Analysis of high school students’ use of digital devices: focus on learning and instruction elements by Ga-young Yun, Sojung Kim

“…The purpose of this study is to categorize high school students’ digital device usage patterns and identify group characteristics, focusing on learning and instructional elements. For this purpose, data from high school students were collected through the Gyeonggi Education Panel Study (GEPS) and clusters were derived through cluster analysis using variables related to digital device use, general learning, subject-specific learning in school, and academic achievement. …”
Get full text

MyWear revolutionizes real-time health monitoring with comparative analysis of machine learning by Krishna Prakash, Musam Naga Harshitha, Golla Naga Lakshmi, Pallem Moses, Madala Sumanth Chowdary, Shonak Bansal, Mohammad Rashed Iqbal Faruque, K. S. Al-Mugren

Get full text

Bibliometric analysis of physical education learning in inclusive schools: trends, contributions, and research impact by Deswita Supriyatni, Soni Nopembri, Hari Yuliarto, Sandey Tantra Paramitha, Muhammad Gilang Ramadhan, Eko Purnomo

“…Objective: This study aims to analyze trends, contributions, and research impact on physical education learning in inclusive schools (2014–2024) using Scopus data, identifying collaborations, research gaps, and future study directions. …”
Get full text

Bibliometric analysis of physical education learning in inclusive schools: trends, contributions, and research impact by Deswita Supriyatni, Soni Nopembri, Hari Yuliarto, Sandey Tantra Paramitha, Muhammad Gilang Ramadhan, Eko Purnomo

“…Objective: This study aims to analyze trends, contributions, and research impact on physical education learning in inclusive schools (2014–2024) using Scopus data, identifying collaborations, research gaps, and future study directions. …”
Get full text

Advancing Image Spam Detection: Evaluating Machine Learning Models Through Comparative Analysis by Mahnoor Jamil, Hristina Mihajloska Trpcheska, Aleksandra Popovska-Mitrovikj, Vesna Dimitrova, Reiner Creutzburg

Get full text

Evaluating integrated learning: A SECI model approach through importance-performance analysis by Sabda Alam Muhammadan, Mochamad Syamsul Ma'arif, Suhendi Suhendi

“…A quantitative research design was employed, utilizing Importance-Performance Analysis (IPA) to assess the perceived importance and performance of three integrated learning models: Learning from Experience, Social Learning, and Formal Learning. …”
Get full text

Evaluating integrated learning: A SECI model approach through importance-performance analysis by Sabda Alam Muhammadan, Mochamad Syamsul Ma'arif, Suhendi Suhendi

“…A quantitative research design was employed, utilizing Importance-Performance Analysis (IPA) to assess the perceived importance and performance of three integrated learning models: Learning from Experience, Social Learning, and Formal Learning. …”
Get full text

Deep learning model for hair artifact removal and Mpox skin lesion analysis and detection by Edeh Michael Onyema, B. Gunapriya, Balasubramanian Prabhu Kavin, Mueen Uddin, Priyan Malarvizhi Kumar, Tehseen Mazhar, Mamoon M. Saeed

Get full text

The Analysis of Communication Strategy of Disabled Sports Information Based on Deep Learning and the Internet of Things by Wanglong Wang, Qingwen Liu, Chuan Shu

“…By constructing deep learning models, extensive data on disabled sports activities are analyzed, enabling the identification and prediction of key factors in information dissemination. …”
Get full text

Image-Based Laser-Beam Diagnostics Using Statistical Analysis and Machine Learning Regression by Tayyab Imran, Muddasir Naeem

“…By integrating deterministic analysis with data-driven forecasting, this methodology offers a robust framework for real-time beam quality evaluation. …”
Get full text

Hybrid learning in post-pandemic higher education systems: an analysis using SEM and DNN by Alvin Muhammad ‘Ainul Yaqin, Ahmad Kamil Muqoffi, Sigit Rahmat Rizalmi, Faishal Arham Pratikno, Remba Yanuar Efranto

Get full text

Deep learning analysis for rheumatologic imaging: current trends, future directions, and the role of human by Jucheol Moon, Pratik Jadhav, Sangtae Choi

“…Traditional imaging techniques, including plain radiography, ultrasounds, computed tomography, and magnetic resonance imaging (MRI), play a critical role in diagnosing and monitoring these conditions, but face limitations like inter-observer variability and time-consuming assessments. Recently, deep learning (DL), a subset of artificial intelligence, has emerged as a promising tool for enhancing medical imaging analysis. …”
Get full text

Machine Learning for Resilient and Sustainable Cities: A Bibliometric Analysis of Smart Urban Technologies by Bin Luan, Xinqun Feng

“…This study aims to conduct a bibliometric analysis of the published research in the fields of smart cities and machine learning, using visualization techniques to reveal the spatiotemporal distribution patterns, research hotspots, and collaborative network structures. …”
Get full text

Comparative analysis of machine learning models for detecting water quality anomalies in treatment plants by P. Prabu, Ala Saleh Alluhaidan, Romana Aziz, Shakila Basheer

“…In addition to developing this model, we present a comparative analysis with several existing machine learning models, demonstrating the effectiveness of our approach in detecting water quality anomalies. …”
Get full text

Dilated cardiomyopathy signature metabolic marker screening: Machine learning and multi-omics analysis by Xiao-Lei Li, Aibibanmu Aizezi, Yan-Peng Li, Yan-Hong Li, Fen Liu, Qian Zhao, Xiang Ma, Dilare Adi, Yi-Tong Ma

“…Methods: We utilized non-targeted metabolomics with a cross-sectional cohort of age- and sex-matched DCM patients and controls. Metabolomics data were analyzed using orthogonal partial least squares-discriminant analysis (OPLS-DA) and pathway analysis. …”
Get full text

Machine learning based risk analysis and predictive modeling of structure fire related casualties by Andres Schmidt, Eric Gemmil, Russ Hoskins

Get full text

Machine-learned interatomic potentials for accurate analysis of the mechanical properties of boron nitride sheets by Vijay Choyal, Mahesh Patil, Nitin Luhadiya, S I Kundalwal

“…We introduced a novel machine-learned interatomic potential (MLIP) by thoroughly discussing the step–by–step MLIP creation process using precise but limited data. …”
Get full text

Understanding forest insect outbreak dynamics: a comparative analysis of machine learning techniques by Roberto Molowny-Horas, Saeed Harati-Asl, Liliana Perez

Get full text

Predicting Credit Card Fraud using Supervised Machine Learning Methods: Comparative Analysis by Güner Altan, Metin Recep Zafer

Get full text

Deep Federated Learning Based Convergence Analysis in Relaying-Aided MEC-IoT Networks by Jun Liu, Tao Cui, Lin Zhang, Yuwei Zhang, Jing Wang, Chao Li, Kai Chen, Huang Huang, Xuan Zhou, Wei Zhou, Sun Li, Suili Feng, Dongqing Xie, Yun Li, Haige Xiang, Kaimeno Dube, Abbarbas Muazu, Nakilavai Rono, Wen Zhou, Fusheng Zhu, Liming Chen, Dan Deng, Zhao Wang, Yajuan Tang

“…We finally present some results to show that the analysis of the convergence error is effective. The work in this paper can provide some theoretical foundation for deep federated learning and computing networks.…”
Get full text