Meena N. Alnuaimi¹, Nourah S. Alqahtani¹, Mohammed Gollapalli², Atta Rahman^1,*, Alaa Alahmadi¹, Aghiad Bakry¹, Mustafa Youldash³, Dania Alkhulaifi¹, Rashad Ahmed⁴, Hesham Al-Musallam¹

CMES-Computer Modeling in Engineering & Sciences, Vol.141, No.3, pp. 2609-2623, 2024, DOI:10.32604/cmes.2024.055303 - 31 October 2024

Abstract Human beings are often affected by a wide range of skin diseases, which can be attributed to genetic factors and environmental influences, such as exposure to sunshine with ultraviolet (UV) rays. If left untreated, these diseases can have severe consequences and spread, especially among children. Early detection is crucial to prevent their spread and improve a patient’s chances of recovery. Dermatology, the branch of medicine dealing with skin diseases, faces challenges in accurately diagnosing these conditions due to the difficulty in identifying and distinguishing between different diseases based on their appearance, type of skin, and… More >

Ruobing Zuo¹, Xiaohan Huang¹, Xuguo Jiao^2,3, Zhenyong Zhang^1,4,5,*

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 >

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 >

Xinliang Tang¹, Xiaotong Ru¹, Jingfang Su^1,*, 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 >

Chun Sha^1,*, 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 >

Dao Thi Le Thuy¹, Trinh Van Loan^2,*, 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 >

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 >

S. Kiruthika Devi¹, Amani Abdulrahman Albraikan², Fahd N. Al-Wesabi³, Mohamed K. Nour⁴, Ahmed Ashour⁵, Anwer Mustafa Hilal^6,*

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 >

Badriyya B. Al-onazi¹, Mohamed K. Nour², Hussain Alshahran³, Mohamed Ahmed Elfaki³, Mrim M. Alnfiai⁴, Radwa Marzouk⁵, Mahmoud Othman⁶, Mahir M. Sharif⁷, Abdelwahed Motwakel^8,*

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 >

Mrityunjoy Gain¹, Md Arifur Rahman¹, Rameswar Debnath¹, Mrim M. Alnfiai², Abdullah Sheikh³, Mehedi Masud³, Anupam Kumar Bairagi^1,*

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 >