Cloth-Changing Person Re-Identification Based on the Backtracking Mechanism by Xuan Liu, Hua Han, Kaiyu Xu, Li Huang

“…Additionally, we introduce a feature infiltration module that integrates body shape features with appearance features extracted by the backbone network, enabling the model to extract clothing-independent features. …”
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A hybrid framework for heart disease prediction using classical and quantum-inspired machine learning techniques by Ankur Kumar, Sanjay Dhanka, Abhinav Sharma, Rohit Bansal, Mochammad Fahlevi, Fazla Rabby, Mohammed Aljuaid

“…The performance of the classical and quantum models has been compared to existing state-of-the-art approaches. …”
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Machine Learning System for Predicting Cardiovascular Disorders in Diabetic Patients by A. Mayya, H. Solieman

“…Performance optimization of the developed classification and prediction models was carried out based on different evaluation metrics.Results. …”
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An improved ShuffleNetV2 method based on ensemble self-distillation for tomato leaf diseases recognition by Shuiping Ni, Yue Jia, Mingfu Zhu, Mingfu Zhu, Yizhe Zhang, Wendi Wang, Shangxin Liu, Yawei Chen

“…This method ensures that the intermediate features from each model are fully preserved to the ensemble model, thereby improving the overall performance of the ensemble model. …”
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A novel approach to skin disease segmentation using a visual selective state spatial model with integrated spatial constraints by Yu Bai, Hai Zhou, Hongjie Zhu, Shimin Wen, Binbin Hu, Haotian Li, Huazhang Wang, Daji Ergu, Fangyao Liu

“…To address limitations in existing approaches, we introduce a novel U-shaped segmentation architecture based on our Residual Space State Block. This efficient model, termed ‘SSR-UNet,’ leverages bidirectional scanning to capture both global and local features in image data, achieving strong performance with low computational complexity. …”
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SMS Scam Detection Application Based on Optical Character Recognition for Image Data Using Unsupervised and Deep Semi-Supervised Learning by Anjali Shinde, Essa Q. Shahra, Shadi Basurra, Faisal Saeed, Abdulrahman A. AlSewari, Waheb A. Jabbar

“…Notably, the K-means feature extraction with vectorizer achieved 91.01% accuracy, and the KNN-Flatten model reached 94.13% accuracy, emerging as the top performer. …”
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Effective Cultivated Land Extraction in Complex Terrain Using High-Resolution Imagery and Deep Learning Method by Zhenzhen Liu, Jianhua Guo, Chenghang Li, Lijun Wang, Dongkai Gao, Yali Bai, Fen Qin

“…The experimental results show that the proposed method achieved an F1 score of 89.55% for the entire study area, with an F1 score ranging from 83.84% to 90.44% in the hilly or transitional zones. Compared to models that solely rely on spectral features, the feature selection-based model demonstrates superior performance in hilly and adjacent mountainous regions, with improvements of 4.5% in Intersection over Union (IoU). …”
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MDNN-DTA: a multimodal deep neural network for drug-target affinity prediction by Xu Gao, Xu Gao, Mengfan Yan, Mengfan Yan, Chengwei Zhang, Chengwei Zhang, Gang Wu, Gang Wu, Jiandong Shang, Jiandong Shang, Congxiang Zhang, Congxiang Zhang, Kecheng Yang, Kecheng Yang

“…Artificial intelligence (AI), especially deep learning models, can automatically extract high-dimensional features from the biological sequences of drug molecules and target proteins. …”
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An Innovative Method of Monitoring Cotton Aphid Infestation Based on Data Fusion and Multi-Source Remote Sensing Using Unmanned Aerial Vehicles by Chenning Ren, Bo Liu, Zhi Liang, Zhonglong Lin, Wei Wang, Xinzheng Wei, Xiaojuan Li, Xiangjun Zou

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Contactless Fatigue Level Diagnosis System Through Multimodal Sensor Data by Younggun Lee, Yongkyun Lee, Sungho Kim, Sitae Kim, Seunghoon Yoo

“…The system integrates non-contact biometric data collection with an AI-driven classification model capable of diagnosing fatigue levels on a 1 to 5 scale with an average accuracy of 89%. …”
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Focused Crawler for Event Detection Using Metaheuristic Algorithms and Knowledge Extraction by Hossein Moradi, Fatemeh Azimzadeh

“…Comparative analysis reveals that the proposed method outperformed alternative models. Nevertheless, when tested across various data models and datasets, the WOA model consistently demonstrated superior performance, albeit exhibiting reduced evaluation metrics for Wikipedia text data.…”
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DGNMDA: Dual Heterogeneous Graph Neural Network Encoder for miRNA-Disease Association Prediction by Daying Lu, Qi Zhang, Chunhou Zheng, Jian Li, Zhe Yin

“…We evaluate our model using 5-fold cross-validation and real-world disease case studies, based on the HMDD V3.2 dataset. …”
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AI-driven diagnosis and health management of autonomous electric vehicle powertrains: An empirical data-driven approach by Hicham El hadraoui, Adila El maghraoui, Oussama Laayati, Erroumayssae Sabani, Mourad Zegrari, Ahmed Chebak

“…The study focuses on identifying the most informative features from time, frequency, and wavelet domains, followed by dimensionality reduction using Principal Component Analysis (PCA) and Correlation Analysis (CA) to enhance classification performance, reduce complexity, and improve model interpretability. …”
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Exploring the upper ocean characteristics of a bay using coastal and regional ocean community model by D. Jaishree, P.T. Ravichandran

“…This model was crucial for investigating and analyzing the circulation features throughout the entire Bay of Bengal, contributing knowledge and insights about the coastal and regional oceanographic community.METHODS: To investigate the temporal variability of the upper ocean in the Bay of Bengal, climatological simulations were performed over eight years using the coastal and regional ocean community model. …”
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Intelligent diagnosis and prediction of pregnancy induced hypertension in obstetrics and gynecology teaching by integrating GA by Xiaolan Li, Xiaolan Li, Xiaolan Li, Fen Kang, Fen Kang, Fen Kang, Xiaojing Li, Xiaojing Li, Xiaojing Li

“…The model achieved superior performance metrics, including the highest recall (0.768), F-score (0.728), and area under the curve (0.832) when compared to other prevalent models. …”
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Color Consistency Anime Style Transfer Based on Hybrid Structural Decomposition Network by Jun Zhang, Yunhua Zhang

“…The proposed HSDN utilizes an encoder to separately extract structural and color features from input images. The structural features are then processed by our style transfer sub-network, which is based on diffusion models. …”
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Adaptive Spectral Correlation Learning Neural Network for Hyperspectral Image Classification by Wei-Ye Wang, Yang-Jun Deng, Yuan-Ping Xu, Ben-Jun Guo, Chao-Long Zhang, Heng-Chao Li

“…Taking advantage of the group convolutional and ConvLSTM3D layers, a new adaptive spectral correlation learning block (ASBlock) is designed as a basic network unit to construct the backbone of a spatial–spectral feature extraction model for learning the spectral information, extracting the spectral-enhanced deep spatial–spectral features. …”
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Temporal generalization in evergreen leaf type classification using tailored Sentinel-2 composites by Peter Hofinger, Jan Dempewolf, Simon Ecke, Hans-Joachim Klemmt

“…Importantly, we found that model performance improved markedly with tree height, leading us to recommend our methodology for trees taller than 20 m. …”
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YOLOv8-MSP-PD: A Lightweight YOLOv8-Based Detection Method for Jinxiu Malus Fruit in Field Conditions by Yi Liu, Xiang Han, Hongjian Zhang, Shuangxi Liu, Wei Ma, Yinfa Yan, Linlin Sun, Linlong Jing, Yongxian Wang, Jinxing Wang

“…To address these challenges, we propose YOLOv8-MSP-PD (YOLOv8 with Multi-Scale Pyramid Fusion and Proportional Distance IoU), a lightweight model built on an enhanced YOLOv8 architecture. We replace the backbone with MobileNetV4, incorporating unified inverted bottleneck (UIB) modules and depth-wise separable convolutions for efficient feature extraction. …”
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DeepCCDS: Interpretable Deep Learning Framework for Predicting Cancer Cell Drug Sensitivity through Characterizing Cancer Driver Signals by Jiashuo Wu, Jiyin Lai, Xilong Zhao, Ziyi Wang, Yongbao Zhang, Liqiang Wang, Yinchun Su, Yalan He, Siyuan Li, Ying Jiang, Junwei Han

“…The signals can reflect key mechanisms influencing cancer cell development and drug response, enhancing the model's predictive performance and interpretability. …”
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