Dr. Aprna Tripathi is an assistant professor in the department of data science and engineering, Manipal University Jaipur. She received her bachelor’s degree in sciences from Kanpur University (1998), master’s in computer applications from HBTI (2002), Kanpur, Master of Technology from Banasthali University (2007), Rajasthan and PhD from National Institute of Technology Allahabad, Prayagraj (2015).
With over 15 years of teaching and research experience, she has established a strong foundation in academia. Her scholarly contributions can be found in prestigious national and international journals and conferences, including those recognized by SCI and Scopus.
Furthermore, she actively contributes as a reviewer for prestigious academic journals. Her areas of specialization include Software Engineering, Software Testing, Data Visualization, and Data Structures & Algorithms. Notably, she has authored a book titled "Component-Based Systems: Estimating Efforts Using Soft Computing Technique
CNN-DBO: Dung Beetle Optimization Based Urban Area Change Detection Using Satellite Images Jambukeshwar Pujari, Javed Wasim, Aprna Tripathi IEEE International Conference on Computational Communication and Information Technology Icccit 2025, 2025 In recent years, Geographic Information Systems (GIS) have garnered a significant deal of interest for their ability to detect changes in metropolitan areas. One of the uses of change detection in satellite photographs is in the field of urban planning. Other applications include environmental monitoring and remote surveillance. A number of different approaches have been suggested in recent times for the purpose of automatically identifying alterations in the land cover based on GIS photographs. However, due to poor texture, cluttered context, and considerable object scale disparities, the convolutional neural network (CNN) method is only partially successful in capturing the context from geographic information systems (GISs). This is despite the fact that it remains an effective method for detecting change in urban environments. The article presents the construction of a CNN-based change detection model using the utilization of Dung Beetle Optimization (DBO). The DBO algorithm is used to adjust the loss parameter in order to increase the overall performance. The approach that was proposed makes use of the Onera satellite data set and displays an overall accuracy of 97.50% percent. In light of this, it appears that the strategy that is proposed is both very efficient and effective.
Multi-objective ANT lion optimization algorithm based mutant test case selection for regression testing Journal of Scientific and Industrial Research, 2021
Event correlation for intrusion detection systems Neelam Dwivedi, Aprna Tripathi Proceedings 2015 IEEE International Conference on Computational Intelligence and Communication Technology Cict 2015, 2015
A new design based software coupling metric Anshu Maheshwari, Aprna Tripathi, Dharmender Singh Kushwaha Proceedings 2014 13th International Conference on Information Technology Icit 2014, 2014
SRS based estimation of software maintenance effort Aprna Tripathi, Bhuvnesh Kumar, Ashish Sharma, Dharmender Singh Kushwaha Proceedings of the 2012 3rd International Conference on Computer and Communication Technology Iccct 2012, 2012
E-Governance challenges and cloud benefits Aprna Tripathi, Bhawana Parihar Proceedings 2011 IEEE International Conference on Computer Science and Automation Engineering Csae 2011, 2011
