Deep Reinforcement Learning-Based Multi-Agent System with Advanced Actor–Critic Framework for Complex Environment by Zihao Cui, Kailian Deng, Hongtao Zhang, Zhongyi Zha, Sayed Jobaer

“…The development of artificial intelligence (AI) game agents that use deep reinforcement learning (DRL) algorithms to process visual information for decision-making has emerged as a key research focus in both academia and industry. …”
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Estimating Tree Diameter at Breast Height (DBH) Using iPad Pro LiDAR Sensor in Boreal Forests by Matthew Guenther, Muditha K. Heenkenda, Brigitte Leblon, Dave Morris, Jason Freeburn

“…Two circle fitting algorithms (Pratt, Taubin) and two ellipse fitting algorithms (Taubin, Szpak) were applied to the extracted cross-sections to estimate DBH. …”
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Review of Modern Forest Fire Detection Techniques: Innovations in Image Processing and Deep Learning by Berk Özel, Muhammad Shahab Alam, Muhammad Umer Khan

“…Furthermore, we explore the utilization of deep learning and machine learning in training intelligent algorithms to recognize fire patterns and features. …”
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Aspects of Developing A Methodology for Managing Digital Financial Assets by A. Yu. Proskuryakov

“…In accordance with the scientific novelty of the research and methodology, a set of interrelated research stages is formed, consisting of an ordered cascade of methods, models and algorithms that perform preliminary analysis, processing and forecasting of financial time series of value indicators.Conclusion. …”
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Machine learning applications in the analysis of sedentary behavior and associated health risks by Ayat S Hammad, Ayat S Hammad, Ali Tajammul, Ismail Dergaa, Ismail Dergaa, Ismail Dergaa, Maha Al-Asmakh, Maha Al-Asmakh

“…As prolonged inactivity becomes a growing public health concern, researchers are increasingly utilizing machine learning (ML) techniques to examine and understand these patterns. …”
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Analysis of Gene Expression Omnibus high-throughput sequencing data for the determination of microribonucleic acids in the blood plasma of patients with glioblastomas by A. A. Pushkin, D. Yu. Gvaldin, N. N. Timoshkina, E. E. Rostorguev, L. Yu. Vladimirova, E. A. Dzenkova

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ML-Based Control Strategy for PHEV Under Predictive Vehicle Usage Behaviour by Aleksandr Doikin, Aleksandr Korsunovs, Felician Campean, Oscar García-Afonso, Enrico Agostinelli

“…This study, based on extended real-world data (journeys history from 10 vehicles over 12 months), shows that trip patterns can be learnt quite effectively using classic ML classification algorithms. …”
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Mapping Vegetation Dynamics in Wyoming: A Multi-Temporal Analysis using Landsat NDVI and Clustering by N. Kuppala, C. Navneet Krishna, V. V. Sajith Variyar, R. Sivanpillai

“…This research focused on mapping vegetation growth patterns in non-irrigated fields using five early season (2019–2023) Normalized Difference Vegetation Index (NDVI) derived from Landsat imagery. …”
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Clinical Phenotypes of Patients with Anti-DFS70/LEDGF Antibodies in a Routine ANA Referral Cohort by Makoto Miyara, Roger Albesa, Jean-Luc Charuel, Mohamed El Amri, Marvin J. Fritzler, Pascale Ghillani-Dalbin, Zahir Amoura, Lucile Musset, Michael Mahler

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Generation of Spatial Structure of Urban Parks Based on Spatial Analysis of Agent-Based Models by Huizi KONG, Tianming LIU, Jiajie LIAO, Liu CUI

“…ObjectiveThis research aims to leverage the advances of computer algorithms to provide scientific analytical methods for spatial simulation and offer a more objective approach to reflect the natural operational laws of interdependencies between elements in the real physical world for landscape design. …”
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Multi-omics derivation of a core gene signature for predicting therapeutic response and characterizing immune dysregulation in inflammatory bowel disease by Mingming Wang, Liping Liang, Zibo Tang, Jimin Han, Lele Wu, Le Liu, Le Liu, Ye Chen, Ye Chen

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CNN Based Fault Classification and Predition of 33kw Solar PV System with IoT Based Smart Data Collection Setup by K. Punitha, G. Sivapriya, T. Jayachitra

“…By analysing data from sensors and system logs, ML algorithms can identify patterns indicative of faults or inefficiencies, such as shading, soiling, or equipment malfunctions, often before they become serious issues. …”
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A novel multi-agent dynamic portfolio optimization learning system based on hierarchical deep reinforcement learning by Ruoyu Sun, Yue Xi, Angelos Stefanidis, Zhengyong Jiang, Jionglong Su

“…To address these problems, we propose a novel multi-agent learning system based on the hierarchical deep reinforcement learning (HDRL) algorithmic framework in this research. Under this framework, the agents work together as a learning system for portfolio optimization. …”
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Appraisal and analysis on various web service composition approaches based on QoS factors by M. Rajeswari, G. Sambasivam, N. Balaji, M.S. Saleem Basha, T. Vengattaraman, P. Dhavachelvan

“…We have illustrated the related technology by analyzing QoS parameters based on existing algorithms with composition patterns and compared the results.…”
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KT-Deblur: Kolmogorov–Arnold and Transformer Networks for Remote Sensing Image Deblurring by Baoyu Zhu, Zekun Li, Qunbo Lv, Zheng Tan, Kai Zhang

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THE IMPROVEMENT OF PROFESSIONAL TRAINING ORGANIZATION OF THE X-RAY SCREENING SYSTEMS OPERATORS BY USING THE EYE MOVEMENTS REGISTRATION SYSTEM AND METHODS OF CLUSTER AND DISCRIMINAN... by A. K. Volkov, V. V. Ionov

“…The X-ray screening systems operators’ professional training is based on the CBT (computer-based training) principle, which has algorithms of adaptive training. These algorithms in existing computer simulators include feedback mechanisms on the basis of trainability exponents – such as the frequency of detecting dangerous objects, the frequency of false alarms and detection time. …”
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Preliminary Electroencephalography-Based Assessment of Anxiety Using Machine Learning: A Pilot Study by Katarzyna Mróz, Kamil Jonak

“…<b>Conclusions</b>: By integrating ML with EEG analysis, this study highlights the potential for future healthcare applications, including neurorehabilitation, anxiety disorder therapy, and predictive clinical models. Future research should focus on optimizing ML algorithms, enhancing personalization, and addressing ethical concerns related to patient privacy.…”
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Video-Driven Artificial Intelligence for Predictive Modelling of Antimicrobial Peptide Generation: Literature Review on Advances and Challenges by Jielu Yan, Zhengli Chen, Jianxiu Cai, Weizhi Xian, Xuekai Wei, Yi Qin, Yifan Li

“…How video-based methodologies and advanced computer vision algorithms can facilitate the development of antimicrobial peptide (AMP) generation models should be further reviewed, structural and functional patterns should be elucidated, and the generative power of in silico pipelines should be enhanced. …”
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Flexible imputation toolkit for electronic health records by Alireza Vafaei Sadr, Jiang Li, Wenke Hwang, Mohammed Yeasin, Ming Wang, Harold Lehmann, Ramin Zand, Vida Abedi

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A comprehensive review of flood damage in mountainous regions: challenges, solutions, and advanced management technologies by Mohammad Roohi, Jila Dehghani, Maryam Irani, Paramiss Mina

“…This work employs techniques such as LiDAR for precise topographic models, integrating remote sensing with hydrological/hydraulic models, and analyzing satellite imagery to study flood patterns and land cover changes. Furthermore, a precise and useful understanding of the effects of floods in these areas has been made possible by the use of machine learning algorithms to forecast flood episodes and evaluate damage, in conjunction with field research and community participation, such as citizen science initiatives for data collection and local knowledge integration. …”
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