Ning Wang, Haoran Lyu^*, Yuchen Fu

CMC-Computers, Materials & Continua, Vol.86, No.1, pp. 1-31, 2026, DOI:10.32604/cmc.2025.068784 - 10 November 2025

Abstract With the widespread use of social media, the propagation of health-related rumors has become a significant public health threat. Existing methods for detecting health rumors predominantly rely on external knowledge or propagation structures, with only a few recent approaches attempting causal inference; however, these have not yet effectively integrated causal discovery with domain-specific knowledge graphs for detecting health rumors. In this study, we found that the combined use of causal discovery and domain-specific knowledge graphs can effectively identify implicit pseudo-causal logic embedded within texts, holding significant potential for health rumor detection. To this end, we… More >

Carlos Rojas¹, Cristian Bravo¹, Carlos Enrique Montenegro-Marín¹, Rubén González-Crespo^2,*

CMC-Computers, Materials & Continua, Vol.85, No.3, pp. 5489-5507, 2025, DOI:10.32604/cmc.2025.067394 - 23 October 2025

Abstract The increasing prevalence of violent incidents in public spaces has created an urgent need for intelligent surveillance systems capable of detecting dangerous objects in real time. While traditional video surveillance relies on human monitoring, this approach suffers from limitations such as fatigue and delayed response times. This study addresses these challenges by developing an automated detection system using advanced deep learning techniques to enhance public safety. Our approach leverages state-of-the-art convolutional neural networks (CNNs), specifically You Only Look Once version 4 (YOLOv4) and EfficientDet, for real-time object detection. The system was trained on a comprehensive… More >

Li Li, Xiao Wang^*, Ran Ding, Linlin Luo, Qinmu Wu, Zhiqin He

CMC-Computers, Materials & Continua, Vol.85, No.1, pp. 1287-1309, 2025, DOI:10.32604/cmc.2025.065670 - 29 August 2025

Abstract Brain tumors are neoplastic diseases caused by the proliferation of abnormal cells in brain tissues, and their appearance may lead to a series of complex symptoms. However, current methods struggle to capture deeper brain tumor image feature information due to the variations in brain tumor morphology, size, and complex background, resulting in low detection accuracy, high rate of misdiagnosis and underdiagnosis, and challenges in meeting clinical needs. Therefore, this paper proposes the CMS-YOLO network model for multi-category brain tumor detection, which is based on the You Only Look Once version 10 (YOLOv10s) algorithm. This model… More >

Kachi Anvesh^1,2, Bharati M. Reshmi^2,3, Shanmugasundaram Hariharan⁴, H. Venkateshwara Reddy⁵, Murugaperumal Krishnamoorthy⁶, Vinay Kukreja⁷, Shih-Yu Chen^8,9,*

CMES-Computer Modeling in Engineering & Sciences, Vol.143, No.2, pp. 1485-1517, 2025, DOI:10.32604/cmes.2025.063239 - 30 May 2025

Abstract Automated classification of retinal fundus images is essential for identifying eye diseases, though there is earlier research on applying deep learning models designed especially for detecting tessellation in retinal fundus images. This study classifies 4 classes of retinal fundus images with 3 diseased fundus images and 1 normal fundus image, by creating a refined VGG16 model to categorize fundus pictures into tessellated, normal, myopia, and choroidal neovascularization groups. The approach utilizes a VGG16 architecture that has been altered with unique fully connected layers and regularization using dropouts, along with data augmentation techniques (rotation, flip, and… More >

Mohamed Abouhawwash^1,2, S. Sridevi³, Suma Christal Mary Sundararajan⁴, Rohit Pachlor⁵, Faten Khalid Karim⁶, Doaa Sami Khafaga^6,*

Computer Systems Science and Engineering, Vol.47, No.1, pp. 239-253, 2023, DOI:10.32604/csse.2023.037812 - 26 May 2023

Abstract One of the significant health issues affecting women that impacts their fertility and results in serious health concerns is Polycystic ovarian syndrome (PCOS). Consequently, timely screening of polycystic ovarian syndrome can help in the process of recovery. Finding a method to aid doctors in this procedure was crucial due to the difficulties in detecting this condition. This research aimed to determine whether it is possible to optimize the detection of PCOS utilizing Deep Learning algorithms and methodologies. Additionally, feature selection methods that produce the most important subset of features can speed up calculation and enhance… More >

B. Divya^*, M. Santhi

Intelligent Automation & Soft Computing, Vol.36, No.2, pp. 1881-1898, 2023, DOI:10.32604/iasc.2023.031573 - 05 January 2023

Abstract Pest detection in agricultural crop fields is the most challenging task, so an effective pest detection technique is required to detect insects automatically. Image processing techniques are widely preferred in agricultural science because they offer multiple advantages like maximal crop protection, improved crop management and productivity. On the other hand, developing the automatic pest monitoring system dramatically reduces the workforce and errors. Existing image processing approaches are limited due to the disadvantages like poor efficiency and less accuracy. Therefore, a successful image processing technique based on FF-GWO-CNN classification algorithm is introduced for effective pest monitoring… More >

Muhammad Irfan¹, Khalil Ullah², Fazal Muhammad^3,*, Salman Khan³, Faisal Althobiani⁴, Muhammad Usman⁵, Mohammed Alshareef⁴, Shadi Alghaffari⁴, Saifur Rahman¹

Intelligent Automation & Soft Computing, Vol.36, No.1, pp. 169-181, 2023, DOI:10.32604/iasc.2023.032337 - 29 September 2022

Abstract The automatic detection of noisy channels in surface Electromyogram (sEMG) signals, at the time of recording, is very critical in making a noise-free EMG dataset. If an EMG signal contaminated by high-level noise is recorded, then it will be useless and can’t be used for any healthcare application. In this research work, a new machine learning-based paradigm is proposed to automate the detection of low-level and high-level noises occurring in different channels of high density and multi-channel sEMG signals. A modified version of mel frequency cepstral coefficients (mMFCC) is proposed for the extraction of features… More >

Wenjing Wang¹, Tengteng Zhao¹, Xiaolong Li^1,*, Lei Huang¹, Wei Zhang¹, Hui Guo²

Journal of Cyber Security, Vol.4, No.1, pp. 1-15, 2022, DOI:10.32604/jcs.2022.026816 - 05 May 2022

Abstract At present, the network security situation is becoming more and more serious. Malicious network attacks such as computer viruses, Trojans and hacker attacks are becoming more and more rampant. National and group network attacks such as network information war and network terrorism have a serious damage to the production and life of the whole society. At the same time, with the rapid development of Internet of Things and the arrival of 5G era, IoT devices as an important part of industrial Internet system, have become an important target of infiltration attacks by hostile forces. This More >

Erssa Arif^1,*, Syed Khuram Shahzad², Muhammad Waseem Iqbal³, Muhammad Arfan Jaffar⁴, Abdullah S. Alshahrani⁵, Ahmed Alghamdi⁶

CMC-Computers, Materials & Continua, Vol.72, No.3, pp. 4615-4630, 2022, DOI:10.32604/cmc.2022.027571 - 21 April 2022

Abstract The conventional Close circuit television (CCTV) cameras-based surveillance and control systems require human resource supervision. Almost all the criminal activities take place using weapons mostly a handheld gun, revolver, pistol, swords etc. Therefore, automatic weapons detection is a vital requirement now a day. The current research is concerned about the real-time detection of weapons for the surveillance cameras with an implementation of weapon detection using Efficient–Net. Real time datasets, from local surveillance department's test sessions are used for model training and testing. Datasets consist of local environment images and videos from different type and resolution More >

Fujia Wei^1,2,*, Liyin Shen¹, Yuanming Xiang², Xingjie Zhang², Yu Tang², Qian Tan²

CMES-Computer Modeling in Engineering & Sciences, Vol.131, No.2, pp. 619-637, 2022, DOI:10.32604/cmes.2022.019082 - 14 March 2022

Abstract Concrete exterior quality is one of the important metrics in evaluating construction project quality. Among the defects affecting concrete exterior quality, bughole is one of the most common imperfections, thus detecting concrete bughole accurately is significant for improving concrete exterior quality and consequently the quality of the whole project. This paper presents a deep learning-based method for detecting concrete surface bugholes in a more objective and automatic way. The bugholes are identified in concrete surface images by Mask R-CNN. An evaluation metric is developed to indicate the scale of concrete bughole. The proposed approach can More >