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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Advancing Hate Speech Detection in Indonesian Language Using Graph Neural Networks and TF-IDF by Syaikha Amirah Zikrina, Fitriyani

“…This study establishes a robust framework for improving hate speech detection, offering a valuable contribution to safer digital environments.…”
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RoBERTa-Based Multi-Feature Integrated BiLSTM and CNN Model for Ceramic Review Analysis by LiHua Yang, Jun Wang, WangRen Qiu

“…To address the limitation that the Robustly Optimized BERT Pretraining Approach (RoBERTa) may not effectively capture local dependencies and salient features within the text, we propose a feature fusion framework based on RoBERTa’s multi-output architecture. …”
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Real time blood detection in CCTV surveillance using attention enhanced InceptionV3 by Adnan Khalil, Fakhre Alam, Dilawar Shah, Irshad khalil, Shujaat Ali, Muhammad Tahir

“…This study proposes a real-time deep learning framework that combines the InceptionV3 architecture with Convolutional Block Attention Modules to enhance spatial and channel-level feature discrimination. …”
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Prediction of power generation and maintenance using AOC‐ResNet50 network by Yueqiang Chu, Wanpeng Cao, Cheng Xiao, Yubin Song

“…Based on the analysis of the OctConv (Octave Convolution) network structure, the AOctConv (Attention Octave Convolution) convolutional neural network structure is proposed, which is combined with the ResNet50 backbone network to obtain AOC‐ResNet50. …”
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Enhanced Brain Tumor Segmentation Using CBAM-Integrated Deep Learning and Area Quantification by Rafiqul Islam, Sazzad Hossain

“…Furthermore, the proposed framework enhances segmentation precision while simultaneously incorporating tumor area measurement, making it a comprehensive tool for early-stage tumor analysis. …”
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Privacy-Oriented Successive Approximation Image Position Follower Processing by Ying Miao, Danyang Shao, Zhimin Yan

“…To solve the detection problem of moving the human body in the dynamic background, the motion target detection module integrates the two ideas of feature information detection and human body model segmentation detection and combines the deep learning framework to complete the detection of the human body by detecting the feature points of key parts of the human body. …”
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Algorithms for the visual analysis of an environment by an autonomous mobile robot for area cleanup by M. E. Beliakov, S. A. K. Diane

“…The purpose of this work is to develop the underlying algorithmics for the vision system of robots executing area cleaning functions.Methods. Within the framework ofthe proposed structure ofthe system for visual analysis ofthe external environment, algorithms for detecting and classifying objects of various appearance have been developed using convolutional neural networks. …”
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Complex Network Analytics for Structural–Functional Decoding of Neural Networks by Jiarui Zhang, Dongxiao Zhang, Hu Lou, Yueer Li, Taijiao Du, Yinjun Gao

“…This work proposes a complex network theory framework decoding structure–function coupling by mapping convolutional layers, fully connected layers, and Dropout modules into graph representations. …”
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Task-Related EEG as a Biomarker for Preclinical Alzheimer’s Disease: An Explainable Deep Learning Approach by Ziyang Li, Hong Wang, Lei Li

“…An interpretable deep learning framework—Interpretable Convolutional Neural Network (InterpretableCNN)—was utilized to identify AD-related EEG features. …”
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Orbital Behavior Intention Recognition for Space Non-Cooperative Targets Under Multiple Constraints by Yuwen Chen, Xiang Zhang, Wenhe Liao, Guoning Wei, Shuhui Fan

“…To address the issue of misclassification and diminished accuracy that is prevalent in existing intent recognition models for non-cooperative spacecraft due to the omission of environmental influences, this paper presents a novel recognition framework leveraging a hybrid neural network subject to multiple constraints. …”
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Knowledge Distillation‐Based Zero‐Shot Learning for Process Fault Diagnosis by Yi Liu, Jiajun Huang, Mingwei Jia

“…With unknown faults as a zero‐shot learning problem, this article proposes a method for detecting and isolating unknown faults based on knowledge distillation within a teacher–student framework. Process data and image data are equivalent in their spatiotemporal dimensions, and convolutional neural networks are selected as the teacher model, pretrained on image data. …”
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Web-Enhanced Vision Transformers and Deep Learning for Accurate Event-Centric Management Categorization in Education Institutions by Khalied M. Albarrak, Shaymaa E. Sorour

“…By categorizing images into five primary event management categories and subcategories, this research provides a robust framework for assessing the social media content generated by KFU’s administrative units. …”
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