Zhimao Lai^1,2, Saad Arif³, Cong Feng⁴, Guangjun Liao⁵, Chuntao Wang^6,*

CMC-Computers, Materials & Continua, Vol.82, No.1, pp. 73-102, 2025, DOI:10.32604/cmc.2024.059370 - 03 January 2025

Abstract With the rapid advancement of visual generative models such as Generative Adversarial Networks (GANs) and stable Diffusion, the creation of highly realistic Deepfake through automated forgery has significantly progressed. This paper examines the advancements in Deepfake detection and defense technologies, emphasizing the shift from passive detection methods to proactive digital watermarking techniques. Passive detection methods, which involve extracting features from images or videos to identify forgeries, encounter challenges such as poor performance against unknown manipulation techniques and susceptibility to counter-forensic tactics. In contrast, proactive digital watermarking techniques embed specific markers into images or videos, facilitating More >

Hassan Saeed¹, Imran Shafi¹, Jamil Ahmad², Adnan Ahmed Khan³, Tahir Khurshaid^4,*, Imran Ashraf^5,*

CMC-Computers, Materials & Continua, Vol.82, No.1, pp. 139-172, 2025, DOI:10.32604/cmc.2024.057587 - 03 January 2025

Abstract Software-related security aspects are a growing and legitimate concern, especially with 5G data available just at our palms. To conduct research in this field, periodic comparative analysis is needed with the new techniques coming up rapidly. The purpose of this study is to review the recent developments in the field of security integration in the software development lifecycle (SDLC) by analyzing the articles published in the last two decades and to propose a way forward. This review follows Kitchenham’s review protocol. The review has been divided into three main stages including planning, execution, and analysis.… More >

Sumaia Mohamed Elhassan¹, Saad Mohamed Darwish^1,*, Saleh Mesbah Elkaffas²

CMES-Computer Modeling in Engineering & Sciences, Vol.142, No.1, pp. 835-867, 2025, DOI:10.32604/cmes.2024.058770 - 17 December 2024

Abstract Lung cancer continues to be a leading cause of cancer-related deaths worldwide, emphasizing the critical need for improved diagnostic techniques. Early detection of lung tumors significantly increases the chances of successful treatment and survival. However, current diagnostic methods often fail to detect tumors at an early stage or to accurately pinpoint their location within the lung tissue. Single-model deep learning technologies for lung cancer detection, while beneficial, cannot capture the full range of features present in medical imaging data, leading to incomplete or inaccurate detection. Furthermore, it may not be robust enough to handle the… More >

Tajmal Hussain, Jongwon Seok^*

CMES-Computer Modeling in Engineering & Sciences, Vol.142, No.1, pp. 231-250, 2025, DOI:10.32604/cmes.2024.056621 - 17 December 2024

Abstract Smart manufacturing and Industry 4.0 are transforming traditional manufacturing processes by utilizing innovative technologies such as the artificial intelligence (AI) and internet of things (IoT) to enhance efficiency, reduce costs, and ensure product quality. In light of the recent advancement of Industry 4.0, identifying defects has become important for ensuring the quality of products during the manufacturing process. In this research, we present an ensemble methodology for accurately classifying hot rolled steel surface defects by combining the strengths of four pre-trained convolutional neural network (CNN) architectures: VGG16, VGG19, Xception, and Mobile-Net V2, compensating for their… More >

Petr Opěla^1,*, Josef Walek^1,*, Jaromír Kopeček²

CMES-Computer Modeling in Engineering & Sciences, Vol.142, No.1, pp. 713-732, 2025, DOI:10.32604/cmes.2024.055219 - 17 December 2024

Abstract In engineering practice, it is often necessary to determine functional relationships between dependent and independent variables. These relationships can be highly nonlinear, and classical regression approaches cannot always provide sufficiently reliable solutions. Nevertheless, Machine Learning (ML) techniques, which offer advanced regression tools to address complicated engineering issues, have been developed and widely explored. This study investigates the selected ML techniques to evaluate their suitability for application in the hot deformation behavior of metallic materials. The ML-based regression methods of Artificial Neural Networks (ANNs), Support Vector Machine (SVM), Decision Tree Regression (DTR), and Gaussian Process Regression More >

Quynh-Anh Thi Bui^1,*, Dam Duc Nguyen¹, Hiep Van Le¹, Indra Prakash², Binh Thai Pham^1,*

CMES-Computer Modeling in Engineering & Sciences, Vol.142, No.1, pp. 691-712, 2025, DOI:10.32604/cmes.2024.054766 - 17 December 2024

Abstract Determination of Shear Bond strength (SBS) at interlayer of double-layer asphalt concrete is crucial in flexible pavement structures. The study used three Machine Learning (ML) models, including K-Nearest Neighbors (KNN), Extra Trees (ET), and Light Gradient Boosting Machine (LGBM), to predict SBS based on easily determinable input parameters. Also, the Grid Search technique was employed for hyper-parameter tuning of the ML models, and cross-validation and learning curve analysis were used for training the models. The models were built on a database of 240 experimental results and three input variables: temperature, normal pressure, and tack coat… More >

Afam Okonkwo^*, Pius Onobhayedo, Charles Igah

Journal on Artificial Intelligence, Vol.6, pp. 379-401, 2024, DOI:10.32604/jai.2024.058649 - 31 December 2024

Abstract Artificial Intelligence, in general, and particularly Natural language Processing (NLP) has made unprecedented progress recently in many areas of life, automating and enabling a lot of activities such as speech recognition, language translations, search engines, and text-generations, among others. Software engineering and Software Development Life Cycle (SDLC) is also not left out. Indeed, one of the most critical starting points of SDLC is the requirement engineering stage which, traditionally, has been dominated by business analysts. Unfortunately, these analysts have always done the job not just in a monotonous way, but also in an error-prone, tedious,… More >

Satya Narayan Das^1,2,*, Mrutyunjaya Panda^2,*

Intelligent Automation & Soft Computing, Vol.39, No.6, pp. 1073-1100, 2024, DOI:10.32604/iasc.2024.057013 - 30 December 2024

Abstract In today’s world of massive data and interconnected networks, it’s crucial to burgeon a secure and efficient digital watermarking method to protect the copyrights of digital content. Existing research primarily focuses on deep learning-based approaches to improve the quality of watermarked images, but they have some flaws. To overcome this, the deep learning digital image watermarking model with highly secure algorithms is proposed to secure the digital image. Recently, quantum logistic maps, which combine the concept of quantum computing with traditional techniques, have been considered a niche and promising area of research that has attracted… More >

Fizza Mansoor¹, Muhammad Affan Alim^2,5,*, Muhammad Taha Jilani³, Muhammad Monsoor Alam^4,5, Mazliham Mohd Su’ud⁵

CMC-Computers, Materials & Continua, Vol.81, No.3, pp. 4603-4624, 2024, DOI:10.32604/cmc.2024.057979 - 19 December 2024

Abstract Software cost estimation is a crucial aspect of software project management, significantly impacting productivity and planning. This research investigates the impact of various feature selection techniques on software cost estimation accuracy using the CoCoMo NASA dataset, which comprises data from 93 unique software projects with 24 attributes. By applying multiple machine learning algorithms alongside three feature selection methods, this study aims to reduce data redundancy and enhance model accuracy. Our findings reveal that the principal component analysis (PCA)-based feature selection technique achieved the highest performance, underscoring the importance of optimal feature selection in improving software More >

Avi Deb Raha¹, Fatema Jannat Dihan², Mrityunjoy Gain¹, Saydul Akbar Murad³, Apurba Adhikary², Md. Bipul Hossain², Md. Mehedi Hassan¹, Taher Al-Shehari⁴, Nasser A. Alsadhan⁵, Mohammed Kadrie⁴, Anupam Kumar Bairagi^1,*

CMC-Computers, Materials & Continua, Vol.81, No.3, pp. 4033-4048, 2024, DOI:10.32604/cmc.2024.057415 - 19 December 2024

Abstract Breast cancer stands as one of the world’s most perilous and formidable diseases, having recently surpassed lung cancer as the most prevalent cancer type. This disease arises when cells in the breast undergo unregulated proliferation, resulting in the formation of a tumor that has the capacity to invade surrounding tissues. It is not confined to a specific gender; both men and women can be diagnosed with breast cancer, although it is more frequently observed in women. Early detection is pivotal in mitigating its mortality rate. The key to curbing its mortality lies in early detection.… More >