SiNC: Saliency-injected neural codes for representation and efficient retrieval of medical radiographs. by Jamil Ahmad, Muhammad Sajjad, Irfan Mehmood, Sung Wook Baik

“…Comprehensive experimental evaluations on the radiology images dataset reveal that the proposed framework achieves high retrieval accuracy and efficiency for scalable image retrieval applications and compares favorably with existing approaches.…”
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Forecasting Surface Velocity Fields Associated With Laboratory Seismic Cycles Using Deep Learning by G. Mastella, F. Corbi, J. Bedford, F. Funiciello, M. Rosenau

“…Here we propose a prediction framework that allows forecasting future surface velocity fields from past ones for analog experiments of megathrust seismic cycles. …”
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Adoption deep learning approach using realistic synthetic data for enhancing network intrusion detection in intelligent vehicle systems by Said A. Salloum, Tarek Gaber, Mohammed Amin Almaiah, Rami Shehab, Romel Al-Ali, Theyazan H.H Aldahyani

“…This dataset was analyzed using a deep learning framework employing a Convolutional Neural Network (CNN), which demonstrated outstanding performance metrics: an accuracy of 99.08%, precision of 98.96%, recall of 99.11%, and an F1 score of 99.03%. …”
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Hybrid Capsule Network for precise and interpretable detection of malaria parasites in blood smear images by Bader Alawfi

“…Grad-CAM visualizations confirm that the model focuses on biologically relevant parasite regions, validating interpretability.DiscussionThe proposed framework delivers a pragmatic and interpretable solution for malaria diagnosis, balancing high accuracy with minimal computational requirements, and demonstrates strong potential for deployment in real-world, resource-limited clinical environments.…”
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MeshHSTGT: hierarchical spatio-temporal fusion for mesh network traffic forecasting by Sunlei Qian, Xiaorong Zhu

“…To address this, we propose MeshHSTGT, a novel hierarchical spatio-temporal framework that synergizes TimesNet for multi-periodic temporal-frequency modeling and a Channel Capacity-Weighted Graph Convolutional Network (CCW-GCN) with Temporal Encoding GRU (TE-GRU) for topology-aware spatial-temporal dependency learning. …”
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Heart failure prognosis risk assessment model based on multimodal data fusion and IoT device monitoring by Zhe Zhang, Dengao Li, Jumin Zhao, Huiting Ma, Fei Wang, Qinglian Hao

“…., age, blood pressure, ejection fraction), and real-time data from IoT devices monitoring physiological parameters. This deep learning framework combines graph neural networks (GNN) and convolutional neural networks (CNN) to extract comprehensive features from diverse data types, thereby improving risk predictions. …”
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Enhancing fairness in disease prediction by optimizing multiple domain adversarial networks. by Bin Li, Xiaoqian Jiang, Kai Zhang, Arif O Harmanci, Bradley Malin, Hongchang Gao, Xinghua Shi, Alzheimer’s Disease Neuroimaging Initiative

“…To enhance fairness, we introduce a framework based on a Multiple Domain Adversarial Neural Network (MDANN), which incorporates multiple adversarial components. …”
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Real-Time Waste Detection and Classification Using YOLOv12-Based Deep Learning Model by Mosharof Hossain Dipo, Fahmid Al Farid, Md. Sifti Al Mahmud, Muntasir Momtaz, Shakila Rahman, Jia Uddin, Hezerul Abdul Karim

“…It is coupled with advanced convolutional neural networks (CNNs), which are used for data collection, real-time waste detection, and classification of the proposed framework. …”
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Wind energy system fault classification using deep CNN and improved PSO‐tuned extreme gradient boosting by Chun‐Yao Lee, Edu Daryl C. Maceren

“…Additionally, the study contributes to methodological advancements in wind turbine fault diagnosis by providing a rigorous framework for fault classification. It is confirmed that utilizing the extracted deep learning features into the resampled data can significantly affect the classification performance metrics. …”
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CNN-ViT: A multi-feature learning based approach for driver drowsiness detection by Madduri Venkateswarlu, Venkata Rami Reddy Chirra

“…This hybrid framework is designed to harness the complementary strengths of CNNs and transformers: CNNs excel at capturing fine-grained local features, while ViT effectively models global dependencies within images. …”
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Deep Reinforcement Learning-Based Deployment Method for Emergency Communication Network by Bo Huang, Yiwei Lu, Hao Ma, Changsheng Yin, Ruopeng Yang, Yongqi Shi, Yu Tao, Yongqi Wen, Yihao Zhong

“…To address these challenges, this study proposes a novel deep reinforcement learning framework with a fully convolutional value network architecture, which achieves breakthroughs in multi-dimensional spatial decision-making through end-to-end feature extraction. …”
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Damage Detection and Identification on Elevator Systems Using Deep Learning Algorithms and Multibody Dynamics Models by Josef Koutsoupakis, Dimitrios Giagopoulos, Panagiotis Seventekidis, Georgios Karyofyllas, Amalia Giannakoula

“…The results indicate that the developed framework can accurately identify damages in the system, hinting at its potential as a powerful structural health monitoring tool for such applications, where manual damage localization would otherwise be considerably time-consuming.…”
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Enhancing Traffic Accident Severity Prediction Using ResNet and SHAP for Interpretability by Ilyass Benfaress, Afaf Bouhoute, Ahmed Zinedine

“…Background/Objectives: This paper presents a Residual Neural Network (ResNet) based framework tailored for structured traffic accident data, aiming to improve accident severity prediction. …”
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Efficient Approach for Brain Tumor Detection and Classification Using Fuzzy Thresholding and Deep Learning Algorithms by Nashaat M. Hussain Hassan, Wadii Boulila

“…This study proposes a novel framework that integrates fuzzy logic-based segmentation with deep learning (DL) techniques to enhance brain tumor detection and classification in magnetic resonance imaging (MRI) scans. …”
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KSC-Net: a biologically inspired spatio-temporal correlation network for video-based human action recognition by Hui Ma, Xuelian Ma

“…To address these limitations, we propose a biologically inspired two-branch convolutional network, termed Key-information Spatio-temporal Correlation Network (KSC-Net). …”
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Visualization Methods for DNA Sequences: A Review and Prospects by Tan Li, Mengshan Li, Yan Wu, Yelin Li

“…The study serves as an important reference for improving intelligent search systems, enriching knowledge bases, and enhancing query systems related to biological sequence visualization, offering a comprehensive framework for future research.…”
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RESE-CNN: Residual Squeeze-and-Excitation Network for High-Contrast Optical Tomography Reconstruction by Zhu Yingkuang, Pan Zhenhua, Li Huajun, Chen Jianyang, Sheng Yihao

“…While this study focuses on high-contrast binary scenarios, the proposed RESE-CNN framework provides a basic architecture for future extensions to weakly absorbing and scattering media where nonlinear reconstruction problems dominate.…”
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Semantic ECG hash similarity graph by Yixian Fang, Shilin Zhang, Yuwei Ren

“…In this paper, we present a novel graph generation learning framework that incorporates semantic hash coding to capture the intricate associations both within and between ECG signals, thereby significantly enhancing the retrieval efficiency of subsequent graph-based deep learning models. …”
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Focal Cosine-Enhanced EfficientNetB0: A Novel Approach to Classifying Breast Histopathological Images by Min Liu, Yuzhen Pei, Minghu Wu, Juan Wang

“…To address challenges in breast histopathology image analysis, including multi-magnification characteristics, insufficient feature extraction in traditional CNNs, and high inter-class similarity coupled with significant intra-class variation among tumor subtypes, this work proposes a focal cosine-enhanced EfficientNetB0 (FCE-EfficientNetB0) classification model. The framework incorporates a multiscale efficient attention mechanism into a multiscale efficient mobile inverted bottleneck conv, where parallel 1D convolutional branches extract features across magnification levels, while the attention mechanism prioritizes clinically relevant patterns. …”
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DBRSNet: a dual-branch remote sensing image segmentation model based on feature interaction and multi-scale feature fusion by Yong Ji, Wenbin Shi, Jingsheng Lei, Jiayin Ding

“…To address these limitations, we introduce DBRSNet, an advanced dual-branch remote sensing segmentation framework that integrates feature interaction with multi-scale feature fusion. …”
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