Nancy Kansal

Previously, I was an Assistant Professor at Noida Institute of Engineering and Technology, Greater Noida, India. I have done my Masters from Ajay Kumar Garg Engineering College, Ghaziabad in Computer Science and Engineering. In my Master thesis project, I worked on Cross Domain Sentiment Analysis Thesis Project under the supervision of Lipika Goel and Sonam Gupta. Finally, I did my Bachelor in computer science and engineering at the Radha Govind Group of Institions, Meerut, where I studied various subjects like Data Structures, Database Management Systems, Automata, Design and Analysis of Algorithms, Operating Systems, Computer Organization and Architecture, Digital Logic Design, Compiler Design, and more.

Publications

Jump to Books, Journal Papers, Conference Proceedings, Workshops and Talks

Books

1. 2022.
   Architecture, Security Vulnerabilities, and the Proposed Countermeasures in Agriculture-Internet-of-Things (AIoT) Systems. Nancy Kansal, Bharat Bhushan, and Shubham Sharma. (2022). In book: Internet of Things and Analytics for Agriculture, Volume 3 (pp.329-353). Springer. DOI: 10.1007/978-981-16-6210-2_16.
   Abstract

   In recent years, along with the rise in the population, the demand for food has also increased which led to the need for industrialization as well as intensification of agricultural sector. The Internet-of-Things (IoT) has been a promising technology that offers extended solutions towards the development of agriculture. Various research institutions and scientific groups, as well as industries, are trying to cope with the challenges by delivering more and more IoT products for agricultural sector. In this paper, we aim to provide a survey of IoT systems, its enabling technologies, and communication technologies. Moreover, we provide insights into IoT enabled agricultural applications along with its architecture and research challenges. Finally, we discussed the security and privacy issues that occur in agriculture IoT along with some cybersecurity attacks.

Journal Papers

2. SIS 2020.
   Cross-domain sentiment classification initiated with Polarity Detection Task. Nancy Kansal, Lipika Goel, and Sonam Gupta. (2020). In: EAI Transactions on Scalable Information Systems. EAI Transactions. DOI: 10.4108/eai.26-5-2020.165965.

   Abstract

   INTRODUCTION: The requirement of the labeled dataset in the source domain makes the Cross Domain Sentiment Classification (CDSC) task complicate in the situation when the dataset is labeled manually.

   OBJECTIVES: To overcome the dependency of CDSC tasks on manual labeling of the dataset by proposing a polarity detection task.

   METHODS: We have proposed the CDSC-PDT method that is the polarity Detection Task (PDT) followed by the CDSC task. The proposed PDT task extracts the polarity of reviews from the source domain using the contextual and relevancy information of words in documents and this automatic labeled dataset is further used to train classifiers to make the further classification.

   RESULTS: Proposed method is comparable to the traditional learning method giving the highest precision 85.7%.

   CONCLUSION: The proposed method does not need to manually label the documents in either of the domain (source or target), hence it overcomes the human intervention and is also time saving and cheap process, unlike traditional CDSC tasks.

3. IJAIML 2020.
   A Literature review on Cross Domain sentiment Analysis using Machine Learning. Nancy Kansal, Lipika Goel, and Sonam Gupta. (2020). IGI-Global. DOI: 10.4018/IJAIML.2020070103.

   Abstract

   Sentiment analysis is the field of NLP which analyzes the sentiments of text written by users on online sites in the form of reviews. These reviews may be either in the form of a word, sentence, document, or ratings. These reviews are used as datasets when applied to train a classifier. These datasets are applied in the annotated form with the positive, negative or neutral labels as an input to train the classifier. This trained classifier is used to test other reviews, either in the same or different domains to know like or dislike of the user for the related field. Various researches have been done in single and cross domain sentiment analysis. The new methods proposed are overcoming the previous ones but according to this survey, no methods best suit the proposed work. In this article, the authors review the methods and techniques that are given by various researchers in cross domain sentiment analysis and how those are compared with the pre-existing methods for the related work.

Conference Proceedings (peer-reviewed)

4. ICRITO-2022.
   Comparative analysis of machine learning algorithms for Classification of US Airlines Tweets. Subham Sharma, Usha Mittal, Ankita Wadhawan, and Nancy Kansal. (2022). In: 10th International Conference on Reliability, Infocom Technologies and Optimization (Trends and Future Directions). IEEE. DOI: 10.1109/ICRITO56286.2022.9964766.
5. ICCST-2022.
   Drowsiness Alertness for Driver Safety. Rohit Gusain and Nancy Kansal. (2022). In: 1st International Conference on Computational Science and Technology . IEEE. DOI: 10.1109/ICCST55948.2022.10040365.

Workshops and Talks

6. Full Stack Training.
   Java Programming, Microservices, and Postgre". Nancy Kansal. (Mar 2022 - Sept 2022). Keynote speaker as part of the Training on Full Stack Web Development (Tied up with Capgemini), NIET.
7. DSA.
   Divide and Conquer Approach and Applications. Nancy Kansal. (May 17, 2021 - May 22, 2021). Keynote speaker in the online workshop on Problem Solving Approach on Data Structures and Algorithms, School of Computer Science and Information Technology, NIET Gr. Noida, IIC-NIET .
8. ITBT-2019.
   A literature Review on Cross Domain Sentiment Analysis using Machine Learning Methods". Nancy Kansal. (2019). Paper presentation In: 6th ITBT 2019 National Conference held in AKGEC, Ghaziabad.

Teaching

In the Fall 2024 term at McMaster, I am a Teaching Assistant for CAS department for the courses COMPSCI 2C03: Data Structures and Algorithms; COMPSCI 3SH3: Operating systems.

In the Spring 2024 term at McMaster, I was a Sessional Instructor for the course COMPSCI 1DM3: Discrete Mathematics for Computer Science.

In the Winter 2024 term at McMaster, I was a Teaching Assistant for CAS department for the course COMPSCI 2DB3: Databases.

In the Fall 2023 term at McMaster, I was a Teaching Assistant for CAS department for the courses SFWRENG 3BB4: Software Design II - Concurrent System Design; SFWRENG 2DM3: Discrete Mathematics with Applications I.

In the Winter 2023 term at McMaster, I was a Teaching Assistant in CAS department for the course COMPSCI 2DB3: Databases.

In 2020-2022 at NIET, I was an Assistant professor in School of Computer Science and Information Technology, Noida Institute of Engineering and Technology, Knowledge Part II, Gr. Noida, India.

I was responsible for the following courses.

Digital Image Processing.

Digital Image Fundamentals, Image Enhancement, Image Restoration, Image Compression and Recognition.

Object Oriented Programming in Java.

Java Introduction, Basics of Java Programming, Packages, Exception Handling and String Handling, Concurrency in Java and I/O Stream, basics of GUI Programming, Generics, and Collections.

Blockchain Architecture and Technology.

Introduction to Blockchain, Consensus, Permissioned Blockchain, Hyperledger Fabric A and B, Use cases 1, 2, and 3.

Develop in Swift Fundamentals.

Introduction to Swift and playgrounds, basics and Object-Oriented concepts, Type Casting, Guard, Scope, Enumeration, XCode: Building, Running, Debugging an App, Controls, Layouts, Segues, Controllers, and guided projects

Academic Services

I have served in the program committee of BOS 2022. I served as the Apple Coding Club Faculty Coordinator for 2022. I served as external reviewer for MYSURUCON (2021 and 2022). I have been Mentor for Bachelor Students of Department of IT for the year 2021 and 2022. I was the Faculty head for Full Stack Training for the Students of School of Computer Science and Information Technology.