Dr. Dipikaben Umakant Thakar’s scientific contributions

Big Data Analytics and Deep Learning are crucial fields due to the use of massive domain-specific data for solving complex challenges in cybersecurity, marketing, and healthcare. Deep Learning facilitates recognition of complex patterns via hierarchical learning and building higher-level abstractions on top of lower-level abstractions, suitable for handling colossal volumes of untagged data. This work discusses how issues in semantic indexing, data annotation, and fast information retrieval could be addressed with Deep Learning. It also recognizes problems such as streaming data, high-dimensionality, and scalability, and in the future, work will target data sampling, improved semantic indexing, and semi-supervised learning methods. In addition, inclusion of distributed computing for scalability is also mentioned among the key areas of future research in Deep Learning models.

Data analytics is now the central field of study in research, led by the explosive growth of data coming from many sources. This article investigates the background of data analytics from the challenge of handling large data and the significance of data engineering. We then consider basic processes such as data cleansing, transformation, and modeling. Various methods of analysis, including exploratory data analysis, predictive modeling, and machine learning, are explained. We also highlight the programming languages and software that are most commonly used in data analytics. The paper then goes on to explain recent advancements in the discipline, including big data, cloud computing, and data privacy. Lastly, this comprehensive review is meant to enable researchers to make use of data analytics to gain useful insights from their data.

Rampant growth in web data due to increased growth at zetta byte scales driven by various types of online data sources including online commerce websites, social networks, web sensors, business websites, and user communities has given a boost to extracting valuable information with the help of Big Data Analytics (BDA). The key strength of BDA is that it can process large datasets and reveal underlying trends. Optimizing BDA processes, though, is a challenge since it involves combining different data assets to produce actionable insights. This paper explores the BDA process and what it can do through three case studies of leading BDA tool users. The research uncovers four core elements of the BDA framework: system coordination, data sourcing, big data application services, and end-user interaction. Besides, key supporting factors such as data security, privacy, and management act as fundamental functions that complement these elements throughout the information and technology value chain.