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  • Open Access

    EDITORIAL

    Multimodal Learning in Image Processing

    Zhixin Chen1,2, Gautam Srivastava3,4,5,*, Shuai Liu1,2,*

    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 >

  • Open Access

    EDITORIAL

    Guest Editorial Special Issue on Industrial Big Data and Artificial Intelligence-Driven Intelligent Perception, Maintenance, and Decision Optimization in Industrial Systems

    Jipu Li1, Haidong Shao2,*, 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 >

  • Open Access

    REVIEW

    A Critical Review of Methods and Challenges in Large Language Models

    Milad Moradi1,*, 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 >

  • Open Access

    ARTICLE

    Reliable Task Offloading for 6G-Based IoT Applications

    Usman Mahmood Malik1, Muhammad Awais Javed2, Ahmad Naseem Alvi2, Mohammed Alkhathami3,*

    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 >

  • Open Access

    ARTICLE

    GPU Usage Time-Based Ordering Management Technique for Tasks Execution to Prevent Running Failures of GPU Tasks in Container Environments

    Joon-Min Gil1, Hyunsu Jeong1, Jihun Kang2,*

    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 >

  • Open Access

    ARTICLE

    A Novel Approach Based on Graph Attention Networks for Fruit Recognition

    Dat Tran-Anh1, Hoai Nam Vu2,3,*

    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 >

  • Open Access

    REVIEW

    A Review of the Numerical Methods for Diblock Copolymer Melts

    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 >

  • Open Access

    ARTICLE

    Optimized Convolutional Neural Networks with Multi-Scale Pyramid Feature Integration for Efficient Traffic Light Detection in Intelligent Transportation Systems

    Yahia Said1,2,*, Yahya Alassaf3, Refka Ghodhbani4, Taoufik Saidani4, Olfa Ben Rhaiem5

    CMC-Computers, Materials & Continua, Vol.82, No.2, pp. 3005-3018, 2025, DOI:10.32604/cmc.2025.060928 - 17 February 2025

    Abstract Transportation systems are experiencing a significant transformation due to the integration of advanced technologies, including artificial intelligence and machine learning. In the context of intelligent transportation systems (ITS) and Advanced Driver Assistance Systems (ADAS), the development of efficient and reliable traffic light detection mechanisms is crucial for enhancing road safety and traffic management. This paper presents an optimized convolutional neural network (CNN) framework designed to detect traffic lights in real-time within complex urban environments. Leveraging multi-scale pyramid feature maps, the proposed model addresses key challenges such as the detection of small, occluded, and low-resolution traffic… More >

  • Open Access

    ARTICLE

    A Novel Proactive AI-Based Agents Framework for an IoE-Based Smart Things Monitoring System with Applications for Smart Vehicles

    Meng-Hua Yen1,*, Nilamadhab Mishra2,*, 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 >

  • Open Access

    ARTICLE

    Addressing Imbalance in Health Datasets: A New Method NR-Clustering SMOTE and Distance Metric Modification

    Hairani Hairani1,2, Triyanna Widiyaningtyas1,*, Didik Dwi Prasetya1, Afrig Aminuddin3

    CMC-Computers, Materials & Continua, Vol.82, No.2, pp. 2931-2949, 2025, DOI:10.32604/cmc.2024.060837 - 17 February 2025

    Abstract An imbalanced dataset often challenges machine learning, particularly classification methods. Underrepresented minority classes can result in biased and inaccurate models. The Synthetic Minority Over-Sampling Technique (SMOTE) was developed to address the problem of imbalanced data. Over time, several weaknesses of the SMOTE method have been identified in generating synthetic minority class data, such as overlapping, noise, and small disjuncts. However, these studies generally focus on only one of SMOTE’s weaknesses: noise or overlapping. Therefore, this study addresses both issues simultaneously by tackling noise and overlapping in SMOTE-generated data. This study proposes a combined approach of… More >

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