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

“…Mel-frequency cepstral coefficients (MFCCs) are extracted as features and fed into a transformer-based residual one-dimensional convolutional neural network (1D-CNN) for classification. …”
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DDI-KGAT: A Graph Attention Network on Biomedical Knowledge Graph for the Prediction of Drug-Drug Interactions by Iqra Naseer Kundi, Shahzad Amin Sheikh, Fahad Mumtaz Malik, Kamran Aziz Bhatti

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Triple dimensional psychology knowledge encouraging graph attention networks to exploit aspect-based sentiment analysis by Xuefeng Shi, Weiping Ding, Min Hu, Xin Kang, Fuji Ren

“…Thus, this paper proposes a novel ABSA network to fully exploit the sentiment features from triple dimensions, namely the psychology knowledge assisted graph attention networks (VADGAT). …”
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Adaptive Echo State Network for crop yield prediction incorporating Fall Armyworm dynamicsMendeley Data by Mulima Chibuye, Jackson Phiri, Phillip Nkunika

“…To support precision pest management, we developed an adaptive Echo State Network (ESN) that predicts annual maize yield while accounting for FAW pressure. …”
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Electroencephalography-Based Pain Detection Using Kernel Spectral Connectivity Network with Preserved Spatio-Frequency Interpretability by Santiago Buitrago-Osorio, Julian Gil-González, Andrés Marino Álvarez-Meza, David Cardenas-Peña, Alvaro Orozco-Gutierrez

“…(i) We employ the Kernel Cross-Spectral Gaussian Functional Connectivity Network (KCS-FCnet) to code pairwise channel dependencies for pain detection. …”
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3D Point Cloud Fusion Method Based on EMD Auto-Evolution and Local Parametric Network by Wen Chen, Hao Chen, Shuting Yang

“…To this end, this paper addresses the problems of shape distortion and sparse point cloud density in existing 3D point cloud fusion methods by proposing a 3D point cloud fusion method based on Earth mover’s distance (EMD) auto-evolution and local parameterization network. Our method is divided into two stages. In the first stage, EMD is introduced as a key metric for evaluating the fusion results, and a point cloud fusion method based on EMD auto-evolution is constructed. …”
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A Hybrid Artificial Neural Network and Particle Swarm Optimization algorithm for Detecting COVID-19 Patients by Alla Ahmad Hassan, Tarik A Rashid

“…We used precision, accuracy score, recall, and F-Measure tests to evaluate the Neural Network with Particle Swarm Optimization algorithms. …”
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CDMRNet: multimodal meta-adaptive reasoning network with dynamic causal modeling and co-evolution of quantum states by Shengwei Wang, Keda Chen, Mengduo Yu, Pengjiao Zhao, Hui Duan

“…Abstract Cross-modal reasoning tasks face persistent challenges such as cross-modal inference of causal dependencies with coarse-grained, weak resistance to noise, and weak interaction of spatial-temporal features. To address these issues, the article proposes a dynamic causal-aware collaborative quantum state evolution multimodal reasoning architecture, Causal-aware Dynamic Multimodal Reasoning Network (CDMRNet). …”
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Integrating Human Mobility Models with Epidemic Modeling: A Framework for Generating Synthetic Temporal Contact Networks by Diaoulé Diallo, Jurij Schoenfeld, René Schmieding, Sascha Korf, Martin J. Kühn, Tobias Hecking

“…However, for most realistic modeling of epidemic spread and the evaluation of countermeasures, there is a critical need for highly resolved, temporal contact networks that encompass multiple venues without sacrificing the intricate dynamics of real-world contacts. …”
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GSINet: Gradual Semantic Interaction Network for Remote Sensing Object Detection Based on Dual Attention Mechanism by Yuntao Xu, Qi Qi, Wei He, Guoyun Zhang, Siyuan Chen, Bing Tu

“…To address these challenges, we explore a novel end-to-end deep RSOD method, termed gradual semantic interaction network (GSINet). Specifically, we first design a gradual semantic information interaction structure, which integrates contextual information across network layers from shallow to deep, enhancing semantic comprehension and detection precision. …”
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Knowledge Improved Hybrid DNN–KAN Framework for Intrusion Detection in Wireless Sensor Networks by M. Sriraghavendra, Muna Elsadig, Ines Hilali Jaghdam, S. Abdel-Khalek, B. Galeebathullah, Salem Alkhalaf

“…The proposed framework preprocesses and merges multiple datasets (WSN, NSL-KDD, and CICIDS2017), extracts features using Principal Component Analysis (PCA), and constructs a knowledge graph to embed expert-defined rules via Graph Convolutional Networks (GCNs). …”
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Harnessing Semantic and Trajectory Analysis for Real-Time Pedestrian Panic Detection in Crowded Micro-Road Networks by Rongyong Zhao, Lingchen Han, Yuxin Cai, Bingyu Wei, Arifur Rahman, Cuiling Li, Yunlong Ma

“…Pedestrian panic behavior is a primary cause of overcrowding and stampede accidents in public micro-road network areas with high pedestrian density. However, reliably detecting such behaviors remains challenging due to their inherent complexity, variability, and stochastic nature. …”
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Research on Damage Detection Methods for Concrete Beams Based on Ground Penetrating Radar and Convolutional Neural Networks by Ning Liu, Ya Ge, Xin Bai, Zi Zhang, Yuhao Shangguan, Yan Li

“…In order to verify the applicability of typical one-dimensional radar signals combined with convolutional neural networks (CNN) in the non-destructive testing of concrete structures, this study created concrete specimens with embedded defects (voids, non-dense solids, and cracks) commonly found in concrete structures in a laboratory setting. …”
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A Segmentation Network with Two Distinct Attention Modules for the Segmentation of Multiple Renal Structures in Ultrasound Images by Youhe Zuo, Jing Li, Jing Tian

“…Specifically, the multi-convolution pixel-wise attention module utilizes the pixel-wise attention to enable the network to focus more effectively on important features at each stage. …”
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Intractable prefrontal and limbic white matter network disruption in adolescents with drug-naïve nonsuicidal self-injury by Yuwei Chen, Xiongxiong Yang, Kaike Liao, Rui Yu, Xinyue Chen, Wenjing Zhang, Nian Liu

“…This study thus sought to investigate the structural connectivity and network features of adolescents with drug-naïve NSSI, while also evaluating the alterations in these parameters following treatment. …”
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Hybrid Optical Wireless Network for Future SAGO-Integrated Communication Based on FSO/VLC Heterogeneous Interconnection by Zhitong Huang, Zhenfang Wang, Minglei Huang, Wei Li, Tong Lin, Peixuan He, Yuefeng Ji

“…An experimental platform is built to evaluate the transmission performance of the presented hybrid network, and a complete data aggregation/transmission/distribution procedure based on heterogeneous interconnection is first realized, which includes two-segment 1.0-m 450-Mb/s orthogonal-frequency-division-multiplexing-based VLC transmissions interconnected by one-segment 430-m 0.96-Gb/s on-off keying (OOK)-based nonturbulent FSO transmission, with the bit error rate under the forward-error-correction limit (3.8 × 10^−3). …”
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FVPNet: A Fuzzy Visual Positioning Perception Network for Small Object Segmentation in Remote Sensing Images by Yu Yan, Jindong Xu, Qianpeng Chong, Guangyi Wei, Zhixiang Wang

“…To address these issues, we propose a fuzzy visual perception network, which incorporates a feature inheritance downsampling (FID) module, a fuzzy refinement subnet, and a small target perception (STP) subnet, along with a hierarchical balance loss that prioritizes small foreground objects to effectively reduce foreground bias in regression. …”
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Pilot study of the capabilities of neural network data analysis in predicting placental disorders: A prospective study by Tatiana E. Belokrinitskaya, Viktor A. Mudrov, Elena S. Nabieva, Andrey S. Nadzhaf-Zade, Antonina S. Zhykharieva, Islom I. Dzhurabaev

“…Meanwhile, there are currently no technologies that can predict the development of placental disorders with a high degree of probability. Aim. To evaluate the capabilities of neural network data analysis in predicting placental disorders. …”
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A Pilot Study: Sleep and Activity Monitoring of Newborn Infants by GRU-Stack-Based Model Using Video Actigraphy and Pulse Rate Variability Features by Ádám Nagy, Zita Lilla Róka, Imre Jánoki, Máté Siket, Péter Földesy, Judit Varga, Miklós Szabó, Ákos Zarándy

“…We also show how pulse rate variability (PRV) data could improve the performance of the classification. The network was trained and evaluated on our own database of 108 h collected at the Neonatal Intensive Care Unit, Dept. of Neonatology of Pediatrics, Semmelweis University, Budapest, Hungary.…”
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