MediScan: A Framework of U-Health and Prognostic AI Assessment on Medical Imaging by Sibtain Syed, Rehan Ahmed, Arshad Iqbal, Naveed Ahmad, Mohammed Ali Alshara

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Hydroelectric Unit Fault Diagnosis Based on Modified Fractional Hierarchical Fluctuation Dispersion Entropy and AdaBoost-SCN by Xing Xiong, Zhexi Xu, Rende Lu, Yisheng Li, Bingyan Li, Fengjiao Wu, Bin Wang

“…Then, a novel method for evaluating the complexity of time-series signals, called MFHFDE, is presented. …”
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Lightweight LSTM and GRU Design for Data-Driven Rotor Position Error Estimation in IPMSM Drives by Yang Zhao, Chee Shen Lim, Fei Xue, Chao Long, Andrew Huey Ping Tan

“…They are proven to better generalize to nontraining operating points or data, constituting an essential feature when dealing with closed-loop control's experimental data. …”
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Binary classification of Low-Rate DoS attacks using Long Short-Term Memory Feed-Forward (LSTM-FF) Intrusion Detection System (IDS) by Suhaila ZeinElabideen Omer, Fazirulhisyam Hashim, Aduwati Sali, Faisul Arif Ahmad

“…The data and size of networks have grown substantially due to the rapid development of the Internet and other communication techniques. …”
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Enhanced Spatiotemporal Landslide Displacement Prediction Using Dynamic Graph-Optimized GNSS Monitoring by Jiangfeng Li, Jiahao Qin, Kaimin Kang, Mingzhi Liang, Kunpeng Liu, Xiaohua Ding

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Using deep learning to capture gravel soil microstructure and hydraulic characteristics by Bin Zhu, Yu-Fei Xie, Xiang-Gang Hu, Dai-Rong Su

“…Furthermore, through the evaluation of permeability, it was shown that the reconstructed realizations effectively captured and represented the actual soil prototype. …”
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Breast Cancer Detection via Multi-Tiered Self-Contrastive Learning in Microwave Radiometric Imaging by Christoforos Galazis, Huiyi Wu, Igor Goryanin

“…This approach enables the detection of subtle thermal abnormalities that may indicate potential issues. <b>Results:</b> We evaluated J-MWR on a dataset of 4932 patients, demonstrating improvements over existing MWR-based neural networks and conventional contrastive learning methods. …”
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Predicting carbon dioxide emissions using deep learning and Ninja metaheuristic optimization algorithm by Anis Ben Ghorbal, Azedine Grine, Ibrahim Elbatal, Ehab M. Almetwally, Marwa M. Eid, El-Sayed M. El-Kenawy

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A Transfer Learning Approach for Diverse Motion Augmentation Under Data Scarcity by Junwon Yoon, Jeon-Seong Kang, Ha-Yoon Song, Beom-Joon Park, Kwang-Woo Jeon, Hyun-Joon Chung, Jang-Sik Park

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Enhanced People Re-identification in CCTV Surveillance Using Deep Learning: A Framework for Real-World Applications by Mossaab Idrissi Alami, Abderrahmane Ez-zahout, Fouzia Omary

“…In this paper, we propose a robust deep learning framework that leverages convolutional neural networks (CNNs) with a customized triplet loss function to overcome these obstacles and improve re-identification accuracy. …”
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Early Breast Cancer Detection Based on Deep Learning: An Ensemble Approach Applied to Mammograms by Youness Khourdifi, Alae El Alami, Mounia Zaydi, Yassine Maleh, Omar Er-Remyly

“…This ensemble approach is evaluated on two benchmark datasets: INbreast and CBIS-DDSM. …”
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ON SOME ASPECTS OF THE ASSESSMENT OF NEGATIVE ANTHROPOGENIC IMPACT ON THE QUALITY OF SURFACE WATER BODIES IN THE ENVIRONMENTAL SAFETY ENSURING SYSTEM by Roman N. Bubenov, Vasiliy I. Borisenko, Andrey A. Danilenko, Lyubov A. Bubenova

“…It has been established that the quality assessment methodology by the Specific Combinatorial Water Pollution Index does not objectively evaluate the quality of surface water bodies taking into account the natural features of water bodies. …”
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YOLO Object Detection for Real-Time Fabric Defect Inspection in the Textile Industry: A Review of YOLOv1 to YOLOv11 by Makara Mao, Min Hong

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Personalized region of interest recommendation through adaptive fusion of multi-dimensional user preferences by Qing Tang, Shenghua Xu, Zhuolu Wang, Yong Wang, Shichuan Liu, Haiqin Hua

“…In addition, category features are extracted from the users’ historical check-in trajectories, and category preferences are calculated by evaluating the semantic similarity between the Point of Interest (POI) categories and user category features within a region using a multi-layer perceptron. …”
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Deep Learning Innovations: ResNet Applied to SAR and Sentinel-2 Imagery by Giuliana Bilotta, Luigi Bibbò, Giuseppe M. Meduri, Emanuela Genovese, Vincenzo Barrile

“…ResNet, noted for its deep residual learning capabilities, significantly enhances the classifier’s proficiency in identifying intricate patterns and features from high-resolution images. A test dataset derived from Sentinel-2 raster images is utilised to evaluate the effectiveness of the neural network (NN). …”
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DaGAM-Trans: Dual graph attention module-based transformer for offline signature forgery detection by Sara Tehsin, Ali Hassan, Farhan Riaz, Inzamam Mashood Nasir

“…The model is evaluated on four publicly available signature datasets: SigComp2011, BHSig260, CEDAR, and UTSig, within a cross-language verification setting. …”
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Token Mixing for Breast Cancer Diagnosis: Pre-Trained MLP-Mixer Models on Mammograms by Hosameldin O. A. Ahmed, Asoke K. Nandi

“…Deep learning, particularly convolutional neural networks (CNNs), has significantly advanced mammographic analysis by automating feature extraction and improving early detection. …”
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Multistage fall detection framework via 3D pose sequences and TCN integration by Leitao Qi, Haibo Sun

“…Subsequently, we introduce a robust fall detection network that leverages temporal convolutions to process the 3D pose sequences, capturing long-term dependencies while maintaining low computational costs for fall event recognition. …”
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Neutrosophic Intelligence for Secure UAV Communication: A Machine Learning Framework for Uncertainty-Aware Link Classification by Muhammad Edmerdash, Waleed khedr, Ehab Rushdy

“…By integrating these neutrosophic values as features for advanced machine learning models—including XGBoost, deep neural networks (DNN), and hybrid architectures—the proposed system achieves high accuracy, adaptability, and explainability in classifying secure versus insecure links. …”
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Scenario modeling of the drug prescription process for children: application of machine learning methods by А. А. Kondrashov, М. М. Kurashov, Е. Е. Loskutova

“…The following model architectures were developed and validated: fully connected neural network (FCNN), convolutional neural network (CNN), One-vs-Rest (OvR) classifier, eXtreme gradient boosting classifier (XGBC), and RandomForestClassifier (RFC). …”
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