Genomic Selection in Alfalfa Across Multiple Ploidy Levels: A Comparative Study Using Machine Learning and Bayesian Methods by Xiaoyue Zhu, Ruixin Zhang, Tianxiang Zhang, Changhong Guo, Yongjun Shu

“…However, for the fall dormancy trait in the diploid genome, more than half of the models showed regular fluctuations, with prediction accuracy increasing as SNP density increased. …”
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Optimization of heat and mass transfer in chemically radiative nanofluids using Cattaneo-Christov fluxes and advanced machine learning techniques by Shazia Habib, Saleem Nasir, Zeeshan Khan, Abdallah S. Berrouk, Waseem Khan, Saeed Islam

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Multi-Modal Dynamic Fusion for Defect Detection in Electronic Products: A Novel Approach Based on Energy and Deep Learning by Yulin Liu, Yang Gao

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Using role-playing games in virtual exchanges: Global experiences in intercultural and multidisciplinary approaches to learning about environmental sustainability by Eduardo Verri Liberado, Kelly Tzoumis

“…This research examines how the use of role-playing games in virtual exchange projects can provide global learning experiences on controversial environmental sustainability projects. …”
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Adoption deep learning approach using realistic synthetic data for enhancing network intrusion detection in intelligent vehicle systems by Said A. Salloum, Tarek Gaber, Mohammed Amin Almaiah, Rami Shehab, Romel Al-Ali, Theyazan H.H Aldahyani

“…Traditional Network Intrusion Detection Systems (NIDS) often fall short in detecting emerging and sophisticated intrusion methods, primarily due to their reliance on static datasets that fail to capture the nuanced dynamics and complexity of modern network intrusions. …”
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Wavelet-Enhanced Deep Learning Ensemble for Accurate Stock Market Forecasting: A Case Study of Nifty 50 Index by Priya Singh, Manoj Jha, Harshita Patel

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First Global Machine Learning Model to Predict the Rate of TEC Index (ROTI) Response to X‐Class Solar Flares by A. Mahmoudian, F. Ghorbali, M. Vazifehkhah Hafteh

“…A nonlinear response of the ionosphere associated with solar flare characteristics including rise/fall time and maximum amplitude is discussed. The first global machine learning (ML) model to predict solar flare impact on Earth's ionosphere through ROTI parameter is developed. …”
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MIML: multiplex image machine learning for high precision cell classification via mechanical traits within microfluidic systems by Khayrul Islam, Ratul Paul, Shen Wang, Yuwen Zhao, Partho Adhikary, Qiying Li, Xiaochen Qin, Yaling Liu

“…Abstract Label-free cell classification is advantageous for supplying pristine cells for further use or examination, yet existing techniques frequently fall short in terms of specificity and speed. In this study, we address these limitations through the development of a novel machine learning framework, Multiplex Image Machine Learning (MIML). …”
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A Novel Graph Reinforcement Learning-Based Approach for Dynamic Reconfiguration of Active Distribution Networks with Integrated Renewable Energy by Hua Zhan, Changxu Jiang, Zhen Lin

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Machine learning-based prediction of torsional behavior for ultra-high-performance concrete beams with variable cross-sectional shapes by Elhabyb Khaoula, Baina Amine, Bellafkih Mostafa, A. Deifalla, Amr El-Said, Mohamed Salama, Ahmed Awad

“…This work introduces a unique Machine Learning (ML) method to accurately anticipate the torsional behavior of UHPCs. …”
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Research on Novel Bearing Fault Diagnosis Method Based on Improved Krill Herd Algorithm and Kernel Extreme Learning Machine by Zhijian Wang, Likang Zheng, Junyuan Wang, Wenhua Du

“…Then, it is imported into the kernel extreme learning machine for fault diagnosis. But considering the kernel function parameters σ and the error penalty factor C will affect the classification accuracy of the kernel extreme learning machine, this paper uses the novel krill herd algorithm (NKH) for their optimization. …”
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The application of machine learning in predicting post-cardiac surgery acute kidney injury in pediatric patients: a systematic review by Sxe Chang Cheong, Shing Lok So, Alexander Lal, Jan Coveliers-Munzi

“…Conventional markers (KDIGO criteria) often fall short for pediatric patients undergoing cardiac surgery. …”
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High-resolution energy consumption forecasting of a university campus power plant based on advanced machine learning techniques by Saad A. Alsamraee, Sanjeev Khanna

“…Effective long-term energy forecasting is essential for efficient management of large institutions like university campuses, yet traditional forecasting methods frequently fall short in capturing complex consumption behaviors. …”
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Generative Artificial Intelligence in Ubiquitous Learning: Evaluating a Chatbot-based Recommendation Engine for Personalized and Context-aware Education by Manel Guettala, Samir Bourekkache, Okba Kazar, Saad Harous

“…Background: Ubiquitous learning environments aim to provide personalized and context-aware educational resources; however, traditional recommendation systems often fall short in meeting these dynamic learner needs.Objective: This study develops and evaluates a chatbot-based recommendation system that uses generative AI and prompt engineering techniques to enhance recommendation accuracy and user engagement in ubiquitous learning contexts.Methods: A ChatGPT-powered chatbot was implemented using few-shot prompting and dynamic context integration to deliver personalized, real-time educational support. …”
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Digital twin based deep learning framework for personalized thermal comfort prediction and energy efficient operation in smart buildings by Ahmad Almadhor, Nejib Ghazouani, Belgacem Bouallegue, Natalia Kryvinska, Shtwai Alsubai, Moez Krichen, Abdullah Al Hejaili, Gabriel Avelino Sampedro

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Using fishery-related data, scientific expertise, and machine learning to improve marine habitat mapping in northeastern Mediterranean waters by Loukas Katikas, Sofia Reizopoulou, Paraskevi Drakopoulou, Vassiliki Vassilopoulou

“…The vast majority of marine seabed priority habitats in the study area appeared to fall outside the borders of the current Natura 2000 sites, which served as the baseline for the declared trawl bans in Greek waters, following the provisions of the EU Marine Action Plan.…”
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Near-Infrared Spectroscopy Machine-Learning Spectral Analysis Tool for Blueberries (<i>Vaccinium corymbosum</i>) Cultivar Discrimination by Pedro Ribeiro, Maria Inês Barbosa, Clara Sousa, Pedro Miguel Rodrigues

“…Spectra were acquired from fresh blueberry leaves collected from two geographic regions and across three seasons. Machine-learning-based models, selected from a pool of 10 classifiers based on their discrimination power under a twofold stratified cross-validation process, were trained/tested with 1 to 20 components obtained by the application of data dimensionality reduction (DDR) techniques (dictionary learning, factor analysis, fast individual component analysis, and principal component analysis) to different near-infrared (NIR) spectra regions’ data, to either analyze a single spectral region and season or combine spectral regions and/or seasons for each side of the blueberry leaf. …”
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Dynamic Spectrum Coexistence of NR-V2X and Wi-Fi 6E Using Deep Reinforcement Learning by Kashish D. Shah, Dhaval K. Patel, Brijesh Soni, Siddhartan Govindasamy, Mehul S. Raval, Mukesh Zaveri

“…Most existing works on coexistence rely on rule-based approaches or classical machine learning algorithms. These approaches may fall short in real-time environments where adaptive decision-making is required. …”
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Interdisciplinary problem-based learning model for standardized dental residency training: from theory to practice in dental trauma management by Yang Shuting, Wang Haohao, Wang Shida, Zheng Liwei, Wan Mian

“…Conventional standardized training falls short in exposing residents to diverse scenarios and fostering interdisciplinary collaboration, essential for dental trauma management. …”
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Suitability of Mechanics-Based and Optimized Machine Learning-Based Models in the Shear Strength Prediction of Slender Beams Without Stirrups by Abayomi B. David, Oladimeji B. Olalusi, Paul O. Awoyera, Lenganji Simwanda

“…This paper assesses the effectiveness of mechanics-based and optimized machine learning (ML) models for predicting shear strength in stirrup-less, slender beams using a dataset of 784 tests. …”
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