Home / Journals / CSSE / Vol.44, No.2, 2023
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  • Open AccessOpen Access

    ARTICLE

    Optimal Deep Convolutional Neural Network with Pose Estimation for Human Activity Recognition

    S. Nandagopal1,*, G. Karthy2, A. Sheryl Oliver3, M. Subha4
    Computer Systems Science and Engineering, Vol.44, No.2, pp. 1719-1733, 2023, DOI:10.32604/csse.2023.028003
    Abstract Human Action Recognition (HAR) and pose estimation from videos have gained significant attention among research communities due to its application in several areas namely intelligent surveillance, human robot interaction, robot vision, etc. Though considerable improvements have been made in recent days, design of an effective and accurate action recognition model is yet a difficult process owing to the existence of different obstacles such as variations in camera angle, occlusion, background, movement speed, and so on. From the literature, it is observed that hard to deal with the temporal dimension in the action recognition process. Convolutional… More >

  • Open AccessOpen Access

    ARTICLE

    A Virtual Cloud Storage Architecture for Enhanced Data Security

    M. Antony Joans Kumar1,*, C. Christopher Columbus2, E. Ben George3, T. Ajith Bosco Raj4
    Computer Systems Science and Engineering, Vol.44, No.2, pp. 1735-1747, 2023, DOI:10.32604/csse.2023.025906
    Abstract The sensitive data stored in the public cloud by privileged users, such as corporate companies and government agencies are highly vulnerable in the hands of cloud providers and hackers. The proposed Virtual Cloud Storage Architecture is primarily concerned with data integrity and confidentiality, as well as availability. To provide confidentiality and availability, the file to be stored in cloud storage should be encrypted using an auto-generated key and then encoded into distinct chunks. Hashing the encoded chunks ensured the file integrity, and a newly proposed Circular Shift Chunk Allocation technique was used to determine the… More >

  • Open AccessOpen Access

    ARTICLE

    Rider Optimization Algorithm Based Optimal Cloud Server Selection in E-Learning

    R. Soundhara Raja Pandian*, C. Christopher Columbus
    Computer Systems Science and Engineering, Vol.44, No.2, pp. 1749-1762, 2023, DOI:10.32604/csse.2023.028014
    Abstract Currently, e-learning is one of the most prevalent educational methods because of its need in today’s world. Virtual classrooms and web-based learning are becoming the new method of teaching remotely. The students experience a lack of access to resources commonly the educational material. In remote locations, educational institutions face significant challenges in accessing various web-based materials due to bandwidth and network infrastructure limitations. The objective of this study is to demonstrate an optimization and queueing technique for allocating optimal servers and slots for users to access cloud-based e-learning applications. The proposed method provides the optimization… More >

  • Open AccessOpen Access

    ARTICLE

    Haemoglobin Measurement from Eye Anterior Ciliary Arteries through Borescope Camera

    Mohamed Abbas Ahamed Farook1,*, S. Rukmanidevi2, N. R. Shanker3
    Computer Systems Science and Engineering, Vol.44, No.2, pp. 1763-1774, 2023, DOI:10.32604/csse.2023.026260
    Abstract Nowadays, smartphones are used as self-health monitoring devices for humans. Self-health monitoring devices help clinicians with big data for accurate diagnosis and guidance for treatment through repetitive measurement. Repetitive measurement of haemoglobin requires for pregnant women, pediatric, pulmonary hypertension and obstetric patients. Noninvasive haemoglobin measurement through conjunctiva leads to inaccurate measurement. The inaccuracy is due to a decrease in the density of goblet cells and acinar units in Meibomian glands in the human eye as age increases. Furthermore, conjunctivitis is a disease in the eye due to inflammation or infection at the conjunctiva. Conjunctivitis is… More >

  • Open AccessOpen Access

    ARTICLE

    Deep-BERT: Transfer Learning for Classifying Multilingual Offensive Texts on Social Media

    Md. Anwar Hussen Wadud1, M. F. Mridha1, Jungpil Shin2,*, Kamruddin Nur3, Aloke Kumar Saha4
    Computer Systems Science and Engineering, Vol.44, No.2, pp. 1775-1791, 2023, DOI:10.32604/csse.2023.027841
    Abstract Offensive messages on social media, have recently been frequently used to harass and criticize people. In recent studies, many promising algorithms have been developed to identify offensive texts. Most algorithms analyze text in a unidirectional manner, where a bidirectional method can maximize performance results and capture semantic and contextual information in sentences. In addition, there are many separate models for identifying offensive texts based on monolingual and multilingual, but there are a few models that can detect both monolingual and multilingual-based offensive texts. In this study, a detection system has been developed for both monolingual… More >

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    ARTICLE

    Brain Tumor Diagnosis Using Sparrow Search Algorithm Based Deep Learning Model

    G. Ignisha Rajathi1, R. Ramesh Kumar2, D. Ravikumar3, T. Joel4, Seifedine Kadry4,5, Chang-Won Jeong6, Yunyoung Nam7,*
    Computer Systems Science and Engineering, Vol.44, No.2, pp. 1793-1806, 2023, DOI:10.32604/csse.2023.024674
    Abstract Recently, Internet of Medical Things (IoMT) has gained considerable attention to provide improved healthcare services to patients. Since earlier diagnosis of brain tumor (BT) using medical imaging becomes an essential task, automated IoMT and cloud enabled BT diagnosis model can be devised using recent deep learning models. With this motivation, this paper introduces a novel IoMT and cloud enabled BT diagnosis model, named IoMTC-HDBT. The IoMTC-HDBT model comprises the data acquisition process by the use of IoMT devices which captures the magnetic resonance imaging (MRI) brain images and transmit them to the cloud server. Besides,… More >

  • Open AccessOpen Access

    ARTICLE

    Moving Multi-Object Detection and Tracking Using MRNN and PS-KM Models

    V. Premanand*, Dhananjay Kumar
    Computer Systems Science and Engineering, Vol.44, No.2, pp. 1807-1821, 2023, DOI:10.32604/csse.2023.026742
    Abstract On grounds of the advent of real-time applications, like autonomous driving, visual surveillance, and sports analysis, there is an augmenting focus of attention towards Multiple-Object Tracking (MOT). The tracking-by-detection paradigm, a commonly utilized approach, connects the existing recognition hypotheses to the formerly assessed object trajectories by comparing the similarities of the appearance or the motion between them. For an efficient detection and tracking of the numerous objects in a complex environment, a Pearson Similarity-centred Kuhn-Munkres (PS-KM) algorithm was proposed in the present study. In this light, the input videos were, initially, gathered from the MOT… More >

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    ARTICLE

    Music Genre Classification Using African Buffalo Optimization

    B. Jaishankar1,*, Raghunathan Anitha2, Finney Daniel Shadrach1, M. Sivarathinabala3, V. Balamurugan4
    Computer Systems Science and Engineering, Vol.44, No.2, pp. 1823-1836, 2023, DOI:10.32604/csse.2023.022938
    Abstract In the discipline of Music Information Retrieval (MIR), categorizing music files according to their genre is a difficult process. Music genre classification is an important multimedia research domain for classification of music databases. In the proposed method music genre classification using features obtained from audio data is proposed. The classification is done using features extracted from the audio data of popular online repository namely GTZAN, ISMIR 2004 and Latin Music Dataset (LMD). The features highlight the differences between different musical styles. In the proposed method, feature selection is performed using an African Buffalo Optimization (ABO), More >

  • Open AccessOpen Access

    ARTICLE

    Designing Software to Analyze Sewing Process of Industrial Knitted Products

    Phan Thanh Thao*, Nguyen Thanh Hung, Pham Thi Le My, Nguyen Xuan Hiep, Duy-Nam Phan
    Computer Systems Science and Engineering, Vol.44, No.2, pp. 1837-1852, 2023, DOI:10.32604/csse.2023.026502
    Abstract In the textile industry, garment manufacturing contains four major processes containing cutting, sewing, finishing, and packaging. Sewing is the most crucial and intricate section, dealing with a large number of varied operations. A successful sewing process needs to be optimized regarding different factors, including time, sewing equipment, and skilled workers. Assembly line flow is combined by a set of operations with a particular sequence. The utmost importance of all garment industry is to arrange the workstations to minimize the number of employees in order to produce at the best productive rate with the most reasonable… More >

  • Open AccessOpen Access

    ARTICLE

    Intelligent Student Mental Health Assessment Model on Learning Management System

    Nasser Ali Aljarallah1,2, Ashit Kumar Dutta3,*, Majed Alsanea4, Abdul Rahaman Wahab Sait5
    Computer Systems Science and Engineering, Vol.44, No.2, pp. 1853-1868, 2023, DOI:10.32604/csse.2023.028755
    Abstract A learning management system (LMS) is a software or web based application, commonly utilized for planning, designing, and assessing a particular learning procedure. Generally, the LMS offers a method of creating and delivering content to the instructor, monitoring students’ involvement, and validating their outcomes. Since mental health issues become common among studies in higher education globally, it is needed to properly determine it to improve mental stability. This article develops a new seven spot lady bird feature selection with optimal sparse autoencoder (SSLBFS-OSAE) model to assess students’ mental health on LMS. The major aim of… More >

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