Enhanced residual attention-based subject-specific network (ErAS-Net): facial expression-based pain classification with multiple attention mechanisms by Mahdi Morsali, Aboozar Ghaffari

“…Through transfer learning and multiple attention mechanisms, the proposed deep learning model is designed to mimic human perception of facial expressions, thereby enhancing its pain recognition ability and capturing the unique features of each individual’s facial expressions based on their specific patterns. …”
Get full text

PARS: A Position-Based Attention for Rumor Detection Using Feedback From Source News by Hanieh Asadi G., Ali Hamzeh, Niloofar Mozafari

“…A post reordering mechanism is applied to reflect the actual dissemination pattern, ensuring that position-sensitive attention captures the most relevant signals. …”
Get full text

Epileptic seizure detection in EEG signals via an enhanced hybrid CNN with an integrated attention mechanism by Sakorn Mekruksavanich, Wikanda Phaphan, Anuchit Jitpattanakul

“…Nevertheless, the intricate characteristics of electroencephalography (EEG) signals, noise, and the want for real-time analysis require enhancement in the creation of dependable detection approaches. Despite advances in machine learning and deep learning, capturing the intricate spatial and temporal patterns in EEG data remains challenging. …”
Get full text

UAV rice panicle blast detection based on enhanced feature representation and optimized attention mechanism by Shaodan Lin, Deyao Huang, Libin Wu, Zuxin Cheng, Dapeng Ye, Haiyong Weng

“…Results The ConvGAM model, leveraging the ConvNeXt-Large backbone network and the Global Attention Mechanism (GAM), achieves outstanding performance in feature extraction, crucial for detecting small and complex disease patterns. …”
Get full text

Real time blood detection in CCTV surveillance using attention enhanced InceptionV3 by Adnan Khalil, Fakhre Alam, Dilawar Shah, Irshad khalil, Shujaat Ali, Muhammad Tahir

“…The model is further optimized through a proposed attention module that intensifies attention to small and minute blood-related patterns, even under challenging conditions such as occlusions, motion blur, and low visibility. …”
Get full text

Explainable AI for zero-day attack detection in IoT networks using attention fusion model by Deepa Krishnan, Swapnil Singh, Vijayan Sugumaran

“…The proposed attention fusion classification model utilizes both long-term and short-term attention mechanisms to capture temporal patterns and protocol-specific features, which improves the differentiation between benign and malicious traffic. …”
Get full text

DFANet: A Deep Feature Attention Network for Building Change Detection in Remote Sensing Imagery by Peigeng Lu, Haiyong Ding, Xiang Tian

“…Second, we introduce a GatedConv module to improve the network’s capability for building edge detection. Finally, Transformer is introduced to capture long-range dependencies across bitemporal images, enabling the network to better understand feature change patterns and the relationships between different regions and land cover categories. …”
Get full text

An Attention-Enhanced 3D-CNN Framework for Spectrogram-Based EEG Analysis in Epilepsy Detection by Ziaullah Khan, Aakanksha Dayal, Hee-Cheol Kim

Get full text

Few-shot network intrusion detection method based on multi-domain fusion and cross-attention. by Congyuan Xu, Donghui Li, Zihao Liu, Jun Yang, Qinfeng Shen, Ningbing Tong

“…To address this challenge, we propose a novel few-shot intrusion detection method that integrates multi-domain feature fusion with a bidirectional cross-attention mechanism. …”
Get full text

Detection of Student Engagement via Transformer-Enhanced Feature Pyramid Networks on Channel-Spatial Attention by A. Naveen, I. Jeena Jacob, Ajay Kumar Mandava

“…One of the most important aspects of contemporary educational systems is student engagement detection, which involves determining how involved, attentive, and active students are in class activities. …”
Get full text

Power transmission system’s fault location, detection, and classification: Pay close attention to transmission nodes by Chiagoziem C. Ukwuoma, Dongsheng Cai, Olusola Bamisile, Ejiyi J. Chukwuebuka, Ekong Favour, Gyarteng S.A. Emmanuel, Acen Caroline, Sabirin F. Abdi

“…The model's capacity to manage unusual data input and unidentified application situations is improved by the inclusion of multi-scale attention. Furthermore, it enables the model to precisely pinpoint fault areas by identifying patterns and connections among system parts, concentrating on specific areas or nodes. …”
Get full text

Securing Industrial IoT Environments: A Fuzzy Graph Attention Network for Robust Intrusion Detection by Safa Ben Atitallah, Maha Driss, Wadii Boulila, Anis Koubaa

“…Ablation studies confirm the essential roles of both fuzzy logic and attention mechanisms in boosting detection accuracy. …”
Get full text

Windows Malware Detection via Enhanced Graph Representations with Node2Vec and Graph Attention Network by Nisa Vuran Sarı, Mehmet Acı, Çiğdem İnan Acı

“…Therefore, developing innovative detection frameworks that can effectively analyze and interpret these complex patterns has become critical. …”
Get full text

Design of an Improved Model for Anomaly Detection in CCTV Systems Using Multimodal Fusion and Attention-Based Networks by V. Srilakshmi, Sai Babu Veesam, Mallu Shiva Rama Krishna, Ravi Kumar Munaganuri, Dulam Devee Sivaprasad

“…Long Short-Term Memory (LSTM) can capture long-range dependencies in sequential data, while Temporal Convolutional Network (TCN) efficiently models temporal patterns using convolutional layers and Transformer Networks fathom the relative importance of temporal features against one another through self-attention, thus improving their detection accuracy for anomalies that happen over a long duration. …”
Get full text

Enhanced detection of accounting fraud using a CNN-LSTM-Attention model optimized by Sparrow search by Peifeng Wu, Yaqiang Chen

“…This paper proposes an enhanced approach to fraud detection by integrating convolutional neural networks (CNN) and long short-term memory (LSTM) networks, complemented by an attention mechanism to prioritize relevant features. …”
Get full text

Gaitformer: a spatial-temporal attention-enhanced network without softmax for Parkinson’s disease early detection by Shupei Jiao, Hua Huo, Wei Liu, Changwei Zhao, Lan Ma, Jinxuan Wang, Ningya Xu, Chen Zhang, Dongfang Li

“…Using these advanced technologies, researchers can deep dive into understanding human gait and movement patterns, providing robust support for applications such as medical diagnosis, rehabilitation, and sports optimization. …”
Get full text

Hybrid Contrastive Learning With Attention-Based Neural Networks for Robust Fraud Detection in Digital Payment Systems by Md Shahin Alam Mozumder, Mohammad Balayet Hossain Sakil, Md Rokibul Hasan, Md Amit Hasan, K. M Nafiur Rahman Fuad, M. F. Mridha, Md Rashedul Islam, Yutaka Watanobe

“…Fraud detection in digital payment systems is a critical challenge due to the growing complexity of transaction patterns and the inherent class imbalance in datasets. …”
Get full text

A novel ensemble model for fall detection: leveraging CNN and BiLSTM with channel and temporal attention by Sarita Sahni, Sweta Jain, Sri Khetwat Saritha

“…To address this issue, researchers propose integrating attention mechanisms, which help prioritize important information from the sensors and reduce the impact of over lapping activity patterns. …”
Get full text

PhishingGNN: Phishing Email Detection Using Graph Attention Networks and Transformer-Based Feature Extraction by Mejdl Safran, Abdulbaset Musleh

“…This study introduces PhishingGNN, a hybrid model that integrates DistilBERT for context-aware text analysis with Graph Attention Networks (GAT) to model email metadata and content as graph structures, detecting subtle phishing patterns overlooked by traditional methods. …”
Get full text

Attention-Guided Sample-Based Feature Enhancement Network for Crowded Pedestrian Detection Using Vision Sensors by Shuyuan Tang, Yiqing Zhou, Jintao Li, Chang Liu, Jinglin Shi

“…In complex and variable urban settings, these compounded occlusion patterns critically limit the efficacy of both one-stage and two-stage pedestrian detectors, leading to suboptimal detection performance. …”
Get full text