Dr Jaya Lakshmi Tangirala PhD
Lecturer
- School of Engineering and Built Environment
- School of Computing and Digital Technologies
- Industry and Innovation Research Institute
Summary
Dr. Jaya is a seasoned educator and researcher with a Ph.D. in Data Analytics, driven by a profound passion for unleashing the transformative potential of data in academia and beyond. With over two decades of experience in computer science and engineering education, her career has been shaped by a commitment to excellence in teaching, research, and innovation. This journey has afforded her the privilege of inspiring and mentoring countless students while staying attuned to the dynamic landscape of technological advancements. As both an educator and researcher, she continually strives to foster a deep appreciation for data-driven problem-solving and empower the next generation of technology professionals.
Jaya’s research focuses on exploring Data Analytics, integrating cutting-edge technologies for positive societal change. Jaya is dedicated to tackling intricate challenges across a spectrum of sectors including education, finance, and healthcare. Currently, her efforts are concentrated in two primary areas. Firstly, Jaya is delving into the problem of link prediction in social networks in various kinds of network models. Secondly, she is endeavouring to enhance customer experiences through the development of innovative algorithms for recommender systems. Furthermore, Jaya’s ongoing research encompasses the exploration of applications in Natural Language Processing, Data Mining, Big Data, and Machine Learning.
About
Dr. Jaya is a seasoned educator and researcher with a Ph.D. in Data Analytics, driven by a profound passion for unleashing the transformative potential of data in academia and beyond. With over two decades of experience in computer science and engineering education, her career has been shaped by a commitment to excellence in teaching, research, and innovation. This journey has afforded her the privilege of inspiring and mentoring countless students while staying attuned to the dynamic landscape of technological advancements. As both an educator and researcher, she continually strives to foster a deep appreciation for data-driven problem-solving and empower the next generation of technology professionals.
Jaya’s research focuses on exploring Data Analytics, integrating cutting-edge technologies for positive societal change. Jaya is dedicated to tackling intricate challenges across a spectrum of sectors including education, finance, and healthcare. Currently, her efforts are concentrated in two primary areas. Firstly, Jaya is delving into the problem of link prediction in social networks in various kinds of network models. Secondly, she is endeavouring to enhance customer experiences through the development of innovative algorithms for recommender systems. Furthermore, Jaya’s ongoing research encompasses the exploration of applications in Natural Language Processing, Data Mining, Big Data, and Machine Learning.
Lecturer
Teaching
School of Engineering and Built Environment , School of Computing and Digital Technologies
College of Business, Technology and Engineering
Subject area: Software Engineering
Courses taught:
- BSc Computer Science
- MSc Software Engineering
Modules taught:
- Algorithms and Data Structures
- Databases and Web
- Work-based Review for Apprenticeship
- Advanced Software Engineering
Publications
Journal articles
Tokala, S., Krishna Enduri, M., Tangirala, J.L., Abdul, A., & Chen, J. (2024). Empowering Quality of Recommendations by Integrating Matrix Factorization Approaches With Louvain Community Detection. IEEE Access, 12, 164028-164062. http://doi.org/10.1109/access.2024.3491829
Nandini, Y.V., Lakshmi, T.J., Enduri, M.K., & Sharma, H. (2024). Link Prediction in Complex Networks Using Average Centrality-Based Similarity Score. Entropy, 26 (6). http://doi.org/10.3390/e26060433
Nandini, Y.V., Tangirala, J.L., Enduri, M.K., Sharma, H., & Ahmad, M.W. (2024). Extending Graph-Based LP Techniques for Enhanced Insights Into Complex Hypergraph Networks. IEEE Access, 12, 51208-51222. http://doi.org/10.1109/access.2024.3385320
Sanku, S.U., Pavani, S.T., Tangirala, J.L., & Chivukula, R. (2024). COVID-19 Literature Mining and Retrieval Using Text Mining Approaches. SN Computer Science, 5. http://doi.org/10.1007/s42979-023-02550-1
Tangirala, J.L., & Bhavani, S.D. (2023). Link prediction approach to recommender systems. Computing. http://doi.org/10.1007/s00607-023-01227-0
Tokala, S., Enduri, M.K., Tangirala, J.L., & Sharma, H. (2023). Community-Based Matrix Factorization (CBMF) Approach for Enhancing Quality of Recommendations. Entropy, 25 (9). http://doi.org/10.3390/e25091360
Kapila, R., Ragunathan, T., Saleti, S., Tangirala, J.L., & Ahmad, M.W. (2023). Heart Disease Prediction Using Novel Quine McCluskey Binary Classifier (QMBC). IEEE Access, 11, 64324-64347. http://doi.org/10.1109/access.2023.3289584
Saleti, S., Tangirala, J.L., & Ahmad, M.W. (2022). Mining High Utility Time Interval Sequences Using MapReduce Approach: Multiple Utility Framework. IEEE Access, 10, 123301-123315. http://doi.org/10.1109/access.2022.3224217
Kandula, L.R.R., Tangirala, J.L., Alla, K., & Chivukula, R. (2022). An intelligent prediction of phishing URLs using ML algorithms. International Journal of Safety and Security Engineering, 12 (3), 381-386. http://doi.org/10.18280/ijsse.120312
Bojjagani, S., Rao, P.V.V., Vemula, D.R., Reddy, B.R., & Lakshmi, T.J. (2022). A secure IoT-based micro-payment protocol for wearable devices. Peer-to-Peer Networking and Applications, 15 (2), 1163-1188. http://doi.org/10.1007/s12083-021-01242-y
Chivukula, R., Tangirala, J.L., Uday, S.S., & Pavani, S.T. (2021). Classifying clinically actionable genetic mutations using KNN and SVM. Indonesian Journal of Electrical Engineering and Computer Science, 24 (3), 1672-1679. http://doi.org/10.11591/ijeecs.v24.i3.pp1672-1679
Chivukula, R., Vamsi, M., Tangirala, J.L., & Harini, M. (2021). Empirical study on Microsoft malware classification. International Journal of Advanced Computer Science and Applications, 12 (3), 509-515. http://doi.org/10.14569/ijacsa.2021.0120361
Jaya Lakshmi, T., & Durga Bhavani, S. (2017). Temporal probabilistic measure for link prediction in collaborative networks. Applied Intelligence, 47 (1), 83-95. http://doi.org/10.1007/s10489-016-0883-y
Conference papers
Nandini, Y.V., Tangirala, J.L., & Enduri, M.K. (2023). Link Prediction in Complex Networks: An Empirical Review. Smart Innovation, Systems and Technologies, 371 (371), 57-67. http://doi.org/10.1007/978-981-99-6706-3_5
Sanku, S.U., Satti, T.P., Tangirala, J.L., & Nandini, Y.V. (2023). Classifying Human Activities Using Machine Learning and Deep Learning Techniques. Smart Innovation, Systems and Technologies, 371 (371), 19-29. http://doi.org/10.1007/978-981-99-6706-3_2
Mani Saketh, C.V.S.S., Pranay, K., Susarla, A., Ravi Ram Karthik, D., Tangirala, J.L., & Nandini, Y.V. (2023). A Study on Influence Maximization in Complex Networks. Smart Innovation, Systems and Technologies, 371 (371), 111-119. http://doi.org/10.1007/978-981-99-6706-3_10
Harsha, K., Yuva Nitya, S., Kota, S., Satyanarayana, K., & Lakshmi, J. (2023). Empirical evaluation of Amazon fine food reviews using Text Mining. 2023 IEEE 8th International Conference for Convergence in Technology (I2CT), 1-5. http://doi.org/10.1109/i2ct57861.2023.10126349
Chivukula, R., Lakshmi, T.J., Sumalatha, S., & Reddy, K.L.R. (2022). Ontology Based Food Recommendation. In Smart Innovation, Systems and Technologies, (pp. 751-759). Springer Nature Singapore: http://doi.org/10.1007/978-981-16-3945-6_74
Tejaswi, D.K., Chauhan, H., Lakshmi, T.J., Swetha, R., & Sri, N.N. (2022). Investigation of Ethereum Price Trends using Machine learning and Deep Learning Algorithms. 2022 2nd International Conference on Intelligent Technologies (CONIT). http://doi.org/10.1109/conit55038.2022.9848000
Saleti, S., Tangirala, J.L., & Thirumalaisamy, R. (2021). Distributed Mining of High Utility Time Interval Sequential Patterns with Multiple Minimum Utility Thresholds. In Lecture Notes in Computer Science, (pp. 86-97). Springer International Publishing: http://doi.org/10.1007/978-3-030-79457-6_8
Chivukula, R., & Lakshmi, T.J. (2021). Mining Heterogeneous Information Networks: A Review. 2021 IEEE Pune Section International Conference (PuneCon), 1-4. http://doi.org/10.1109/punecon52575.2021.9686506
Chivukula, R., Jaya Lakshmi, T., Ranganadha Reddy Kandula, L., & Alla, K. (2021). A Study of Cyber Security Issues and Challenges. 2021 IEEE Bombay Section Signature Conference (IBSSC), 1-5. http://doi.org/10.1109/ibssc53889.2021.9673270
Lakshmi, T.J., & Bhavani, S.D. (2018). Link Prediction Measures in Various Types of Information Networks: A Review. 2018 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM), 1160-1167. http://doi.org/10.1109/asonam.2018.8508295
Jaya Lakshmi, T., & Bhavani, S.D. (2015). Enhancement to community-based multi-relational link prediction using co-occurrence probability feature. Proceedings of the Second ACM IKDD Conference on Data Sciences, 86-91. http://doi.org/10.1145/2732587.2732599
Lakshmi, T.J., & Prasad, C.S.R. (2014). A study on classifying imbalanced datasets. 2014 First International Conference on Networks & Soft Computing (ICNSC2014), 141-145. http://doi.org/10.1109/cnsc.2014.6906652
Lakshmi, T.J., & Bhavani, S.D. (2014). Heterogeneous link prediction based on multi relational community information. 2014 Sixth International Conference on Communication Systems and Networks (COMSNETS), 1-4. http://doi.org/10.1109/comsnets.2014.6734932
Book chapters
Jaya Lakshmi, T., & Durga Bhavani, S. (2017). Link Prediction in Temporal Heterogeneous Networks. In Lecture Notes in Computer Science. (pp. 83-98). Springer International Publishing: http://doi.org/10.1007/978-3-319-57463-9_6