Ecotoxicity prediction of chemical compounds using machine learning and different molecular structure representations by Michał Marek, Rafał Kurczab

“…Advancements in computational tools have facilitated interdisciplinary approaches in toxicology, enabling chemists to explore the toxicity and ecotoxicity of chemical compounds while minimizing ethically questionable or hazardous methods. This paper presents the development of models for predicting chemical ecotoxicity (HC50) based on machine learning algorithms and different molecular representations. …”
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Subtyping first-episode psychosis based on longitudinal symptom trajectories using machine learning by Yanan Liu, Sara Jalali, Ridha Joober, Martin Lepage, Srividya Iyer, Jai Shah, David Benrimoh

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The effect of using interactive e-books on students’ mastery of learning competencies in science 9 by Sharon M. Ananayo

“…In addition, e-book utilization does not present any learning differences when compared to the two-way radio-guided modular distance learning modality indicating that this strategy is as effective as the two-way radio-guided instruction. …”
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The Impact of Virtual Reality Technology on Jordan’s Learning Environment and Medical Informatics among Physicians by Malik Jawarneh, Marwan Alshar'e, Deshinta Arrova Dewi, Mohammad Al Nasar, Rasha Almajed, Amer Ibrahim

“…The purpose of this study is to look into the components that affect the behavioral intention of Jordanians to use Virtual Reality Technology in the Learning Environment (VRTLE). For the educational and entertainment industries, virtual reality presents both obstacles and opportunities. …”
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Deep learning-based CAD system for Alzheimer’s diagnosis using deep downsized KPLS by Syrine Neffati, Kawther Mekki, Mohsen Machhout

“…Initially, deep learning techniques are utilized to extract features from the AD brain MRIs. …”
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BIM log mining framework using deep learning for productivity assessment in construction facilities by Ali Akbar, Younghee Chang, Jinwoo Song, Seojoon Lee, Sanghyeon Park, Jinhyun Bae, Soonwook Kwon

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Learning Style Preferences, Strategy Use and Chinese Undergraduate EFL Students’ English Achievements by Meihua Liu

“…The present study thus examined Chinese undergraduate EFL (English as a FL) students’ preferences for learning styles, use of learning strategies and their predictive effects on their English achievements. …”
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Application of Ensemble Learning and VISSIM in Intersection Traffic Flow Prediction and Signal Timing Optimization by Yutong Rou, Chao Liang, Zhizhan Lu

“…Initially, an ensemble learning algorithm accurately predicts intersection traffic flow. …”
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Medium-sized protein language models perform well at transfer learning on realistic datasets by Luiz C. Vieira, Morgan L. Handojo, Claus O. Wilke

“…While larger models, such as the 15 billion parameter model ESM-2, promise to capture more complex patterns in sequence space, they also present practical challenges due to their high dimensionality and high computational cost. …”
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Hybrid Approach for WDM Network Restoration: Deep Reinforcement Learning and Graph Neural Networks by Isaac Ampratwum, Amiya Nayak

“…Ensuring robust and efficient service restoration in Wavelength Division Multiplexing (WDM) networks is crucial for maintaining network reliability amidst failures caused by disasters, equipment malfunctions, or power outages. This article presents a hybrid framework that integrates Deep Reinforcement Learning (DRL) and Graph Neural Networks (GNN) to optimize WDM network restoration. …”
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Coordinated Traffic-Signal Control of Wide Area Network via Hierarchical Reinforcement Learning by Takumi Saiki, Sachiyo Arai

“…Therefore, this paper presents a hierarchical architecture that divides global and local controls. …”
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GPS-Aided Deep Learning for Beam Prediction and Tracking in UAV mmWave Communication by Vendi Ardianto Nugroho, Byung Moo Lee

“…However, a robust beam management remains challenging due to significant path loss and the dynamic mobility of UAVs, which can destabilize the UAV-base station (BS) link. This research presents a GPS-aided deep learning (DL) model that simultaneously predicts current and future optimal beams for UAV mmWave communications, maintaining a Top-1 prediction accuracy exceeding 70% and an average power loss below 0.6 dB across all prediction steps. …”
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Plastic Litter Detection in the Environment Using Hyperspectral Aerial Remote Sensing and Machine Learning by Marco Balsi, Monica Moroni, Soufyane Bouchelaghem

“…Plastic waste has become a critical environmental issue, necessitating effective methods for detection and monitoring. This article presents a machine-learning-based methodology and embedded solution to detect plastic waste in the environment using an airborne hyperspectral sensor operating in the short-wave infrared (SWIR) band. …”
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RECENT DEVELOPMENTS OF THE WAR IN UKRAINE. POSSIBLE AND NECESSARY LESSONS TO LEARN FOR ROMANIA AND THE ROMANIAN ARMY by Constantin MINCU

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Continuous Multi-Target Approaching Control of Hyper-Redundant Manipulators Based on Reinforcement Learning by Han Xu, Chen Xue, Quan Chen, Jun Yang, Bin Liang

“…However, controlling these manipulators is challenging because of infinite inverse kinematic solutions. In this paper, we present a novel reinforcement learning-based control method for hyper-redundant manipulators, integrating path and configuration planning. …”
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System for PCB Defect Detection Using Visual Computing and Deep Learning for Production Optimization by Gabriel Gomes de Oliveira, Gabriel Caumo Vaz, Marcos Antonio Andrade, Yuzo Iano, Leandro Ronchini Ximenes, Rangel Arthur

“…To guarantee the quality of the products in the manufacturing process, it is crucial to identify defects during the production stage of an electronic device. This study presents a system based on traditional visual computing and new deep learning methods to detect defects in electronic devices during the manufacturing process. …”
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Federated Learning for privacy-Friendly Health Apps: A Case Study on Ovulation Tracking by Nikolaos Pavlidis, Andreas Sendros, Theodoros Tsiolakis, Periklis Kostamis, Christos Karasoulas, Eleni Briola, Christos Chrysanthos Nikolaidis, Vasilis Perifanis, George Drosatos, Eleftheria Katsiri, Despoina Elisavet Filippidou, Anastasios Manos, Pavlos S. Efraimidis

“…Health applications often need to balance advanced AI functionalities with sufficient privacy measures to ensure user engagement. This paper presents the architecture of FLORA, a privacy-first ovulation-tracking application that leverages federated learning (FL), privacy-enhancing technologies (PETs), and blockchain to protect user data while delivering accurate and personalized health insights. …”
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Machine learning based disruption prediction using long short-term memory in KSTAR by Jeongwon Lee, Jayhyun Kim, Jinsu Kim, Sang-hee Hahn, Hyunsun Han, Giwook Shin, Yong-Su Na, Yong Un Nam

“…This study presents a machine learning model for predicting plasma disruptions using the KSTAR database. …”
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Deep learning enhanced deciphering of brain activity maps for discovery of therapeutics for brain disorders by Xianrui Zhang, Zhen Liu, Xuan Luo, Yi Cao, Wencong Zhang, Honglin Li, Wei Li, Shuk Han Cheng, Stephen J. Haggarty, Xin Wang, Peng Shi

“…Summary: This study presents an artificial intelligence enhanced in vivo screening platform, DeepBAM, which enables deep learning of large-scale whole brain activity maps (BAMs) from living, drug-responsive larval zebrafish for neuropharmacological prediction. …”
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Aero-Engine Blade Defect Detection: A Systematic Review of Deep Learning Models by Yusra Abdulrahman, M. A. Mohammed Eltoum, Abdulla Ayyad, Brain Moyo, Yahya Zweiri

“…The visual inspection of aero-engine blades is a complex process that requires extensive knowledge and experience. This paper presents a systematic literature review (SLR) of deep learning models for detecting defects in aero-engine blades. …”
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