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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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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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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Flower Automata Pattern-Based Discrimination of Fibromyalgia From Control Subjects Using Fusion of Sleep EEG and ECG Signals by Prabal Datta Barua, Makiko Kobayashi, Sengul Dogan, Mehmet Baygin, Turker Tuncer, Jose Kunnel Paul, Thomas Iype, U. R. Acharya

Subjects: “…Attention maximum pooling…”
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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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JMoE-FAP: A novel model for telecom network fraud victimization pattern analysis by Tuo Shi, Jing Hu, Danyang Li, Min Chen

“…The identified patterns can be used to design focused awareness campaigns, enhance fraud detection algorithms, and improve law enforcement training, thereby significantly increasing the effectiveness of anti-fraud initiatives.…”
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An improved multi‐scale YOLOv8 for apple leaf dense lesion detection and recognition by Shixin Huo, Na Duan, Zhizheng Xu

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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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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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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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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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STID-Net: Optimizing Intrusion Detection in IoT with Gradient Descent by James Deva Koresh Hezekiah, Usha Nandini Duraisamy, Kalaichelvi Nallusamy, Avudaiammal Ramalingam, Saranya Chandran, Murugesan Rajeswari Thiyagupriyadharsan, Periasamy Selvaraju, Rajagopal Maheswar

“…Unlike traditional models, STID-Net has an improved ability to identify irregular patterns in dynamic datasets. This work is also equipped with an attention mechanism for enhancing the detection of long-term dependencies in intrusion patterns. …”
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Temperature and Precipitation Extremes in the Brazilian Legal Amazon: A Summary of Climatological Patterns and Detected Trends by Wanderson Luiz-Silva, Anna Carolina Fernandes Bazzanela, Claudine Pereira Dereczynski, Antonio Carlos Oscar-Júnior, Igor Pinheiro Raupp

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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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Traffic environment perception algorithm based on multi-task feature fusion and orthogonal attention by Zhengfeng LI, Mingen ZHONG, Yihong ZHANG, Kang FAN, Zhiying DENG, Jiawei TAN

“…The integration of complementary pattern information deepens feature sharing, thereby enhancing the recognition accuracy of each task. …”
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RaViT-AE: Unsupervised Anomaly Detection for Intelligent Cultural Heritage Monitoring Using Region-Attentive ViT Autoencoder by Dohyung Kwon, Jeongmin Yu

“…Region-attentive patch projection enhances detection by applying higher-dimensional embeddings to regions of petroglyph images that show a higher likelihood of anomalies, effectively extracting features and recognizing complex patterns. …”
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Attention Lempel-Ziv complexity: an improved Lempel-Ziv complexity with high computational efficiency for bearing early fault detection by Jiancheng Yin, Wentao Sui, Xuye Zhuang, Yunlong Sheng

“…Lempel-Ziv complexity assesses time series anomalies by quantifying the amount of novel patterns within the time series. It has been effectively utilized in assessing bearing fault severity and classification. …”
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