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All Journal International Journal of Engineering, Science and Information Technology
Bin Ariffin, Shamsul Arrieya
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Toward Ultra-Reliable Low-Latency V2X: A Hybrid Deep Learning Approach for Intelligent Vehicular Networks Jiang, Yi; Bin Ariffin, Shamsul Arrieya
International Journal of Engineering, Science and Information Technology Vol 5, No 3 (2025)
Publisher : Malikussaleh University, Aceh, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52088/ijesty.v5i3.1536

Abstract

Safe and efficient vehicular networks in contemporary intelligent transportation systems necessitate ultra-reliable and low-latency communication (URLLC) requirements acting as the base foundation. Researchers combined Convolutional Neural Networks (CNN) with Long Short-Term Memory (LSTM) networks for creating their Hybrid Deep Learning-Based V2X Framework to improve V2X real-time decision-making abilities. The system's first operation phase acquires diverse Vehicle-to-Everything data from V2V, V2I, V2P and V2N sources which contain GPS locations and vehicle speed readings side by side with Received Signal Strength Indicator (RSSI) measurements along with channel status data. The preprocessing method applies normalization strategies (Min-Max Scaling and Sliding Window Method) together with data reduction methods and time-series transformations to create ready-to-use modelling inputs. Through traffic data sources CNN modules decode road layout features and vehicle distributions next to detecting signal interference sequences but LSTM modules analyze signal variations and handover delay effects and identify congested area evolutions. Processor layers integrate both spatial and temporal elements to produce a unified representation that enables predictions for optimal communication standards. The system maintains dependable communication in dense and mobile environments by enabling adaptive routing and dynamic power control along with stable link selection mechanics. The proposed hybrid framework will benefit the next-generation V2X network by achieving computational efficiency alongside predictive accuracy for autonomous driving and smart traffic management functionalities. The proposed hybrid framework boosts the V2X network by ensuring both computational efficiency and predictive accuracy for autonomous driving, enabling improved traffic management. This integration enhances vehicle coordination, real-time safety, and congestion forecasting for future transportation systems.
Enhancing Healthcare Data Security and Integrity through Blockchain Technology in Hospital Information Systems Ming, Hu; Bin Ariffin, Shamsul Arrieya
International Journal of Engineering, Science and Information Technology Vol 5, No 4 (2025)
Publisher : Malikussaleh University, Aceh, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52088/ijesty.v5i4.1715

Abstract

The goal of this research is to develop and test a blockchain-based framework whose target is to increase the level of security of, integrity , and operational efficiency in hospital information systems (HIS). As the increasing number of sensitive healthcare data and the increasing threats to data privacy, the following system integrates AES encryption, multi-factor authentication (MFA), role-based access control (RBAC), blockchain storage and smart contracts to ensure secure and transparent data management. The methodology was that the framework was implemented in a simulated environment using the Hyperledger Fabric v2.2 on Ubuntu 20.04, and performance was measured for various metrics such as encryption time, transaction time, storage efficiency, scalability, and system responsiveness. Comparative analysis was done to evaluate the user related metrics like ease of use, adoption rate and user satisfaction against the benchmarked metrics reported in the previous body of literature. The results indicate that the blockchain-based system is better than the traditional cloud-based and distributed systems, offering 80% of speeds of transactions, 90% storage efficacy, 85% of scaling, 95% of security, 90% of ease of use, and 80% of adoption. These results show the potentiality of blockchain in improving reliability, auditability, and trustworthiness of information systems in healthcare.
Co-Authors Hu Ming Yi Jiang