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  • Open Access

    ARTICLE

    An Improved YOLOv5s-Based Smoke Detection System for Outdoor Parking Lots

    Ruobing Zuo1, Xiaohan Huang1, Xuguo Jiao2,3, Zhenyong Zhang1,4,5,*

    CMC-Computers, Materials & Continua, Vol.80, No.2, pp. 3333-3349, 2024, DOI:10.32604/cmc.2024.050544 - 15 August 2024

    Abstract In the rapidly evolving urban landscape, outdoor parking lots have become an indispensable part of the city’s transportation system. The growth of parking lots has raised the likelihood of spontaneous vehicle combustion, a significant safety hazard, making smoke detection an essential preventative step. However, the complex environment of outdoor parking lots presents additional challenges for smoke detection, which necessitates the development of more advanced and reliable smoke detection technologies. This paper addresses this concern and presents a novel smoke detection technique designed for the demanding environment of outdoor parking lots. First, we develop a novel… More >

  • Open Access

    ARTICLE

    KurdSet: A Kurdish Handwritten Characters Recognition Dataset Using Convolutional Neural Network

    Sardar Hasen Ali*, Maiwan Bahjat Abdulrazzaq

    CMC-Computers, Materials & Continua, Vol.79, No.1, pp. 429-448, 2024, DOI:10.32604/cmc.2024.048356 - 25 April 2024

    Abstract Handwritten character recognition (HCR) involves identifying characters in images, documents, and various sources such as forms surveys, questionnaires, and signatures, and transforming them into a machine-readable format for subsequent processing. Successfully recognizing complex and intricately shaped handwritten characters remains a significant obstacle. The use of convolutional neural network (CNN) in recent developments has notably advanced HCR, leveraging the ability to extract discriminative features from extensive sets of raw data. Because of the absence of pre-existing datasets in the Kurdish language, we created a Kurdish handwritten dataset called (KurdSet). The dataset consists of Kurdish characters, digits,… More >

  • Open Access

    ARTICLE

    A Transmission and Transformation Fault Detection Algorithm Based on Improved YOLOv5

    Xinliang Tang1, Xiaotong Ru1, Jingfang Su1,*, Gabriel Adonis2

    CMC-Computers, Materials & Continua, Vol.76, No.3, pp. 2997-3011, 2023, DOI:10.32604/cmc.2023.038923 - 08 October 2023

    Abstract On the transmission line, the invasion of foreign objects such as kites, plastic bags, and balloons and the damage to electronic components are common transmission line faults. Detecting these faults is of great significance for the safe operation of power systems. Therefore, a YOLOv5 target detection method based on a deep convolution neural network is proposed. In this paper, Mobilenetv2 is used to replace Cross Stage Partial (CSP)-Darknet53 as the backbone. The structure uses depth-wise separable convolution toreduce the amount of calculation and parameters; improve the detection rate. At the same time, to compensate for… More >

  • 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 - 07 September 2023

    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. 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 - 26 May 2023

    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… 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 - 29 April 2023

    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… 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 - 03 April 2023

    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… 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 - 31 March 2023

    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 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 - 20 January 2023

    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… 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 - 16 August 2022

    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… More >

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