Parneeta Dhaliwal

Work place: Department of Computer Science and Technology, Manav Rachna University, Faridabad Sector 43, Haryana 121001, India

E-mail: parneeta07@gmail.com

Website:

Research Interests:

Biography

Parneeta Dhaliwal was born in 1980.She is presently working as an Associate Professor in CST Department, Manav Rachna University, Faridabad. She has over 16 years of academics & research experience. She is currently serving as Head, Research Cluster of Computing Lab at Manav Rachna University. She has worked as full time Teaching & Research Fellow for four years at Netaji Subhas Institute of Technology, University of Delhi. She has been awarded Ph.D. for thesis in the area of “Machine Learning” from Netaji Subhas Institute of Technology, University of Delhi in 2017.She has published many research papers in international level journals with good indexing and high impact factor.

She has presented very high-quality research papers in National & International Conferences and also attended various workshops & FDPs. She is currently mentoring Ph. D students working in the area of Blockchain Technology and Digital Forensics. She has mentored many consultancy projects in latest technical areas for the industry. She has mentored many student teams in their projects at the State Level and National level Competitions. She is the University Coordinator for MRU Code Chef Campus Chapter. The chapter works towards improving the culture of competitive programming in the Campus and organizes various events, workshops, and contests from time to time.

She is a life member of Computer Society of India. She is currently serving as a reviewer with many reputed international journals and conferences. She has also contributed as Session chairs and as a Resource Person in reputed international conferences and FDPs, respectively.

Author Articles
Detailed Study of Wine Dataset and its Optimization

By Parneeta Dhaliwal Suyash Sharma Lakshay Chauhan

DOI: https://doi.org/10.5815/ijisa.2022.05.04, Pub. Date: 8 Oct. 2022

The consumption of wine these days is becoming more common in social gatherings and to monitor the health of individuals it's very important to maintain the quality of the wine. For the assessment of wine quality many methods have been proposed. We have described a technique to pre-process the “Vinho Verde” wine dataset. The dataset consists of red and white wine samples. The wine dataset size has been reduced from a total of 13 attributes to 9 attributes without any loss of performance. This has been validated through various classification techniques like Random Forest Classifier, Decision tree Classifiers, K-Nearest Neighbor Classifier and Artificial Neural Network Classifier. These classifiers have been compared based on two performance metrics of accuracy and RMSE values. Among the three classifiers Random Forest tends to outperform the other two classifiers in various measures for predicting the quality of the wine.

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