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Search Results (16)
  • Open Access

    ARTICLE

    Gaussian Optimized Deep Learning-based Belief Classification Model for Breast Cancer Detection

    Areej A. Malibari1, Marwa Obayya2, Mohamed K. Nour3, Amal S. Mehanna4, Manar Ahmed Hamza5,*, Abu Sarwar Zamani5, Ishfaq Yaseen5, Abdelwahed Motwakel5

    CMC-Computers, Materials & Continua, Vol.73, No.2, pp. 4123-4138, 2022, DOI:10.32604/cmc.2022.030492

    Abstract With the rapid increase of new cases with an increased mortality rate, cancer is considered the second and most deadly disease globally. Breast cancer is the most widely affected cancer worldwide, with an increased death rate percentage. Due to radiologists’ processing of mammogram images, many computer-aided diagnoses have been developed to detect breast cancer. Early detection of breast cancer will reduce the death rate worldwide. The early diagnosis of breast cancer using the developed computer-aided diagnosis (CAD) systems still needed to be enhanced by incorporating innovative deep learning technologies to improve the accuracy and sensitivity of the detection system with… More >

  • Open Access

    ARTICLE

    Optimal Cooperative Spectrum Sensing Based on Butterfly Optimization Algorithm

    Noor Gul1,2, Saeed Ahmed1,3, Atif Elahi4, Su Min Kim1, Junsu Kim1,*

    CMC-Computers, Materials & Continua, Vol.71, No.1, pp. 369-387, 2022, DOI:10.32604/cmc.2022.022260

    Abstract Since the introduction of the Internet of Things (IoT), several researchers have been exploring its productivity to utilize and organize the spectrum assets. Cognitive radio (CR) technology is characterized as the best aspirant for wireless communications to augment IoT competencies. In the CR networks, secondary users (SUs) opportunistically get access to the primary users (PUs) spectrum through spectrum sensing. The multipath issues in the wireless channel can fluster the sensing ability of the individual SUs. Therefore, several cooperative SUs are engaged in cooperative spectrum sensing (CSS) to ensure reliable sensing results. In CSS, security is still a major concern for… More >

  • Open Access

    ARTICLE

    A Transfer Learning-Enabled Optimized Extreme Deep Learning Paradigm for Diagnosis of COVID-19

    Ahmed Reda*, Sherif Barakat, Amira Rezk

    CMC-Computers, Materials & Continua, Vol.70, No.1, pp. 1381-1399, 2022, DOI:10.32604/cmc.2022.019809

    Abstract Many respiratory infections around the world have been caused by coronaviruses. COVID-19 is one of the most serious coronaviruses due to its rapid spread between people and the lowest survival rate. There is a high need for computer-assisted diagnostics (CAD) in the area of artificial intelligence to help doctors and radiologists identify COVID-19 patients in cloud systems. Machine learning (ML) has been used to examine chest X-ray frames. In this paper, a new transfer learning-based optimized extreme deep learning paradigm is proposed to identify the chest X-ray picture into three classes, a pneumonia patient, a COVID-19 patient, or a normal… More >

  • Open Access

    ARTICLE

    Monarch Butterfly Optimization for Reliable Scheduling in Cloud

    B. Gomathi1, S. T. Suganthi2,*, Karthikeyan Krishnasamy3, J. Bhuvana4

    CMC-Computers, Materials & Continua, Vol.69, No.3, pp. 3693-3710, 2021, DOI:10.32604/cmc.2021.018159

    Abstract Enterprises have extensively taken on cloud computing environment since it provides on-demand virtualized cloud application resources. The scheduling of the cloud tasks is a well-recognized NP-hard problem. The Task scheduling problem is convoluted while convincing different objectives, which are dispute in nature. In this paper, Multi-Objective Improved Monarch Butterfly Optimization (MOIMBO) algorithm is applied to solve multi-objective task scheduling problems in the cloud in preparation for Pareto optimal solutions. Three different dispute objectives, such as makespan, reliability, and resource utilization, are deliberated for task scheduling problems.The Epsilon-fuzzy dominance sort method is utilized in the multi-objective domain to elect the foremost… More >

  • Open Access

    ARTICLE

    Protein detection in spermatids and spermatozoa of the butterfly Euptoieta hegesia (Lepidoptera)

    KARINA MANCINI, HEIDI DOLDER

    BIOCELL, Vol.28, No.3, pp. 299-310, 2004, DOI:10.32604/biocell.2004.28.299

    Abstract This study was undertaken to detect protein components in both sperm types of the butterfly Euptoieta hegesia. These spermatozoa possess complex extracellular structures for which the composition and functional significance are still unclear. In the apyrene sperm head, the proteic cap presented an external ring and an internal dense content; basic proteins were detected only in external portions. In the tail, the paracrystalline core of mitochondrial derivatives and the axoneme are rich in proteins. The extratesticular spermatozoa are covered by a proteic coat, which presented two distinct layers. In eupyrene spermatozoa, acrosome and nucleus were negatively stained, probably because of… More >

  • Open Access

    ARTICLE

    Anti-Noise Quantum Network Coding Protocol Based on Bell States and Butterfly Network Model

    Zhexi Zhang1, Zhiguo Qu1,2,*

    Journal of Quantum Computing, Vol.1, No.2, pp. 89-109, 2019, DOI:10.32604/jqc.2019.07415

    Abstract How to establish a secure and efficient quantum network coding algorithm is one of important research topics of quantum secure communications. Based on the butterfly network model and the characteristics of easy preparation of Bell states, a novel anti-noise quantum network coding protocol is proposed in this paper. The new protocol encodes and transmits classical information by virtue of Bell states. It can guarantee the transparency of the intermediate nodes during information, so that the eavesdropper Eve disables to get any information even if he intercepts the transmitted quantum states. In view of the inevitability of quantum noise in quantum… More >

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