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What is Data Scarcity

Confluence of AI, Machine, and Deep Learning in Cyber Forensics
It is the unavailability of data that could possibly satisfy the need of the system to increase the accuracy and prediction dynamics.
Published in Chapter:
Challenges of Developing AI Applications in the Evolving Digital World and Recommendations to Mitigate Such Challenges: A Conceptual View
Srinivasan Vaidyanathan (Cognizant, India), Madhumitha Sivakumar (Sri Sivasubramaniya Nadar College of Engineering, India), and Baskaran Kaliamourthy (Atto Technology Solutions LLC, Dallas, USA)
Copyright: © 2021 |Pages: 22
DOI: 10.4018/978-1-7998-4900-1.ch011
Abstract
These intelligence in the systems are not organic but programmed. In spite of being extensively used, they suffer from setbacks that are to be addressed to expand their usage and a sense of trust in humans. This chapter focuses on the different hurdles faced during the course of adopting the technology namely data privacy, data scarcity, bias, unexplainable Blackbox nature of AI, etc. Techniques like adversarial forgetting, federated learning approach are providing promising results to address various issues like bias, data privacy are being researched widely to check their competency to mitigate these problems. Hardware advancements and the need for enhancing the skillset in the artificial intelligence domain are also elucidated. Recommendations to resolve each major challenge faced are also addressed in this chapter to give an idea about the areas that need improvement.
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