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  • Open Access

    ARTICLE

    A Fractional Drift Diffusion Model for Organic Semiconductor Devices

    Yi Yang*, Robert A. Nawrocki, Richard M. Voyles, Haiyan H. Zhang

    CMC-Computers, Materials & Continua, Vol.69, No.1, pp. 237-266, 2021, DOI:10.32604/cmc.2021.017439

    Abstract Because charge carriers of many organic semiconductors (OSCs) exhibit fractional drift diffusion (Fr-DD) transport properties, the need to develop a Fr-DD model solver becomes more apparent. However, the current research on solving the governing equations of the Fr-DD model is practically nonexistent. In this paper, an iterative solver with high precision is developed to solve both the transient and steady-state Fr-DD model for organic semiconductor devices. The Fr-DD model is composed of two fractional-order carriers (i.e., electrons and holes) continuity equations coupled with Poisson’s equation. By treating the current density as constants within each pair of consecutive grid nodes, a… More >

  • Open Access

    ARTICLE

    Enabling Reachability Across Multiple Domains Without Controller Synchronization in SDN

    Nauman Khan1,2, Rosli Bin Salleh1,*, Ihsan Ali1, Zahid Khan3, Noman Mazhar1, Roobaea Alroobaea4, Fahad Almansour5, Usman Ali1

    CMC-Computers, Materials & Continua, Vol.69, No.1, pp. 945-965, 2021, DOI:10.32604/cmc.2021.017394

    Abstract Software-defined networking (SDN) makes network agile and flexible due to its programmable approach. An extensive network has multiple domains in SDN for the scalability and performance of the network. However, the inter-domain link is also crucial for the stability of the entire network on the data plane layer. More than one inter-domain connection enhances the scalability of the data plane layer. However, it faces a reachability problem with the principal root, which causes forwarding loops and packet drops in the network, thereby degrading network performance. The proposed solution is a multiple controller architecture; however, this approach increases the complexity and… More >

  • Open Access

    ARTICLE

    An Efficient CNN-Based Automated Diagnosis Framework from COVID-19 CT Images

    Walid El-Shafai1, Noha A. El-Hag2, Ghada M. El-Banby3, Ashraf A. M. Khalaf2, Naglaa F. Soliman4,*, Abeer D. Algarni4, Fathi E. Abd El-Samie1,4

    CMC-Computers, Materials & Continua, Vol.69, No.1, pp. 1323-1341, 2021, DOI:10.32604/cmc.2021.017385

    Abstract Corona Virus Disease-2019 (COVID-19) continues to spread rapidly in the world. It has dramatically affected daily lives, public health, and the world economy. This paper presents a segmentation and classification framework of COVID-19 images based on deep learning. Firstly, the classification process is employed to discriminate between COVID-19, non-COVID, and pneumonia by Convolutional Neural Network (CNN). Then, the segmentation process is applied for COVID-19 and pneumonia CT images. Finally, the resulting segmented images are used to identify the infected region, whether COVID-19 or pneumonia. The proposed CNN consists of four Convolutional (Conv) layers, four batch normalization layers, and four Rectified… More >

  • Open Access

    ARTICLE

    LOA-RPL: Novel Energy-Efficient Routing Protocol for the Internet of Things Using Lion Optimization Algorithm to Maximize Network Lifetime

    Sankar Sennan1, Somula Ramasubbareddy2, Anand Nayyar3,4, Yunyoung Nam5,*, Mohamed Abouhawwash6,7

    CMC-Computers, Materials & Continua, Vol.69, No.1, pp. 351-371, 2021, DOI:10.32604/cmc.2021.017360

    Abstract Energy conservation is a significant task in the Internet of Things (IoT) because IoT involves highly resource-constrained devices. Clustering is an effective technique for saving energy by reducing duplicate data. In a clustering protocol, the selection of a cluster head (CH) plays a key role in prolonging the lifetime of a network. However, most cluster-based protocols, including routing protocols for low-power and lossy networks (RPLs), have used fuzzy logic and probabilistic approaches to select the CH node. Consequently, early battery depletion is produced near the sink. To overcome this issue, a lion optimization algorithm (LOA) for selecting CH in RPL… More >

  • Open Access

    ARTICLE

    Ecological Security Evaluation Algorithm for Resource-Exhausted Cities Based on the PSR Model

    Xiaozhou Li1,2, Zhenggang Xu1, Yuanyuan Fu3, Qi Jin1, Yunlin Zhao1,*, Neal N. Xiong4

    CMC-Computers, Materials & Continua, Vol.69, No.1, pp. 985-1001, 2021, DOI:10.32604/cmc.2021.017357

    Abstract Today, resource depletion threatens a number of resource-based cities in China. The ecological security problem caused by the long-term exploitation of natural resources is a key issue to be solved in the development of resource-exhausted cities. Using 23 indicators, this study evaluated the ecological security status and development trends of 21 resource-exhausted cities in China from 2011 to 2017. The results showed that from 2011 to 2015, the overall ecological security of this type of city was low, with over 60% of the cities at an unsafe level. However, ecological security improved rapidly after 2016, and by 2017, all of… More >

  • Open Access

    ARTICLE

    Guided Intra-Patch Smoothing Graph Filtering for Single-Image Denoising

    Yibin Tang1, Ying Chen2, Aimin Jiang1, Jian Li1, Yan Zhou1,*, Hon Keung Kwan3

    CMC-Computers, Materials & Continua, Vol.69, No.1, pp. 67-80, 2021, DOI:10.32604/cmc.2021.017300

    Abstract Graph filtering is an important part of graph signal processing and a useful tool for image denoising. Existing graph filtering methods, such as adaptive weighted graph filtering (AWGF), focus on coefficient shrinkage strategies in a graph-frequency domain. However, they seldom consider the image attributes in their graph-filtering procedure. Consequently, the denoising performance of graph filtering is barely comparable with that of other state-of-the-art denoising methods. To fully exploit the image attributes, we propose a guided intra-patch smoothing AWGF (AWGF-GPS) method for single-image denoising. Unlike AWGF, which employs graph topology on patches, AWGF-GPS learns the topology of superpixels by introducing the… More >

  • Open Access

    ARTICLE

    AntiFlamPred: An Anti-Inflammatory Peptide Predictor for Drug Selection Strategies

    Fahad Alotaibi1, Muhammad Attique2,3, Yaser Daanial Khan2,*

    CMC-Computers, Materials & Continua, Vol.69, No.1, pp. 1039-1055, 2021, DOI:10.32604/cmc.2021.017297

    Abstract Several autoimmune ailments and inflammation-related diseases emphasize the need for peptide-based therapeutics for their treatment and established substantial consideration. Though, the wet-lab experiments for the investigation of anti-inflammatory proteins/peptides (“AIP”) are usually very costly and remain time-consuming. Therefore, before wet-lab investigations, it is essential to develop in-silico identification models to classify prospective anti-inflammatory candidates for the facilitation of the drug development process. Several anti-inflammatory prediction tools have been proposed in the recent past, yet, there is a space to induce enhancement in prediction performance in terms of precision and efficiency. An exceedingly accurate anti-inflammatory prediction model is proposed, named AntiFlamPred… More >

  • Open Access

    ARTICLE

    Deep Learning Approach for Cosmetic Product Detection and Classification

    Se-Won Kim1, Sang-Woong Lee2,*

    CMC-Computers, Materials & Continua, Vol.69, No.1, pp. 713-725, 2021, DOI:10.32604/cmc.2021.017292

    Abstract As the amount of online video content is increasing, consumers are becoming increasingly interested in various product names appearing in videos, particularly in cosmetic-product names in videos related to fashion, beauty, and style. Thus, the identification of such products by using image recognition technology may aid in the identification of current commercial trends. In this paper, we propose a two-stage deep-learning detection and classification method for cosmetic products. Specifically, variants of the YOLO network are used for detection, where the bounding box for each given input product is predicted and subsequently cropped for classification. We use four state-of-the-art classification networks,… More >

  • Open Access

    ARTICLE

    Reversible Data Hiding Based on Varying Radix Numeral System

    J. Hemalatha1, S. Geetha2, R. Geetha3, C. Balasubramanian4, Daniela Elena Popescu5, D. Jude Hemanth6,*

    CMC-Computers, Materials & Continua, Vol.69, No.1, pp. 283-300, 2021, DOI:10.32604/cmc.2021.017203

    Abstract A novel image reversible data-hiding scheme based on primitive and varying radix numerical model is presented in this article. Using varying radix, variable sum of data may be embedded in various pixels of images. This scheme is made adaptive using the correlation of the neighboring pixels. Messages are embedded as blocks of non-uniform length in the high-frequency regions of the rhombus mean interpolated image. A higher amount of data is embedded in the high-frequency regions and lesser data in the low-frequency regions of the image. The size of the embedded data depends on the statistics of the pixel distribution in… More >

  • Open Access

    ARTICLE

    Cryptographic Based Secure Model on Dataset for Deep Learning Algorithms

    Muhammad Tayyab1,*, Mohsen Marjani1, N. Z. Jhanjhi1, Ibrahim Abaker Targio Hashim2, Abdulwahab Ali Almazroi3, Abdulaleem Ali Almazroi4

    CMC-Computers, Materials & Continua, Vol.69, No.1, pp. 1183-1200, 2021, DOI:10.32604/cmc.2021.017199

    Abstract Deep learning (DL) algorithms have been widely used in various security applications to enhance the performances of decision-based models. Malicious data added by an attacker can cause several security and privacy problems in the operation of DL models. The two most common active attacks are poisoning and evasion attacks, which can cause various problems, including wrong prediction and misclassification of decision-based models. Therefore, to design an efficient DL model, it is crucial to mitigate these attacks. In this regard, this study proposes a secure neural network (NN) model that provides data security during model training and testing phases. The main… More >

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