Industry 5.0 represents a paradigm shift from fully automated production toward intelligent, human-centric manufacturing environments where collaborative robots, artificial intelligence (AI), edge computing, digital twins, Industrial Internet of Things (IIoT), and cyber-physical systems (CPS) operate in harmony with human workers. Unlike Industry 4.0, which primarily emphasized automation and productivity, Industry 5.0 focuses on resilience, sustainability, worker well-being, and personalized manufacturing. This study presents a comprehensive research framework for Industry 5.0-oriented human-centric robotic manufacturing systems by integrating collaborative robotics, AI-assisted decision-making, adaptive sensing, real-time monitoring, and intelligent manufacturing analytics. The proposed framework enables seamless interaction between human operators and robotic systems while ensuring operational safety, productivity, flexibility, and energy efficiency. A detailed literature review identifies current advancements, research gaps, and technological challenges associated with human-robot collaboration. The research methodology introduces an intelligent architecture incorporating multi-modal sensing, AI-based decision support, digital twin simulation, and adaptive robotic control. Comparative performance metrics demonstrate improvements in production efficiency, safety compliance, response time, and system adaptability. The findings indicate that Industry 5.0 technologies significantly enhance manufacturing performance while promoting sustainable and worker-centered industrial environments. The study concludes with future research directions involving explainable artificial intelligence, federated learning, autonomous collaborative robots, and resilient cyber-physical manufacturing ecosystems.
Narendra Karmarkar, Iyengar P. K.· International Journal of Int...· 0 citations
The rapid evolution of intelligent robotic systems has increased the demand for embedded computing architectures capable of performing complex perception, decision-making, and control operations under strict energy constraints. Traditional robotic platforms often rely on centralized cloud computing or high-performance processors, resulting in increased communication latency, energy consumption, and reduced autonomy. Energy-aware embedded intelligence overcomes these drawbacks by directly embedding lightweight artificial intelligence (AI) algorithms, hardware accelerators, adaptive power management and edge-based decision-making capabilities into robotic platforms. We propose an energy-smart embedded intelligence framework for a smart robotic application, engaging the ensemble of (i) embedded AI (ii) sensor fusion mechanism (iii), Nature-mimicry based energy optimization strategy and (iv) real-time autonomous control. We have designed robotics based on dynamic workload scheduling and intelligent resource allocation for dynamically balancing the computational performance with power consumption. The paper introduces the results of an analysis on state-of-the-art in embedded intelligence methods, short description of research gaps and also insight regarding energy-efficient architectures effectiveness that addresses mobile robots, autonomous robotic systems, and more broadly industrial robots. Comparative evaluation shows that energy-aware embedded intelligence can drastically reduce energy consumption and maintain high accuracy and responsiveness. It suggests that enhanced edge-intelligence architectures will drive sustainable, autonomous, and scalable operation of next-generation robotic systems in Industry 5.0 settings.
Narendra Karmarkar· International Journal of Int...· 0 citations
The rapid digital transformation of healthcare has generated enormous volumes of heterogeneous medical data from hospitals, research laboratories, diagnostic centers, and wearable healthcare devices. These distributed datasets present unprecedented opportunities for developing intelligent clinical decision support systems using artificial intelligence (AI). However, strict privacy regulations, institutional policies, and cybersecurity concerns significantly restrict the sharing of sensitive patient information across organizations. Federated Learning (FL) has emerged as a promising distributed machine learning paradigm that enables collaborative model training without exchanging raw medical data. While conventional FL offers inherent advantages, it is subjected to challenges including centralized aggregation and impacts from malicious participants such as participating adversarial agents performing model poisoning attacks or moving around in a vehicular network leading to non-transparent trust management. Decentralization consensus, immutability, traceability and tamper-proof transaction record capabilities of blockchain technology is an excellent complement to federated learning. This paper proposes a novel blockchain based federated learning framework for non-sensitive cross-institutional medical data research. We present a solution which integrates decentralized blockchain networks with federated model aggregation offering secure parameter exchange, transparent participant validation, tamper-resistant audit trails and increased trust between collaborating healthcare institutions. Smart contracts provide data security, model privacy and automated protocol of database access as well as a rudimentary sense of control to prevent forgery. The framework achieves substantial gains in privacy preservation, collaborative intelligence, system scalability, and adversarial attack robustness. This integration enables safe, reliable and scalable collaborative medical intelligence for efficient multi-institutional medical research, accelerating AI-based health innovations, and maintaining compliance with recent healthcare privacy regulations.
Narendra Karmarkar· International Journal of Eme...· 0 citations
Recent enterprise digitalization has transformed workplaces into intelligent environments powered by cloud computing, IoT, big data, AI, and collaborative platforms. Among these technologies, AI has emerged as a key enabler of human–AI collaboration, enhancing decision-making, automating routine tasks, supporting organizational learning, and improving productivity. However, organizations continue to face challenges related to trust, transparency, explainability, ethical governance, interoperability, and employee acceptance. This paper proposes an **Intelligent Human–AI Collaboration Framework for Future Digital Workplaces** consisting of four stages: workplace data integration, intelligent knowledge processing, collaborative decision intelligence, and adaptive feedback optimization. The framework integrates enterprise data, AI-driven analytics, human expertise, and explainable AI to deliver transparent, adaptive, and trustworthy decision support while maintaining human oversight. It also incorporates responsible AI principles, including fairness, accountability, privacy, and ethical governance. By combining collaborative intelligence with continuous learning, the framework aims to improve decision accuracy, operational efficiency, employee productivity, organizational agility, and sustainable digital transformation across diverse industries.
Narendra Karmarkar, Iyengar P. K.· International Journal of Eme...· 0 citations
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 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
An Edge AI-based autonomous monitoring framework that integrates Industrial Internet of Things sensors, edge computing, deep learning models, and cloud platforms for efficient industrial monitoring that improves prediction accuracy, minimizes downtime, enhances product quality, strengthens cybersecurity, and supports sustainable manufacturing.
Narendra Karmarkar· International Journal of Mod...· 0 citations
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
The proposed Explainable AI-Based Predictive Maintenance Framework (XAI-PMF) addresses this challenge by integrating IIoT sensing, intelligent feature engineering, hybrid machine learning, and explainability techniques such as SHAP, LIME, and rule extraction.
Narendra Karmarkar· International Journal of Mod...· 0 citations
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