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Search Results (22)
  • Open Access

    ARTICLE

    Comparative Analysis for Evaluating Wind Energy Resources Using Intelligent Optimization Algorithms and Numerical Methods

    Musaed Alrashidi*

    Computer Systems Science and Engineering, Vol.47, No.1, pp. 491-513, 2023, DOI:10.32604/csse.2023.038628 - 26 May 2023

    Abstract Statistical distributions are used to model wind speed, and the two-parameters Weibull distribution has proven its effectiveness at characterizing wind speed. Accurate estimation of Weibull parameters, the scale (c) and shape (k), is crucial in describing the actual wind speed data and evaluating the wind energy potential. Therefore, this study compares the most common conventional numerical (CN) estimation methods and the recent intelligent optimization algorithms (IOA) to show how precise estimation of c and k affects the wind energy resource assessments. In addition, this study conducts technical and economic feasibility studies for five sites in the northern… More >

  • Open Access

    ARTICLE

    Assessment of Different Optimization Algorithms for a Thermal Conduction Problem

    Mohammad Reza Hajmohammadi1, Javad Najafiyan1, Giulio Lorenzini2,*

    FDMP-Fluid Dynamics & Materials Processing, Vol.19, No.1, pp. 233-244, 2023, DOI:10.32604/fdmp.2023.019763 - 02 August 2022

    Abstract In this study, three computational approaches for the optimization of a thermal conduction problem are critically compared. These include a Direct Method (DM), a Genetic Algorithm (GA), and a Pattern Search (PS) technique. The optimization aims to minimize the maximum temperature of a hot medium (a medium with uniform heat generation) using a constant amount of high conductivity materials (playing the role of fixed factor constraining the considered problem). The principal goal of this paper is to determine the most efficient and fastest option among the considered ones. It is shown that the examined three 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 - 18 May 2022

    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… More >

  • Open Access

    ARTICLE

    Evolutionary Intelligence and Deep Learning Enabled Diabetic Retinopathy Classification Model

    Bassam A. Y. Alqaralleh1,*, Fahad Aldhaban1, Anas Abukaraki2, Esam A. AlQaralleh3

    CMC-Computers, Materials & Continua, Vol.73, No.1, pp. 87-101, 2022, DOI:10.32604/cmc.2022.026729 - 18 May 2022

    Abstract Diabetic Retinopathy (DR) has become a widespread illness among diabetics across the globe. Retinal fundus images are generally used by physicians to detect and classify the stages of DR. Since manual examination of DR images is a time-consuming process with the risks of biased results, automated tools using Artificial Intelligence (AI) to diagnose the disease have become essential. In this view, the current study develops an Optimal Deep Learning-enabled Fusion-based Diabetic Retinopathy Detection and Classification (ODL-FDRDC) technique. The intention of the proposed ODL-FDRDC technique is to identify DR and categorize its different grades using retinal More >

  • Open Access

    ARTICLE

    A Novel Binary Emperor Penguin Optimizer for Feature Selection Tasks

    Minakshi Kalra1, Vijay Kumar2, Manjit Kaur3, Sahar Ahmed Idris4, Şaban Öztürk5, Hammam Alshazly6,*

    CMC-Computers, Materials & Continua, Vol.70, No.3, pp. 6239-6255, 2022, DOI:10.32604/cmc.2022.020682 - 11 October 2021

    Abstract Nowadays, due to the increase in information resources, the number of parameters and complexity of feature vectors increases. Optimization methods offer more practical solutions instead of exact solutions for the solution of this problem. The Emperor Penguin Optimizer (EPO) is one of the highest performing meta-heuristic algorithms of recent times that imposed the gathering behavior of emperor penguins. It shows the superiority of its performance over a wide range of optimization problems thanks to its equal chance to each penguin and its fast convergence features. Although traditional EPO overcomes the optimization problems in continuous search… More >

  • Open Access

    ARTICLE

    Estimation of Locational Marginal Pricing Using Hybrid Optimization Algorithms

    M. Bhoopathi1,*, P. Palanivel2

    Intelligent Automation & Soft Computing, Vol.31, No.1, pp. 143-159, 2022, DOI:10.32604/iasc.2022.017705 - 03 September 2021

    Abstract At present, the restructured electricity market has been a prominent research area and attracted attention. The motivation of the restructuring in the power system is to introduce the competition at various levels and to generate a correct economic signal to reduce the generation cost. As a result, it is required to have an effective price scheme to deliver useful information about the power. The pricing mechanism is dependent on the demand at the load level, the generator bids, and the limits of the transmission network. To address the congestion charges, Locational Marginal Pricing (LMP) is… More >

  • Open Access

    ARTICLE

    Optimization of Sentiment Analysis Using Teaching-Learning Based Algorithm

    Abdullah Muhammad, Salwani Abdullah, Nor Samsiah Sani*

    CMC-Computers, Materials & Continua, Vol.69, No.2, pp. 1783-1799, 2021, DOI:10.32604/cmc.2021.018593 - 21 July 2021

    Abstract Feature selection and sentiment analysis are two common studies that are currently being conducted; consistent with the advancements in computing and growing the use of social media. High dimensional or large feature sets is a key issue in sentiment analysis as it can decrease the accuracy of sentiment classification and make it difficult to obtain the optimal subset of the features. Furthermore, most reviews from social media carry a lot of noise and irrelevant information. Therefore, this study proposes a new text-feature selection method that uses a combination of rough set theory (RST) and teaching-learning… More >

  • Open Access

    ARTICLE

    Optimizing Service Composition (SC) Using Smart Multistage Forward Search (SMFS)

    Issam Alhadid1, Hassan Tarawneh2, Khalid Kaabneh2, Ra’ed Masa’deh3, Nawaf N. Hamadneh4,*, Muhammad Tahir5, Sufian Khwaldeh1

    Intelligent Automation & Soft Computing, Vol.28, No.2, pp. 321-336, 2021, DOI:10.32604/iasc.2021.014892 - 01 April 2021

    Abstract

    Service Oriented Architecture (SOA) is a style of software design where Web Services (WS) provide services to the other components through a communication protocol over a network. WS components are managed, updated, and rearranged at runtime to provide the business processes as SCs, which consist of a set of WSs that can be invoked in a specific order to fulfill the clients’ requests. According to the Service Level Agreement (SLA) requirements, WS selection and composition are significant perspectives of research to meet the clients’ expectations. This paper presents an effective technique using SMFS that attempts to

    More >

  • Open Access

    ARTICLE

    A New Metaheuristic Optimization Algorithms for Brushless Direct Current Wheel Motor Design Problem

    M. Premkumar1, R. Sowmya2, Pradeep Jangir3, Kottakkaran Sooppy Nisar4,*, Mujahed Aldhaifallah5

    CMC-Computers, Materials & Continua, Vol.67, No.2, pp. 2227-2242, 2021, DOI:10.32604/cmc.2021.015565 - 05 February 2021

    Abstract The Equilibrium Optimizer (EO), Grey Wolf Optimizer (GWO), and Whale Optimizer (WO) algorithms are being recently developed for engineering optimization problems. In this paper, the EO, GWO, and WO algorithms are applied individually for a brushless direct current (BLDC) design optimization problem. The EO algorithm is inspired by the models utilized to find the system’s dynamic state and equilibrium state. The GWO and WO algorithms are inspired by the hunting behavior of the wolf and the whale, respectively. The primary purpose of any optimization technique is to find the optimal configuration by maximizing motor efficiency… More >

  • Open Access

    ARTICLE

    Global Levy Flight of Cuckoo Search with Particle Swarm Optimization for Effective Cluster Head Selection in Wireless Sensor Network

    Vijayalakshmi. K1,*, Anandan. P2

    Intelligent Automation & Soft Computing, Vol.26, No.2, pp. 303-311, 2020, DOI:10.31209/2020.100000165

    Abstract The advent of sensors that are light in weight, small-sized, low power and are enabled by wireless network has led to growth of Wireless Sensor Networks (WSNs) in multiple areas of applications. The key problems faced in WSNs are decreased network lifetime and time delay in transmission of data. Several key issues in the WSN design can be addressed using the Multi-Objective Optimization (MOO) Algorithms. The selection of the Cluster Head is a NP Hard optimization problem in nature. The CH selection is also challenging as the sensor nodes are organized in clusters. Through partitioning… More >

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