Hamayun Khan^1,*, Muhammad Atif Imtiaz², Hira Siddique³, Muhammad Tausif Afzal Rana⁴, Arshad Ali⁵, Muhammad Zeeshan Baig⁶, Saif ur Rehman⁷, Yazed Alsaawy⁵

Computer Systems Science and Engineering, Vol.49, pp. 317-331, 2025, DOI:10.32604/csse.2025.061118 - 19 March 2025

Abstract The migration of tasks aided by machine learning (ML) predictions IN (DPM) is a system-level design technique that is used to reduce energy by enhancing the overall performance of the processor. In this paper, we address the issue of system-level higher task dissipation during the execution of parallel workloads with common deadlines by introducing a machine learning-based framework that includes task migration using energy-efficient earliest deadline first scheduling (EA-EDF). ML-based EA-EDF enhances the overall throughput and optimizes the energy to avoid delay and performance degradation in a multiprocessor system. The proposed system model allocates processors… More >

Md Naeem Hossain¹, Md. Abdur Rahim², Md Mustafizur Rahman^1,3,*, Devarajan Ramasamy¹

CMC-Computers, Materials & Continua, Vol.82, No.3, pp. 3643-3692, 2025, DOI:10.32604/cmc.2025.061749 - 06 March 2025

Abstract The automotive sector is crucial in modern society, facilitating essential transportation needs across personal, commercial, and logistical domains while significantly contributing to national economic development and employment generation. The transformative impact of Artificial Intelligence (AI) has revolutionised multiple facets of the automotive industry, encompassing intelligent manufacturing processes, diagnostic systems, control mechanisms, supply chain operations, customer service platforms, and traffic management solutions. While extensive research exists on the above aspects of AI applications in automotive contexts, there is a compelling need to synthesise this knowledge comprehensively to guide and inspire future research. This review introduces a… More >

Isha Kiran¹, Shahzad Ali^2,3, Sajawal ur Rehman Khan^4,5, Musaed Alhussein⁶, Sheraz Aslam^7,8,*, Khursheed Aurangzeb^6,*

CMC-Computers, Materials & Continua, Vol.82, No.3, pp. 5057-5078, 2025, DOI:10.32604/cmc.2025.058724 - 06 March 2025

Abstract Cardiovascular disease (CVD) remains a leading global health challenge due to its high mortality rate and the complexity of early diagnosis, driven by risk factors such as hypertension, high cholesterol, and irregular pulse rates. Traditional diagnostic methods often struggle with the nuanced interplay of these risk factors, making early detection difficult. In this research, we propose a novel artificial intelligence-enabled (AI-enabled) framework for CVD risk prediction that integrates machine learning (ML) with eXplainable AI (XAI) to provide both high-accuracy predictions and transparent, interpretable insights. Compared to existing studies that typically focus on either optimizing ML… More >

Eliel Martins¹, Javier Bermejo Higuera^2,*, Ricardo Sant’Ana¹, Juan Ramón Bermejo Higuera², Juan Antonio Sicilia Montalvo², Diego Piedrahita Castillo³

CMES-Computer Modeling in Engineering & Sciences, Vol.142, No.3, pp. 3031-3067, 2025, DOI:10.32604/cmes.2025.061080 - 03 March 2025

Abstract The growing threat of malware, particularly in the Portable Executable (PE) format, demands more effective methods for detection and classification. Machine learning-based approaches exhibit their potential but often neglect semantic segmentation of malware files that can improve classification performance. This research applies deep learning to malware detection, using Convolutional Neural Network (CNN) architectures adapted to work with semantically extracted data to classify malware into malware families. Starting from the Malconv model, this study introduces modifications to adapt it to multi-classification tasks and improve its performance. It proposes a new innovative method that focuses on byte More >

L. S. Sundar^*, Sérgio M. O. Tavares, António M. B. Pereira, Antonio C. M. Sousa

Frontiers in Heat and Mass Transfer, Vol.23, No.1, pp. 131-162, 2025, DOI:10.32604/fhmt.2025.055854 - 26 February 2025

Abstract The experimental analysis takes too much time-consuming process and requires considerable effort, while, the Artificial Neural Network (ANN) algorithms are simple, affordable, and fast, and they allow us to make a relevant analysis in establishing an appropriate relationship between the input and output parameters. This paper deals with the use of back-propagation ANN algorithms for the experimental data of heat transfer coefficient, Nusselt number, and friction factor of water-based Fe₃O₄-TiO₂ magnetic hybrid nanofluids in a mini heat sink under magnetic fields. The data considered for the ANN network is at different Reynolds numbers (239 to 1874),… 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 >

Meng-Hua Yen^1,*, Nilamadhab Mishra^2,*, Win-Jet Luo³, Chu-En Lin¹

CMC-Computers, Materials & Continua, Vol.82, No.2, pp. 1839-1855, 2025, DOI:10.32604/cmc.2025.060903 - 17 February 2025

Abstract The Internet of Everything (IoE) coupled with Proactive Artificial Intelligence (AI)-Based Learning Agents (PLAs) through a cloud processing system is an idea that connects all computing resources to the Internet, making it possible for these devices to communicate with one another. Technologies featured in the IoE include embedding, networking, and sensing devices. To achieve the intended results of the IoE and ease life for everyone involved, sensing devices and monitoring systems are linked together. The IoE is used in several contexts, including intelligent cars’ protection, navigation, security, and fuel efficiency. The Smart Things Monitoring System… More >

Beike Yu¹, Dafang Wang^1,*, Xing Cui², Bowen Yang¹

CMC-Computers, Materials & Continua, Vol.82, No.2, pp. 2687-2702, 2025, DOI:10.32604/cmc.2024.059594 - 17 February 2025

Abstract Realistic urban scene generation has been extensively studied for the sake of the development of autonomous vehicles. However, the research has primarily focused on the synthesis of vehicles and pedestrians, while the generation of cyclists is rarely presented due to its complexity. This paper proposes a perspective-aware and realistic cyclist generation method via object retrieval. Images, semantic maps, and depth labels of objects are first collected from existing datasets, categorized by class and perspective, and calculated by an algorithm newly designed according to imaging principles. During scene generation, objects with the desired class and perspective… More >

Ahmed Alhussen¹, Arshiya Sajid Ansari^2,*, Mohammad Sajid Mohammadi³

CMC-Computers, Materials & Continua, Vol.82, No.2, pp. 2909-2929, 2025, DOI:10.32604/cmc.2024.059382 - 17 February 2025

Abstract Voice, motion, and mimicry are naturalistic control modalities that have replaced text or display-driven control in human-computer communication (HCC). Specifically, the vocals contain a lot of knowledge, revealing details about the speaker’s goals and desires, as well as their internal condition. Certain vocal characteristics reveal the speaker’s mood, intention, and motivation, while word study assists the speaker’s demand to be understood. Voice emotion recognition has become an essential component of modern HCC networks. Integrating findings from the various disciplines involved in identifying vocal emotions is also challenging. Many sound analysis techniques were developed in the… More >