencephalograph (EEG) Data Obtained by Consumer-Grade EEG Device by Kei Suzuki, Tipporn Laohakangvalvit, Midori Sugaya

“…The feature selection methods were Light Gradient Boosting Machine (LightGBM) feature importance, mutual information, ReliefF and ElasticNet coefficients. …”
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A Comparative Study of Machine Learning and Deep Learning Models for Automatic Parkinson’s Disease Detection from Electroencephalogram Signals by Sankhadip Bera, Zong Woo Geem, Young-Im Cho, Pawan Kumar Singh

“…<b>Methods:</b> We propose an innovative EEG-based PD detection approach by integrating advanced spectral feature engineering with machine learning and deep learning models. …”
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IoT Integrated Graphene Derivative-Based Capacitive Sensors with Machine Learning Classification for Precision Soil Moisture Monitoring Process by Ukani Neema Amish, Khera Shelej, Chakole Saurabh S.

“…Afterward, the k-means clustering method, supported by the elbow method, enabled our correct classification into dry, moderate, and wet moisture levels with silhouette scores of 0.88 (GO) and 0.91 (rGO) Sets. …”
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The Intelligent Diagnosis of a Hydraulic Plunger Pump Based on the MIGLCC-DLSTM Method Using Sound Signals by Liqiang Ma, Anqi Jiang, Wanlu Jiang

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Driver Injury Prediction and Factor Analysis in Passenger Vehicle-to-Passenger Vehicle Collision Accidents Using Explainable Machine Learning by Peng Liu, Weiwei Zhang, Xuncheng Wu, Wenfeng Guo, Wangpengfei Yu

“…Most existing studies focus on macro-level predictions, such as accident frequency, but lack detailed collision-level analysis, which limits the precision of severity prediction. …”
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Performance Analysis of Machine Learning Techniques for Server Health Monitoring Using Time Series Data Against DDOS Attacks by Sajja Ratan Kumar, Valli Kumari Vatsavayi

“…In Stage I: DDoS Attack Detection, binary classification techniques&#x2014;machine learning methods that divide data into two groups&#x2014;analyze network traffic to differentiate normal traffic from DDoS attack traffic. …”
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Predicting Calcein Release from Ultrasound-Targeted Liposomes: A Comparative Analysis of Random Forest and Support Vector Machine by Ibrahim Shomope MS, Kelly M. Percival BS, Nabil M. Abdel Jabbar PhD, Ghaleb A. Husseini PhD

“…Methods Liposomes loaded with calcein and targeted with seven different moieties (cRGD, estrone, folate, Herceptin, hyaluronic acid, lactobionic acid, and transferrin) were synthesized using the thin-film hydration method. …”
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Microbial Load And Public Health Risks of Contaminated Keypads of Selected Automated Teller Machines of Some Banks in Abraka-Nigeria. by Omotejohwo Emily Okolosi-Patani Ahwinahwi Ufuoma Shalom Clifford Isioma

“…This study is aimed at microbiologically evaluating the keypads of automated teller machines of some banks in Abraka-Nigeria. A total of 32 sterile swab sticks moistened with normal saline were used to swab the keypads of each ATM at different time intervals (morning and evening), after which the swab sticks were transferred immediately to the laboratory and analysed using standard microbiological methods. …”
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Predicting at-risk students in the early stage of a blended learning course via machine learning using limited data by Zahra Azizah, Tomoya Ohyama, Xiumin Zhao, Yuichi Ohkawa, Takashi Mitsuishi

“…The Shapley additive explanations (SHAP) method emphasizes the importance of time-management variables, particularly study in time, for identifying at-risk students. …”
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A Machine-Learning-Based Ocean-Current Velocity Inversion Model Using OCN From Sentinel-1 Observations by Yang Bai, Yubin Zhang, Xudong Zhang, Xiaofeng Li

“…However, traditional methods, such as the Doppler centroid anomaly (DCA) and along-track interferometry methods, face challenges, such as low inversion accuracy, poor robustness, and limited data sources. …”
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Watershed scale soil moisture estimation model using machine learning and remote sensing in a data-scarce context by Marcelo Bueno, Carlos Baca García, Nilton Montoya, Pedro Rau, Hildo Loayza

“…The results indicate that the proposed method demonstrated spatial and hydrological coherence, along with a satisfactory downscaling quality. …”
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Exploring Machine Learning Classification of Movement Phases in Hemiparetic Stroke Patients: A Controlled EEG-tDCS Study by Rishishankar E. Suresh, M S Zobaer, Matthew J. Triano, Brian F. Saway, Parneet Grewal, Nathan C. Rowland

“…Eight machine learning algorithms and five ensemble methods were used to classify two movement phases (hold posture and reaching) during each of these periods. …”
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An Improved Fault Diagnosis Method for Rolling Bearing Based on Relief-F and Optimized Random Forests Algorithm by Yueyi Yang, Jiabo Zhai, Haiquan Wang, Xiaobin Xu, Yabo Hu, Jinxia Wen

“…The Relief-F ranking method is utilized to assess the quality of time–frequency domain features, and the top-ranked features with high weight gain are selected for identifying the fault modes. …”
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A Bearing Fault Diagnosis Method Combining Multi-Source Information and Multi-Domain Information Fusion by Tao Sui, Yixiang Feng, Sitian Sui, Xueran Xie, Hui Li, Xiuzhi Liu

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Machine learning and artificial intelligence in type 2 diabetes prediction: a comprehensive 33-year bibliometric and literature analysis by Mahreen Kiran, Ying Xie, Nasreen Anjum, Graham Ball, Barbara Pierscionek, Duncan Russell

“…It highlights the growing complexity of the field and identifies key trends, methodologies, and research gaps.MethodsA systematic methodology guided the literature selection process, starting with keyword identification using Term Frequency-Inverse Document Frequency (TF-IDF) and expert input. …”
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Integrating ultrasound and clinical risk factors to predict carotid plaque vulnerability in gout patients: a machine learning approach by Yabin Fang, Yabin Fang, Kaiyi Yang, Kaiyi Yang, Xinyu Gao, Xinyu Gao, Yiran Gong, Yiran Gong, Yaxin Deng, Xiang Xu, Xiang Xu, Jing Xu, Jing Xu, Lei Yan, Lei Yan, Jinshu Zeng, Jinshu Zeng, Shuqiang Chen

“…ObjectivesThis study aimed to identify independent risk factors for carotid plaque (CP) vulnerability in patients with gout and to develop a predictive model incorporating both gout-specific and cardiovascular factors.MethodThis study was designed as a retrospective cohort analysis that enrolled patients with newly diagnosed gout. …”
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Assessment of sand nourishment dynamics under repeated storm impact supported by machine learning-based analysis of UAV data by Jan Tiede, Joshua Leon Lovell, Christian Jordan, Armin Moghimi, Torsten Schlurmann

“…This study examines the development of a large-scale sand nourishment (600,000 m³) in the southwestern Baltic Sea over 25 months (October 2021–November 2023) using UAV-derived digital surface models (DSMs) and machine learning (ML). High-frequency, multi-temporal UAV surveys enabled detailed analyses of the development of the nourished beach and dune. …”
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