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  • Open Access

    ARTICLE

    Solar Energy Harvesting Using a Timer-Based Relay Selection

    Raed Alhamad1,*, Hatem Boujemaa2

    CMC-Computers, Materials & Continua, Vol.74, No.1, pp. 2149-2159, 2023, DOI:10.32604/cmc.2023.033222

    Abstract In this paper, the throughput and delay of cooperative communications are derived when solar energy is used and relay node is selected using a timer. The source and relays harvest energy from sun using a photo voltaic system. The harvested power is used by the source to transmit data to the relays. Then, a selected relay amplifies the signal to the destination. Opportunistic, partial and reactive relay selection are used. The relay transmits when its timer elapses. The timer is set to a value proportional to the inverse of its Signal to Noise Ratio (SNR). Therefore, the relay with largest… More >

  • Open Access

    ARTICLE

    Effective Return Rate Prediction of Blockchain Financial Products Using Machine Learning

    K. Kalyani1, Velmurugan Subbiah Parvathy2, Hikmat A. M. Abdeljaber3, T. Satyanarayana Murthy4, Srijana Acharya5, Gyanendra Prasad Joshi6, Sung Won Kim7,*

    CMC-Computers, Materials & Continua, Vol.74, No.1, pp. 2303-2316, 2023, DOI:10.32604/cmc.2023.033162

    Abstract In recent times, financial globalization has drastically increased in different ways to improve the quality of services with advanced resources. The successful applications of bitcoin Blockchain (BC) techniques enable the stockholders to worry about the return and risk of financial products. The stockholders focused on the prediction of return rate and risk rate of financial products. Therefore, an automatic return rate bitcoin prediction model becomes essential for BC financial products. The newly designed machine learning (ML) and deep learning (DL) approaches pave the way for return rate predictive method. This study introduces a novel Jellyfish search optimization based extreme learning… More >

  • Open Access

    ARTICLE

    Robust and Reusable Fuzzy Extractors from Non-Uniform Learning with Errors Problem

    Joo Woo1, Jonghyun Kim1, Jong Hwan Park2,*

    CMC-Computers, Materials & Continua, Vol.74, No.1, pp. 1985-2003, 2023, DOI:10.32604/cmc.2023.033102

    Abstract A fuzzy extractor can extract an almost uniform random string from a noisy source with enough entropy such as biometric data. To reproduce an identical key from repeated readings of biometric data, the fuzzy extractor generates a helper data and a random string from biometric data and uses the helper data to reproduce the random string from the second reading. In 2013, Fuller et al. proposed a computational fuzzy extractor based on the learning with errors problem. Their construction, however, can tolerate a sub-linear fraction of errors and has an inefficient decoding algorithm, which causes the reproducing time to increase significantly. In 2016,… More >

  • Open Access

    ARTICLE

    An Optimal DPM Based Energy-Aware Task Scheduling for Performance Enhancement in Embedded MPSoC

    Hamayun Khan1,*, Irfan Ud Din2, Arshad Ali3, Mohammad Husain3

    CMC-Computers, Materials & Continua, Vol.74, No.1, pp. 2097-2113, 2023, DOI:10.32604/cmc.2023.032999

    Abstract Minimizing the energy consumption to increase the life span and performance of multiprocessor system on chip (MPSoC) has become an integral chip design issue for multiprocessor systems. The performance measurement of computational systems is changing with the advancement in technology. Due to shrinking and smaller chip size power densities on-chip are increasing rapidly that increasing chip temperature in multi-core embedded technologies. The operating speed of the device decreases when power consumption reaches a threshold that causes a delay in complementary metal oxide semiconductor (CMOS) circuits because high on-chip temperature adversely affects the life span of the chip. In this paper… More >

  • Open Access

    ARTICLE

    Cyberbullying-related Hate Speech Detection Using Shallow-to-deep Learning

    Daniyar Sultan1,2, Aigerim Toktarova3,*, Ainur Zhumadillayeva4, Sapargali Aldeshov5,6, Shynar Mussiraliyeva1, Gulbakhram Beissenova6,7, Abay Tursynbayev8, Gulmira Baenova4, Aigul Imanbayeva6

    CMC-Computers, Materials & Continua, Vol.74, No.1, pp. 2115-2131, 2023, DOI:10.32604/cmc.2023.032993

    Abstract Communication in society had developed within cultural and geographical boundaries prior to the invention of digital technology. The latest advancements in communication technology have significantly surpassed the conventional constraints for communication with regards to time and location. These new platforms have ushered in a new age of user-generated content, online chats, social network and comprehensive data on individual behavior. However, the abuse of communication software such as social media websites, online communities, and chats has resulted in a new kind of online hostility and aggressive actions. Due to widespread use of the social networking platforms and technological gadgets, conventional bullying… More >

  • Open Access

    ARTICLE

    Fractional Order Environmental and Economic Model Investigations Using Artificial Neural Network

    Wajaree Weera1, Chantapish Zamart1, Zulqurnain Sabir2,3, Muhammad Asif Zahoor Raja4, Afaf S. Alwabli5, S. R. Mahmoud6, Supreecha Wongaree7, Thongchai Botmart1,*

    CMC-Computers, Materials & Continua, Vol.74, No.1, pp. 1735-1748, 2023, DOI:10.32604/cmc.2023.032950

    Abstract The motive of these investigations is to provide the importance and significance of the fractional order (FO) derivatives in the nonlinear environmental and economic (NEE) model, i.e., FO-NEE model. The dynamics of the NEE model achieves more precise by using the form of the FO derivative. The investigations through the non-integer and nonlinear mathematical form to define the FO-NEE model are also provided in this study. The composition of the FO-NEE model is classified into three classes, execution cost of control, system competence of industrial elements and a new diagnostics technical exclusion cost. The mathematical FO-NEE system is numerically studied… More >

  • Open Access

    ARTICLE

    Lung Cancer Detection Using Modified AlexNet Architecture and Support Vector Machine

    Iftikhar Naseer1,*, Tehreem Masood1, Sheeraz Akram1, Arfan Jaffar1, Muhammad Rashid2, Muhammad Amjad Iqbal3

    CMC-Computers, Materials & Continua, Vol.74, No.1, pp. 2039-2054, 2023, DOI:10.32604/cmc.2023.032927

    Abstract Lung cancer is the most dangerous and death-causing disease indicated by the presence of pulmonary nodules in the lung. It is mostly caused by the instinctive growth of cells in the lung. Lung nodule detection has a significant role in detecting and screening lung cancer in Computed tomography (CT) scan images. Early detection plays an important role in the survival rate and treatment of lung cancer patients. Moreover, pulmonary nodule classification techniques based on the convolutional neural network can be used for the accurate and efficient detection of lung cancer. This work proposed an automatic nodule detection method in CT… More >

  • Open Access

    ARTICLE

    Efficient Scalable Template-Matching Technique for Ancient Brahmi Script Image

    Sandeep Kaur*, Bharat Bhushan Sagar

    CMC-Computers, Materials & Continua, Vol.74, No.1, pp. 1541-1559, 2023, DOI:10.32604/cmc.2023.032857

    Abstract Analysis and recognition of ancient scripts is a challenging task as these scripts are inscribed on pillars, stones, or leaves. Optical recognition systems can help in preserving, sharing, and accelerate the study of the ancient scripts, but lack of standard dataset for such scripts is a major constraint. Although many scholars and researchers have captured and uploaded inscription images on various websites, manual searching, downloading and extraction of these images is tedious and error prone. Web search queries return a vast number of irrelevant results, and manually extracting images for a specific script is not scalable. This paper proposes a… More >

  • Open Access

    ARTICLE

    Split-n-Swap: A New Modification of the Twofish Block Cipher Algorithm

    Awny Sayed1,2, Maha Mahrous3, Enas Elgeldawi1,*

    CMC-Computers, Materials & Continua, Vol.74, No.1, pp. 1723-1734, 2023, DOI:10.32604/cmc.2023.032838

    Abstract Securing digital data from unauthorized access throughout its entire lifecycle has been always a critical concern. A robust data security system should protect the information assets of any organization against cybercriminal activities. The Twofish algorithm is one of the well-known symmetric key block cipher cryptographic algorithms and has been known for its rapid convergence. But when it comes to security, it is not the preferred cryptographic algorithm to use compared to other algorithms that have shown better security. Many applications and social platforms have adopted other symmetric key block cipher cryptographic algorithms such as the Advanced Encryption Standard (AES) algorithm… More >

  • Open Access

    ARTICLE

    Automated Brain Tumor Diagnosis Using Deep Residual U-Net Segmentation Model

    R. Poonguzhali1, Sultan Ahmad2, P. Thiruvannamalai Sivasankar3, S. Anantha Babu3, Pranav Joshi4, Gyanendra Prasad Joshi5, Sung Won Kim6,*

    CMC-Computers, Materials & Continua, Vol.74, No.1, pp. 2179-2194, 2023, DOI:10.32604/cmc.2023.032816

    Abstract Automated segmentation and classification of biomedical images act as a vital part of the diagnosis of brain tumors (BT). A primary tumor brain analysis suggests a quicker response from treatment that utilizes for improving patient survival rate. The location and classification of BTs from huge medicinal images database, obtained from routine medical tasks with manual processes are a higher cost together in effort and time. An automatic recognition, place, and classifier process was desired and useful. This study introduces an Automated Deep Residual U-Net Segmentation with Classification model (ADRU-SCM) for Brain Tumor Diagnosis. The presented ADRU-SCM model majorly focuses on… More >

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