Uncovering implicit Seismogenic associated regions towards promoting urban resilience by Roya Habibi, Ali Asghar Alesheikh

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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

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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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Neural Network Approach for Fatigue Crack Prediction in Asphalt Pavements Using Falling Weight Deflectometer Data by Bishal Karki, Sayla Prova, Mayzan Isied, Mena Souliman

“…This study develops an artificial neural network (ANN) model to predict the onset and progression of fatigue cracking. …”
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Sky Temperature Forecasting in Djibouti: An Integrated Approach Using Measured Climate Data and Artificial Neural Networks by Hamda Abdi, Abdou Idris, Anh Dung Tran Le

“…The purpose of this paper is to provide a comprehensive analysis of the sky temperature measurement conducted, for the first time, in Djibouti, with a pyrgeometer, a tool designed to measure longwave radiation as a component of thermal radiation, and an artificial neural network (ANN) model for improved sky temperature forecasting. …”
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Examining changes in social care referrals during the 2021 Winter Storm Uri in the Unite Texas network by Zackery White, Amanda Terry, Gillian Feldmeth, Alyssa Altstaedter, Halima Ahmadi-Montecalvo

“…Future research should investigate whether these patterns reflect intentional, proactive coordination decisions versus incidental network activity, assess their consistency across different hazards and networks, and evaluate client and provider satisfaction with the referral process and outcomes.…”
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Enhancing sound-based classification of birds and anurans with spectrogram representations and acoustic indices in neural network architectures by Fábio Felix Dias, Moacir Antonelli Ponti, Rosane Minghim

“…With that, we propose guidelines for using neural networks to classify species based on their sound patterns, even for a small dataset. …”
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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

“…This article proposes a novel Hybrid Contrastive Learning framework integrating Siamese Networks with Attention-Based Neural Networks to effectively distinguish fraudulent from legitimate transactions. …”
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Classification of Short-Segment Pediatric Heart Sounds Based on a Transformer-Based Convolutional Neural Network by Md Hassanuzzaman, Samit Kumar Ghosh, Mohammad Nurul Akhtar Hasan, Mohammad Abdullah Al Mamun, Khawza I. Ahmed, Raqibul Mostafa, Ahsan H. Khandoker

“…Phonocardiograms (PCGs), as a non-invasive and cost-effective diagnostic modality, capture vital acoustic signals that reflect the mechanical activity of the heart and can reveal pathological patterns associated with various CHD types. This study investigates the minimum signal duration required for accurate automatic classification of heart sounds and evaluates signal quality using the root mean square of successive differences (RMSSD) and the zero-crossing rate (ZCR). …”
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Forecasting Stock Market Volatility Using Hybrid of Adaptive Network of Fuzzy Inference System and Wavelet Functions by Abdullah H. Alenezy, Mohd Tahir Ismail, S. Al Wadi, Muhammad Tahir, Nawaf N. Hamadneh, Jamil J. Jaber, Waqar A. Khan

“…This study aims to model and enhance the forecasting accuracy of Saudi Arabia stock exchange (Tadawul) data patterns using the daily stock price indices data with 2026 observations from October 2011 to December 2019. …”
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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

“…To address this, we introduce a novel architecture termed the Attention-Guided Feature Enhancement Network (AGFEN), designed within the deep convolutional neural network framework. …”
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An Autism Spectrum Disorder Identification Method Based on 3D-CNN and Segmented Temporal Decision Network by Zhiling Liu, Ye Chen, Xinrui Dong, Jing Liu

“…This approach not only enhances the efficiency of spatiotemporal feature extraction but also improves the model’s ability to learn complex brain activity patterns. (3) Results: The proposed method was evaluated on the ABIDE dataset, which includes 1035 subjects from 17 different brain imaging centers. …”
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DepthFormer: Depth‐Enhanced Transformer Network for Semantic Segmentation of the Martian Surface From Rover Images by Yuan Ma, Zhaojin Li, Bo Wu, Ran Duan

“…The stereo images acquired by the Zhurong rover along its traverse are used for training and testing the DepthFormer network. Different from regular deep‐learning networks only dealing with three bands (red, green and blue) of images, the DepthFormer incorporates the depth information available from the stereo images as the fourth band in the network to enable more accurate segmentation of various surface features. …”
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Unseen Attack Detection in Software-Defined Networking Using a BERT-Based Large Language Model by Mohammed N. Swileh, Shengli Zhang

“…Our approach transforms network flow data into a format interpretable by language models, allowing BERT-base-uncased to capture intricate patterns and relationships within network traffic. …”
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Early breast cancer detection in CT scans using convolutional neural bidirectional feature pyramid network by Tahani Jaser Alahmadi, Adeel Ahmed, Amjad Rehman, Abeer Rashad Mirdad, Bayan Al Ghofaily, Shehryar Ali

“…Using convolutional neural networks, we defined several layers to classify the diseased and normal CT scan images. …”
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Bind: large-scale biological interaction network discovery through knowledge graph-driven machine learning by Naafey Aamer, Muhammad Nabeel Asim, Aamer Iqbal Bhatti, Andreas Dengel

“…Methods We developed BIND (Biological Interaction Network Discovery), a comprehensive framework utilizing 11 Knowledge Graph Embedding Methods evaluated on 8 million interactions across 30 biological relationships and 129,000 nodes. …”
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CeRNA network reveals potential diagnostic biomarkers or immunotherapy targets for Hypopharyngeal squamous cell carcinoma by Xi Yang, Chun Feng, Donghui Jiang, Xin Xu, Yingying Zhang, Jin Wang, Xiaoguang He

“…In addition, the diagnostic value of the biomarkers and their relevance to the tumor microenvironment and immunotherapy were evaluated. A ceRNA network based on mRNAs, circRNAs, and miRNAs was established. …”
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A holistic framework for intradialytic hypotension prediction using generative adversarial networks-based data balancing by Hsuan-Ming Lin, JrJung Lyu

“…Traditional oversampling methods often struggle with complex clinical data. This study evaluates an enhanced conditional Wasserstein Generative Adversarial Network with Gradient Penalty (CWGAN-GP) framework to improve IDH prediction by generating high-utility synthetic data for balancing. …”
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Changes of functional brain network topology associated with nutritional indicator of patients with recurrent major depressive disorder by Guanghui Liu, Yanli Jiang, Chunpeng Zhou, Xiangxiang Wang, Min Dai, Jinping Zhang, Jing Yu, Gangzhong Zhang

“…The level of BDNF were positively related to nodal strength of right amygdala and nodal global efficiency of right amygdala of rMDD patients.ConclusionOur findings revealed distinct patterns of brain network topology between fMDD and rMDD patients, with rMDD patients showing more widespread disruptions, which might be associated with greater number of relapses and worse level of neurological nutrition. …”
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Carotid plaque segmentation and classification using MRI-based plaque texture analysis and convolutional neural network by Zakarya Hasan Ahmed Abu Alregal, Gehad Abdullah Amran, Ali A. Al-Bakhrani, Saleh Abdul Amir Mohammad, Amerah Alabrah, Lubna Alkhalil, Abdalla Ibrahim, Maryam Ghaffar, Maryam Ghaffar

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