Home / Advanced Search

  • Title/Keywords

  • Author/Affliations

  • Journal

  • Article Type

  • Start Year

  • End Year

Update SearchingClear
  • Articles
  • Online
Search Results (332)
  • Open Access

    ARTICLE

    DeepFake Videos Detection Based on Texture Features

    Bozhi Xu1, Jiarui Liu1, Jifan Liang1, Wei Lu1,*, Yue Zhang2

    CMC-Computers, Materials & Continua, Vol.68, No.1, pp. 1375-1388, 2021, DOI:10.32604/cmc.2021.016760 - 22 March 2021

    Abstract In recent years, with the rapid development of deep learning technologies, some neural network models have been applied to generate fake media. DeepFakes, a deep learning based forgery technology, can tamper with the face easily and generate fake videos that are difficult to be distinguished by human eyes. The spread of face manipulation videos is very easy to bring fake information. Therefore, it is important to develop effective detection methods to verify the authenticity of the videos. Due to that it is still challenging for current forgery technologies to generate all facial details and the… More >

  • Open Access

    ARTICLE

    Computer Decision Support System for Skin Cancer Localization and Classification

    Muhammad Attique Khan1, Tallha Akram2, Muhammad Sharif1, Seifedine Kadry3, Yunyoung Nam4,*

    CMC-Computers, Materials & Continua, Vol.68, No.1, pp. 1041-1064, 2021, DOI:10.32604/cmc.2021.016307 - 22 March 2021

    Abstract In this work, we propose a new, fully automated system for multiclass skin lesion localization and classification using deep learning. The main challenge is to address the problem of imbalanced data classes, found in HAM10000, ISBI2018, and ISBI2019 datasets. Initially, we consider a pre-trained deep neural network model, DarkeNet19, and fine-tune the parameters of third convolutional layer to generate the image gradients. All the visualized images are fused using a High-Frequency approach along with Multilayered Feed-Forward Neural Network (HFaFFNN). The resultant image is further enhanced by employing a log-opening based activation function to generate a… More >

  • Open Access

    ARTICLE

    Classification of COVID-19 CT Scans via Extreme Learning Machine

    Muhammad Attique Khan1, Abdul Majid1, Tallha Akram2, Nazar Hussain1, Yunyoung Nam3,*, Seifedine Kadry4, Shui-Hua Wang5, Majed Alhaisoni6

    CMC-Computers, Materials & Continua, Vol.68, No.1, pp. 1003-1019, 2021, DOI:10.32604/cmc.2021.015541 - 22 March 2021

    Abstract Here, we use multi-type feature fusion and selection to predict COVID-19 infections on chest computed tomography (CT) scans. The scheme operates in four steps. Initially, we prepared a database containing COVID-19 pneumonia and normal CT scans. These images were retrieved from the Radiopaedia COVID-19 website. The images were divided into training and test sets in a ratio of 70:30. Then, multiple features were extracted from the training data. We used canonical correlation analysis to fuse the features into single vectors; this enhanced the predictive capacity. We next implemented a genetic algorithm (GA) in which an More >

  • Open Access

    ARTICLE

    Entropy-Based Watermarking Approach for Sensitive Tamper Detection of Arabic Text

    Fahd N. Al-Wesabi1,2,*

    CMC-Computers, Materials & Continua, Vol.67, No.3, pp. 3635-3648, 2021, DOI:10.32604/cmc.2021.015865 - 01 March 2021

    Abstract The digital text media is the most common media transferred via the internet for various purposes and is very sensitive to transfer online with the possibility to be tampered illegally by the tampering attacks. Therefore, improving the security and authenticity of the text when it is transferred via the internet has become one of the most difficult challenges that researchers face today. Arabic text is more sensitive than other languages due to Harakat’s existence in Arabic diacritics such as Kasra, and Damma in which making basic changes such as modifying diacritic arrangements can lead to… More >

  • Open Access

    ARTICLE

    1D-CNN: Speech Emotion Recognition System Using a Stacked Network with Dilated CNN Features

    Mustaqeem, Soonil Kwon*

    CMC-Computers, Materials & Continua, Vol.67, No.3, pp. 4039-4059, 2021, DOI:10.32604/cmc.2021.015070 - 01 March 2021

    Abstract Emotion recognition from speech data is an active and emerging area of research that plays an important role in numerous applications, such as robotics, virtual reality, behavior assessments, and emergency call centers. Recently, researchers have developed many techniques in this field in order to ensure an improvement in the accuracy by utilizing several deep learning approaches, but the recognition rate is still not convincing. Our main aim is to develop a new technique that increases the recognition rate with reasonable cost computations. In this paper, we suggested a new technique, which is a one-dimensional dilated… More >

  • Open Access

    ARTICLE

    Multiclass Stomach Diseases Classification Using Deep Learning Features Optimization

    Muhammad Attique Khan1, Abdul Majid1, Nazar Hussain1, Majed Alhaisoni2, Yu-Dong Zhang3, Seifedine Kadry4, Yunyoung Nam5,*

    CMC-Computers, Materials & Continua, Vol.67, No.3, pp. 3381-3399, 2021, DOI:10.32604/cmc.2021.014983 - 01 March 2021

    Abstract In the area of medical image processing, stomach cancer is one of the most important cancers which need to be diagnose at the early stage. In this paper, an optimized deep learning method is presented for multiple stomach disease classification. The proposed method work in few important steps—preprocessing using the fusion of filtering images along with Ant Colony Optimization (ACO), deep transfer learning-based features extraction, optimization of deep extracted features using nature-inspired algorithms, and finally fusion of optimal vectors and classification using Multi-Layered Perceptron Neural Network (MLNN). In the feature extraction step, pre-trained Inception V3… More >

  • Open Access

    ARTICLE

    Deep Learning Approach for COVID-19 Detection in Computed Tomography Images

    Mohamad Mahmoud Al Rahhal1, Yakoub Bazi2,*, Rami M. Jomaa3, Mansour Zuair2, Naif Al Ajlan2

    CMC-Computers, Materials & Continua, Vol.67, No.2, pp. 2093-2110, 2021, DOI:10.32604/cmc.2021.014956 - 05 February 2021

    Abstract With the rapid spread of the coronavirus disease 2019 (COVID-19) worldwide, the establishment of an accurate and fast process to diagnose the disease is important. The routine real-time reverse transcription-polymerase chain reaction (rRT-PCR) test that is currently used does not provide such high accuracy or speed in the screening process. Among the good choices for an accurate and fast test to screen COVID-19 are deep learning techniques. In this study, a new convolutional neural network (CNN) framework for COVID-19 detection using computed tomography (CT) images is proposed. The EfficientNet architecture is applied as the backbone… More >

  • Open Access

    ARTICLE

    An Effective Memory Analysis for Malware Detection and Classification

    Rami Sihwail*, Khairuddin Omar, Khairul Akram Zainol Ariffin

    CMC-Computers, Materials & Continua, Vol.67, No.2, pp. 2301-2320, 2021, DOI:10.32604/cmc.2021.014510 - 05 February 2021

    Abstract The study of malware behaviors, over the last years, has received tremendous attention from researchers for the purpose of reducing malware risks. Most of the investigating experiments are performed using either static analysis or behavior analysis. However, recent studies have shown that both analyses are vulnerable to modern malware files that use several techniques to avoid analysis and detection. Therefore, extracted features could be meaningless and a distraction for malware analysts. However, the volatile memory can expose useful information about malware behaviors and characteristics. In addition, memory analysis is capable of detecting unconventional malware, such… More >

  • Open Access

    ARTICLE

    Epithelial Layer Estimation Using Curvatures and Textural Features for Dysplastic Tissue Detection

    Afzan Adam1,*, Abdul Hadi Abd Rahman1, Nor Samsiah Sani1, Zaid Abdi Alkareem Alyessari1, Nur Jumaadzan Zaleha Mamat2, Basela Hasan3

    CMC-Computers, Materials & Continua, Vol.67, No.1, pp. 761-777, 2021, DOI:10.32604/cmc.2021.014599 - 12 January 2021

    Abstract Boundary effect in digital pathology is a phenomenon where the tissue shapes of biopsy samples get distorted during the sampling process. The morphological pattern of an epithelial layer is greatly affected. Theoretically, the shape deformation model can normalise the distortions, but it needs a 2D image. Curvatures theory, on the other hand, is not yet tested on digital pathology images. Therefore, this work proposed a curvature detection to reduce the boundary effects and estimates the epithelial layer. The boundary effect on the tissue surfaces is normalised using the frequency of a curve deviates from being… More >

  • Open Access

    ARTICLE

    Automatic Text Summarization Using Genetic Algorithm and Repetitive Patterns

    Ebrahim Heidary1, Hamïd Parvïn2,3,4,*, Samad Nejatian5,6, Karamollah Bagherifard1,6, Vahideh Rezaie6,7, Zulkefli Mansor8, Kim-Hung Pho9

    CMC-Computers, Materials & Continua, Vol.67, No.1, pp. 1085-1101, 2021, DOI:10.32604/cmc.2021.013836 - 12 January 2021

    Abstract Taking into account the increasing volume of text documents, automatic summarization is one of the important tools for quick and optimal utilization of such sources. Automatic summarization is a text compression process for producing a shorter document in order to quickly access the important goals and main features of the input document. In this study, a novel method is introduced for selective text summarization using the genetic algorithm and generation of repetitive patterns. One of the important features of the proposed summarization is to identify and extract the relationship between the main features of the… More >

Displaying 261-270 on page 27 of 332. Per Page