The Fourth Industrial Revolution is accelerating the adoption of Industry 4.0 through intelligent computing, Industrial Internet of Things (IIoT), edge computing, and AI-driven automation. Traditional cloud-based industrial systems often experience latency, bandwidth limitations, network congestion, and privacy concerns, making them unsuitable for real-time manufacturing applications. This paper proposes an Edge Intelligence framework that integrates distributed edge computing, real-time AI analytics, and autonomous decision-making to process industrial IoT data locally. The architecture consists of four layers: perception, edge intelligence, autonomous decision, and cloud coordination. Industrial sensors, PLCs, and robotic systems collect operational data, while lightweight machine learning, deep learning, and reinforcement learning models perform feature extraction, anomaly detection, predictive analytics, and adaptive control with minimal latency. A mathematical optimization model minimizes processing delay, energy consumption, and resource utilization while maximizing accuracy and efficiency. Experimental evaluation demonstrates improved real-time performance, decision accuracy, fault detection, scalability, and resource utilization, making the framework well suited for next-generation smart manufacturing and sustainable industrial automation.
V. Sethi· International Journal of Int...· 0 citations
The integration of Artificial Intelligence (AI) as a collaborative partner is transforming the future of work. Rather than replacing human labor, modern AI systems enhance human capabilities through cognitive augmentation, adaptive workflows, and cooperative problem-solving. This paper presents a multidisciplinary analysis of human–AI collaboration across sectors such as healthcare, engineering, finance, education, and creative industries. A conceptual framework is proposed for dynamic task allocation between humans and AI based on uncertainty, contextual reasoning, and interpretability requirements. The study also examines socio-technical challenges including trust, ethical alignment, skill transformation, and organizational resilience. Using a domain-agnostic evaluation approach, collaboration effectiveness is measured through metrics such as cognitive load distribution, error reduction, adaptability, and explainability. The findings indicate that hybrid intelligence systems outperform both purely human and fully automated systems in complex and uncertain environments. The study concludes that the future of work will depend on co-evolutionary human-AI collaboration, requiring organizational restructuring, policy development, and ethical safeguards to ensure sustainable productivity and innovation.
V. Sethi· International Journal of Eme...· 0 citations
Results demonstrate that smart sensor-based systems outperform conventional sensing systems in efficiency, fault detection, responsiveness, energy savings, and data accuracy, making them a key enabler of Industry 4.0 and future autonomous engineering applications.
V. Sethi· International Journal of Mod...· 0 citations