Carlos Quiterio Gómez Muñoz¹, Fausto Pedro García Márquez^2,*, Jorge Bernabé Sanjuán³

CMES-Computer Modeling in Engineering & Sciences, Vol.144, No.3, pp. 3369-3386, 2025, DOI:10.32604/cmes.2025.069225 - 30 September 2025

Abstract Due to the continuous increase in global energy demand, photovoltaic solar energy generation and associated maintenance requirements have significantly expanded. One critical maintenance challenge in photovoltaic installations is detecting hot spots, localized overheating defects in solar cells that drastically reduce efficiency and can lead to permanent damage. Traditional methods for detecting these defects rely on manual inspections using thermal imaging, which are costly, labor-intensive, and impractical for large-scale installations. This research introduces an automated hybrid system based on two specialized convolutional neural networks deployed in a cascaded architecture. The first convolutional neural network efficiently detects More >

Shih-Lin Lin^*

CMC-Computers, Materials & Continua, Vol.83, No.1, pp. 537-557, 2025, DOI:10.32604/cmc.2025.061379 - 26 March 2025

Abstract Tires are integral to vehicular systems, directly influencing both safety and overall performance. Traditional tire pressure inspection methods—such as manual or gauge-based approaches—are often time-consuming, prone to inconsistency, and lack the flexibility needed to meet diverse operational demands. In this research, we introduce an AI-driven tire pressure detection system that leverages an enhanced GoogLeNet architecture incorporating a novel Softplus-LReLU activation function. By combining the smooth, non-saturating characteristics of Softplus with a linear adjustment term, this activation function improves computational efficiency and helps stabilize network gradients, thereby mitigating issues such as gradient vanishing and neuron death.… More >

Jungpil Shin^1,*, Md. Al Mehedi Hasan², Abu Saleh Musa Miah¹, Kota Suzuki¹, Koki Hirooka¹

CMES-Computer Modeling in Engineering & Sciences, Vol.139, No.3, pp. 2605-2625, 2024, DOI:10.32604/cmes.2023.046334 - 11 March 2024

Abstract Sign language recognition is vital for enhancing communication accessibility among the Deaf and hard-of-hearing communities. In Japan, approximately 360,000 individuals with hearing and speech disabilities rely on Japanese Sign Language (JSL) for communication. However, existing JSL recognition systems have faced significant performance limitations due to inherent complexities. In response to these challenges, we present a novel JSL recognition system that employs a strategic fusion approach, combining joint skeleton-based handcrafted features and pixel-based deep learning features. Our system incorporates two distinct streams: the first stream extracts crucial handcrafted features, emphasizing the capture of hand and body… More >

Wenxing Zhang¹, Jingbing Li^1,2,*, Uzair Aslam Bhatti^1,2, Jing Liu³, Junhua Zheng¹, Yen-Wei Chen⁴

CMC-Computers, Materials & Continua, Vol.75, No.1, pp. 565-586, 2023, DOI:10.32604/cmc.2023.036317 - 06 February 2023

Abstract The field of medical images has been rapidly evolving since the advent of the digital medical information era. However, medical data is susceptible to leaks and hacks during transmission. This paper proposed a robust multi-watermarking algorithm for medical images based on GoogLeNet transfer learning to protect the privacy of patient data during transmission and storage, as well as to increase the resistance to geometric attacks and the capacity of embedded watermarks of watermarking algorithms. First, a pre-trained GoogLeNet network is used in this paper, based on which the parameters of several previous layers of the… More >

Trong Hieu Luu¹, Phan Nguyen Ky Phuc^2,*, Zhiqiu Yu³, Duy Dung Pham¹, Huu Trong Cao¹

CMC-Computers, Materials & Continua, Vol.73, No.2, pp. 3251-3262, 2022, DOI:10.32604/cmc.2022.029663 - 16 June 2022

Abstract In recent years, the COVID-19 pandemic has negatively impacted all aspects of social life. Due to ease in the infected method, i.e., through small liquid particles from the mouth or the nose when people cough, sneeze, speak, sing, or breathe, the virus can quickly spread and create severe problems for people’s health. According to some research as well as World Health Organization (WHO) recommendation, one of the most economical and effective methods to prevent the spread of the pandemic is to ask people to wear the face mask in the public space. A face mask… More >

Farman Ali¹, Sadia Khan², Arbab Waseem Abbas², Babar Shah³, Tariq Hussain², Dongho Song^4,*, Shaker EI-Sappagh^5,6, Jaiteg Singh⁷

CMC-Computers, Materials & Continua, Vol.72, No.1, pp. 73-92, 2022, DOI:10.32604/cmc.2022.024103 - 24 February 2022

Abstract Medical Image Analysis (MIA) is one of the active research areas in computer vision, where brain tumor detection is the most investigated domain among researchers due to its deadly nature. Brain tumor detection in magnetic resonance imaging (MRI) assists radiologists for better analysis about the exact size and location of the tumor. However, the existing systems may not efficiently classify the human brain tumors with significantly higher accuracies. In addition, smart and easily implementable approaches are unavailable in 2D and 3D medical images, which is the main problem in detecting the tumor. In this paper, More >

Sofia Nishath, P. S. Nithya Darisini^*

CMC-Computers, Materials & Continua, Vol.72, No.1, pp. 349-364, 2022, DOI:10.32604/cmc.2022.023935 - 24 February 2022

Abstract Crowd Anomaly Detection has become a challenge in intelligent video surveillance system and security. Intelligent video surveillance systems make extensive use of data mining, machine learning and deep learning methods. In this paper a novel approach is proposed to identify abnormal occurrences in crowded situations using deep learning. In this approach, Adaptive GoogleNet Neural Network Classifier with Multi-Objective Whale Optimization Algorithm are applied to predict the abnormal video frames in the crowded scenes. We use multiple instance learning (MIL) to dynamically develop a deep anomalous ranking framework. This technique predicts higher anomalous values for abnormal More >

R. Rajakumari^1,*, L. Kalaivani²

Intelligent Automation & Soft Computing, Vol.32, No.2, pp. 1089-1107, 2022, DOI:10.32604/iasc.2022.020178 - 17 November 2021

Abstract Breast cancer is a commonly diagnosed disease in women. Early detection, a personalized treatment approach, and better understanding are necessary for cancer patients to survive. In this work, a deep learning network and traditional convolution network were both employed with the Digital Database for Screening Mammography (DDSM) dataset. Breast cancer images were subjected to background removal followed by Wiener filtering and a contrast limited histogram equalization (CLAHE) filter for image restoration. Wavelet packet decomposition (WPD) using the Daubechies wavelet level 3 (db3) was employed to improve the smoothness of the images. For breast cancer recognition,… More >

P. Siva Satya Sreedhar^1,*, N. Nandhagopal²

Intelligent Automation & Soft Computing, Vol.31, No.3, pp. 1331-1344, 2022, DOI:10.32604/iasc.2022.020918 - 09 October 2021

Abstract The current trend in Image Fusion (IF) algorithms concentrate on the fusion process alone. However, pay less attention to critical issues such as the similarity between the two input images, features that participate in the Image Fusion. This paper addresses these two issues by deliberately attempting a new Image Fusion framework with Convolutional Neural Network (CNN). CNN has features like pre-training and similarity score, but functionalities are limited. A CNN model with classification prediction and similarity estimation are introduced as Classification Similarity Networks (CSN) to address these issues. ResNet50 and GoogLeNet are modified as the More >

Adi Alhudhaif^1,*, Ammar Saeed², Talha Imran², Muhammad Kamran³, Ahmed S. Alghamdi³, Ahmed O. Aseeri¹, Shtwai Alsubai¹

Computer Systems Science and Engineering, Vol.40, No.1, pp. 223-235, 2022, DOI:10.32604/csse.2022.018430 - 26 August 2021

Abstract Image classification is a core field in the research area of image processing and computer vision in which vehicle classification is a critical domain. The purpose of vehicle categorization is to formulate a compact system to assist in real-world problems and applications such as security, traffic analysis, and self-driving and autonomous vehicles. The recent revolution in the field of machine learning and artificial intelligence has provided an immense amount of support for image processing related problems and has overtaken the conventional, and handcrafted means of solving image analysis problems. In this paper, a combination of… More >