Home / Advanced Search

  • Title/Keywords

  • Author/Affliations

  • Journal

  • Article Type

  • Start Year

  • End Year

Update SearchingClear
  • Articles
  • Online
Search Results (5)
  • Open Access


    Model for Generating Scale-Free Artificial Social Networks Using Small-World Networks

    Farhan Amin, Gyu Sang Choi*

    CMC-Computers, Materials & Continua, Vol.73, No.3, pp. 6367-6391, 2022, DOI:10.32604/cmc.2022.029927

    Abstract The Internet of Things (IoT) has the potential to be applied to social networks due to innovative characteristics and sophisticated solutions that challenge traditional uses. Social network analysis (SNA) is a good example that has recently gained a lot of scientific attention. It has its roots in social and economic research, as well as the evaluation of network science, such as graph theory. Scientists in this area have subverted predefined theories, offering revolutionary ones regarding interconnected networks, and they have highlighted the mystery of six degrees of separation with confirmation of the small-world phenomenon. The motivation of this study is… More >

  • Open Access


    An Improved Genetic Algorithm for Automated Convolutional Neural Network Design

    Rahul Dubey*, Jitendra Agrawal

    Intelligent Automation & Soft Computing, Vol.32, No.2, pp. 747-763, 2022, DOI:10.32604/iasc.2022.020975

    Abstract Extracting the features from an image is a cumbersome task. Initially, this task was performed by domain experts through a process known as handcrafted feature design. A deep embedding technique known as convolutional neural networks (CNNs) later solved this problem by introducing the feature learning concept, through which the CNN is directly provided with images. This CNN then learns the features of the image, which are subsequently given as input to the further layers for an intended task like classification. CNNs have demonstrated astonishing performance in several practicable applications in the last few years. Nevertheless, the pursuance of CNNs primarily… More >

  • Open Access


    Neutrosophic Mathematical Programming for Optimization of Multi-Objective Sustainable Biomass Supply Chain Network Design

    Mohammad Fallah*, Hamed Nozari

    CMES-Computer Modeling in Engineering & Sciences, Vol.129, No.2, pp. 927-951, 2021, DOI:10.32604/cmes.2021.017511

    Abstract In this paper, a multi-objective sustainable biomass supply chain network under uncertainty is designed by neutrosophic programming method. In this method, for each objective function of the problem, three functions of truth membership, non-determination and falsehood are considered. Neutrosophic programming method in this paper simultaneously seeks to optimize the total costs of the supply chain network, the amount of greenhouse gas emissions, the number of potential people hired and the time of product transfer along the supply chain network. To achieve the stated objective functions, strategic decisions such as locating potential facilities and tactical decisions such as optimal product flow… More >

  • Open Access


    GUI-Based DL-Network Designer for KISTI’s Supercomputer Users

    Jaegwang Lee, Jongsuk R. Lee, Sunil Ahn*

    CMC-Computers, Materials & Continua, Vol.69, No.2, pp. 1611-1629, 2021, DOI:10.32604/cmc.2021.016803

    Abstract With the increase in research on AI (Artificial Intelligence), the importance of DL (Deep Learning) in various fields, such as materials, biotechnology, genomes, and new drugs, is increasing significantly, thereby increasing the number of deep-learning framework users. However, to design a deep neural network, a considerable understanding of the framework is required. To solve this problem, a GUI (Graphical User Interface)-based DNN (Deep Neural Network) design tool is being actively researched and developed. The GUI-based DNN design tool can design DNNs quickly and easily. However, the existing GUI-based DNN design tool has certain limitations such as poor usability, framework dependency,… More >

  • Open Access


    On Network Designs with Coding Error Detection and Correction Application

    Mahmoud Higazy1,2,*, Taher A. Nofal1

    CMC-Computers, Materials & Continua, Vol.67, No.3, pp. 3401-3418, 2021, DOI:10.32604/cmc.2021.015790

    Abstract The detection of error and its correction is an important area of mathematics that is vastly constructed in all communication systems. Furthermore, combinatorial design theory has several applications like detecting or correcting errors in communication systems. Network (graph) designs (GDs) are introduced as a generalization of the symmetric balanced incomplete block designs (BIBDs) that are utilized directly in the above mentioned application. The networks (graphs) have been represented by vectors whose entries are the labels of the vertices related to the lengths of edges linked to it. Here, a general method is proposed and applied to construct new networks designs.… More >

Displaying 1-10 on page 1 of 5. Per Page  

Share Link