Online interaction learning: A way towards sustainability and buoyant EFL students by Seyedhamed Sadoughvanini, Hamed Ghaemi

“…However, more research on the role of buoyancy in relation to achievement and online tactics to increase individual students’ achievement and overall online interaction learning model is warranted based on the findings of the present study. …”
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Multi‐task learning using GNet features and SVM classifier for signature identification by Anamika Jain, Satish Kumar Singh, Krishna Pratap Singh

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Machine Learning Recognizes Stages of Parkinson’s Disease Using Magnetic Resonance Imaging by Artur Chudzik

“…Using interpretable metrics, such as the volume- and centroid-based spatial distances, models achieved high diagnostic accuracy, presenting a promising framework for early-stage PD identification based on MRI scans.…”
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Learning from Outputs: Improving Multi-Object Tracking Performance by Tracker Fusion by Vincenzo M. Scarrica, Antonino Staiano

“…This paper presents an approach to improving visual object tracking performance by dynamically fusing the results of two trackers, where the scheduling of trackers is determined by a support vector machine (SVM). …”
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Maintaining Diversity in Parallel Problem Solving: The Influence of Network Structure and Learning Strategy by Hua Zhang, Chunhui Cao

“…First, we present a simulation framework that consists of external environment, communication networks, and agents’ learning strategies. …”
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Towards digital health: Integrating federated learning and crowdsensing through the Contigo app by Daniel Flores-Martin, Sergio Laso, Javier Berrocal, Juan M. Murillo

“…This digitization supposes a challenge from the point of view of privacy and the treatment of sensitive personal data while providing non-intrusive and easy-to-use digital mechanisms. This paper presents Contigo: a health monitoring system that integrates a mobile application and a web platform for detecting anomalies using Federated Learning techniques. …”
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A Hybrid Deep Learning Ensemble Model for Credit Card Fraud Detection by Emmanuel Ileberi, Yanxia Sun

“…The rising volume of online transactions has concurrently increased the incidence of credit card fraud, presenting severe challenges to financial institutions and consumers alike. …”
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Learning Sensor Based Risk Map Augmentation for Risk Aware UAS Operation by Bhaskar Trivedi, Manfred Huber

“…While this can sometimes be done based on prior information, such as street and building  plans in cities, a priori information is often not complete, making it essential that risk representations can be augmented in real time based on sensor information. This paper presents an approach to risk map augmentation that uses learned risk identification from aerial pictures to fuse additional information with prior data into a dynamically changing risk map that allows effective re-planning of navigation strategies.…”
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Advancing crop recommendation system with supervised machine learning and explainable artificial intelligence by Sourabh Shastri, Sachin Kumar, Vibhakar Mansotra, Rohit Salgotra

“…This work presents a crop recommendation model based on Gradient Boosting trained on a crop recommendation dataset. …”
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Basket2vec: Learning Retail Basket Representations From Transactional Data by Bryan V. Piguave, Andres G. Abad

“…In this work, we present the novel basket2vec methodology to learn a rich retail basket representation in a latent space with an aim to preserve the co-occurrence relationship between items. …”
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Machine learning algorithms can predict emotional valence across ungulate vocalizations by Romain A. Lefèvre, Ciara C.R. Sypherd, Élodie F. Briefer

“…If vocal expression of emotions has been conserved throughout evolution, direct between-species comparisons using the same set of acoustic indicators should be possible. The present study used a machine learning algorithm (eXtreme Gradient Boosting [XGBoost]) to distinguish between contact calls indicating positive (pleasant) and negative (unpleasant) emotional valence, produced in various contexts by seven species of ungulates. …”
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Assessing learning and development through transdisciplinary problem-based invention education offerings by Stephanie Couch, Kelli M. Paul, Michelle Sullivan, Sabiha Sultana

“…Invention Education (IvE), a form of problem-based learning, presents new challenges for educational assessments in public schooling because traditional assessments were designed to evaluate learning in singular disciplines. …”
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Structural Competency and the Medical Learning Environment—An Overdue Paradigm Shift in Medical Education by Iman F. Hassan, Rebecca Leeds, Ijeoma Nnodim Opara, Thuy D. Bui, Sharon E. Connor, Sejal Shah, Shwetha Iyer

“…However, our present-day clinical settings fail to create a learning environment in which SC skills can be effectively taught and operationalized. …”
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Experiences and lessons learned from two virtual, hands-on microbiome bioinformatics workshops. by Matthew R Dillon, Evan Bolyen, Anja Adamov, Aeriel Belk, Emily Borsom, Zachary Burcham, Justine W Debelius, Heather Deel, Alex Emmons, Mehrbod Estaki, Chloe Herman, Christopher R Keefe, Jamie T Morton, Renato R M Oliveira, Andrew Sanchez, Anthony Simard, Yoshiki Vázquez-Baeza, Michal Ziemski, Hazuki E Miwa, Terry A Kerere, Carline Coote, Richard Bonneau, Rob Knight, Guilherme Oliveira, Piraveen Gopalasingam, Benjamin D Kaehler, Emily K Cope, Jessica L Metcalf, Michael S Robeson Ii, Nicholas A Bokulich, J Gregory Caporaso

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Automatic identification of Dong Son antique glass artifacts using evolving learning by Anh-Khoi Ngo-Ho, Hoang-Bac Bui, Van-Trieu Pham

“…Our research is motivated by the unique conditions present in archaeology, where we seek to employ evolving learning algorithms to archaeological databases, comparing and selecting the most suitable model that aligns with the archeological dataset's performance. …”
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Detection of Victimization Patterns and Risk of Gender Violence Through Machine Learning Algorithms by Edna Rocio Bernal-Monroy, Erika Dajanna Castañeda-Monroy, Rafael Ricardo Rentería-Ramos, Sixto Enrique Campaña-Bastidas, Jessica Barrera, Tania Maribel Palacios-Yampuezan, Olga Lucía González Gustin, Carlos Fernando Tobar-Torres, Zeneida Rocio Ceballos-Villada

“…This paper explores the application of machine learning techniques and statistical analysis to identify the patterns of victimization and the risk of gender-based violence in San Andrés de Tumaco, Nariño, Colombia. …”
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Advances in Composite Power System Reliability Assessment: Trends and Machine Learning Role by Chiranjeevi Yarramsetty, Tukaram Moger, Debashisha Jena, Veeranki Srinivasa Rao

“…A comparative examination of conventional and Machine Learning (ML)-based methods demonstrates that deep learning models, such as Convolutional Neural Networks, offer substantial reductions in computational time while maintaining reliability assessment precision. …”
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Multi-UAV Reinforcement Learning With Realistic Communication Models: Recent Advances and Challenges by Tiziana Cattai, Francesco Frattolillo, Andrea Lacava, Prasanna Raut, Jennifer Simonjan, Salvatore D'Oro, Tommaso Melodia, Evgenii Vinogradov, Enrico Natalizio, Stefania Colonnese, Francesca Cuomo, Luca Iocchi

“…The interest in applications related to Multi-Unmanned Aerial Vehicle (UAV) systems has been growing exponentially inthe last few years. Reinforcement Learning (RL) presents one of the most popular alternatives for solving Multi-UAV tasks, thanks to its flexible requirements for modelingthe problem. …”
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DRAFTS: A Deep-learning-based Radio Fast Transient Search Pipeline by Yong-Kun Zhang, Di Li, Yi Feng, Chao-Wei Tsai, Pei Wang, Chen-Hui Niu, Hua-Xi Chen, Yu-Hao Zhu

“…Traditional search algorithms are often inefficient, time-consuming, and generate a high number of false positives. In this paper, we present DRAFTS , a deep-learning-based radio fast transient search pipeline. …”
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Discovery of 118 New Ultracool Dwarf Candidates Using Machine-learning Techniques by Hunter Brooks, Dan Caselden, J. Davy Kirkpatrick, Yadukrishna Raghu, Charles A. Elachi, Jake Grigorian, Asa Trek, Andrew Washburn, Hiro Higashimura, Aaron M. Meisner, Adam C. Schneider, Jacqueline K. Faherty, Federico Marocco, Christopher R. Gelino, Jonathan Gagné, Thomas P. Bickle, Shih-Yun Tang, Austin Rothermich, Adam J. Burgasser, Marc J. Kuchner, Paul Beaulieu, John Bell, Guillaume Colin, Giovanni Colombo, Alexandru Dereveanco, Deiby Pozo Flores, Konstantin Glebov, Leopold Gramaize, Les Hamlet, Ken Hinckley, Martin Kabatnik, Frank Kiwy, David W. Martin, Raúl F. Palma Méndez, Billy Pendrill, Lizzeth Ruiz, John Sanchez, Arttu Sainio, Jörg Schümann, Manfred Schonau, Christopher Tanner, Nikolaj Stevnbak, Andres Stenner, Melina Thévenot, Vinod Thakur, Nikita V. Voloshin, Zbigniew Wȩdracki, The Backyard Worlds: Planet 9 Collaboration

“…We present the discovery of 118 new ultracool dwarf candidates, discovered using a new machine-learning tool, named SMDET , applied to time-series images from the Wide-field Infrared Survey Explorer. …”
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