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All Journal Bulletin of Electrical Engineering and Informatics
Shaik Abdul Hameed
VNR Vignana Jyothi Institute of Engineering and Technology
Electrical & Electronics Engineering

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A machine learning framework for dynamic and balanced computing resource allocation in 5G networks Shaik Abdul Hameed; Indurthi Ravindra Kumar; Chavali Amaresh; Kanchana Rajendran; Zarapala Sunitha Bai; Maganti Syamala
Bulletin of Electrical Engineering and Informatics Vol 15, No 1: February 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v15i1.11138

Abstract

The swift expansion of fifth-generation (5G) networks has heightened the difficulty of distributing computing and transmission resources amidst the demands for extensive connectivity, ultra-low latency, and high throughput. This paper presents an innovative hybrid framework that combines deep learning (DL) with bird swarm optimization (BSO) to achieve dynamic and balanced resource allocation in mobile edge–cloud environments. A DL model based on long short-term memory (LSTM) forecasts user demand and channel conditions, while BSO enhances offloading and power distribution to reduce latency, energy usage, and expenses. In a setup utilizing non-orthogonal multiple access (NOMA) and mobile edge computing (MEC), the proposed DL–BSO approach demonstrates an impressive improvement of up to 54% compared to heuristic methods in simulations that reflect realistic traffic and channel conditions. The framework demonstrates a strong ability to adjust to different loads, rendering it ideal for applications that require low latency, including autonomous driving and augmented reality. The constraints involve dependence on precise forecasts and scalability issues in extensive implementations, which will be tackled in forthcoming research focused on 6G advancements.
A proactive approach to software security using DCodeBERT for vulnerability management Indurthi Ravindra Indurthi; Shaik Abdul Hameed; Polasi Sushma; Jose Pitchaiya; Veeramreddy Surya Narayana Reddy; Maganti Syamala
Bulletin of Electrical Engineering and Informatics Vol 15, No 1: February 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v15i1.11100

Abstract

The complexity of modern software has increased security risks, emphasizing the need for automated detection and correction. DCodeBERT, a CodeBERT-based vulnerability detection and remediation framework, is introduced in this study. DCodeBERT uses a multi-task learning framework with shared-private layers, gradient normalization, and uncertainty weighting to stand out. This architecture lets the model capture general representations while preserving task-specific details. From open-source repositories and vetted vulnerability databases, 85,000 code snippets—vulnerable, clean, and repaired—were collected. C, C++, Java, and Python programming languages (PLs) make this dataset highly usable. DCodeBERT surpasses CodeGPT, VulDeePecker, CodeT5 Small, GraphCodeBERT, and Devign in accuracy, precision, recall, and F1-score. Statistics show that the improvements are significant, and qualitative inspection shows that the resulting patches fix buffer overflows and injection problems within semantic validity. This novel approach combines multi-task optimization with natural and PL semantic integration for high cross-language performance. The findings show that DCodeBERT improves vulnerability management in software development settings.
Co-Authors Chavali Amaresh Indurthi Ravindra Indurthi Indurthi Ravindra Kumar Jose Pitchaiya Kanchana Rajendran Maganti Syamala Polasi Sushma Veeramreddy Surya Narayana Reddy Zarapala Sunitha Bai