Influence of the ICT learning model on quality improvement at Mandiri LPK SNU’R Bogor by Iwan Armawan, Amiruddin Saleh, Pudji Muljono, Musa Hubeis

Subjects: “…availability of media, quality of learning, digital learning, learning model…”
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A randomized trial of blended first aid education for the public

Subjects: “…e-learning…”
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A Collaborative Privacy Preserved Federated Learning Framework for Pneumonia Detection using Diverse Chest X-ray Data Silos by Shagun Sharma, Kalpna Guleria

Subjects: “…federated learning…”
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In-Service Teacher Professional Development: Challenges and Opportunities for Innovating the Trichronous Modality of Delivery in Vietnam’s EFL Education by Tuyen Van Nguyen, Helena Sit

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A feature selection and scoring scheme for dimensionality reduction in a machine learning task by PHILEMON UTEN EMMOH, christopher ifeanyi Eke, Timothy Moses

“… Selection of important features is very vital in machine learning tasks involving high-dimensional dataset with large features. …”
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A Systematic Review of Self-directed Learning in Medical Education in Undergraduate Medical Students by Dharmendra Kumar Gupta, Arunima Chaudhuri, Dip Gaine

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Lessons Learned by Yugoslav Military Experts from the Soviet Invasion of Afghanistan 1979-1981 by A. Y. Timofeev

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A machine learning and clustering-based approach for county-level COVID-19 analysis. by Charles Nicholson, Lex Beattie, Matthew Beattie, Talayeh Razzaghi, Sixia Chen

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Predicting the Number of Passengers in Public Transportation Areas Using the Deep Learning Model LSTM by Joko Siswanto, Sri Yulianto Joko Prasetyo, Sutarto Wijono, Evi Maria, Untung Rahardja

“…They are required by the planning and management authority for bus public transport. A deep learning-based LSTM prediction model is proposed to predict the number of passengers in 4 bus public transportation areas (central, north, south, and west), evaluated by MSLE, MAPE, and SMAPE with dropout, neuron, and train-test variations. …”
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Storylines from Newcomers’ Perspectives: The Complexities of Learning Mathematics in a Norwegian Introductory Class by Shanthi Gretta Pasanha, Annica Andersson, Beth Herbel-Eisenmann, Anita Movik Simensen

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Deep learning-based prediction of mortality using brain midline shift and clinical information by An-Rong Wu, Sun-Yuan Hsieh, Hsin-Hung Chou, Cheng-Shih Lai, Jo-Ying Hung, Bow Wang, Yi-Shan Tsai

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Integrating machine learning with mendelian randomization for unveiling causal gene networks in glioblastoma multiforme by Lixin Du, Pan Wang, Xiaoting Qiu, Zhigang Li, Jianlan Ma, Pengfei Chen

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Applications and challenges in designing VHH-based bispecific antibodies: leveraging machine learning solutions by Michael Mullin, James McClory, Winston Haynes, Justin Grace, Nathan Robertson, Gino van Heeke

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End-to-End Architecture for English Reading and Writing Content Assessment Based on Prompt Learning by Su-Qin Wu, Ming-Yong Pang, Xue-Mei Sun, Xin-Jian Wang, Yun-Peng Ji

Subjects: “…Prompt learning…”
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Retracted: Deep Learning-Based Detection and Identification Method for Sports Health Video Dissemination by Discrete Dynamics in Nature and Society

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Frontier machine learning techniques for melanoma skin cancer identification and categorization: An in-Depth review by Viomesh Singh, Kavita A. Sultanpure, Harshwardhan Patil

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Multistage System-Based Machine Learning Techniques for Intrusion Detection in WiFi Network by Vu Viet Thang, F. F. Pashchenko

“…The aim of machine learning is to develop algorithms that can learn from data and solve specific problems in some context as human do. …”
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Analyzing and forecasting under-5 mortality trends in Bangladesh using machine learning techniques. by Shayla Naznin, Md Jamal Uddin, Ishmam Ahmad, Ahmad Kabir

“…This study employs machine learning models, including Linear Regression, Ridge Regression, Lasso Regression, Bayesian Ridge, Decision Tree, Gradient Boosting, XGBoost, and CatBoost, to forecast future trends in under-5 mortality. …”
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Modeling students’ intentions to learn data science: Using an extended theory of planned behavior by Ram B. Basnet, David J. Lemay, Paul Bazelais

“…Knowledge Management & E-Learning: An International Journal…”
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Categorizing and Determining of BI-RAD Breast Cancer Score by Using Deep Learning Techniques by Azhar Kassem Flayeh, Ali Douik

“…This work utilized deep learning techniques and algorithms to accurately categorize breast cancer at 93%, 92%, 90%, and 93% for each category (C2, C3, C4, and C5). …”
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