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  • Open Access

    ARTICLE

    A Modified Firefly Optimization Algorithm-Based Fuzzy Packet Scheduler for MANET

    Mercy Sharon Devadas1, N. Bhalaji1,*, Xiao-Zhi Gao2

    Intelligent Automation & Soft Computing, Vol.36, No.3, pp. 2685-2702, 2023, DOI:10.32604/iasc.2023.031636

    Abstract In Mobile ad hoc Networks (MANETs), the packet scheduling process is considered the major challenge because of error-prone connectivity among mobile nodes that introduces intolerable delay and insufficient throughput with high packet loss. In this paper, a Modified Firefly Optimization Algorithm improved Fuzzy Scheduler-based Packet Scheduling (MFPA-FSPS) Mechanism is proposed for sustaining Quality of Service (QoS) in the network. This MFPA-FSPS mechanism included a Fuzzy-based priority scheduler by inheriting the merits of the Sugeno Fuzzy inference system that potentially and adaptively estimated packets’ priority for guaranteeing optimal network performance. It further used the modified Firefly Optimization Algorithm to optimize the… More >

  • Open Access

    ARTICLE

    Firefly-CDDL: A Firefly-Based Algorithm for Cyberbullying Detection Based on Deep Learning

    Monirah Al-Ajlan*, Mourad Ykhlef

    CMC-Computers, Materials & Continua, Vol.75, No.1, pp. 19-34, 2023, DOI:10.32604/cmc.2023.033753

    Abstract There are several ethical issues that have arisen in recent years due to the ubiquity of the Internet and the popularity of social media and community platforms. Among them is cyberbullying, which is defined as any violent intentional action that is repeatedly conducted by individuals or groups using online channels against victims who are not able to react effectively. An alarmingly high percentage of people, especially teenagers, have reported being cyberbullied in recent years. A variety of approaches have been developed to detect cyberbullying, but they require time-consuming feature extraction and selection processes. Moreover, no approach to date has examined… More >

  • Open Access

    ARTICLE

    Swarm Optimization and Machine Learning for Android Malware Detection

    K. Santosh Jhansi1,2,*, P. Ravi Kiran Varma2, Sujata Chakravarty3

    CMC-Computers, Materials & Continua, Vol.73, No.3, pp. 6327-6345, 2022, DOI:10.32604/cmc.2022.030878

    Abstract Malware Security Intelligence constitutes the analysis of applications and their associated metadata for possible security threats. Application Programming Interfaces (API) calls contain valuable information that can help with malware identification. The malware analysis with reduced feature space helps for the efficient identification of malware. The goal of this research is to find the most informative features of API calls to improve the android malware detection accuracy. Three swarm optimization methods, viz., Ant Lion Optimization (ALO), Cuckoo Search Optimization (CSO), and Firefly Optimization (FO) are applied to API calls using auto-encoders for identification of most influential features. The nature-inspired wrapper-based algorithms… More >

  • Open Access

    ARTICLE

    Bio-inspired Hybrid Feature Selection Model for Intrusion Detection

    Adel Hamdan Mohammad1,*, Tariq Alwada’n2, Omar Almomani3, Sami Smadi3, Nidhal ElOmari4

    CMC-Computers, Materials & Continua, Vol.73, No.1, pp. 133-150, 2022, DOI:10.32604/cmc.2022.027475

    Abstract Intrusion detection is a serious and complex problem. Undoubtedly due to a large number of attacks around the world, the concept of intrusion detection has become very important. This research proposes a multilayer bio-inspired feature selection model for intrusion detection using an optimized genetic algorithm. Furthermore, the proposed multilayer model consists of two layers (layers 1 and 2). At layer 1, three algorithms are used for the feature selection. The algorithms used are Particle Swarm Optimization (PSO), Grey Wolf Optimization (GWO), and Firefly Optimization Algorithm (FFA). At the end of layer 1, a priority value will be assigned for each… More >

  • Open Access

    ARTICLE

    Hybrid Renewable Energy System Using Cuckoo Firefly Optimization

    M. E. Shajini Sheeba1,*, P. Jagatheeswari2

    Intelligent Automation & Soft Computing, Vol.34, No.2, pp. 1141-1156, 2022, DOI:10.32604/iasc.2022.024549

    Abstract With abundant and non-polluting benefits in nature, sources of renewable energy have reached vast concentrations. This paper first discusses the number of MPPT (Maximum Power Point Tracking) techniques utilized by wind and photovoltaic (PV) to create hybrid systems for generating wind-PV energy. This hybrid system complements each other day and night to enable continuous power output. Then, a new MPPT technique was proposed to extract maximum power using a newly developed hybrid optimization algorithm, namely the Cukoo Fire Fly method (CFF). The CFF algorithm is derived from the integration of the cuckoo search (CS) algorithm and the Firefly (FF) optimization… More >

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