The Application of Deep Learning in Quality Assurance for U.S. Pharmaceutical Manufacturing
Keywords:
Deep Learning, Quality Assurance, Pharmaceutical ManufacturingAbstract
Deep Learning has recently been utilized in the Artificial Intelligence (AI) for Automation of Process Inspections and Product Quality Control domains. Generally, in this field, prior models of deep learning are exploited to construct individual deep learning models for each production line or product. To build such models, a great deal of data (a few tens of thousands of images) is required. Since each production line needs an inspection system that uses deep learning models, in a recent approach, a transformer on the base model is proposed to mitigate the need for huge data by re-training the model for each new line [1]. When building product quality inspection systems, there are still various issues to be resolved. Most targets of deep learning methods in product inspections are limited to surface defects, but recently internal quality checking methods using Deep Learning are proposed.
Downloads
References
Pelluru, Karthik. "Integrate security practices and compliance requirements into DevOps processes." MZ Computing Journal 2.2 (2021): 1-19.
Nimmagadda, Venkata Siva Prakash. "Artificial Intelligence and Blockchain Integration for Enhanced Security in Insurance: Techniques, Models, and Real-World Applications." African Journal of Artificial Intelligence and Sustainable Development 1.2 (2021): 187-224.
Singh, Puneet. "Transforming Healthcare through AI: Enhancing Patient Outcomes and Bridging Accessibility Gaps." Journal of Artificial Intelligence Research 4.1 (2024): 220-232.
Rambabu, Venkatesha Prabhu, Chandrashekar Althati, and Amsa Selvaraj. "ETL vs. ELT: Optimizing Data Integration for Retail and Insurance Analytics." Journal of Computational Intelligence and Robotics 3.1 (2023): 37-84.
Krothapalli, Bhavani, Chandan Jnana Murthy, and Jim Todd Sunder Singh. "Cross-Industry Enterprise Integration: Best Practices from Insurance and Retail." Journal of Science & Technology 3.2 (2022): 46-97.
Amsa Selvaraj, Priya Ranjan Parida, and Chandan Jnana Murthy, “Enhancing Automotive Safety and Efficiency through AI/ML-Driven Telematics Solutions”, J. Computational Intel. & Robotics, vol. 3, no. 2, pp. 82–122, Oct. 2023.
Pradeep Manivannan, Sharmila Ramasundaram Sudharsanam, and Jim Todd Sunder Singh, “Leveraging Integrated Customer Data Platforms and MarTech for Seamless and Personalized Customer Journey Optimization”, J. of Artificial Int. Research and App., vol. 1, no. 1, pp. 139–174, Mar. 2021
Jasrotia, Manojdeep Singh. "Unlocking Efficiency: A Comprehensive Approach to Lean In-Plant Logistics." International Journal of Science and Research (IJSR) 13.3 (2024): 1579-1587.
Gayam, Swaroop Reddy. "AI for Supply Chain Visibility in E-Commerce: Techniques for Real-Time Tracking, Inventory Management, and Demand Forecasting." Distributed Learning and Broad Applications in Scientific Research 5 (2019): 218-251.
Nimmagadda, Venkata Siva Prakash. "AI-Powered Predictive Analytics for Credit Risk Assessment in Finance: Advanced Techniques, Models, and Real-World Applications." Distributed Learning and Broad Applications in Scientific Research 5 (2019): 251-286.
Putha, Sudharshan. "AI-Driven Decision Support Systems for Insurance Policy Management." Distributed Learning and Broad Applications in Scientific Research 5 (2019): 326-359.
Sahu, Mohit Kumar. "Machine Learning Algorithms for Automated Underwriting in Insurance: Techniques, Tools, and Real-World Applications." Distributed Learning and Broad Applications in Scientific Research 5 (2019): 286-326.
Kasaraneni, Bhavani Prasad. "Advanced AI Techniques for Fraud Detection in Travel Insurance: Models, Applications, and Real-World Case Studies." Distributed Learning and Broad Applications in Scientific Research 5 (2019): 455-513.
Kondapaka, Krishna Kanth. "Advanced AI Models for Portfolio Management and Optimization in Finance: Techniques, Applications, and Real-World Case Studies." Distributed Learning and Broad Applications in Scientific Research 5 (2019): 560-597.
Kasaraneni, Ramana Kumar. "AI-Enhanced Claims Processing in Insurance: Automation and Efficiency." Distributed Learning and Broad Applications in Scientific Research 5 (2019): 669-705.
Pattyam, Sandeep Pushyamitra. "Advanced AI Algorithms for Predictive Analytics: Techniques and Applications in Real-Time Data Processing and Decision Making." Distributed Learning and Broad Applications in Scientific Research 5 (2019): 359-384.
Kuna, Siva Sarana. "AI-Powered Customer Service Solutions in Insurance: Techniques, Tools, and Best Practices." Distributed Learning and Broad Applications in Scientific Research 5 (2019): 588-629.
Gayam, Swaroop Reddy. "Artificial Intelligence for Financial Fraud Detection: Advanced Techniques for Anomaly Detection, Pattern Recognition, and Risk Mitigation." African Journal of Artificial Intelligence and Sustainable Development 1.2 (2021): 377-412.
Nimmagadda, Venkata Siva Prakash. "Artificial Intelligence for Automated Loan Underwriting in Banking: Advanced Models, Techniques, and Real-World Applications." Journal of Artificial Intelligence Research and Applications 2.1 (2022): 174-218.
Putha, Sudharshan. "AI-Driven Molecular Docking Simulations: Enhancing the Precision of Drug-Target Interactions in Computational Chemistry." African Journal of Artificial Intelligence and Sustainable Development 1.2 (2021): 260-300.
Sahu, Mohit Kumar. "Machine Learning Algorithms for Enhancing Supplier Relationship Management in Retail: Techniques, Tools, and Real-World Case Studies." Distributed Learning and Broad Applications in Scientific Research 6 (2020): 227-271.
Kasaraneni, Bhavani Prasad. "Advanced AI Techniques for Predictive Maintenance in Health Insurance: Models, Applications, and Real-World Case Studies." Distributed Learning and Broad Applications in Scientific Research 5 (2019): 513-546.
Kondapaka, Krishna Kanth. "Advanced AI Models for Retail Supply Chain Network Design and Optimization: Techniques, Applications, and Real-World Case Studies." Distributed Learning and Broad Applications in Scientific Research 5 (2019): 598-636.
Kasaraneni, Ramana Kumar. "AI-Enhanced Clinical Trial Design: Streamlining Patient Recruitment, Monitoring, and Outcome Prediction." Distributed Learning and Broad Applications in Scientific Research 5 (2019): 706-746.
Pattyam, Sandeep Pushyamitra. "AI in Data Science for Financial Services: Techniques for Fraud Detection, Risk Management, and Investment Strategies." Distributed Learning and Broad Applications in Scientific Research 5 (2019): 385-416.
Kuna, Siva Sarana. "AI-Powered Techniques for Claims Triage in Property Insurance: Models, Tools, and Real-World Applications." Australian Journal of Machine Learning Research & Applications 1.1 (2021): 208-245.
Pradeep Manivannan, Sharmila Ramasundaram Sudharsanam, and Jim Todd Sunder Singh, “Trends, Future and Potential of Omnichannel Marketing through Integrated MarTech Stacks”, J. Sci. Tech., vol. 2, no. 2, pp. 269–300, Jun. 2021
Selvaraj, Akila, Mahadu Vinayak Kurkute, and Gunaseelan Namperumal. "Strategic Project Management Frameworks for Mergers and Acquisitions in Large Enterprises: A Comprehensive Analysis of Integration Best Practices." Journal of Artificial Intelligence Research and Applications 1.2 (2021): 200-248.
Selvaraj, Amsa, Akila Selvaraj, and Deepak Venkatachalam. "Generative Adversarial Networks (GANs) for Synthetic Financial Data Generation: Enhancing Risk Modeling and Fraud Detection in Banking and Insurance." Journal of Artificial Intelligence Research 2.1 (2022): 230-269.
Krishnamoorthy, Gowrisankar, Mahadu Vinayak Kurkute, and Jeevan Sreeram. "Integrating LLMs into AI-Driven Supply Chains: Best Practices for Training, Development, and Deployment in the Retail and Manufacturing Industries." Journal of Artificial Intelligence Research and Applications 4.1 (2024): 592-627.
Paul, Debasish, Rajalakshmi Soundarapandiyan, and Praveen Sivathapandi. "Optimization of CI/CD Pipelines in Cloud-Native Enterprise Environments: A Comparative Analysis of Deployment Strategies." Journal of Science & Technology 2.1 (2021): 228-275.
Venkatachalam, Deepak, Gunaseelan Namperumal, and Amsa Selvaraj. "Advanced Techniques for Scalable AI/ML Model Training in Cloud Environments: Leveraging Distributed Computing and AutoML for Real-Time Data Processing." Journal of Artificial Intelligence Research 2.1 (2022): 131-177.
Namperumal, Gunaseelan, Deepak Venkatachalam, and Akila Selvaraj. "Enterprise Integration Post-M&A: Managing Complex IT Projects for Large-Scale Organizational Alignment." Journal of Artificial Intelligence Research and Applications 1.2 (2021): 248-291.
Kurkute, Mahadu Vinayak, Deepak Venkatachalam, and Priya Ranjan Parida. "Enterprise Architecture and Project Management Synergy: Optimizing Post-M&A Integration for Large-Scale Enterprises." Journal of Science & Technology 3.2 (2022): 141-182.
Soundarapandiyan, Rajalakshmi, Gowrisankar Krishnamoorthy, and Debasish Paul. "The Role of Infrastructure as Code (IaC) in Platform Engineering for Enterprise Cloud Deployments." Journal of Science & Technology 2.2 (2021): 301-344.
Sivathapandi, Praveen, Rajalakshmi Soundarapandiyan, and Gowrisankar Krishnamoorthy. "Platform Engineering for Multi-Cloud Enterprise Architectures: Design Patterns and Best Practices." Australian Journal of Machine Learning Research & Applications 1.1 (2021): 132-183.
Sudharsanam, Sharmila Ramasundaram, Venkatesha Prabhu Rambabu, and Yeswanth Surampudi. "Scaling CI/CD Pipelines in Microservices Architectures for Large Enterprises: Performance and Reliability Considerations." Journal of Artificial Intelligence Research and Applications 1.2 (2021): 115-160.
