Deep Learning Applications in Smart Manufacturing for Revitalizing the U.S. Pharmaceutical Sector
Keywords:
Smart Manufacturing, Pharmaceutical SectorAbstract
Deep learning, a subset of artificial intelligence (AI) and machine learning (ML), broadens the idea of neural networks by incorporating more complex architectures in order to abstract concepts of a higher order [1]. Deep learning models excel particularly well in processing unstructured and semi-structured data (i.e., photographs, videos, voice, text, etc.,) and have achieved extraordinary results in several industry sectors in recent years. Smart manufacturing is the adoption of advanced and modern technologies as well as the employment of enhanced and new strategies and initiatives in manufacturing process(es) with the intention of boosting operational efficiency, enhancing quality, optimizing the supply chain, increasing personalization, and minimizing costs [2]. This paper attempts to address the applications of deep learning as a key technology in smart manufacturing strategies in the context of revitalizing the pharmaceutical sector of the USA industry. The development of the deep learning architecture, the exploration of smart manufacturing principles, and the investigation of deep learning’s advantages and applications in smart manufacturing strategy are both addressed.
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