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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Triplet Spatial Reconstruction Attention-Based Lightweight Ship Component Detection for Intelligent Manufacturing by Bocheng Feng, Zhenqiu Yao, Chuanpu Feng

“…To address the requirements of shipbuilding industrial applications, a Triplet Spatial Reconstruction Attention (TSA) mechanism that combines threshold-based feature separation with triplet parallel processing is proposed, and a lightweight You Only Look Once Ship (YOLO-Ship) detection network is constructed. …”
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SYSTEM AND ALGORITHM OF INTELLIGENT BIOMEDICAL SIGNAL PROCESSING AND ANALYSIS FOR HUMAN HEALTH STATUS REMOTE MONITORING SYSTEM by Nguyen Trong Tuyen, Tran Trong Huu, Nguyen Mau Thach, Zafar M. Yuldashev

“…The system and intelligent monitoring algorithm is tested with the use of heart rate remote control and atrial fibrillation episode detection system. …”
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Random walk based snapshot clustering for detecting community dynamics in temporal networks by Filip Blašković, Tim O. F. Conrad, Stefan Klus, Nataša Djurdjevac Conrad

“…We also provide a low-dimensional representation of entire snapshots, placing those with similar community structure close to each other in the feature space. To validate our approach, we develop an agent-based algorithm that generates synthetic datasets with the desired characteristic properties, enabling thorough testing and benchmarking. …”
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Detection and Measurement of Displacement and Velocity of Single Moving Object in a Stationary Background by Amr Reda. R. Almaddah, Tauseef Ahmad, Abdullah Dubai

“…The work we have done addresses the issue of robustly detection of feature points, detected multiple of local features are characterized by the intensity changes in both horizontal and vertical direction which is called corner features.  …”
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Ensemble-based multiclass lung cancer classification using hybrid CNN-SVD feature extraction and selection method. by Md Sabbir Hossain, Niloy Basak, Md Aslam Mollah, Md Nahiduzzaman, Mominul Ahsan, Julfikar Haider

“…The extracted features were then processed by a set of ML algorithms along with a voting ensemble approach. …”
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Robust Face Detection and Identification under Occlusion using MTCNN and RESNET50 by Eiman Wahab, Wajeeha Shafique, Habiba Amir, Sameena Javed, Muhammad Marouf

“…We used the face detector algorithm called Multi-Task Cascaded Convolutional Neural Network (MTCNN) for face detection with 99.8% accuracy. …”
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Infrared Ship Detection in Complex Nearshore Scenes Based on Improved YOLOv5s by Xiuwen Liu, Mingchen Liu, Yong Yin

“…Experimental results demonstrate that CGSE-YOLOv5s achieves a mean average precision (mAP@0.5) of 94.8%, outperforming YOLOv5s by 1.3% and surpassing other detection algorithms.…”
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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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SPL-YOLOv8: A Lightweight Method for Rape Flower Cluster Detection and Counting Based on YOLOv8n by Yue Fang, Chenbo Yang, Jie Li, Jingmin Tu

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YOLO-CSMD: Integrating Improved Convolutional Techniques for Manhole Cover Defect Detection by Zhiwang Xu, Haowei Luo, Huijie Zhu, Wanfa Sun, Shengying Yang

“…Recent detection algorithms for manhole cover defects exhibit limited detection capabilities, which frequently encounter the issue of missed detection. …”
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Comparative analysis of features phenotype limb-girdle muscular dystrophy 2А and 2I types by I. V. Sharkova, E. L. Dadali, O. P. Ryzhkova, V. N. Evdokimenkov

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Miniaturized Near-Infrared Analyzer for Quantitative Detection of Trace Water in Ethylene Glycol by Qunling Luo, Zhiqiang Guo, Danping Lin, Boxue Chang, Yinlan Ruan

“…To address the limitations of a traditional Fourier-transform infrared (FTIR) spectrometer, including its bulky size, high cost, and unsuitability for on-site industrial detection, this study developed a Fourier-transform near-infrared (FT-NIR) absorption testing system utilizing Micro-Electro-Mechanical System (MEMS) technology for detecting trace water content in ethylene glycol. …”
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A Context-Aware Android Malware Detection Approach Using Machine Learning by Mohammed N. AlJarrah, Qussai M. Yaseen, Ahmad M. Mustafa

“…Several combinations of API Calls, permissions, and contextual features were used. These combinations were fed into different machine-learning algorithms to show the significance of using the selected contextual features in detecting Android malware. …”
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Construction of a feature gene and machine prediction model for inflammatory bowel disease based on multichip joint analysis by Yan Chaosheng, Sun Haowen, Rao Jingjing, Dai Yuanyuan, Duan Wenhui, Sheng Yingyue, Xue Yuzheng

“…On the basis of methods such as artificial neural networks (ANNs), machine learning techniques, and the SHAP model, we developed a diagnostic model for IBD. To select genetic features, we utilized three machine learning algorithms, namely, least absolute shrinkage and selection operator (LASSO), support vector machine (SVM), and random forest (RF), to identify differentially expressed genes. …”
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FEATURES OF LYMPHADENECTOMY PERFORMANCE IN PATIENTS WITH STOMACH CANCER IN RADICAL SURGERY IN CASE OF VARIANT STRUCTURE OF CELIAC TRUNK by V. M. Sedov, I. N. Danilov, A. N. Yaitskiy, A. A. Zakharenko, K. N. Vovin, A. L. Bykova

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Improving and simulating urban landscape image recognition using combination optimization and fuzzy K-means algorithm by Lihua Yang, Yuhui Zheng

“…CO specifically integrates the genetic algorithm (GA) to efficiently search for the optimal subset of features that maximize the performance of a convolutional neural network (CNN) based on extracted features. …”
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Improved YOLOv8 Algorithm was Used to Segment Cucumber Seedlings Under Complex Artificial Light Conditions by Duokuo Zhang, Na Li, Mingfu Zhao, Kun Xu

“…The effectiveness and superiority of this approach are validated through comparisons with other widely used segmentation detection algorithms.…”
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MobileNetV3: an efficient deep learning-based feature selection and classification technique for cardiovascular disease by B. Dhanalaxmi, B. Naveen Kumar, Yeligeti Raju, Rama Seshagiri Rao Channapragada

“…Next, use the EfficientNetV2 approach to extract the features from the data, and then use the Reptile Search Algorithm (RSA) to choose the most significant features from the collected features. …”
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Classification Model for Bot-IoT Attack Detection Using Correlation and Analysis of Variance by Firgiawan Faira, Dandy Pramana Hostiadi

“…This research focuses on detecting Bot-IoT activity using the Bot-IoT UNSW Canberra 2018 dataset. …”
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