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  • Open Access

    ARTICLE

    Breast Cancer Diagnosis Using Feature Selection Approaches and Bayesian Optimization

    Erkan Akkur1, Fuat TURK2,*, Osman Erogul1

    Computer Systems Science and Engineering, Vol.45, No.2, pp. 1017-1031, 2023, DOI:10.32604/csse.2023.033003

    Abstract Breast cancer seriously affects many women. If breast cancer is detected at an early stage, it may be cured. This paper proposes a novel classification model based improved machine learning algorithms for diagnosis of breast cancer at its initial stage. It has been used by combining feature selection and Bayesian optimization approaches to build improved machine learning models. Support Vector Machine, K-Nearest Neighbor, Naive Bayes, Ensemble Learning and Decision Tree approaches were used as machine learning algorithms. All experiments were tested on two different datasets, which are Wisconsin Breast Cancer Dataset (WBCD) and Mammographic Breast Cancer Dataset (MBCD). Experiments were… More >

  • Open Access

    ARTICLE

    Bayesian Computation for the Parameters of a Zero-Inflated Cosine Geometric Distribution with Application to COVID-19 Pandemic Data

    Sunisa Junnumtuam, Sa-Aat Niwitpong*, Suparat Niwitpong

    CMES-Computer Modeling in Engineering & Sciences, Vol.135, No.2, pp. 1229-1254, 2023, DOI:10.32604/cmes.2022.022098

    Abstract A new three-parameter discrete distribution called the zero-inflated cosine geometric (ZICG) distribution is proposed for the first time herein. It can be used to analyze over-dispersed count data with excess zeros. The basic statistical properties of the new distribution, such as the moment generating function, mean, and variance are presented. Furthermore, confidence intervals are constructed by using the Wald, Bayesian, and highest posterior density (HPD) methods to estimate the true confidence intervals for the parameters of the ZICG distribution. Their efficacies were investigated by using both simulation and real-world data comprising the number of daily COVID-19 positive cases at the… More > Graphic Abstract

    Bayesian Computation for the Parameters of a Zero-Inflated Cosine Geometric Distribution with Application to COVID-19 Pandemic Data

  • Open Access

    ARTICLE

    Certrust: An SDN-Based Framework for the Trust of Certificates against Crossfire Attacks in IoT Scenarios

    Lei Yan1, Maode Ma2, Dandan Li1, Xiaohong Huang1,*, Yan Ma1, Kun Xie1

    CMES-Computer Modeling in Engineering & Sciences, Vol.134, No.3, pp. 2137-2162, 2023, DOI:10.32604/cmes.2022.022462

    Abstract The low-intensity attack flows used by Crossfire attacks are hard to distinguish from legitimate flows. Traditional methods to identify the malicious flows in Crossfire attacks are rerouting, which is based on statistics. In these existing mechanisms, the identification of malicious flows depends on the IP address. However, the IP address is easy to be changed by attacks. Compared with the IP address, the certificate is more challenging to be tampered with or forged. Moreover, the traffic trend in the network is towards encryption. The certificates are popularly utilized by IoT devices for authentication in encryption protocols. DTLShps proposed a new… More > Graphic Abstract

    Certrust: An SDN-Based Framework for the Trust of Certificates against Crossfire Attacks in IoT Scenarios

  • Open Access

    ARTICLE

    Change Point Detection for Process Data Analytics Applied to a Multiphase Flow Facility

    Rebecca Gedda1,*, Larisa Beilina2, Ruomu Tan3

    CMES-Computer Modeling in Engineering & Sciences, Vol.134, No.3, pp. 1737-1759, 2023, DOI:10.32604/cmes.2022.019764

    Abstract Change point detection becomes increasingly important because it can support data analysis by providing labels to the data in an unsupervised manner. In the context of process data analytics, change points in the time series of process variables may have an important indication about the process operation. For example, in a batch process, the change points can correspond to the operations and phases defined by the batch recipe. Hence identifying change points can assist labelling the time series data. Various unsupervised algorithms have been developed for change point detection, including the optimisation approach which minimises a cost function with certain… More > Graphic Abstract

    Change Point Detection for Process Data Analytics Applied to a Multiphase Flow Facility

  • Open Access

    ARTICLE

    Designing Bayesian Two-Sided Group Chain Sampling Plan for Gamma Prior Distribution

    Waqar Hafeez1, Nazrina Aziz1,2,*

    Computer Systems Science and Engineering, Vol.44, No.2, pp. 1069-1079, 2023, DOI:10.32604/csse.2023.022047

    Abstract Acceptance sampling is used to decide either the whole lot will be accepted or rejected, based on inspection of randomly sampled items from the same lot. As an alternative to traditional sampling plans, it is possible to use Bayesian approaches using previous knowledge on process variation. This study presents a Bayesian two-sided group chain sampling plan (BTSGChSP) by using various combinations of design parameters. In BTSGChSP, inspection is based on preceding as well as succeeding lots. Poisson function is used to derive the probability of lot acceptance based on defective and non-defective products. Gamma distribution is considered as a suitable… More >

  • Open Access

    ARTICLE

    Deep Feature Bayesian Classifier for SAR Target Recognition with Small Training Set

    Liguo Zhang1,2, Zilin Tian1, Yan Zhang3,*, Tong Shuai4, Shuo Liang4, Zhuofei Wu5

    Journal of New Media, Vol.4, No.2, pp. 59-71, 2022, DOI:10.32604/jnm.2022.029360

    Abstract In recent years, deep learning algorithms have been popular in recognizing targets in synthetic aperture radar (SAR) images. However, due to the problem of overfitting, the performance of these models tends to worsen when just a small number of training data are available. In order to solve the problems of overfitting and an unsatisfied performance of the network model in the small sample remote sensing image target recognition, in this paper, we uses a deep residual network to autonomously acquire image features and proposes the Deep Feature Bayesian Classifier model (RBnet) for SAR image target recognition. In the RBnet, a… More >

  • Open Access

    ARTICLE

    A Hybrid Regularization-Based Multi-Frame Super-Resolution Using Bayesian Framework

    Mahmoud M. Khattab1,2,*, Akram M Zeki1, Ali A. Alwan3, Belgacem Bouallegue2, Safaa S. Matter4, Abdelmoty M. Ahmed2

    Computer Systems Science and Engineering, Vol.44, No.1, pp. 35-54, 2023, DOI:10.32604/csse.2023.025251

    Abstract The prime purpose for the image reconstruction of a multi-frame super-resolution is to reconstruct a higher-resolution image through incorporating the knowledge obtained from a series of relevant low-resolution images, which is useful in numerous fields. Nevertheless, super-resolution image reconstruction methods are usually damaged by undesirable restorative artifacts, which include blurring distortion, noises, and stair-casing effects. Consequently, it is always challenging to achieve balancing between image smoothness and preservation of the edges inside the image. In this research work, we seek to increase the effectiveness of multi-frame super-resolution image reconstruction by increasing the visual information and improving the automated machine perception,… More >

  • Open Access

    ARTICLE

    Multilevel Modelling for Surgical Tool Calibration Using LINEX Loss Function

    Mansour F. Yassen1,2,*

    CMC-Computers, Materials & Continua, Vol.73, No.1, pp. 1691-1706, 2022, DOI:10.32604/cmc.2022.029701

    Abstract Quantifying the tool–tissue interaction forces in surgery can be utilized in the training of inexperienced surgeons, assist them better use surgical tools and avoid applying excessive pressures. The voltages read from strain gauges are used to approximate the unknown values of implemented forces. To this objective, the force-voltage connection must be quantified in order to evaluate the interaction forces during surgery. The progress of appropriate statistical learning approaches to describe the link between the genuine force applied on the tissue and numerous outputs obtained from sensors installed on surgical equipment is a key problem. In this study, different probabilistic approaches… More >

  • Open Access

    ARTICLE

    Sensitive Information Protection Model Based on Bayesian Game

    Yuzhen Liu1,2, Zhe Liu3, Xiaoliang Wang1,2,*, Qing Yang4, Guocai Zuo5, Frank Jiang6

    CMC-Computers, Materials & Continua, Vol.73, No.1, pp. 883-898, 2022, DOI:10.32604/cmc.2022.029002

    Abstract

    A game measurement model considering the attacker's knowledge background is proposed based on the Bayesian game theory aiming at striking a balance between the protection of sensitive information and the quality of service. We quantified the sensitive level of information according to the user's personalized sensitive information protection needs. Based on the probability distribution of sensitive level and attacker's knowledge background type, the strategy combination of service provider and attacker was analyzed, and a game-based sensitive information protection model was constructed. Through the combination of strategies under Bayesian equilibrium, the information entropy was used to measure the leakage of sensitive… More >

  • Open Access

    ARTICLE

    Bayesian Convolution for Stochastic Epidemic Model

    Mukhsar1,*, Ansari Saleh Ahmar2, M. A. El Safty3, Hamed El-Khawaga4,5, M. El Sayed6

    Intelligent Automation & Soft Computing, Vol.34, No.2, pp. 1175-1186, 2022, DOI:10.32604/iasc.2022.025214

    Abstract Dengue Hemorrhagic Fever (DHF) is a tropical disease that always attacks densely populated urban communities. Some factors, such as environment, climate and mobility, have contributed to the spread of the disease. The Aedes aegypti mosquito is an agent of dengue virus in humans, and by inhibiting its life cycle it can reduce the spread of the dengue disease. Therefore, it is necessary to involve the dynamics of mosquito's life cycle in a model in order to obtain a reliable risk map for intervention. The aim of this study is to develop a stochastic convolution susceptible, infective, recovered-susceptible, infective (SIR-SI) model… More >

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