Explainable Spatio-Temporal Inference Network for Car-Sharing Demand Prediction by Nihad Brahimi, Huaping Zhang, Zahid Razzaq

“…Predicting vehicle demand is challenging due to the interconnections of temporal, spatial, and spatio-temporal features. This paper presents the Explainable Spatio-Temporal Inference Network (eX-STIN), a new approach that improves upon our prior Unified Spatio-Temporal Inference Prediction Network (USTIN) model. …”
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Models, systems, networks in economics, engineering, nature and society by S.V. Chuprov, A.V. Babkin

Exploring New Dimensions of Archives: Finding Audiovisually Similar Programmes with the Help of Neural Networks by Sara Veldhoen, Rana Klein, Mari Wigham, Roeland Ordelman, Teng Long, Nanne van Noord

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Performance Evaluation of Intrusion Detection System using Selected Features and Machine Learning Classifiers by Raja Azlina Raja Mahmood, AmirHossien Abdi, Masnida Hussin

“…Feature selection finds the most relevant features, thus reduces the dimensionality and complexity to analyze the network traffic.  …”
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Leveraging Multi-Modality and Enhanced Temporal Networks for Robust Violence Detection by Gwangho Na, Jaepil Ko, Kyungjoo Cheoi

“…Additionally, we refine the multi-scale temporal network (MTN) to improve feature extraction across multiple temporal scales between video snippets. …”
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Application of Ontology Matching Algorithm Based on Linguistic Features in English Pronunciation Quality Evaluation by Shan Zhu

“…This paper uses the method of tree kernel function to measure the similarity of two ontology trees. According to the features of nodes in ontology tree, methods to calculate the external features and internal features of nodes are proposed, respectively. …”
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Temporal Graph Attention Network for Spatio-Temporal Feature Extraction in Research Topic Trend Prediction by Zhan Guo, Mingxin Lu, Jin Han

“…This necessity arises from the fact that research topics exhibit both temporal trend features and spatial correlation features. This study proposes a Temporal Graph Attention Network (T-GAT) to extract the spatio-temporal features of research topics and predict their trends. …”
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Intelligent Cyber-Attack Detection in IoT Networks Using IDAOA-Based Wrapper Feature Selection by Mohammed Abdullah, Ryna Svyd

“…The IDAOA serves as a wrapper-based feature selection method, optimizing the identification of the most impactful features while balancing local exploration and global exploitation. …”
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Research on traffic representation in network anomaly detection by SUN Jianwen, ZHANG Bin, CHANG Heyu

“…Subsequently, the evolution of traffic representation in network anomaly detection was systematically reviewed, providing a comprehensive analysis of its forms, feature learning, and application in anomaly detection both globally and domestically. …”
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A Feature Dynamic Enhancement and Global Collaboration Guidance Network for Remote Sensing Image Compression by Q. Z. Fang, S. B. Gu, J. G. Wang, L. L. Zhang

“…Deep learning-based remote sensing image compression methods show great potential, but traditional convolutional networks mainly focus on local feature extraction and show obvious limitations in dynamic feature learning and global context modeling. …”
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MFENet: A Multi-Feature Extraction Network for Enhanced Emotion Detection Using EEG and STFT by N. Ramesh Babu, Viswanathan Vadivel

“…To address these challenges, this paper introduces MFENet, a lightweight convolutional neural network tailored for emotion recognition from electroencephalogram (EEG) data. …”
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DFANet: A Deep Feature Attention Network for Building Change Detection in Remote Sensing Imagery by Peigeng Lu, Haiyong Ding, Xiang Tian

“…However, most existing methods have limitations in detecting building edges and addressing pseudo-changes, and lack the ability to model feature context. In this paper, we introduce DFANet—a Deep Feature Attention Network specifically designed for building CD in RS imagery. …”
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Detection of Student Engagement via Transformer-Enhanced Feature Pyramid Networks on Channel-Spatial Attention by A. Naveen, I. Jeena Jacob, Ajay Kumar Mandava

“…This study proposes a novel real-time detection framework that leverages Transformer-enhanced Feature Pyramid Networks (FPN) with Channel-Spatial Attention (CSA), referred to as BiusFPN_CSA. …”
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Liver Tumor Prediction using Attention-Guided Convolutional Neural Networks and Genomic Feature Analysis by S. Edwin Raja, J. Sutha, P. Elamparithi, K. Jaya Deepthi, S.D. Lalitha

“…To overcome these hurdles, this study presents two integrated approaches namely, – Attention-Guided Convolutional Neural Networks (AG-CNNs), and Genomic Feature Analysis Module (GFAM). …”
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Feature Selection and Hyper-parameter Tuning Technique using Neural Network for Stock Market Prediction by Karanveer Singh, Rahul Tiwari, Prashant Johri, Ahmed A. Elngar

“…The dataset has 310 features, and the entries are more than 100000 per week. …”
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Classification of arteriovenous fistula sounds using a convolutional block attention module and long short-term memory neural network by Jun Zhang, Rongxi Zhang, Xinming Shu, Hongtao Zhang

“…Features were extracted by combining Mel-Frequency Cepstral Coefficients with Mel-Spectrogram data, generating a novel set of feature parameters. …”
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Voice Spoofing Detection Through Residual Network, Max Feature Map, and Depthwise Separable Convolution by Il-Youp Kwak, Sungsu Kwag, Junhee Lee, Youngbae Jeon, Jeonghwan Hwang, Hyo-Jung Choi, Jong-Hoon Yang, So-Yul Han, Jun Ho Huh, Choong-Hoon Lee, Ji Won Yoon

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MFHG-DDI: An Enhanced Hybrid Graph Method Leveraging Multiple Features for Predicting Drug–Drug Interactions by Xiang Li, Xiangmin Ji, Chengzhen Xu, Jie Hou, Xiaoyu Zhao, Guodong Peng

“…Specifically, we constructed a heterogeneous network incorporating multiple drug features and DDI information. …”
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Style Transfer and Topological Feature Analysis of Text-Based CAPTCHA via Generative Adversarial Networks by Tao Xue, Zixuan Guo, Zehang Yin, Yu Rong

“…This study introduces topological feature analysis as a new method for evaluating text-based CAPTCHAs. …”
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Smart insole-based abnormal gait identification: Deep sequential networks and feature ablation study by Beomjoon Park, Minhye Kim, Dawoon Jung, Jinwook Kim, Kyung-Ryoul Mun

“…This study aims to classify nine gait types including one normal and eight abnormal gaits, using sequential network-based models and diverse feature combinations obtained from insole sensors. …”
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