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  • Open Access

    ARTICLE

    Covid-19 Detection Using Deep Correlation-Grey Wolf Optimizer

    K. S. Bhuvaneshwari1, Ahmed Najat Ahmed2, Mehedi Masud3, Samah H. Alajmani4, Mohamed Abouhawwash5,6,*

    Computer Systems Science and Engineering, Vol.46, No.3, pp. 2933-2945, 2023, DOI:10.32604/csse.2023.034288 - 03 April 2023

    Abstract The immediate and quick spread of the coronavirus has become a life-threatening disease around the globe. The widespread illness has dramatically changed almost all sectors, moving from offline to online, resulting in a new normal lifestyle for people. The impact of coronavirus is tremendous in the healthcare sector, which has experienced a decline in the first quarter of 2020. This pandemic has created an urge to use computer-aided diagnosis techniques for classifying the Covid-19 dataset to reduce the burden of clinical results. The current situation motivated me to choose correlation-based development called correlation-based grey wolf… More >

  • Open Access

    ARTICLE

    Learning-Based Artificial Algae Algorithm with Optimal Machine Learning Enabled Malware Detection

    Khaled M. Alalayah1, Fatma S. Alrayes2, Mohamed K. Nour3, Khadija M. Alaidarous1, Ibrahim M. Alwayle1, Heba Mohsen4, Ibrahim Abdulrab Ahmed5, Mesfer Al Duhayyim6,*

    Computer Systems Science and Engineering, Vol.46, No.3, pp. 3103-3119, 2023, DOI:10.32604/csse.2023.034034 - 03 April 2023

    Abstract Malware is a ‘malicious software program that performs multiple cyberattacks on the Internet, involving fraud, scams, nation-state cyberwar, and cybercrime. Such malicious software programs come under different classifications, namely Trojans, viruses, spyware, worms, ransomware, Rootkit, botnet malware, etc. Ransomware is a kind of malware that holds the victim’s data hostage by encrypting the information on the user’s computer to make it inaccessible to users and only decrypting it; then, the user pays a ransom procedure of a sum of money. To prevent detection, various forms of ransomware utilize more than one mechanism in their attack… More >

  • Open Access

    ARTICLE

    Automatic Sentimental Analysis by Firefly with Levy and Multilayer Perceptron

    D. Elangovan1,*, V. Subedha2

    Computer Systems Science and Engineering, Vol.46, No.3, pp. 2797-2808, 2023, DOI:10.32604/csse.2023.031988 - 03 April 2023

    Abstract The field of sentiment analysis (SA) has grown in tandem with the aid of social networking platforms to exchange opinions and ideas. Many people share their views and ideas around the world through social media like Facebook and Twitter. The goal of opinion mining, commonly referred to as sentiment analysis, is to categorise and forecast a target’s opinion. Depending on if they provide a positive or negative perspective on a given topic, text documents or sentences can be classified. When compared to sentiment analysis, text categorization may appear to be a simple process, but number… More >

  • Open Access

    ARTICLE

    DDoS Attack Detection in Cloud Computing Based on Ensemble Feature Selection and Deep Learning

    Yousef Sanjalawe1,2,*, Turke Althobaiti3,4

    CMC-Computers, Materials & Continua, Vol.75, No.2, pp. 3571-3588, 2023, DOI:10.32604/cmc.2023.037386 - 31 March 2023

    Abstract Intrusion Detection System (IDS) in the cloud Computing (CC) environment has received paramount interest over the last few years. Among the latest approaches, Deep Learning (DL)-based IDS methods allow the discovery of attacks with the highest performance. In the CC environment, Distributed Denial of Service (DDoS) attacks are widespread. The cloud services will be rendered unavailable to legitimate end-users as a consequence of the overwhelming network traffic, resulting in financial losses. Although various researchers have proposed many detection techniques, there are possible obstacles in terms of detection performance due to the use of insignificant traffic… More >

  • Open Access

    ARTICLE

    Feature Selection with Deep Belief Network for Epileptic Seizure Detection on EEG Signals

    Srikanth Cherukuvada, R. Kayalvizhi*

    CMC-Computers, Materials & Continua, Vol.75, No.2, pp. 4101-4118, 2023, DOI:10.32604/cmc.2023.036207 - 31 March 2023

    Abstract The term Epilepsy refers to a most commonly occurring brain disorder after a migraine. Early identification of incoming seizures significantly impacts the lives of people with Epilepsy. Automated detection of epileptic seizures (ES) has dramatically improved the life quality of the patients. Recent Electroencephalogram (EEG) related seizure detection mechanisms encountered several difficulties in real-time. The EEGs are the non-stationary signal, and seizure patterns would change with patients and recording sessions. Further, EEG data were disposed to wide noise varieties that adversely moved the recognition accuracy of ESs. Artificial intelligence (AI) methods in the domain of… More >

  • Open Access

    ARTICLE

    Grey Wolf-Based Method for an Implicit Authentication of Smartphone Users

    Abdulwahab Ali Almazroi, Mohamed Meselhy Eltoukhy*

    CMC-Computers, Materials & Continua, Vol.75, No.2, pp. 3729-3741, 2023, DOI:10.32604/cmc.2023.036020 - 31 March 2023

    Abstract Smartphones have now become an integral part of our everyday lives. User authentication on smartphones is often accomplished by mechanisms (like face unlock, pattern, or pin password) that authenticate the user’s identity. These technologies are simple, inexpensive, and fast for repeated logins. However, these technologies are still subject to assaults like smudge assaults and shoulder surfing. Users’ touch behavior while using their cell phones might be used to authenticate them, which would solve the problem. The performance of the authentication process may be influenced by the attributes chosen (from these behaviors). The purpose of this… More >

  • Open Access

    ARTICLE

    Predicting Dementia Risk Factors Based on Feature Selection and Neural Networks

    Ashir Javeed1,2, Ana Luiza Dallora2, Johan Sanmartin Berglund2,*, Arif Ali4, Peter Anderberg2,3, Liaqat Ali5

    CMC-Computers, Materials & Continua, Vol.75, No.2, pp. 2491-2508, 2023, DOI:10.32604/cmc.2023.033783 - 31 March 2023

    Abstract Dementia is a disorder with high societal impact and severe consequences for its patients who suffer from a progressive cognitive decline that leads to increased morbidity, mortality, and disabilities. Since there is a consensus that dementia is a multifactorial disorder, which portrays changes in the brain of the affected individual as early as 15 years before its onset, prediction models that aim at its early detection and risk identification should consider these characteristics. This study aims at presenting a novel method for ten years prediction of dementia using on multifactorial data, which comprised 75 variables.… More >

  • Open Access

    ARTICLE

    Bayesian Deep Learning Enabled Sentiment Analysis on Web Intelligence Applications

    Abeer D. Algarni*

    CMC-Computers, Materials & Continua, Vol.75, No.2, pp. 3399-3412, 2023, DOI:10.32604/cmc.2023.026687 - 31 March 2023

    Abstract In recent times, web intelligence (WI) has become a hot research topic, which utilizes Artificial Intelligence (AI) and advanced information technologies on the Web and Internet. The users post reviews on social media and are employed for sentiment analysis (SA), which acts as feedback to business people and government. Proper SA on the reviews helps to enhance the quality of the services and products, however, web intelligence techniques are needed to raise the company profit and user fulfillment. With this motivation, this article introduces a new modified pigeon inspired optimization based feature selection (MPIO-FS) with… More >

  • Open Access

    ARTICLE

    Performance Analysis of Intrusion Detection System in the IoT Environment Using Feature Selection Technique

    Moody Alhanaya, Khalil Hamdi Ateyeh Al-Shqeerat*

    Intelligent Automation & Soft Computing, Vol.36, No.3, pp. 3709-3724, 2023, DOI:10.32604/iasc.2023.036856 - 15 March 2023

    Abstract The increasing number of security holes in the Internet of Things (IoT) networks creates a question about the reliability of existing network intrusion detection systems. This problem has led to the developing of a research area focused on improving network-based intrusion detection system (NIDS) technologies. According to the analysis of different businesses, most researchers focus on improving the classification results of NIDS datasets by combining machine learning and feature reduction techniques. However, these techniques are not suitable for every type of network. In light of this, whether the optimal algorithm and feature reduction techniques can… More >

  • Open Access

    ARTICLE

    Classifying Big Medical Data through Bootstrap Decision Forest Using Penalizing Attributes

    V. Gowri1,*, V. Vijaya Chamundeeswari2

    Intelligent Automation & Soft Computing, Vol.36, No.3, pp. 3675-3690, 2023, DOI:10.32604/iasc.2023.035817 - 15 March 2023

    Abstract Decision forest is a well-renowned machine learning technique to address the detection and prediction problems related to clinical data. But, the traditional decision forest (DF) algorithms have lower classification accuracy and cannot handle high-dimensional feature space effectively. In this work, we propose a bootstrap decision forest using penalizing attributes (BFPA) algorithm to predict heart disease with higher accuracy. This work integrates a significance-based attribute selection (SAS) algorithm with the BFPA classifier to improve the performance of the diagnostic system in identifying cardiac illness. The proposed SAS algorithm is used to determine the correlation among attributes… More >

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