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  • Open Access

    ARTICLE

    Ensemble 1D DenseNet Damage Identification Method Based on Vibration Acceleration

    Chun Sha1,*, Chaohui Yue2, Wenchen Wang3

    Structural Durability & Health Monitoring, Vol.17, No.5, pp. 369-381, 2023, DOI:10.32604/sdhm.2023.027948

    Abstract Convolution neural networks in deep learning can solve the problem of damage identification based on vibration acceleration. By combining multiple 1D DenseNet submodels, a new ensemble learning method is proposed to improve identification accuracy. 1D DenseNet is built using standard 1D CNN and DenseNet basic blocks, and the acceleration data obtained from multiple sampling points is brought into the 1D DenseNet training to generate submodels after offset sampling. When using submodels for damage identification, the voting method ideas in ensemble learning are used to vote on the results of each submodel, and then vote centrally. Finally, the cantilever damage problem… More >

  • Open Access

    ARTICLE

    Music Genre Classification Using DenseNet and Data Augmentation

    Dao Thi Le Thuy1, Trinh Van Loan2,*, Chu Ba Thanh3, Nguyen Hieu Cuong1

    Computer Systems Science and Engineering, Vol.47, No.1, pp. 657-674, 2023, DOI:10.32604/csse.2023.036858

    Abstract It can be said that the automatic classification of musical genres plays a very important role in the current digital technology world in which the creation, distribution, and enjoyment of musical works have undergone huge changes. As the number of music products increases daily and the music genres are extremely rich, storing, classifying, and searching these works manually becomes difficult, if not impossible. Automatic classification of musical genres will contribute to making this possible. The research presented in this paper proposes an appropriate deep learning model along with an effective data augmentation method to achieve high classification accuracy for music… More >

  • Open Access

    ARTICLE

    Detection Algorithm of Knee Osteoarthritis Based on Magnetic Resonance Images

    Xin Wang*, Shuang Liu, Chang-Cai Zhou

    Intelligent Automation & Soft Computing, Vol.37, No.1, pp. 221-234, 2023, DOI:10.32604/iasc.2023.036766

    Abstract Knee osteoarthritis (OA) is a common disease that impairs knee function and causes pain. Currently, studies on the detection of knee OA mainly focus on X-ray images, but X-ray images are insensitive to the changes in knee OA in the early stage. Since magnetic resonance (MR) imaging can observe the early features of knee OA, the knee OA detection algorithm based on MR image is innovatively proposed to judge whether knee OA is suffered. Firstly, the knee MR images are preprocessed before training, including a region of interest clipping, slice selection, and data augmentation. Then the data set was divided… 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

    Arabic Sign Language Gesture Classification Using Deer Hunting Optimization with Machine Learning Model

    Badriyya B. Al-onazi1, Mohamed K. Nour2, Hussain Alshahran3, Mohamed Ahmed Elfaki3, Mrim M. Alnfiai4, Radwa Marzouk5, Mahmoud Othman6, Mahir M. Sharif7, Abdelwahed Motwakel8,*

    CMC-Computers, Materials & Continua, Vol.75, No.2, pp. 3413-3429, 2023, DOI:10.32604/cmc.2023.035303

    Abstract Sign language includes the motion of the arms and hands to communicate with people with hearing disabilities. Several models have been available in the literature for sign language detection and classification for enhanced outcomes. But the latest advancements in computer vision enable us to perform signs/gesture recognition using deep neural networks. This paper introduces an Arabic Sign Language Gesture Classification using Deer Hunting Optimization with Machine Learning (ASLGC-DHOML) model. The presented ASLGC-DHOML technique mainly concentrates on recognising and classifying sign language gestures. The presented ASLGC-DHOML model primarily pre-processes the input gesture images and generates feature vectors using the densely connected… More >

  • Open Access

    ARTICLE

    An Improved Encoder-Decoder CNN with Region-Based Filtering for Vibrant Colorization

    Mrityunjoy Gain1, Md Arifur Rahman1, Rameswar Debnath1, Mrim M. Alnfiai2, Abdullah Sheikh3, Mehedi Masud3, Anupam Kumar Bairagi1,*

    Computer Systems Science and Engineering, Vol.46, No.1, pp. 1059-1077, 2023, DOI:10.32604/csse.2023.034809

    Abstract Colorization is the practice of adding appropriate chromatic values to monochrome photographs or videos. A real-valued luminance image can be mapped to a three-dimensional color image. However, it is a severely ill-defined problem and not has a single solution. In this paper, an encoder-decoder Convolutional Neural Network (CNN) model is used for colorizing gray images where the encoder is a Densely Connected Convolutional Network (DenseNet) and the decoder is a conventional CNN. The DenseNet extracts image features from gray images and the conventional CNN outputs a * b * color channels. Due to a large number of desaturated color components compared to saturated… More >

  • Open Access

    ARTICLE

    Detection of Diabetic Retinopathy from Retinal Images Using DenseNet Models

    R. Nandakumar1, P. Saranya2,*, Vijayakumar Ponnusamy3, Subhashree Hazra2, Antara Gupta2

    Computer Systems Science and Engineering, Vol.45, No.1, pp. 279-292, 2023, DOI:10.32604/csse.2023.028703

    Abstract A prevalent diabetic complication is Diabetic Retinopathy (DR), which can damage the retina’s veins, leading to a severe loss of vision. If treated in the early stage, it can help to prevent vision loss. But since its diagnosis takes time and there is a shortage of ophthalmologists, patients suffer vision loss even before diagnosis. Hence, early detection of DR is the necessity of the time. The primary purpose of the work is to apply the data fusion/feature fusion technique, which combines more than one relevant feature to predict diabetic retinopathy at an early stage with greater accuracy. Mechanized procedures for… More >

  • Open Access

    ARTICLE

    Big Data Analytics: Deep Content-Based Prediction with Sampling Perspective

    Waleed Albattah, Saleh Albahli*

    Computer Systems Science and Engineering, Vol.45, No.1, pp. 531-544, 2023, DOI:10.32604/csse.2023.021548

    Abstract The world of information technology is more than ever being flooded with huge amounts of data, nearly 2.5 quintillion bytes every day. This large stream of data is called big data, and the amount is increasing each day. This research uses a technique called sampling, which selects a representative subset of the data points, manipulates and analyzes this subset to identify patterns and trends in the larger dataset being examined, and finally, creates models. Sampling uses a small proportion of the original data for analysis and model training, so that it is relatively faster while maintaining data integrity and achieving… More >

  • Open Access

    ARTICLE

    Detection of COVID-19 and Pneumonia Using Deep Convolutional Neural Network

    Md. Saiful Islam, Shuvo Jyoti Das, Md. Riajul Alam Khan, Sifat Momen*, Nabeel Mohammed

    Computer Systems Science and Engineering, Vol.44, No.1, pp. 519-534, 2023, DOI:10.32604/csse.2023.025282

    Abstract COVID-19 has created a panic all around the globe. It is a contagious disease caused by Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2), originated from Wuhan in December 2019 and spread quickly all over the world. The healthcare sector of the world is facing great challenges tackling COVID cases. One of the problems many have witnessed is the misdiagnosis of COVID-19 cases with that of healthy and pneumonia cases. In this article, we propose a deep Convolutional Neural Network (CNN) based approach to detect COVID+ (i.e., patients with COVID-19), pneumonia and normal cases, from the chest X-ray images. COVID-19 detection… More >

  • Open Access

    ARTICLE

    Wall Cracks Detection in Aerial Images Using Improved Mask R-CNN

    Wei Chen1, Caoyang Chen1,*, Mi Liu1, Xuhong Zhou2, Haozhi Tan3, Mingliang Zhang4

    CMC-Computers, Materials & Continua, Vol.73, No.1, pp. 767-782, 2022, DOI:10.32604/cmc.2022.028571

    Abstract The present paper proposes a detection method for building exterior wall cracks since manual detection methods have high risk and low efficiency. The proposed method is based on Unmanned Aerial Vehicle (UAV) and computer vision technology. First, a crack dataset of 1920 images was established using UAV to collect the images of a residential building exterior wall under different lighting conditions. Second, the average crack detection precisions of different methods including the Single Shot MultiBox Detector, You Only Look Once v3, You Only Look Once v4, Faster Regional Convolutional Neural Network (R-CNN) and Mask R-CNN methods were compared. Then, the… More >

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