Analysis of space solar array arc images based on deep learning techniques by Afaf M. Abd El-Hameed, Ahmed S. Farahat, Khaled Y. Youssef, M. Elfarran, I. M. Selim

“…Furthermore, algorithms and image processing tools, such as Python and Maxim-DL, are employed to examine variations in intensities and spatial variation in the arced region. …”
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A Machine Learning Approach to Wireless Propagation Modeling in Industrial Environment by Mohammad Hossein Zadeh, Marina Barbiroli, Franco Fuschini

“…The proposed model relies on a combination of predictive algorithms, including a linear regression model and a Multi-Layer Perceptron, working collaboratively to model the relationship between the considered propagation markers and input features like frequency and machine size, spacing, and density. …”
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Multiomics integration and machine learning reveal prognostic programmed cell death signatures in gastric cancer by Zihao Bai, Hao Wang, Jingru Han, Jia An, Zhaocong Yang, Xuming Mo

“…This study applied multiomics approaches, integrating transcriptomic, epigenetic, and somatic mutation data, with consensus clustering algorithms to classify GC molecular subtypes and assess their biological and immunological features. …”
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An end-to-end deep learning solution for automated LiDAR tree detection in the urban environment by Julian R. Rice, G. Andrew Fricker, Jonathan Ventura

“…Specifically, we develop and train a novel PointNet-based neural network architecture to predict tree locations directly from LiDAR data augmented with multi-spectral imagery. …”
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Artificial intelligence and machine learning in modern cardiology: Advancements in diagnosis, treatment and patient monitoring by Szymon Kopciał, Dawid Piecuch, Edyta Hańczyk, Karolina Kornatowska, Natalia Pawelec, Weronika Mazur

“…Additionally, AI-driven algorithms support personalized treatment strategies, real-time remote monitoring, and precision-guided coronary interventions, reducing procedural complications. …”
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Enhancing emergency response efficiency through advanced urban logistics: The role of driver psychophysiology and vehicle dynamics in mitigating socio-economic impacts by Yurii Davidich, Dávid Földes, Andrii Galkin

“…Utilising regression models and physiological assessments, we developed predictive models that link these factors to emergency response times. …”
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Machine learning-assisted quantitative metabolomics of West African patients with advanced breast cancer by Aboubacar Dit Tietie Bissan, Mathieu Michel, Xavier Dieu, Cinzia Bocca, Awo Emmanuela Hilda Amegonou, Fatoumata Matokoma Sidibe, Madani Ly, Bocary Sidi Koné, Nènè Oumou Kesso Barry, Kletigui Casimir Dembélé, Bakary Cissé, Bourèma Kouriba, Gilles Simard, Delphine Mirebeau-Prunier, Juan Manuel Chao de la Barca, Zahra Ouzzif, Pascal Reynier

“…The data were compared with those of 79 healthy controls using a combination of several machine learning approaches and statistical analyses. The predictive models obtained with the machine learning algorithms were comparable, with the best AUC of 0.878 obtained with ridge logistic regression using Boruta feature selection. …”
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Comparative Analysis of Control Strategies for Microgrid Energy Management with a Focus on Reinforcement Learning by Parisa Mohammadi, Razieh Darshi, Saeed Shamaghdari, Pierluigi Siano

“…Traditional methods, including classical, metaheuristic, model predictive, stochastic, and robust control techniques, have been extensively studied. …”
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Thermal Conductivity Characteristics of Thermal Insulation Materials Immersed in Water for Cold-Region Tunnels by Youyun Li, Yongmei Sun, Yuan Zhuang, LiMin Duan, Ke Xie

“…The correlation between gray volume moisture content and mass moisture content is determined by comparing different algorithms of gray volume moisture content and volume moisture content. …”
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Efficient text-to-video retrieval via multi-modal multi-tagger derived pre-screening by Yingjia Xu, Mengxia Wu, Zixin Guo, Min Cao, Mang Ye, Jorma Laaksonen

“…In this work, we present a plug-and-play multi-modal multi-tagger-driven pre-screening framework, which pre-screens a substantial number of videos before applying any TVR algorithms, thereby efficiently reducing the search space of videos. …”
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The Advanced Actuarial Data Science Based AI-Driven Solutions for Automated Loss Reserving Under IFRS 17 in Non-Life Insurance by Brighton Mahohoho, Charles Chimedza, Florance Matarise, Sheunesu Munyira

“…Among eight evaluated machine learning algorithms, the Random Forest (RANGER) outperformed others for estimating Aggregate Comprehensive Automated Actuarial Loss Reserves (ACAALR). …”
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Reverberation Mapping of Lamppost and Wind Structures in Accretion Thin Disks by James Hung-Hsu Chan, Joshua Fagin, Henry Best, Matthew J. O’Dowd

“…To address the discrepancy where disk sizes exceed those predicted by standard models, we explore two extensions to disk size estimates within the UV/optical wavelength range: disk winds and color correction. …”
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Providing contexts for classification of transients in a wide-area sky survey: An application of noise-induced cluster ensemble by Tossapon Boongoen, Natthakan Iam-On, James Mullaney

“…In particular, it enhances predictive performance of basic classifiers by lifting F1 scores from less than 0.1 to around 0.3–0.5. …”
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A Comprehensive Overview of the Development and Research of Energy Savings of Electric Wheel Loader by Xiaotao Fei, Zuo Cheng, Shaw Voon Wong, Muhammad Amin Azman, Dawei Wang, Xiuxian Zhang, Qiuchen Shao, Qingqiu Lin

“…The analysis reveals that integrating optimal control strategies with machine learning algorithms—such as model predictive control (MPC)—is a critical pathway to achieving energy-efficient and assisted driving in next-generation EWLs. …”
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Evolution and Knowledge Structure of Wearable Technologies for Vulnerable Road User Safety: A CiteSpace-Based Bibliometric Analysis (2000–2025) by Gang Ren, Zhihuang Huang, Tianyang Huang, Gang Wang, Jee Hang Lee

“…Comparative analysis across pedestrians, cyclists and motorcyclists, and persons with disabilities shows three parallel transitions: single- to multisensory interfaces, reactive to predictive systems, and isolated devices to V2X-enabled ecosystems. …”
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Artificial Intelligence in Chest Radiography—A Comparative Review of Human and Veterinary Medicine by Andrea Rubini, Roberto Di Via, Vito Paolo Pastore, Francesca Del Signore, Martina Rosto, Andrea De Bonis, Francesca Odone, Massimo Vignoli

“…Deep learning-based models assist radiologists by detecting patterns, generating probability maps, and predicting outcomes like heart failure. However, AI is still supplementary to clinical expertise due to challenges such as data limitations, algorithmic biases, and the need for extensive validation. …”
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On the Choice of Training Data for Machine Learning of Geostrophic Mesoscale Turbulence by F. E. Yan, J. Mak, Y. Wang

“…Abstract Data plays a central role in data‐driven methods, but is not often the subject of focus in investigations of machine learning algorithms as applied to Earth System Modeling related problems. …”
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Two‐stage video anomaly detection based on dual‐stream networks and multi‐instance learning by Dejun Zhang, Wenbo Fang, Yuhang Liu, Zirong Lyu, Chen Xiong, Zhan Wang

“…Therefore, it is crucial to develop intelligent algorithms for video anomaly detection. The method can detect whether segments of a video contain abnormal events. …”
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Artificial intelligence-based automated breast ultrasound radiomics for breast tumor diagnosis and treatment: a narrative review by Yinglin Guo, Ning Li, Chonghui Song, Juan Yang, Yinglan Quan, Hongjiang Zhang

“…Radiomics, an emerging field, focuses on extracting high-dimensional quantitative features from medical imaging data and utilizing them to construct predictive models for disease diagnosis, prognosis, and treatment evaluation. …”
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Combination of Remote Sensing and Artificial Intelligence in Fruit Growing: Progress, Challenges, and Potential Applications by Danielle Elis Garcia Furuya, Édson Luis Bolfe, Taya Cristo Parreiras, Jayme Garcia Arnal Barbedo, Thiago Teixeira Santos, Luciano Gebler

“…The analysis included the use of remote sensing (orbital and proximal) imagery and ML/DL algorithms to map crop areas, detect diseases, and monitor crop development, among other analyses. …”
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