Rapid urbanization, industrialization, increasing vehicle emissions, fossil fuel consumption, and construction activities have significantly deteriorated urban air quality, posing serious risks to public health, the environment, and the economy. Traditional air quality monitoring systems, which rely on fixed monitoring stations and statistical forecasting methods, often lack adequate spatial coverage and fail to capture the complex relationships among environmental factors. Artificial Intelligence (AI) offers an effective alternative by integrating data from IoT sensors, satellite observations, meteorological stations, traffic systems, and historical pollution records to generate accurate real-time air quality predictions. This paper presents an AI-based predictive framework for urban air quality management that combines data preprocessing, feature engineering, machine learning, deep learning, and ensemble models. The framework analyzes key environmental parameters, including particulate matter (PM₂.₅ and PM₁₀), gaseous pollutants, weather conditions, traffic density, and industrial emissions. Advanced preprocessing techniques improve data quality, while algorithms such as Random Forest, Support Vector Machine (SVM), Artificial Neural Networks (ANN), Long Short-Term Memory (LSTM), and Gradient Boosting enhance forecasting accuracy. The proposed model supports intelligent decision-making by enabling early pollution warnings, optimized traffic management, industrial emission control, and sustainable urban planning. Experimental results demonstrate that AI-based models outperform conventional statistical approaches in prediction accuracy and computational efficiency. Overall, the framework provides a scalable and reliable solution for smart city applications, contributing to healthier, more sustainable, and resilient urban environments.
Seshagiri N, Narendra Karmarkar· International Journal of Mod...· 0 citations
The rapid advancement of Industry 4.0 has accelerated the development of intelligent and autonomous smart factories powered by Industrial Internet of Things (IIoT) devices, cyber-physical systems (CPS), and advanced manufacturing technologies. Although cloud computing offers significant computational and storage capabilities, it suffers from latency, bandwidth limitations, privacy concerns, and delayed decision-making in time-critical industrial environments. This paper proposes an Integrated Intelligent Edge Computing Architecture for Real-Time Smart Factory Operations, combining edge computing, Artificial Intelligence (AI), digital twins, and predictive analytics to enable real-time local data processing with seamless cloud integration. The framework employs machine learning for anomaly detection, deep learning for automated quality inspection, reinforcement learning for adaptive production scheduling, and predictive models for equipment health monitoring. Secure communication protocols enhance data protection and system reliability. Experimental results demonstrate improved latency, prediction accuracy, manufacturing efficiency, energy utilization, fault detection, and operational resilience compared with conventional cloud-based approaches. The proposed architecture provides a scalable and sustainable solution for next-generation autonomous smart factories, improving equipment reliability, reducing operational costs, and increasing manufacturing productivity.
Seshagiri N· International Journal of Mod...· 0 citations
Decision Intelligence (DI) combines Artificial Intelligence (AI), Machine Learning (ML), analytics, and domain expertise to improve organizational decision-making. However, the growing volume of multimodal data, including text, images, sensor data, and numerical information, presents significant challenges for conventional decision support systems. This study proposes a Multimodal AI Framework for Decision Intelligence Systems that integrates diverse data sources to enhance prediction accuracy, contextual understanding, and operational efficiency. The proposed architecture consists of four layers: data ingestion, multimodal processing, fusion intelligence, and decision orchestration. It employs Natural Language Processing (NLP), Computer Vision (CV), time-series analytics, and transformer-based fusion techniques to generate predictive insights, automated recommendations, and explainable decisions. The framework is applicable across healthcare, finance, manufacturing, retail, and intelligent governance. Performance evaluation demonstrates that multimodal AI significantly outperforms traditional unimodal systems by improving prediction accuracy, reducing decision latency, and enhancing contextual awareness. The proposed framework supports faster, more reliable, and explainable decision-making, providing a scalable solution for next-generation enterprise decision intelligence and data-driven strategic planning.
Seshagiri N· International Journal of Art...· 0 citations
Industry 4.0 has accelerated the adoption of the Industrial Internet of Things (IIoT), enabling intelligent communication among industrial devices, edge systems, and cloud platforms for smart manufacturing. However, conventional centralized security approaches are increasingly vulnerable to cyber threats, data tampering, and single-point failures. This paper proposes a secure blockchain-enabled IIoT architecture that integrates industrial sensing, edge computing, distributed ledger technology, cloud analytics, and intelligent decision-making. The framework employs device authentication, encrypted communication, decentralized consensus, smart contracts, and machine learning-based anomaly detection to enhance data integrity, secure information sharing, and cyber resilience. Experimental evaluation demonstrates improvements in communication security, authentication accuracy, transparency, scalability, latency, and throughput, making the proposed architecture a robust and scalable solution for secure next-generation smart engineering and industrial automation.
Seshagiri N· International Journal of Mod...· 0 citations
The rapid integration of Internet of Things (IoT), Artificial Intelligence (AI), cloud computing, edge computing, and advanced communication technologies is transforming traditional urban infrastructure into intelligent smart cities. Conventional resource allocation methods struggle to manage dynamic urban environments, creating a need for adaptive and decentralized decision-making systems. This study proposes a Multi-Agent Reinforcement Learning (MARL) framework for intelligent resource allocation across transportation, energy, water, healthcare, emergency response, and communication systems. Each urban subsystem functions as an autonomous learning agent that optimizes local decisions while coordinating to improve overall city performance. The framework combines IoT sensing, edge intelligence, cloud analytics, and deep reinforcement learning to enable real-time, adaptive resource management. It enhances resource utilization, reduces energy consumption and response time, improves system reliability, and supports scalable urban operations. The proposed approach also provides a foundation for future smart city technologies, including digital twins, federated learning, autonomous edge intelligence, and 6G networks, promoting sustainable, resilient, and efficient urban development.
Seshagiri N· International Journal of Eme...· 0 citations
Rapid urbanization and increasing traffic demand require intelligent transportation systems that can adapt in real time. Traditional centralized traffic management is limited in handling dynamic traffic conditions, leading to congestion, delays, higher fuel consumption, and reduced road safety. This study proposes a Multi-Agent Autonomous Control Framework that integrates Multi-Agent Systems (MAS), Artificial Intelligence (AI), Deep Reinforcement Learning (DRL), Graph Neural Networks (GNN), Internet of Things (IoT), Vehicle-to-Everything (V2X) communication, and edge-cloud computing. The framework enables traffic signals, vehicles, roadside units, and other transportation entities to operate as autonomous, cooperative agents that exchange real-time information and make distributed decisions. By analyzing traffic, environmental, and infrastructure data, the proposed architecture dynamically optimizes signal control, routing, and emergency response. Simulation results demonstrate improvements in traffic flow, congestion reduction, travel time, fuel efficiency, road safety, scalability, and system resilience. The framework provides a scalable and intelligent foundation for future smart cities, connected autonomous transportation, and sustainable urban mobility.
Seshagiri N· International Journal of Mod...· 0 citations
Experimental results demonstrate high model accuracy, reduced privacy leakage, lower communication overhead, faster convergence, enhanced scalability, and strong resilience against security attacks, making the proposed framework suitable for next-generation privacy-preserving smart applications.
Seshagiri N· International Journal of Mod...· 0 citations
Autonomous robotic systems are increasingly deployed in industrial automation, healthcare, logistics, agriculture, defense, and intelligent transportation, where they must make complex decisions in dynamic environments. Reinforcement Learning (RL) enables robots to learn optimal actions through interaction with their environment, but most deep RL models function as black boxes, limiting transparency and trust in safety-critical applications. This paper proposes an Explainable Reinforcement Learning for Autonomous Robotic Decision Making (XORL) framework that integrates reinforcement learning with Explainable AI (XAI) to improve decision interpretability. The framework combines multimodal sensor data, policy optimization, confidence estimation, reward decomposition, policy visualization, and decision traceability to generate understandable explanations for robotic actions. It evaluates performance using metrics such as navigation success, obstacle avoidance, learning stability, computational efficiency, explanation consistency, and reliability. Experimental results demonstrate that XORL enhances decision transparency, operator trust, safety awareness, and autonomous task performance while maintaining competitive learning efficiency, supporting the development of trustworthy and human-centric autonomous robotic systems.
Seshagiri N· International Journal of Int...· 0 citations
This paper proposes the Foundation Model-Based Predictive Analytics Framework for Multi-Domain Decision Intelligence (FMPA-MDI), an integrated architecture that combines heterogeneous data acquisition, multimodal preprocessing, semantic representation learning, transformer-based predictive reasoning, retrieval-augmented learning, knowledge graph integration, explainable AI (XAI), and intelligent decision optimization.
Seshagiri N· International Journal of Mac...· 0 citations
The proposed hybrid methodology integrates workload prediction, adaptive scheduling, and resource consolidation, demonstrating significant energy savings without compromising system performance is proposed.
Seshagiri N· International Journal of Dat...· 0 citations
This study proposes a Federated Predictive Learning with Privacy-Aware Model Aggregation (FPL-PAMA) framework, suitable for applications including healthcare, IoT, smart manufacturing, transportation, and financial fraud detection, providing a secure and scalable solution for next-generation distributed intelligent systems.
Mahabala H. N., Seshagiri N· International Journal of Mac...· 0 citations