Assessing the influence of multimedia instruction on knowledge transfer in higher education: Multimodal learning experiences by Daigo Bernadetha Haule, Thanaa Shaker, Godfrey Yohanes Nkwera

“…Notwithstanding challenges such as curriculum misalignment and resistance to change, the study emphasizes the overwhelmingly positive impact of multimedia on learning outcomes. …”
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Watch Your Callback: Offline Anomaly Detection Using Machine Learning in ROS 2 by Jeonghwan Kang, Kyounghwan Kim, Donghyun Kwon

“…Based on our evaluation, Isolation Forest achieved an F1 score of up to 0.94 with a false positive rate of 1.3%, OCSVM reached an F1 score of up to 0.98 with a false positive rate of around 1.7%, and the Autoencoder attained an F1 score of up to 0.99 with a false positive rate as low as 1.0%.…”
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Exploring the Controlling Factors of Watershed Streamflow Variability Using Hydrological and Machine Learning Models by Bingbing Ding, Xinxiao Yu, Guodong Jia

“…This study demonstrated the potential of integrating hydrological models with machine learning by constructing two machine learning methods, Extreme Gradient Boosting (XGBoost) and Random Forest (RF), based on the input and output data from the Soil and Water Assessment Tool (SWAT) and comparing their streamflow simulation performances. …”
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Transformative applications in biology education: A case study on the efficacy of adaptive learning with numerical insights by Shin I. Aleksandrovich, Tyan Ramazan, Raissa Utegaliyeva, Balzat Sarimbayeva, Gaukhar Keubassova, Rakhima Bissalyyeva, Kuanysh Syman, Gaukhar Abdikarimova

“…Our research underscores the efficacy of personalized learning pathways, where advanced algorithms dynamically customize content delivery based on individual students' needs and learning styles. …”
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Predicting executive functioning from walking features in Parkinson’s disease using machine learning by Artur Piet, Johanna Geritz, Pascal Garcia, Mona Irsfeld, Frédéric Li, Xinyu Huang, Muhammad Tausif Irshad, Julius Welzel, Clint Hansen, Walter Maetzler, Marcin Grzegorzek, Nico Bunzeck

“…Walking features were quantified using an inertial measurement unit (IMU) system positioned at the patient’s lower back. The analyses included five imputation methods and four regression approaches to predict executive functioning, as measured using the Trail-Making Test (TMT). …”
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An interpretable deep learning model for the accurate prediction of mean fragmentation size in blasting operations by Baoqian Huan, Xianglong Li, Jianguo Wang, Tao Hu, Zihao Tao

“…The study is based on a database containing 105 samples derived from both previous research and field collection. …”
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Learning engagement and psychological flexibility among Chinese adolescents: a moderated mediation model by Pengfei Yue, Pengfei Yue, Jiaxin Zhang

“…While previous studies have used professional Acceptance and Commitment Therapy (ACT) interventions to enhance psychological flexibility, our study proposes a new approach to enhance psychological flexibility based on the Development-in-Sociocultural-Context Model for Childrenl Acceptance and CommitmenMethodsThis study, adopting a positivist research approach, utilized a cross-sectional design. …”
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Effective Detection of Malicious Uniform Resource Locator (URLs) Using Deep-Learning Techniques by Yirga Yayeh Munaye, Aneas Bekele Workneh, Yenework Belayneh Chekol, Atinkut Molla Mekonen

“…To address these limitations, we propose a deep learning-based approach aimed at developing an effective model for detecting malicious URLs. …”
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A real-time AI tool for hybrid learning recommendation in education: Preliminary results by Chaman Verma

“…This study created an innovative AI tool utilizing the Support Vector Machine (SVM) algorithm on primary samples of Hungarian informatics students to assess their suitability for adopting hybrid learning in their studies. This paper also explained the strength of the model with Shapley Additive exPlanations (SHAP) values of the Explainable Artificial Intelligence (XAI) method. …”
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An evolution of forensic linguistics: From manual analysis to machine learning – A narrative review by R. Thamizh Mani, Vikram Palimar, Mamatha Shivananda Pai, T.S. Shwetha, M. Nirmal Krishnan

“…By synthesizing 77 studies, the analysis reveals that ML algorithms—notably deep learning and computational stylometry—outperform manual methods in processing large datasets rapidly and identifying subtle linguistic patterns (e.g., authorship attribution accuracy increased by 34 % in ML models). …”
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Optimising test intervals for individuals with type 2 diabetes: A machine learning approach. by Sasja Maria Pedersen, Nicolai Damslund, Trine Kjær, Kim Rose Olsen

“…Model performance is evaluated using ROC-AUC, and optimal intervals are determined based on a "time-to-next-positive-test" concept, with different durations associated with specific intervals.…”
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The perception of dental students and educators about e-learning during COVID − 19 pandemic by Shaimaa M. Fouda, Marwa Madi, Passent Ellakany, Nourhan M. Aly, Hala A. Bahgat, Abeer E. Elembaby, Jorma I. Virtanen, Jehan AlHumaid

“…Satisfaction and attitude towards technology-based learning (TBL) were assessed. Two binary logistic regression models were performed to assess the association of factors (age, gender, Grade Point Average, IT skills and experience in TBL) with educators and students’ overall satisfaction and attitude towards TBL. …”
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Interpretable machine learning approach for TBM tunnel crown convergence prediction with Bayesian optimization by Wanrui Hu, Kai Wu, Kai Wu, Heng Liu, Weibang Luo, Xingxing Li, Peng Guan

“…This study proposes an interpretable machine learning method based on Bayesian optimization (BO) and SHapley Additive exPlanations (SHAP) for predicting crown convergence (CC) in TBM tunnels. …”
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Implementation of a hospice community service redesign: Qualitative research identifying lessons learned by Kate Sugar, Candy McCabe, Alison Llewellyn, Wayne de Leeuw, Maggie Crowe, Claire Prendergast, Charlotte Spence, Natasha Bradley

“…Aim: We aimed to capture learning from the implementation experiences of hospice stakeholders during the first 12 months of a hospice community services redesign. …”
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Breast Cancer Detection on Dual-View Sonography via Data-Centric Deep Learning by Ting-Ruen Wei, Michele Hell, Aren Vierra, Ran Pang, Young Kang, Mahesh Patel, Yuling Yan

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Enhancing eLoran Timing Accuracy via Machine Learning With Meteorological and Terrain Data by Taewon Kang, Seunghyeon Park, Pyo-Woong Son, Jiwon Seo

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Sex Estimation Through Orbital Measurements: A Machine Learning Approach for Forensic Science by George Triantafyllou, George G. Botis, Maria Piagkou, Konstantinos Papanastasiou, George Tsakotos, Ioannis Paschopoulos, George K. Matsopoulos, Stavroula Papadodima

“…The human skull has important landmarks that can serve as reliable sex estimation predictors. Methods: In this study, orbital measurements from 92 dried skulls, comprising 35 males and 57 females, were utilized to develop a machine-learning-based classifier for sex estimation with potential applications in forensic science. …”
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Sensitivity to Instruction Strategies in Motor Learning Is Predicted by Anterior–Posterior TMS Motor Thresholds by Michael L. Perrier, Kylee R. Graham, Jessica E. Vander Vaart, W. Richard Staines, Sean K. Meehan

“…<b>Objectives:</b> This study used controllable pulse parameter TMS (cTMS) to assess how AP-sensitive interneuron excitability interacts with explicit knowledge to influence motor learning. <b>Methods</b>: Seventy-two participants were grouped as AP-positive (n = 36) and AP-negative groups (n = 36) based on whether an AP threshold could be obtained before reaching maximal stimulator output. …”
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Distance Learning For Military Doctors in Crisis Situations: The DILEMMA Cross-Sectional Study Results by Nataliia Sydorova, Valerii Savytskyi, Taras Kuts

“…Methods: The data presented reflect the results of 147 trainees’ survey dedicated to their satisfaction and ways to improve distance learning component of training/retraining at UMMA during such crisis situations as pandemics and hostilities. …”
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External Validation of Persistent Severe Acute Kidney Injury Prediction With Machine Learning Model by Simone Zappalà, PhD, Francesca Alfieri, MS, Andrea Ancona, PhD, Antonio M. Dell’Anna, MD, Kianoush B. Kashani, MD, MS

“…Results from the Amsterdam UMCdb (531 patients, 59 [11.11%] positive) and MIMIC-III (495 patients, 57 [11.52%] positive) cohorts were obtained in a prior development study. …”
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