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  • Open Access

    ARTICLE

    False Alarm Reduction in ICU Using Ensemble Classifier Approach

    V. Ravindra Krishna Chandar1,*, M. Thangamani2

    Intelligent Automation & Soft Computing, Vol.34, No.1, pp. 165-181, 2022, DOI:10.32604/iasc.2022.022339 - 15 April 2022

    Abstract

    During patient monitoring, false alert in the Intensive Care Unit (ICU) becomes a major problem. In the category of alarms, pseudo alarms are regarded as having no clinical or therapeutic significance, and thus they result in fatigue alarms. Artifacts are misrepresentations of tissue structures produced by imaging techniques. These Artifacts can invalidate the Arterial Blood Pressure (ABP) signal. Therefore, it is very important to develop algorithms that can detect artifacts. However, ABP has algorithmic shortcomings and limitations of design. This study is aimed at developing a real-time enhancement of independent component analysis (EICA) and time-domain

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  • Open Access

    ARTICLE

    Wavelet Based Detection of Outliers in Volatility Time Series Models

    Khudhayr A. Rashedi1,2,*, Mohd Tahir Ismail1, Abdeslam Serroukh3, S. Al wadi4

    CMC-Computers, Materials & Continua, Vol.72, No.2, pp. 3835-3847, 2022, DOI:10.32604/cmc.2022.026476 - 29 March 2022

    Abstract We introduce a new wavelet based procedure for detecting outliers in financial discrete time series. The procedure focuses on the analysis of residuals obtained from a model fit, and applied to the Generalized Autoregressive Conditional Heteroskedasticity (GARCH) like model, but not limited to these models. We apply the Maximal-Overlap Discrete Wavelet Transform (MODWT) to the residuals and compare their wavelet coefficients against quantile thresholds to detect outliers. Our methodology has several advantages over existing methods that make use of the standard Discrete Wavelet Transform (DWT). The series sample size does not need to be a More >

  • Open Access

    ARTICLE

    Fast and Accurate Thoracic SPECT Image Reconstruction

    Afef Houimli1,*, IssamBen Mhamed2, Bechir Letaief1,3,4, Dorra Ben-Sellem1,3,4

    CMES-Computer Modeling in Engineering & Sciences, Vol.131, No.2, pp. 881-904, 2022, DOI:10.32604/cmes.2022.016705 - 14 March 2022

    Abstract In Single-Photon Emission Computed Tomography (SPECT), the reconstructed image has insufficient contrast, poor resolution and inaccurate volume of the tumor size due to physical degradation factors. Generally, nonstationary filtering of the projection or the slice is one of the strategies for correcting the resolution and therefore improving the quality of the reconstructed SPECT images. This paper presents a new 3D algorithm that enhances the quality of reconstructed thoracic SPECT images and reduces the noise level with the best degree of accuracy. The suggested algorithm is composed of three steps. The first one consists of denoising… More >

  • Open Access

    ARTICLE

    Enhancement of Biomass Material Characterization Images Using an Improved U-Net

    Zuozheng Lian1, Hong Zhao2,*, Qianjun Zhang1, Haizhen Wang1, E. Erdun3

    CMC-Computers, Materials & Continua, Vol.72, No.1, pp. 1515-1528, 2022, DOI:10.32604/cmc.2022.024779 - 24 February 2022

    Abstract For scanning electron microscopes with high resolution and a strong electric field, biomass materials under observation are prone to radiation damage from the electron beam. This results in blurred or non-viable images, which affect further observation of material microscopic morphology and characterization. Restoring blurred images to their original sharpness is still a challenging problem in image processing. Traditional methods can't effectively separate image context dependency and texture information, affect the effect of image enhancement and deblurring, and are prone to gradient disappearance during model training, resulting in great difficulty in model training. In this paper,… More >

  • Open Access

    ARTICLE

    Cancelable Multi-biometric Template Generation Based on Dual-Tree Complex Wavelet Transform

    Ahmed M. Ayoup1,*, Ashraf A. M. Khalaf1, Fahad Alraddady2, Fathi E. Abd El-Samie3, Walid El-Shafai3,5, Salwa M. Serag Eldin2,4

    Intelligent Automation & Soft Computing, Vol.33, No.2, pp. 1289-1304, 2022, DOI:10.32604/iasc.2022.024381 - 08 February 2022

    Abstract In this article, we introduce a new cancelable biometric template generation layout depending on selective encryption technology and Dual-Tree Complex Wavelet Transform (DT-CWT) fusion. The input face biometric is entered into the automatic face-segmentation (Viola-Jones) algorithm to detect the object in a short time. Viola-Jones algorithm can detect the left eye, right eye, nose, and mouth of the input biometric image. The encoder can choose the left or right eye to generate a cancelable biometric template. The selected eye image of size M × N is XORed with the created pseudo-random number (PRN) matrix CM × N to… More >

  • Open Access

    ARTICLE

    Research on Thunderstorm Identification Based on Discrete Wavelet Transform

    Xiaopeng Li1, Ziyuan Xu3,4, Jin Han1,*, Xingming Sun1,2, Yi Cao5

    Intelligent Automation & Soft Computing, Vol.33, No.2, pp. 1153-1166, 2022, DOI:10.32604/iasc.2022.023261 - 08 February 2022

    Abstract Lightning has been one of the most talked-about natural disasters worldwide in recent years, as it poses a great threat to all industries and can cause huge economic losses. Thunderstorms are often accompanied by natural phenomena such as lightning strikes and lightning, and many scholars have studied deeply the regulations of thunderstorm generation, movement and dissipation to reduce the risk of lightning damage. Most of the current methods for studying thunderstorms focus on using more complex algorithms based on radar or lightning data, which increases the computational burden and reduces the computational efficiency to some… More >

  • Open Access

    ARTICLE

    Hybrid Approach for Taxonomic Classification Based on Deep Learning

    Naglaa. F. Soliman1,*, Samia M. Abd-Alhalem2, Walid El-Shafai2, Salah Eldin S. E. Abdulrahman3, N. Ismaiel3, El-Sayed M. El-Rabaie2, Abeer D. Algarni1, Fatimah Algarni4, Amel A. Alhussan5, Fathi E. Abd El-Samie1,2

    Intelligent Automation & Soft Computing, Vol.32, No.3, pp. 1881-1891, 2022, DOI:10.32604/iasc.2022.017683 - 09 December 2021

    Abstract Recently, deep learning has opened a remarkable research direction in the track of bioinformatics, especially for the applications that need classification and regression. With deep learning techniques, DNA sequences can be classified with high accuracy. Firstly, a DNA sequence should be represented, numerically. After that, DNA features are extracted from the numerical representations based on deep learning techniques to improve the classification process. Recently, several architectures have been developed based on deep learning for DNA sequence classification. Convolutional Neural Network (CNN) and Recurrent Neural Network (RNN) are the default deep learning architectures used for this… More >

  • Open Access

    ARTICLE

    Synovial Sarcoma Classification Technique Using Support Vector Machine and Structure Features

    P. Arunachalam1, N. Janakiraman1,*, Arun Kumar Sivaraman2, A. Balasundaram3, Rajiv Vincent2, Sita Rani4, Barnali Dey5, A. Muralidhar2, M. Rajesh2

    Intelligent Automation & Soft Computing, Vol.32, No.2, pp. 1241-1259, 2022, DOI:10.32604/iasc.2022.022573 - 17 November 2021

    Abstract Digital clinical histopathology technique is used for accurately diagnosing cancer cells and achieving optimal results using Internet of Things (IoT) and blockchain technology. The cell pattern of Synovial Sarcoma (SS) cancer images always appeared as spindle shaped cell (SSC) structures. Identifying the SSC and its prognostic indicator are very crucial problems for computer aided diagnosis, especially in healthcare industry applications. A constructive framework has been proposed for the classification of SSC feature components using Support Vector Machine (SVM) with the assistance of relevant Support Vectors (SVs). This framework used the SS images, and it has… More >

  • Open Access

    ARTICLE

    A Robust 3-D Medical Watermarking Based on Wavelet Transform for Data Protection

    Xiaorui Zhang1,3,*, Wenfang Zhang1, Wei Sun2, Xingming Sun1, Sunil Kumar Jha4

    Computer Systems Science and Engineering, Vol.41, No.3, pp. 1043-1056, 2022, DOI:10.32604/csse.2022.022305 - 10 November 2021

    Abstract In a telemedicine diagnosis system, the emergence of 3D imaging enables doctors to make clearer judgments, and its accuracy also directly affects doctors’ diagnosis of the disease. In order to ensure the safe transmission and storage of medical data, a 3D medical watermarking algorithm based on wavelet transform is proposed in this paper. The proposed algorithm employs the principal component analysis (PCA) transform to reduce the data dimension, which can minimize the error between the extracted components and the original data in the mean square sense. Especially, this algorithm helps to create a bacterial foraging More >

  • Open Access

    ARTICLE

    X-Ray Covid-19 Detection Based on Scatter Wavelet Transform and Dense Deep Neural Network

    Ali Sami Al-Itbi*, Ahmed Bahaaulddin A. Alwahhab, Ali Mohammed Sahan

    Computer Systems Science and Engineering, Vol.41, No.3, pp. 1255-1271, 2022, DOI:10.32604/csse.2022.021980 - 10 November 2021

    Abstract Notwithstanding the discovery of vaccines for Covid-19, the virus's rapid spread continues due to the limited availability of vaccines, especially in poor and emerging countries. Therefore, the key issues in the present COVID-19 pandemic are the early identification of COVID-19, the cautious separation of infected cases at the lowest cost and curing the disease in the early stages. For that reason, the methodology adopted for this study is imaging tools, particularly computed tomography, which have been critical in diagnosing and treating the disease. A new method for detecting Covid-19 in X-rays and CT images has… More >

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