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  • Open Access


    Modified Metaheuristics with Weighted Majority Voting Ensemble Deep Learning Model for Intrusion Detection System

    Mahmoud Ragab1,2,*, Sultanah M. Alshammari2,3, Abdullah S. Al-Malaise Al-Ghamdi2,4

    Computer Systems Science and Engineering, Vol.47, No.2, pp. 2497-2512, 2023, DOI:10.32604/csse.2023.041446

    Abstract The Internet of Things (IoT) system has confronted dramatic growth in high dimensionality and data traffic. The system named intrusion detection systems (IDS) is broadly utilized for the enhancement of security posture in an IT infrastructure. An IDS is a practical and suitable method for assuring network security and identifying attacks by protecting it from intrusive hackers. Nowadays, machine learning (ML)-related techniques were used for detecting intrusion in IoTs IDSs. But, the IoT IDS mechanism faces significant challenges because of physical and functional diversity. Such IoT features use every attribute and feature for IDS self-protection… More >

  • Open Access


    Pulmonary Diseases Decision Support System Using Deep Learning Approach

    Yazan Al-Issa1, Ali Mohammad Alqudah2,*, Hiam Alquran3,2, Ahmed Al Issa4

    CMC-Computers, Materials & Continua, Vol.73, No.1, pp. 311-326, 2022, DOI:10.32604/cmc.2022.025750

    Abstract Pulmonary diseases are common throughout the world, especially in developing countries. These diseases include chronic obstructive pulmonary diseases, pneumonia, asthma, tuberculosis, fibrosis, and recently COVID-19. In general, pulmonary diseases have a similar footprint on chest radiographs which makes them difficult to discriminate even for expert radiologists. In recent years, many image processing techniques and artificial intelligence models have been developed to quickly and accurately diagnose lung diseases. In this paper, the performance of four popular pretrained models (namely VGG16, DenseNet201, DarkNet19, and XceptionNet) in distinguishing between different pulmonary diseases was analyzed. To the best of… More >

  • Open Access


    Robust Interactive Method for Hand Gestures Recognition Using Machine Learning

    Amal Abdullah Mohammed Alteaimi1,*, Mohamed Tahar Ben Othman1,2

    CMC-Computers, Materials & Continua, Vol.72, No.1, pp. 577-595, 2022, DOI:10.32604/cmc.2022.023591

    Abstract The Hand Gestures Recognition (HGR) System can be employed to facilitate communication between humans and computers instead of using special input and output devices. These devices may complicate communication with computers especially for people with disabilities. Hand gestures can be defined as a natural human-to-human communication method, which also can be used in human-computer interaction. Many researchers developed various techniques and methods that aimed to understand and recognize specific hand gestures by employing one or two machine learning algorithms with a reasonable accuracy. This work aims to develop a powerful hand gesture recognition model with… More >

  • Open Access


    Profiling Casualty Severity Levels of Road Accident Using Weighted Majority Voting

    Saba Awan1, Zahid Mehmood2,*, Hassan Nazeer Chaudhry3, Usman Tariq4, Amjad Rehman5, Tanzila Saba5, Muhammad Rashid6

    CMC-Computers, Materials & Continua, Vol.71, No.3, pp. 4609-4626, 2022, DOI:10.32604/cmc.2022.019404

    Abstract To determine the individual circumstances that account for a road traffic accident, it is crucial to consider the unplanned connections amongst various factors related to a crash that results in high casualty levels. Analysis of the road accident data concentrated mainly on categorizing accidents into different types using individually built classification methods which limit the prediction accuracy and fitness of the model. In this article, we proposed a multi-model hybrid framework of the weighted majority voting (WMV) scheme with parallel structure, which is designed by integrating individually implemented multinomial logistic regression (MLR) and multilayer perceptron… More >

  • Open Access


    Research on Copyright Protection Method of Material Genome Engineering Data Based on Zero-Watermarking

    Lulu Cui2,3,*, Yabin Xu1,2,3

    Journal on Big Data, Vol.2, No.2, pp. 53-62, 2020, DOI:10.32604/jbd.2020.010590

    Abstract In order to effectively solve the problem of copyright protection of materials genome engineering data, this paper proposes a method for copyright protection of materials genome engineering data based on zero-watermarking technology. First, the important attribute values are selected from the materials genome engineering database; then, use the method of remainder to group the selected attribute values and extract eigenvalues; then, the eigenvalues sequence is obtained by the majority election method; finally, XOR the sequence with the actual copyright information to obtain the watermarking information and store it in the third-party authentication center. When a More >

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