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  • Open Access

    ARTICLE

    Efficient Classification of Remote Sensing Images Using Two Convolution Channels and SVM

    Khalid A. AlAfandy1, Hicham Omara2, Hala S. El-Sayed3, Mohammed Baz4,*, Mohamed Lazaar5, Osama S. Faragallah6, Mohammed Al Achhab1

    CMC-Computers, Materials & Continua, Vol.72, No.1, pp. 739-753, 2022, DOI:10.32604/cmc.2022.022457

    Abstract Remote sensing image processing engaged researchers’ attentiveness in recent years, especially classification. The main problem in classification is the ratio of the correct predictions after training. Feature extraction is the foremost important step to build high-performance image classifiers. The convolution neural networks can extract images’ features that significantly improve the image classifiers’ accuracy. This paper proposes two efficient approaches for remote sensing images classification that utilizes the concatenation of two convolution channels’ outputs as a features extraction using two classic convolution models; these convolution models are the ResNet 50 and the DenseNet 169. These elicited features have been used by… More >

  • Open Access

    ARTICLE

    Robust Frequency Estimation Under Additive Mixture Noise

    Yuan Chen1, Yulu Tian1, Dingfan Zhang2, Longting Huang3,*, Jingxin Xu4

    CMC-Computers, Materials & Continua, Vol.72, No.1, pp. 1671-1684, 2022, DOI:10.32604/cmc.2022.022371

    Abstract In many applications such as multiuser radar communications and astrophysical imaging processing, the encountered noise is usually described by the finite sum of -stable variables. In this paper, a new parameter estimator is developed, in the presence of this new heavy-tailed noise. Since the closed-form PDF of the -stable variable does not exist except and , we take the sum of the Cauchy () and Gaussian () noise as an example, namely, additive Cauchy-Gaussian (ACG) noise. The probability density function (PDF) of the mixed random variable, can be calculated by the convolution of the Cauchy's PDF and Gaussian's PDF. Because… More >

  • Open Access

    ARTICLE

    Non-integer Order Control Scheme for Pressurized Water Reactor Core Power

    Ibrahim M. Mehedi1,2,*, Maher H. AL-Sereihy2, Asmaa Ubaid Al-Saggaf2, Ubaid M. Al-Saggaf1,2

    CMC-Computers, Materials & Continua, Vol.72, No.1, pp. 651-662, 2022, DOI:10.32604/cmc.2022.022013

    Abstract Tracking load changes in a pressurized water reactor (PWR) with the help of an efficient core power control scheme in a nuclear power station is very important. The reason is that it is challenging to maintain a stable core power according to the reference value within an acceptable tolerance for the safety of PWR. To overcome the uncertainties, a non-integer-based fractional order control method is demonstrated to control the core power of PWR. The available dynamic model of the reactor core is used in this analysis. Core power is controlled using a modified state feedback approach with a non-integer integral… More >

  • Open Access

    ARTICLE

    Efficient Load Balancing with MANET Propagation of Least Common Multiple Routing and Fuzzy Logic

    V. Gayatri*, M. Senthil Kumaran

    CMC-Computers, Materials & Continua, Vol.72, No.1, pp. 1831-1845, 2022, DOI:10.32604/cmc.2022.021857

    Abstract Mobile Ad Hoc Network (MANET) is a group of node that would interrelate among each other through one multi-hop wireless link, wherein the nodes were able to move in response to sudden modifications. The objective of MANET routing protocol is to quantify the route and compute the best path, but there exists a major decrease in energy efficiency, difficulty in hop selection, cost estimation, and efficient load-balancing. In this paper, a novel least common multipath-based routing has been proposed. Multipath routing is used to find a multipath route from source and destination. Load balancing is of primary importance in the… More >

  • Open Access

    ARTICLE

    Nonlinear Dynamics of Nervous Stomach Model Using Supervised Neural Networks

    Zulqurnain Sabir1, Manoj Gupta2, Muhammad Asif Zahoor Raja3, N. Seshagiri Rao4, Muhammad Mubashar Hussain5, Faisal Alanazi6, Orawit Thinnukool7, Pattaraporn Khuwuthyakorn7,*

    CMC-Computers, Materials & Continua, Vol.72, No.1, pp. 1627-1644, 2022, DOI:10.32604/cmc.2022.021462

    Abstract The purpose of the current investigations is to solve the nonlinear dynamics based on the nervous stomach model (NSM) using the supervised neural networks (SNNs) along with the novel features of Levenberg-Marquardt backpropagation technique (LMBT), i.e., SNNs-LMBT. The SNNs-LMBT is implemented with three different types of sample data, authentication, testing and training. The ratios for these statistics to solve three different variants of the nonlinear dynamics of the NSM are designated 75% for training, 15% for validation and 10% for testing, respectively. For the numerical measures of the nonlinear dynamics of the NSM, the Runge-Kutta scheme is implemented to form… More >

  • Open Access

    ARTICLE

    Weighted-adaptive Inertia Strategy for Multi-objective Scheduling in Multi-clouds

    Mazen Farid1,3,*, Rohaya Latip1,2, Masnida Hussin1, Nor Asilah Wati Abdul Hamid1

    CMC-Computers, Materials & Continua, Vol.72, No.1, pp. 1529-1560, 2022, DOI:10.32604/cmc.2022.021410

    Abstract One of the fundamental problems associated with scheduling workflows on virtual machines in a multi-cloud environment is how to find a near-optimum permutation. The workflow scheduling involves assigning independent computational jobs with conflicting objectives to a set of virtual machines. Most optimization methods for solving non-deterministic polynomial-time hardness (NP-hard) problems deploy multi-objective algorithms. As such, Pareto dominance is one of the most efficient criteria for determining the best solutions within the Pareto front. However, the main drawback of this method is that it requires a reasonably long time to provide an optimum solution. In this paper, a new multi-objective minimum… More >

  • Open Access

    ARTICLE

    Cloud Data Encryption and Authentication Based on Enhanced Merkle Hash Tree Method

    J. Stanly Jayaprakash1, Kishore Balasubramanian2, Rossilawati Sulaiman3, Mohammad Kamrul Hasan3,*, B. D. Parameshachari4, Celestine Iwendi5

    CMC-Computers, Materials & Continua, Vol.72, No.1, pp. 519-534, 2022, DOI:10.32604/cmc.2022.021269

    Abstract Many organizations apply cloud computing to store and effectively process data for various applications. The user uploads the data in the cloud has less security due to the unreliable verification process of data integrity. In this research, an enhanced Merkle hash tree method of effective authentication model is proposed in the multi-owner cloud to increase the security of the cloud data. Merkle Hash tree applies the leaf nodes with a hash tag and the non-leaf node contains the table of hash information of child to encrypt the large data. Merkle Hash tree provides the efficient mapping of data and easily… More >

  • Open Access

    ARTICLE

    Hyperchaos and MD5 Based Efficient Color Image Cipher

    Muhammad Samiullah1, Waqar Aslam1, Saima Sadiq2, Arif Mehmood1, Gyu Sang Choi3,*

    CMC-Computers, Materials & Continua, Vol.72, No.1, pp. 1645-1670, 2022, DOI:10.32604/cmc.2022.021019

    Abstract While designing and developing encryption algorithms for text and images, the main focus has remained on security. This has led to insufficient attention on the improvement of encryption efficiency, enhancement of hyperchaotic sequence randomness, and dynamic DNA-based S-box. In this regard, a new symmetric block cipher scheme has been proposed. It uses dynamic DNA-based S-box connected with MD5 and a hyperchaotic system to produce confusion and diffusion for encrypting color images. Our proposed scheme supports various size color images. It generates three DNA based S-boxes for substitution namely DNA_1_s-box, DNA_2_s-box and DNA_3_s-box, each of size . Next, the 4D hyperchaotic… More >

  • Open Access

    ARTICLE

    An Optimal Deep Learning for Cooperative Intelligent Transportation System

    K. Lakshmi1, Srinivas Nagineni2, E. Laxmi Lydia3, A. Francis Saviour Devaraj4, Sachi Nandan Mohanty5, Irina V. Pustokhina6,*, Denis A. Pustokhin7

    CMC-Computers, Materials & Continua, Vol.72, No.1, pp. 19-35, 2022, DOI:10.32604/cmc.2022.020244

    Abstract Cooperative Intelligent Transport System (C-ITS) plays a vital role in the future road traffic management system. A vital element of C-ITS comprises vehicles, road side units, and traffic command centers, which produce a massive quantity of data comprising both mobility and service-related data. For the extraction of meaningful and related details out of the generated data, data science acts as an essential part of the upcoming C-ITS applications. At the same time, prediction of short-term traffic flow is highly essential to manage the traffic accurately. Due to the rapid increase in the amount of traffic data, deep learning (DL) models… More >

  • Open Access

    ARTICLE

    Melanoma Identification Through X-ray Modality Using Inception-v3 Based Convolutional Neural Network

    Saad Awadh Alanazi*

    CMC-Computers, Materials & Continua, Vol.72, No.1, pp. 37-55, 2022, DOI:10.32604/cmc.2022.020118

    Abstract Melanoma, also called malignant melanoma, is a form of skin cancer triggered by an abnormal proliferation of the pigment-producing cells, which give the skin its color. Melanoma is one of the skin diseases, which is exceptionally and globally dangerous, Skin lesions are considered to be a serious disease. Dermoscopy-based early recognition and detection procedure is fundamental for melanoma treatment. Early detection of melanoma using dermoscopy images improves survival rates significantly. At the same time, well-experienced dermatologists dominate the precision of diagnosis. However, precise melanoma recognition is incredibly hard due to several factors: low contrast between lesions and surrounding skin, visual… More >

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