Improving multi-talker binaural DOA estimation by combining periodicity and spatial features in convolutional neural networks by Reza Varzandeh, Simon Doclo, Volker Hohmann

“…Abstract Deep neural network-based direction of arrival (DOA) estimation systems often rely on spatial features as input to learn a mapping for estimating the DOA of multiple talkers. …”
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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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Measurement error evaluation method for voltage transformers in distribution networks based on self-attention and graph convolutional networks by Xiujuan Zeng, Tong Liu, Huiqin Xie, Dajiang Wang, Jihong Xiao

“…This paper uses the connection relationship between distribution transformers and voltage transformers to predict the secondary voltage of voltage transformers through the secondary voltage of transformers, constructing a voltage transfer characteristic model between the two to achieve accurate evaluation of voltage transformer errors. To address the challenge of extracting complex nonlinear features from multivariate electrical data, a combined model of a self-attention mechanism and a graph convolutional network (GCN) is proposed. …”
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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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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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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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High‐order multilayer attention fusion network for 3D object detection by Baowen Zhang, Yongyong Zhao, Chengzhi Su, Guohua Cao

“…By incorporating filtering and non‐linear activation, we extract deep semantic information from the fused multi‐modal features. To maximize the effectiveness of the fused salient feature information, we introduce an attention mechanism that dynamically evaluates the importance of pooled features at each level, enabling adaptive weighted fusion of significant and secondary features. …”
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Equipment Maintenance Support Effectiveness Evaluation Based on Improved Generative Adversarial Network and Radial Basis Function Network by Zhen Li, Jianping Hao, Cuijuan Gao

“…In this paper, a neural network evaluation model based on an improved generative adversarial network (GAN) and radial basis function (RBF) network is proposed to amplify maintenance support samples. …”
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A human pose estimation network based on YOLOv8 framework with efficient multi-scale receptive field and expanded feature pyramid network by Shaobin Cai, Han Xu, Wanchen Cai, Yuchang Mo, Liansuo Wei

“…To address these issues, we propose EE-YOLOv8, a human pose estimation network based on the YOLOv8 framework, which integrates Efficient Multi-scale Receptive Field (EMRF) and Expanded Feature Pyramid Network (EFPN). …”
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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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Applying a Deep Neural Network and Feature Engineering to Assess the Impact of Attacks on Autonomous Vehicles by Sara Ftaimi, Tomader Mazri

“…By integrating deep neural network technology with feature engineering, the proposed system provides a comprehensive impact assessment. …”
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Enhancing Anomaly Detection in Structured Data Using Siamese Neural Networks as a Feature Extractor by Elizabeth P. Chou, Bo-Cheng Hsieh

“…However, in this study, we introduce a novel approach by leveraging the feature extraction capabilities of SNN, inspired by the powerful representation learning ability of neural networks. …”
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Feature Multi-Scale Enhancement and Adaptive Dynamic Fusion Network for Infrared Small Target Detection by Zenghui Xiong, Zhiqiang Sheng, Yao Mao

“…In response to these issues, we propose a deep learning model called the Feature Multi-Scale Enhancement and Adaptive Dynamic Fusion Network (FMADNet). …”
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VSS-SpatioNet: a multi-scale feature fusion network for multimodal image integrations by Zeyu Xiang

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