An Opinion Mining for Indian Premier League Using Machine Learning Techniques
- Authors: KP Dubey, S Agrawal
- Conference: 2019 4th International Conference on Internet of Things: Smart Innovation, Usage, and Application
- Pages: 25
- Year: 2019
- Summary: This paper presents a sentiment analysis model for social media data related to the Indian Premier League (IPL). The authors employed machine learning techniques to classify public opinions, enabling better understanding of audience engagement and predicting trends in sports sentiment.
Comparing Classification and Regression Tree and Support Vector Machine for Analyzing Sentiments for IPL
- Author: Arti Singh
- Journal: International Journal on Recent and Innovation Trends in Computing and Communication (IJRITCC)
- Volume: 4
- Issue: 6
- Pages: 172-175
- Year: 2016
- ISSN: 2321-8169
- Summary: This study compares the performance of two machine learning algorithms, Classification and Regression Tree (CART) and Support Vector Machine (SVM), for sentiment analysis on IPL data. The research evaluates the accuracy and effectiveness of both approaches for sports sentiment analysis.
AI Application in Production
- Author: Arti Singh
- Publisher: Taylor & Francis
- Book Title: Industry 4.0: Enabling Technologies and Applications
- Chapter: AI Application in Production
- Year: 2024
- URL: Link to book
- Summary: This book chapter explores the integration of Artificial Intelligence (AI) in manufacturing and production processes. It highlights AI-driven innovations, predictive maintenance, process optimization, and intelligent automation in modern industrial setups.
Automated Invoice Data Extraction: Advancements and Challenges in OCR-Based Approaches
- Authors: Arti Singh, Sneha Kanwade, Siddhant Shendge, Amoksh Layane, Kohsheen Tikoo
- Journal: International Journal of Scientific Research in Engineering and Management (IJSREM)
- Volume: 8
- Pages: 1-6
- ISSN: 2582-3930
- Year: 2024
- Summary: This paper addresses the growing need for automated invoice data extraction using Optical Character Recognition (OCR) technologies. It discusses the latest advancements, the challenges faced, and potential solutions to enhance accuracy in invoice processing systems.
An In-Depth Analysis of Sentiment Polarity Using Various Machine Learning Algorithms
- Author: Arti Singh
- Conference: 8th International Conference on ISDIA 2024
- Volume: 1107
- Pages: 157–167
- Year: 2024
- Summary: This research investigates the effectiveness of different machine learning algorithms for sentiment polarity detection. The study evaluates models such as SVM, Random Forest, and Extremely Randomized Trees to improve sentiment classification accuracy in social media data.
Comparative Study of Machine Learning Algorithms for Sentiment Polarity
- Author: Arti Singh
- Conference: IRF International Conference
- Pages: 1-5
- Year: 2017
- Summary: The paper compares several machine learning techniques, including Naive Bayes, Decision Trees, and SVM, for sentiment polarity classification. It emphasizes the importance of selecting the appropriate algorithm for accurate sentiment detection in online text data.
Conclusion:
Mrs. Arti Singh is a strong candidate for the Best Researcher Award, given her consistent research output in machine learning and applied AI domains, industry-relevant research contributions, and dedication to academic excellence. Her work bridges the gap between theory and practice, making her a valuable contributor to the field of computational intelligence and Industry 4.0 applications.
With increased focus on high-impact journals, research funding, and industry collaborations, she has the potential to emerge as a leading figure in her field. Therefore, she is highly deserving of recognition through the Best Researcher Award.