Computer Science & Engineering and Information Technology (CSE&IT)

Dr. Rajni Mohana

Dr. Rajni Mohana
Associate Professor
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Biography :

Dr. Rajni Mohana is currently working as Associate Professor in the Department of CSE, at Jaypee University of Information Technology (JUIT), Waknaghat, India. She has over 17 years of experience in Academia. She received B.Tech degree in Computer Science and Engineering from BPUT,ROURKELA, Orissa, India in 2003 and M.Tech. degree in Computer Science and Engineering from Punjab Technical University, Jalandhar, India in 2009. She has completed Ph.D. degree in Computer Science and Engineering from Jaypee University of Information Technology,Solan, Himachal Pradesh, India in 2013, working on interoperability problem in service-oriented computing.
Her research interests are sentiment analysis, cloud computing.She has published her research in various impact factor journals like MDPI Senors, proceedings of national academy of sciences physical sciences. Dr. Rajni has published around 20 papers in various ESCI and Scopus Journals. She has guided two Ph.D students and is currently guiding three more students. She has guided 4 post graduate student.
She is also a reviewer for various renowned International journals and International conferences. She was TPC Chair of 2015 International Conference on Image Information Processing at Jaypee University of Information Technology, Waknaghat, Solan, H.P. on 21-24 December 2015 and also served as TPC Co-Chair of 2017 International Conference on Image Information Processing at Jaypee University of Information Technology, Waknaghat, Solan, H.P. on December 21-23, 2017. She has been a guest editor of SCPE and has edited books.


B.Tech(CSE) form  MITS , Orissa

 M.Tech(CSE). 2009 from DAVIET , Jalandhar

Ph.D (CSE), 2013 from Jaypee University of Information Technology, Wakhnaghat, Solan


Research Groups:

Software Engineering

Natural Language processing

Interest Areas:

Software Engineering and Information Systems

Service Oriented Architecure, Aspect Oriented Programming, Model Driven Architecture

Natural Language processing

Sentiment Analysis, Opinion mining, Text mining

M.Tech Students Supervised:

Ashima Kukkar 142204 - Anaphora Resolution in Hindi

Akanksha Puri 152206 - Temporal Sentiment Analysis Using Temporal Synset and Metadata

Pallavi Chandel 152213 -  An Ontology Based Privacy Model For Secure Dissemination In Web Service Composition.

PhDs Thesis Submitted:

Sukhnadan Kaur (2014 -19) Thesis Title: Holistic Multilingual Sentiment Analysis on Reviews in Social Media  

Ashima Kukkar (2016 - 20) Thesis Title: Bug Summarization Severity Classification and Assignment for Automated Bug Resolution Process

PhDs Under Progress:

Mandeep (2016 -) Broad Area: Agent Based Cloud Computing

Gauravdeep Sharma (2017 -)  Broad Area: Cloud Security

Open Project Titles:

1. Fake Review Detection
Fake reviews seem to be everywhere these days, leaving customers unsure of which products or businesses are any good. Whether you’re shopping on Amazon, checking out a restaurant on Tripadvisor, or reading about a potential employer on Glassdoor, there’s always a risk that the reviews you’re reading are fake. One of the reasons why fake reviews can be so hard to spot is that they come in various forms. They mainly form two main types: human-generated fake reviews and computer-generated fake reviews. However, both human-generated and computer-generated reviews can be positive or negative in sentiment, and can be aimed at both increasing or decreasing the overall rating, or boosting the total number of reviews to help add credibility to the score.

2. Crop Yield Production
Crop yield is a standard measurement of the amount of agricultural production harvested yield of a crop per unit of land area. Crop yield is the measure most often used for cereal, grain, or legumes; and typically is measured in bushels, tons, or pounds per acre in the U.S. To estimate crop yield, producers usually count the amount of a given crop harvested in a sample area. Then the harvested crop is weighed, and the crop yield of the entire field is extrapolated from the sample. Crop yield can also refer to the actual seed generation from the plant. For example, a grain of wheat yielding three new grains of wheat would have a crop yield of 1:3. Sometimes crop yield is referred to as "agricultural output."

3. Fault Tolerance in Fog Computing
Fault Tolerance simply means a system’s ability to continue operating uninterrupted despite the failure of one or more of its components. This is true whether it is a computer system, a cloud cluster, a network, or something else. In other words, fault tolerance refers to how an operating system (OS) responds to and allows for software or hardware malfunctions and failures. The goal of fault-tolerant computer systems is to ensure business continuity and high availability by preventing disruptions arising from a single point of failure. Fault tolerance solutions, therefore, tend to focus most on mission-critical applications or systems. Fault-tolerant computing may include several levels of tolerance:

  • At the lowest level, the ability to respond to a power failure, for example.
  • A step up: during a system failure, the ability to use a backup system immediately.
  • Enhanced fault tolerance: a disk fails, and mirrored disks take over for it immediately. This provides functionality despite partial system failure, or graceful degradation, rather than an immediate breakdown and loss of function.
  • High-level fault tolerant computing: multiple processors collaborate to scan data and output to detect errors, and then immediately correct them.
  • Fault tolerance software may be part of the OS interface, allowing the programmer to check critical data at specific points during a transaction.

4. Machine Translation
Machine translation is the process of using artificial intelligence to automatically translate text from one language to another without human involvement. Modern machine translation goes beyond simple word-to-word translation to communicate the full meaning of the original language text in the target language. It analyzes all text elements and recognizes how the words influence one another. The idea of using computers to translate human languages automatically first emerged in the early 1950s. However, at the time, the complexity of translation was far higher than early estimates by computer scientists. It required enormous data processing power and storage, which was beyond the capabilities of early machines.

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