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  • Open Access


    Hybrid Dipper Throated and Grey Wolf Optimization for Feature Selection Applied to Life Benchmark Datasets

    Doaa Sami Khafaga1, El-Sayed M. El-kenawy2,3, Faten Khalid Karim1,*, Mostafa Abotaleb4, Abdelhameed Ibrahim5, Abdelaziz A. Abdelhamid6,7, D. L. Elsheweikh8

    CMC-Computers, Materials & Continua, Vol.74, No.2, pp. 4531-4545, 2023, DOI:10.32604/cmc.2023.033042

    Abstract Selecting the most relevant subset of features from a dataset is a vital step in data mining and machine learning. Each feature in a dataset has 2n possible subsets, making it challenging to select the optimum collection of features using typical methods. As a result, a new metaheuristics-based feature selection method based on the dipper-throated and grey-wolf optimization (DTO-GW) algorithms has been developed in this research. Instability can result when the selection of features is subject to metaheuristics, which can lead to a wide range of results. Thus, we adopted hybrid optimization in our method of… More >

  • Open Access


    Novel Optimized Feature Selection Using Metaheuristics Applied to Physical Benchmark Datasets

    Doaa Sami Khafaga1, El-Sayed M. El-kenawy2, Fadwa Alrowais1,*, Sunil Kumar3, Abdelhameed Ibrahim4, Abdelaziz A. Abdelhamid5,6

    CMC-Computers, Materials & Continua, Vol.74, No.2, pp. 4027-4041, 2023, DOI:10.32604/cmc.2023.033039

    Abstract In data mining and machine learning, feature selection is a critical part of the process of selecting the optimal subset of features based on the target data. There are 2n potential feature subsets for every n features in a dataset, making it difficult to pick the best set of features using standard approaches. Consequently, in this research, a new metaheuristics-based feature selection technique based on an adaptive squirrel search optimization algorithm (ASSOA) has been proposed. When using metaheuristics to pick features, it is common for the selection of features to vary across runs, which can lead… More >

  • Open Access


    Data Mining with Comprehensive Oppositional Based Learning for Rainfall Prediction

    Mohammad Alamgeer1, Amal Al-Rasheed2, Ahmad Alhindi3, Manar Ahmed Hamza4,*, Abdelwahed Motwakel4, Mohamed I. Eldesouki5

    CMC-Computers, Materials & Continua, Vol.74, No.2, pp. 2725-2738, 2023, DOI:10.32604/cmc.2023.029163

    Abstract Data mining process involves a number of steps from data collection to visualization to identify useful data from massive data set. the same time, the recent advances of machine learning (ML) and deep learning (DL) models can be utilized for effectual rainfall prediction. With this motivation, this article develops a novel comprehensive oppositional moth flame optimization with deep learning for rainfall prediction (COMFO-DLRP) Technique. The proposed CMFO-DLRP model mainly intends to predict the rainfall and thereby determine the environmental changes. Primarily, data pre-processing and correlation matrix (CM) based feature selection processes are carried out. In More >

  • Open Access


    Blockchain Driven Metaheuristic Route Planning in Secure Wireless Sensor Networks

    M. V. Rajesh1, T. Archana Acharya2, Hafis Hajiyev3, E. Laxmi Lydia4, Haya Mesfer Alshahrani5, Mohamed K Nour6, Abdullah Mohamed7, Mesfer Al Duhayyim8,*

    CMC-Computers, Materials & Continua, Vol.74, No.1, pp. 933-949, 2023, DOI:10.32604/cmc.2023.032549

    Abstract Recently, Internet of Things (IoT) has been developed into a field of research and it purposes at linking many sensors enabling devices mostly to data collection and track applications. Wireless sensor network (WSN) is a vital element of IoT paradigm since its inception and has developed into one of the chosen platforms for deploying many smart city application regions such as disaster management, intelligent transportation, home automation, smart buildings, and other such IoT-based application. The routing approaches were extremely-utilized energy efficient approaches with an initial drive that is, for balancing the energy amongst sensor nodes.… More >

  • Open Access


    Metaheuristics-based Clustering with Routing Technique for Lifetime Maximization in Vehicular Networks

    P. Muthukrishnan*, P. Muthu Kannan

    CMC-Computers, Materials & Continua, Vol.74, No.1, pp. 1107-1122, 2023, DOI:10.32604/cmc.2023.031962

    Abstract Recently, vehicular ad hoc networks (VANETs) finds applicability in different domains such as security, rescue operations, intelligent transportation systems (ITS), etc. VANET has unique features like high mobility, limited mobility patterns, adequate topology modifications, and wireless communication. Despite the benefits of VANET, scalability is a challenging issue which could be addressed by the use of cluster-based routing techniques. It enables the vehicles to perform intercluster communication via chosen CHs and optimal routes. The main drawback of VANET network is the network unsteadiness that results in minimum lifetime. In order to avoid reduced network lifetime in… More >

  • Open Access


    Malicious URL Classification Using Artificial Fish Swarm Optimization and Deep Learning

    Anwer Mustafa Hilal1,2,*, Aisha Hassan Abdalla Hashim1, Heba G. Mohamed3, Mohamed K. Nour4, Mashael M. Asiri5, Ali M. Al-Sharafi6, Mahmoud Othman7, Abdelwahed Motwakel2

    CMC-Computers, Materials & Continua, Vol.74, No.1, pp. 607-621, 2023, DOI:10.32604/cmc.2023.031371

    Abstract Cybersecurity-related solutions have become familiar since it ensures security and privacy against cyberattacks in this digital era. Malicious Uniform Resource Locators (URLs) can be embedded in email or Twitter and used to lure vulnerable internet users to implement malicious data in their systems. This may result in compromised security of the systems, scams, and other such cyberattacks. These attacks hijack huge quantities of the available data, incurring heavy financial loss. At the same time, Machine Learning (ML) and Deep Learning (DL) models paved the way for designing models that can detect malicious URLs accurately and… More >

  • Open Access


    Sailfish Optimizer with EfficientNet Model for Apple Leaf Disease Detection

    Mazen Mushabab Alqahtani1, Ashit Kumar Dutta2, Sultan Almotairi3, M. Ilayaraja4, Amani Abdulrahman Albraikan5, Fahd N. Al-Wesabi6,7,*, Mesfer Al Duhayyim8

    CMC-Computers, Materials & Continua, Vol.74, No.1, pp. 217-233, 2023, DOI:10.32604/cmc.2023.025280

    Abstract Recent developments in digital cameras and electronic gadgets coupled with Machine Learning (ML) and Deep Learning (DL)-based automated apple leaf disease detection models are commonly employed as reasonable alternatives to traditional visual inspection models. In this background, the current paper devises an Effective Sailfish Optimizer with EfficientNet-based Apple Leaf disease detection (ESFO-EALD) model. The goal of the proposed ESFO-EALD technique is to identify the occurrence of plant leaf diseases automatically. In this scenario, Median Filtering (MF) approach is utilized to boost the quality of apple plant leaf images. Moreover, SFO with Kapur's entropy-based segmentation technique More >

  • Open Access


    Optimal Deep Belief Network Enabled Malware Detection and Classification Model

    P. Pandi Chandran1,*, N. Hema Rajini2, M. Jeyakarthic3

    Intelligent Automation & Soft Computing, Vol.35, No.3, pp. 3349-3364, 2023, DOI:10.32604/iasc.2023.029946

    Abstract Cybercrime has increased considerably in recent times by creating new methods of stealing, changing, and destroying data in daily lives. Portable Document Format (PDF) has been traditionally utilized as a popular way of spreading malware. The recent advances of machine learning (ML) and deep learning (DL) models are utilized to detect and classify malware. With this motivation, this study focuses on the design of mayfly optimization with a deep belief network for PDF malware detection and classification (MFODBN-MDC) technique. The major intention of the MFODBN-MDC technique is for identifying and classifying the presence of malware… More >

  • Open Access


    Honey Badger Algorithm Based Clustering with Routing Protocol for Wireless Sensor Networks

    K. Arutchelvan1, R. Sathiya Priya1,*, C. Bhuvaneswari2

    Intelligent Automation & Soft Computing, Vol.35, No.3, pp. 3199-3212, 2023, DOI:10.32604/iasc.2023.029804

    Abstract Wireless sensor network (WSN) includes a set of self-organizing and homogenous nodes employed for data collection and tracking applications. It comprises a massive set of nodes with restricted energy and processing abilities. Energy dissipation is a major concern involved in the design of WSN. Clustering and routing protocols are considered effective ways to reduce the quantity of energy dissipation using metaheuristic algorithms. In order to design an energy aware cluster-based route planning scheme, this study introduces a novel Honey Badger Based Clustering with African Vulture Optimization based Routing (HBAC-AVOR) protocol for WSN. The presented HBAC-AVOR… More >

  • Open Access


    Improved Rat Swarm Based Multihop Routing Protocol for Wireless Sensor Networks

    H. Manikandan1,*, D. Narasimhan2

    Intelligent Automation & Soft Computing, Vol.35, No.3, pp. 2925-2939, 2023, DOI:10.32604/iasc.2023.029754

    Abstract Wireless sensor networks (WSNs) encompass a massive set of sensor nodes, which are self-configurable, inexpensive, and compact. The sensor nodes undergo random deployment in the target area and transmit data to base station using inbuilt transceiver. For reducing energy consumption and lengthen lifetime of WSN, multihop routing protocols can be designed. This study develops an improved rat swarm optimization based energy aware multi-hop routing (IRSO-EAMHR) protocol for WSN. An important intention of the IRSO-EAMHR method is for determining optimal routes to base station (BS) in the clustered WSN. Primarily, a weighted clustering process is performed More >

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