Volume XIII, Issue III, July-Sep.-2025

Title–DIVING SAFETY MONITORING SYSTEM: A DEEP LEARNING BASED DRIVER DROWSINESS DETECTION USING YOLOV8

Author– Sudharani Bangarimath, Dr. Sangram Patil and Jambukeshwar Pujari

Abstract

Traffic accidents kill and injure most people. Traffic accident injuries kill a million people annually, according to the WHO. Lack of sleep, rest, or fatigue can cause drivers to fall asleep, endangering themselves and others. This research focuses on driver sleepiness detection and response. The artificial intelligence represents advanced technology, offering solutions to create a majority of vision-based applications. In this research, a driver drowsiness detection system has been developed utilizing the deep learning Yolo v8 algorithm. An image dataset comprising two classes, namely drowsiness and non-drowsiness, is utilized for training with Yolov8. Image classification is conducted based on the condition of the driver’s eyes, specifically whether they are open or closed. The validation of the trained model indicates an accuracy of 96.85% and an F1-Score of 97.5%.

Index Term–Deep Learning, Driver Drowsiness, AI. WHO, Vision-based Applications.

Reference to this paper should be made as follows:  Sudharani Bangarimath, Dr. Sangram Patil and Jambukeshwar Pujari, (2025), “Diving Safety Monitoring System: A Deep Learning Based Driver Drowsiness Detection Using Yolov8” Int. J. Electronics Engineering and Applications, Vol. XIII, No. 3, pp. 01-12.

Title–ARTIFICIAL INTELLIGENCE ENABLED BLOCK CHAIN FRAMEWORK FOR TRANSPARENT SUPPLY CHAIN MONITORING AND ETHICAL RESOURCE UTILIZATION

Author– Abhishek Kumar dwiwedi¹, Abhishek Kumar²

Abstract

The integration of Artificial Intelligence (AI) and Blockchain technologies presents a transformative opportunity to address persistent challenges in supply chain management, including transparency, traceability, ethical sourcing, and resource optimization. This paper proposes an AI-enabled Blockchain Framework designed to enhance transparent supply chain monitoring and promote ethical resource utilization across global logistics networks. The proposed model leverages blockchain’s immutable ledger to ensure trust, accountability, and data provenance, while AI-driven analytics provide real-time insights for predictive decision-making, anomaly detection, and optimization of supply chain operations. Smart contracts are utilized to automate compliance verification, sustainability auditing, and fair-trade validation, thereby minimizing human bias and fraud. Furthermore, machine learning algorithms analyze transactional and sensor-based data to identify unethical practices and inefficiencies, ensuring responsible resource consumption and reducing environmental impact. The framework is validated through simulated and real-world scenarios to evaluate its performance in scalability, security, and transparency. Experimental results demonstrate significant improvements in supply chain visibility, operational efficiency, and ethical compliance compared to conventional systems. This research highlights the potential of integrating AI and blockchain as a unified architecture for building next-generation, trustworthy, sustainable, and intelligent supply chains.

Index Term–Artificial Intelligence (AI), Blockchain, Supply Chain Management, Transparency, Ethical Resource Utilization, Smart Contracts, Sustainability, Internet of Things (IoT), Traceability, Data Integrity.

Reference to this paper should be made as follows:  Abhishek Kumar dwiwedi, Abhishek Kumar,(2025), “Regression Based Sub – Image Matching Methodology For Recognizing An Indian Paper Bill With A Partially Captured Bill Image” Int. J. Electronics Engineering and Applications, Vol. XIII, No. 3, pp. 13-28.

Title–ARTIFICIAL INTELLIGENCE POWERED SMART GOVERNANCE SYSTEMS USING BLOCKCHAIN AND QUANTUM SAFE TECHNOLOGIES

Author– Dr. Deepanjal Shrestha  and Dr. YYY. Nagabhushan

Abstract

This The rapid evolution of digital governance systems has transformed how governments deliver services, ensure transparency, and engage with citizens. However, existing e-governance frameworks face critical challenges related to data security, privacy, interoperability, and trust. This paper proposes an Artificial Intelligence (AI)-Powered Smart Governance System integrated with Blockchain and Quantum-Safe Technologies to build a resilient, transparent, and secure digital governance infrastructure. The proposed model leverages AI for intelligent decision-making, predictive analytics, and process automation, while blockchain ensures data immutability, decentralized control, and verifiable transactions among stakeholders. To counter emerging quantum threats, post-quantum cryptographic algorithms are embedded within the blockchain framework to provide long-term data protection and security assurance. The system architecture enables real-time data sharing, citizen identity management, and automated policy execution through smart contracts, thereby reducing corruption and administrative inefficiencies. Experimental simulations and performance evaluations demonstrate enhanced security, scalability, and operational efficiency compared to conventional governance models. This research highlights a futuristic paradigm for trustworthy, AI-driven governance ecosystems capable of withstanding quantum-era security challenges and ensuring sustainable digital transformation in public administration.

Index Term–Artificial Intelligence (AI), Blockchain, Smart Governance, Quantum-Safe Cryptography, Post-Quantum Cryptography (PQC), Smart Contracts, Digital Transformation, E-Governance, Cybersecurity.

Reference to this paper should be made as follows:  Dr. Deepanjal Shrestha and Dr. YYY. Nagabhushan,(2025), “Regression Based Sub – Image Matching Methodology For Recognizing An Indian Paper Bill With A Partially Captured Bill Image” Int. J. Electronics Engineering and Applications, Vol. XIII, No. 3, pp. 29-45.

Title–ARTIFICIAL INTELLIGENCE DRIVEN DIGITAL TWIN FRAMEWORK FOR PROGNOSTICS, PREDICTIVE MAINTENANCE, AND PROCESS OPTIMIZATION IN INDUSTRY 4.0 ENVIRONMENTS

Author– Digvijay Singh and Ritesh Singh

Abstract

The advent of Industry 4.0 has accelerated the integration of Artificial Intelligence (AI) and Digital Twin (DT) technologies to enable intelligent, data-driven industrial ecosystems. This paper proposes an AI-driven Digital Twin framework for prognostics, predictive maintenance, and process optimization within smart manufacturing environments. The proposed framework leverages real-time data acquisition, sensor fusion, and machine learning-based analytics to mirror the physical system digitally, enabling continuous monitoring, fault detection, and failure prediction. By incorporating AI algorithms such as deep learning and reinforcement learning, the framework enhances the accuracy of prognostic models and facilitates adaptive decision-making for maintenance scheduling and resource allocation. Furthermore, it enables dynamic optimization of production processes, minimizing downtime, energy consumption, and operational costs. The framework’s modular architecture ensures scalability across diverse industrial domains, including manufacturing, energy, and logistics. Experimental validation and simulation results demonstrate the framework’s effectiveness in improving system reliability, operational efficiency, and lifecycle management. This study contributes to the advancement of smart factory systems by establishing a robust foundation for self-aware, autonomous, and sustainable industrial operations powered by AI-driven Digital Twins.

Index Term–Artificial Intelligence, Digital Twin, Predictive Maintenance, Prognostics, Process Optimization, Industry 4.0, Machine Learning, Smart Manufacturing, Cyber-Physical Systems.

Reference to this paper should be made as follows:  Digvijay Singh and Ritesh Singh, (2025), “Regression Based Sub – Image Matching Methodology For Recognizing An Indian Paper Bill With A Partially Captured Bill Image” Int. J. Electronics Engineering and Applications, Vol. XIII, No. 3, pp. 46-63.

Title–BLOCKCHAIN BASED DECENTRALIZED FRAMEWORK FOR ENHANCING TRUST AND TRANSPARENCY IN ARTIFICIAL INTELLIGENCE APPLICATIONS

Author–  Rohith Varma Vegesna , I Wayan budi sentana , and Rajesh Kumar Tiwari

Abstract

The rapid advancement of Artificial Intelligence (AI) has revolutionized diverse sectors, including healthcare, finance, governance, and autonomous systems. However, the increasing dependence on AI-driven decision-making raises significant concerns regarding trust, transparency, data integrity, and accountability. This research proposes a blockchain-based decentralized framework to address these challenges by ensuring secure, traceable, and tamper-proof management of AI models and datasets. The proposed system leverages the immutability and consensus mechanisms of blockchain to record AI decision processes, data provenance, and model updates in a transparent and verifiable manner. By integrating smart contracts, the framework enables automated validation and auditing of AI operations without relying on centralized authorities. Furthermore, distributed storage and cryptographic techniques enhance the confidentiality and integrity of sensitive data while maintaining accessibility across nodes. Experimental analysis and case studies demonstrate that the blockchain-integrated AI ecosystem not only strengthens user trust but also mitigates ethical and security risks associated with opaque AI systems. This study highlights the potential of blockchain-AI convergence in building explainable, accountable, and trustworthy intelligent systems for future digital infrastructures.

Index Term–Quantum Blockchain, Hybrid Model, Quantum Cryptography, Secure Data Exchange, Computational Efficiency, Post-Quantum Security, Smart Contracts, Consensus Mechanism, Quantum Key Distribution (QKD), Decentralized Networks.

Reference to this paper should be made as follows:  I Wayan Budi Sentana, and Rajesh Kumar Tiwari (2025), “Climate Pattern Prediction Using Hybrid Spatiotemporal Models” Int. J. Electronics Engineering and Applications, Vol. XIII, No. 3, pp. 64-79.