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  • Open Access

    ARTICLE

    Hybrid Cuckoo Search Algorithm for Scheduling in Cloud Computing

    Manoj Kumar*, Suman

    CMC-Computers, Materials & Continua, Vol.71, No.1, pp. 1641-1660, 2022, DOI:10.32604/cmc.2022.021793 - 03 November 2021

    Abstract Cloud computing has gained widespread popularity over the last decade. Scheduling problem in cloud computing is prejudiced due to enormous demands of cloud users. Meta-heuristic techniques in cloud computing have exhibited high performance in comparison to traditional scheduling algorithms. This paper presents a novel hybrid Nesterov Accelerated Gradient-based Cuckoo Search Algorithm (NAGCSA) to address the scheduling issue in cloud computing. Nesterov Accelerated Gradient can address trapping at local minima in CSA by updating the position using future approximation. The local search in the proposed algorithm is performed by using Nesterov Accelerated Gradient, while the global More >

  • Open Access

    ARTICLE

    Fuzzy Based Hybrid Focus Value Estimation for Multi Focus Image Fusion

    Muhammad Ahmad1,*, M. Arfan Jaffar1, Fawad Nasim1, Tehreem Masood1, Sheeraz Akram2

    CMC-Computers, Materials & Continua, Vol.71, No.1, pp. 735-752, 2022, DOI:10.32604/cmc.2022.019691 - 03 November 2021

    Abstract

    Due to limited depth-of-field of digital single-lens reflex cameras, the scene content within a limited distance from the imaging plane remains in focus while other objects closer to or further away from the point of focus appear as blurred (out-of-focus) in the image. Multi-Focus Image Fusion can be used to reconstruct a fully focused image from two or more partially focused images of the same scene. In this paper, a new Fuzzy Based Hybrid Focus Measure (FBHFM) for multi-focus image fusion has been proposed. Optimal block size is very critical step for multi-focus image fusion.

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  • Open Access

    ARTICLE

    Optimized U-Net Segmentation and Hybrid Res-Net for Brain Tumor MRI Images Classification

    R. Rajaragavi1,*, S. Palanivel Rajan2

    Intelligent Automation & Soft Computing, Vol.32, No.1, pp. 1-14, 2022, DOI:10.32604/iasc.2022.021206 - 26 October 2021

    Abstract A brain tumor is a portion of uneven cells, need to be detected earlier for treatment. Magnetic Resonance Imaging (MRI) is a routinely utilized procedure to take brain tumor images. Manual segmentation of tumor is a crucial task and laborious. There is a need for an automated system for segmentation and classification for tumor surgery and medical treatments. This work suggests an efficient brain tumor segmentation and classification based on deep learning techniques. Initially, Squirrel search optimized bidirectional ConvLSTM U-net with attention gate proposed for brain tumour segmentation. Then, the Hybrid Deep ResNet and Inception More >

  • Open Access

    ARTICLE

    Performance Analysis of Photovoltaic Systems and Energy Return on the Environment Economy

    Murad A. A. Almekhlafi1, Fahd N. Al-Wesabi2,3, Anwer Mustafa Hilal4, Manar Ahmed Hamza4,*, Abdelzahir Abdelmaboud5, Mohammed Rizwanullah4

    Intelligent Automation & Soft Computing, Vol.32, No.1, pp. 557-571, 2022, DOI:10.32604/iasc.2022.020576 - 26 October 2021

    Abstract Using fossil fuels and non-renewable forms of energy has many adverse effects on the global ecosystem, and global demand exceeds the limited available resources. Renewable energy is one of the essential elements of social and economic development in any civilized country. This study comprises a feasibility study of the implementation of PV systems in a hybrid diesel network and analyzes the relationship between the effective uses of photovoltaic systems, the return of energy to the environment, and that country’s national economy. As a potential solution for the public and private utilities, the sunny web design More >

  • Open Access

    ARTICLE

    Hybrid GLFIL Enhancement and Encoder Animal Migration Classification for Breast Cancer Detection

    S. Prakash1,*, M. Vinoth Kumar2, R. Saravana Ram3, Miodrag Zivkovic4, Nebojsa Bacanin4, Milos Antonijevic4

    Computer Systems Science and Engineering, Vol.41, No.2, pp. 735-749, 2022, DOI:10.32604/csse.2022.020533 - 25 October 2021

    Abstract Breast cancer has become the second leading cause of death among women worldwide. In India, a woman is diagnosed with breast cancer every four minutes. There has been no known basis behind it, and detection is extremely challenging among medical scientists and researchers due to unknown reasons. In India, the ratio of women being identified with breast cancer in urban areas is 22:1. Symptoms for this disease are micro calcification, lumps, and masses in mammogram images. These sources are mostly used for early detection. Digital mammography is used for breast cancer detection. In this study,… More >

  • Open Access

    ARTICLE

    Hybrid Metaheuristics Web Service Composition Model for QoS Aware Services

    P. Rajeswari*, K. Jayashree

    Computer Systems Science and Engineering, Vol.41, No.2, pp. 511-524, 2022, DOI:10.32604/csse.2022.020352 - 25 October 2021

    Abstract Recent advancements in cloud computing (CC) technologies signified that several distinct web services are presently developed and exist at the cloud data centre. Currently, web service composition gains maximum attention among researchers due to its significance in real-time applications. Quality of Service (QoS) aware service composition concerned regarding the election of candidate services with the maximization of the whole QoS. But these models have failed to handle the uncertainties of QoS. The resulting QoS of composite service identified by the clients become unstable and subject to risks of failing composition by end-users. On the other… More >

  • Open Access

    ARTICLE

    A Hybrid Deep Learning-Based Unsupervised Anomaly Detection in High Dimensional Data

    Amgad Muneer1,2,*, Shakirah Mohd Taib1,2, Suliman Mohamed Fati3, Abdullateef O. Balogun1, Izzatdin Abdul Aziz1,2

    CMC-Computers, Materials & Continua, Vol.70, No.3, pp. 5363-5381, 2022, DOI:10.32604/cmc.2022.021113 - 11 October 2021

    Abstract Anomaly detection in high dimensional data is a critical research issue with serious implication in the real-world problems. Many issues in this field still unsolved, so several modern anomaly detection methods struggle to maintain adequate accuracy due to the highly descriptive nature of big data. Such a phenomenon is referred to as the “curse of dimensionality” that affects traditional techniques in terms of both accuracy and performance. Thus, this research proposed a hybrid model based on Deep Autoencoder Neural Network (DANN) with five layers to reduce the difference between the input and output. The proposed… More >

  • Open Access

    ARTICLE

    Hybrid Sensor Selection Technique for Lifetime Extension of Wireless Sensor Networks

    Khaled M. Fouad1 , Basma M. Hassan2,*, Omar M. Salim3

    CMC-Computers, Materials & Continua, Vol.70, No.3, pp. 4965-4985, 2022, DOI:10.32604/cmc.2022.020926 - 11 October 2021

    Abstract

    Energy conservation is a crucial issue to extend the lifetime of wireless sensor networks (WSNs) where the battery capacity and energy sources are very restricted. Intelligent energy-saving techniques can help designers overcome this issue by reducing the number of selected sensors that report environmental measurements by eliminating all replicated and unrelated features. This paper suggests a Hybrid Sensor Selection (HSS) technique that combines filter-wrapper method to acquire a rich-informational subset of sensors in a reasonable time. HSS aims to increase the lifetime of WSNs by using the optimal number of sensors. At the same time, HSS

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  • Open Access

    ARTICLE

    Handover Mechanism Based on Underwater Hybrid Software-Defined Modem in Advanced Diver Networks

    K. M. Delphin Raj1, Sun-Ho Yum1, Jinyoung Lee2, Eunbi Ko3, Soo-Yong Shin2, Soo-Hyun Park3,*

    CMC-Computers, Materials & Continua, Vol.70, No.3, pp. 5721-5743, 2022, DOI:10.32604/cmc.2022.020870 - 11 October 2021

    Abstract For the past few decades, the internet of underwater things (IoUT) obtained a lot of attention in mobile aquatic applications such as oceanography, diver network monitoring, unmanned underwater exploration, underwater surveillance, location tracking system, etc. Most of the IoUT applications rely on acoustic medium. The current IoUT applications face difficulty in delivering a reliable communication system due to the various technical limitations of IoUT environment such as low data rate, attenuation, limited bandwidth, limited battery, limited memory, connectivity problem, etc. One of the significant applications of IoUT include monitoring underwater diver networks. In order to… More >

  • Open Access

    ARTICLE

    A Hybrid Model for Reliability Aware and Energy-Efficiency in Multicore Systems

    Samar Nour1,*, Sameh A. Salem1,2, Shahira M. Habashy1

    CMC-Computers, Materials & Continua, Vol.70, No.3, pp. 4447-4466, 2022, DOI:10.32604/cmc.2022.020775 - 11 October 2021

    Abstract Recently, Multicore systems use Dynamic Voltage/Frequency Scaling (DV/FS) technology to allow the cores to operate with various voltage and/or frequencies than other cores to save power and enhance the performance. In this paper, an effective and reliable hybrid model to reduce the energy and makespan in multicore systems is proposed. The proposed hybrid model enhances and integrates the greedy approach with dynamic programming to achieve optimal Voltage/Frequency (Vmin/F) levels. Then, the allocation process is applied based on the available workloads. The hybrid model consists of three stages. The first stage gets the optimum safe voltage More >

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