Development of Teaching Materials Based on Melayu Culture IPAS Learning in Elementary Schools by Guslinda, Munjiatun, Intan Kartika Sari, Afra Alifarani

“…At the Define stage, an analysis of the material presented in teaching materials is carried out which is adapted to students. …”
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An efficient swarm evolution algorithm with probability learning for the black and white coloring problem by Zhiqiang Zhang, Li Zhang, Xiujun Zhang

“…This problem is a NP-complete problem, widely used in reagent product storage in chemical industry and the solution to the problem of black and white queens in chess. The paper presents a swarm evolution algorithm based on improved simulated annealing search and evolutionary operation with probability learning mechanism. …”
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Malware Detection in Forensic Memory Dumps: The Use of Deep Meta-Learning Models by Yalçın Özkan

“…The present study aimed to design a high-performance deep meta-learning model that could be utilized in classification predictions using forensic memory datasets and propose a framework that would ensure the generalization and consistency of the predictions with the help of this model. …”
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Graph-contrast ransomware detection (GCRD) with advanced feature selection and deep learning by Suneeta Satpathy, Pratik Kumar Swain

“…CLR enhances feature representation through self-supervised learning, improving generalizability to various ransomware families. …”
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Copula-Driven Learning Techniques for Physical Layer Authentication Using Multimodal Data by Sahana Srikanth, Sanjeev Gurugopinath, Sami Muhaidat

“…In this paper, we present a study on copula-driven learning techniques for physical layer authentication (PLA) in wireless communication, using data from multiple modalities. …”
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Machine learning applications in energy systems: current trends, challenges, and research directions by Saad Aslam, Pyi Phyo Aung, Ahmad Sahban Rafsanjani, Anwar P. P. Abdul Majeed

“…Building on its proven success, researchers are increasingly adopting ML-driven approaches to accelerate advances in energy systems. This work presents a detailed review of current ML-driven research trends in energy systems, outlines the associated challenges, and provides potential research directions and recommendations. …”
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ANALYSIS OF THE FACTORS INFLUENCING THE OFFLINE LEARNING READINESS DURING THE COVID-19 PANDEMIC by Arlina Dhian Sulistyowati, Devi Permata Sari, Fitriana Noor Khayati, Ambar Winarti, Ria May Syaroh

“…The results of the Chi-Square test showed that physical, psychological, and material readiness influenced offline learning readiness. Conclusions: The present study also concluded that physical readiness was the most dominant factor which affects offline learning readiness.…”
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A Damage Classification Approach for Structural Health Monitoring Using Machine Learning by Diego Tibaduiza, Miguel Ángel Torres-Arredondo, Jaime Vitola, Maribel Anaya, Francesc Pozo

“…As a contribution, this work presents a damage detection and classification methodology which includes the use of data collected from a structure under different structural states by means of a piezoelectric sensor network taking advantage of the use of guided waves, hierarchical nonlinear principal component analysis (h-NLPCA), and machine learning. …”
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Neural mechanisms of learned suppression uncovered by probing the hidden attentional priority map by Changrun Huang, Dirk van Moorselaar, Joshua Foster, Mieke Donk, Jan Theeuwes

“…These findings suggest that learned distractor suppression is a reactive process, providing new insights into learned spatial distractor suppression mechanisms.…”
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A Hybrid Deep Transfer Learning Framework for Delamination Identification in Composite Laminates by Muhammad Haris Yazdani, Muhammad Muzammil Azad, Salman Khalid, Heung Soo Kim

“…To overcome these limitations, this study presents a hybrid deep transfer learning (HTL) framework to identify delamination in composite laminates. …”
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Deep Learning in Digital Breast Tomosynthesis: Current Status, Challenges, and Future Trends by Ruoyun Wang, Fanxuan Chen, Haoman Chen, Chenxing Lin, Jincen Shuai, Yutong Wu, Lixiang Ma, Xiaoqu Hu, Min Wu, Jin Wang, Qi Zhao, Jianwei Shuai, Jingye Pan

“…Deep learning (DL) has been shown to effectively increase the processing efficiency and diagnostic accuracy of DBT images. …”
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Plant Disease Prognosis Using Spatial-Exploitation-Based Deep-Learning Models by Jayavani Vankara, Sekharamahanti S. Nandini, Murali Krishna Muddada, N. Satya Chitra Kuppili, K Sowjanya Naidu

“…The use of deep convolutional neural networks for image categorization has greatly improved accuracy. In this paper, we present a framework for automating disease detection by the use of a tailored DL architecture. …”
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Comparison of Deep Learning Techniques for RF-Based Human Posture Detection Systems by Eugene Casmin, Miriam Rodrigues, Americo Alves, Rodolfo Oliveira

“…We show that although the SP-based solution presents high accuracy, the hybrid SP/DL solutions are advantageous in terms of classification accuracy and robustness at multiple distances, albeit requiring higher computation time. …”
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EEG-Based Assessment of Cognitive Resilience via Interpretable Machine Learning Models by Ioannis Kakkos, Elias Tzavellas, Eleni Feleskoura, Stamatis Mourtakos, Eleftherios Kontopodis, Ioannis Vezakis, Theodosis Kalamatianos, Emmanouil Synadinakis, George K. Matsopoulos, Ioannis Kalatzis, Errikos M. Ventouras, Aikaterini Skouroliakou

“…Background: Cognitive resilience is a critical factor in high-performance environments such as military operations, where sustained stress can impair attention and decision-making. In the present study, we utilized EEG and machine learning to assess cognitive resilience in elite military personnel. …”
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Evaluating the Efficacy of Deep Learning Models for Identifying Manipulated Medical Fundus Images by Ho-Jung Song, Ju-Hyuck Han, You-Sang Cho, Yong-Suk Kim

“…The proposed approach for detecting manipulated fundus images through a deep learning model demonstrates higher performance than that of ophthalmologists, making it an effective method.…”
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The Adversarial Robust and Generalizable Stereo Matching for Infrared Binocular Based on Deep Learning by Bowen Liu, Jiawei Ji, Cancan Tao, Jujiu Li, Yingxun Wang

“…This paper presents a novel deep-learning-based depth optimization method that obviates the need for large infrared image datasets and adapts seamlessly to any specific infrared camera. …”
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The Effect of Organizational Learning on Improving Organizational Effectiveness: The Mediating Role of Operational Flexibility by Rashad Said Dabwaan Al-Makhlaafi, Abdulrahman Al-Sufyani, Faisal Ali Mohammed Ali

“…This study provides a novel contribution to the academic literature by presenting an innovative theoretical model that enhances the understanding of how organizational learning influences organizational effectiveness through operational flexibility. …”
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Advanced Deep Learning Based Predictive Maintenance of DC Microgrids: Correlative Analysis by M. Y. Arafat, M. J. Hossain, Li Li

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High-Performance Real-Time Human Activity Recognition Using Machine Learning by Pardhu Thottempudi, Biswaranjan Acharya, Fernando Moreira

“…Human Activity Recognition (HAR) is a vital technology in domains such as healthcare, fitness, and smart environments. This paper presents an innovative HAR system that leverages machine-learning algorithms deployed on the B-L475E-IOT01A Discovery Kit, a highly efficient microcontroller platform designed for low-power, real-time applications. …”
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Exploration of physiological sensors, features, and machine learning models for pain intensity estimation. by Fatemeh Pouromran, Srinivasan Radhakrishnan, Sagar Kamarthi

“…This subjective pain assessment results in suboptimal treatment plans, over-prescription of opioids, and drug-seeking behavior among patients. In the present study, we explored automatic objective pain intensity estimation machine learning models using inputs from physiological sensors. …”
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