Rotating Machinery Fault Detection Using Support Vector Machine via Feature Ranking by Harry Hoa Huynh, Cheol-Hong Min

“…Especially the use of machine learning algorithms has been very popular in all areas, including fault detection. …”
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Effective Detection of Malicious Uniform Resource Locator (URLs) Using Deep-Learning Techniques by Yirga Yayeh Munaye, Aneas Bekele Workneh, Yenework Belayneh Chekol, Atinkut Molla Mekonen

“…By effectively extracting and utilizing informative features, the model accurately classified URLs into benign and malicious categories, thereby improving detection capabilities. This study highlights the significance of our deep learning approach in strengthening cybersecurity by integrating advanced algorithms that enhance detection accuracy, bolster defense mechanisms, and contribute to a safer digital environment.…”
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SynthSecureNet: An Improved Deep Learning Architecture with Application to Intelligent Violence Detection by Ntandoyenkosi Zungu, Peter Olukanmi, Pitshou Bokoro

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An optimization model of computer network security based on GABP neural network algorithm by Jiangang Wang, Xiaoyan Wang

“…Objective This research proposed the investigation of the Genetic Algorithm with Back Propagation Neural Network (GA-BPNN) model for computer network safety. …”
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Machine learning for Parkinson’s disease: a comprehensive review of datasets, algorithms, and challenges by Sahar Shokrpour, AmirMehdi MoghadamFarid, Sepideh Bazzaz Abkenar, Mostafa Haghi Kashani, Mohammad Akbari, Mostafa Sarvizadeh

“…Our SLR identifies the datasets and ML algorithms used for PD diagnosis, as well as their merits, limitations, and evaluation factors. …”
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Deep Belief Network-Based Multifeature Fusion Music Classification Algorithm and Simulation by Tianzhuo Gong

“…In this paper, the multifeature fusion music classification algorithm and its simulation results are studied by deep confidence networks, the multifeature fusion music database is established and preprocessed, and then features are extracted. …”
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The OPS-SAT benchmark for detecting anomalies in satellite telemetry by Bogdan Ruszczak, Krzysztof Kotowski, David Evans, Jakub Nalepa

“…It is time-consuming, error-prone and human dependent, thus automated data-driven algorithms have been emerging at a steady pace. However, there are no available datasets of real satellite telemetry with annotations to verify anomaly detection models. …”
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A Review of Stall Detection in Subsonic Axial Compressors by Kellie N. Wilson, Golam Gause Jaman, Anish Thapa, Amirthavarshini Vivekananda, Mitchell Lowe, Zachary Grima, Marco P. Schoen

“…This paper reviews the major contributions in these listed pursuits and presents the latest methods and algorithms for stall precursor detection in low-speed axial compressors. …”
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Machine Learning-Based Network Detection Research for SDNs by Lai Jiayue

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Graph convolution network for fraud detection in bitcoin transactions by Ahmad Asiri, K. Somasundaram

“…This provides a better platform for criminals for their illicit transactions. New algorithms have been proposed to detect illicit transactions. …”
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A Lightweight Direction-Aware Network for Vehicle Detection by Luxia Yang, Yilin Hou, Hongrui Zhang, Chuanghui Zhang

“…Vehicle detection algorithms, which are essential to intelligent traffic management and control systems, have attracted growing attention. …”
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Deep-Learning-CNN for Detecting Covered Faces with Niqab by Abdulaziz A. Alashbi, Mohd Shahrizal Sunar, Zieb Alqahtani

“…Despite the high performance of current state-of-the-art face detection algorithms, the detection of occluded and covered faces is an unsolved problem and is still worthy of study. …”
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Algorithm for forming a geo-map of citizen appeals using artificial intelligence by Mallaev Oybek, Gazatov Jamoliddin, Aliyev Jaloliddin

“…This article presents the development of a method, mechanism, module, and algorithm for the automatic analysis, real-time visualization, and intelligent resolution of citizen appeals using artificial intelligence (AI) and geo-mapping technologies. …”
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SMART HYBRID MODELS FOR IMPROVED BREAST CANCER DETECTION by Nageswara Rao Gali, Panduranga Vital Terlapu, Yasaswini Mandavakuriti, Sai Manoj Somu, Madhavi Varanasi, Vijay Telugu, Maheswara Rao V V R

“…Many researchers have turned to Machine Learning algorithms to develop prognosis systems. We employ a combination of deep learning (DL) and machine learning (ML) algorithms for BC identification. …”
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Advancements in Intelligent Sensing Technologies for Food Safety Detection by Wenhui Jiang, Changhong Liu, Wei Liu, Lei Zheng

“…This review systematically examines the fundamental principles of intelligent sensing technologies, including optical, electrochemical, machine olfaction, and machine gustatory systems, along with their practical applications in detecting microbial, chemical, and physical hazards in food products. …”
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anomaly: Detection of Anomalous Structure in Time Series Data by Alex Fisch, Daniel Grose, Idris A. Eckley, Paul Fearnhead, Lawrence Bardwell

“…The anomaly package has been developed to provide users with a choice of anomaly detection methods and, in particular, provides an implementation of the recently proposed collective and point anomaly family of anomaly detection algorithms. …”
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Advances in machine learning for the detection and characterization of microplastics in the environment by M. Maksuda Khanam, M. Khabir Uddin, Julhash U. Kazi

“…Recent advances in machine learning (ML) have revolutionized the field of microplastic research by automating and enhancing detection processes. In particular, algorithms such as support vector machines, random forests, and convolutional neural networks have demonstrated considerable success in classifying microplastics based on chemical signatures and visual characteristics. …”
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Predictive modeling for rework detection in sustainable building projects by AbdulLateef Olanrewaju, Kafayat Shobowale

“…The results revealed that Random Forest and AdaBoost outperformed the other algorithms. The Adaboost achieved an accuracy of 90%, with a precision of 0.80, a recall of 0.76, and an F1-score of 0.77. …”
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AC-YOLO: citrus detection in the natural environment of orchards by Xu Xiao, Yaonan Wang, Yiming Jiang, Haotian Wu, Zhe Zhang, Rujing Wang

“…By comparing the orange recognition effects of different network models such as Fast R-CNN, Center Net, YOLOV4 series algorithms, and YOLOV5 series algorithms on the panorama image, the improved YOLOV4 network model has an accuracy rate of 96.19%, a recall rate of 95.47%, and an average accuracy of 97.27%, Compared with the original YOLOv4 model, it has increased by 1.07, 2.59, and 2.02 percentage points respectively. …”
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Neurophysiological Approaches to Lie Detection: A Systematic Review by Bewar Neamat Taha, Muhammet Baykara, Talha Burak Alakuş

“…The goal is to summarize commonly used EEG signal processing techniques, feature extraction methods, and classification algorithms, identifying those that yield the highest accuracy in lie detection tasks. …”
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