Differences in improving learning outcomes and student learning motivation by S. Sudirman, Kms. Muhammad Amin Fauzi, Anita Yus

“…The research instruments used were tests and a motivation questionnaire. Data analysis was conducted using t-test and N-Gain analysis. …”
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Advancing shock prediction: leveraging prior knowledge and self-controlled data for enhanced model accuracy and generalizability by Cheng-Yu Tsai, Xiu-Rong Huang, Po-Tsun Kuo, Tzu-Tao Chen, Yun-Kai Yeh, Kuan-Yuan Chen, Arnab Majumdar, Chien-Hua Tseng

“…This study aims to develop an enhanced machine learning model that improves predictive performance by utilizing self-controlled data and applying feature engineering informed by medical knowledge to physiological waveforms, enabling the prediction of shock one hour in advance without relying on blood tests. …”
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Identification of potential diagnostic markers and molecular mechanisms of asthma and ulcerative colitis based on bioinformatics and machine learning by Chenxuyu Zhang, Chenxuyu Zhang, Zheng Luo, Liang Ji

“…Shared genes between asthma and UC were derived by intersecting DEGs with UC-associated modules, followed by functional enrichment and protein-protein interaction (PPI) analysis. Machine learning identified hub genes, validated through external datasets using ROC curves, nomograms, and boxplots. …”
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Text Classification Techniques: A Holistic Review, Observational Analysis, and Experimental Investigation by Kamal Taha, Paul D. Yoo, Chan Yeun, Aya Taha

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Article RETRACTED due to manipulation by the authors Differences in Achievement in Physical Education Learning Outcomes for High School Students Based on Parental Occupation: Analy... by Riko Septiantoko, Mukhammad Murdiono, Saliman Saliman, Hendra Setyawan, José Vicente García-Jiménez, Francesca Latino, Francesco Tafuri, Nuridin Widya Pranoto, Ari Wibowo Kurniawan, Khoiril Anam, Abdul Aziz Purnomo Shidiq, Muhammad Imam Rahmatullah, Özgür Eken

“…Yogyakarta Indonesia. Data analysis used descriptive statistics and inferential factorial one-way ANOVA. …”
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Exploring the Impact of Artificial Intelligence on Healthcare Management: A Combined Systematic Review and Machine-Learning Approach by Vito Santamato, Caterina Tricase, Nicola Faccilongo, Massimo Iacoviello, Agostino Marengo

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Park Development, Potential Measurement, and Site Selection Study Based on Interpretable Machine Learning—A Case Study of Shenzhen City, China by Haihong Li, Li He

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Long-Term Predictive Modelling of the Craniofacial Complex Using Machine Learning on 2D Cephalometric Radiographs by Michael Myers, Michael D. Brown, Sarkhan Badirli, George J. Eckert, Diane Helen-Marie Johnson, Hakan Turkkahraman

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Automatic detection and prediction of epileptic EEG signals based on nonlinear dynamics and deep learning: a review by Shixiao Tan, Zhen Tang, Qiang He, Ying Li, Yuliang Cai, Jiawei Zhang, Di Fan, Zhenkai Guo

“…In recent years, nonlinear dynamics methods such as chaos theory, fractal analysis, and entropy computation have provided new perspectives for EEG signal analysis, while deep learning approaches like convolutional neural networks and long short-term memory networks further enhance the robustness of dynamical pattern recognition through end-to-end nonlinear feature extraction. …”
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Reflections on Students’ Synchronous Learning to Engage Autonomous Learning by Khulaifiyah Khulaifiyah, Andri Eko Prabowo, Andi Idayani, Erna Nawir

“…The data is described from 100 respondents to by descriptive analysis approach. …”
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Application of Formal Concept Analysis and Clustering Algorithms to Analyze Customer Segments by I Gede Bintang Arya Budaya, I Komang Dharmendra, Evi Triandini

“…The complexity of customer data makes manual analysis of the customer segments difficult, so applying machine learning to segment the customer can be the solution. …”
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Prediction of Innovation Values of Countries Using Data Mining Decision Trees and a Comparative Application with Linear Regression Model by Merve Doğruel, Seniye Ümit Fırat

“…This study includes regression tree analysis and linear regression analysis and comparison using the indicators within the dimensions below the subscales of the GII score andNRI index based on supervised machine learning. …”
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A hybrid deep learning-based approach for optimal genotype by environment selection by Zahra Khalilzadeh, Motahareh Kashanian, Saeed Khaki, Lizhi Wang

“…Genotype-environment interactions introduce large variations in crop-climate responses, and are hard to factor in to breeding programs. Data-driven approaches, particularly those based on machine learning, can help guide breeding efforts by factoring in genotype-environment interactions when making yield predictions. …”
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Machine learning-based prediction model for post-stroke cerebral-cardiac syndrome: a risk stratification study by Tingyu Zhang, Zelin Hao, Qunlian Jiang, Linhui Zhu, Lifang Ye

“…CCS diagnosis was based on cardiac dysfunction occurring after stroke, excluding pre-existing cardiac diseases. Five machine learning models, including Logistic Regression, Random Forest, Support Vector Machine (SVM), XGBoost, and Deep Neural Network, were trained on 80% of the data and tested on the remaining 20%. …”
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A Survey of Ransomware Detection Methods by Saleh Alzahrani, Yang Xiao, Sultan Asiri, Jianying Zheng, Tieshan Li

“…The detection methods are categorized into machine-learning-based and non-machine-learning-based methods, discussing the advantages and drawbacks of each. …”
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Classification of Real-World Objects Using Supervised ML-Assisted Polarimetry: Cost/Benefit Analysis by Rui M. S. Pereira, Filipe Oliveira, Nazar Romanyshyn, Irene Estevez, Joel Borges, Stephane Clain, Mikhail I. Vasilevskiy

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MCAUnet: a deep learning framework for automated quantification of body composition in liver cirrhosis patients by Jiening Wang, Shuqi Xia, Jie Zhang, Xinyi Wang, Cai Zhao, Wen Zheng

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Integrated single-cell and transcriptome sequencing data reveal the value of IL1RAP in gastric cancer microenvironment and prognosis by Weifeng Yang, Xiaohua Wu, Jian Wang, Wenquan Ou, Xing Huang

“…BackgroundInvestigating the pivotal role of IL1RAP in the tumor microenvironment of gastric cancer.MethodDownload and collate transcriptomic and single-cell data from gastric cancer patients. Three machine learning algorithms identified distinct sets of prognostic genes in gastric cancer patients. …”
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Comparing the effect of pre-anesthesia clonidine and tranexamic acid on intraoperative bleeding volume in rhinoplasty: a machine learning approach by Zahra Asghari Varzaneh, Akram Hemmatipour, Hadi Kazemi-Arpanahi

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A Distributed Machine Learning-Based Scheme for Real-Time Highway Traffic Flow Prediction in Internet of Vehicles by Hani Alnami, Imad Mahgoub, Hamzah Al-Najada, Easa Alalwany

“…However, these detection systems do not support real-time analysis. Centralized machine learning methods face a number of challenges due to the sheer volume of traffic data that needs to be processed in real-time. …”
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