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  • Open Access

    ARTICLE

    Enhancing Fire Detection Performance Based on Fine-Tuned YOLOv10

    Trong Thua Huynh*, Hoang Thanh Nguyen, Du Thang Phu

    CMC-Computers, Materials & Continua, Vol.81, No.2, pp. 2281-2298, 2024, DOI:10.32604/cmc.2024.057954 - 18 November 2024

    Abstract In recent years, early detection and warning of fires have posed a significant challenge to environmental protection and human safety. Deep learning models such as Faster R-CNN (Faster Region based Convolutional Neural Network), YOLO (You Only Look Once), and their variants have demonstrated superiority in quickly detecting objects from images and videos, creating new opportunities to enhance automatic and efficient fire detection. The YOLO model, especially newer versions like YOLOv10, stands out for its fast processing capability, making it suitable for low-latency applications. However, when applied to real-world datasets, the accuracy of fire prediction is… More >

  • Open Access

    ARTICLE

    A Secure Method for Data Storage and Transmission in Sustainable Cloud Computing

    Muhammad Usman Sana1,*, Zhanli Li1, Tayybah Kiren2, Hannan Bin Liaqat3, Shahid Naseem3, Atif Saeed4

    CMC-Computers, Materials & Continua, Vol.75, No.2, pp. 2741-2757, 2023, DOI:10.32604/cmc.2023.036093 - 31 March 2023

    Abstract Cloud computing is a technology that provides secure storage space for the customer’s massive data and gives them the facility to retrieve and transmit their data efficiently through a secure network in which encryption and decryption algorithms are being deployed. In cloud computation, data processing, storage, and transmission can be done through laptops and mobile devices. Data Storing in cloud facilities is expanding each day and data is the most significant asset of clients. The important concern with the transmission of information to the cloud is security because there is no perceivability of the client’s… More >

  • Open Access

    ARTICLE

    Detection of Left Ventricular Cavity from Cardiac MRI Images Using Faster R-CNN

    Zakarya Farea Shaaf1,*, Muhammad Mahadi Abdul Jamil1, Radzi Ambar1, Ahmed Abdu Alattab2,3, Anwar Ali Yahya3,4, Yousef Asiri4

    CMC-Computers, Materials & Continua, Vol.74, No.1, pp. 1819-1835, 2023, DOI:10.32604/cmc.2023.031900 - 22 September 2022

    Abstract The automatic localization of the left ventricle (LV) in short-axis magnetic resonance (MR) images is a required step to process cardiac images using convolutional neural networks for the extraction of a region of interest (ROI). The precise extraction of the LV’s ROI from cardiac MRI images is crucial for detecting heart disorders via cardiac segmentation or registration. Nevertheless, this task appears to be intricate due to the diversities in the size and shape of the LV and the scattering of surrounding tissues across different slices. Thus, this study proposed a region-based convolutional network (Faster R-CNN)… More >

  • Open Access

    ARTICLE

    A Study on Cascade R-CNN-Based Dangerous Goods Detection Using X-Ray Image

    Sang-Hyun Lee*

    CMC-Computers, Materials & Continua, Vol.73, No.2, pp. 4245-4260, 2022, DOI:10.32604/cmc.2022.026012 - 16 June 2022

    Abstract X-ray inspection equipment is divided into small baggage inspection equipment and large cargo inspection equipment. In the case of inspection using X-ray scanning equipment, it is possible to identify the contents of goods, unauthorized transport, or hidden goods in real-time by-passing cargo through X-rays without opening it. In this paper, we propose a system for detecting dangerous objects in X-ray images using the Cascade Region-based Convolutional Neural Network (Cascade R-CNN) model, and the data used for learning consists of dangerous goods, storage media, firearms, and knives. In addition, to minimize the overfitting problem caused by… More >

  • Open Access

    ARTICLE

    A Study on Small Pest Detection Based on a CascadeR-CNN-Swin Model

    Man-Ting Li, Sang-Hyun Lee*

    CMC-Computers, Materials & Continua, Vol.72, No.3, pp. 6155-6165, 2022, DOI:10.32604/cmc.2022.025714 - 21 April 2022

    Abstract This study aims to detect and prevent greening disease in citrus trees using a deep neural network. The process of collecting data on citrus greening disease is very difficult because the vector pests are too small. In this paper, since the amount of data collected for deep learning is insufficient, we intend to use the efficient feature extraction function of the neural network based on the Transformer algorithm. We want to use the Cascade Region-based Convolutional Neural Networks (Cascade R-CNN) Swin model, which is a mixture of the transformer model and Cascade R-CNN model to… More >

  • Open Access

    ARTICLE

    FASTER–RCNN for Skin Burn Analysis and Tissue Regeneration

    C. Pabitha*, B. Vanathi

    Computer Systems Science and Engineering, Vol.42, No.3, pp. 949-961, 2022, DOI:10.32604/csse.2022.021086 - 08 February 2022

    Abstract Skin is the largest body organ that is prone to the environment most specifically. Therefore the skin is susceptible to many damages, including burn damage. Burns can endanger life and are linked to high morbidity and mortality rates. Effective diagnosis with the help of accurate burn zone and wound depth evaluation is important for clinical efficacy. The following characteristics are associated with the skin burn wound, such as healing, infection, painand stress and keloid formation. Tissue regeneration also takes a significant amount of time for formation while considering skin healing after a burn injury. Deep… More >

  • Open Access

    ARTICLE

    Defect-Detection Model for Underground Parking Lots Using Image Object-Detection Method

    Hyun Kyu Shin1, Si Woon Lee2, Goo Pyo Hong3, Lee Sael2, Sang Hyo Lee4, Ha Young Kim5,*

    CMC-Computers, Materials & Continua, Vol.66, No.3, pp. 2493-2507, 2021, DOI:10.32604/cmc.2021.014170 - 28 December 2020

    Abstract The demand for defect diagnoses is gradually gaining ground owing to the growing necessity to implement safe inspection methods to ensure the durability and quality of structures. However, conventional manpower-based inspection methods not only incur considerable cost and time, but also cause frequent disputes regarding defects owing to poor inspections. Therefore, the demand for an effective and efficient defect-diagnosis model for concrete structures is imminent, as the reduction in maintenance costs is significant from a long-term perspective. Thus, this paper proposes a deep learning-based image object-identification method to detect the defects of paint peeling, leakage… More >

  • Open Access

    ARTICLE

    A Novel Steganography Scheme Combining Coverless Information Hiding and Steganography

    Ruohan Meng1,2, Zhili Zhou1,2, Qi Cui1,2, Xingming Sun1,2,*, Chengsheng Yuan1,2,3

    Journal of Information Hiding and Privacy Protection, Vol.1, No.1, pp. 43-48, 2019, DOI:10.32604/jihpp.2019.05797

    Abstract At present, the coverless information hiding has been developed. However, due to the limited mapping relationship between secret information and feature selection, it is challenging to further enhance the hiding capacity of coverless information hiding. At the same time, the steganography algorithm based on object detection only hides secret information in foreground objects, which contribute to the steganography capacity is reduced. Since object recognition contains multiple objects and location, secret information can be mapped to object categories, the relationship of location and so on. Therefore, this paper proposes a new steganography algorithm based on object More >

  • Open Access

    ARTICLE

    Text Detection and Recognition for Natural Scene Images Using Deep Convolutional Neural Networks

    Xianyu Wu1, Chao Luo1, Qian Zhang2, Jiliu Zhou1, Hao Yang1, 3, *, Yulian Li1

    CMC-Computers, Materials & Continua, Vol.61, No.1, pp. 289-300, 2019, DOI:10.32604/cmc.2019.05990

    Abstract Words are the most indispensable information in human life. It is very important to analyze and understand the meaning of words. Compared with the general visual elements, the text conveys rich and high-level moral information, which enables the computer to better understand the semantic content of the text. With the rapid development of computer technology, great achievements have been made in text information detection and recognition. However, when dealing with text characters in natural scene images, there are still some limitations in the detection and recognition of natural scene images. Because natural scene image has… More >

  • Open Access

    ARTICLE

    A Fusion Steganographic Algorithm Based on Faster R-CNN

    Ruohan Meng1,2, Steven G. Rice3, Jin Wang4, Xingming Sun1,2,*

    CMC-Computers, Materials & Continua, Vol.55, No.1, pp. 1-16, 2018, DOI:10.3970/cmc.2018.055.001

    Abstract The aim of information hiding is to embed the secret message in a normal cover media such as image, video, voice or text, and then the secret message is transmitted through the transmission of the cover media. The secret message should not be damaged on the process of the cover media. In order to ensure the invisibility of secret message, complex texture objects should be chosen for embedding information. In this paper, an approach which corresponds multiple steganographic algorithms to complex texture objects was presented for hiding secret message. Firstly, complex texture regions are selected More >

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