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  • Open Access

    ARTICLE

    SwinVid: Enhancing Video Object Detection Using Swin Transformer

    Abdelrahman Maharek1,2,*, Amr Abozeid2,3, Rasha Orban1, Kamal ElDahshan2

    Computer Systems Science and Engineering, Vol.48, No.2, pp. 305-320, 2024, DOI:10.32604/csse.2024.039436

    Abstract What causes object detection in video to be less accurate than it is in still images? Because some video frames have degraded in appearance from fast movement, out-of-focus camera shots, and changes in posture. These reasons have made video object detection (VID) a growing area of research in recent years. Video object detection can be used for various healthcare applications, such as detecting and tracking tumors in medical imaging, monitoring the movement of patients in hospitals and long-term care facilities, and analyzing videos of surgeries to improve technique and training. Additionally, it can be used in telemedicine to help diagnose… More >

  • Open Access

    ARTICLE

    A Novel Deep Learning-Based Model for Classification of Wheat Gene Expression

    Amr Ismail1, Walid Hamdy1,2, Aya M. Al-Zoghby3, Wael A. Awad3, Ahmed Ismail Ebada3, Yunyoung Nam4, Byeong-Gwon Kang4,*, Mohamed Abouhawwash5,6

    Computer Systems Science and Engineering, Vol.48, No.2, pp. 273-285, 2024, DOI:10.32604/csse.2023.038192

    Abstract Deep learning (DL) plays a critical role in processing and converting data into knowledge and decisions. DL technologies have been applied in a variety of applications, including image, video, and genome sequence analysis. In deep learning the most widely utilized architecture is Convolutional Neural Networks (CNN) are taught discriminatory traits in a supervised environment. In comparison to other classic neural networks, CNN makes use of a limited number of artificial neurons, therefore it is ideal for the recognition and processing of wheat gene sequences. Wheat is an essential crop of cereals for people around the world. Wheat Genotypes identification has… More >

  • Open Access

    ARTICLE

    Movement Function Assessment Based on Human Pose Estimation from Multi-View

    Lingling Chen1,2,*, Tong Liu1, Zhuo Gong1, Ding Wang1

    Computer Systems Science and Engineering, Vol.48, No.2, pp. 321-339, 2024, DOI:10.32604/csse.2023.037865

    Abstract Human pose estimation is a basic and critical task in the field of computer vision that involves determining the position (or spatial coordinates) of the joints of the human body in a given image or video. It is widely used in motion analysis, medical evaluation, and behavior monitoring. In this paper, the authors propose a method for multi-view human pose estimation. Two image sensors were placed orthogonally with respect to each other to capture the pose of the subject as they moved, and this yielded accurate and comprehensive results of three-dimensional (3D) motion reconstruction that helped capture their multi-directional poses.… More >

  • Open Access

    ARTICLE

    Deep Learning-Based Mask Identification System Using ResNet Transfer Learning Architecture

    Arpit Jain1, Nageswara Rao Moparthi1, A. Swathi2, Yogesh Kumar Sharma1, Nitin Mittal3, Ahmed Alhussen4, Zamil S. Alzamil5,*, MohdAnul Haq5

    Computer Systems Science and Engineering, Vol.48, No.2, pp. 341-362, 2024, DOI:10.32604/csse.2023.036973

    Abstract Recently, the coronavirus disease 2019 has shown excellent attention in the global community regarding health and the economy. World Health Organization (WHO) and many others advised controlling Corona Virus Disease in 2019. The limited treatment resources, medical resources, and unawareness of immunity is an essential horizon to unfold. Among all resources, wearing a mask is the primary non-pharmaceutical intervention to stop the spreading of the virus caused by Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) droplets. All countries made masks mandatory to prevent infection. For such enforcement, automatic and effective face detection systems are crucial. This study presents a face… More >

  • Open Access

    CORRECTION

    Correction: Spatio Temporal Tourism Tracking System Based on Adaptive Convolutional Neural Network

    L. Maria Michael Visuwasam1,*, D. Paul Raj2

    Computer Systems Science and Engineering, Vol.48, No.1, pp. 267-267, 2024, DOI:10.32604/csse.2023.047461

    Abstract This article has no abstract. More >

  • Open Access

    CORRECTION

    Correction: Prediction of Alzheimer’s Using Random Forest with Radiomic Features

    Anuj Singh*, Raman Kumar, Arvind Kumar Tiwari

    Computer Systems Science and Engineering, Vol.48, No.1, pp. 269-269, 2024, DOI:10.32604/csse.2023.047533

    Abstract This article has no abstract. More >

  • Open Access

    CORRECTION

    Correction: Learning-Based Metaheuristic Approach for Home Healthcare Optimization Problem

    Mariem Belhor1,2,3, Adnen El-Amraoui1,*, Abderrazak Jemai2, François Delmotte1

    Computer Systems Science and Engineering, Vol.48, No.1, pp. 271-271, 2024, DOI:10.32604/csse.2023.048573

    Abstract This article has no abstract. More >

  • Open Access

    ARTICLE

    Heart Disease Prediction Using Convolutional Neural Network with Elephant Herding Optimization

    P. Nandakumar, R. Subhashini*

    Computer Systems Science and Engineering, Vol.48, No.1, pp. 57-75, 2024, DOI:10.32604/csse.2023.042294

    Abstract Heart disease is a major cause of death for many people in the world. Each year the death rate of people affected with heart disease increased a lot. Machine learning models have been widely used for the prediction of heart disease from the different University of California Irvine (UCI) Machine Learning Repositories. But, due to certain data, it predicts less accurately, whereas, for large data, its sub-model deep learning is used. Our literature work has identified that only traditional methods are used for the prediction of heart disease. It will produce less accuracy. To produce more efficacy, Euclidean Distance was… More >

  • Open Access

    REVIEW

    AI-Based UAV Swarms for Monitoring and Disease Identification of Brassica Plants Using Machine Learning: A Review

    Zain Anwar Ali1,2,*, Dingnan Deng1, Muhammad Kashif Shaikh3, Raza Hasan4, Muhammad Aamir Khan2

    Computer Systems Science and Engineering, Vol.48, No.1, pp. 1-34, 2024, DOI:10.32604/csse.2023.041866

    Abstract Technological advances in unmanned aerial vehicles (UAVs) pursued by artificial intelligence (AI) are improving remote sensing applications in smart agriculture. These are valuable tools for monitoring and disease identification of plants as they can collect data with no damage and effects on plants. However, their limited carrying and battery capacities restrict their performance in larger areas. Therefore, using multiple UAVs, especially in the form of a swarm is more significant for monitoring larger areas such as crop fields and forests. The diversity of research studies necessitates a literature review for more progress and contribution in the agricultural field. In this… More >

  • Open Access

    ARTICLE

    Letter Recognition Reinvented: A Dual Approach with MLP Neural Network and Anomaly Detection

    Nesreen M. Alharbi*, Ahmed Hamza Osman, Arwa A. Mashat, Hasan J. Alyamani

    Computer Systems Science and Engineering, Vol.48, No.1, pp. 175-198, 2024, DOI:10.32604/csse.2023.041044

    Abstract Recent years have witnessed significant advancements in the field of character recognition, thanks to the revolutionary introduction of machine learning techniques. Among various types of character recognition, offline Handwritten Character Recognition (HCR) is comparatively more challenging as it lacks temporal information, such as stroke count and direction, ink pressure, and unexpected handwriting variability. These issues contribute to a poor level of precision, which calls for the adoption of anomaly detection techniques to enhance Optical Character Recognition (OCR) schemes. Previous studies have not researched unsupervised anomaly detection using MLP for handwriting recognition. Therefore, this study proposes a novel approach for enhanced… More >

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