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Vijayalakshmi Kakulapati

Sreenidhi Institute of Science and Technology, India

Prof. Vijayalakshmi Kakulapati received a Ph.D. in Computer Science and Engineering from Jawaharlal Nehru Technological University (JNTU), Hyderabad. She is currently a professor in the Department of Information Technology, Sreenidhi Institute of Science and Technology, Hyderabad. She has twenty-six years of industry and teaching experience and is a member of various professional bodies, including the Institute of Electrical and Electronics Engineer (IEEE), Association for Computing Machinery (ACM), Computer Science Teachers Association (CSTA), LMISTE, LMCSI, International Association of Computer Science and Information Technology (IACSIT), FIETE, and more. She has more than 160 publications in national and international journals and conferences, 30 book chapters, and 4 books to her credit. She has received numerous awards, including Excellence in Research, Best Reviewer, appreciation awards, and more. Her areas of research include theoretical and practical information retrieval problems as well as machine learning applied to large-scale textual applications. Her research has focused on retrieval models, query/document representations, term weighting, term proximity models, and learning to rank (machine-learned ranking functions). She is also passionate about seeing research problems applied to real-world problems, especially those dealing with large, complex data sets. Along these lines, she is working with evaluating and designing novel search algorithms for web search and summarization. Currently, Dr. Kakulapati is working with big data analytics, health informatics, the Internet of Things, deep learning, artificial intelligence, and data sciences.

1books edited

2chapters authored

Latest work with IntechOpen by Vijayalakshmi Kakulapati

Data is often open to all users and sharers. Governments provide data on publicly available websites and this data may pertain to specific regions or be aggregate data on national or international issues. Data that is in the public domain but not in a machine-readable format is considered public data and may only be accessible via a right-of-access request. Maintaining accuracy and management is a major obstacle when it comes to data systems and solutions. Data governance describes the rules, procedures, and responsibilities that outline the data's acquisition, storage, retrieval and use. Data security and privacy refer to safeguards put in place to protect information from being seen, copied, distributed, altered, or destroyed without permission. Data integration and interoperability involve combining and exchanging data from many sources, systems, and formats, as well as facilitating data sharing and collaboration across various platforms, apps, and organizations. Defining data standards, implementing data quality checks, assigning data ownership and responsibility, and monitoring data performance and utilization are all important steps toward resolving the data quality problem. This book contains two sections. “Trends and Challenges of Open Data” and “Case Studies”. Each section contains three chapters.

New Trends and Challenges in Open Data

New Trends and Challenges in Open Data

Edited by Vijayalakshmi Kakulapati

Books edited

Open Data

Open Data

Edited by Vijayalakshmi Kakulapati

Chapters authored

Knowledge Extraction from Open Data Repository

By Vijayalakshmi Kakulapati

The explosion of affluent social networks, online communities, and jointly generated information resources has accelerated the convergence of technological and social networks producing environments that reveal both the framework of the underlying information arrangements and the collective formation of their members. In studying the consequences of these developments, we face the opportunity to analyze the POD repository at unprecedented scale levels and extract useful information from query log data. This chapter aim is to improve the performance of a POD repository from a different point of view. Firstly, we propose a novel query recommender system to help users shorten their query sessions. The idea is to find shortcuts to speed up the user interaction with the open data repository and decrease the number of queries submitted. The proposed model, based on pseudo-relevance feedback, formalizes exploiting the knowledge mined from query logs to help users rapidly satisfy their information need.

Part of the book: Open Data

Analysis of Trends and Challenges of Public Open Data in Health Care Industry Using Artificial Intelligence

By Vijayalakshmi Kakulapati

Understanding the public open data being gathered and analyzed is necessary before we can discuss health data analytics and its function in the healthcare industry. A significant quantity of health data is also being obtained, kept, and analyzed, in addition to data on the operations and procedures of the commercial side of the healthcare industry. Any information about a patient’s or a population’s health is referred to as “health data.” Medical professionals and administrators may find areas that need improvement or are in danger by using data from the health industry. With this knowledge, they may take steps to improve any areas where patient care is deficient and elevate the standard of care for all patients. Lab findings, vital sign recordings, prescription diaries, and computerized medical records all include enormous amounts of data. A change in the patient’s health or the possibility of experiencing a major consequence may be detected by physicians and nurses using artificial intelligence (AI) techniques to spot data trends. Due to the complexity and expansion of data in the healthcare sector, AI will be employed there with greater frequency. Numerous types of AI are already being utilized by health insurance companies, medical organizations, and biological sciences enterprises. Solutions can be put into three main categories: operational tasks, patient engagement and participation, and medication and diagnosis recommendations. The health sector uses AI and data engineering to improve the processing and analysis of health data, compensation settlements, and other clinical records. The objective of this chapter is to learn about the capabilities of AI in using public open data as well as the trends and challenges in patient data.

Part of the book: New Trends and Challenges in Open Data

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