Chhavi Rana

Work place: Department of CSE, UIET, M.D. University, Rohtak, 124001, India



Research Interests: Information Systems, Data Mining, Information Retrieval, Data Structures and Algorithms


Dr. Chhavi Rana belongs to Haryana. She is an Assistant Professor in Department of Computer Science and Engineering, University Institute of Engineering and Technology, Maharshi Dayanand University, Rohtak, Haryana. She received her Ph.D degree in Web Mining from NIT, Kurukshetra University, Haryana in 2014. Her research areas include information management, information retrieval, ICT and data mining.

She has published more than 50 research papers in reputed International Journals and Conferences. She has an experience of more than 10 years in teaching data mining.

She has also been reviewer on IEEE Transaction’s on Systems, Man and Cybernetics: Systems, Artificial Intelligence Review, Springer as well as Inderscience Publishers. She has also published 4 books on her research work.

Author Articles
An Application-oriented Review of Deep Learning in Recommender Systems

By Jyoti Shokeen Chhavi Rana

DOI:, Pub. Date: 8 May 2019

The development in technology has gifted huge set of alternatives. In the modern era, it is difficult to select relevant items and information from the large amount of available data. Recommender systems have been proved helpful in choosing relevant items. Several algorithms for recommender systems have been proposed in previous years. But recommender systems implementing these algorithms suffer from various challenges. Deep learning is proved successful in speech recognition, image processing and object detection. In recent years, deep learning has been also proved effective in handling information overload and recommending items. This paper gives a brief overview of various deep learning techniques and their implementation in recommender systems for various applications. The increasing research in recommender systems using deep learning proves the success of deep learning techniques over traditional methods of recommender systems.

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Multi Featured Fuzzy based Block Weight Assignment and Block Frequency Map Model for Transformation Invariant Facial Recognition

By Kapil Juneja Chhavi Rana

DOI:, Pub. Date: 8 Mar. 2018

Misalignment of the camera, some jerk during capture is natural that results some tilt or geometric transformed photo. The accurate recognition on these misaligned facial images is one of the biggest challenges in real time systems. In this paper, a fuzzy enabled multi-parameter based model is presented, which is applied to individual blocks to assign block weights. At first, the model has divided the image into square segments of fixed size. Each segmented division is analyzed under directional, structural and texture features. Fuzzy rule is applied on the obtained quantized values for each segment and to assign weights to each segment. While performing the recognition process, each weighted block is compared with all weighted-feature blocks of training set. A weight-ratio to exactly map and one-to-all map methods are assigned to identify overall matching accuracy. The work is applied on FERET and LFW datasets with rotational, translational and skewed transformation. The comparative observations are taken against KPCA and ICA methods. The proportionate transformation specific observations show that the model has improved the accuracy up to 30% for rotational and skewed transformation and in case of translation the improvement is up to 11%.

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