The concept of a smart campus has emerged with advances in ICT, IoT, cloud computing, and data analytics, enabling higher education institutions to improve efficiency, sustainability, and user experience. This paper presents an integrated technology framework that connects infrastructure, services, and stakeholders through intelligent systems. It highlights the role of IoT for real-time monitoring, cloud platforms for scalable data management, and AI/ML for predictive decision-making. The proposed architecture consists of sensing, network, data processing, and application layers to ensure interoperability among heterogeneous systems. The study also emphasizes the importance of cybersecurity, privacy, and governance in maintaining system integrity. Results show that smart campus implementation enhances energy efficiency, operational performance, and user satisfaction. Finally, the paper provides a roadmap for developing scalable, sustainable, and intelligent campus ecosystems.
Nandhini Ravi· International Journal of Eme...· 0 citations
Scalable Event-Driven Architectures (EDAs) provide an efficient approach for real-time big data processing by enabling low-latency, asynchronous, and continuous handling of high-speed data streams generated from IoT devices, cloud platforms, social media, and enterprise systems. This study presents a scalable framework that integrates distributed event brokers, stream processing engines, cloud-native microservices, and scalable storage solutions. The architecture utilizes publish-subscribe communication, event sourcing, and distributed stream analytics to support real-time decision-making and dynamic scalability. Technologies such as Apache Kafka, Apache Flink, Apache Spark Streaming, and Kubernetes enhance throughput, fault tolerance, and system resilience. Key components include event producers, brokers, stream processors, consumers, and monitoring services. Performance evaluation based on throughput, latency, scalability, fault tolerance, and resource utilization demonstrates that EDAs outperform traditional batch-processing and request-response systems. The framework also addresses challenges such as event ordering, state management, event replay, and observability through checkpointing, event partitioning, distributed tracing, and container orchestration. The proposed architecture is applicable to financial analytics, smart cities, healthcare, e-commerce, cybersecurity, and industrial automation. Overall, EDAs offer a scalable, reliable, and flexible foundation for next-generation real-time analytics and big data applications.
Nandhini Ravi· International Journal of App...· 0 citations
The integration of Artificial Intelligence (AI) into the Industrial Internet of Things (IIoT) has enabled predictive analytics, autonomous control, and optimized operations. However, the increasing reliance on complex and opaque machine learning models raises concerns regarding trust, accountability, and regulatory compliance in critical industrial environments. Explainable AI (XAI) aims to address these concerns by providing transparent and interpretable decision-making processes. This paper explores the intersection of XAI and IIoT, highlighting the challenges of applying explainable models in real-time, data-intensive industrial contexts. We survey existing XAI techniques and evaluate their suitability for IIoT applications, such as predictive maintenance, quality assurance, and anomaly detection. Additionally, we discuss evaluation metrics, present case studies, and propose a framework for integrating XAI into IIoT pipelines. Our findings demonstrate the potential of XAI to enhance transparency, user trust, and operational safety in next-generation industrial systems.
Nandhini Ravi· International Journal of Mac...· 0 citations
Industry 4.0 has transformed conventional manufacturing into intelligent, automated, and connected production environments. Intelligent robotic pick-and-sort systems improve productivity, flexibility, product quality, and operational efficiency by overcoming the limitations of traditional rule-based automation. This paper presents an AI-enabled framework integrating computer vision, deep learning, robotic manipulation, edge computing, and the Industrial Internet of Things (IIoT) for dynamic manufacturing applications. Convolutional Neural Networks (CNNs) provide accurate object detection and classification, while intelligent motion planning and reinforcement learning optimize robotic grasping and movement. Sensor fusion, edge computing, and IIoT connectivity enable real-time monitoring, low-latency decision-making, and predictive maintenance, improving system reliability and reducing downtime. Performance is evaluated using object detection accuracy, sorting accuracy, processing time, throughput, energy efficiency, and overall system reliability. Compared with conventional automation, the proposed framework offers greater adaptability, higher sorting accuracy, and improved operational performance in dynamic production environments. The study concludes that intelligent robotic pick-and-sort systems are a key technology for smart factories, supporting flexible manufacturing, mass customization, and sustainable industrial production, with future opportunities in digital twins, explainable AI, cloud-edge intelligence, and collaborative human-robot systems.
Nandhini Ravi· International Journal of Int...· 0 citations
This research proposes an Explainable Machine Learning (XML)–based framework to assess software quality by integrating code metrics, defect datasets, and advanced interpretability methods such as SHAP, LIME, and permutation importance.
Nandhini Ravi· International Journal of Mac...· 0 citations
Experimental results show that AI-driven knowledge graphs significantly enhance decision accuracy, reduce ambiguity, and improve interpretability, achieving up to 85–92% higher decision efficiency compared to traditional methods.
Venkatesh Iyer, Nandhini Ravi· International Journal of Art...· 0 citations