DSCnet: detection of drug and alcohol addiction mechanisms based on multi-angle feature learning from the hybrid representation of EEG by Jing Wu, Nan Zhang, Qilei Ye, Xiaorui Zheng, Minmin Shao, Xian Chen, Hui Huang

“…Electroencephalography (EEG) is commonly used to analyze addiction mechanisms, but traditional feature extraction methods such as time-frequency analysis, Principal Component Analysis (PCA), and Independent Component Analysis (ICA) fail to capture complex relationships between variables.MethodsThis paper proposes DSCnet, a novel neural network model for addiction detection. …”
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Epidemiological features of travel-related cases Mpox (Monkeypox) cases detected at airports in Pakistan; Insights from Screening Surveillance by Dr Nadia Noreen

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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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AFF_CGE: Combined Attention-Aware Feature Fusion and Communication Graph Embedding Learning for Detecting Encrypted Malicious Traffic by Junhao Liu, Guolin Shao, Hong Rao, Xiangjun Li, Xuan Huang

“…While encryption enhances data security, it also presents significant challenges for network traffic analysis, especially in detecting malicious activities. …”
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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

“…Vehicle detection in aerial imagery is of paramount importance for various intelligent transportation applications, including vehicle tracking, traffic control, and traffic behavior analysis. …”
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Detection of Orienting Response to Novel Sounds in Healthy Elderly Subjects: A Machine Learning Approach Using EEG Features by Emine Elif Tülay

“…Different classifiers including Support Vector Machine (SVM) with Linear and Polynomial kernels, Linear Discriminant Analysis (LDA), and Naive Bayes were fed by ERSP features obtained from varying frequency bands and time domains. …”
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A hybrid approach for intrusion detection in vehicular networks using feature selection and dimensionality reduction with optimized deep learning. by Fayaz Hassan, Zafi Sherhan Syed, Aftab Ahmed Memon, Saad Said Alqahtany, Nadeem Ahmed, Mana Saleh Al Reshan, Yousef Asiri, Asadullah Shaikh

“…We proposed a hybrid approach uses automated feature engineering via correlation-based feature selection (CFS) and principal component analysis (PCA)-based dimensionality reduction to reduce feature matrix size before a series of dense layers are used for classification. …”
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Artificial intelligence with feature fusion empowered enhanced brain stroke detection and classification for disabled persons using biomedical images by Mohammed Alsieni, Khaled H. Alyoubi

“…Artificial intelligence technologies, primarily deep learning (DL), have been widely employed in medical imaging, utilizing automated detection methods. This paper presents an Enhanced Brain Stroke Detection and Classification using Artificial Intelligence with Feature Fusion Technologies (EBSDC-AIFFT) model. …”
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Panoramic radiographic features for machine learning based detection of mandibular third molar root and inferior alveolar canal contact by A. Canberk Ulusoy, Tuğçe Toprak, M. Alper Selver, Pelin Güneri, Betül İlhan

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QuadTPat: Quadruple Transition Pattern-based explainable feature engineering model for stress detection using EEG signals by Veysel Yusuf Cambay, Irem Tasci, Gulay Tasci, Rena Hajiyeva, Sengul Dogan, Turker Tuncer

“…An XFE model has been presented to detect stress automatically. The presented XFE model has four main phases, and these are (i) channel transformer and quadruple transition pattern (QuadTPat)-based feature generation, (ii) feature selection deploying cumulative weighted neighborhood component analysis (CWNCA), (iii) explainable results creation with DLob and (iv) classification with t algorithm-based k-nearest neighbors (tkNN) classifier. …”
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Modelling radiological features fusion and explainable AI in pneumonia detection: A graph- based deep learning and transformer approach by Pratham Kaushik, Eshika Jain, Vinay Kukreja, Shanmugasundaram Hariharan, Murugaperumal Krishnamoorthy, Vandana Ahuja, Abhishek Bhattacherjee, Rajesh Kumar Kaushal, Shih-Yu Chen

“…Objectives: This study aims to develop a deep learning-based methodology for pneumonia detection using lung CT scans. The research focuses on integrating U-Net for lung segmentation, graph-based feature representations, and transformer-based models to improve interpretability and diagnostic accuracy. …”
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Aberrant fragmentomic features of circulating cell-free mitochondrial DNA enable early detection and prognosis prediction of hepatocellular carcinoma by Yang Liu, Fan Peng, Siyuan Wang, Huanmin Jiao, Kaixiang Zhou, Wenjie Guo, Shanshan Guo, Miao Dang, Huanqin Zhang, Weizheng Zhou, Xu Guo, Jinliang Xing

“…Moreover, we constructed a random forest algorithm-based HCC detection model by utilizing ccf-mtDNA fragmentomic features. …”
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An analytics-driven model for identifying autism spectrum disorder using eye tracking by Deblina Mazumder Setu

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Deep learning-based crop health enhancement through early disease prediction by Venkata Santhosh Yakkala, Krishna Vamsi Nusimala, Badisa Gayathri, Sriya Kanamarlapudi, S. S. Aravinth, Ayodeji Olalekan Salau, S. Srithar

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Progressive multi-scale attention neural network for pneumonia classification in chest X-rays by Mohammad Reza Mahdiani

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2D Animal Skeletons Keypoint Detection: Research Progress and Future Trends by Pengfei Ma, Ronghua Gao, Qifeng Li, Qinyang Yu, Rong Wang, Chengrong Lai, Weiwei Huang, Peng Hao, Zhaoyang Wang, Xuwen Li

“…The paper not only summarizes different model algorithms, datasets, and evaluation metrics related to animal keypoint detection but also integrates various application scenarios, highlighting distinct features under different focal points. …”
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Adversarial Threats to Cloud IDS: Robust Defense With Adversarial Training and Feature Selection by Hariprasad Holla, Shashidhar Reddy Polepalli, Arun Ambika Sasikumar

“…The increasing adoption of cloud-based infrastructures necessitates robust cybersecurity measures, particularly in Intrusion Detection Systems (IDS). While Machine Learning (ML)-based IDS solutions improve attack detection, they remain highly vulnerable to adversarial attacks, where subtle perturbations deceive the model and evade detection. …”
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LocaLock: Enhancing Multi-Object Tracking in Satellite Videos via Local Feature Matching by Lingyu Kong, Zhiyuan Yan, Hanru Shi, Ting Zhang, Lei Wang

“…Multi-object tracking (MOT) in satellite videos is a challenging task due to the small size and blurry features of objects, which often lead to intermittent detection and tracking instability. …”
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Early detection of Alzheimer’s disease in structural and functional MRI by Rudrani Maity, Vellupillai Mariappan Raja Sankari, Umapathy Snekhalatha, Umapathy Snekhalatha, Shubashini Velu, Tahani Jaser Alahmadi, Zaid Ali Alhababi, Hend Khalid Alkahtani

“…Among some DL techniques, VGG-16-RF classifier has given better accuracy of 96.87%.ConclusionThe novelty of this work lies in the seamless integration of advanced segmentation techniques with hybrid classifiers, offering a robust and scalable framework for early AD detection. The proposed study demonstrates a significant advancement in the early detection of Alzheimer’s disease by integrating state-of-the-art deep learning models and comprehensive functional connectivity analysis. …”
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Malware classification method based on static multiple-feature fusion by Bo-wen SUN, Yan-yi HUANG, Qiao-kun WEN, Bin TIAN, Peng WU, Qi LI

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