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  • Open Access

    ARTICLE

    An Imbalanced Dataset and Class Overlapping Classification Model for Big Data

    Mini Prince1,*, P. M. Joe Prathap2

    Computer Systems Science and Engineering, Vol.44, No.2, pp. 1009-1024, 2023, DOI:10.32604/csse.2023.024277 - 15 June 2022

    Abstract Most modern technologies, such as social media, smart cities, and the internet of things (IoT), rely on big data. When big data is used in the real-world applications, two data challenges such as class overlap and class imbalance arises. When dealing with large datasets, most traditional classifiers are stuck in the local optimum problem. As a result, it’s necessary to look into new methods for dealing with large data collections. Several solutions have been proposed for overcoming this issue. The rapid growth of the available data threatens to limit the usefulness of many traditional methods.… More >

  • 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 - 15 June 2022

    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… More >

  • Open Access

    ARTICLE

    The Neurosurgical Challenge of Primary Central Nervous System Lymphoma Diagnosis: A Multimodal Intraoperative Imaging Approach to Overcome Frameless Neuronavigated Biopsy Sampling Errors

    Roberto Altieri1,2,*, Francesco Certo1, Marco Garozzo1, Giacomo Cammarata1, Massimiliano Maione1, Giuseppa Fiumanò3, Giuseppe Broggi4, Giada Maria Vecchio4, Rosario Caltabiano4, Gaetano Magro4, Giuseppe Barbagallo1

    Oncologie, Vol.24, No.4, pp. 693-706, 2022, DOI:10.32604/oncologie.2022.025393 - 31 December 2022

    Abstract Background: Intracranial lymphoma remains a challenging differential diagnosis in daily neurosurgical practice. We analyzed our early experience with a surgical series of frameless neuronavigated biopsies in Primary CNS Lymphomas (PCNSLs), highlighting the importance of using an intraoperative combined imaging protocol (5-ALA fluorescence, i-CT and 11C-MET-PET) to overcome potential targeting errors secondary to tumor volume reduction after corticosteroid therapy. Materials and Methods: All patients treated for PCNLSs at our center in a 24-month period (1/1/2019 to 31/12/2020) were analyzed. Our cohort included 6 patients (4 males), with a median age of 67 years (59–82). A total of 45 samples… More >

  • Open Access

    ARTICLE

    A Novel Multiple Dependent State Sampling Plan Based on Time Truncated Life Tests Using Mean Lifetime

    Pramote Charongrattanasakul1, Wimonmas Bamrungsetthapong2,*, Poom Kumam3

    CMC-Computers, Materials & Continua, Vol.73, No.3, pp. 4611-4626, 2022, DOI:10.32604/cmc.2022.030856 - 28 July 2022

    Abstract The design of a new adaptive version of the multiple dependent state (AMDS) sampling plan is presented based on the time truncated life test under the Weibull distribution. We achieved the proposed sampling plan by applying the concept of the double sampling plan and existing multiple dependent state sampling plans. A warning sign for acceptance number was proposed to increase the probability of current lot acceptance. The optimal plan parameters were determined simultaneously with nonlinear optimization problems under the producer’s risk and consumer’s risk. A simulation study was presented to support the proposed sampling plan. More >

  • Open Access

    ARTICLE

    MCBC-SMOTE: A Majority Clustering Model for Classification of Imbalanced Data

    Jyoti Arora1, Meena Tushir2, Keshav Sharma1, Lalit Mohan1, Aman Singh3,*, Abdullah Alharbi4, Wael Alosaimi4

    CMC-Computers, Materials & Continua, Vol.73, No.3, pp. 4801-4817, 2022, DOI:10.32604/cmc.2022.025960 - 28 July 2022

    Abstract Datasets with the imbalanced class distribution are difficult to handle with the standard classification algorithms. In supervised learning, dealing with the problem of class imbalance is still considered to be a challenging research problem. Various machine learning techniques are designed to operate on balanced datasets; therefore, the state of the art, different under-sampling, over-sampling and hybrid strategies have been proposed to deal with the problem of imbalanced datasets, but highly skewed datasets still pose the problem of generalization and noise generation during resampling. To over-come these problems, this paper proposes a majority clustering model for… More >

  • Open Access

    ARTICLE

    Medical Image Demosaicing Based Design of Newton Gregory Interpolation Algorithm

    E. P. Kannan1,*, S. S. Vinsley2, T. V. Chithra3

    Intelligent Automation & Soft Computing, Vol.34, No.3, pp. 1675-1691, 2022, DOI:10.32604/iasc.2022.022707 - 25 May 2022

    Abstract In this paper, Field-Programmable Gate Array (FPGA) implementation-based image demosaicing is carried out. The Newton Gregory interpolation algorithm is designed based on FPGA frame work. Interpolation is the method of assessing the value of a function for any in-between value of self-regulating variable, whereas the method of computing the value of the function outside the specified range is named extrapolation. The natural images are collected from Kodak image database and medical images are collected from UPOL (University of Phoenix Online) database. The proposed algorithm is executed on using Xilinx ISE (Integrated Synthesis Environment) Design Suite… More >

  • Open Access

    ARTICLE

    A 78-MHz BW Continuous-Time Sigma-Delta ADC with Programmable VCO Quantizer

    Sha Li1,2, Qiao Meng1,*, Irfan Tariq1, Xi Chen3

    CMC-Computers, Materials & Continua, Vol.72, No.3, pp. 6079-6090, 2022, DOI:10.32604/cmc.2022.027404 - 21 April 2022

    Abstract This article presents a high speed third-order continuous-time (CT) sigma-delta analog-to-digital converter (SDADC) based on voltage-controlled oscillator (VCO), featuring a digital programmable quantizer structure. To improve the overall performance, not only oversampling technique but also noise-shaping enhancing technique is used to suppress in-band noise. Due to the intrinsic first-order noise-shaping of the VCO quantizer, the proposed third-order SDADC can realize forth-order noise-shaping ideally. As a bright advantage, the proposed programmable VCO quantizer is digital-friendly, which can simplify the design process and improve anti-interference capability of the circuit. A 4-bit programmable VCO quantizer clocked at 2.5 GHz,… More >

  • Open Access

    ARTICLE

    Modelling an Efficient Clinical Decision Support System for Heart Disease Prediction Using Learning and Optimization Approaches

    Sridharan Kannan*

    CMES-Computer Modeling in Engineering & Sciences, Vol.131, No.2, pp. 677-694, 2022, DOI:10.32604/cmes.2022.018580 - 14 March 2022

    Abstract With the worldwide analysis, heart disease is considered a significant threat and extensively increases the mortality rate. Thus, the investigators mitigate to predict the occurrence of heart disease in an earlier stage using the design of a better Clinical Decision Support System (CDSS). Generally, CDSS is used to predict the individuals’ heart disease and periodically update the condition of the patients. This research proposes a novel heart disease prediction system with CDSS composed of a clustering model for noise removal to predict and eliminate outliers. Here, the Synthetic Over-sampling prediction model is integrated with the… More >

  • Open Access

    ARTICLE

    Aluminum Alloy Fatigue Crack Damage Prediction Based on Lamb Wave-Systematic Resampling Particle Filter Method

    Gaozheng Zhao1, Changchao Liu1, Lingyu Sun1, Ning Yang2, Lei Zhang1, Mingshun Jiang1, Lei Jia1, Qingmei Sui1,*

    Structural Durability & Health Monitoring, Vol.16, No.1, pp. 81-96, 2022, DOI:10.32604/sdhm.2022.016905 - 11 February 2022

    Abstract Fatigue crack prediction is a critical aspect of prognostics and health management research. The particle filter algorithm based on Lamb wave is a potential tool to solve the nonlinear and non-Gaussian problems on fatigue growth, and it is widely used to predict the state of fatigue crack. This paper proposes a method of lamb wave-based early fatigue microcrack prediction with the aid of particle filters. With this method, which the changes in signal characteristics under different fatigue crack lengths are analyzed, and the state- and observation-equations of crack extension are established. Furthermore, an experiment is More >

  • Open Access

    ARTICLE

    Mu-Net: Multi-Path Upsampling Convolution Network for Medical Image Segmentation

    Jia Chen1, Zhiqiang He1, Dayong Zhu1, Bei Hui1,*, Rita Yi Man Li2, Xiao-Guang Yue3,4,5

    CMES-Computer Modeling in Engineering & Sciences, Vol.131, No.1, pp. 73-95, 2022, DOI:10.32604/cmes.2022.018565 - 24 January 2022

    Abstract Medical image segmentation plays an important role in clinical diagnosis, quantitative analysis, and treatment process. Since 2015, U-Net-based approaches have been widely used for medical image segmentation. The purpose of the U-Net expansive path is to map low-resolution encoder feature maps to full input resolution feature maps. However, the consecutive deconvolution and convolutional operations in the expansive path lead to the loss of some high-level information. More high-level information can make the segmentation more accurate. In this paper, we propose MU-Net, a novel, multi-path upsampling convolution network to retain more high-level information. The MU-Net mainly More >

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