Personal Information

Name: Ravneil Nand
Position: Assistant Lecturer in computer science and information system
Email: Ravneil.nand@usp.ac.fj
Phone: +679 3232032

 

Detail Information

Mr. Ravneil Nand is an Assistant Lecturer in the Discipline of CS/IS at the School of Information Technology, Engineering Mathematics & Physics (STEMP). He holds a BSc, BeTech and MSc from the USP.

Interest: Making friends, visiting new places, reading and games.

Research Interest: Artificial intelligence, optimization, algorithms, time-series prediction and data mining.

Members of an organization: Institute of Electrical and Electronics Engineers (IEEE) and International Association of Engineers (IAENG)

  • Bachelor of Science in CS,
  • Bachelor of Technology in Engineering, and
  • Master of Science in computer science

Undergraduate Courses:
CS140, IS314 and IS333

Journals:

  1. Nand, R., Chaudhary, K. and Sharma, B., 2024. Single Depot Multiple Travelling Salesman Problem Solved With Preference-Based Stepping Ahead Firefly Algorithm. IEEE Access12, pp.26655-26666.
  2. Nand, R., Reddy, E., Chaudhary, K. and Sharma, B., 2024. Preference Based Stepping ahead Firefly Algorithm for solving Real-World uncapacitated Examination Timetabling Problem. IEEE Access.
  3. Nand, R., Sharma, B. and Chaudhary, K., 2022. An introduction of preference based stepping ahead firefly algorithm for the uncapacitated examination timetabling. PeerJ Computer Science8, p.e1068.
  4. Nand, R., Sharma, B.N. and Chaudhary, K., 2021. Stepping ahead firefly algorithm and hybridization with evolution strategy for global optimization problems. Applied Soft Computing109, p.107517.
  5. Sharma, B., Nand, R., Naseem, M. and Reddy, E.V., 2020. Effectiveness of online presence in a blended higher learning environment in the Pacific. Studies in Higher Education, 45(8), pp.1547-1565.

Conferences:

  1. Nand, R., Chand, A. and Reddy, E., 2022, December. Comparative Analysis of Classification Techniques on Olympic Games Datasets. In 2022 IEEE Asia-Pacific Conference on Computer Science and Data Engineering (CSDE)(pp. 1-6). IEEE.
  2. Reddy, E. and Nand, R., 2022, December. A Comparative Analysis of Swarm Intelligent Algorithms. In 2022 IEEE Asia-Pacific Conference on Computer Science and Data Engineering (CSDE) (pp. 1-5). IEEE.
  3. Nand, Ravneil, “A Preference-based Stepping ahead Artificial Bee Colony Algorithm for Global Optimization” (2022). PACIS 2022 Proceedings. 138. https://aisel.aisnet.org/pacis2022/138
  4. Naseem, M., Reddy, E. and Nand, R., 2021, December. Statistical Methods for Data mining Mathematics students’ online presence. In 2021 IEEE Asia-Pacific Conference on Computer Science and Data Engineering (CSDE) (pp. 1-6). IEEE.
  5. Nand, R., Chand, A. and Reddy, E., 2021, October. Data Mining Students’ performance in a Higher Learning Environment. In 2021 3rd Novel Intelligent and Leading Emerging Sciences Conference (NILES) (pp. 241-245). IEEE.
  6. Aung, M.H., Seluka, P.T., Fuata, J.T.R., Tikoisuva, M.J., Cabealawa, M.S. and Nand, R., 2020, December. Random forest classifier for detecting credit card fraud based on performance metrics. In 2020 IEEE Asia-Pacific Conference on Computer Science and Data Engineering (CSDE) (pp. 1-6). IEEE.
  7. Nand, R., Chand, A. and Naseem, M., 2020, December. Analyzing students’ online presence in undergraduate courses using Clustering. In 2020 IEEE Asia-Pacific Conference on Computer Science and Data Engineering (CSDE) (pp. 1-6). IEEE.
  8. Nand, R., Chaudhary, K. and Sharma, B., 2020, July. Stepping ahead based hybridization of meta-heuristic model for solving Global Optimization Problems. In 2020 IEEE Congress on Evolutionary Computation (CEC) (pp. 1-8). IEEE.
  9. Nand, R. and Sharma, P., 2019. Iteration split with Firefly Algorithm and Genetic Algorithm to Solve Multidimensional Knapsack Problems. 2019 IEEE Asia-Pacific Conference on Computer Science and Data Engineering(CSDE), 1–7. Anais… IEEE.
  10. Chand, A. and Nand, R., 2019, December. Rainfall prediction using Artificial Neural network in the South Pacific region. In 2019 IEEE Asia-Pacific Conference on Computer Science and Data Engineering (CSDE) (pp. 1-7). IEEE.
  11. Nand, R. and Sharma, A., 2019, June. Meta-heuristic approaches to tackle skill based group allocation of students in project based learning courses. In 2019 IEEE Congress on Evolutionary Computation (CEC) (pp. 1782-1789). IEEE.
  12. Nand, R., Sharma, A. and Reddy, K., 2018, December. Skill-based group allocation of students for project-based learning courses using genetic algorithm: Weighted penalty model. In 2018 IEEE International Conference on Teaching, Assessment, and Learning for Engineering (TALE) (pp. 394-400). IEEE.
  13. Nand, R., Sharma, A. and Reddy, K., 2018, December. Skill-Based Group Allocation of Students for Project-Based Learning Courses Using Genetic Algorithm: Weightless Penalty Model. In 2018 IEEE International Conference on Teaching, Assessment, and Learning for Engineering (TALE) (pp. 431-437). IEEE.
  14. Sharma, B.N., Nand, R., Naseem, M., Reddy, E., Narayan, S.S. and Reddy, K., 2018, December. Smart learning in the Pacific: Design of new pedagogical tools. In 2018 IEEE International Conference on Teaching, Assessment, and Learning for Engineering (TALE) (pp. 573-580). IEEE.
  15. Nand, R., 2016, July. Neuron-synapse level problem decomposition method for cooperative coevolution of recurrent networks for time series prediction. In 2016 IEEE congress on evolutionary computation (CEC) (pp. 3102-3109). IEEE.
  16. Nand, R., Naseem, M., Reddy, E. and Sharma, B.N., 2017, December. Combinational problem decomposition method for Cooperative Coevolution of Recurrent Networks for Time Series Prediction. In 2017 4th Asia-Pacific World Congress on Computer Science and Engineering (APWC on CSE) (pp. 69-74). IEEE.
  17. Nand, R., 2016, July. Neuron-synapse level problem decomposition method for cooperative coevolution of recurrent networks for time series prediction. In 2016 IEEE congress on evolutionary computation (CEC) (pp. 3102-3109). IEEE.
  18. Nand, R. and Chandra, R., 2016. Coevolutionary feature selection and reconstruction in neuro-evolution for time series prediction. In Artificial Life and Computational Intelligence: Second Australasian Conference, ACALCI 2016, Canberra, ACT, Australia, February 2-5, 2016, Proceedings 2 (pp. 285-297). Springer International Publishing.
  19. Nand, R. and Chandra, R., 2016. Competitive island cooperative neuro-evolution of feedforward networks for time series prediction. In Artificial Life and Computational Intelligence: Second Australasian Conference, ACALCI 2016, Canberra, ACT, Australia, February 2-5, 2016, Proceedings 2 (pp. 160-170). Springer International Publishing.
  20. Nand, R., Reddy, E. and Naseem, M., 2016. Neuron-network level problem decomposition method for cooperative coevolution of recurrent networks for time series prediction. In Neural Information Processing: 23rd International Conference, ICONIP 2016, Kyoto, Japan, October 16–21, 2016, Proceedings, Part III 23 (pp. 38-48).
USP Chat Service
Lets start: