sweta srivastava
@amity.edu/aset
Assistant Professor CSE
Amity University, Noida
RESEARCH INTERESTS
Text mining, soft computing, machine learning, AI
Scopus Publications
Scholar Citations
Scholar h-index
Scholar i10-index
Scopus Publications
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.
Vinayak Nayar, Tushar Malik, Arbab Badar Khan, and Sweta Srivastava
Springer Nature Singapore
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.
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.
Akash Kashyap, Kunal Yadav, and Sweta Srivastava
Springer Nature Singapore
Sweta Srivastava and Sudip Kumar Sahana
Springer Science and Business Media LLC
Thompson Stephan, Fadi Al-Turjman, K. Suresh Joseph, Balamurugan Balusamy, and Sweta Srivastava
Elsevier BV
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.
Sweta Srivastava and Sudip Kumar Sahana
Springer Singapore
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.
Sweta Srivastava and Sudip Kumar Sahana
Springer Singapore
Sweta Srivastava and Sudip Kumar Sahana
Springer Science and Business Media LLC
Sweta Srivastava and Sudip Kumar Sahana
Springer India