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Search Results (21)
  • Open Access

    ARTICLE

    Deep Convolution Neural Networks for Image-Based Android Malware Classification

    Amel Ksibi1,*, Mohammed Zakariah2, Latifah Almuqren1, Ala Saleh Alluhaidan1

    CMC-Computers, Materials & Continua, Vol.82, No.3, pp. 4093-4116, 2025, DOI:10.32604/cmc.2025.059615 - 06 March 2025

    Abstract The analysis of Android malware shows that this threat is constantly increasing and is a real threat to mobile devices since traditional approaches, such as signature-based detection, are no longer effective due to the continuously advancing level of sophistication. To resolve this problem, efficient and flexible malware detection tools are needed. This work examines the possibility of employing deep CNNs to detect Android malware by transforming network traffic into image data representations. Moreover, the dataset used in this study is the CIC-AndMal2017, which contains 20,000 instances of network traffic across five distinct malware categories: a.… More >

  • Open Access

    ARTICLE

    Diagnosing Retinal Eye Diseases: A Novel Transfer Learning Approach

    Mohammed Salih Ahmed1, Atta Rahman2,*, Yahya Alhabboub1, Khalid Alzahrani1, Hassan Baragbah1, Basel Altaha1, Hussein Alkatout1, Sardar Asad Ali Biabani3,4, Rashad Ahmed5, Aghiad Bakry2

    Intelligent Automation & Soft Computing, Vol.40, pp. 149-175, 2025, DOI:10.32604/iasc.2025.059080 - 12 February 2025

    Abstract This study rigorously evaluates the potential of transfer learning in diagnosing retinal eye diseases using advanced models such as YOLOv8, Xception, ConvNeXtTiny, and VGG16. All models were trained on the esteemed RFMiD dataset, which includes images classified into six critical categories: Diabetic Retinopathy (DR), Macular Hole (MH), Diabetic Neuropathy (DN), Optic Disc Changes (ODC), Tesselated Fundus (TSLN), and normal cases. The research emphasizes enhancing model performance by prioritizing recall metrics, a crucial strategy aimed at minimizing false negatives in medical diagnostics. To address the challenge of imbalanced data, we implemented effective preprocessing techniques, including cropping,… More >

  • Open Access

    ARTICLE

    Contemporary Study for Detection of COVID-19 Using Machine Learning with Explainable AI

    Saad Akbar1,2, Humera Azam1, Sulaiman Sulmi Almutairi3,*, Omar Alqahtani4, Habib Shah4, Aliya Aleryani4

    CMC-Computers, Materials & Continua, Vol.80, No.1, pp. 1075-1104, 2024, DOI:10.32604/cmc.2024.050913 - 18 July 2024

    Abstract The prompt spread of COVID-19 has emphasized the necessity for effective and precise diagnostic tools. In this article, a hybrid approach in terms of datasets as well as the methodology by utilizing a previously unexplored dataset obtained from a private hospital for detecting COVID-19, pneumonia, and normal conditions in chest X-ray images (CXIs) is proposed coupled with Explainable Artificial Intelligence (XAI). Our study leverages less preprocessing with pre-trained cutting-edge models like InceptionV3, VGG16, and VGG19 that excel in the task of feature extraction. The methodology is further enhanced by the inclusion of the t-SNE (t-Distributed… More >

  • Open Access

    ARTICLE

    Sleep Posture Classification Using RGB and Thermal Cameras Based on Deep Learning Model

    Awais Khan1, Chomyong Kim2, Jung-Yeon Kim2, Ahsan Aziz1, Yunyoung Nam3,*

    CMES-Computer Modeling in Engineering & Sciences, Vol.140, No.2, pp. 1729-1755, 2024, DOI:10.32604/cmes.2024.049618 - 20 May 2024

    Abstract Sleep posture surveillance is crucial for patient comfort, yet current systems face difficulties in providing comprehensive studies due to the obstruction caused by blankets. Precise posture assessment remains challenging because of the complex nature of the human body and variations in sleep patterns. Consequently, this study introduces an innovative method utilizing RGB and thermal cameras for comprehensive posture classification, thereby enhancing the analysis of body position and comfort. This method begins by capturing a dataset of sleep postures in the form of videos using RGB and thermal cameras, which depict six commonly adopted postures: supine,… More > Graphic Abstract

    Sleep Posture Classification Using RGB and Thermal Cameras Based on Deep Learning Model

  • Open Access

    ARTICLE

    Automatic Finding of Brain-Tumour Group Using CNN Segmentation and Moth-Flame-Algorithm, Selected Deep and Handcrafted Features

    Imad Saud Al Naimi1,2,*, Syed Alwee Aljunid Syed Junid1, Muhammad lmran Ahmad1,*, K. Suresh Manic2,3

    CMC-Computers, Materials & Continua, Vol.79, No.2, pp. 2585-2608, 2024, DOI:10.32604/cmc.2024.046461 - 15 May 2024

    Abstract Augmentation of abnormal cells in the brain causes brain tumor (BT), and early screening and treatment will reduce its harshness in patients. BT’s clinical level screening is usually performed with Magnetic Resonance Imaging (MRI) due to its multi-modality nature. The overall aims of the study is to introduce, test and verify an advanced image processing technique with algorithms to automatically extract tumour sections from brain MRI scans, facilitating improved accuracy. The research intends to devise a reliable framework for detecting the BT region in the two-dimensional (2D) MRI slice, and identifying its class with improved… More >

  • Open Access

    ARTICLE

    Optimized Deep Learning Approach for Efficient Diabetic Retinopathy Classification Combining VGG16-CNN

    Heba M. El-Hoseny1,*, Heba F. Elsepae2, Wael A. Mohamed2, Ayman S. Selmy2

    CMC-Computers, Materials & Continua, Vol.77, No.2, pp. 1855-1872, 2023, DOI:10.32604/cmc.2023.042107 - 29 November 2023

    Abstract Diabetic retinopathy is a critical eye condition that, if not treated, can lead to vision loss. Traditional methods of diagnosing and treating the disease are time-consuming and expensive. However, machine learning and deep transfer learning (DTL) techniques have shown promise in medical applications, including detecting, classifying, and segmenting diabetic retinopathy. These advanced techniques offer higher accuracy and performance. Computer-Aided Diagnosis (CAD) is crucial in speeding up classification and providing accurate disease diagnoses. Overall, these technological advancements hold great potential for improving the management of diabetic retinopathy. The study’s objective was to differentiate between different classes… More >

  • Open Access

    ARTICLE

    Predicting Lumbar Spondylolisthesis: A Hybrid Deep Learning Approach

    Deepika Saravagi1, Shweta Agrawal2,*, Manisha Saravagi3, Sanjiv K. Jain4, Bhisham Sharma5, Abolfazl Mehbodniya6,*, Subrata Chowdhury7, Julian L. Webber6

    Intelligent Automation & Soft Computing, Vol.37, No.2, pp. 2133-2151, 2023, DOI:10.32604/iasc.2023.039836 - 21 June 2023

    Abstract Spondylolisthesis is a chronic disease, and a timely diagnosis of it may help in avoiding surgery. Disease identification in x-ray radiographs is very challenging. Strengthening the feature extraction tool in VGG16 has improved the classification rate. But the fully connected layers of VGG16 are not efficient at capturing the positional structure of an object in images. Capsule network (CapsNet) works with capsules (neuron clusters) rather than a single neuron to grasp the properties of the provided image to match the pattern. In this study, an integrated model that is a combination of VGG16 and CapsNet… More >

  • Open Access

    ARTICLE

    Identification of Tuberculosis and Coronavirus Patients Using Hybrid Deep Learning Models

    Mohammed A. Al Ghamdi*

    CMC-Computers, Materials & Continua, Vol.76, No.1, pp. 881-894, 2023, DOI:10.32604/cmc.2023.037826 - 08 June 2023

    Abstract Considerable resources, technology, and efforts are being utilized worldwide to eradicate the coronavirus. Although certain measures taken to prevent the further spread of the disease have been successful, efforts to completely wipe out the coronavirus have been insufficient. Coronavirus patients have symptoms similar to those of chest Tuberculosis (TB) or pneumonia patients. Chest tuberculosis and coronavirus are similar because both diseases affect the lungs, cause coughing and produce an irregular respiratory system. Both diseases can be confirmed through X-ray imaging. It is a difficult task to diagnose COVID-19, as coronavirus testing kits are neither excessively… More >

  • Open Access

    ARTICLE

    HIUNET: A Hybrid Inception U-Net for Diagnosis of Diabetic Retinopathy

    S. Deva Kumar, S. Venkatramaphanikumar*, K. Venkata Krishna Kishore

    Intelligent Automation & Soft Computing, Vol.37, No.1, pp. 1013-1032, 2023, DOI:10.32604/iasc.2023.038165 - 29 April 2023

    Abstract Type 2 diabetes patients often suffer from microvascular complications of diabetes. These complications, in turn, often lead to vision impairment. Diabetic Retinopathy (DR) detection in its early stage can rescue people from long-term complications that could lead to permanent blindness. In this study, we propose a complex deep convolutional neural network architecture with an inception module for automated diagnosis of DR. The proposed novel Hybrid Inception U-Net (HIUNET) comprises various inception modules connected in the U-Net fashion using activation maximization and filter map to produce the image mask. First, inception blocks were used to enlarge… More >

  • Open Access

    ARTICLE

    Improved Siamese Palmprint Authentication Using Pre-Trained VGG16-Palmprint and Element-Wise Absolute Difference

    Mohamed Ezz, Waad Alanazi, Ayman Mohamed Mostafa*, Eslam Hamouda, Murtada K. Elbashir, Meshrif Alruily

    Computer Systems Science and Engineering, Vol.46, No.2, pp. 2299-2317, 2023, DOI:10.32604/csse.2023.036567 - 09 February 2023

    Abstract Palmprint identification has been conducted over the last two decades in many biometric systems. High-dimensional data with many uncorrelated and duplicated features remains difficult due to several computational complexity issues. This paper presents an interactive authentication approach based on deep learning and feature selection that supports Palmprint authentication. The proposed model has two stages of learning; the first stage is to transfer pre-trained VGG-16 of ImageNet to specific features based on the extraction model. The second stage involves the VGG-16 Palmprint feature extraction in the Siamese network to learn Palmprint similarity. The proposed model achieves More >

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