Disha Handa

Work place: Department of University Institute of Computing, Chandigarh University, Mohali, Punjab, India



Research Interests: Programming Language Theory, Data Structures and Algorithms, Computational Learning Theory, Computer systems and computational processes


Dr. Disha Handa is Academic Coordinator (Specialization) in University Institute of Computing, Chandigarh University (NIRF ranked). She is the former women scientist (WOS-B) from 2017-2021(June). She has completed her PhD in Parallel cryptographic algorithms in 2015. Her research areas are Acoustic analysis, Parallel programming models and machine learning models. Recently, she has completed the project “Design and development of a smart back panel for women security” which is based on women’s scream patterns.

Author Articles
An Experimental and Statistical Analysis to Assess impact of Regional Accent on Distress Non-linguistic Scream of Young Women

By Disha Handa Renu Vig Mukesh Kumar Namarta Vij

DOI:, Pub. Date: 8 Aug. 2023

Scream is recognized as constant and ear-splitting non-linguistic verbal communication that has no phonological structure. This research is based on the study to assess the effect of regional accent on distress screams of women of a very specific age group. The primary goal of this research is to identify the components of non-speech sound so that the region of origin of the speaker can be determined. Furthermore, this research can aid in the development of security techniques based on emotions to prevent and report criminal activities where victims used to yell for help. For the time being, we have limited the study to women because women are the primary victims of all types of criminal’s activities. The Non-Speech corpus has been used to explore different parameters of scream samples collected from three different regions by using high-reliability audio recordings. The detailed investigation is based on the vocal characteristics of female speakers. Further, the investigations have been verified with bi-variate, partial correlation and one-way ANOVA to find out the impact of region-based accent non-speech distress signal. Results from the correlation techniques indicate that out of four attributes only jitter varies with respect to the specific region. Whereas ANOVA depicts that there is no significant regional impact on distress non-speech signals.

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Building Predictive Model by Using Data Mining and Feature Selection Techniques on Academic Dataset

By Mukesh Kumar Nidhi Bhisham Sharma Disha Handa

DOI:, Pub. Date: 8 Aug. 2022

In the field of education, every institution stores a significant amount of data in digital form on the academic performance of students. If this data is correctly analysed to discover any pattern related to student learning, it can assist the institution in achieving a favorable outcome in the future. Because of this, the use of data mining techniques makes it much simpler to unearth previously concealed information or detect patterns in student data. We use a variety of data mining methods, such as Naive Bayes, Random Forest, Decision Tree, Multilayer Perceptron, and Decision Table, to predict the academic performance of individual students. In the real world, a dataset may contain many features, yet the mining process may only place significance on some of those aspects. The correlation attribute evaluator, the information gain attribute evaluator, and the gain ratio attribute evaluator are some of the feature selection methods that are used in data mining to remove features that are not important for the mining process. Other feature selection methods include the gain ratio attribute evaluator and the gain ratio attribute evaluator. In conclusion, each classification algorithm that is designed using some feature selection methods enhances the overall predictive performance of the algorithms, which in turn improves the performance of the algorithms overall.

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