Synchronous Analysis of Speech Production and Lips Movement to Detect Parkinson’s Disease Using Deep Learning Methods by Cristian David Ríos-Urrego, Daniel Escobar-Grisales, Juan Rafael Orozco-Arroyave

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Small object detection in side-scan sonar images based on SOCA-YOLO and image restoration by Xiaodong Cui, Jiale Zhang, Lingling Zhang, Qunfei Zhang, Jing Han

“…Furthermore, the model integrates the standardized CBAM (Convolutional Block Attention Module) attention mechanism, enabling adaptive focus on salient regions of small targets in sonar images, thereby significantly improving detection robustness in complex underwater environments. …”
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IRFNet: Cognitive-Inspired Iterative Refinement Fusion Network for Camouflaged Object Detection by Guohan Li, Jingxin Wang, Jianming Wei, Zhengyi Xu

“…Camouflaged Object Detection (COD) aims to identify objects that are intentionally concealed within their surroundings through appearance, texture, or pattern adaptations. …”
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Automatic Detection of Landslide Surface Cracks from UAV Images Using Improved U-Network by Hao Xu, Li Wang, Bao Shu, Qin Zhang, Xinrui Li

“…Surface cracks are key indicators of landslide deformation, crucial for early landslide identification and deformation pattern analysis. However, due to the complex terrain and landslide extent, manual surveys or traditional digital image processing often face challenges with efficiency, precision, and interference susceptibility in detecting these cracks. …”
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A deep learning-based detection model and illumination-adaptive behavioral analysis for soldier crabs in the intertidal zone by Liangjun Li, Zhihao Ren, Cheng Tang, Shengning Lu, Yong Liang

“…However, due to their small size and tidal-driven activity patterns, conventional behavior detection methods suffer from low efficiency and considerable observer bias, particularly under dark conditions where detection errors and omissions are prevalent. …”
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MT-CMVAD: A Multi-Modal Transformer Framework for Cross-Modal Video Anomaly Detection by Hantao Ding, Shengfeng Lou, Hairong Ye, Yanbing Chen

“…Video anomaly detection (VAD) faces significant challenges in multimodal semantic alignment and long-term temporal modeling within open surveillance scenarios. …”
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A hybrid deep learning-based intrusion detection system for EV and UAV charging stations by Rosebell Paul, Mercy Paul Selvan

“…The SS-ADRNN model incorporates squirrel search optimization, which is inspired by the foraging behaviour of squirrels, to dynamically adjust charging station operations based on environmental conditions and demand patterns. Additionally, attention mechanisms are employed to prioritize relevant input features, enabling the model to focus on critical information during decision-making processes. …”
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GNN-EADD: Graph Neural Network-Based E-Commerce Anomaly Detection via Dual-Stage Learning by Zhouhang Shao, Xuran Wang, Enkai Ji, Shiyang Chen, Jin Wang

“…Our key contributions include: 1) A heterogeneous graph representation incorporating products, sellers, and buyers as nodes with their relationships as edges; 2) A novel dual-stage learning framework combining unsupervised graph embedding with semi-supervised fine-tuning; and 3) An attention mechanism that effectively captures complex patterns within network structures. …”
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ID3RSNet: cross-subject driver drowsiness detection from raw single-channel EEG with an interpretable residual shrinkage network by Xiao Feng, Xiao Feng, Zhongyuan Guo, Zhongyuan Guo, Sam Kwong

“…To address these issues, we propose a novel interpretable residual shrinkage network, namely, ID3RSNet, for cross-subject driver drowsiness detection using single-channel EEG signals. First, a base feature extractor is employed to extract the essential features of EEG frequencies; to enhance the discriminative feature learning ability, the residual shrinkage building unit with attention mechanism is adopted to perform adaptive feature recalibration and soft threshold denoising inside the residual network is further applied to achieve automatic feature extraction. …”
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The Role of Cognitive Control in Language Comprehension: Commentary on Kuz et al. (2024) by Jared M. Novick, Susan Teubner-Rhodes, Albert E. Kim

“…By highlighting the linking assumptions behind the visual-world paradigm, we argue that eye movement patterns reflect syntactic parsing decisions and cannot be explained by visual attention alone. …”
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Concomitant body-wide trauma patterns in patients with head and neck injuries: a comparison based on the trauma register DGU® by the German trauma society and the dortmund maxillof... by Ákos Bicsák, Fatma Topcuoglu, Stefan Hassfeld, Rolf Lefering, Lars Bonitz, Jens-Peter Stahl

“…Conclusions Patients with HNI tend to have slightly different injury pattern in the whole body than non-HNI patients. Head and neck injuries in different locations show different patterns of concomitant injuries. …”
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StyleGraph: A Heterogeneous Graph Neural Framework for Stylistic and Semantic Rumor Detection on Social Media by Haider Jaffar, Ali Mohades, Mohammad Ebrahim Shiri

“…Identifying and controlling misinformation circulated on social media is known as rumor detection. The growing prevalence of deceptive content presents a significant challenge that necessitates advanced detection techniques, beyond classical machine learning. …”
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Enhanced tuberculosis detection using Vision Transformers and explainable AI with a Grad-CAM approach on chest X-rays by K. Vanitha, T. R. Mahesh, V. Vinoth Kumar, Suresh Guluwadi

“…The ViT model utilizes self-attention mechanisms to extract long-range dependencies and complex patterns directly from the raw pixel information, whereas Grad-CAM offers visual explanations of model decisions about highlighting significant regions in the X-rays. …”
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GDText-VM: an arbitrary-shaped scene text detector based on globally deformable VMamba by Yingnan Zhao, Zheng Hu, Fangqi Ding, Jielin Jiang, Xiaolong Xu

“…Unlike the original VMamba, GDText-VM integrates deformable convolutions to enhance focus on local regions and reduces reliance on cross-shaped activation patterns. Additionally, to improve the capability of GDText-VM to fit text contours in the Fourier domain, this study introduces an innovative Global Attention Shuffle Module (GASM). …”
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GradCAM-PestDetNet: A deep learning-based hybrid model with explainable AI for pest detection and classification by Ramitha Vimala, Saharsh Mehrotra, Satish Kumar, Pooja Kamat, Arunkumar Bongale, Ketan Kotecha

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A hybrid deep learning model EfficientNet with GRU for breast cancer detection from histopathology images by M. Pradeepa, B. Sharmila, M. Nirmala

“…Abstract Breast cancer remains a significant global health challenge among women, with histopathological image analysis playing a critical role in early detection. However, existing diagnostic models often struggle to extract complex patterns from high-resolution tissue images, limiting their diagnostic accuracy and generalization. …”
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Performance Comparison of Facial Emotion Recognition: Introducing a Model within the Driver Assistance Framework based on Deep Learning with LBP Feature Extraction for In-Vehicle A... by Ehsan Ghasemi, Seyyed Mohammad Razavi, Sajad Mohamadzadeh

“…Secondly, the Local Binary Patterns (LBP) feature descriptor is utilized to extract features from the identified eye, nose, and mouth regions. …”
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Diverse behavior clustering of students on campus with macroscopic attention by Wanghu Chen, Zongjuan Wu, Siqi Zeng, Hongle Guo, Jing Li

“…Abstract Analyzing multi-source heterogeneous behavioral data of individuals in complex environments and discovering effective patterns is a challenging topic. Since cognitive psychology believes that all behaviors can be regarded as attention to different objects, this paper proposes an analysis framework based on Macroscopic Attention (MA) to characterize the diverse behavior of individuals. …”
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CPS-IIoT-P2Attention: Explainable Privacy-Preserving With Scaled Dot-Product Attention in Cyber-Physical System-Industrial IoT Network by Yakub Kayode Saheed, Joshua Ebere Chukwuere

“…In this research, we propose a new privacy-preservation via Pearson correlation coefficient and agglomerative clustering with Bidirectional long short-term memory (BiLSTM) integrated with a scaled dot-product attention for cyber-attacks detection in CPS-IIoT. …”
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Predator gaze captures both human and chimpanzee attention. by Will Whitham, Bradley Karstadt, Nicola C Anderson, Walter F Bischof, Steven J Schapiro, Alan Kingstone, Richard Coss, Elina Birmingham, Jessica L Yorzinski

“…Primates can rapidly detect potential predators and modify their behavior based on the level of risk. …”
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