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  • Open Access

    ARTICLE

    Application of the Deep Convolutional Neural Network for the Classification of Auto Immune Diseases

    Fayaz Muhammad1, Jahangir Khan1, Asad Ullah1, Fasee Ullah1, Razaullah Khan2, Inayat Khan2, Mohammed ElAffendi3, Gauhar Ali3,*

    CMC-Computers, Materials & Continua, Vol.77, No.1, pp. 647-664, 2023, DOI:10.32604/cmc.2023.038748

    Abstract IIF (Indirect Immune Florescence) has gained much attention recently due to its importance in medical sciences. The primary purpose of this work is to highlight a step-by-step methodology for detecting autoimmune diseases. The use of IIF for detecting autoimmune diseases is widespread in different medical areas. Nearly 80 different types of autoimmune diseases have existed in various body parts. The IIF has been used for image classification in both ways, manually and by using the Computer-Aided Detection (CAD) system. The data scientists conducted various research works using an automatic CAD system with low accuracy. The diseases in the human body… More >

  • Open Access

    ARTICLE

    A Degradation Type Adaptive and Deep CNN-Based Image Classification Model for Degraded Images

    Huanhua Liu, Wei Wang*, Hanyu Liu, Shuheng Yi, Yonghao Yu, Xunwen Yao

    CMES-Computer Modeling in Engineering & Sciences, Vol.138, No.1, pp. 459-472, 2024, DOI:10.32604/cmes.2023.029084

    Abstract Deep Convolutional Neural Networks (CNNs) have achieved high accuracy in image classification tasks, however, most existing models are trained on high-quality images that are not subject to image degradation. In practice, images are often affected by various types of degradation which can significantly impact the performance of CNNs. In this work, we investigate the influence of image degradation on three typical image classification CNNs and propose a Degradation Type Adaptive Image Classification Model (DTA-ICM) to improve the existing CNNs’ classification accuracy on degraded images. The proposed DTA-ICM comprises two key components: a Degradation Type Predictor (DTP) and a Degradation Type… More >

  • Open Access

    ARTICLE

    A Novel Attack on Complex APUFs Using the Evolutionary Deep Convolutional Neural Network

    Ali Ahmadi Shahrakht1, Parisa Hajirahimi2, Omid Rostami3, Diego Martín4,*

    Intelligent Automation & Soft Computing, Vol.37, No.3, pp. 3059-3081, 2023, DOI:10.32604/iasc.2023.040502

    Abstract As the internet of things (IoT) continues to expand rapidly, the significance of its security concerns has grown in recent years. To address these concerns, physical unclonable functions (PUFs) have emerged as valuable tools for enhancing IoT security. PUFs leverage the inherent randomness found in the embedded hardware of IoT devices. However, it has been shown that some PUFs can be modeled by attackers using machine-learning-based approaches. In this paper, a new deep learning (DL)-based modeling attack is introduced to break the resistance of complex XAPUFs. Because training DL models is a problem that falls under the category of NP-hard… More >

  • Open Access

    ARTICLE

    A Novel Multi-Stage Bispectral Deep Learning Method for Protein Family Classification

    Amjed Al Fahoum*, Ala’a Zyout, Hiam Alquran, Isam Abu-Qasmieh

    CMC-Computers, Materials & Continua, Vol.76, No.1, pp. 1173-1193, 2023, DOI:10.32604/cmc.2023.038304

    Abstract Complex proteins are needed for many biological activities. Folding amino acid chains reveals their properties and functions. They support healthy tissue structure, physiology, and homeostasis. Precision medicine and treatments require quantitative protein identification and function. Despite technical advances and protein sequence data exploration, bioinformatics’ “basic structure” problem—the automatic deduction of a protein’s properties from its amino acid sequence—remains unsolved. Protein function inference from amino acid sequences is the main biological data challenge. This study analyzes whether raw sequencing can characterize biological facts. A massive corpus of protein sequences and the Globin-like superfamily’s related protein families generate a solid vector representation.… More >

  • Open Access

    ARTICLE

    Multi-Classification of Polyps in Colonoscopy Images Based on an Improved Deep Convolutional Neural Network

    Shuang Liu1,2,3, Xiao Liu1, Shilong Chang1, Yufeng Sun4, Kaiyuan Li1, Ya Hou1, Shiwei Wang1, Jie Meng5, Qingliang Zhao6, Sibei Wu1, Kun Yang1,2,3,*, Linyan Xue1,2,3,*

    CMC-Computers, Materials & Continua, Vol.75, No.3, pp. 5837-5852, 2023, DOI:10.32604/cmc.2023.034720

    Abstract Achieving accurate classification of colorectal polyps during colonoscopy can avoid unnecessary endoscopic biopsy or resection. This study aimed to develop a deep learning model that can automatically classify colorectal polyps histologically on white-light and narrow-band imaging (NBI) colonoscopy images based on World Health Organization (WHO) and Workgroup serrAted polypS and Polyposis (WASP) classification criteria for colorectal polyps. White-light and NBI colonoscopy images of colorectal polyps exhibiting pathological results were firstly collected and classified into four categories: conventional adenoma, hyperplastic polyp, sessile serrated adenoma/polyp (SSAP) and normal, among which conventional adenoma could be further divided into three sub-categories of tubular adenoma,… More >

  • Open Access

    ARTICLE

    Intelligent Deep Convolutional Neural Network Based Object Detection Model for Visually Challenged People

    S. Kiruthika Devi1, Amani Abdulrahman Albraikan2, Fahd N. Al-Wesabi3, Mohamed K. Nour4, Ahmed Ashour5, Anwer Mustafa Hilal6,*

    Computer Systems Science and Engineering, Vol.46, No.3, pp. 3191-3207, 2023, DOI:10.32604/csse.2023.036980

    Abstract Artificial Intelligence (AI) and Computer Vision (CV) advancements have led to many useful methodologies in recent years, particularly to help visually-challenged people. Object detection includes a variety of challenges, for example, handling multiple class images, images that get augmented when captured by a camera and so on. The test images include all these variants as well. These detection models alert them about their surroundings when they want to walk independently. This study compares four CNN-based pre-trained models: Residual Network (ResNet-50), Inception v3, Dense Convolutional Network (DenseNet-121), and SqueezeNet, predominantly used in image recognition applications. Based on the analysis performed on… More >

  • Open Access

    ARTICLE

    An Innovative Bispectral Deep Learning Method for Protein Family Classification

    Isam Abu-Qasmieh, Amjed Al Fahoum*, Hiam Alquran, Ala’a Zyout

    CMC-Computers, Materials & Continua, Vol.75, No.2, pp. 3971-3991, 2023, DOI:10.32604/cmc.2023.037431

    Abstract Proteins are essential for many biological functions. For example, folding amino acid chains reveals their functionalities by maintaining tissue structure, physiology, and homeostasis. Note that quantifiable protein characteristics are vital for improving therapies and precision medicine. The automatic inference of a protein’s properties from its amino acid sequence is called “basic structure”. Nevertheless, it remains a critical unsolved challenge in bioinformatics, although with recent technological advances and the investigation of protein sequence data. Inferring protein function from amino acid sequences is crucial in biology. This study considers using raw sequencing to explain biological facts using a large corpus of protein… More >

  • Open Access

    ARTICLE

    Type 2 Diabetes Risk Prediction Using Deep Convolutional Neural Network Based-Bayesian Optimization

    Alawi Alqushaibi1,2,*, Mohd Hilmi Hasan1,2, Said Jadid Abdulkadir1,2, Amgad Muneer1,2, Mohammed Gamal1,2, Qasem Al-Tashi3, Shakirah Mohd Taib1,2, Hitham Alhussian1,2

    CMC-Computers, Materials & Continua, Vol.75, No.2, pp. 3223-3238, 2023, DOI:10.32604/cmc.2023.035655

    Abstract Diabetes mellitus is a long-term condition characterized by hyperglycemia. It could lead to plenty of difficulties. According to rising morbidity in recent years, the world’s diabetic patients will exceed 642 million by 2040, implying that one out of every ten persons will be diabetic. There is no doubt that this startling figure requires immediate attention from industry and academia to promote innovation and growth in diabetes risk prediction to save individuals’ lives. Due to its rapid development, deep learning (DL) was used to predict numerous diseases. However, DL methods still suffer from their limited prediction performance due to the hyperparameters… More >

  • Open Access

    ARTICLE

    Squirrel Search Optimization with Deep Convolutional Neural Network for Human Pose Estimation

    K. Ishwarya, A. Alice Nithya*

    CMC-Computers, Materials & Continua, Vol.74, No.3, pp. 6081-6099, 2023, DOI:10.32604/cmc.2023.034654

    Abstract Human pose estimation (HPE) is a procedure for determining the structure of the body pose and it is considered a challenging issue in the computer vision (CV) communities. HPE finds its applications in several fields namely activity recognition and human-computer interface. Despite the benefits of HPE, it is still a challenging process due to the variations in visual appearances, lighting, occlusions, dimensionality, etc. To resolve these issues, this paper presents a squirrel search optimization with a deep convolutional neural network for HPE (SSDCNN-HPE) technique. The major intention of the SSDCNN-HPE technique is to identify the human pose accurately and efficiently.… More >

  • Open Access

    ARTICLE

    Optimal Deep Convolutional Neural Network for Vehicle Detection in Remote Sensing Images

    Saeed Masoud Alshahrani1, Saud S. Alotaibi2, Shaha Al-Otaibi3, Mohamed Mousa4, Anwer Mustafa Hilal5,*, Amgad Atta Abdelmageed5, Abdelwahed Motwakel5, Mohamed I. Eldesouki6

    CMC-Computers, Materials & Continua, Vol.74, No.2, pp. 3117-3131, 2023, DOI:10.32604/cmc.2023.033038

    Abstract Object detection (OD) in remote sensing images (RSI) acts as a vital part in numerous civilian and military application areas, like urban planning, geographic information system (GIS), and search and rescue functions. Vehicle recognition from RSIs remained a challenging process because of the difficulty of background data and the redundancy of recognition regions. The latest advancements in deep learning (DL) approaches permit the design of effectual OD approaches. This study develops an Artificial Ecosystem Optimizer with Deep Convolutional Neural Network for Vehicle Detection (AEODCNN-VD) model on Remote Sensing Images. The proposed AEODCNN-VD model focuses on the identification of vehicles accurately… More >

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