sweta srivastava

@amity.edu/aset

Assistant Professor CSE
Amity University, Noida



              

https://researchid.co/swetasrivastava

RESEARCH INTERESTS

Text mining, soft computing, machine learning, AI

15

Scopus Publications

202

Scholar Citations

5

Scholar h-index

4

Scholar i10-index

Scopus Publications

  • Extraction of Handwritten Text from Doctors’ Prescriptions
    Madhurima Mitra, Shrey Goyal, Tanmay Agrawal, and Sweta Srivastava
    Springer Nature Singapore

  • Mobile app review analysis for crowdsourcing of software requirements: a mapping study of automated and semi-automated tools
    Rhodes Massenon, Ishaya Gambo, Roseline Oluwaseun Ogundokun, Ezekiel Adebayo Ogundepo, Sweta Srivastava, Saurabh Agarwal, and Wooguil Pak
    PeerJ
    Mobile app reviews are valuable for gaining user feedback on features, usability, and areas for improvement. Analyzing these reviews manually is difficult due to volume and structure, leading to the need for automated techniques. This mapping study categorizes existing approaches for automated and semi-automated tools by analyzing 180 primary studies. Techniques include topic modeling, collocation finding, association rule-based, aspect-based sentiment analysis, frequency-based, word vector-based, and hybrid approaches. The study compares various tools for analyzing mobile app reviews based on performance, scalability, and user-friendliness. Tools like KEFE, MERIT, DIVER, SAFER, SIRA, T-FEX, RE-BERT, and AOBTM outperformed baseline tools like IDEA and SAFE in identifying emerging issues and extracting relevant information. The study also discusses limitations such as manual intervention, linguistic complexities, scalability issues, and interpretability challenges in incorporating user feedback. Overall, this mapping study outlines the current state of feature extraction from app reviews, suggesting future research and innovation opportunities for extracting software requirements from mobile app reviews, thereby improving mobile app development.

  • Optimizing Real-Time Performance in ML-Based Application Layer Firewalls
    Vinayak Nayar, Tushar Malik, Arbab Badar Khan, and Sweta Srivastava
    Springer Nature Singapore

  • Machine Learning Techniques for Twitter Spam Detection: Comparative Insights and Real-Time Application
    Yashvardhan Asthana, Rahul Chhabra, and Sweta Srivastava
    IEEE
    In today's world of digital communication, platforms like Twitter are essential for connecting people globally. But there's a problem – spam on Twitter can be a big issue for users. Our main goal is to make a system that can spot and stop spam using fancy computer techniques. We looked at many tweets on Twitter, trying to understand how spam and regular tweets are spread out. We found a problem with our data – there were way more regular tweets than spam ones. To fix it, we used something called Synthetic Minority Over-sampling Technique (SMOTE) to make the number of tweets more equal. After that, we made and tested 13 computer models, looking at how good they were using important measures like accuracy and recall. The result is a strong system that can tell the difference between spam and real tweets on Twitter. This helps make online talks better, keeps users safe, and makes sure Twitter stays a good place to be. Since spam tactics keep changing, our work is an important step in making social media safer. This means everyone can enjoy a safer and better time online.

  • An Innovative Hybrid Biologically Inspired Method for Traffic Optimization Problem
    Sweta Srivastava, Thompson Stephan, and Sudip Kumar Sahana
    World Scientific Pub Co Pte Ltd
    The transport network and road services are the foundation for the development of human civilization. It is immensely essential to manage network congestion as well as to minimize the travel time of the growing traffic load on the road network. Traffic signals may play an important role in managing the mounting traffic. This work relies on reducing the total time lag at the traffic signals, thus reducing the overall travel period. The model is designed on a bi-level framework. The overall wait time is optimized at the traffic signals by the upper level while the User Equilibrium (UE) is estimated by the lower level. Biologically inspired metaheuristic methods like Bat Algorithm (BA), Genetic algorithms (GA), Ant Colony Optimization (ACO), and many others demonstrated optimized outcomes for bi-level problems. To improve the desirability of the metaheuristic techniques an innovative method encapsulating the desirability of both BA and GA is proposed to evaluate the traffic optimization problem (TOP). While BA helps in faster convergence GA diversifies the search space. A comparative analysis has been carried out with the parent algorithms as well as an existing ACO-GA-based model. It was observed that the proposed BA-GA method performs better than the rest of the techniques.

  • Twitter Sentiment Analysis on Oxygen Supply During Covid 19 Outbreak
    Akash Kashyap, Kunal Yadav, and Sweta Srivastava
    Springer Nature Singapore

  • A survey on traffic optimization problem using biologically inspired techniques
    Sweta Srivastava and Sudip Kumar Sahana
    Springer Science and Business Media LLC

  • Artificial intelligence inspired energy and spectrum aware cluster based routing protocol for cognitive radio sensor networks
    Thompson Stephan, Fadi Al-Turjman, K. Suresh Joseph, Balamurugan Balusamy, and Sweta Srivastava
    Elsevier BV

  • Application of Bat Algorithm for Transport Network Design Problem
    Sweta Srivastava and Sudip Kumar Sahana
    Hindawi Limited
    The requirement of the road services and transportation network development planning came into existence with the development of civilization. In the modern urban transport scenario with the forever mounting amount of vehicles, it is very much essential to tackle network congestion and to minimize the travel time. This work is based on determining the optimal wait time at traffic signals for the microscopic discrete model. The problem is formulated as a bilevel model. The upper layer optimizes the travel time by reducing the wait time at traffic signal and the lower layer solves the stochastic user equilibrium. Soft computing techniques like Genetic Algorithms, Ant Colony Optimization, and many other biologically inspired techniques prove to give good results for bilevel problems. Here this work uses Bat Intelligence to solve the transport network design problem. The results are compared with the existing techniques.

  • Bat algorithm-based traffic signal optimization problem
    Sweta Srivastava and Sudip Kumar Sahana
    Springer Singapore

  • The insects of nature-inspired computational intelligence
    Sweta Srivastava and Sudip Kumar Sahana
    IGI Global
    The desirable merits of the intelligent computational algorithms and the initial success in many domains have encouraged researchers to work towards the advancement of these techniques. A major plunge in algorithmic development to solve the increasingly complex problems turned out as breakthrough towards the development of computational intelligence (CI) techniques. Nature proved to be one of the greatest sources of inspiration for these intelligent algorithms. In this chapter, computational intelligence techniques inspired by insects are discussed. These techniques make use of the skills of intelligent agent by mimicking insect behavior suitable for the required problem. The diversities in the behavior of the insect families and similarities among them that are used by researchers for generating intelligent techniques are also discussed in this chapter.

  • The insects of innovative computational intelligence
    Sweta Srivastava and Sudip Kumar Sahana
    Springer Singapore

  • Nested hybrid evolutionary model for traffic signal optimization
    Sweta Srivastava and Sudip Kumar Sahana
    Springer Science and Business Media LLC

  • ACONN—A multicast routing implementation
    Sweta Srivastava and Sudip Kumar Sahana
    Springer India

  • Hybrid microscopic discrete evolutionary model for traffic signal optimization


RECENT SCHOLAR PUBLICATIONS

  • Optimizing Real-Time Performance in ML-Based Application Layer Firewalls.
    N Vinayak, M Tushar, AB Khan, S Srivastava
    Proceedings of Fifth International Conference on Computing, Communications 2024

  • Mobile app review analysis for crowdsourcing of software requirements: a mapping study of automated and semi-automated tools
    R Massenon, I Gambo, RO Ogundokun, EA Ogundepo, S Srivastava, ...
    PeerJ Computer Science 2024

  • Extraction of Handwritten Text from Doctors’ Prescriptions
    M Mitra, S Goyal, T Agrawal, S Srivastava
    International Conference on Information and Communication Technology for 2024

  • Machine Learning Techniques for Twitter Spam Detection: Comparative Insights and Real-Time Application
    Y Asthana, R Chhabra, S Srivastava
    14th International Conference on Cloud Computing, Data Science & Engineering 2024

  • Twitter Sentiment Analysis on Oxygen Supply During Covid 19 Outbreak.
    A Kashyap, K Yadav, S Srivastava.
    Lecture Notes in Electrical Engineering 925, 655-665. 2022

  • An innovative hybrid biologically inspired method for traffic optimization problem
    S Srivastava, T Stephan, SK Sahana
    International Journal on Artificial Intelligence Tools 31 (02), 2240004 2022

  • A survey on traffic optimization problem using biologically inspired techniques
    S Srivastava, SK Sahana
    Natural Computing 19 (4), 647-661 2020

  • Artificial intelligence inspired energy and spectrum aware cluster based routing protocol for cognitive radio sensor networks
    T Stephan, F Al-Turjman, KS Joseph, B Balusamy, S Srivastava
    Journal of Parallel and Distributed Computing 142, 90-105 2020

  • Application of bat algorithm for transport network design problem
    S Srivastava, SK Sahana
    Applied Computational Intelligence and soft computing 2019 (1), 9864090 2019

  • Bat algorithm-based traffic signal optimization problem
    S Srivastava, SK Sahana
    Soft Computing for Problem Solving: SocProS 2017, Volume 1, 927-936 2019

  • Nested hybrid evolutionary model for traffic signal optimization
    S Srivastava, SK Sahana
    Applied intelligence 46 (1), 113-123 2017

  • The Insects of Innovative Computational Intelligence
    S Srivastava, SK Sahana
    Advances in Computational Intelligence: Proceedings of International 2017

  • The Insects of Nature-Inspired Computational Intelligence
    S Srivastava, SK Sahana
    Handbook of Research on Soft Computing and Nature-Inspired Algorithms, 398-428 2017

  • ACONN—A Multicast Routing Implementation
    S Srivastava, SK Sahana
    Computational Intelligence in Data Mining—Volume 2: Proceedings of the 2016

  • Hbrid Microscopic Discrete Evolutionary Model for Traffic Signal Optimization
    S Srivastava, SK Sahana, D Pant, P Mahanti
    Journal of Next Generation Information Technology 6 (2), 1 2015

MOST CITED SCHOLAR PUBLICATIONS

  • Artificial intelligence inspired energy and spectrum aware cluster based routing protocol for cognitive radio sensor networks
    T Stephan, F Al-Turjman, KS Joseph, B Balusamy, S Srivastava
    Journal of Parallel and Distributed Computing 142, 90-105 2020
    Citations: 59

  • Application of bat algorithm for transport network design problem
    S Srivastava, SK Sahana
    Applied Computational Intelligence and soft computing 2019 (1), 9864090 2019
    Citations: 52

  • Nested hybrid evolutionary model for traffic signal optimization
    S Srivastava, SK Sahana
    Applied intelligence 46 (1), 113-123 2017
    Citations: 50

  • A survey on traffic optimization problem using biologically inspired techniques
    S Srivastava, SK Sahana
    Natural Computing 19 (4), 647-661 2020
    Citations: 18

  • Hbrid Microscopic Discrete Evolutionary Model for Traffic Signal Optimization
    S Srivastava, SK Sahana, D Pant, P Mahanti
    Journal of Next Generation Information Technology 6 (2), 1 2015
    Citations: 8

  • Bat algorithm-based traffic signal optimization problem
    S Srivastava, SK Sahana
    Soft Computing for Problem Solving: SocProS 2017, Volume 1, 927-936 2019
    Citations: 4

  • Machine Learning Techniques for Twitter Spam Detection: Comparative Insights and Real-Time Application
    Y Asthana, R Chhabra, S Srivastava
    14th International Conference on Cloud Computing, Data Science & Engineering 2024
    Citations: 3

  • Twitter Sentiment Analysis on Oxygen Supply During Covid 19 Outbreak.
    A Kashyap, K Yadav, S Srivastava.
    Lecture Notes in Electrical Engineering 925, 655-665. 2022
    Citations: 2

  • The Insects of Innovative Computational Intelligence
    S Srivastava, SK Sahana
    Advances in Computational Intelligence: Proceedings of International 2017
    Citations: 2

  • ACONN—A Multicast Routing Implementation
    S Srivastava, SK Sahana
    Computational Intelligence in Data Mining—Volume 2: Proceedings of the 2016
    Citations: 2

  • Mobile app review analysis for crowdsourcing of software requirements: a mapping study of automated and semi-automated tools
    R Massenon, I Gambo, RO Ogundokun, EA Ogundepo, S Srivastava, ...
    PeerJ Computer Science 2024
    Citations: 1

  • An innovative hybrid biologically inspired method for traffic optimization problem
    S Srivastava, T Stephan, SK Sahana
    International Journal on Artificial Intelligence Tools 31 (02), 2240004 2022
    Citations: 1