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  • Open Access

    ARTICLE

    Feature Selection Using Grey Wolf Optimization with Random Differential Grouping

    R. S. Latha1,*, B. Saravana Balaji2, Nebojsa Bacanin3, Ivana Strumberger3, Miodrag Zivkovic3, Milos Kabiljo3

    Computer Systems Science and Engineering, Vol.43, No.1, pp. 317-332, 2022, DOI:10.32604/csse.2022.020487 - 23 March 2022

    Abstract Big data are regarded as a tremendous technology for processing a huge variety of data in a short time and with a large storage capacity. The user’s access over the internet creates massive data processing over the internet. Big data require an intelligent feature selection model by addressing huge varieties of data. Traditional feature selection techniques are only applicable to simple data mining. Intelligent techniques are needed in big data processing and machine learning for an efficient classification. Major feature selection algorithms read the input features as they are. Then, the features are preprocessed and… More >

  • Open Access

    ARTICLE

    Dipper Throated Optimization Algorithm for Unconstrained Function and Feature Selection

    Ali E. Takieldeen1, El-Sayed M. El-kenawy1,2, Mohammed Hadwan3,4,5,*, Rokaia M. Zaki6,7

    CMC-Computers, Materials & Continua, Vol.72, No.1, pp. 1465-1481, 2022, DOI:10.32604/cmc.2022.026026 - 24 February 2022

    Abstract Dipper throated optimization (DTO) algorithm is a novel with a very efficient metaheuristic inspired by the dipper throated bird. DTO has its unique hunting technique by performing rapid bowing movements. To show the efficiency of the proposed algorithm, DTO is tested and compared to the algorithms of Particle Swarm Optimization (PSO), Whale Optimization Algorithm (WOA), Grey Wolf Optimizer (GWO), and Genetic Algorithm (GA) based on the seven unimodal benchmark functions. Then, ANOVA and Wilcoxon rank-sum tests are performed to confirm the effectiveness of the DTO compared to other optimization techniques. Additionally, to demonstrate the proposed More >

  • Open Access

    ARTICLE

    Malware Detection Using Decision Tree Based SVM Classifier for IoT

    Anwer Mustafa Hilal1,*, Siwar Ben Haj Hassine2, Souad Larabi-Marie-Sainte3, Nadhem Nemri2, Mohamed K. Nour4, Abdelwahed Motwakel1, Abu Sarwar Zamani1, Mesfer Al Duhayyim5

    CMC-Computers, Materials & Continua, Vol.72, No.1, pp. 713-726, 2022, DOI:10.32604/cmc.2022.024501 - 24 February 2022

    Abstract The development in Information and Communication Technology has led to the evolution of new computing and communication environment. Technological revolution with Internet of Things (IoTs) has developed various applications in almost all domains from health care, education to entertainment with sensors and smart devices. One of the subsets of IoT is Internet of Medical things (IoMT) which connects medical devices, hardware and software applications through internet. IoMT enables secure wireless communication over the Internet to allow efficient analysis of medical data. With these smart advancements and exploitation of smart IoT devices in health care technology… More >

  • Open Access

    ARTICLE

    Anomaly Detection for Internet of Things Cyberattacks

    Manal Alanazi*, Ahamed Aljuhani

    CMC-Computers, Materials & Continua, Vol.72, No.1, pp. 261-279, 2022, DOI:10.32604/cmc.2022.024496 - 24 February 2022

    Abstract The Internet of Things (IoT) has been deployed in diverse critical sectors with the aim of improving quality of service and facilitating human lives. The IoT revolution has redefined digital services in different domains by improving efficiency, productivity, and cost-effectiveness. Many service providers have adapted IoT systems or plan to integrate them as integral parts of their systems’ operation; however, IoT security issues remain a significant challenge. To minimize the risk of cyberattacks on IoT networks, anomaly detection based on machine learning can be an effective security solution to overcome a wide range of IoT… More >

  • Open Access

    ARTICLE

    A Novel Framework for Windows Malware Detection Using a Deep Learning Approach

    Abdulbasit A. Darem*

    CMC-Computers, Materials & Continua, Vol.72, No.1, pp. 461-479, 2022, DOI:10.32604/cmc.2022.023566 - 24 February 2022

    Abstract Malicious software (malware) is one of the main cyber threats that organizations and Internet users are currently facing. Malware is a software code developed by cybercriminals for damage purposes, such as corrupting the system and data as well as stealing sensitive data. The damage caused by malware is substantially increasing every day. There is a need to detect malware efficiently and automatically and remove threats quickly from the systems. Although there are various approaches to tackle malware problems, their prevalence and stealthiness necessitate an effective method for the detection and prevention of malware attacks. The More >

  • Open Access

    ARTICLE

    Real Time Brain Tumor Prediction Using Adaptive Neuro Fuzzy Technique

    Duraimurugan Nagendiran1,*, S. P. Chokkalingam2

    Intelligent Automation & Soft Computing, Vol.33, No.2, pp. 983-996, 2022, DOI:10.32604/iasc.2022.023982 - 08 February 2022

    Abstract Uncontrollable growth of cells may lead to brain tumors and may cause permanent damages to the brain or even death. To make early diagnosis and treatment, identifying the position and size of tumors is identified as a tedious and troublesome problem among the existing computer-aided diagnosis systems. Moreover, the progression of tumors may vary among the patients with respect to shape, location, and volume. Therefore, to effectively classify and diagnose the brain tumor images according to severity stages follows the sequence of processing such as pre-processing, segmentation, feature extraction, and classification techniques to carrying out More >

  • Open Access

    ARTICLE

    Identification of Bio-Markers for Cancer Classification Using Ensemble Approach and Genetic Algorithm

    K. Poongodi1,*, A. Sabari2

    Intelligent Automation & Soft Computing, Vol.33, No.2, pp. 939-953, 2022, DOI:10.32604/iasc.2022.023038 - 08 February 2022

    Abstract The microarray gene expression data has a large number of genes with different expression levels. Analyzing and classifying datasets with entire gene space is quite difficult because there are only a few genes that are informative. The identification of bio-marker genes is significant because it improves the diagnosis of cancer disease and personalized medicine is suggested accordingly. Initially, the parallelized minimum redundancy and maximum relevance ensemble (mRMRe) is employed to select top m informative genes. The selected genes are then fed into the Genetic Algorithm (GA) that selects the optimal set of genes heuristically, which More >

  • Open Access

    ARTICLE

    Feature Selection Based on IoT Aware QDA Node Authentication in 5G Networks

    M. P. Haripriya*, P. Venkadesh

    Intelligent Automation & Soft Computing, Vol.33, No.2, pp. 825-836, 2022, DOI:10.32604/iasc.2022.022940 - 08 February 2022

    Abstract The coming generation in mobile networks is the fifth generation (5G), which appears to be the promoter of the upcoming digital world. 5G is defined by a single piece of cellular access technology or a combination of advanced access technologies. Rather, 5G is a true network assembler that provides consistent support for a slew of novel network topologies. Prior generations provide as a suitable starting point and give support for the security architecture for 5G security. Through authentication and cryptography techniques, many works have tackled the security issues in 3G and 4G networks in an… More >

  • Open Access

    ARTICLE

    Mathematical Modelling of Quantum Kernel Method for Biomedical Data Analysis

    Mahmoud Ragab1,2,3, Ehab Bahauden Ashary4, Maha Farouk S. Sabir5, Adel A. Bahaddad5, Romany F. Mansour6,*

    CMC-Computers, Materials & Continua, Vol.71, No.3, pp. 5441-5457, 2022, DOI:10.32604/cmc.2022.024545 - 14 January 2022

    Abstract This study presents a novel method to detect the medical application based on Quantum Computing (QC) and a few Machine Learning (ML) systems. QC has a primary advantage i.e., it uses the impact of quantum parallelism to provide the consequences of prime factorization issue in a matter of seconds. So, this model is suggested for medical application only by recent researchers. A novel strategy i.e., Quantum Kernel Method (QKM) is proposed in this paper for data prediction. In this QKM process, Linear Tunicate Swarm Algorithm (LTSA), the optimization technique is used to calculate the loss… More >

  • Open Access

    ARTICLE

    Metaheuristic Optimization Algorithm for Signals Classification of Electroencephalography Channels

    Marwa M. Eid1,*, Fawaz Alassery2, Abdelhameed Ibrahim3, Mohamed Saber4

    CMC-Computers, Materials & Continua, Vol.71, No.3, pp. 4627-4641, 2022, DOI:10.32604/cmc.2022.024043 - 14 January 2022

    Abstract Digital signal processing of electroencephalography (EEG) data is now widely utilized in various applications, including motor imagery classification, seizure detection and prediction, emotion classification, mental task classification, drug impact identification and sleep state classification. With the increasing number of recorded EEG channels, it has become clear that effective channel selection algorithms are required for various applications. Guided Whale Optimization Method (Guided WOA), a suggested feature selection algorithm based on Stochastic Fractal Search (SFS) technique, evaluates the chosen subset of channels. This may be used to select the optimum EEG channels for use in Brain-Computer Interfaces More >

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