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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Software Defect Prediction Based on Effective Fusion of Multiple Features by Chaozheng Zhang, Junhua Wu

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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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A model for shale gas well production prediction based on improved artificial neural network by LIN Hun, SUN Xinyi, SONG Xixiang, MENG Chun, XIONG Wenxin, HUANG Junhe, LIU Hongbo, LIU Cheng

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Osteoarthritis Classification Using Hybrid Quantum Convolutional Neural Network by Devansh Tikariha, Abdul Moomin, D. Jeyamani, P. Rukmani

“…Using a QCNN, this model harnesses the ability of quantum computing to represent high-dimensional data transformations, a novel approach that complements classical CNN layers by exploring patterns that are not captured in traditional networks. The initial results showed a high classification accuracy of 97.26%, suggesting that quantum-enhanced layers can significantly bolster feature extraction and classification in medical diagnostics. …”
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Probabilistic regression for autonomous terrain relative navigation via multi-modal feature learning by Ickbum Kim, Sandeep Singh

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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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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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Leveraging explainable artificial intelligence for early detection and mitigation of cyber threat in large-scale network environments by G. Nalinipriya, S. Rama Sree, K. Radhika, E. Laxmi Lydia, Faten Khalid Karim, Mohamad Khairi Ishak, Samih M. Mostafa

“…The insights and hidden trends detected from network data and the architecture of a data-driven ML to avoid this attack are essential to establishing an intelligent security system. …”
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An Approach using Skeleton-based Representations and Neural Networks for Yoga Pose Recognition by Nguyen Hai Thanh, Truong Nguyen Nhat, Pham Linh Thuy Thi, Pham Ngoc Huynh

“…Therefore, we present an approach grounded in skeleton-based feature extraction and neural networks to find a solution to the recognition of yoga postures, creating a premise for researching a smart virtual trainer that supports home workouts for users from input image data converted into skeleton data through MoveNet. …”
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Authorship Classification in a Resource Constraint Language Using Convolutional Neural Networks by Md. Rajib Hossain, Mohammed Moshiul Hoque, M. Ali Akber Dewan, Nazmul Siddique, Md. Nazmul Islam, Iqbal H. Sarker

“…This paper presents an authorship classification approach made of Convolution Neural Networks (CNN) comprising four modules: embedding model generation, feature representation, classifier training and classifier testing. …”
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Swin Transformer With Late-Fusion Feature Aggregation for Multi-Modal Vehicle Reidentification by Reza Fuad Rachmadi, Supeno Mardi Susiki Nugroho, I. Ketut Eddy Purnama

“…In this paper, we proposed a Swin Transformer classifier with late-fusion feature aggregation networks called SAFA (Self-Attention Feature Aggregation) for multi-modal vehicle reidentification problems. …”
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A novel multi-scale and fine-grained network for large choroidal vessels segmentation in OCT by Wei Huang, Qifeng Yan, Lei Mou, Yitian Zhao, Wei Chen, Wei Chen, Wei Chen

“…In this paper, we propose a novel multi-scale and fine-grained network called MFGNet. Since choroidal vessels are small targets, long-range dependencies need to be considered, therefore, we developed a two-branch fine-grained feature extraction module that can mix the long-range information extracted by TransFormer with the local information extracted by convolution in parallel, introducing information exchange between the two branches. …”
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The Evolution of Machine Learning in Vibration and Acoustics: A Decade of Innovation (2015–2024) by Jacek Lukasz Wilk-Jakubowski, Lukasz Pawlik, Damian Frej, Grzegorz Wilk-Jakubowski

“…In the context of these processes, a review of machine learning techniques was conducted, including convolutional neural networks (CNNs), recurrent neural networks (RNNs), long short-term memory (LSTM), autoencoders, support vector machines (SVMs), decision trees (DTs), nearest neighbor search (NNS), K-means clustering, and random forests. …”
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An Enhanced Bio-inspired GWO–DE Technique for Efficient Feature Selection in the EEG-RSVP Paradigm by S. Abinayaa, S. S. Sridhar

“…To resolve these issues, we present a bio-inspired hybrid optimization framework where Differential Evolution (DE) is integrated with Grey Wolf Optimization (GWO) to improve the efficiency of feature selections. …”
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Enhancing Efficiency and Regularization in Convolutional Neural Networks: Strategies for Optimized Dropout by Mehdi Ghayoumi

“…<b>Methods:</b> We introduce <i>Probabilistic Feature Importance Dropout</i> (PFID), a novel regularization method that assigns dropout rates based on the probabilistic significance of individual features. …”
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