Improving Reinforcement Learning Experiments in Unity through Waypoint Utilization by Caleb Koresh, Volkan Ustun, Rajay Kumar, Tim Aris

“…Multi-agent Reinforcement Learning (MARL) models teams of agents that learn by dynamically interacting with an environment and each other, presenting opportunities to train adaptive models for team-based scenarios. …”
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Reward estimation with scheduled knowledge distillation for dialogue policy learning by Junyan Qiu, Haidong Zhang, Yiping Yang

“…Those issues may slow down and degrade the policy learning significantly. In this paper, we present a novel scheduled knowledge distillation framework for dialogue policy learning, which trains a compact student reward estimator by distilling the prior knowledge of user goals from a large teacher model. …”
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EFL Learners’ Perceptions on the Use of English Movies in Learning Vocabulary by Rosalia, Indah Tri Purwanti, Novitri

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Embedded Machine Learning-Based Voltage Fingerprinting for Automotive Cybersecurity by Pierpaolo Dini, Michele Zappavigna, Ettore Soldaini, Sergio Saponara

“…This research presents the development and validation of an embedded system for real-time intrusion detection in automotive environments, leveraging voltage-based ECU fingerprinting. …”
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abnormal data detection and learning their behavior by abnormality and satisficing theory by masood abessi, Elahe Hajigol Yazdi

“…This is important to promote confidence to a limited number of records for effective learning of abnormality. In this study, a new approach based on the abnormality theory and satisficing theory presented for confidence improvement of abnormal data detection and learning. …”
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Constructing multicomponent cluster expansions with machine-learning and chemical embedding by Yann L. Müller, Anirudh Raju Natarajan

“…The eCE model simultaneously learns a low dimensional embedding of site basis functions along with the weights of an energy model. …”
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Machine learning-enhanced monitoring of global copper mining areas by Houxuan Li, Peng Wang, Tim T. Werner, Bin Chen, Wei-Qiang Chen

“…However, as copper mining activities expand worldwide, they often result in significant environmental impacts, yet the monitoring approaches and up-to-date databases remain limited. In this study, we present a high-resolution, site-specific database of global copper mining activities, developed using a machine learning approach that leverages Earth observation images and various dispersed data sources. …”
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Optimization of machine learning algorithms for proteomic analysis using topsis by Javanbakht T., Chakravorty S.

“…The present study focuses on a new application of the TOPSIS method for the optimization of machine learning algorithms, supervised neural networks (SNN), the quick classifier (QC), and genetic algorithm (GA) for proteomic analysis. …”
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Building Resilience: Youth Learning Through Earthquake Shaking Simulations by Gemma Musacchio, Agostino Goretti, Fabrizio Meroni

“…This paper illustrates how youth education can foster resilience and promote risk awareness through interactive learning. It presents <i>Shake It!</i>, an engaging, hands-on educational module designed for middle school students that integrates risk education with experiential activities. …”
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Machine Learning in E-Commerce: Trends, Applications, and Future Challenges by Elias Dritsas, Maria Trigka

“…The rapid evolution of e-commerce has been significantly influenced by the integration of machine learning (ML) and data science techniques. The present survey provides a comprehensive overview of how ML methods are applied across various functional domains in e-commerce, including personalized recommendations, dynamic pricing, fraud detection, customer segmentation, and behavioral analysis. …”
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Investigating of Machine Learning Based Algorithms for Liver Cirrhosis Prediction by Young Choi, Eunsung Oh

“…This paper presents a study on utilizing machine learning algorithms for predicting liver cirrhosis with a focus on enhancing accuracy rates. …”
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Anomalous electroweak physics unraveled via evidential deep learning by Brandon Kriesten, T. J. Hobbs

“…In this study, we leverage recent machine learning (ML) developments in evidential deep learning (EDL) for UQ to separate data (aleatoric) and knowledge (epistemic) uncertainties in a model-discrimination setting. …”
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Methodological Critique of Teaching and Learning Methods from the Perspective of Embodiment by Bijan Babaei, Bakhtiar Shabani Varaki

“…Objective: The main objective of the present study is to explain the methodological difficulties of teaching and learning approaches from the perspective of embodiment. …”
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Current Trends in Class Imbalance Learning for Software Defect Prediction by Somya R. Goyal

“…An analysis on the current trends addressing the Class Imbalanced Learning methods is essential in domain of SDP. This article presents a review of the contributions made over the period from year 2019 to 2024 towards handling class imbalance while applying learning machines for SDP. …”
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Dempster-Shafer ensemble learning framework for air pollution nowcasting by Le Trung H., Nguyen Huynh A.D., Ha Quang P., Tran Minh Q., Ahmed Masrur, Kong Jing, Barthelemy Xavier, Duc Hiep, Jiang Ningbo, Azzi Merched, Riley Matthew

“…Deep-learning has emerged as a powerful approach to significantly improve forecast accuracy for air quality estimation. …”
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Advancing personalized diagnosis and treatment using deep learning architecture by Rahat Ullah, Nadeem Sarwar, Mohammed Naif Alatawi, Abeer Abdullah Alsadhan, Hathal Salamah Alwageed, Maqbool Khan, Aitizaz Ali

“…Autoimmune disorders (AID) present significant challenges due to their complex etiologies and diverse clinical manifestations. …”
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A review on machine learning applications in hydrogen energy systems by Zaid Allal, Hassan N. Noura, Ola Salman, Flavien Vernier, Khaled Chahine

“…Adopting machine learning (ML) in hydrogen systems is a promising approach that enhances the efficiency, reliability, and sustainability of hydrogen power systems and revolutionizes the hydrogen energy sector to optimize energy usage/management and promote sustainability. …”
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A recent survey on instance-dependent positive and unlabeled learning by Chen Gong, Muhammad Imran Zulfiqar, Chuang Zhang, Shahid Mahmood, Jian Yang

“…In this survey, we first present the preliminary knowledge of PU learning, and then review the representative instance-dependent PU learning settings and methods. …”
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RL4CEP: reinforcement learning for updating CEP rules by Afef Mdhaffar, Ghassen Baklouti, Yassine Rebai, Mohamed Jmaiel, Bernd Freisleben

“…Abstract This paper presents RL4CEP, a reinforcement learning (RL) approach to dynamically update complex event processing (CEP) rules. …”
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Electronic Portfolio as a Mean of Reflection Formation for Students Learning by Margolis A.A., Safronova M.A., Drobyazko A.A., Kuravsky L.S., Golovanova I.A., Pominov D.A.

“…The article presents the results of a study aimed at the reflection formation of students to the educational program they are mastering as a program of preparation for professional activity through a specially organized educational environment, including the use of the "Electronic Portfolio" tool and regular interaction with a supervisor. …”
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