Home / Advanced Search

  • Title/Keywords

  • Author/Affliations

  • Journal

  • Article Type

  • Start Year

  • End Year

Update SearchingClear
  • Articles
  • Online
Search Results (272)
  • Open Access

    ARTICLE

    Rao Algorithms-Based Structure Optimization for Heterogeneous Wireless Sensor Networks

    Shereen K. Refaay1, Samia A. Ali2, Moumen T. El-Melegy2, Louai A. Maghrabi3, Hamdy H. El-Sayed1,*

    CMC-Computers, Materials & Continua, Vol.78, No.1, pp. 873-897, 2024, DOI:10.32604/cmc.2023.044982

    Abstract The structural optimization of wireless sensor networks is a critical issue because it impacts energy consumption and hence the network’s lifetime. Many studies have been conducted for homogeneous networks, but few have been performed for heterogeneous wireless sensor networks. This paper utilizes Rao algorithms to optimize the structure of heterogeneous wireless sensor networks according to node locations and their initial energies. The proposed algorithms lack algorithm-specific parameters and metaphorical connotations. The proposed algorithms examine the search space based on the relations of the population with the best, worst, and randomly assigned solutions. The proposed algorithms can be evaluated using any… More >

  • Open Access

    ARTICLE

    Intelligent Solution System for Cloud Security Based on Equity Distribution: Model and Algorithms

    Sarah Mustafa Eljack1,*, Mahdi Jemmali2,3,4, Mohsen Denden6,7, Mutasim Al Sadig1, Abdullah M. Algashami1, Sadok Turki5

    CMC-Computers, Materials & Continua, Vol.78, No.1, pp. 1461-1479, 2024, DOI:10.32604/cmc.2023.040919

    Abstract In the cloud environment, ensuring a high level of data security is in high demand. Data planning storage optimization is part of the whole security process in the cloud environment. It enables data security by avoiding the risk of data loss and data overlapping. The development of data flow scheduling approaches in the cloud environment taking security parameters into account is insufficient. In our work, we propose a data scheduling model for the cloud environment. The model is made up of three parts that together help dispatch user data flow to the appropriate cloud VMs. The first component is the… More >

  • Open Access

    ARTICLE

    Evaluating the Efficacy of Latent Variables in Mitigating Data Poisoning Attacks in the Context of Bayesian Networks: An Empirical Study

    Shahad Alzahrani1, Hatim Alsuwat2, Emad Alsuwat3,*

    CMES-Computer Modeling in Engineering & Sciences, Vol.139, No.2, pp. 1635-1654, 2024, DOI:10.32604/cmes.2023.044718

    Abstract Bayesian networks are a powerful class of graphical decision models used to represent causal relationships among variables. However, the reliability and integrity of learned Bayesian network models are highly dependent on the quality of incoming data streams. One of the primary challenges with Bayesian networks is their vulnerability to adversarial data poisoning attacks, wherein malicious data is injected into the training dataset to negatively influence the Bayesian network models and impair their performance. In this research paper, we propose an efficient framework for detecting data poisoning attacks against Bayesian network structure learning algorithms. Our framework utilizes latent variables to quantify… More >

  • Open Access

    ARTICLE

    Highly Accurate Golden Section Search Algorithms and Fictitious Time Integration Method for Solving Nonlinear Eigenvalue Problems

    Chein-Shan Liu1, Jian-Hung Shen2, Chung-Lun Kuo1, Yung-Wei Chen2,*

    CMES-Computer Modeling in Engineering & Sciences, Vol.139, No.2, pp. 1317-1335, 2024, DOI:10.32604/cmes.2023.030618

    Abstract This study sets up two new merit functions, which are minimized for the detection of real eigenvalue and complex eigenvalue to address nonlinear eigenvalue problems. For each eigen-parameter the vector variable is solved from a nonhomogeneous linear system obtained by reducing the number of eigen-equation one less, where one of the nonzero components of the eigenvector is normalized to the unit and moves the column containing that component to the right-hand side as a nonzero input vector. 1D and 2D golden section search algorithms are employed to minimize the merit functions to locate real and complex eigenvalues. Simultaneously, the real… More >

  • Open Access

    ARTICLE

    A Work Review on Clinical Laboratory Data Utilizing Machine Learning Use-Case Methodology

    Uma Ramasamy*, Sundar Santhoshkumar

    Journal of Intelligent Medicine and Healthcare, Vol.2, pp. 1-14, 2024, DOI:10.32604/jimh.2023.046995

    Abstract More than 140 autoimmune diseases have distinct autoantibodies and symptoms, and it makes it challenging to construct an appropriate model using Machine Learning (ML) for autoimmune disease. Arthritis-related autoimmunity requires special attention. Although many conventional biomarkers for arthritis have been established, more biomarkers of arthritis autoimmune diseases remain to be identified. This review focuses on the research conducted using data obtained from clinical laboratory testing of real-time arthritis patients. The collected data is labelled the Arthritis Profile Data (APD) dataset. The APD dataset is the retrospective data with many missing values. We undertook a comprehensive APD dataset study comprising four… More >

  • Open Access

    ARTICLE

    Optimization Algorithms of PERT/CPM Network Diagrams in Linear Diophantine Fuzzy Environment

    Mani Parimala1, Karthikeyan Prakash1, Ashraf Al-Quran2,*, Muhammad Riaz3, Saeid Jafari4

    CMES-Computer Modeling in Engineering & Sciences, Vol.139, No.1, pp. 1095-1118, 2024, DOI:10.32604/cmes.2023.031193

    Abstract The idea of linear Diophantine fuzzy set (LDFS) theory with its control parameters is a strong model for machine learning and optimization under uncertainty. The activity times in the critical path method (CPM) representation procedures approach are initially static, but in the Project Evaluation and Review Technique (PERT) approach, they are probabilistic. This study proposes a novel way of project review and assessment methodology for a project network in a linear Diophantine fuzzy (LDF) environment. The LDF expected task time, LDF variance, LDF critical path, and LDF total expected time for determining the project network are all computed using LDF… More > Graphic Abstract

    Optimization Algorithms of PERT/CPM Network Diagrams in Linear Diophantine Fuzzy Environment

  • Open Access

    ARTICLE

    A Comparative Study of Metaheuristic Optimization Algorithms for Solving Real-World Engineering Design Problems

    Elif Varol Altay, Osman Altay, Yusuf Özçevik*

    CMES-Computer Modeling in Engineering & Sciences, Vol.139, No.1, pp. 1039-1094, 2024, DOI:10.32604/cmes.2023.029404

    Abstract Real-world engineering design problems with complex objective functions under some constraints are relatively difficult problems to solve. Such design problems are widely experienced in many engineering fields, such as industry, automotive, construction, machinery, and interdisciplinary research. However, there are established optimization techniques that have shown effectiveness in addressing these types of issues. This research paper gives a comparative study of the implementation of seventeen new metaheuristic methods in order to optimize twelve distinct engineering design issues. The algorithms used in the study are listed as: transient search optimization (TSO), equilibrium optimizer (EO), grey wolf optimizer (GWO), moth-flame optimization (MFO), whale… More >

  • Open Access

    REVIEW

    Multi-Robot Privacy-Preserving Algorithms Based on Federated Learning: A Review

    Jiansheng Peng1,2,*, Jinsong Guo1, Fengbo Bao1, Chengjun Yang2, Yong Xu2, Yong Qin2

    CMC-Computers, Materials & Continua, Vol.77, No.3, pp. 2971-2994, 2023, DOI:10.32604/cmc.2023.041897

    Abstract The robotics industry has seen rapid development in recent years due to the Corona Virus Disease 2019. With the development of sensors and smart devices, factories and enterprises have accumulated a large amount of data in their daily production, which creates extremely favorable conditions for robots to perform machine learning. However, in recent years, people’s awareness of data privacy has been increasing, leading to the inability to circulate data between different enterprises, resulting in the emergence of data silos. The emergence of federated learning provides a feasible solution to this problem, and the combination of federated learning and multi-robot systems… More >

  • Open Access

    ARTICLE

    Optimizing Deep Learning for Computer-Aided Diagnosis of Lung Diseases: An Automated Method Combining Evolutionary Algorithm, Transfer Learning, and Model Compression

    Hassen Louati1,2, Ali Louati3,*, Elham Kariri3, Slim Bechikh2

    CMES-Computer Modeling in Engineering & Sciences, Vol.138, No.3, pp. 2519-2547, 2024, DOI:10.32604/cmes.2023.030806

    Abstract Recent developments in Computer Vision have presented novel opportunities to tackle complex healthcare issues, particularly in the field of lung disease diagnosis. One promising avenue involves the use of chest X-Rays, which are commonly utilized in radiology. To fully exploit their potential, researchers have suggested utilizing deep learning methods to construct computer-aided diagnostic systems. However, constructing and compressing these systems presents a significant challenge, as it relies heavily on the expertise of data scientists. To tackle this issue, we propose an automated approach that utilizes an evolutionary algorithm (EA) to optimize the design and compression of a convolutional neural network… More >

  • Open Access

    ARTICLE

    Comparison among Classical, Probabilistic and Quantum Algorithms for Hamiltonian Cycle Problem

    Giuseppe Corrente1,2,*, Carlo Vincenzo Stanzione3,4, Vittoria Stanzione5

    Journal of Quantum Computing, Vol.5, pp. 55-70, 2023, DOI:10.32604/jqc.2023.044786

    Abstract The Hamiltonian cycle problem (HCP), which is an NP-complete problem, consists of having a graph G with nodes and m edges and finding the path that connects each node exactly once. In this paper we compare some algorithms to solve a Hamiltonian cycle problem, using different models of computations and especially the probabilistic and quantum ones. Starting from the classical probabilistic approach of random walks, we take a step to the quantum direction by involving an ad hoc designed Quantum Turing Machine (QTM), which can be a useful conceptual project tool for quantum algorithms. Introducing several constraints to the graphs,… More >

Displaying 1-10 on page 1 of 272. Per Page