BiEHFFNet: A Water Body Detection Network for SAR Images Based on Bi-Encoder and Hybrid Feature Fusion by Bin Han, Xin Huang, Feng Xue

“…To address this issue, a bi-encoder and hybrid feature fuse network (BiEHFFNet) is proposed for achieving accurate water body detection. …”
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Adaptive Graph Convolutional Network with Deep Sequence and Feature Correlation Learning for Porosity Prediction from Well-Logging Data by Long Hao, Xun Wang, Yunlong Dong, Peizhi Zhao, Peifu Han, Xue Li, Fengrui Jing, Chuchu Zhai

“…To better capture these correlations, we propose an adaptive graph convolutional network (GCN) with learning deep sequence and feature correlation graphs. …”
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Flooded Infrastructure Change Detection in Deeply Supervised Networks Based on Multi-Attention-Constrained Multi-Scale Feature Fusion by Gang Qin, Shixin Wang, Futao Wang, Suju Li, Zhenqing Wang, Jinfeng Zhu, Ming Liu, Changjun Gu, Qing Zhao

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Fast real-time detection and counting of thrips in greenhouses with multi-level feature attention and fusion by Zhangzhang He, Xinyue Chen, Ying Gao, Yu Zhang, Yuheng Guo, Tong Zhai, Xiaochen Wei, Huan Li, Haipeng Zhu, Yongkun Fu, Zhiliang Zhang, Zhiliang Zhang

“…In this work, we propose the Thrip Counting and Detection Network (TCD-Net). TCD-Net is an fully convolutional network consisting of a backbone network, a feature pyramid, and an output head. …”
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An Automatic Gastrointestinal Polyp Detection System in Video Endoscopy Using Fusion of Color Wavelet and Convolutional Neural Network Features by Mustain Billah, Sajjad Waheed, Mohammad Motiur Rahman

“…This system captures the video streams from endoscopic video and, in the output, it shows the identified polyps. Color wavelet (CW) features and convolutional neural network (CNN) features of video frames are extracted and combined together which are used to train a linear support vector machine (SVM). …”
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Weighted Feature Fusion Network Based on Large Kernel Convolution and Transformer for Multi-Modal Remote Sensing Image Segmentation by Jianxia Wang, Shaozu Qiu, Jia Cai, Xiaoming Zhang

“…To solve this problem, a weighted feature fusion network based on large kernel convolution and Transformer (LTFCNet) was proposed. …”
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BaAM-YOLO: a balanced feature fusion and attention mechanism based vehicle detection network in aerial images by Xunxun Zhang, Xu Zhu

“…To address these issues, we propose a balanced feature fusion and attention mechanism-based vehicle detection network, termed BaAM-YOLO, specifically designed for aerial images. …”
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Cyber intrusion detection using ensemble of deep learning with prediction scoring based optimized feature sets for IOT networks by Deepesh M. Dhanvijay, Mrinai M. Dhanvijay, Vaishali H. Kamble

“…Detecting intrusions in Internet of Things (IoT) networks is critical for maintaining cybersecurity. …”
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Maritime man-overboard search based on MOB-Detector with modulated deformable convolution and bi-directional feature fusion network by Guokang Xu, Jianchuan Yin, Jianchuan Yin, Jinfeng Zhang, Nini Wang

“…MOB-Detector utilizes the bi-directional feature fusion network to integrate location and semantic features effectively. …”
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Comparative analysis of deep neural network architectures for renewable energy forecasting: enhancing accuracy with meteorological and time-based features by Sunawar Khan, Tehseen Mazhar, Muhammad Amir Khan, Tariq Shahzad, Wasim Ahmad, Afsha Bibi, Mamoon M. Saeed, Habib Hamam

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ESL-YOLO: Small Object Detection with Effective Feature Enhancement and Spatial-Context-Guided Fusion Network for Remote Sensing by Xiangyue Zheng, Yijuan Qiu, Gang Zhang, Tao Lei, Ping Jiang

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An electrical load forecasting model based on a novel closed loop neural networks and interaction gain feature selection by Gholamreza Memarzadeh, Faezeh Amirteimoury, Hossein Noori, Farshid Keynia

“…The ensemble framework leverages these selected features for point forecasting, while the interval forecasting mechanism evaluates and adjusts the predictions iteratively. …”
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Keypoints-Based Multi-Cue Feature Fusion Network (MF-Net) for Action Recognition of ADHD Children in TOVA Assessment by Wanyu Tang, Chao Shi, Yuanyuan Li, Zhonglan Tang, Gang Yang, Jing Zhang, Ling He

“…The system aims to assess ADHD symptoms as described in the DSM-V by extracting features from human body and facial keypoints. For human body keypoints, we introduce the Multi-scale Features and Frame-Attention Adaptive Graph Convolutional Network (MSF-AGCN) to extract irregular and impulsive motion features. …”
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MFDAFF-Net: Multiscale Frequency-Aware and Dual Attention-Guided Feature Fusion Network for UAV Imagery Object Detection by Shu Tian, Bingxi Zhang, Lin Cao, Lihong Kang, Jing Tian, Xiangwei Xing, Bo Shen, Chunzhuo Fan, Kangning Du, Chong Fu, Ye Zhang

“…Then, we design a dual attention-guided adaptive feature fusion network (DAAFFN) as the specific feature fusion strategy. …”
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Computer-aided diagnosis of Haematologic disorders detection based on spatial feature learning networks using blood cell images by Jamal Alsamri, Hamed Alqahtani, Ali M. Al-Sharafi, Abdulbasit A. Darem, Khalid Nazim, Abdul Sattar, Menwa Alshammeri, Ahmad A. Alzahrani, Marwa Obayya

“…This study presents a novel Computer-Aided Diagnosis of Haematologic Disorders Detection Based on Spatial Feature Learning Networks with Hybrid Model (CADHDD-SFLNHM) approach using Blood Cell Images. …”
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SA3C-ID: a novel network intrusion detection model using feature selection and adversarial training by Wanwei Huang, Haobin Tian, Lei Wang, Sunan Wang, Kun Wang, Songze Li

“…However, traditional intrusion detection methods exhibit several limitations, including insufficient feature extraction from network data, high model complexity, and data imbalance, which result in issues like low detection efficiency, as well as frequent false positives and missed alarms. …”
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Leveraging federated learning for DoS attack detection in IoT networks based on ensemble feature selection and deep learning models by Tasneem Qasem Al-Ghadi, Selvakumar Manickam, I. Dewa Made Widia, Eka Ratri Noor Wulandari, Shankar Karuppayah

“…These findings underscore the significant impact of feature selection on learning performance and provide valuable insights into optimizing deep learning-based DoS detection in IoT networks.…”
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Harnessing Multiple Level Features to Improve Segmentation Performance of Deep Neural Network: A Case Study in Magnetic Resonance Imaging of Nasopharyngeal Cancer by Rongzhi Mao, Liangxu Xie, Xiaohua Lu, Jialu Pei, Xiaojun Xu, Shan Chang

“…The improved generalization ability suggests that harnessing multi-level features in deep neural networks can improve segmentation performance.…”
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Facial Feature Recognition with Multi-task Learning and Attention-based Enhancements by M. Rohani, H. Farsi, S. Mohamadzadeh

“…Facial feature recognition (FFR) has witnessed a remarkable surge in recent years, driven by its extensive applications in identity recognition, security, and intelligent imaging. …”
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Comparative Performance Analysis of Optimization Algorithms in Artificial Neural Networks for Stock Price Prediction by Ekaprana Wijaya, Moch. Arief Soeleman, Pulung Nurtantio Andono

“…This study aims to enhance price prediction accuracy using Artificial Neural Networks (ANN) by comparing three optimization methods: Stochastic Gradient Descent (SGD), Adam, and RMSprop. …”
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