Transfer Learning Strategies for AI Applications: Analyzing transfer learning strategies and their effectiveness in adapting AI models to new tasks and domains
Keywords:
Transfer learning, artificial intelligence, feature extraction, fine-tuning, domain adaptation, meta-learning, continual learning, model adaptation, model generalization, domain shiftAbstract
Transfer learning has emerged as a powerful technique in the field of artificial intelligence (AI), allowing models to leverage knowledge from one domain or task to improve performance on another. This paper provides a comprehensive analysis of transfer learning strategies in AI applications, focusing on their effectiveness in adapting models to new tasks and domains. We review prominent transfer learning approaches, including feature extraction, fine-tuning, and domain adaptation, highlighting their strengths and limitations. Furthermore, we discuss recent advancements in transfer learning, such as meta-learning and continual learning, and their implications for AI applications. Through a series of experiments and case studies, we evaluate the performance of transfer learning strategies across different datasets and domains, shedding light on best practices and challenges in their implementation. Our findings suggest that transfer learning can significantly improve the efficiency and effectiveness of AI models, particularly in scenarios with limited labeled data or domain shifts. We conclude with recommendations for future research directions to further enhance the applicability of transfer learning in AI applications.
References
Pargaonkar, Shravan. "A Review of Software Quality Models: A Comprehensive Analysis." Journal of Science & Technology 1.1 (2020): 40-53.
Buddha, Govind Prasad, and Rahul Pulimamidi. "The Future Of Healthcare: Artificial Intelligence's Role In Smart Hospitals And Wearable Health Devices." Tuijin Jishu/Journal of Propulsion Technology 44.5 (2023): 2498-2504.
Bayraktar, Necmi. "Non-invasive alternative for phosphodiesterase inhibitor-refractory erectile dysfunction: Real-life experience with low-intensity extracorporeal shockwave therapy." Medicine 102.45 (2023): e35939.
Bayraktar, Necmi, and Fazil Tuncay Aki. "Laparoscopy-assisted peritoneal dialysis catheter placement using a modified minimally invasive approach: A retrospective observational study." Medicine 102.43 (2023): e35814.
Bayraktar, Necmi. "Prevalence of Family Refusal and Associated Factors in Declared Brain Death: A Six-Year Retrospective Study in Northern Cyprus." Transplantation Proceedings. Vol. 55. No. 7. Elsevier, 2023.
Kolay, Srikanta, Kumar Sankar Ray, and Abhoy Chand Mondal. "K+ means: An enhancement over k-means clustering algorithm." arXiv preprint arXiv:1706.02949 (2017).
Ray, Kumar S., and Srikanta Kolay. "Application of Approximate Equality for Reduction of Feature Vector Dimension." Journal of Pattern Recognition Research 1 (2016): 26-40.
Varela, Damián Tuset. "Artificial Intelligence on the Global Stage: Transforming Diplomacy and International Relations." Advances in Deep Learning Techniques 4.1 (2024): 53-57.
Varela, Damián Tuset. "AI Arms Races: Implications for Global Stability." Journal of Computational Intelligence and Robotics 1.2 (2021): 1-5.
Varela, Damián Tuset. "Artificial Intelligence in Humanitarian Aid and Development: A New Paradigm for International Cooperation." Journal of Artificial Intelligence Research 1.2 (2021): 1-4.
Varela, Damián Tuset. "Navigating Cyber Diplomacy in the Governance of Emerging AI Technologies: Lessons from Transatlantic Cooperation." Journal of Artificial Intelligence General science (JAIGS) ISSN: 3006-4023 2.1 (2024): 110-124.
Varela, Damián Tuset. "Diplomacy in the Age of AI: Challenges and Opportunities." Journal of Artificial Intelligence General science (JAIGS) ISSN: 3006-4023 2.1 (2024): 98-109.
Varela, Damián Tuset. "El Derecho en el siglo XXI: de lineal a circular." Diario La Ley 10409 (2023): 3.
Dey, Sudipto, et al. "Methods and systems for selecting a machine learning algorithm." U.S. Patent Application No. 18/514,181.
Dey, Sudipto, and Pulla Reddy P. Yeduru. "Methods and systems for predicting prescription directions using machine learning algorithm." U.S. Patent Application No. 18/242,098.
Dey, Sudipto, et al. "Methods and systems for automatic prescription processing using machine learning algorithm." U.S. Patent No. 11,848,086. 19 Dec. 2023.
Dey, Sudipto, and Pulla Reddy P. Yeduru. "Methods and systems for predicting prescription directions using machine learning algorithm." U.S. Patent No. 11,783,186. 10 Oct. 2023.
Dey, Sudipto, et al. "Microservice architecture with automated non-intrusive event tracing." U.S. Patent Application No. 17/499,966.
Dey, Sudipto, and Pulla Reddy P. Yeduru. "Methods and systems for predicting prescription directions using machine learning algorithm." U.S. Patent No. 11,468,320. 11 Oct. 2022.
Dey, Sudipto, and Pulla Reddy P. Yeduru. "Methods and systems for predicting prescription directions using machine learning algorithm." U.S. Patent No. 11,468,320. 11 Oct. 2022.
Veronin, Michael A., et al. "Opioids and frequency counts in the US Food and Drug Administration Adverse Event Reporting System (FAERS) database: A quantitative view of the epidemic." Drug, Healthcare and Patient Safety (2019): 65-70.
Dixit, Rohit R., Robert P. Schumaker, and Michael A. Veronin. "A Decision Tree Analysis of Opioid and Prescription Drug Interactions Leading to Death Using the FAERS Database." IIMA/ICITED Joint Conference 2018. INTERNATIONAL INFORMATION MANAGEMENT ASSOCIATION, 2018.
Veronin, Michael A., Robert P. Schumaker, and Rohit Dixit. "The irony of MedWatch and the FAERS database: an assessment of data input errors and potential consequences." Journal of Pharmacy Technology 36.4 (2020): 164-167.
Schumaker, Robert P., et al. "Calculating a Severity Score of an Adverse Drug Event Using Machine Learning on the FAERS Database." IIMA/ICITED UWS Joint Conference. INTERNATIONAL INFORMATION MANAGEMENT ASSOCIATION, 2017.
Veronin, Michael A., et al. "A systematic approach to'cleaning'of drug name records data in the FAERS database: a case report." International Journal of Big Data Management 1.2 (2020): 105-118.
Dixit, Rohit R. "Predicting Fetal Health using Cardiotocograms: A Machine Learning Approach." Journal of Advanced Analytics in Healthcare Management 6.1 (2022): 43-57.
Schumaker, Robert P., Michael A. Veronin, and Rohit R. Dixit. "Determining Mortality Likelihood of Opioid Drug Combinations using Decision Tree Analysis." (2022).
Dixit, Rohit R. "Risk Assessment for Hospital Readmissions: Insights from Machine Learning Algorithms." Sage Science Review of Applied Machine Learning 4.2 (2021): 1-15.
Veronin, Michael A., et al. "Irony of the FAERS Database: An Analysis of Data Input Errors and Potential Consequences." IIMA/ICITED Joint Conference 2018. INTERNATIONAL INFORMATION MANAGEMENT ASSOCIATION, 2018.
Schumaker, Robert P., et al. "A data driven approach to profile potential SARS-CoV-2 drug interactions using TylerADE." Journal of International Technology and Information Management 30.3 (2021): 108-142.
Dossa, Kossivi Fabrice, et al. "Economic analysis of sesame (Sesamum indicum L.) production in Northern Benin." Frontiers in Sustainable Food Systems 6 (2023): 1015122.
Dossa, Kossivi Fabrice, and Yann Emmanuel Miassi. "Exploring the nexus of climate variability, population dynamics, and maize production in Togo: implications for global warming and food security." Farming System 1.3 (2023): 100053.
Li, Xiaying, Belle Li, and Su-Je Cho. "Empowering Chinese Language Learners from Low-Income Families to Improve Their Chinese Writing with ChatGPT’s Assistance Afterschool." Languages 8.4 (2023): 238.
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