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. …”
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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. …”
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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. …”
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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. …”
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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. …”
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DSAT: a dynamic sparse attention transformer for steel surface defect detection with hierarchical feature fusion by Shouluan Wu, Hui Yang, Liefa Liao, Chao Song, Yating Fang, Jianglong Fu, Tan Li

“…These defects exhibit diverse morphological characteristics and complex patterns, which pose substantial challenges to traditional detection models, particularly regarding multi-scale feature extraction and information retention across network depths. …”
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Attention Enhanced InceptionNeXt-Based Hybrid Deep Learning Model for Lung Cancer Detection by Burhanettin Ozdemir, Emrah Aslan, Ishak Pacal

“…The use of InceptionNeXt blocks facilitates multi-scale feature processing, making the model particularly effective for complex and diverse lung nodule patterns. Similarly, including grid attention improves the model’s capacity to identify spatial relationships across different sections of the picture, whereas block attention focuses on capturing hierarchical and contextual information, allowing for precise identification and categorization of lung nodules. …”
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Multi-Modal Deep Learning for Lung Cancer Detection Using Attention-Based Inception-ResNet by Mohamed Hosny, Ibrahim A. Elgendy, Mousa Ahmad Albashrawi

“…Comparative experiments unveiled that the proposed model outperformed conventional DL architectures in lung cancer detection. The proposed system, utilizing advanced attention mechanisms and multi-modal imaging capabilities, has the potential to revolutionize early lung cancer diagnosis and extend its impact to other critical diseases. …”
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gamUnet: designing global attention-based CNN architectures for enhanced oral cancer detection and segmentation by Jinyang Zhang, Hongxin Ding, Hongxin Ding, Runchuan Zhu, Weibin Liao, Weibin Liao, Junfeng Zhao, Junfeng Zhao, Min Gao, Xiaoyun Zhang

“…Its infiltrative growth patterns and poorly defined boundaries, coupled with the complex architecture of the oral cavity, make accurate segmentation particularly difficult. …”
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MMG-Based Motion Segmentation and Recognition of Upper Limb Rehabilitation Using the YOLOv5s-SE by Gangsheng Cao, Shen Jia, Qing Wu, Chunming Xia

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The Elevational Distribution Patterns of Plant Diversity and Phylogenetic Structure Vary Geographically Across Eight Subtropical Mountains by Kuiling Zu, Fusheng Chen, Chao Huang, Yuanqiu Liu, Fangchao Wang, Guojin Zhu, Wensheng Bu, Xiangmin Fang, Liping Guo

“…We also detected the elevational patterns and their relationship between different groups. …”
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Reduced temporal and spatial stability of neural activity patterns predict cognitive control deficits in children with ADHD by Zhiyao Gao, Katherine Duberg, Stacie L. Warren, Li Zheng, Stephen P. Hinshaw, Vinod Menon, Weidong Cai

“…Additionally, children with ADHD exhibit more heterogeneous neural response patterns across individuals compared to TD children. …”
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Brain Tumour Segmentation and Grading Using Local and Global Context-Aggregated Attention Network Architecture by Ahmed Abdulhakim Al-Absi, Rui Fu, Nadhem Ebrahim, Mohammed Abdulhakim Al-Absi, Dae-Ki Kang

“…Segmentation refers to the identification and delineation of tumour regions in medical images, while classification classifies based on tumour characteristics, such as the size, location and enhancement pattern. The main aim of this research is to design and develop an intelligent model that can detect and grade tumours more effectively. …”
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Multi-Modal Fused-Attention Network for Depression Level Recognition Based on Enhanced Audiovisual Cues by Yihan Zhou, Xiaokang Yu, Zixi Huang, Feierdun Palati, Zeyu Zhao, Zihan He, Yuan Feng, Yuxi Luo

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An Improved YOLOv5 Model for Lithographic Hotspot Detection by Mu Lin, Wenjing He, Jiale Liu, Fencheng Li, Jun Luo, Yijiang Shen

“…In this paper, we propose a hotspot detection method to improve the precision and recall rate of the fatal pinching and bridging error due to the poor printability of certain layout patterns by embedding a spatial attention mechanism into the YOLOv5 model. …”
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Boosting Cognitive Focus via Attention Types Detection using Brain-Computer Interfaces: A Pilot Study by Mihai-Robert BEU, Tudor DURDUMAN-BURTESCU, David GHEORGHICĂ ISTRATE

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A Hybrid CNN-LSTM Model With Attention Mechanism for Improved Intrusion Detection in Wireless IoT Sensor Networks by Pendukeni Phalaagae, Adamu Murtala Zungeru, Abid Yahya, Boyce Sigweni, Selvaraj Rajalakshmi

“…The proposed model enhances IoT intrusion detection by integrating a novel hybrid CNN-LSTM with an attention mechanism, thereby improving feature extraction and temporal pattern recognition. …”
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Spike-Driven Channel-Temporal Attention Network with Multi-Scale Convolution for Energy-Efficient Bearing Fault Detection by JinGyo Lim, Seong-Eun Kim

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A deep learning approach to detect diseases in pomegranate fruits via hybrid optimal attention capsule network by P. Sajitha, A. Diana Andrushia, N. Anand, M.Z. Naser, Eva Lubloy

“…In post-harvest pomegranate fruit disease detection, deep learning has great potential to extract complex patterns and features from large datasets. …”
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MultiSHTM: Multi-Level Attention Enabled Bi-Directional Model for the Summarization of Chart Images by Indra Kumari, Hansung Lee

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