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Advances in AI Techniques in Convergence ICT

Submission Deadline: 30 May 2025 View: 639 Submit to Special Issue

Guest Editors

Dr. Ji Su Park

Email: jisupark@jj.ac.kr

Affiliation: Department of Computer Science Engineering, Jeonju University, Jeonju, 55069, Republic of Korea

Homepage:

Research Interests: IIoT, Cloud Computing, Mobile computing

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Dr. Yan Li

Email: leeyeon@inha.ac.kr

Affiliation: Department of Electrical and Computer Engineering, Inha University, Incheon, 22212, Republic of Korea

Homepage:

Research Interests: Deep Learning, Database, IoT, Computer Vision

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Summary

The recent advancements in smart devices, mobile networks, and computing technologies are ushering us into a new era of Convergence ICT. To enable these smart devices to provide intelligent services, machine learning techniques are essential for training powerful predictive models. A common approach involves collecting distributed user data to a central cloud for deep learning model training. However, transferring massive amounts of data to the cloud center can cause significant transmission pressure on the backbone network. Additionally, increasing concerns about data privacy and the enforcement of privacy protection laws make it impractical to transmit data from end devices to the cloud.

Despite these advancements, existing AI techniques face several limitations that require novel solutions to better comprehend and improve their potential for decision-making in various real-world applications. Key challenges for employing diverse AI techniques include network management, communication efficiency, client selection and scheduling, resource management, security and privacy concerns, incentive mechanisms, and service management and pricing. Addressing these challenges calls for various techniques, including but not limited to:

· Data augmentation

· Active learning

· Multi-task learning

· Knowledge distillation

· Model compression

· Game theory

· Trust and reputation systems

· Multi-objective optimization

· Reinforcement learning

· AI based algorithm/method in Convergence ICT

· Privacy and Security in Convergence ICT

· Data Analysis in Convergence ICT

Keywords

Convergence ICT, AI Techniques, Security, Data Analysis

Published Papers

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Open Access

ARTICLE

Integration of Federated Learning and Graph Convolutional Networks for Movie Recommendation Systems
Sony Peng, Sophort Siet, Ilkhomjon Sadriddinov, Dae-Young Kim, Kyuwon Park, Doo-Soon Park
CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2025.061166
（This article belongs to the Special Issue: Advances in AI Techniques in Convergence ICT)
Abstract Recommendation systems (RSs) are crucial in personalizing user experiences in digital environments by suggesting relevant content or items. Collaborative filtering (CF) is a widely used personalization technique that leverages user-item interactions to generate recommendations. However, it struggles with challenges like the cold-start problem, scalability issues, and data sparsity. To address these limitations, we develop a Graph Convolutional Networks (GCNs) model that captures the complex network of interactions between users and items, identifying subtle patterns that traditional methods may overlook. We integrate this GCNs model into a federated learning (FL) framework, enabling the model to learn… More >

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ARTICLE

Smart Contract Vulnerability Detection Using Large Language Models and Graph Structural Analysis
Ra-Yeon Choi, Yeji Song, Minsoo Jang, Taekyung Kim, Jinhyun Ahn, Dong-Hyuk Im
CMC-Computers, Materials & Continua, Vol.83, No.1, pp. 785-801, 2025, DOI:10.32604/cmc.2025.061185
（This article belongs to the Special Issue: Advances in AI Techniques in Convergence ICT)
Abstract Smart contracts are self-executing programs on blockchains that manage complex business logic with transparency and integrity. However, their immutability after deployment makes programming errors particularly critical, as such errors can be exploited to compromise blockchain security. Existing vulnerability detection methods often rely on fixed rules or target specific vulnerabilities, limiting their scalability and adaptability to diverse smart contract scenarios. Furthermore, natural language processing approaches for source code analysis frequently fail to capture program flow, which is essential for identifying structural vulnerabilities. To address these limitations, we propose a novel model that integrates textual and structural… More >

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Advances in AI Techniques in Convergence ICT

Guest Editors

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Published Papers

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