Data Science Research Topics

Most Pertinent And Significant Data Science Research Topics

The first and most significant stage in producing academic papers or essays is to choose the correct big data study subjects. Data science research topics are becoming increasingly popular among academics and practitioners. Data research has a diverse base that spans a wide range of scholarly papers in several fields of study.

Data Visualization.

Aims and Objective:

Over 50 researchers and practitioners gathered in 1997 for the AAAI Workshop on AI Approach to Fraud Detection and Risk Management to explore fraud detection, computer intrusion detection, and risk assessment. This research summarises the essential points, including explanations of frequent problems in specific application fields and potential solutions based on various AI methodologies. Furthermore, it will present an online learning approach for dealing with real-time insurance network changes, showing that it beats other online algorithms.

Use of Augmented Analysis.

Aims and Objective:

Augmented Reality and infographics are two of the most commonly dealt with technical advancements in education today. This study aims to find out how these two features are widely employed in today’s learning settings, and their significance in academic studies is growing. The overall goal of this study is to conduct a content analysis of publications in the field of education utilizing augmented reality technology and infographics. Content analysis will assess the publications that were pertinent to this study. The sample will consist of published papers and infographics about Augmented Reality technology. The information gathered will be evaluated using descriptive statistical approaches such as percentages and frequencies. It will be expected that the study’s findings will provide insight into future research.

Logistic regression Modeling of Software quality.

Aims and Objective:

This paper’s main objective is to use the prior probabilities and costs of misclassification to a logistic regression-based classification rule for a software quality model. The advantages of logistic regression over other classification modeling approaches include interpretable coefficients. This paper also includes examples of how to interpret coefficients, apply prior probabilities, use costs of misclassifications, and an integrated technique for employing logistic regression in software quality modeling. The concepts will be demonstrated using a case study of a significant subsystem of a real-time military system.

Relationship of Data science to big data.

Aims and Objective:

Data science is inextricably linked to other concepts such as big data and data-driven decision-making. This research’s aim is to explore what’s the relationship between these two. This paper argues that for data science to be helpful in business, it’s necessary to know how it relates to other essential ideas and begin defining the fundamental principles underpinning data science. This research will evaluate whether it can be much better grasped and convey what data science offers. Furthermore, we should only call it data science if we accept big data. This article provides a viewpoint that encompasses all of these ideas. Finally, a limited list of essential data science ideas will be provided as examples.

Fraud detection AI.

Aims and Objective:

The goal of this study is to define artificial intelligence (AI) technologies and how they function to detect money laundering through wire transfer screening. The technological and public policy ramifications of the federal government’s broad deployment of AI technology for fraud detection will be assessed in this study. Its findings apply to a wide range of different AI-based fraud detection applications in the public and commercial sectors.

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