Ravikumar Penugonda

About

I am working as an Assistant Professor in computer science and engineering at the IIIT – RK Valley, Rajiv Gandhi University of Knowledge Technologies, Andhra Pradesh, India. Currently, pursuing a Ph.D. in Computer and information systems at the University of Aizu, Aizu Wakamatsu, Fukushima, Japan on a deputation basis. I have received my Master of Engineering degree in computer science from the Indian Institute of Science, Bangalore, Karnataka, India. I have received my bachelor’s degree in Computer science and engineering department at Sri Venkateshwara University College of Engineering. My current research interests include data mining, air pollution data analytics, traffic congestion data analytics, recommender systems, and time series classification. I have published several papers in reputed international conferences, such as IEEE International Conference on Fuzzy Systems (FUZZ-IEEE), IEEE International Conference on Big Data (IEEE BigData), IEEE Symposium on Computational Intelligence and Data Mining (CIDM), International Conference on Industrial, Engineering and Other Applications of Applied Intelligent Systems (IEA/AIE), International Conference on Soft Computing and Machine Intelligence (ISCMI).

Employment

• IIIT RK Valley, RGUKT - AP Assistant Professor
  2013 - Present

Education

Education history is unavailable.

Projects & Funding

Projects & funding information is unavailable.

Publications (15)

• Intelligent Tutoring in a Driving Simulator: Enhancing Driving Proficiency With AI-Driven Skill Assessment and Personalized Coaching Generation
  2026 DOI: 10.1145/3742413.3789060
  +Save
• 3P-ECLAT: mining partial periodic patterns in columnar temporal databases
  Applied Intelligence 2024 DOI: 10.1007/s10489-023-05172-5
  +Save
• A fundamental approach to discover closed periodic-frequent patterns in very large temporal databases
  Applied Intelligence 2023 DOI: 10.1007/s10489-023-04811-1
  +Save
• Finding Stable Periodic-Frequent Itemsets in Big Columnar Databases
  IEEE Access 2023 DOI: 10.1109/ACCESS.2023.3241313
  +Save
• k-PFPMiner: Top-k Periodic Frequent Patterns in Big Temporal Databases
  IEEE Access 2023 DOI: 10.1109/ACCESS.2023.3325839
  +Save
• Efficient Discovery of Partial Periodic Patterns in Large Temporal Databases
  Electronics 2022 DOI: 10.3390/electronics11101523
  +Save
• Discovering Top-k Periodic-Frequent Patterns in Very Large Temporal Databases
  2022 DOI: 10.1007/978-3-031-24094-2_14
  +Save
• Towards Efficient Discovery of Partial Periodic Patterns in Columnar Temporal Databases
  2022 DOI: 10.1007/978-3-031-21967-2_12
  +Save
• Towards Efficient Discovery of Stable Periodic Patterns in Big Columnar Temporal Databases
  2022 DOI: 10.1007/978-3-031-08530-7_70
  +Save
• Efficient Discovery of Periodic-Frequent Patterns in Columnar Temporal Databases
  Electronics 2021 DOI: 10.3390/electronics10121478
  +Save