Preliminary Electroencephalography-Based Assessment of Anxiety Using Machine Learning: A Pilot Study by Katarzyna Mróz, Kamil Jonak

“…<b>Background</b>: Recent advancements in machine learning (ML) have significantly influenced the analysis of brain signals, particularly electroencephalography (EEG), enhancing the detection of complex neural patterns. …”
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Non-invasive detection of Parkinson’s disease based on speech analysis and interpretable machine learning by Huanqing Xu, Wei Xie, Mingzhen Pang, Ya Li, Luhua Jin, Fangliang Huang, Xian Shao

“…To address class imbalance, synthetic minority oversampling technique (SMOTE) was applied. Several machine learning algorithms, including K-Nearest Neighbors (KNN), Support Vector Machine (SVM), Decision Trees, Random Forests, and Neural Networks, were implemented and evaluated. …”
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MACHINE LEARNING AND DEEP LEARNING: A COMPARATIVE ANALYSIS FOR APPLE LEAF DISEASE DETECTION by Anupam Bonkra, Sunil Pathak, Amandeep Kaur

“…This work employs five classification algorithms Inception V3, Decision Tree, Support Vector Machine (SVM), and Random Forest to create a model for detecting diseases on apple leaves. …”
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Employing Data Mining Techniques and Machine Learning Models in Classification of Students’ Academic Performance. by Hussein, Alkattan, Alhumaima, Ali Subhi, Oluwaseun, Adelaja A., Abotaleb, Mostafa, Mijwil, Maad M., Pradeep, Mishra, Sekiwu, Denis, Bamwerinde, Wilson, Turyasingura, Benson

“…The research indicates that the use of machine learning models and data mining methods can reveal hidden patterns and relationships in big data, making them indispensable tools in the field of education analysis. …”
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What factors enhance students' achievement? A machine learning and interpretable methods approach. by Hui Mao, Ribesh Khanal, ChengZhang Qu, HuaFeng Kong, TingYao Jiang

“…This study addresses these limitations by employing an ensemble of five machine learning algorithms (SVM, DT, ANN, RF, and XGBoost) to model multivariate relationships between four behavioral and six instructional predictors, using final exam performance as our outcome variable. …”
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PREDICTION INTERVALS IN MACHINE LEARNING: RESIDUAL BOOTSTRAP AND QUANTILE REGRESSION FOR CASH FLOW ANALYSIS by Wa Ode Rahmalia Safitri, Farit Mochamad Afendi, Budi Susetyo

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The Machine Learning-Based Task Automation Framework for Human Resource Management in MNC Companies by Suchitra Deviprasad, N. Madhumithaa, I. Walter Vikas, Archana Yadav, Geetha Manoharan

“…The ML-based task automation framework utilizes automation bots which can simulate all processes of HR management such as recruitment, time attendance, tracking employee records, scheduling calendar, and office administration tasks. The machine learning-based task automation framework utilizes predictive analytics to identify trends, patterns, behaviour, anomalies, and important insights from the large volumes of structured and unstructured data.…”
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A Data-Driven Machine Vision Framework for Quality Management in Photovoltaic Module Manufacturing by In-Bae Lee, Youngjin Kim, Sojung Kim

“…Autonomous decision-making algorithms are devised to recognize incorrect patterns of PV modules in terms of product quality. …”
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Parsimonious and explainable machine learning for predicting mortality in patients post hip fracture surgery by Fouad Trad, Bassel Isber, Ryan Yammine, Khaled Hatoum, Dana Obeid, Mohammad Chahine, Rachid Haidar, Ghada El-Hajj Fuleihan, Ali Chehab

“…In this study, we developed machine learning (ML) algorithms to estimate 30-day mortality risk post-hip fracture surgery in the elderly using data from the National Surgical Quality Improvement Program (NSQIP 2012–2017, n = 62,492 patients). …”
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Influenza virus genotype to phenotype predictions through machine learning: a systematic review by Laura K. Borkenhagen, Martin W. Allen, Jonathan A. Runstadler

“…Background: There is great interest in understanding the viral genomic predictors of phenotypic traits that allow influenza A viruses to adapt to or become more virulent in different hosts. Machine learning techniques have demonstrated promise in addressing this critical need for other pathogens because the underlying algorithms are especially well equipped to uncover complex patterns in large datasets and produce generalizable predictions for new data. …”
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Progress and trends on machine learning in proteomics during 1997-2024: a bibliometric analysis by Chao Tan, Hao Liu, Zhen Zhang, Xinyu Liu, Yinquan Ai, Xiumin Wu, Enlin Jian, Yongyan Song, Jin Yang

“…ObjectiveDespite growing interest in the application of machine learning (ML) in proteomics, a comprehensive and systematic mapping of this research domain has been lacking. …”
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Predicting anemia management in dialysis patients using open-source machine learning libraries by Takahiro Inoue, Norio Hanafusa, Yuki Kawaguchi, Ken Tsuchiya

“…These models closely mirrored actual prescribing patterns, suggesting feasibility for clinical integration. …”
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Machine learning-based identification of exosome-related biomarkers and drugs prediction in nasopharyngeal carcinoma by Zhengyu Wei, Guoli Wang, Yanghao Hu, Chongchang Zhou, Yuna Zhang, Yi Shen, Yaowen Wang

“…Results Through the application of three machine learning algorithms, five key genes (LTF, IDH1, ITGAV, CCL2, and LGALS3BP) were identified for the construction of a diagnostic model. …”
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Trust-driven approach to enhance early forest fire detection using machine learning by Tayyab Khan, Karan Singh, Bhoopesh Singh Bhati, Khaleel Ahmad, Amal Al-Rasheed, Masresha Getahun, Ben Othman Soufiene

“…Our technique combines trust mechanisms with machine learning algorithms to create a very advanced forest fire detection system.…”
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Identification and validation of the nicotine metabolism-related signature of bladder cancer by bioinformatics and machine learning by Yating Zhan, Min Weng, Yangyang Guo, Dingfeng Lv, Feng Zhao, Zejun Yan, Junhui Jiang, Yanyi Xiao, Lili Yao

“…Prognostic differentially expressed genes (DEGs) were filtered via differentially expression analysis and univariate Cox regression analysis. Integrative machine learning combination based on 10 machine learning algorithms was used for the construction of robust signature. …”
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Machine Learning Techniques Applied to COVID-19 Prediction: A Systematic Literature Review by Yunyun Cheng, Rong Cheng, Ting Xu, Xiuhui Tan, Yanping Bai

“…Since the outbreak of COVID-19, there has been an influx of research on predictive modelling, with artificial intelligence (AI) techniques, particularly machine learning (ML) methods, becoming the dominant research direction due to their superior capability in processing multidimensional datasets and capturing complex nonlinear transmission patterns. …”
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Cluster Analysis of RF-EMF Exposure to Detect Time Patterns in Urban Environment: A Model-Based Approach by Nicola Pasquino, Nunzia Solmonte, Nikola Djuric, Dragan Kljajic, Snezana Djuric

“…Advanced techniques for data analysis, based on machine learning like clustering, can decompose daily variations in EMF exposure into distinct patterns, providing a clearer understanding of how exposure fluctuates over time. …”
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Inter-Turn Fault Diagnosis of Induction Motors Based on Current Vector Pattern Analysis in Stationary Coordinate Frame by Inyeol Yun, Hyunwoo Kim, Ju Lee, Sung-Gu Lee

“…In this study, a current vector pattern is analyzed for inter-turn fault (ITF) diagnosis of induction machines (IMs), and an ITF diagnosis algorithm is proposed. …”
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Enhanced Network Traffic Classification Using Bayesian-Optimized Logistic Regression and Random Forest Algorithm by Manisankar Sannigrahi, R. Thandeeswaran

“…The research evaluates four key machine learning algorithms: Random forest, logistic regression, support vector machine (SVM), and K-nearest neighbors (K-NN). …”
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Solving Maxwell Equations in Hybrid Electrical Vehicle to Optimal Design by Multi-Objective Optimization Algorithms by Abdulaziz Alkuhayli, Aymen Flah, Alireza Hosseinpour, Claude Ziad El-Bayeh, Lukas Prokop, Vojtech Blazek

“…Next, various magnetization patterns, which are presented based on Fourier series, are considered to investigate their influences on the performance of the machine. …”
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