AI in Data Recovery and Data Analysis

Title

AI in Data Recovery and Data Analysis

Creator

Singh A.; Joshi A.; Sankhla M.S.; Saini K.; Choudhary S.K.

Description

The use of artificial intelligence (AI) techniques for data collection and analysis is examined in this chapter. It also looks at the benefits, challenges, and future directions. It provides a broad overview of AI techniques and illustrates the use of generative adversarial networks (GANs), recurrent neural networks (RNNs), convolutional neural networks (CNNs), etc. in this area. Data recovery is an essential process when trying to recover lost or damaged data. For AI methods like CNN, the retrieval of image and video data has shown great promise. Using the power of deep learning, CNNs can search for patterns in data, assisting in the reconstruction and restoration of lost information. On the other hand, RNNs excel at retrieving serial data, such as text or time series data. These networks can efficiently learn dependencies and contexts, which makes it possible to precisely reconstruct missing or imperfect sequences. AI-based data analytics provides businesses with insightful information and opportunities. GANs, for example, are increasingly being used to generate and improve data, enabling organizations to expand the size of their datasets and improve the efficacy of their analytical models. Large amounts of data can also be divided up using A-based clustering algorithms, which are also well classified and provide insightful analysis and interpretation. In the gathering and analysis of data, AI has many benefits. Businesses can process and analyze enormous amounts of data in a fraction of the time thanks to this productivity-boosting automation of challenging and time-consuming tasks. By reducing bias and human error, AI techniques also increase accuracy, resulting in results that are more dependable and consistent. Additionally, AI-driven insights assist businesses in spotting trends, uncovering buried patterns, and coming to wise decisions that may not be apparent using traditional analytics methods. Due to privacy concerns, ethical considerations, interpretability, transparency, and accountability, AI deployment in data recovery and analysis is difficult. Future directions include collaboration between humans and AI, edge computing integration, and privacy-preserving methods. In conclusion, organizations looking to maximize their data assets stand to benefit greatly from the application of AI techniques to data analytics and data retrieval. 2024 selection and editorial matter, Kavita Saini, Swaroop S. Sonone, Mahipal Singh Sankhla, and Naveen Kumar.

Source

Artificial Intelligence in Forensic Science: an Emerging Technology in Criminal Investigation Systems, pp. 142-164.

Date

2024-01-01

Publisher

Taylor and Francis

Coverage

Singh A., School of Forensic Science and Risk Management, Rashtriya Raksha University, Gujarat, India; Joshi A., Department of Forensic Science, School of Life Science, Christ Deemed-to-be University, Karnataka, Bangalore, India; Sankhla M.S., Department of Forensic Science, University Center for Research and Development (UCRD), Chandigarh University, Punjab, Mohali, India; Saini K., School of Computing Science & Engineering (SCSE), Galgotias University, Delhi, India; Choudhary S.K., Rashtriya Raksha University, Gujarat, India

Rights

Restricted Access

Relation

ISBN: 978-104001919-1; 978-103226337-3

Format

Online

Language

English

Type

Book chapter

Identifier

https://doi.org/10.4324/9781003287810-10

https://www.scopus.com/inward/record.uri?eid=2-s2.0-85201869737&doi=10.4324%2f9781003287810-10&partnerID=40&md5=9f96ac0cd416f3adb9351efdf47e6400