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  • Open Access


    RDA- CNN: Enhanced Super Resolution Method for Rice Plant Disease Classification

    K. Sathya1,*, M. Rajalakshmi2

    Computer Systems Science and Engineering, Vol.42, No.1, pp. 33-47, 2022, DOI:10.32604/csse.2022.022206

    Abstract In the field of agriculture, the development of an early warning diagnostic system is essential for timely detection and accurate diagnosis of diseases in rice plants. This research focuses on identifying the plant diseases and detecting them promptly through the advancements in the field of computer vision. The images obtained from in-field farms are typically with less visual information. However, there is a significant impact on the classification accuracy in the disease diagnosis due to the lack of high-resolution crop images. We propose a novel Reconstructed Disease Aware–Convolutional Neural Network (RDA-CNN), inspired by recent CNN architectures, that integrates image super… More >

  • Open Access


    Deep Convolutional Neural Network Approach for COVID-19 Detection

    Yu Xue1,2,*, Bernard-Marie Onzo1, Romany F. Mansour3,4, Shoubao Su4

    Computer Systems Science and Engineering, Vol.42, No.1, pp. 201-211, 2022, DOI:10.32604/csse.2022.022158

    Abstract Coronavirus disease 2019 (Covid-19) is a life-threatening infectious disease caused by a newly discovered strain of the coronaviruses. As by the end of 2020, Covid-19 is still not fully understood, but like other similar viruses, the main mode of transmission or spread is believed to be through droplets from coughs and sneezes of infected persons. The accurate detection of Covid-19 cases poses some questions to scientists and physicians. The two main kinds of tests available for Covid-19 are viral tests, which tells you whether you are currently infected and antibody test, which tells if you had been infected previously. Routine… More >

  • Open Access


    Towards Improving Predictive Statistical Learning Model Accuracy by Enhancing Learning Technique

    Ali Algarni1, Mahmoud Ragab2,3,4,*, Wardah Alamri5, Samih M. Mostafa6

    Computer Systems Science and Engineering, Vol.42, No.1, pp. 303-318, 2022, DOI:10.32604/csse.2022.022152

    Abstract The accuracy of the statistical learning model depends on the learning technique used which in turn depends on the dataset’s values. In most research studies, the existence of missing values (MVs) is a vital problem. In addition, any dataset with MVs cannot be used for further analysis or with any data driven tool especially when the percentage of MVs are high. In this paper, the authors propose a novel algorithm for dealing with MVs depending on the feature selection (FS) of similarity classifier with fuzzy entropy measure. The proposed algorithm imputes MVs in cumulative order. The candidate feature to be… More >

  • Open Access


    An Efficient On-Demand Virtual Machine Migration in Cloud Using Common Deployment Model

    C. Saravanakumar1,*, R. Priscilla1, B. Prabha2, A. Kavitha3, M. Prakash4, C. Arun5

    Computer Systems Science and Engineering, Vol.42, No.1, pp. 245-256, 2022, DOI:10.32604/csse.2022.022122

    Abstract Cloud Computing provides various services to the customer in a flexible and reliable manner. Virtual Machines (VM) are created from physical resources of the data center for handling huge number of requests as a task. These tasks are executed in the VM at the data center which needs excess hosts for satisfying the customer request. The VM migration solves this problem by migrating the VM from one host to another host and makes the resources available at any time. This process is carried out based on various algorithms which follow a predefined capacity of source VM leads to the capacity… More >

  • Open Access


    Deep Reinforcement Learning Empowered Edge Collaborative Caching Scheme for Internet of Vehicles

    Xin Liu1, Siya Xu1, Chao Yang2, Zhili Wang1,*, Hao Zhang3, Jingye Chi1, Qinghan Li4

    Computer Systems Science and Engineering, Vol.42, No.1, pp. 271-287, 2022, DOI:10.32604/csse.2022.022103

    Abstract With the development of internet of vehicles, the traditional centralized content caching mode transmits content through the core network, which causes a large delay and cannot meet the demands for delay-sensitive services. To solve these problems, on basis of vehicle caching network, we propose an edge collaborative caching scheme. Road side unit (RSU) and mobile edge computing (MEC) are used to collect vehicle information, predict and cache popular content, thereby provide low-latency content delivery services. However, the storage capacity of a single RSU severely limits the edge caching performance and cannot handle intensive content requests at the same time. Through… More >

  • Open Access


    Autonomous Unbiased Study Group Formation Algorithm for Rapid Knowledge Propagation

    Monday Eze1,*, Charles Okunbor2, Solomon Esomu3, Nneka Richard-Nnabu4, Kayode Oladapo1, Oghenetega Avwokuruaye5, Abisola Olayiwola6, Akpovi Ominike7, Godwin Odulaja8, Oluwatobi Akinmerese1

    Computer Systems Science and Engineering, Vol.42, No.1, pp. 15-31, 2022, DOI:10.32604/csse.2022.021964

    Abstract Knowledge propagation is a necessity, both in academics and in the industry. The focus of this work is on how to achieve rapid knowledge propagation using collaborative study groups. The practice of knowledge sharing in study groups finds relevance in conferences, workshops, and class rooms. Unfortunately, there appears to be only few researches on empirical best practices and techniques on study groups formation, especially for achieving rapid knowledge propagation. This work bridges this gap by presenting a workflow driven computational algorithm for autonomous and unbiased formation of study groups. The system workflow consists of a chronology of stages, each made… More >

  • Open Access


    Developing Engagement in the Learning Management System Supported by Learning Analytics

    Suraya Hamid1, Shahrul Nizam Ismail1, Muzaffar Hamzah2,*, Asad W. Malik3

    Computer Systems Science and Engineering, Vol.42, No.1, pp. 335-350, 2022, DOI:10.32604/csse.2022.021927

    Abstract Learning analytics is an emerging technique of analysing student participation and engagement. The recent COVID-19 pandemic has significantly increased the role of learning management systems (LMSs). LMSs previously only complemented face-to-face teaching, something which has not been possible between 2019 to 2020. To date, the existing body of literature on LMSs has not analysed learning in the context of the pandemic, where an LMS serves as the only interface between students and instructors. Consequently, productive results will remain elusive if the key factors that contribute towards engaging students in learning are not first identified. Therefore, this study aimed to perform… More >

  • Open Access


    Analysis of Critical Factors in Manufacturing by Adopting a Cloud Computing Service

    Hsin-Pin Fu1,*, Tsung-Sheng Chang2, Chien-Hung Liu3, Li-Chun Liu1

    Computer Systems Science and Engineering, Vol.42, No.1, pp. 213-227, 2022, DOI:10.32604/csse.2022.021767

    Abstract The advantages of a cloud computing service are cost advantages, availability, scalability, flexibility, reduced time to market, and dynamic access to computing resources. Enterprises can improve the successful adoption rate of cloud computing services if they understand the critical factors. To find critical factors, this study first reviewed the literature and established a three-layer hierarchical factor table for adopting a cloud computing service based on the Technology-Organization-Environment framework. Then, a hybrid method that combines two multi-criteria decision-making tools—called the Fuzzy Analytic Network Process method and the concept of VlseKriterijumska Optimizacija I Kompromisno Resenje acceptable advantage—was used to objectively identify critical… More >

  • Open Access


    Automated Deep Learning Based Cardiovascular Disease Diagnosis Using ECG Signals

    S. Karthik1, M. Santhosh1,*, M. S. Kavitha1, A. Christopher Paul2

    Computer Systems Science and Engineering, Vol.42, No.1, pp. 183-199, 2022, DOI:10.32604/csse.2022.021698

    Abstract Automated biomedical signal processing becomes an essential process to determine the indicators of diseased states. At the same time, latest developments of artificial intelligence (AI) techniques have the ability to manage and analyzing massive amounts of biomedical datasets results in clinical decisions and real time applications. They can be employed for medical imaging; however, the 1D biomedical signal recognition process is still needing to be improved. Electrocardiogram (ECG) is one of the widely used 1-dimensional biomedical signals, which is used to diagnose cardiovascular diseases. Computer assisted diagnostic models find it difficult to automatically classify the 1D ECG signals owing to… More >

  • Open Access


    Machine Learning-Based Pruning Technique for Low Power Approximate Computing

    B. Sakthivel1,*, K. Jayaram2, N. Manikanda Devarajan3, S. Mahaboob Basha4, S. Rajapriya5

    Computer Systems Science and Engineering, Vol.42, No.1, pp. 397-406, 2022, DOI:10.32604/csse.2022.021637

    Abstract Approximate Computing is a low power achieving technique that offers an additional degree of freedom to design digital circuits. Pruning is one of the types of approximate circuit design technique which removes logic gates or wires in the circuit to reduce power consumption with minimal insertion of error. In this work, a novel machine learning (ML) -based pruning technique is introduced to design digital circuits. The machine-learning algorithm of the random forest decision tree is used to prune nodes selectively based on their input pattern. In addition, an error compensation value is added to the original output to reduce an… More >

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