AI-Enhanced Data Science: Techniques for Improved Data Visualization and Interpretation
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Abstract
AI-enhanced data science is transforming the way data is visualized and interpreted, offering more accurate, efficient, and insightful methods to comprehend complex datasets. This paper explores key AI techniques that improve data visualization, such as machine learning-driven pattern recognition, automated chart generation, and natural language generation (NLG). These techniques enable non-technical users to better understand data trends, outliers, and relationships, thus enhancing decision-making processes. Furthermore, the integration of AI with traditional data visualization tools is examined, highlighting its ability to dynamically interpret data in real time, customize visual outputs, and handle large-scale datasets with ease. The paper also addresses the challenges and future directions in AI-driven visualization, including the ethical implications of AI biases in data interpretation.
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Kim, G., Humble, J., & Debois, P. (2016). The DevOps Handbook: How to Create World-Class Agility, Reliability, & Security in Technology Organizations. IT Revolution Press.
Ransford, B., Clarke, D., & Duquennoy, S. (2019). Security for the Internet of Things: A Survey of Existing Protocols and Open Research Issues. IEEE Access, 7, 12950-12988.
Chandrasekaran, K. C., & Meghanathan, N. (2017). Big Data Analytics: A Hands-On Approach. CRC Press.
Rubinoff, S. (2018). Web and Network Data Science: Modeling Techniques in Predictive Analytics. CRC Press.
Liu, S., Yu, S., & Guo, Y. (2019). A survey on security threats and defensive techniques of machine learning: A data driven view. Journal of Network and Computer Applications, 131, 36-57.
Parnin, C., & Bird, C. (2016). Usage, costs, and benefits of continuous integration in open-source projects. Empirical Software Engineering, 21(3), 1-35.
Bass, L., Weber, I., & Zhu, L. (2015). DevOps: A Software Architect’s Perspective. Addison-Wesley.
Haines, M., & Righter, R. (2016). Securing DevOps: Security in the Cloud. O'Reilly Media.
Fitzgerald, B., Stol, K. J., & O'Sullivan, P. (2014). Continuous software engineering and beyond: Trends and challenges. Information and Software Technology, 56(5), 365-386.
Le, V. H., & Chua, T. S. (2017). A survey on data fusion in the era of big data. ACM Computing Surveys (CSUR), 49(1), 1-42.
O'Reilly, T., & Battelle, J. (2009). Web Squared: Web 2.0 Five Years On. O'Reilly Media.
Luiijf, E. A., & Buijs, J. C. (2017). Securing Smart Cities. Springer.
Dhiman, V. (2020). PROACTIVE SECURITY COMPLIANCE: LEVERAGING PREDICTIVE ANALYTICS IN WEB APPLICATIONS. JOURNAL OF BASIC SCIENCE AND ENGINEERING, 17(1).
Dhiman, V. (2019). DYNAMIC ANALYSIS TECHNIQUES FOR WEB APPLICATION VULNERABILITY DETECTION. JOURNAL OF BASIC SCIENCE AND ENGINEERING, 16(1
Rubinoff, S. (2018). Web and Network Data Science: Modeling Techniques in Predictive Analytics. CRC Press.
Mettikolla, P., Calander, N., Luchowski, R., Gryczynski, I., Gryczynski, Z., & Borejdo, J. (2010). Kinetics of a single cross-bridge in familial hypertrophic cardiomyopathy heart muscle measured by reverse Kretschmann fluorescence. Journal of Biomedical Optics, 15(1), 017011-017011.
Mettikolla, P., Luchowski, R., Gryczynski, I., Gryczynski, Z., Szczesna-Cordary, D., & Borejdo, J. (2009). Fluorescence lifetime of actin in the familial hypertrophic cardiomyopathy transgenic heart. Biochemistry, 48(6), 1264-1271.
Mettikolla, P., Calander, N., Luchowski, R., Gryczynski, I., Gryczynski, Z., & Borejdo, J. (2010). Observing cycling of a few cross‐bridges during isometric contraction of skeletal muscle. Cytoskeleton, 67(6), 400-411.
Muthu, P., Mettikolla, P., Calander, N., & Luchowski, R. 458 Gryczynski Z, Szczesna-Cordary D, and Borejdo J. Single molecule kinetics in, 459, 989-998.
Chandrasekaran, K. C., & Meghanathan, N. (2017). Big Data Analytics: A Hands-On Approach. CRC Press.
Ransford, B., Clarke, D., & Duquennoy, S. (2019). Security for the Internet of Things: A Survey of Existing Protocols and Open Research Issues. IEEE Access, 7, 12950-12988.
Forsgren, N., Humble, J., & Kim, G. (2018). Accelerate: The Science of Lean Software and DevOps: Building and Scaling High Performing Technology Organizations. IT Revolution Press.
Parnin, C., & Bird, C. (2016). Usage, costs, and benefits of continuous integration in open-source projects. Empirical Software Engineering, 21(3), 1-35.
Pombinho, J., & Silva, A. R. (2018). DevSecOps: Shifting Security Left with Continuous Delivery. Proceedings of the 1st International Workshop on Secure Development Lifecycle.
Pires, M., & Duboc, L. (2017). Towards a DevSecOps process model: Organizational patterns of integration of security in DevOps. Journal of Systems and Software, 130, 141-159.
Rubinoff, S., & Rajkumar, T. (2016). Applied Data Science: Lessons Learned for the Data-Driven Business. O'Reilly Media.