HAF-YOLO: Dynamic Feature Aggregation Network for Object Detection in Remote-Sensing Images by Pengfei Zhang, Jian Liu, Jianqiang Zhang, Yiping Liu, Jiahao Shi

“…The growing use of remote-sensing technologies has placed greater demands on object-detection algorithms, which still face challenges. This study proposes a hierarchical adaptive feature aggregation network (HAF-YOLO) to improve detection precision in remote-sensing images. …”
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Anomaly detection with grid sentinel framework for electric vehicle charging stations in a smart grid environment by V. Thiruppathy Kesavan, Md. Jakir Hossen, R. Gopi, Emerson Raja Joseph

“…This technology can also detect and respond to suspicious movements dynamically using powerful machine learning algorithms (long short-term memory (LSTM), random forest, and autoencoder models), ensuring safety. …”
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Sequential Hybrid Integration of U-Net and Fully Convolutional Networks with Mask R-CNN for Enhanced Building Boundary Segmentation from Satellite Imagery by Rojgar Qarani Ismael, Haval Abduljabbar Sadeq

“… In the recent years, building boundary segmentation obtained significant advancement through using deep learning. The present algorithms, such as Convolutional Neural Network (CNN) are unable to detect buildings in challenging urban areas like occlusions. …”
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Integrative review in PhD admissions: A case study of efficiently minimizing bias while maximizing the student narrative. by Minerva A Orellana, Danielle J Beetler, Carmen J Silvano, Ryan Wuertz, Jennifer L Weisbrod, Lewis R Roberts, Anthony J Windebank, Felicity T Enders, Marina R Walther-Antonio

“…Effects modification models were used to assess odds of reaching each admissions phase, adjusting for UR status and review process. …”
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HOW IS ARTIFICIAL INTELLIGENCE CHANGING HR? ADAPTIVE MANAGEMENT FOR THE NEW ENVIRONMENT by Dmytro Antoniuk, Bjoern Sven Ivens, Olexandr Kolyada

“…The practical significance of the study lies in its recommendations for organisations seeking to implement adaptive AI-based HR models. It provides insights on how to optimise the use of AI for talent management, improve HR efficiency and address ethical considerations. …”
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When Two are Better Than One: Synthesizing Heavily Unbalanced Data by Francisco Ferreira, Nuno Lourenco, Bruno Cabral, Joao Paulo Fernandes

“…Additionally, privacy is crucial, and it is usually forbidden, or not possible, to share the data of organizations and individuals for creating or improving models.In this paper we propose a framework for private data sharing based on synthetic data generation using <italic>Generative Adversarial Networks (GAN)</italic> that learns the specificities of financial transactions data and generates fictitious data that keeps the utility of the original datasets. …”
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Opportunities of Digitalization of Economic Subjects in Educational Process by L. N. Babkina, O. V. Skotarenko

“…Thus, institutions of higher professional education should become so-called points of growth or locomotives of growth of the level of application of information and computer technologies, algorithms for solving economic and management problems based on the use of economic and mathematical methods and models that students master in the process of studying the relevant disciplines. …”
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A systematic review of UAV and AI integration for targeted disease detection, weed management, and pest control in precision agriculture by Iftekhar Anam, Naiem Arafat, Md Sadman Hafiz, Jamin Rahman Jim, Md Mohsin Kabir, M.F. Mridha

“…The focus of this study is on the incorporation of machine learning and deep learning algorithms into these UAV systems. We have conducted a thorough analysis of recent studies, particularly 2022–24, to evaluate the effectiveness of different unmanned aerial vehicle models, sensor types, and computational methods to improve crop monitoring and disease control strategies. …”
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Integrated artificial intelligence approach for well-log fluid identification in dual-medium tight sandstone gas reservoirs by Wurong Wang, Wurong Wang, Linbo Qu, Linbo Qu, Dali Yue, Dali Yue, Wei Li, Wei Li, Junlong Liu, Wujun Jin, Jialin Fu, Jialin Fu, Jiarui Zhang, Jiarui Zhang, Dongxia Chen, Dongxia Chen, Qiaochu Wang, Qiaochu Wang, Sha Li, Sha Li

“…Reservoir classification based on geological genetic mechanism significantly reduces data noise and prediction ambiguity, thereby improving the efficiency of model training.DiscussionThe final model is constructed by an ensemble method that integrates multiple sub-models, including fuzzy C-means clustering (FCM), gradient boosting decision tree (GBDT), backpropagation neural network (BPNN), random forests (RF), and light gradient boosting machines (LightGBM). …”
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Hand gestures classification of sEMG signals based on BiLSTM-metaheuristic optimization and hybrid U-Net-MobileNetV2 encoder architecture by Khosro Rezaee, Safoura Farsi Khavari, Mojtaba Ansari, Fatemeh Zare, Mohammad Hossein Alizadeh Roknabadi

“…Six standard databases were utilized, achieving an average accuracy of 90.23% with our proposed model, showcasing a 3–4% average accuracy improvement and a 10% variance reduction. …”
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SFMattingNet: A Trimap-Free Deep Image Matting Approach for Smoke and Fire Scenes by Shihui Ma, Zhaoyang Xu, Hongping Yan

“…Since image matting algorithms compute these alpha values, the quality of the alpha composition directly depends on the performance of the smoke and fire matting methods. …”
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Estimating Leaf Chlorophyll Fluorescence Parameters Using Partial Least Squares Regression with Fractional-Order Derivative Spectra and Effective Feature Selection by Jie Zhuang, Quan Wang

“…Chlorophyll fluorescence (ChlF) parameters serve as non-destructive indicators of vegetation photosynthetic function and are widely used as key input parameters in photosynthesis–fluorescence models. The rapid acquisition of the spatiotemporal dynamics of ChlF parameters is crucial for enhancing remote sensing applications and improving carbon cycle modeling. …”
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Synthetic Data-Enhanced Classification of Prevalent Osteoporotic Fractures Using Dual-Energy X-Ray Absorptiometry-Based Geometric and Material Parameters by Luca Quagliato, Jiin Seo, Jiheun Hong, Taeyong Lee, Yoon-Sok Chung

“…It included 9,260 patients, aged 55 to 99, comprising 242 femur fracture (FX) cases and 9,018 non-fracture (NFX) cases. To model the association of the bone’s current health status with prevalent FXs, three prediction algorithms—extreme gradient boosting (XGB), support vector machine, and multilayer perceptron—were trained using two-dimensional dual-energy X-ray absorptiometry (2D-DXA) analysis results and subsequently benchmarked. …”
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Housing Price Prediction - Machine Learning and Geostatistical Methods by Cellmer Radosław, Kobylińska Katarzyna

“…Machine learning algorithms are increasingly often used to predict real estate prices because they generate more accurate results than conventional statistical or geostatistical methods. …”
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Research on Quantitative Evaluation of Defects in Ferromagnetic Materials Based on Electromagnetic Non-Destructive Testing by Xiangyi Hu, Ruijie Xie, Ruotian Wang, Jiapeng Wang, Haichao Cai, Xiaoqiang Wang, Xiang Li, Qingzhu Guan, Jianhua Zhang

“…To accurately describe the quantitative relationship between ENDT signals and defect dimensional parameters, the electromagnetic model and electromagnetic induction model are introduced in this paper to elucidate the physical mechanism of ENDT, as both models provide a basis for the selection of the constitutive relationship for simulation analysis. …”
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Benchmarking Federated Few-Shot Learning for Video-Based Action Recognition by Nguyen Anh Tu, Nartay Aikyn, Nursultan Makhanov, Assanali Abu, Kok-Seng Wong, Min-Ho Lee

“…Additionally, we explore three meta-learning paradigms and three FL algorithms to investigate their effectiveness and suggest the optimal choices for performance improvement. …”
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Review: Multiobject tracking in livestock − from farm animal management to state-of-the-art methods by M.H. Nidhi, K. Liu, K.J. Flay

“…Improvements in detection association strategies and motion models, as well as innovations in multi-camera tracking, can lead to improved animal health, productivity, and welfare in the livestock industry. …”
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Smartphone-derived multidomain features including voice, finger-tapping movement and gait aid early identification of Parkinson’s disease by Wee-Shin Lim, Sung-Pin Fan, Shu-I Chiu, Meng-Ciao Wu, Pu-He Wang, Kun-Pei Lin, Yung-Ming Chen, Pei-Ling Peng, Jyh-Shing Roger Jang, Chin-Hsien Lin

“…An integrated multimodal model using a support vector machine improved performance to 0.86 and achieved 0.82 for identifying early-stage PD during the “off” phase. …”
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UAV Collision Avoidance in Unknown Scenarios with Causal Representation Disentanglement by Zhun Fan, Zihao Xia, Che Lin, Gaofei Han, Wenji Li, Dongliang Wang, Yindong Chen, Zhifeng Hao, Ruichu Cai, Jiafan Zhuang

“…By concentrating on causal aspects during the policy learning phase, the impact of non-causal factors is minimized, thereby improving the generalizability of DRL models. Experimental results demonstrate that our technique achieves reliable navigation and effective collision avoidance in unseen scenarios, surpassing state-of-the-art deep reinforcement learning algorithms.…”
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Deep learning-based crop health enhancement through early disease prediction by Venkata Santhosh Yakkala, Krishna Vamsi Nusimala, Badisa Gayathri, Sriya Kanamarlapudi, S. S. Aravinth, Ayodeji Olalekan Salau, S. Srithar

“…Additionally, the model aims to identify specific diseases affecting the crops if they are found to be diseased. …”
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