Graph-Based COVID-19 Detection Using Conditional Generative Adversarial Network by Imran Ihsan, Azhar Imran, Tahir Sher, Mahmood Basil A. Al-Rawi, Mohammed A. Elmeligy, Muhammad Salman Pathan

“…The proposed methodology encompasses four distinct phases: initial segmentation of raw chest radiographs employing Conditional Generative Adversarial Networks (CGAN), followed by feature extraction through a tailored pipeline integrating both manual computer vision algorithms and pre-trained Deep Neural Network (DNN) models. …”
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LCFC-Laptop: A Benchmark Dataset for Detecting Surface Defects in Consumer Electronics by Hua-Feng Dai, Jyun-Rong Wang, Quan Zhong, Dong Qin, Hao Liu, Fei Guo

“…The resulting dataset has been made publicly available to support the research community in developing, testing, and refining advanced surface defect detection algorithms under realistic conditions. To the best of our knowledge, this is the first comprehensive, multiclass, multi-defect dataset for surface defect detection in the consumer electronics domain that provides pixel-level ground-truth annotations and is explicitly designed for real-world applications.…”
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Drought Detection in Satellite Imagery: A Layered Ensemble Machine Learning Approach by Muhammad Owais Raza, Naeem Ahmed Mahoto, Mana Saleh Al Reshan, Ali Alqazzaz, Adel Rajab, Asadullah Shaikh

“…The proposed approach combines conventional machine learning algorithms (Support Vector Machine (SVM), Logistic Regression (LR), Decision Tree (DT), Random Forest (RF), Naive Bayes (NB), and k-Nearest Neighbor (k-NN)) with ensemble methods (Bagging and Voting) in a layered fashion for detecting drought from satellite imagery. …”
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Explainability of Network Intrusion Detection Using Transformers: A Packet-Level Approach by Pahavalan Rajkumardheivanayahi, Ryan Berry, Nicholas U. Costagliola, Lance Fiondella, Nathaniel D. Bastian, Gokhan Kul

“…Network Intrusion Detection Systems (NIDS) are critical in ensuring the security of connected computer systems by actively detecting and preventing unauthorized activities and malicious attacks. …”
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Deep Attention Networks With Multi-Temporal Information Fusion for Sleep Apnea Detection by Meng Jiao, Changyue Song, Xiaochen Xian, Shihao Yang, Feng Liu

“…Our experiment results demonstrate that (1) compared with benchmark methods, the proposed DAN-MTIF exhibits excellent performance with 0.9106 accuracy, 0.9396 precision, 0.8470 sensitivity, 0.9588 specificity, and 0.8909 <inline-formula><tex-math notation="LaTeX">$F_{1}$</tex-math></inline-formula> score at per-segment level; (2) DAN-MTIF can effectively extract features with a higher degree of discrimination from ECG segments of multiple timescales than those with a single time scale, ensuring a better SA detection performance; (3) the overall performance of deep learning methods is better than the classical machine learning algorithms, highlighting the superior performance of deep learning approaches for SA detection.…”
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Machine Learning Models in the Detection of MB2 Canal Orifice in CBCT Images by Shishir Shetty, Meliz Yuvali, Ilker Ozsahin, Saad Al-Bayatti, Sangeetha Narasimhan, Mohammed Alsaegh, Hiba Al-Daghestani, Raghavendra Shetty, Renita Castelino, Leena R David, Dilber Uzun Ozsahin

“…Conclusion: The success rates (AUC, precision, recall) of ML algorithms in the detection of MB2 were remarkable in our study. …”
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A lightweight deep-learning model for parasite egg detection in microscopy images by Wenbin Xu, Qiang Zhai, Jizhong Liu, Xingyu Xu, Jing Hua

“…Abstract Background Intestinal parasitic infections are still a serious public health problem in developing countries, and the diagnosis of parasitic infections requires the first step of parasite/egg detection of samples. Automated detection can eliminate the dependence on professionals, but the current detection algorithms require large computational resources, which increases the lower limit of automated detection. …”
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Tensor RT optimized driver drowsiness detection system using edge device by Chandramohan Dhasarathan, Sambasivam Gnanasekaran, Arnab Pattanayak, Gourav Kumar, Kartik Vig, Vaibhav Narain, K.M. Deva Narayan, Sunidhi Garg

“…The system uses transfer learning techniques for implementing CNN model algorithms to analyze live video from the camera module, allowing for real-time detection of driver behavior such as fatigue or distraction. …”
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A Synergy Between Machine Learning and Formal Concept Analysis for Crowd Detection by Anas M. Al-Oraiqat, Oleksandr Drieiev, Sattam Almatarneh, Mohammadnoor Injadat, Karim A. Al-Oraiqat, Hanna Drieieva, Yassin M. Y. Hasan

“…To enhance public safety, crowd detection and prevention systems have essentially become a natural means to manage diverse crowded areas, such as urban settings, transportation hubs, and event venues. …”
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Automated Lightweight Model for Asthma Detection Using Respiratory and Cough Sound Signals by Shuting Xu, Ravinesh C. Deo, Oliver Faust, Prabal D. Barua, Jeffrey Soar, Rajendra Acharya

“…<b>Methods:</b> To build an automated, lightweight model for asthma detection, tested separately with respiratory and cough sounds to assess their suitability for detecting asthma and COPD, the proposed AI models integrate the following ML algorithms: RF, SVM, DT, NN, and KNN, with an overall aim to demonstrate the efficacy of the proposed method for future clinical use. …”
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Diagnostic Performance of Artificial Intelligence–Based Methods for Tuberculosis Detection: Systematic Review by Seng Hansun, Ahmadreza Argha, Ivan Bakhshayeshi, Arya Wicaksana, Hamid Alinejad-Rokny, Greg J Fox, Siaw-Teng Liaw, Branko G Celler, Guy B Marks

“…ObjectiveWe aimed to provide a comprehensive evaluation of AI-based algorithms for TB detection across various data modalities. …”
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Automated Surface Crack Identification of Reinforced Concrete Members Using an Improved YOLOv4-Tiny-Based Crack Detection Model by Sofía Rajesh, K. S. Jinesh Babu, M. Chengathir Selvi, M. Chellapandian

“…This paper employed an enhanced You Only Look Once (YOLO) v4-tiny algorithm, based on the Crack Detection Model (CDM), to accurately identify and classify crack types in reinforced concrete (RC) members. …”
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Analysis of Interference Magnetic Field Characteristics of Underwater Gliders by Taotao Xie, Dawei Xiao, Jiawei Zhang, Qing Ji

“…To meet the objective of equipping underwater gliders with magnetic field sensors for underwater target detection, this paper proposes an adaptive filtering method based on the Recursive Least Squares (RLS) algorithm. …”
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Enhancing Real-Time Emotion Recognition in Classroom Environments Using Convolutional Neural Networks: A Step Towards Optical Neural Networks for Advanced Data Processing by Nuphar Avital, Idan Egel, Ido Weinstock, Dror Malka

“…The algorithm encompasses four key steps: image acquisition, preprocessing, emotion detection, and emotion recognition. …”
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Stationauxiliary operation technology for sonar digital image processing based on machine vision by DAI Zhonghua, JIN Qiaoyuan, YANG Ding, CHENG Yu

“…First, the transient signal detection algorithm is used to extract the threat target mutation signal. …”
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Robust face mask detection in complex scenarios using YOLOv8 and context-aware convolutions by Yingjie Wei, Huili Li, Yuanfei He, Li Li, Qiongshuai Lyu, Yu Yang

“…Abstract Aiming to address the challenges of reduced detection accuracy in face mask applications due to mutual occlusion, lighting variations, and detection distance, this paper proposes a face mask detection algorithm tailored for complex environments. …”
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Eye Movement Tracking Using Opencv Python by mohammed satar, Basim Alshammari, Hayder Jasim

“…For this reason, open-source libraries like OpenCV enable high-level programming to implement reliable and accurate detection algorithms like Haar Cascade. Since everything is processed in real-time, payment must be made quickly. …”
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Region-of-Interest Extraction Method to Increase Object-Detection Performance in Remote Monitoring System by Hyeong-GI Jeon, Kyoung-Hee Lee

“…Then it is used as the reference to be compared with a target image to extract the ROIs by our DSSIM-based area filtering algorithm. The background areas beside the ROIs in the image are filled with a single color—either black or white to reduce data size, or a highly saturated color to improve object detection performance. …”
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FedAware: a distributed IoT intrusion detection method based on fractal shrinking autoencoder by Keyuan Qiu, Meifang Yan, Tao Luo, Feng Chen

“…Abstract The goal of intrusion detection is to prevent potential security threats by analysing activities in a network or system and identifying abnormal or malicious behaviours in a timely manner. …”
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A Novel Multi-Step Forecasting-Based Approach for Enhanced Burst Detection in Water Distribution Systems by Xi Wan, Raziyeh Farmani, Edward Keedwell, Xiao Zhou

“…For an online burst detection method based on flow time series data, the challenge arises in the variability of anomaly definitions across different datasets, rendering a one-size-fits-all anomaly detection algorithm impossible. …”
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