Depth-Enhanced Tumor Detection Framework for Breast Histopathology Images by Integrating Adaptive Multi-Scale Fusion, Semantic Depth Calibration, and Boundary-Guided Detection by A. Robert Singh, Suganya Athisayamani, Hariharasitaraman S, Faten Khalid Karim, Jose Varela-Aldas, Samih M. Mostafa

“…Experimental results demonstrate that the proposed framework significantly improves detection accuracy by 98% and boundary delineation compared to existing methods. …”
Get full text

Metering Automation System 3.0 Base Version Based on Machine Learning by Sheng Li, Leping Zhang, Hang Dai, Lukun Zeng, Yuan Ai, Shuang Qi, Yuanzhai Cui

“…This study proposes a hybrid DSCNN-CBAM-BiLSTM framework that synergistically integrates depthwise separable convolutions, dual attention mechanisms, and bidirectional temporal modeling to address these challenges. …”
Get full text

An Ensemble of Vision-Language Transformer-Based Captioning Model With Rotatory Positional Embeddings by K. B. Sathyanarayana, Dinesh Naik

“…Traditional models, primarily employing an encoder-decoder framework with Convolutional Neural Networks (CNNs), often struggle to capture the complex spatial and sequential relationships inherent in visual data. …”
Get full text

A systematic review of deep learning methods for community detection in social networks by Mohamed El-Moussaoui, Mohamed Hanine, Ali Kartit, Monica Garcia Villar, Monica Garcia Villar, Monica Garcia Villar, Helena Garay, Helena Garay, Helena Garay, Isabel de la Torre Díez

“…It also examines the variety of social networks, datasets, evaluation metrics, and employed frameworks in these studies.DiscussionHowever, the analysis highlights several challenges, such as scalability, understanding how the models work (interpretability), and the need for solutions that can adapt to different types of networks. …”
Get full text

Intrusion Detection System Framework for SDN-Based IoT Networks Using Deep Learning Approaches With XAI-Based Feature Selection Techniques and Domain-Constrained Features by Manlaibaatar Tserenkhuu, Md Delwar Hossain, Yuzo Taenaka, Youki Kadobayashi

“…Two recent flow-based datasets were used to train and assess models to validate the proposed framework. We conducted an extensive set of experiments using the subsets of features derived by XAI-based feature selection techniques, and compared their performance against each other, the baseline, and state-of-the-art models. …”
Get full text

Securing Urban Landscape: Cybersecurity Mechanisms for Resilient Smart Cities by Qiang Lyu, Sujuan Liu, Zhouyuan Shang

“…This article explores a novel approach to enhancing cybersecurity in smart cities by integrating Convolutional Neural Networks (CNNs) with Genetic Algorithms (GAs). …”
Get full text

Sticky Trap-Embedded Machine Vision for Tea Pest Monitoring: A Cross-Domain Transfer Learning Framework Addressing Few-Shot Small Target Detection by Kunhong Li, Yi Li, Xuan Wen, Jingsha Shi, Linsi Yang, Yuyang Xiao, Xiaosong Lu, Jiong Mu

Get full text

A Deep Learning Framework for Using Search Engine Data to Predict Influenza-Like Illness and Distinguish Epidemic and Nonepidemic Seasons: Multifeature Time Series Analysis by Ji Li, Xiangyu Yan, Xingjie Chu, Ying Zhang, Guoliang Liu, Lin Li, Yue Li, Xiaochun Dong, Zihan Mei, Zhengkun Liu, Jinyue Yuan, Xiaohan Sun, Chunxia Cao

“…The study finally used the convolutional long short-term memory (CLSTM) network framework to predict influenza epidemics with 1‐3 weeks ahead for the all-time period and epidemic + nonepidemic period. …”
Get full text

Real-Time Pipeline Leak Detection: A Hybrid Deep Learning Approach Using Acoustic Emission Signals by Faisal Saleem, Zahoor Ahmad, Jong-Myon Kim

“…A Gaussian filter minimizes background noise and clarifies these features further. The core of the framework combines convolutional neural networks (CNNs) with long short-term memory (LSTM), ensuring a comprehensive examination of both spatial and temporal features of AE signals. …”
Get full text

Weapon detection with FMR-CNN and YOLOv8 for enhanced crime prevention and security by Shanthi P, Manjula V

“…This study proposes a hybrid deep learning framework that merges a Faster region convolutional neural network and Mask Region Convolutional Neural Network, named FMR-CNN. …”
Get full text

Predicting drug combination side effects based on a metapath-based heterogeneous graph neural network by Leixia Tian, Qi Wang, Zhiheng Zhou, Xiya Liu, Ming Zhang, Guiying Yan

“…MAEM-SSHIN and GCN-CSHIN provided a united novel framework for predicting potential side effects in combinatorial drug therapies. …”
Get full text

Smart intrusion detection model to identify unknown attacks for improved road safety and management by Faisal Alshammari, Abdullah Alsaleh

“…ACIDS integrates convolutional neural networks (CNN) for hierarchical feature extraction, the synthetic minority over-sampling technique (SMOTE) to address class imbalance and an open-set classification framework to detect novel attack patterns. …”
Get full text

Load Forecasting Based on Multiple Load Features and TCN-GRU Neural Network by Haofeng ZHENG, Guohua YANG, Wenjun KANG, Zhiyuan LIU, Shitao LIU, Hong WU, Honghao ZHANG

“…To improve the prediction accuracy, a multi-load feature combination (MLFC) is proposed, and a load prediction framework is constructed by combining Temporal Convolutional Network (TCN) and Gated Recurrent Unit (GRU). …”
Get full text

Daily insider threat detection with hybrid TCN transformer architecture by Xiaoyun Ye, Huangrongbin Cui, Faqin Luo, Jinlong Wang, Xiaoyun Xiong, Wencui Zhang, Jiawei Yu, Wenhao Zhao

“…This framework combines the strengths of Temporal Convolutional Networks (TCNs) and the Transformer architecture. …”
Get full text

KHNN: Hypercomplex-valued neural networks computations via Keras using TensorFlow and PyTorch by Agnieszka Niemczynowicz, Radosław A. Kycia

“…However, no general framework exists for constructing hypercomplex neural networks. …”
Get full text

Automated image-based condition assessment of the built environment: A state-of-the-art investigation of damage characteristics and detection requirements by Leila Farahzadi, Ibrahim Odeh, Mahdi Kioumarsi, Behrouz Shafei

“…Considering this critical gap, the current study systematically investigated various types of damage and how they can be evaluated in a condition assessment framework. For the automated detection, localization, and measurement of damage, various convolutional neural network, support vector machine, and classification-based methods were examined, including their advantages and limitations. …”
Get full text

Research on knowledge tracing based on learner fatigue state by Haoyu Wang, Qianxi Wu, Chengke Bao, Weidong Ji, Guohui Zhou

“…This method combines the Grit theory to evaluate the learner’s fatigue state and explores the potential impact of learning tasks on the learner’s fatigue state through deep graph convolutional networks. In particular, this article employs a multilayer perceptual network with scaled dot-product attention to process information dynamically, focusing on the critical information the learner needs at a given moment and effectively incorporating it into the knowledge framework. …”
Get full text

A Hybrid Deep Learning Approach for Integrating Transient Electromagnetic and Magnetic Data to Enhance Subsurface Anomaly Detection by Zhijie Qu, Yuan Gao, Shiyan Li, Xiaojuan Zhang

“…In this study, we introduce a novel deep learning framework, MagEMNet, designed to jointly invert EM and magnetic responses. …”
Get full text

Alzheimer’s disease diagnosis by 3D-SEConvNeXt by Zhongyi Hu, Yuhang Wang, Lei Xiao

“…Therefore, our work aims to develop a new deep learning framework to tackle this challenge. Our proposed model integrates ConvNeXt with three-dimensional (3D) convolution and incorporates a 3D Squeeze-and-Excitation (3D-SE) attention mechanism to enhance early classification of AD. …”
Get full text

CNN-Based Multi-Object Detection and Segmentation in 3D LiDAR Data for Dynamic Industrial Environments by Danilo Giacomin Schneider , Marcelo Ricardo Stemmer

“…Furthermore, we integrate our CNN-based detection and segmentation model into a Robot Operating System 2 (ROS2) framework, facilitating communication between mobile robots and a centralized node for data aggregation and map creation. …”
Get full text