Home / Advanced Search

  • Title/Keywords

  • Author/Affliations

  • Journal

  • Article Type

  • Start Year

  • End Year

Update SearchingClear
  • Articles
  • Online
Search Results (37)
  • Open Access

    ARTICLE

    Weber Law Based Approach for Multi-Class Image Forgery Detection

    Arslan Akram1,3, Javed Rashid2,3,4, Arfan Jaffar1, Fahima Hajjej5, Waseem Iqbal6, Nadeem Sarwar7,*

    CMC-Computers, Materials & Continua, Vol.78, No.1, pp. 145-166, 2024, DOI:10.32604/cmc.2023.041074

    Abstract Today’s forensic science introduces a new research area for digital image analysis for multimedia security. So, Image authentication issues have been raised due to the wide use of image manipulation software to obtain an illegitimate benefit or create misleading publicity by using tempered images. Exiting forgery detection methods can classify only one of the most widely used Copy-Move and splicing forgeries. However, an image can contain one or more types of forgeries. This study has proposed a hybrid method for classifying Copy-Move and splicing images using texture information of images in the spatial domain. Firstly, images are divided into equal… More >

  • Open Access

    ARTICLE

    A New Method for Diagnosis of Leukemia Utilizing a Hybrid DL-ML Approach for Binary and Multi-Class Classification on a Limited-Sized Database

    Nilkanth Mukund Deshpande1,2, Shilpa Gite3,4,*, Biswajeet Pradhan5,6, Abdullah Alamri7, Chang-Wook Lee8,*

    CMES-Computer Modeling in Engineering & Sciences, Vol.139, No.1, pp. 593-631, 2024, DOI:10.32604/cmes.2023.030704

    Abstract Infection of leukemia in humans causes many complications in its later stages. It impairs bone marrow’s ability to produce blood. Morphological diagnosis of human blood cells is a well-known and well-proven technique for diagnosis in this case. The binary classification is employed to distinguish between normal and leukemia-infected cells. In addition, various subtypes of leukemia require different treatments. These sub-classes must also be detected to obtain an accurate diagnosis of the type of leukemia. This entails using multi-class classification to determine the leukemia subtype. This is usually done using a microscopic examination of these blood cells. Due to the requirement… More > Graphic Abstract

    A New Method for Diagnosis of Leukemia Utilizing a Hybrid DL-ML Approach for Binary and Multi-Class Classification on a Limited-Sized Database

  • Open Access

    ARTICLE

    Recognizing Breast Cancer Using Edge-Weighted Texture Features of Histopathology Images

    Arslan Akram1,2, Javed Rashid2,3,4, Fahima Hajjej5, Sobia Yaqoob1,6, Muhammad Hamid7, Asma Irshad8, Nadeem Sarwar9,*

    CMC-Computers, Materials & Continua, Vol.77, No.1, pp. 1081-1101, 2023, DOI:10.32604/cmc.2023.041558

    Abstract Around one in eight women will be diagnosed with breast cancer at some time. Improved patient outcomes necessitate both early detection and an accurate diagnosis. Histological images are routinely utilized in the process of diagnosing breast cancer. Methods proposed in recent research only focus on classifying breast cancer on specific magnification levels. No study has focused on using a combined dataset with multiple magnification levels to classify breast cancer. A strategy for detecting breast cancer is provided in the context of this investigation. Histopathology image texture data is used with the wavelet transform in this technique. The proposed method comprises… More >

  • Open Access

    ARTICLE

    Internet of Things (IoT) Security Enhancement Using XGboost Machine Learning Techniques

    Dana F. Doghramachi1,*, Siddeeq Y. Ameen2

    CMC-Computers, Materials & Continua, Vol.77, No.1, pp. 717-732, 2023, DOI:10.32604/cmc.2023.041186

    Abstract The rapid adoption of the Internet of Things (IoT) across industries has revolutionized daily life by providing essential services and leisure activities. However, the inadequate software protection in IoT devices exposes them to cyberattacks with severe consequences. Intrusion Detection Systems (IDS) are vital in mitigating these risks by detecting abnormal network behavior and monitoring safe network traffic. The security research community has shown particular interest in leveraging Machine Learning (ML) approaches to develop practical IDS applications for general cyber networks and IoT environments. However, most available datasets related to Industrial IoT suffer from imbalanced class distributions. This study proposes a… More >

  • Open Access

    ARTICLE

    Integrated Generative Adversarial Network and XGBoost for Anomaly Processing of Massive Data Flow in Dispatch Automation Systems

    Wenlu Ji1, Yingqi Liao1,*, Liudong Zhang2

    Intelligent Automation & Soft Computing, Vol.37, No.3, pp. 2825-2848, 2023, DOI:10.32604/iasc.2023.039618

    Abstract Existing power anomaly detection is mainly based on a pattern matching algorithm. However, this method requires a lot of manual work, is time-consuming, and cannot detect unknown anomalies. Moreover, a large amount of labeled anomaly data is required in machine learning-based anomaly detection. Therefore, this paper proposes the application of a generative adversarial network (GAN) to massive data stream anomaly identification, diagnosis, and prediction in power dispatching automation systems. Firstly, to address the problem of the small amount of anomaly data, a GAN is used to obtain reliable labeled datasets for fault diagnosis model training based on a few labeled… More >

  • Open Access

    ARTICLE

    COVID TCL: A Joint Metric Loss Function for Diagnosing COVID-19 Patient in the Early and Incubation Period

    Rui Wen1,*, Jie Zhou2, Zhongliang Shen1, Xiaorui Zhang2,3,4, Sunil Kumar Jha5

    Computer Systems Science and Engineering, Vol.47, No.1, pp. 187-204, 2023, DOI:10.32604/csse.2023.037889

    Abstract Convolution Neural Networks (CNN) can quickly diagnose COVID-19 patients by analyzing computed tomography (CT) images of the lung, thereby effectively preventing the spread of COVID-19. However, the existing CNN-based COVID-19 diagnosis models do consider the problem that the lung images of COVID-19 patients in the early stage and incubation period are extremely similar to those of the non-COVID-19 population. Which reduces the model’s classification sensitivity, resulting in a higher probability of the model misdiagnosing COVID-19 patients as non-COVID-19 people. To solve the problem, this paper first attempts to apply triplet loss and center loss to the field of COVID-19 image… More >

  • Open Access

    ARTICLE

    Forecasting the Municipal Solid Waste Using GSO-XGBoost Model

    Vaishnavi Jayaraman1, Arun Raj Lakshminarayanan1,*, Saravanan Parthasarathy1, A. Suganthy2

    Intelligent Automation & Soft Computing, Vol.37, No.1, pp. 301-320, 2023, DOI:10.32604/iasc.2023.037823

    Abstract Waste production rises in tandem with population growth and increased utilization. The indecorous disposal of waste paves the way for huge disaster named as climate change. The National Environment Agency (NEA) of Singapore oversees the sustainable management of waste across the country. The three main contributors to the solid waste of Singapore are paper and cardboard (P&C), plastic, and food scraps. Besides, they have a negligible rate of recycling. In this study, Machine Learning techniques were utilized to forecast the amount of garbage also known as waste audits. The waste audit would aid the authorities to plan their waste infrastructure.… More >

  • Open Access

    ARTICLE

    An Improved Ensemble Learning Approach for Heart Disease Prediction Using Boosting Algorithms

    Shahid Mohammad Ganie1, Pijush Kanti Dutta Pramanik2, Majid Bashir Malik3, Anand Nayyar4, Kyung Sup Kwak5,*

    Computer Systems Science and Engineering, Vol.46, No.3, pp. 3993-4006, 2023, DOI:10.32604/csse.2023.035244

    Abstract Cardiovascular disease is among the top five fatal diseases that affect lives worldwide. Therefore, its early prediction and detection are crucial, allowing one to take proper and necessary measures at earlier stages. Machine learning (ML) techniques are used to assist healthcare providers in better diagnosing heart disease. This study employed three boosting algorithms, namely, gradient boost, XGBoost, and AdaBoost, to predict heart disease. The dataset contained heart disease-related clinical features and was sourced from the publicly available UCI ML repository. Exploratory data analysis is performed to find the characteristics of data samples about descriptive and inferential statistics. Specifically, it was… More >

  • Open Access

    ARTICLE

    Early Detection of Alzheimer’s Disease Based on Laplacian Re-Decomposition and XGBoosting

    Hala Ahmed1, Hassan Soliman1, Shaker El-Sappagh2,3,4, Tamer Abuhmed4,*, Mohammed Elmogy1

    Computer Systems Science and Engineering, Vol.46, No.3, pp. 2773-2795, 2023, DOI:10.32604/csse.2023.036371

    Abstract The precise diagnosis of Alzheimer’s disease is critical for patient treatment, especially at the early stage, because awareness of the severity and progression risks lets patients take preventative actions before irreversible brain damage occurs. It is possible to gain a holistic view of Alzheimer’s disease staging by combining multiple data modalities, known as image fusion. In this paper, the study proposes the early detection of Alzheimer’s disease using different modalities of Alzheimer’s disease brain images. First, the preprocessing was performed on the data. Then, the data augmentation techniques are used to handle overfitting. Also, the skull is removed to lead… More >

  • Open Access

    ARTICLE

    SMOGN, MFO, and XGBoost Based Excitation Current Prediction Model for Synchronous Machine

    Ping-Huan Kuo1,2, Yu-Tsun Chen1, Her-Terng Yau1,2,*

    Computer Systems Science and Engineering, Vol.46, No.3, pp. 2687-2709, 2023, DOI:10.32604/csse.2023.036293

    Abstract The power factor is the ratio between the active and apparent power, and it is available to determine the operational capability of the intended circuit or the parts. The excitation current of the synchronous motor is an essential parameter required for adjusting the power factor because it determines whether the motor is under the optimal operating status. Although the excitation current should predict with the experimental devices, such a method is unsuitable for online real-time prediction. The artificial intelligence algorithm can compensate for the defect of conventional measurement methods requiring the measuring devices and the model optimization is compared during… More >

Displaying 1-10 on page 1 of 37. Per Page