DR. SHIVANI V VORA

@ckpcet.ac.in

Assistant Professor, Information Technology Department
C. K. Pithawala College of Engineering and Technology

DR. SHIVANI V VORA


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https://researchid.co/vshivani

RESEARCH, TEACHING, or OTHER INTERESTS

Computer Engineering, Computer Science, Artificial Intelligence, Human-Computer Interaction
5

Scopus Publications

Scopus Publications

  • HDEL: a hierarchical deep ensemble approach for text-based emotion detection
    Shivani Vora, Rupa G. Mehta
    Multimedia Tools and Applications, 2025
  • Impact of balancing techniques for imbalanced class distribution on twitter data for emotion analysis: A case study
    Shivani Vasantbhai Vora, Rupa G. Mehta, Shreyas Kishorkumar Patel
    Research Anthology on Applying Social Networking Strategies to Classrooms and Libraries, 2022
    Continuously growing technology enhances creativity and simplifies humans' lives and offers the possibility to anticipate and satisfy their unmet needs. Understanding emotions is a crucial part of human behavior. Machines must deeply understand emotions to be able to predict human needs. Most tweets have sentiments of the user. It inherits the imbalanced class distribution. Most machine learning (ML) algorithms are likely to get biased towards the majority classes. The imbalanced distribution of classes gained extensive attention as it has produced many research challenges. It demands efficient approaches to handle the imbalanced data set. Strategies used for balancing the distribution of classes in the case study are handling redundant data, resampling training data, and data augmentation. Six methods related to these techniques have been examined in a case study. Upon conducting experiments on the Twitter dataset, it is seen that merging minority classes and shuffle sentence methods outperform other techniques.
  • Impact of Balancing Techniques for Imbalanced Class Distribution on Twitter Data for Emotion Analysis: A Case Study
    Shivani Vasantbhai Vora, Rupa G. Mehta, Shreyas Kishorkumar Patel
    Research Anthology on Implementing Sentiment Analysis Across Multiple Disciplines Volume I IV, 2022
    Continuously growing technology enhances creativity and simplifies humans' lives and offers the possibility to anticipate and satisfy their unmet needs. Understanding emotions is a crucial part of human behavior. Machines must deeply understand emotions to be able to predict human needs. Most tweets have sentiments of the user. It inherits the imbalanced class distribution. Most machine learning (ML) algorithms are likely to get biased towards the majority classes. The imbalanced distribution of classes gained extensive attention as it has produced many research challenges. It demands efficient approaches to handle the imbalanced data set. Strategies used for balancing the distribution of classes in the case study are handling redundant data, resampling training data, and data augmentation. Six methods related to these techniques have been examined in a case study. Upon conducting experiments on the Twitter dataset, it is seen that merging minority classes and shuffle sentence methods outperform other techniques.
  • Impact of balancing techniques for imbalanced class distribution on Twitter data for emotion analysis: A case study
    Shivani Vasantbhai Vora, Rupa G. Mehta, Shreyas Kishorkumar Patel
    Data Preprocessing Active Learning and Cost Perceptive Approaches for Resolving Data Imbalance, 2021
    Continuously growing technology enhances creativity and simplifies humans' lives and offers the possibility to anticipate and satisfy their unmet needs. Understanding emotions is a crucial part of human behavior. Machines must deeply understand emotions to be able to predict human needs. Most tweets have sentiments of the user. It inherits the imbalanced class distribution. Most machine learning (ML) algorithms are likely to get biased towards the majority classes. The imbalanced distribution of classes gained extensive attention as it has produced many research challenges. It demands efficient approaches to handle the imbalanced data set. Strategies used for balancing the distribution of classes in the case study are handling redundant data, resampling training data, and data augmentation. Six methods related to these techniques have been examined in a case study. Upon conducting experiments on the Twitter dataset, it is seen that merging minority classes and shuffle sentence methods outperform other techniques.
  • Effective stress detection using physiological parameters
    Monika Chauhan, Shivani V. Vora, Dipak Dabhi
    Proceedings of 2017 International Conference on Innovations in Information Embedded and Communication Systems Iciiecs 2017, 2017
    In today's word one of the major leading factors to health problem is STRESS. The basic parameters on which stress can be identified are heart rate, galvanic skin response, body temperature, blood pressure, which provides detailed information of the state of mind of a person. These parameters varying from person to person on the basis of certain things such as their body condition, age and gender. The main goal of the system is to analyze the mental stress through physiological data using electrocardiograph in different positions and moods. Different pre-processing techniques can be used for stress detection. In feature extraction discrete wavelet transform can apply. Many classifiers like artificial neural network, support vector machine, Bayesian network, and decision tree are using to get more accurate results based on accuracy.