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  • Open Access

    ARTICLE

    ECO-BAT: A New Routing Protocol for Energy Consumption Optimization Based on BAT Algorithm in WSN

    Mohammed Kaddi1,*, Abdallah Banana2, Mohammed Omari1

    CMC-Computers, Materials & Continua, Vol.66, No.2, pp. 1497-1510, 2021, DOI:10.32604/cmc.2020.012116

    Abstract Wireless sensor network (WSN) has been widely used due to its vast range of applications. The energy problem is one of the important problems influencing the complete application. Sensor nodes use very small batteries as a power source and replacing them is not an easy task. With this restriction, the sensor nodes must conserve their energy and extend the network lifetime as long as possible. Also, these limits motivate much of the research to suggest solutions in all layers of the protocol stack to save energy. So, energy management efficiency becomes a key requirement in WSN design. The efficiency of… More >

  • Open Access

    ARTICLE

    Different Decaying Wood Effects on Bacterial Diversity: Insights from Molecular Methods

    Mu Peng1, Yanli Jing1,#, Qiuyu Wang1, Shaopeng Yan1,2,*

    Phyton-International Journal of Experimental Botany, Vol.90, No.1, pp. 207-222, 2021, DOI:10.32604/phyton.2020.012424

    Abstract Decaying wood is a novel key factor required for biodiversity and function of a forest, as it provides a good account of substrate and habitats for various organisms. Herein, the bacterial diversity in decaying wood of Betula platyphylla was discussed through high throughput sequencing. Our results showed that most of the obtained sequences belonged to the phyla Firmicutes, Proteobacteria, Bacteroidetes, Actinobacteria, Acidobacteria and Verrucomicrobia. Bacterial community compositions in samples with higher moisture content were obviously different than that with lower content, which could be reflected by richness estimators, diversity indices, and cluster and heatmap analysis. All three networks were non-random… More >

  • Open Access

    ARTICLE

    Deep Learning for Distinguishing Computer Generated Images and Natural Images: A Survey

    Bingtao Hu*, Jinwei Wang

    Journal of Information Hiding and Privacy Protection, Vol.2, No.2, pp. 95-105, 2020, DOI:10.32604/jihpp.2020.010464

    Abstract With the development of computer graphics, realistic computer graphics (CG) have become more and more common in our field of vision. This rendered image is invisible to the naked eye. How to effectively identify CG and natural images (NI) has been become a new issue in the field of digital forensics. In recent years, a series of deep learning network frameworks have shown great advantages in the field of images, which provides a good choice for us to solve this problem. This paper aims to track the latest developments and applications of deep learning in the field of CG and… More >

  • Open Access

    REVIEW

    A Survey of GAN-Generated Fake Faces Detection Method Based on Deep Learning

    Xin Liu*, Xiao Chen

    Journal of Information Hiding and Privacy Protection, Vol.2, No.2, pp. 87-94, 2020, DOI:10.32604/jihpp.2020.09839

    Abstract In recent years, with the rapid growth of generative adversarial networks (GANs), a photo-realistic face can be easily generated from a random vector. Moreover, the faces generated by advanced GANs are very realistic. It is reasonable to acknowledge that even a well-trained viewer has difficulties to distinguish artificial from real faces. Therefore, detecting the face generated by GANs is a necessary work. This paper mainly introduces some methods to detect GAN-generated fake faces, and analyzes the advantages and disadvantages of these models based on the network structure and evaluation indexes, and the results obtained in the respective data sets. On… More >

  • Open Access

    ARTICLE

    Image Retrieval Based on Deep Feature Extraction and Reduction with Improved CNN and PCA

    Rongyu Chen, Lili Pan*, Yan Zhou, Qianhui Lei

    Journal of Information Hiding and Privacy Protection, Vol.2, No.2, pp. 67-76, 2020, DOI:10.32604/jihpp.2020.010472

    Abstract With the rapid development of information technology, the speed and efficiency of image retrieval are increasingly required in many fields, and a compelling image retrieval method is critical for the development of information. Feature extraction based on deep learning has become dominant in image retrieval due to their discrimination more complete, information more complementary and higher precision. However, the high-dimension deep features extracted by CNNs (convolutional neural networks) limits the retrieval efficiency and makes it difficult to satisfy the requirements of existing image retrieval. To solving this problem, the high-dimension feature reduction technology is proposed with improved CNN and PCA… More >

  • Open Access

    ARTICLE

    Parametric Evaluation of Routing Algorithms in Network on Chip Architecture

    Mohammad Behrouzian Nejad

    Computer Systems Science and Engineering, Vol.35, No.5, pp. 367-375, 2020, DOI:10.32604/csse.2020.35.367

    Abstract Considering that routing algorithms for the Network on Chip (NoC) architecture is one of the key issues that determine its ultimate performance, several things have to be considered for developing new routing algorithms. This includes examining the strengths, capabilities, and weaknesses of the commonly proposed algorithms as a starting point for developing new ones.
    Because most of the algorithms presented are based on the well-known algorithms that are studied and evaluated in this research. Finally, according to the results produced under different conditions, better decisions can be made when using the aforementioned algorithms as well as when presenting new routing… More >

  • Open Access

    ARTICLE

    Duty Cycling Centralized Hierarchical Routing Protocol With Content Analysis Duty Cycling Mechanism for Wireless Sensor Networks

    Anar A. Hady

    Computer Systems Science and Engineering, Vol.35, No.5, pp. 347-355, 2020, DOI:10.32604/csse.2020.35.347

    Abstract In this paper, a Duty Cycling Centralized Hierarchical Protocol (DCCHP) has been proposed for wireless sensor networks. DCCHP is an energy efficient protocol that prolongs the lifetime of the network by applying a duty cycling mechanism named DCM that chooses the nodes that send unimportant data in a certain epoch to be candidates to be put to sleep. But if the proposed equations for choosing the cluster head nodes put any of them in a high priority it works in the active mode. When comparing DCCHP to the previously proposed LEACH-CS, LEACH-C protocols, using a simulation study, DCCHP in average… More >

  • Open Access

    ARTICLE

    A Frame Work for Categorise the Innumerable Vulnerable Nodes in Mobile Adhoc Network

    Gundala Swathi*

    Computer Systems Science and Engineering, Vol.35, No.5, pp. 335-345, 2020, DOI:10.32604/csse.2020.35.335

    Abstract Researches in wireless mobile ad hoc networks have an inherent challenge of vulnerable diagnosis due to the diverse behaviour pattern of the vulnerable nodes causing heterogeneous vtype1, vtype2, vtupe3 and vtype4 faults. This paper proposes a protocol for the diagnosis of vulnerability nodes with threephases of clustering, vulnerable detection and vulnerable fault classification in wireless networks. This protocol employs the technique of probabilistic neural network for classification of vulnerable nodes and detects vulnerable nodes through timeout mechanism and vtype3, vtype4, vtype1, vtype2 nodes through the method of analysis variance. Network simulator NS-2.3.35 is employed for performance evaluation of the protocol. More >

  • Open Access

    ARTICLE

    Automatic and Robust Segmentation of Multiple Sclerosis Lesions with Convolutional Neural Networks

    H. M. Rehan Afzal1,2,*, Suhuai Luo1, Saadallah Ramadan1,2, Jeannette Lechner-Scott1,2,3, Mohammad Ruhul Amin3, Jiaming Li4, M. Kamran Afzal5

    CMC-Computers, Materials & Continua, Vol.66, No.1, pp. 977-991, 2021, DOI:10.32604/cmc.2020.012448

    Abstract The diagnosis of multiple sclerosis (MS) is based on accurate detection of lesions on magnetic resonance imaging (MRI) which also provides ongoing essential information about the progression and status of the disease. Manual detection of lesions is very time consuming and lacks accuracy. Most of the lesions are difficult to detect manually, especially within the grey matter. This paper proposes a novel and fully automated convolution neural network (CNN) approach to segment lesions. The proposed system consists of two 2D patchwise CNNs which can segment lesions more accurately and robustly. The first CNN network is implemented to segment lesions accurately,… More >

  • Open Access

    ARTICLE

    Soft Robotic Glove Controlling Using Brainwave Detection for Continuous Rehabilitation at Home

    Talit Jumphoo1, Monthippa Uthansakul1, Pumin Duangmanee1, Naeem Khan2, Peerapong Uthansakul1,*

    CMC-Computers, Materials & Continua, Vol.66, No.1, pp. 961-976, 2021, DOI:10.32604/cmc.2020.012433

    Abstract The patients with brain diseases (e.g., Stroke and Amyotrophic Lateral Sclerosis (ALS)) are often affected by the injury of motor cortex, which causes a muscular weakness. For this reason, they require rehabilitation with continuous physiotherapy as these diseases can be eased within the initial stages of the symptoms. So far, the popular control system for robot-assisted rehabilitation devices is only of two types which consist of passive and active devices. However, if there is a control system that can directly detect the motor functions, it will induce neuroplasticity to facilitate early motor recovery. In this paper, the control system, which… More >

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