A Comprehensive Analysis of Imbalance Signal Prediction in the Japanese Electricity Market Using Machine Learning Techniques by Kaiyao Jiang, Yuji Yamada

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Integrating learner characteristics and generative AI affordances to enhance self-regulated learning: a configurational analysis by Xiu-Yi Wu, Thomas K. F. Chiu

“…Abstract This study investigates the configurational impact of generative artificial intelligence (GenAI) tools on self-regulated learning (SRL) across various educational levels using a 28-week fuzzy-set qualitative comparative analysis (fsQCA) approach. …”
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Hybrid time series and machine learning models for forecasting cardiovascular mortality in India: an age specific analysis by M Darshan Teja, G Mokesh Rayalu

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Determination of lung cancer exhaled breath biomarkers using machine learning-a new analysis framework by Tlotlo Cassandra Setlhare, Atlang Gild Mpolokang, Emmanuel Flahaut, George Chimowa

“…This study establishes a new framework integrating machine learning and confounder elimination for biomarker confirmation.…”
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Multi-granularity sentiment analysis and learning outcome prediction for Chinese educational texts based on transformer architecture by Xinyue Gao, Quanrong Fang

“…Furthermore, we develop a predictive model that integrates these sentiment features with learning behavior data to estimate students’ academic performance accurately. …”
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Analysis of anomalous behaviour in network systems using deep reinforcement learning with convolutional neural network architecture by Mohammad Hossein Modirrousta, Parisa Forghani Arani, Reza Kazemi, Mahdi Aliyari‐Shoorehdeli

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The Comprehensive Analysis of Weighted Gene Co-Expression Network Analysis and Machine Learning Revealed Diagnostic Biomarkers for Breast Implant Illness Complicated with Breast Ca... by Huang Z, Wang H, Pang H, Zeng M, Zhang G, Liu F

“…Enrichment analysis, the protein–protein interaction network (PPI), and machine learning algorithms were performed to explore the hub genes. …”
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A comparative analysis of machine learning algorithms with tree-structured parzen estimator for liver disease prediction by Rakibul Islam, Azrin Sultana, MD. Nuruzzaman Tuhin

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Integrating Problem-Based Learning into E-Modules : A Development Study with Validation and Student Response Analysis by Linda Safitri, Nurkhairo Hidayati, Suryanti Suryanti

“…The instruments employed in this study were a validation sheet and a student response questionnaire. Data analysis was conducted by calculating the results of the validation test and the students' responses. …”
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The Impact of ICT on Primary School Students’ Natural Science Learning in Support of Diversity: A Meta-Analysis by José Gabriel Soriano-Sánchez

“…The methodology used for this systematic review and meta-analysis followed PRISMA guidelines, drawing data from the Scopus and Web of Science databases. …”
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Artificial Intelligence-Assisted Machine Learning Methods for Forecasting Green Bond Index: A Comparative Analysis by Ahmed İhsan Şimşek, Yunus Emre Gür, Emre Bulut

“…The data from 1 June 2021 to 29 April 2024, collected from many sources, was separated into training and test sets, and standard preparation was conducted for each. …”
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A social media analysis of students’ perspective toward online learning during the COVID-19 pandemic by Kanetasya Sabilla, Romi Bhakti Hartarto

“…Since March 2020, approximately 45 million students in Indonesia have adapted to online learning due to the COVID-19 pandemic. This study aimed to analyze Indonesian students’ perceptions of online schools using social media data, specifically Twitter. …”
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Predicting diabetic peripheral neuropathy through advanced plantar pressure analysis: a machine learning approach by Mehewish Musheer Sheikh, Mamatha Balachandra, Narendra V. G., Arun G. Maiya

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Fluid-Structure Interaction Analysis of Carotid Artery Blood Flow with Machine Learning Algorithm and OpenFOAM by Banu Körbahti, Murad Kucur

“…The research includes the effect of arterial wall deformation by fluid-structure analysis. Pulsatile velocity cycle is trained for 144 patients with different hemodynamic parameters, by machine learning algorithm using blood flow velocity measured from 337 points of the carotid artery. …”
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Which is Better: E-Book or Printed Book? A Meta-Analysis of Educational Materials in Language Learning by Virgiawan Listanto, Janu Arlinwibowo, Anggraeni Dian Permatasari, Khofifa Najma Iftitah, Ence Oos Mukhamad Anwas

“…Statistical analyses included tests for heterogeneity, publication bias, total effect size, and moderator variables using analysis of variance. The findings indicate that e-books positively influence the learning process as compared to printed books, with a medium standardized effect size of 0.5. …”
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Advanced comparative analysis of machine learning algorithms for early Parkinson's disease detection using vocal biomarkers by Ajay Kumar, Jay Parkash Singh, Priyanka Paygude, Rachan Daimary, Sandeep Prasad

“…Objective To investigate the potential of voice analysis—specifically sustained vowel phonation—as a non-invasive, cost-effective diagnostic method for early detection of Parkinson's disease (PD) using machine learning techniques. …”
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Design and implementation of classical literature sentiment analysis system based on ensemble learning and graph neural network by Qianru Gao, Jiachen Huang

“…This study is committed to the design and implementation of a classical literature sentiment analysis system based on ensemble learning and graph neural network, with the goal of breaking through the limitations of traditional methods and realizing the refined analysis of classical literature sentiment tendencies. …”
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Predicting the Compression Capacity of Screw Piles in Sand Using Machine Learning Trained on Finite Element Analysis by David Igoe, Pouya Zahedi, Hossein Soltani-Jigheh

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Machine-learning-aided analysis of relationship between crystal defects and macroscopic mechanical properties of TWIP steel by Marino Tanaka, Kai Sasaki, Jesada Punyafu, Mayu Muramatsu, Mitsuhiro Murayama

“…Abstract Establishing efficient methods to obtain quantitative data on crystal defect evolution is vital for understanding material properties. …”
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Bagging Vs. Boosting in Ensemble Machine Learning? An Integrated Application to Fraud Risk Analysis in the Insurance Sector by Ruixing Ming, Osama Mohamad, Nisreen Innab, Mohamed Hanafy

“…Addressing the pressing challenge of insurance fraud, which significantly impacts financial losses and trust within the insurance industry, this study introduces an innovative automated detection system utilizing ensemble machine learning (EML) algorithms. The approach encompasses four strategic phases: 1) Tackling data imbalance through diverse re-sampling methods (Over-sampling, Under-sampling, and Hybrid); 2) Optimizing feature selection (Filtering, Wrapping, and Embedding) to enhance model accuracy; 3) employing binary classification techniques (Bagging and Boosting) for effective fraud identification; and 4) applying explanatory model analysis (Shapley Additive Explanations, Break-down plot, and variable-importance Measure) to evaluate the influence of individual features on model performance. …”
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