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  • Open Access


    Anomaly Detection Based on Discrete Wavelet Transformation for Insider Threat Classification

    Dong-Wook Kim1, Gun-Yoon Shin1, Myung-Mook Han2,*

    Computer Systems Science and Engineering, Vol.46, No.1, pp. 153-164, 2023, DOI:10.32604/csse.2023.034589

    Abstract Unlike external attacks, insider threats arise from legitimate users who belong to the organization. These individuals may be a potential threat for hostile behavior depending on their motives. For insider detection, many intrusion detection systems learn and prevent known scenarios, but because malicious behavior has similar patterns to normal behavior, in reality, these systems can be evaded. Furthermore, because insider threats share a feature space similar to normal behavior, identifying them by detecting anomalies has limitations. This study proposes an improved anomaly detection methodology for insider threats that occur in cybersecurity in which a discrete wavelet transformation technique is applied… More >

  • Open Access


    Application of Zero-Watermarking for Medical Image in Intelligent Sensor Network Security

    Shixin Tu, Yuanyuan Jia, Jinglong Du*, Baoru Han*

    CMES-Computer Modeling in Engineering & Sciences, Vol.136, No.1, pp. 293-321, 2023, DOI:10.32604/cmes.2023.022308

    Abstract The field of healthcare is considered to be the most promising application of intelligent sensor networks. However, the security and privacy protection of medical images collected by intelligent sensor networks is a hot problem that has attracted more and more attention. Fortunately, digital watermarking provides an effective method to solve this problem. In order to improve the robustness of the medical image watermarking scheme, in this paper, we propose a novel zero-watermarking algorithm with the integer wavelet transform (IWT), Schur decomposition and image block energy. Specifically, we first use IWT to extract low-frequency information and divide them into non-overlapping blocks,… More >

  • Open Access


    Implementation of VLSI on Signal Processing-Based Digital Architecture Using AES Algorithm

    Mohanapriya Marimuthu1, Santhosh Rajendran2, Reshma Radhakrishnan2, Kalpana Rengarajan3, Shahzada Khurram4, Shafiq Ahmad5, Abdelaty Edrees Sayed5, Muhammad Shafiq6,*

    CMC-Computers, Materials & Continua, Vol.74, No.3, pp. 4729-4745, 2023, DOI:10.32604/cmc.2023.033020

    Abstract Continuous improvements in very-large-scale integration (VLSI) technology and design software have significantly broadened the scope of digital signal processing (DSP) applications. The use of application-specific integrated circuits (ASICs) and programmable digital signal processors for many DSP applications have changed, even though new system implementations based on reconfigurable computing are becoming more complex. Adaptable platforms that combine hardware and software programmability efficiency are rapidly maturing with discrete wavelet transformation (DWT) and sophisticated computerized design techniques, which are much needed in today’s modern world. New research and commercial efforts to sustain power optimization, cost savings, and improved runtime effectiveness have been initiated… More >

  • Open Access


    Efficient Authentication System Using Wavelet Embeddings of Otoacoustic Emission Signals

    V. Harshini1, T. Dhanwin1, A. Shahina1,*, N. Safiyyah2, A. Nayeemulla Khan2

    Computer Systems Science and Engineering, Vol.45, No.2, pp. 1851-1867, 2023, DOI:10.32604/csse.2023.028136

    Abstract Biometrics, which has become integrated with our daily lives, could fall prey to falsification attacks, leading to security concerns. In our paper, we use Transient Evoked Otoacoustic Emissions (TEOAE) that are generated by the human cochlea in response to an external sound stimulus, as a biometric modality. TEOAE are robust to falsification attacks, as the uniqueness of an individual’s inner ear cannot be impersonated. In this study, we use both the raw 1D TEOAE signals, as well as the 2D time-frequency representation of the signal using Continuous Wavelet Transform (CWT). We use 1D and 2D Convolutional Neural Networks (CNN) for… More >

  • Open Access


    Brain Tumor Classification Using Image Fusion and EFPA-SVM Classifier

    P. P. Fathimathul Rajeena1,*, R. Sivakumar2

    Intelligent Automation & Soft Computing, Vol.35, No.3, pp. 2837-2855, 2023, DOI:10.32604/iasc.2023.030144

    Abstract An accurate and early diagnosis of brain tumors based on medical imaging modalities is of great interest because brain tumors are a harmful threat to a person’s health worldwide. Several medical imaging techniques have been used to analyze brain tumors, including computed tomography (CT) and magnetic resonance imaging (MRI). CT provides information about dense tissues, whereas MRI gives information about soft tissues. However, the fusion of CT and MRI images has little effect on enhancing the accuracy of the diagnosis of brain tumors. Therefore, machine learning methods have been adopted to diagnose brain tumors in recent years. This paper intends… More >

  • Open Access


    Gamma Correction for Brightness Preservation in Natural Images

    Navleen S Rekhi1,2,*, Jagroop S Sidhu2, Amit Arora2

    Computer Systems Science and Engineering, Vol.44, No.3, pp. 2791-2807, 2023, DOI:10.32604/csse.2023.026976

    Abstract Due to improper acquisition settings and other noise artifacts, the image degraded to yield poor mean preservation in brightness. The simplest way to improve the preservation is the implementation of histogram equalization. Because of over-enhancement, it failed to preserve the mean brightness and produce the poor quality of the image. This paper proposes a multi-scale decomposition for brightness preservation using gamma correction. After transformation to hue, saturation and intensity (HSI) channel, the 2D- discrete wavelet transform decomposed the intensity component into low and high-pass coefficients. At the next phase, gamma correction is used by auto-tuning the scale value. The scale… More >

  • Open Access


    Anomaly Detection and Pattern Differentiation in Monitoring Data from Power Transformers

    Jun Zhao1, Shuguo Gao1, Yunpeng Liu2,3, Quan Wang2,*, Ziqiang Xu2, Yuan Tian1, Lu Sun1

    Energy Engineering, Vol.119, No.5, pp. 1811-1828, 2022, DOI:10.32604/ee.2022.020490

    Abstract Aiming at the problem of abnormal data generated by a power transformer on-line monitoring system due to the influences of transformer operation state change, external environmental interference, communication interruption, and other factors, a method of anomaly recognition and differentiation for monitoring data was proposed. Firstly, the empirical wavelet transform (EWT) and the autoregressive integrated moving average (ARIMA) model were used for time series modelling of monitoring data to obtain the residual sequence reflecting the anomaly monitoring data value, and then the isolation forest algorithm was used to identify the abnormal information, and the monitoring sequence was segmented according to the… More >

  • Open Access


    Human Fatty Liver Monitoring Using Nano Sensor and IoMT

    Srilekha Muthukaruppankaruppiah1,*, Shanker Rajendiran Nagalingam2, Priya Murugasen3, Rajesh Nandaamarnath4

    Intelligent Automation & Soft Computing, Vol.35, No.2, pp. 2309-2323, 2023, DOI:10.32604/iasc.2023.029598

    Abstract Malfunction of human liver happens due to non-alcoholic fatty liver. Fatty liver measurement is used for grading hepatic steatosis, fibrosis and cirrhosis. The various imaging techniques for measuring fatty liver are Magnetic Resonance Imaging, Ultrasound and Computed Tomography. Imaging modalities lead to the exposure of harmful radiation of electromagnetic waves because of frequent measurement. The continuous monitoring of fatty liver is never achieved through imaging techniques. In this paper, the human fatty liver measured through a Fatty Liver Sensor (FLS). The continuous monitoring of the fatty liver is achieved through the FLS. FLS is fabricated through the screen-printing with materials… More >

  • Open Access


    High Efficiency Crypto-Watermarking System Based on Clifford-Multiwavelet for 3D Meshes Security

    Wajdi Elhamzi1,2,*, Malika Jallouli3, Yassine Bouteraa1,4

    CMC-Computers, Materials & Continua, Vol.73, No.2, pp. 4329-4347, 2022, DOI:10.32604/cmc.2022.030954

    Abstract Since 3D mesh security has become intellectual property, 3D watermarking algorithms have continued to appear to secure 3D meshes shared by remote users and saved in distant multimedia databases. The novelty of our approach is that it uses a new Clifford-multiwavelet transform to insert copyright data in a multiresolution domain, allowing us to greatly expand the size of the watermark. After that, our method does two rounds of insertion, each applying a different type of Clifford-wavelet transform. Before being placed into the Clifford-multiwavelet coefficients, the watermark, which is a mixture of the mesh description, source mesh signature (produced using SHA512),… More >

  • Open Access


    Optimal IoT Based Improved Deep Learning Model for Medical Image Classification

    Prasanalakshmi Balaji1,*, B. Sri Revathi2, Praveetha Gobinathan3, Shermin Shamsudheen3, Thavavel Vaiyapuri4

    CMC-Computers, Materials & Continua, Vol.73, No.2, pp. 2275-2291, 2022, DOI:10.32604/cmc.2022.028560

    Abstract Recently medical image classification plays a vital role in medical image retrieval and computer-aided diagnosis system. Despite deep learning has proved to be superior to previous approaches that depend on handcrafted features; it remains difficult to implement because of the high intra-class variance and inter-class similarity generated by the wide range of imaging modalities and clinical diseases. The Internet of Things (IoT) in healthcare systems is quickly becoming a viable alternative for delivering high-quality medical treatment in today’s e-healthcare systems. In recent years, the Internet of Things (IoT) has been identified as one of the most interesting research subjects in… More >

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