Vol. 4 No. 2 (2024): African Journal of Artificial Intelligence and Sustainable Development
Articles

The Application of Deep Learning Techniques in Advanced Robotics for Medicine Manufacturing in the USA

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  • Dr. Peter Ivanov
Dr. Peter Ivanov
Professor of Artificial Intelligence, Lomonosov Moscow State University, Russia

Published 28-09-2024

Keywords

  • Advanced Robotics,
  • Medicine Manufacturing
Creative Commons License

This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.

How to Cite

[1]
Dr. Peter Ivanov, “The Application of Deep Learning Techniques in Advanced Robotics for Medicine Manufacturing in the USA”, African J. of Artificial Int. and Sust. Dev., vol. 4, no. 2, pp. 178–192, Sep. 2024, Accessed: Dec. 03, 2024. [Online]. Available: https://africansciencegroup.com/index.php/AJAISD/article/view/159

Abstract

Humanity is attracted to and fascinated by wondrous miracles. From the invention of the wheel to the advancement of Artificial Intelligence (AI), the ability of humans to simulate and ameliorate natural landscapes, using artificially simulated materials, has been an intriguing phenomenon. Following this thought process of AI, its growing adoption in medical settings has led scientists to envision the idea of building nanobots for the surgical process, in tandem with neural networks, computer-assisted systems, and robotics machinery, to handle complicated surgical techniques [1]. With this idea in mind, specific queries arise: How far have scientists gone in realization of this ideology? There is a growing concern among medical practitioners regarding the constraints, challenges, and efficacy of AI systems in advanced robotic machinery applied on patients. This review explores the application of deep learning techniques in advanced robotics for medicine manufacturing in the USA, covering recent advancements and breakthroughs.

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References

  1. Pelluru, Karthik. "Integrate security practices and compliance requirements into DevOps processes." MZ Computing Journal 2.2 (2021): 1-19.
  2. 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.
  3. Singh, Puneet. "Transforming Healthcare through AI: Enhancing Patient Outcomes and Bridging Accessibility Gaps." Journal of Artificial Intelligence Research 4.1 (2024): 220-232.
  4. 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.
  5. 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.
  6. 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.
  7. 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
  8. 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.
  9. 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.
  10. 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.
  11. Putha, Sudharshan. "AI-Driven Decision Support Systems for Insurance Policy Management." Distributed Learning and Broad Applications in Scientific Research 5 (2019): 326-359.
  12. 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.
  13. 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.
  14. 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.
  15. Kasaraneni, Ramana Kumar. "AI-Enhanced Claims Processing in Insurance: Automation and Efficiency." Distributed Learning and Broad Applications in Scientific Research 5 (2019): 669-705.
  16. 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.
  17. 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.
  18. 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.
  19. 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.
  20. 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.
  21. 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.
  22. 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.
  23. 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.
  24. 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.
  25. 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.
  26. 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.
  27. 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
  28. Selvaraj, Akila, Praveen Sivathapandi, and Deepak Venkatachalam. "Artificial Intelligence-Enhanced Telematics Systems for Real-Time Driver Behaviour Analysis and Accident Prevention in Modern Vehicles." Journal of Artificial Intelligence Research 3.1 (2023): 198-239.
  29. Paul, Debasish, Gowrisankar Krishnamoorthy, and Sharmila Ramasundaram Sudharsanam. "Platform Engineering for Continuous Integration in Enterprise Cloud Environments: A Case Study Approach." Journal of Science & Technology 2.3 (2021): 179-214.
  30. Namperumal, Gunaseelan, Akila Selvaraj, and Priya Ranjan Parida. "Optimizing Talent Management in Cloud-Based HCM Systems: Leveraging Machine Learning for Personalized Employee Development Programs." Journal of Science & Technology 3.6 (2022): 1-42.
  31. Soundarapandiyan, Rajalakshmi, Priya Ranjan Parida, and Yeswanth Surampudi. "Comprehensive Cybersecurity Framework for Connected Vehicles: Securing Vehicle-to-Everything (V2X) Communication Against Emerging Threats in the Automotive Industry." Cybersecurity and Network Defense Research 3.2 (2023): 1-41.
  32. Sivathapandi, Praveen, Debasish Paul, and Akila Selvaraj. "AI-Generated Synthetic Data for Stress Testing Financial Systems: A Machine Learning Approach to Scenario Analysis and Risk Management." Journal of Artificial Intelligence Research 2.1 (2022): 246-287.
  33. Sudharsanam, Sharmila Ramasundaram, Deepak Venkatachalam, and Debasish Paul. "Securing AI/ML Operations in Multi-Cloud Environments: Best Practices for Data Privacy, Model Integrity, and Regulatory Compliance." Journal of Science & Technology 3.4 (2022): 52-87.
  34. Prabu Ravichandran. “Analysis on Agile Software Development Using Cloud Computing Based on Agile Methodology and Scrum Framework”. International Journal on Recent and Innovation Trends in Computing and Communication, vol. 12, no. 2, Sept. 2024, pp. 865-71

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