As artificial intelligence (AI) increasingly drives decision-making in finance, ensuring transparency and trust becomes essential, particularly in high-stakes applications like portfolio optimization. This paper explores the integration of Explainable AI (XAI) interfaces within cloud-deployed portfolio optimization systems, aiming to bridge the gap between advanced AI models and financial professionals. We outline a cloud-native architecture that embeds explainability into each stage of the portfolio optimization lifecycle, from data ingestion to user interaction. Various XAI methods, including feature attribution and model-agnostic explanations, are examined in the context of financial decision-making. We present design considerations for building user-facing interfaces that make model decisions interpretable and actionable. A case study illustrates how such interfaces can enhance user trust and improve portfolio strategy validation. Finally, we discuss technical, regulatory, and usability challenges, and propose future research directions for deploying responsible AI in cloud-based financial systems.
R. Sharma, Mohammed Asif Khan· International Journal of Art...· 0 citations
The evolution of Industry 4.0 has brought forth an increasing demand for flexibility, adaptability, and intelligence in manufacturing systems. Modular smart manufacturing cells, with their inherent reconfigurability, are becoming essential components in modern production environments. This paper presents an AI-based framework for dynamic reconfiguration of such cells, enabling rapid adaptation to changing production demands, equipment failures, and optimization goals. Leveraging machine learning and real-time data analytics, the proposed system autonomously identifies optimal reconfiguration strategies with minimal human intervention. A case study is presented to validate the framework, demonstrating significant improvements in operational efficiency and system responsiveness. The results highlight the transformative potential of AI in achieving truly autonomous and resilient manufacturing systems.
Mohammed Asif Khan, Shalini Gupta· International Journal of Mac...· 0 citations
The rise of lights-out manufacturing—facilities operating autonomously without human intervention—has redefined the landscape of industrial automation. However, these systems often rely on pre-designed parts and rigid workflows, limiting their adaptability. This paper explores the integration of generative AI models into lights-out manufacturing environments to enable on-demand, real-time product design. By leveraging the capabilities of neural networks such as Generative Adversarial Networks (GANs) and diffusion-based models, manufacturing systems can autonomously create, evaluate, and iterate product designs without human input. We propose an architectural framework for integrating AI-driven design generation with digital twin-based production pipelines, highlight key use cases such as rapid prototyping and design optimization, and assess the technical challenges associated with quality control, validation, and data integrity. Our analysis suggests that generative AI can dramatically enhance the responsiveness and efficiency of fully automated facilities, marking a significant step toward fully autonomous product lifecycles.
Mohammed Asif Khan, Shalini Gupta· International Journal of Mac...· 0 citations
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