Abqa Javed¹, Muhammad Shoaib^1,*, Abdul Jaleel², Mohamed Deriche³, Sharjeel Nawaz⁴

CMC-Computers, Materials & Continua, Vol.82, No.3, pp. 4977-4994, 2025, DOI:10.32604/cmc.2025.057359 - 06 March 2025

Abstract Incorporation of explainability features in the decision-making web-based systems is considered a primary concern to enhance accountability, transparency, and trust in the community. Multi-domain Sentiment Analysis is a significant web-based system where the explainability feature is essential for achieving user satisfaction. Conventional design methodologies such as object-oriented design methodology (OODM) have been proposed for web-based application development, which facilitates code reuse, quantification, and security at the design level. However, OODM did not provide the feature of explainability in web-based decision-making systems. X-OODM modifies the OODM with added explainable models to introduce the explainability feature for… More >

Shitharth Selvarajan^1,2,3,*, Hariprasath Manoharan⁴, Taher Al-Shehari⁵, Hussain Alsalman⁶, Taha Alfakih⁷

CMC-Computers, Materials & Continua, Vol.82, No.3, pp. 4339-4369, 2025, DOI:10.32604/cmc.2025.056100 - 06 March 2025

Abstract This research presents an analysis of smart grid units to enhance connected units’ security during data transmissions. The major advantage of the proposed method is that the system model encompasses multiple aspects such as network flow monitoring, data expansion, control association, throughput, and losses. In addition, all the above-mentioned aspects are carried out with neural networks and adaptive optimizations to enhance the operation of smart grid networks. Moreover, the quantitative analysis of the optimization algorithm is discussed concerning two case studies, thereby achieving early convergence at reduced complexities. The suggested method ensures that each communication More >

Gang Hao, Peng Liang^*, Ziyuan Li, Huimin Zhao, Hong Zhang

CMC-Computers, Materials & Continua, Vol.82, No.3, pp. 5221-5238, 2025, DOI:10.32604/cmc.2025.055739 - 06 March 2025

Abstract In recent years, the detection of image copy-move forgery (CMFD) has become a critical challenge in verifying the authenticity of digital images, particularly as image manipulation techniques evolve rapidly. While deep convolutional neural networks (DCNNs) have been widely employed for CMFD tasks, they are often hindered by a notable limitation: the progressive reduction in spatial resolution during the encoding process, which leads to the loss of critical image details. These details are essential for the accurate detection and localization of image copy-move forgery. To overcome the limitations of existing methods, this paper proposes a Transformer-based… More >

Zhixin Chen^1,2, Gautam Srivastava^3,4,5,*, Shuai Liu^1,2,*

CMC-Computers, Materials & Continua, Vol.82, No.2, pp. 3615-3618, 2025, DOI:10.32604/cmc.2025.062313 - 17 February 2025

Abstract This article has no abstract. More >

Jipu Li¹, Haidong Shao^2,*, Yun Kong³, Zhuyun Chen⁴

CMC-Computers, Materials & Continua, Vol.82, No.2, pp. 3609-3613, 2025, DOI:10.32604/cmc.2024.062183 - 17 February 2025

Abstract This article has no abstract. More >

Milad Moradi^1,*, Ke Yan², David Colwell², Matthias Samwald³, Rhona Asgari¹

CMC-Computers, Materials & Continua, Vol.82, No.2, pp. 1681-1698, 2025, DOI:10.32604/cmc.2025.061263 - 17 February 2025

Abstract This critical review provides an in-depth analysis of Large Language Models (LLMs), encompassing their foundational principles, diverse applications, and advanced training methodologies. We critically examine the evolution from Recurrent Neural Networks (RNNs) to Transformer models, highlighting the significant advancements and innovations in LLM architectures. The review explores state-of-the-art techniques such as in-context learning and various fine-tuning approaches, with an emphasis on optimizing parameter efficiency. We also discuss methods for aligning LLMs with human preferences, including reinforcement learning frameworks and human feedback mechanisms. The emerging technique of retrieval-augmented generation, which integrates external knowledge into LLMs, is More >

Usman Mahmood Malik¹, Muhammad Awais Javed², Ahmad Naseem Alvi², Mohammed Alkhathami^3,*

CMC-Computers, Materials & Continua, Vol.82, No.2, pp. 2255-2274, 2025, DOI:10.32604/cmc.2025.061254 - 17 February 2025

Abstract Fog computing is a key enabling technology of 6G systems as it provides quick and reliable computing, and data storage services which are required for several 6G applications. Artificial Intelligence (AI) algorithms will be an integral part of 6G systems and efficient task offloading techniques using fog computing will improve their performance and reliability. In this paper, the focus is on the scenario of Partial Offloading of a Task to Multiple Helpers (POMH) in which larger tasks are divided into smaller subtasks and processed in parallel, hence expediting task completion. However, using POMH presents challenges… More >

Joon-Min Gil¹, Hyunsu Jeong¹, Jihun Kang^2,*

CMC-Computers, Materials & Continua, Vol.82, No.2, pp. 2199-2213, 2025, DOI:10.32604/cmc.2025.061182 - 17 February 2025

Abstract In a cloud environment, graphics processing units (GPUs) are the primary devices used for high-performance computation. They exploit flexible resource utilization, a key advantage of cloud environments. Multiple users share GPUs, which serve as coprocessors of central processing units (CPUs) and are activated only if tasks demand GPU computation. In a container environment, where resources can be shared among multiple users, GPU utilization can be increased by minimizing idle time because the tasks of many users run on a single GPU. However, unlike CPUs and memory, GPUs cannot logically multiplex their resources. Additionally, GPU memory… More >

Dat Tran-Anh¹, Hoai Nam Vu^2,3,*

CMC-Computers, Materials & Continua, Vol.82, No.2, pp. 2703-2722, 2025, DOI:10.32604/cmc.2025.061086 - 17 February 2025

Abstract Counterfeit agricultural products pose a significant challenge to global food security and economic stability, necessitating advanced detection mechanisms to ensure authenticity and quality. To address this pressing issue, we introduce iGFruit, an innovative model designed to enhance the detection of counterfeit agricultural products by integrating multimodal data processing. Our approach utilizes both image and text data for comprehensive feature extraction, employing advanced backbone models such as Vision Transformer (ViT), Normalizer-Free Network (NFNet), and Bidirectional Encoder Representations from Transformers (BERT). These extracted features are fused and processed using a Graph Attention Network (GAT) to capture intricate More >

Youngjin Hwang, Seungyoon Kang, Junseok Kim^*

CMC-Computers, Materials & Continua, Vol.82, No.2, pp. 1811-1838, 2025, DOI:10.32604/cmc.2025.061071 - 17 February 2025

Abstract This review paper provides a comprehensive introduction to various numerical methods for the phase-field model used to simulate the phase separation dynamics of diblock copolymer melts. Diblock copolymer systems form complex structures at the nanometer scale and play a significant role in various applications. The phase-field model, in particular, is essential for describing the formation and evolution of these structures and is widely used as a tool to effectively predict the movement of phase boundaries and the distribution of phases over time. In this paper, we discuss the principles and implementations of various numerical methodologies More >