Nancy Kansal

Assistant professor

Information Technology,
School of Computer Science and Information Technology
Noida Institute of Engineering and Technology
Knowledge Part II, Gr. Noida, India.

Previously, I was a Masters Scholar in the Ajay Kumar Garg Engineering College, Ghaziabad at the Computer Science and Engineering Department, where 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 sujects 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.


Jump to Books, Journal Papers, Workshops


  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.

    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

  1. 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.

    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.

  2. 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.

    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.

    Workshops and Talks

    1. DSA.
      Divide and Conquer Approach and Applications. Nancy Kansal. (2021).