Home / Advanced Search

  • Title/Keywords

  • Author/Affliations

  • Journal

  • Article Type

  • Start Year

  • End Year

Update SearchingClear
  • Articles
  • Online
Search Results (48)
  • Open Access

    ARTICLE

    Multiple-Object Tracking Using Histogram Stamp Extraction in CCTV Environments

    Ye-Yeon Kang1, Geon Park1, Hyun Yoo2, Kyungyong Chung1,*

    CMC-Computers, Materials & Continua, Vol.77, No.3, pp. 3619-3635, 2023, DOI:10.32604/cmc.2023.043566

    Abstract Object tracking, an important technology in the field of image processing and computer vision, is used to continuously track a specific object or person in an image. This technology may be effective in identifying the same person within one image, but it has limitations in handling multiple images owing to the difficulty in identifying whether the object appearing in other images is the same. When tracking the same object using two or more images, there must be a way to determine that objects existing in different images are the same object. Therefore, this paper attempts to determine the same object… More >

  • Open Access

    ARTICLE

    Reversible Data Hiding with Contrast Enhancement Using Bi-histogram Shifting and Image Adjustment for Color Images

    Goma Tshivetta Christian Fersein Jorvialom1,2, Lord Amoah1,2,*

    Journal of Quantum Computing, Vol.4, No.3, pp. 183-197, 2022, DOI:10.32604/jqc.2022.039913

    Abstract Prior versions of reversible data hiding with contrast enhancement (RDHCE) algorithms strongly focused on enhancing the contrast of grayscale images. However, RDHCE has recently witnessed a rise in contrast enhancement algorithms concentrating on color images. This paper implies a method for color images that uses the RGB (red, green, and blue) color model and is based on bi-histogram shifting and image adjustment. Bi-histogram shifting is used to embed data and image adjustment to achieve contrast enhancement by adjusting the images resulting from each channel of the color images before combining them to generate the final enhanced image. Images are first… More >

  • Open Access

    ARTICLE

    Alzheimer’s Disease Stage Classification Using a Deep Transfer Learning and Sparse Auto Encoder Method

    Deepthi K. Oommen*, J. Arunnehru

    CMC-Computers, Materials & Continua, Vol.76, No.1, pp. 793-811, 2023, DOI:10.32604/cmc.2023.038640

    Abstract Alzheimer’s Disease (AD) is a progressive neurological disease. Early diagnosis of this illness using conventional methods is very challenging. Deep Learning (DL) is one of the finest solutions for improving diagnostic procedures’ performance and forecast accuracy. The disease’s widespread distribution and elevated mortality rate demonstrate its significance in the older-onset and younger-onset age groups. In light of research investigations, it is vital to consider age as one of the key criteria when choosing the subjects. The younger subjects are more susceptible to the perishable side than the older onset. The proposed investigation concentrated on the younger onset. The research used… More >

  • Open Access

    ARTICLE

    Cancer Regions in Mammogram Images Using ANFIS Classifier Based Probability Histogram Segmentation Algorithm

    V. Swetha*, G. Vadivu

    Intelligent Automation & Soft Computing, Vol.37, No.1, pp. 707-726, 2023, DOI:10.32604/iasc.2023.035483

    Abstract Every year, the number of women affected by breast tumors is increasing worldwide. Hence, detecting and segmenting the cancer regions in mammogram images is important to prevent death in women patients due to breast cancer. The conventional methods obtained low sensitivity and specificity with cancer region segmentation accuracy. The high-resolution standard mammogram images were supported by conventional methods as one of the main drawbacks. The conventional methods mostly segmented the cancer regions in mammogram images concerning their exterior pixel boundaries. These drawbacks are resolved by the proposed cancer region detection methods stated in this paper. The mammogram images are classified… More >

  • Open Access

    ARTICLE

    Histogram-Based Decision Support System for Extraction and Classification of Leukemia in Blood Smear Images

    Neenavath Veeraiah1,*, Youseef Alotaibi2, Ahmad F. Subahi3

    Computer Systems Science and Engineering, Vol.46, No.2, pp. 1879-1900, 2023, DOI:10.32604/csse.2023.034658

    Abstract An abnormality that develops in white blood cells is called leukemia. The diagnosis of leukemia is made possible by microscopic investigation of the smear in the periphery. Prior training is necessary to complete the morphological examination of the blood smear for leukemia diagnosis. This paper proposes a Histogram Threshold Segmentation Classifier (HTsC) for a decision support system. The proposed HTsC is evaluated based on the color and brightness variation in the dataset of blood smear images. Arithmetic operations are used to crop the nucleus based on automated approximation. White Blood Cell (WBC) segmentation is calculated using the active contour model… More >

  • Open Access

    ARTICLE

    An Intelligent Decision Support System for Lung Cancer Diagnosis

    Ahmed A. Alsheikhy1,*, Yahia F. Said1, Tawfeeq Shawly2

    Computer Systems Science and Engineering, Vol.46, No.1, pp. 799-817, 2023, DOI:10.32604/csse.2023.035269

    Abstract Lung cancer is the leading cause of cancer-related death around the globe. The treatment and survival rates among lung cancer patients are significantly impacted by early diagnosis. Most diagnostic techniques can identify and classify only one type of lung cancer. It is crucial to close this gap with a system that detects all lung cancer types. This paper proposes an intelligent decision support system for this purpose. This system aims to support the quick and early detection and classification of all lung cancer types and subtypes to improve treatment and save lives. Its algorithm uses a Convolutional Neural Network (CNN)… More >

  • Open Access

    ARTICLE

    A Novel Soft Clustering Method for Detection of Exudates

    Kittipol Wisaeng*

    Computer Systems Science and Engineering, Vol.46, No.1, pp. 1039-1058, 2023, DOI:10.32604/csse.2023.034901

    Abstract One of the earliest indications of diabetes consequence is Diabetic Retinopathy (DR), the main contributor to blindness worldwide. Recent studies have proposed that Exudates (EXs) are the hallmark of DR severity. The present study aims to accurately and automatically detect EXs that are difficult to detect in retinal images in the early stages. An improved Fusion of Histogram–Based Fuzzy C–Means Clustering (FHBFCM) by a New Weight Assignment Scheme (NWAS) and a set of four selected features from stages of pre-processing to evolve the detection method is proposed. The features of DR train the optimal parameter of FHBFCM for detecting EXs… More >

  • Open Access

    ARTICLE

    Vibration-Based Fault Diagnosis Study on a Hydraulic Brake System Using Fuzzy Logic with Histogram Features

    Alamelu Manghai T Marimuthu1, Jegadeeshwaran Rakkiyannan2,*, Lakshmipathi Jakkamputi1, Sugumaran Vaithiyanathan1, Sakthivel Gnanasekaran2

    Structural Durability & Health Monitoring, Vol.16, No.4, pp. 383-396, 2022, DOI:10.32604/sdhm.2022.011396

    Abstract The requirement of fault diagnosis in the field of automobiles is growing higher day by day. The reliability of human resources for the fault diagnosis is uncertain. Brakes are one of the major critical components in automobiles that require closer and active observation. This research work demonstrates a fault diagnosis technique for monitoring the hydraulic brake system using vibration analysis. Vibration signals of a rotating element contain dynamic information about its health condition. Hence, the vibration signals were used for the brake fault diagnosis study. The study was carried out on a brake fault diagnosis experimental setup. The vibration signals… More >

  • 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 >

Displaying 1-10 on page 1 of 48. Per Page