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
Agentic Artificial Intelligence (Agentic AI) represents the next generation of intelligent systems capable of autonomous sensing, reasoning, planning, and action with minimal human intervention. Unlike traditional AI, Agentic AI integrates large language models, reinforcement learning, multi-agent systems, planning mechanisms, orchestration layers, and memory modules to enable adaptive and goal-oriented decision-making. This paper explores Agentic AI architectures for autonomous business applications, highlighting their role in finance, healthcare, supply chain, enterprise resource planning, customer relationship management, and industrial operations. A layered architecture comprising perception, reasoning, orchestration, and execution layers is proposed to support autonomous analysis, strategic planning, and optimized action execution. The framework also incorporates memory, monitoring, and governance modules to enhance transparency, reliability, and explainability. Experimental evaluation demonstrates that the proposed architecture improves workflow automation, decision accuracy, operational efficiency, resource utilization, and response time while reducing manual intervention. The findings indicate that Agentic AI provides a scalable and robust foundation for future autonomous enterprise systems. The study also discusses key challenges, including explainability, governance, ethics, and trust, emphasizing their importance for successful enterprise adoption. Overall, Agentic AI architectures offer significant potential to accelerate intelligent automation and drive the next generation of business transformation.
Narendra Karmarkar, Iyengar P.K· International Journal of Mod...· 0 citations
Modern enterprises generate massive volumes of data from cloud platforms, IoT devices, enterprise applications, social media, and AI systems, creating challenges in data integration, governance, scalability, security, and real-time analytics. Traditional data management approaches often struggle to handle these complex and distributed environments. This paper proposes an Autonomous Data Fabric (ADF) architecture that combines AI/ML, metadata-driven automation, knowledge graphs, intelligent orchestration, and policy-based governance to enable seamless, self-managing enterprise data ecosystems. The framework supports automated data discovery, semantic integration, adaptive workflows, continuous monitoring, and intelligent resource optimization while ensuring data quality, security, and compliance. Experimental results demonstrate that the proposed ADF significantly improves data integration efficiency, governance, analytics performance, operational cost, and decision-making compared to conventional systems. Its scalable and self-adaptive design supports hybrid cloud, multi-cloud, edge, and on-premises environments, making it a robust solution for enterprise digital transformation and next-generation intelligent data management.
Narendra Karmarkar· International Journal of Dat...· 0 citations
Corporate Social Responsibility (CSR) has evolved from a voluntary initiative into a strategic business function that strengthens competitiveness, stakeholder trust, and sustainable development. Digital technologies such as Artificial Intelligence (AI), cloud computing, blockchain, big data, IoT, and social media have transformed CSR by enabling responsible innovation, transparency, sustainable operations, ethical AI, data privacy, cybersecurity, and digital inclusion. This study proposes an integrated CSR framework that combines Environmental, Social, and Governance (ESG) principles with digital technologies to measure CSR performance through quantitative indicators. It also addresses challenges such as cyber threats, algorithmic bias, privacy concerns, misinformation, and regulatory compliance. The framework supports evidence-based decision-making, real-time sustainability monitoring, transparent reporting, and improved stakeholder engagement. The findings demonstrate that digital CSR enhances organizational resilience, brand reputation, customer trust, regulatory compliance, and long-term sustainability, making CSR a key strategic capability in the digital marketplace.
Narendra Karmarkar· International Journal of Com...· 0 citations
The digitalization of government services has increased the efficiency, accessibility and transparency of public administration, with an impact on the speed of change. Government service digitalization has increased the efficiency, accessibility and transparency of public administration, which affects the speed of change. But traditional digital identity management systems still have a lot of issues to overcome, such as centralised data storage, identity theft, unauthorised access, data tampering, privacy breach, and inadequate interoperability between government departments. These issues erode citizens' confidence and affect the safe and secure service delivery of e-Governance. A decentralized, immutable, transparent and cryptographically secure platform for identity management has emerged in the form of blockchain technology. Blockchain-based digital identities give citizens more control over their personal data and allow existing governments to provide secure digital identities and trusted authentication for information sharing between governments and individuals. The paper provides a detailed research on Blockchain-Enabled Identity Systems for Secure e-Governance by exploring various aspects of blockchain-based identity systems, including technologies, components, authentication mechanisms, and security features, which contribute to secure e-governance. The proposed framework combines Distributed Ledger Technology (DLT), smart contracts, cryptographic verification, permissioned blockchain networks, and decentralized identity management to create a secure identity infrastructure for public service delivery. Additionally, the paper explores the opportunities and prospects of blockchain for improving data integrity, transparency, accountability, and efficiency, and curbing fraud and administrative burden. The proposed framework clearly shows how a blockchain-based identity system can play a significant role in strengthening citizen-centric governance, especially when it comes to improving authentication accuracy, safeguarding sensitive information, simplifying the cross-departmental verification of citizens' identities, and enabling secure digital transformation initiatives within modern governments.
Narendra Karmarkar· International Journal of Mod...· 0 citations
The rapid evolution of Industry 4.0 has accelerated the adoption of intelligent manufacturing systems requiring real-time monitoring, predictive maintenance, and autonomous decision-making. Traditional cloud-based solutions often suffer from latency, bandwidth limitations, and data privacy concerns, making them unsuitable for time-critical industrial applications. This paper presents an Edge AI-based autonomous monitoring framework that integrates Industrial Internet of Things (IIoT) sensors, edge computing, deep learning models, and cloud platforms for efficient industrial monitoring. Real-time sensor data, including temperature, vibration, pressure, humidity, and power consumption, are processed locally using Convolutional Neural Networks (CNNs), Long Short-Term Memory (LSTM) networks, and anomaly detection algorithms to identify equipment faults and optimize operations. Only summarized insights are transmitted to the cloud, reducing communication overhead while enabling scalable enterprise-level analytics. The proposed framework improves prediction accuracy, minimizes downtime, enhances product quality, strengthens cybersecurity, and supports sustainable manufacturing. It provides a scalable and resilient solution for smart factories across industries, enabling intelligent automation, predictive analytics, and efficient autonomous industrial operations.
Narendra Karmarkar· International Journal of Mod...· 0 citations
The rapid evolution of Industry 4.0 has accelerated the adoption of autonomous robotic systems in intelligent manufacturing. Traditional robot task planning methods are limited in dynamic production environments due to their dependence on rule-based programming and deterministic algorithms. In contrast, Generative Artificial Intelligence (GenAI), including Large Language Models (LLMs) and multimodal foundation models, enables robots to understand natural language, perform contextual reasoning, generate adaptive task plans, and respond intelligently to changing industrial conditions. This paper presents a Generative AI-based framework for robotic task planning that integrates multimodal perception, semantic reasoning, LLM-based planning, reinforcement learning, and execution monitoring. The framework enhances planning accuracy, adaptability, collaboration, and operational efficiency while reducing execution errors and planning complexity. It is applicable to smart assembly, automated inspection, warehouse logistics, predictive maintenance, collaborative robotics, and flexible manufacturing. The study concludes that Generative AI offers a promising foundation for scalable, adaptive, and human-centric industrial automation, with future research directed toward continual learning, explainable AI, federated robotic intelligence, edge AI, and trustworthy autonomous decision-making.
Narendra Karmarkar· International Journal of Int...· 0 citations
Industry 4.0 has transformed manufacturing through the integration of Industrial IoT (IIoT), cyber-physical systems, cloud computing, and artificial intelligence, making predictive maintenance (PdM) a key strategy for improving equipment reliability. Unlike traditional maintenance, AI-driven PdM analyzes real-time sensor data to predict equipment failures before they occur. However, many AI models operate as black boxes, limiting trust and interpretability. The proposed Explainable AI-Based Predictive Maintenance Framework (XAI-PMF) addresses this challenge by integrating IIoT sensing, intelligent feature engineering, hybrid machine learning (Random Forest, Gradient Boosting, LSTM, and Transformers), and explainability techniques such as SHAP, LIME, and rule extraction. These methods provide transparent fault predictions and maintenance recommendations by highlighting the factors influencing equipment degradation. Continuous learning further enables adaptive model updates as new operational data become available. Overall, the framework improves prediction accuracy, reduces downtime and false alarms, enhances maintenance scheduling, and supports trustworthy, intelligent asset management for next-generation smart factories.
Narendra Karmarkar· International Journal of Mod...· 0 citations
Scientific publishing, digital repositories, patents, and multidisciplinary research datasets have expanded rapidly, making traditional literature review methods increasingly inefficient. Large Language Models (LLMs) address this challenge by enabling intelligent knowledge discovery, semantic search, literature summarization, research gap identification, hypothesis generation, citation assistance, and academic writing support. By integrating Retrieval-Augmented Generation (RAG), vector databases, knowledge graphs, citation networks, and domain-specific ontologies, LLMs improve contextual relevance, reduce hallucinations, and enhance research accuracy. These capabilities accelerate interdisciplinary collaboration, automate research workflows, and support evidence-based decision-making. However, challenges such as hallucination, bias, outdated knowledge, explainability, privacy, intellectual property, reproducibility, and computational requirements remain significant. Modern AI-assisted research systems increasingly incorporate human-in-the-loop validation, explainable AI, and responsible governance to ensure trustworthy outcomes. This study presents a conceptual framework that combines semantic retrieval, intelligent reasoning, automated literature analysis, and workflow orchestration, demonstrating how LLM-powered systems can transform scientific research into scalable, accurate, ethical, and collaborative knowledge discovery processes.
Narendra Karmarkar, Iyengar P.K· International Journal of Eme...· 0 citations