The Role of Education in Building National Soft Power: An Empirical Analysis From a Global Perspective Using Deep Neural Networks by Yun Bai

“…To address this, this paper proposed a novel deep neural network (DNN) model designed to predict soft power based on educational factors. …”
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V³IP Model: A Multiview Vegetation Information Perception Network for Wetland Mapping With Hyperspectral Imagery by Chunyan Yu, Feihong Zhou, Chi Yu, Enyu Zhao, Yulei Wang, Yabin Hu, Haoyang Yu

“…In this article, we present a novel multiview vegetation information perception (V³IP) network for HSI wetland mapping, which significantly enhances the discriminative representation of feature extraction by incorporating three presented vegetation perception components. …”
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Integrating weighted gene co-expression network analysis and machine learning to elucidate neural characteristics in a mouse model of depression by Jinli Gao, Qinglang Wang, Jie Liu, Siqian Zheng, Jiahong Liu, Zhiyong Gao, Cheng Zhu

“…Depression-related gene modules were identified and subjected to feature selection using the random forest model. The biological relevance of selected genes was further assessed, and model accuracy was validated through performance evaluation.ResultsOur findings revealed significant differential expression of genes such as Oprm1, BDNF, Tph2, and Zfp769 in the depression mouse model (p < 0.05). …”
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A Hybrid Deep Learning Model for Network Intrusion Detection System Using Seq2Seq and ConvLSTM-Subnets by S. Hariharan, Y. Annie Jerusha, G. Suganeshwari, S. P. Syed Ibrahim, Uday Tupakula, Vijay Varadharajan

“…This enables the model to leverage the spatial feature extraction capabilities of Convolutional Neural Networks (CNN) alongside the sequential learning strengths of LSTM networks. …”
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A Novel Approach Based on Hypergraph Convolutional Neural Networks for Cartilage Shape Description and Longitudinal Prediction of Knee Osteoarthritis Progression by John B. Theocharis, Christos G. Chadoulos, Andreas L. Symeonidis

“…We propose two novel models, namely, the <i>C_Shape.Net</i> and the predictor network. The <i>C_Shape.Net</i> operates on a hypergraph of volumetric nodes, especially designed to represent the surface and volumetric features of the cartilage. …”
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HSF: A Hybrid SVM-RF Machine Learning Framework for Dual-Plane DDoS Detection and Mitigation in Software-Defined Networks by Abdinasir Hirsi, Lukman Audah, Mohammed A. Alhartomi, Adeb Salh, Godwin Okon Ansa, Mustafa Maad Hamdi, Diani Galih Saputri, Salman Ahmed, Abdullahi Farah

“…Software-defined networking (SDN) has revolutionized network management by centralizing control through software, thereby enabling dynamic traffic adjustments that are independent of the data plane. …”
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Alterations of white matter functional networks in pediatric drug-resistant temporal lobe epilepsy: A graph theory analysis study by Kexin Huang, Yuxin Xie, Haifeng Ran, Jie Hu, Yulun He, Gaoqiang Xu, Guiqin Chen, Qiane Yu, Xuhong Li, Junwei Liu, Heng Liu, Tijiang Zhang

“…Therefore, we combine graph theory with resting-state functional magnetic resonance imaging (rs-fMRI) and T1-weighted imaging (T1WI) to investigate the topological features of the WM network in children with DRTLE, discover potential biomarkers, and understand the underlying neurological mechanisms. …”
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Detecting All-to-One Backdoor Attacks in Black-Box DNNs via Differential Robustness to Noise by Hao Fu, Prashanth Krishnamurthy, Siddharth Garg, Farshad Khorrami

“…The necessity for black-box A2O backdoor defenses arises, particularly in scenarios where only the network&#x2019;s input and output are accessible. …”
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Construction of a CURE Community to Empower Faculty and Accelerate Pedagogical Change by Laura M. Christian, Kristin M. Fox, Anthony J. Bell Jr., Sue DeChenne-Peters, Joseph Provost PhD, Jessica K. Bell, J. Ellis Bell

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Industrial Image Anomaly Detection via Synthetic-Anomaly Contrastive Distillation by Junxian Li, Mingxing Li, Shucheng Huang, Gang Wang, Xinjing Zhao

“…<i>SACD</i> comprises two pivotal components: (1) a reverse distillation (RD) paradigm whereby a pre-trained teacher network extracts hierarchically structured representations, subsequently guiding the student network with inverse architectural configuration to establish hierarchical feature alignment; (2) a group of feature calibration (<i>FeaCali</i>) modules designed to refine the student’s outputs by eliminating anomalous feature responses. …”
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Development of an upper limb muscle strength rehabilitation assessment system using particle swarm optimisation by Chuangan Zhou, Siqi Wang, Meiyi Wu, Wei Lai, Junyu Yao, Xingyue Gou, Hui Ye, Jun Yi, Dong Cao

“…PurposeThis study develops a particle swarm optimization (PSO)-based assessment system for evaluating upper extremity and shoulder joint muscle strength with potential application to stroke rehabilitation. …”
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DMCCT: Dual-Branch Multi-Granularity Convolutional Cross-Substitution Transformer for Hyperspectral Image Classification by Laiying Fu, Xiaoyong Chen, Yanan Xu, Xiao Li

“…In the field of hyperspectral image classification, deep learning technology, especially convolutional neural networks, has achieved remarkable progress. However, convolutional neural network models encounter challenges in hyperspectral image classification due to limitations in their receptive fields. …”
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Optimizing Precipitation Forecasting and Agricultural Water Resource Allocation Using the Gaussian-Stacked-LSTM Model by Maofa Wang, Bingcheng Yan, Yibo Zhang, Lu Zhang, Pengcheng Wang, Jingjing Huang, Weifeng Shan, Haijun Liu, Chengcheng Wang, Yimin Wen

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Coronary Artery Disease Detection Based on a Novel Multi-Modal Deep-Coding Method Using ECG and PCG Signals by Chengfa Sun, Changchun Liu, Xinpei Wang, Yuanyuan Liu, Shilong Zhao

“…To remove irrelevant information while preserving discriminative features, we add an autoencoder network to compress feature dimension. …”
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Hemoglobin state-flux: A finite-state model representation of the hemoglobin signal for evaluation of the resting state and the influence of disease. by Randall L Barbour, Harry L Graber, San-Lian S Barbour

“…The developed methodology integrates concepts from stochastic network theory with known modulatory features of the vascular bed, and in doing so provides access to a previously unrecognized dense feature space that is shown to have promising diagnostic potential. …”
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Enhanced Cleft Lip and Palate Classification Using SigLIP 2: A Comparative Study with Vision Transformers and Siamese Networks by Oraphan Nantha, Benjaporn Sathanarugsawait, Prasong Praneetpolgrang

“…A comparative analysis is conducted, evaluating the performance of our original ViT-Siamese network model (using BiomedCLIP) against a new model leveraging SigLIP 2 for feature extraction. …”
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FRPNet: A Lightweight Multi-Altitude Field Rice Panicle Detection and Counting Network Based on Unmanned Aerial Vehicle Images by Yuheng Guo, Wei Zhan, Zhiliang Zhang, Yu Zhang, Hongshen Guo

“…The architecture integrates three core innovations: a CSP-ScConv backbone with self-calibrating convolutions for efficient multi-scale feature extraction; a Feature Pyramid Shared Convolution (FPSC) module that replaces pooling with multi-branch dilated convolutions to preserve fine-grained spatial information; and a Dynamic Bidirectional Feature Pyramid Network (DynamicBiFPN) employing input-adaptive kernels to optimize cross-scale feature fusion. …”
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A Hybrid Transformers-based Convolutional Neural Network Model for Keratoconus Detection in Scheimpflug-based Dynamic Corneal Deformation Videos by Hazem Abdelmotaal, Rossen Mihaylov Hazarbasanov, Ramin Salouti, M. Hossein Nowroozzadeh, Suphi Taneri, Ali H. Al-Timemy, Alexandru Lavric, Hidenori Takahashi, Siamak Yousefi

“…Methods: We used transfer learning for feature extraction from DCDVs. These feature maps were augmented by self-attention to model long-range dependencies before classification to identify keratoconus directly. …”
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Deep Neural Network-Based Detection of Modulated Jamming in Free-Space Optical Systems: Theory and Performance Under Atmospheric Fading by Manav R. Bhatnagar

“…We propose a deep neural network (DNN)-based binary classifier designed to discriminate between clean and jammed received frames. …”
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Deep Residual Network With Integrated StarDist Nuclei Segmentation for Papillary Thyroid Cancer Identification: A Pathologist-Inspired Approach by Nabila Husna Shabrina, Dadang Gunawan, Mia Rizkinia, Agnes Stephanie Harahap, Mohammad Ikhsan, Rifai Chai, Maria Francisca Ham

“…The proposed model enhances feature localization by integrating nucleus segmentation with Deep Residual Networks, yielding a practical and efficient solution for histopathological PTC analysis.…”
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