Publication Profile
Google Scholar
Educational Details
Ms. Swati Patel holds a Master of Science in Advanced Computing Technologies from Birkbeck, University of London (2022-2023), where her projects included Principal Component Analysis on the Pima Indians Diabetes Dataset and predicting DDoS attacks using Darknet Time-Series data. She earned a Master’s degree in Computer Science from SES’s R. C. Patel Institute of Technology (NMU, India, 2012-2014), completing her thesis on software birthmark-based theft detection of JavaScript programs. Her Bachelor’s degree in Computer Engineering was completed at PSGVPM’s D. N. Patel College of Engineering, Shahada (NMU, India, 2008-2012), where she developed an Online Voting System using ASP.NET and SQL.
Professional Experience
Swati Patel is a Data Analyst and Entrepreneur with proven expertise in designing and optimizing workflows, data visualization, and software development. She served as a Data Analyst at SSP Group PLC, UK (2023-2024), where she created interactive Power BI dashboards to analyze sales data, optimized EPOS systems for data accuracy, and reduced data processing time by 30% through SQL optimization. As an entrepreneur, she successfully led Skyline Software Solutions (2014-2022), developing over 35 applications in Java, .NET, and other technologies. She managed end-to-end project execution, providing high-quality software solutions to diverse industries.
Research Interest
Swati’s research focuses on applied machine learning, natural language processing, and predictive modeling. She is particularly interested in statistical methods for data anomaly detection, speech-based health diagnostics, and secure systems in computing environments. Her work also extends to innovative clustering techniques for theft detection in software and dimensionality reduction in large datasets.
Author Metrics
- Publications: 2 IEEE papers, 8 journal papers, 5 books.
- Key Topics: Data science, machine learning, NLP, secure computing systems, predictive analytics.
- Notable Works:
- “A Review on Statistical Analysis-Based Approaches for Data Poison Detection Using Machine Learning.”
- “Automated Depression Assessment from Speech Signals Using Pitch and Energy Features.”
- “Long Short-Term Memory and Gated Recurrent Unit Networks for Accurate Stock Price Prediction.”
- Books: COVID-19 Data Analysis for the United Kingdom and Data Exploration and Machine Learning using R.
Publication Top Notes
- Software Birthmark Based Theft Detection of JavaScript Programs Using Agglomerative Clustering and Frequent Subgraph Mining
- Authors: S.J. Patel, T.M. Pattewar
- Conference: 2014 International Conference on Embedded Systems (ICES)
- Pages: 63–68
- Citations: 9
- Year: 2014
- Summary: This paper presents a novel method for detecting software theft in JavaScript programs using software birthmarks. The approach employs agglomerative clustering and frequent subgraph mining to identify similarities between programs, aiding in theft detection.
- K-Means Clustering Algorithm: Implementation and Critical Analysis
- Author: S. Patel
- Publisher: Scholars’ Press
- Citations: 8
- Year: 2019
- Summary: This publication provides an in-depth exploration of the K-means clustering algorithm, including its implementation and a critical analysis of its efficiency and limitations in clustering diverse datasets.
- Software Birthmark Based Theft Detection of JavaScript Programs Using Agglomerative Clustering and Improved Frequent Subgraph Mining
- Authors: S. Patel, T. Pattewar
- Conference: 2014 International Conference on Advances in Electronics, Computers, and Communications
- Citations: 7
- Year: 2014
- Summary: This paper builds upon earlier research by introducing an improved method for frequent subgraph mining, enhancing the detection accuracy of JavaScript program theft through advanced birthmark-based techniques.
- Software Birthmark for Theft Detection of JavaScript Programs: A Survey
- Authors: S.J. Patel, T.M. Pattewar
- Journal: IJAFRC (International Journal of Advanced Foundation and Research in Computers)
- Volume: 1, Issue 2, Pages: 29–38
- Citations: 2
- Year: 2014
- Summary: This survey reviews existing methods for detecting software theft in JavaScript programs, with a focus on software birthmark techniques. It outlines challenges, solutions, and future research directions.
- Emerging Trends in Computer Technology (NCETCT)
- Authors: S.J. Patel, T.M. Pattewar
- Journal: IJCA (International Journal of Computer Applications)
- Conference Issue: NCETCT, Number 1
- Year: 2014
- Summary: This conference paper discusses advancements in computer technology with a focus on software security. It explores the use of software birthmarks as a means of detecting and preventing intellectual property theft in programming.
Conclusion
Ms. Swati Patel is a highly qualified and deserving candidate for the Best Researcher Award. Her combination of academic excellence, impactful research, and real-world contributions to artificial intelligence and software engineering set her apart as an innovative thinker. To maximize her potential and visibility, she could focus on strengthening her citation impact, pursuing international collaborations, and contributing to higher-impact conferences. Overall, her track record reflects dedication, skill, and the ability to drive meaningful advancements in her field.