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  • Open Access

    ARTICLE

    Hybrid of Distributed Cumulative Histograms and Classification Model for Attack Detection

    Mostafa Nassar1, Anas M. Ali1,2, Walid El-Shafai1,3, Adel Saleeb1, Fathi E. Abd El-Samie1, Naglaa F. Soliman4, Hussah Nasser AlEisa5,*, Hossam Eldin H. Ahmed1

    Computer Systems Science and Engineering, Vol.45, No.2, pp. 2235-2247, 2023, DOI:10.32604/csse.2023.032156

    Abstract Traditional security systems are exposed to many various attacks, which represents a major challenge for the spread of the Internet in the future. Innovative techniques have been suggested for detecting attacks using machine learning and deep learning. The significant advantage of deep learning is that it is highly efficient, but it needs a large training time with a lot of data. Therefore, in this paper, we present a new feature reduction strategy based on Distributed Cumulative Histograms (DCH) to distinguish between dataset features to locate the most effective features. Cumulative histograms assess the dataset instance patterns of the applied features… More >

  • Open Access

    ARTICLE

    Improved Model for Genetic Algorithm-Based Accurate Lung Cancer Segmentation and Classification

    K. Jagadeesh1,*, A. Rajendran2

    Computer Systems Science and Engineering, Vol.45, No.2, pp. 2017-2032, 2023, DOI:10.32604/csse.2023.029169

    Abstract Lung Cancer is one of the hazardous diseases that have to be detected in earlier stages for providing better treatment and clinical support to patients. For lung cancer diagnosis, the computed tomography (CT) scan images are to be processed with image processing techniques and effective classification process is required for appropriate cancer diagnosis. In present scenario of medical data processing, the cancer detection process is very time consuming and exactitude. For that, this paper develops an improved model for lung cancer segmentation and classification using genetic algorithm. In the model, the input CT images are pre-processed with the filters called… More >

  • Open Access

    ARTICLE

    P-ROCK: A Sustainable Clustering Algorithm for Large Categorical Datasets

    Ayman Altameem1, Ramesh Chandra Poonia2, Ankit Kumar3, Linesh Raja4, Abdul Khader Jilani Saudagar5,*

    Intelligent Automation & Soft Computing, Vol.35, No.1, pp. 553-566, 2023, DOI:10.32604/iasc.2023.027579

    Abstract Data clustering is crucial when it comes to data processing and analytics. The new clustering method overcomes the challenge of evaluating and extracting data from big data. Numerical or categorical data can be grouped. Existing clustering methods favor numerical data clustering and ignore categorical data clustering. Until recently, the only way to cluster categorical data was to convert it to a numeric representation and then cluster it using current numeric clustering methods. However, these algorithms could not use the concept of categorical data for clustering. Following that, suggestions for expanding traditional categorical data processing methods were made. In addition to… More >

  • Open Access

    ARTICLE

    Histogram Matched Chest X-Rays Based Tuberculosis Detection Using CNN

    Joe Louis Paul Ignatius1,*, Sasirekha Selvakumar1, Kavin Gabriel Joe Louis Paul2, Aadhithya B. Kailash1, S. Keertivaas1, S. A. J. Akarvin Raja Prajan1

    Computer Systems Science and Engineering, Vol.44, No.1, pp. 81-97, 2023, DOI:10.32604/csse.2023.025195

    Abstract Tuberculosis (TB) is a severe infection that mostly affects the lungs and kills millions of people’s lives every year. Tuberculosis can be diagnosed using chest X-rays (CXR) and data-driven deep learning (DL) approaches. Because of its better automated feature extraction capability, convolutional neural networks (CNNs) trained on natural images are particularly effective in image categorization. A combination of 3001 normal and 3001 TB CXR images was gathered for this study from different accessible public datasets. Ten different deep CNNs (Resnet50, Resnet101, Resnet152, InceptionV3, VGG16, VGG19, DenseNet121, DenseNet169, DenseNet201, MobileNet) are trained and tested for identifying TB and normal cases. This… More >

  • Open Access

    ARTICLE

    Adaptive Resource Allocation Neural Network-Based Mammogram Image Segmentation and Classification

    P. Indra, G. Kavithaa*

    Intelligent Automation & Soft Computing, Vol.34, No.2, pp. 877-893, 2022, DOI:10.32604/iasc.2022.025982

    Abstract Image processing innovations assume a significant part in diagnosing and distinguishing diseases and monitoring these diseases’ quality. In Medical Images, detection of breast cancer in its earlier stage is most important in this field. Because of the low contrast and uncertain design of the tumor cells in breast images, it is still challenging to classify breast tumors only by visual testing by the radiologists. Hence, improvement of computer-supported strategies has been introduced for breast cancer identification. This work presents an efficient computer-assisted method for breast cancer classification of digital mammograms using Adaptive Resource Allocation Network (ARAN). At first, breast cancer… More >

  • Open Access

    ARTICLE

    Background Subtraction in Surveillance Systems Using Local Spectral Histograms and Linear Regression

    S. Hariharan1,*, R. Venkatesan2

    Intelligent Automation & Soft Computing, Vol.34, No.1, pp. 407-422, 2022, DOI:10.32604/iasc.2022.025309

    Abstract Background subtraction is a fundamental and crucial task for computer vision-based automatic video analysis due to various challenging situations that occur in real-world scenarios. This paper presents a novel background subtraction method by estimating the background model using linear regression and local spectral histogram which captures combined spectral and texture features. Different linear filters are applied on the image window centered at each pixel location and the features are captured via these filter responses. Each feature has been approximated by a linear combination of two representative features, each of which corresponds to either a background or a foreground pixel. These… More >

  • Open Access

    ARTICLE

    A Post-Processing Algorithm for Boosting Contrast of MRI Images

    B. Priestly Shan1, O. Jeba Shiney1, Sharzeel Saleem2, V. Rajinikanth3, Atef Zaguia4, Dilbag Singh5,*

    CMC-Computers, Materials & Continua, Vol.72, No.2, pp. 2749-2763, 2022, DOI:10.32604/cmc.2022.023057

    Abstract Low contrast of Magnetic Resonance (MR) images limits the visibility of subtle structures and adversely affects the outcome of both subjective and automated diagnosis. State-of-the-art contrast boosting techniques intolerably alter inherent features of MR images. Drastic changes in brightness features, induced by post-processing are not appreciated in medical imaging as the grey level values have certain diagnostic meanings. To overcome these issues this paper proposes an algorithm that enhance the contrast of MR images while preserving the underlying features as well. This method termed as Power-law and Logarithmic Modification-based Histogram Equalization (PLMHE) partitions the histogram of the image into two… More >

  • Open Access

    ARTICLE

    Detection of Microbial Activity in Silver Nanoparticles Using Modified Convolution Network

    D. Devina Merin1,*, P. Jagatheeswari2

    Intelligent Automation & Soft Computing, Vol.33, No.3, pp. 1849-1860, 2022, DOI:10.32604/iasc.2022.024495

    Abstract The Deep learning (DL) network is an effective technique that has extended application in medicine, robotics, biotechnology, biometrics and communication. The unique architecture of DL networks can be trained according to classify any complex tasks in a limited duration. In the proposed work a deep convolution neural network of DL is trained to classify the antimicrobial activity of silver nanoparticles (AgNP). The process involves two processing steps; synthesis of silver nanoparticles and classification (SEM) of AgNP based on the antimicrobial activity. AgNP images from scanning electron microscope are pre-processed using Adaptive Histogram Equalization in the networking system and the DL… More >

  • Open Access

    ARTICLE

    Segmentation of Cervical Cancer by OLHT Based DT-CWT Techniques

    P. R. Sheebha Rani1,*, R. Jemila Rose2

    Intelligent Automation & Soft Computing, Vol.33, No.3, pp. 1579-1592, 2022, DOI:10.32604/iasc.2022.023587

    Abstract Every year, cervical cancer (CC) is the leading cause of death in women around the world. If detected early enough, this cancer can be treated, and patients will receive adequate care. This study introduces a novel ultrasound-based method for detecting CC. The Oriented Local Histogram Technique (OLHT) is used to improve the image corners in the cervical image (CI), and the Dual-Tree Complex Wavelet Transform (DT-CWT) is used to build a multi-resolution image (CI). Wavelet, and Local Binary Pattern are among the elements retrieved from this improved multi-resolution CI (LBP). The retrieved appearance is trained and tested using a feed-forward… More >

  • Open Access

    ARTICLE

    Robust Interactive Method for Hand Gestures Recognition Using Machine Learning

    Amal Abdullah Mohammed Alteaimi1,*, Mohamed Tahar Ben Othman1,2

    CMC-Computers, Materials & Continua, Vol.72, No.1, pp. 577-595, 2022, DOI:10.32604/cmc.2022.023591

    Abstract The Hand Gestures Recognition (HGR) System can be employed to facilitate communication between humans and computers instead of using special input and output devices. These devices may complicate communication with computers especially for people with disabilities. Hand gestures can be defined as a natural human-to-human communication method, which also can be used in human-computer interaction. Many researchers developed various techniques and methods that aimed to understand and recognize specific hand gestures by employing one or two machine learning algorithms with a reasonable accuracy. This work aims to develop a powerful hand gesture recognition model with a 100% recognition rate. We… More >

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