About This Journal

Computer and Telecommunication Engineering (CTE, eISSN: 3029-2298) is an international open access journal involving the study of computer and telecommunication systems. The journal welcomes submissions from worldwide researchers and practitioners in the field of computer and telecommunication, which can be original research articles, review articles, case reports, commentaries, etc. All submissions will undergo a rigorous double blind peer review process.



Latest Articles
Open Access
Article ID: 3170
by Huu Q. Tran, Viet-Thanh Pham, Sy Ngo
Comput. Telecommun. Eng. 2025, 3(1);   
Received: 18 December 2024; Accepted: 26 February 2025; Available online: 20 March 2025;
Issue release: 31 March 2025
Abstract This survey explores the integration of machine learning (ML), deep learning (DL), and reinforcement learning (RL) within wireless communications. It reviews various methods, algorithms, and applications while addressing the challenges and future research directions in this field. The paper highlights the necessity of intelligent techniques to enhance the performance and management of wireless networks, driven by the increasing complexity and demand for higher efficiency. Key areas of focus include network optimization, resource management, security, signal recognition, channel coding, traffic prediction, access control, and energy optimization. The survey also discusses emerging techniques such as federated learning, transfer learning, and multi-agent reinforcement learning, emphasizing their potential to revolutionize wireless communication systems.
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Open Access
Article ID: 3168
by Bowen Li, Chuan Zhang
Comput. Telecommun. Eng. 2025, 3(1);   
Received: 18 December 2024; Accepted: 11 March 2025; Available online: 19 March 2025;
Issue release: 31 March 2025
Abstract Integrating Large Language Models (LLMs) into traditional back-end systems can significantly reduce development overhead and enhance flexibility. This paper presents a novel approach using a fine-tuned LLama3 model as a modular back-end component capable of processing JSON-formatted inputs and outputs. We developed a specialized dataset through advanced prompt engineering with the Phi-3.5 model and fine-tuned LLama3 using Quantized Low-Rank Adaptation (QLoRA) on a single NVIDIA T4 GPU. An API layer was designed to facilitate seamless communication between clients and the LLM, effectively replacing conventional application logic. Our fine-tuned model achieved an average accuracy of 76.5¥% and a response time of 3.56 s across 100 test cases, demonstrating its effectiveness in handling back-end tasks. This work underscores the potential of LLMs to transform AI-driven back-end architectures, offering scalable and efficient solutions for modern web services.
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Open Access
Article ID: 3166
by Bowen Li, Bohan Cheng, Patrick D. Taylor, Dale A. Osborne, Fengling Han, Robert Shen, Iqbal Gondal
Comput. Telecommun. Eng. 2025, 3(1);   
Received: 18 December 2024; Accepted: 17 February 2025; Available online: 24 February 2025;
Issue release: 31 March 2025
Abstract Evaluating large numbers of hackathon submissions quickly, fairly, and at scale is a persistent challenge. Existing automated grading systems often struggle with bias, limited scalability, and a lack of transparency. In this paper, we present a novel hybrid evaluation framework that leverages large language models (LLMs) and a weighted scoring mechanism to address these issues. Our approach classifies hackathon submissions using LLMs, converts Jupyter notebooks to markdown for consistent analysis, and integrates multiple evaluation factors—from technical quality to video presentations—into a single, balanced score. Through dynamic prompt engineering and iterative refinement against manually benchmarked evaluations, we mitigate prompt design biases and ensure stable, fair outcomes. We validate our method in a multi-campus GenAI and Cybersecurity hackathon, demonstrating improved scalability, reduced evaluator workload, and transparent feedback. Our results highlight the potential of hybrid AI-driven frameworks to enhance fairness, adaptability, and efficiency in large-scale educational and competitive environments.
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Open Access
Article ID: 3118
by Cheryl Ann Alexander, Lidong Wang
Comput. Telecommun. Eng. 2025, 3(1);   
Received: 2 December 2024; Accepted: 7 March 2025; Available online: 14 March 2025;
Issue release: 31 March 2025
Abstract Cyber risks have been a major concern even if more advanced technologies have been used to deter or mitigate cyberattacks. Much research has been conducted in the areas of cyber risks and cybersecurity. Handling cyber risks needs the specific support of the theories, frameworks, and models of cyber risk management. This paper introduces theories for managing cyber risks, frameworks for handling cyber risks, models for managing cyber risks, and cyber risk management and practices. Cyber risk management and threat intelligence provide their technologies and standards. Healthcare organizations must provide robust cybersecurity procedures. Big data analytics, artificial intelligence (AI)/machine learning (ML)/deep learning (DL), etc., have thus far offered significant advances in cybersecurity for healthcare agencies. This paper will also present a case study of managing cyber risks, which will demonstrate how successful these theories, frameworks, models, and practices have been in healthcare. This paper is not a more in-depth qualitative or quantitative analysis but focuses on identifying, justifying, and describing certain key issues regarding cyber risks.
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