Note: Due to very high competition and fast paced research in the field of computer science, any publication in a top-tier conference (i.e., CORE rank A* and A) were treated on par with any major journal publication. CORE 2010 ranking were employed to rank conferences.

Note: (i) Citation count was last updated on 2-Feb-2020. (ii) "SJR" stands for "Scientific Journal Rankings". A click on each rank will direct you to the respective SJR page.

S.No.	Publication	Citation count	Impact factor	SJR
27	P. Veena, R. Uday Kiran, P. Ravikumar, P. Likhitha, Yuto Hayamizu, Kazuo Goda, Masashi Toyoda, Koji Zettsu, Sourabh Shrivastava: A Fundamental Approach to Discover Closed Periodic-Frequent Patterns in Very Large Temporal Databases. To be appeared in the Journal of Applied Intelligence 2023	NA	5.524
26	Hong N. Dao, Penugonda Ravikumar, Palla Likhitha, Rage Uday Kiran, Yutaka Watanobe, Incheon Paik: Finding Stable Periodic-Frequent Itemsets in Big Columnar Databases. IEEE Access 11: 12504-12524 (2023)	NA	5.524
25	Phuc-Thinh Nguyen, Minh-Son Dao, Michael A. Riegler, Rage Uday Kiran, Thai-Thinh Dang, Duy-Dong Le, Kieu-Chinh Nguyen-Ly, Thanh-Qui Pham, Van-Luong Nguyen: Training Performance Indications for Amateur Athletes Based on Nutrition and Activity Lifelogs. Algorithms 16(1): 30 (2023)	NA	5.524
24	Rage Uday Kiran, Pamalla Veena, Penugonda Ravikumar, Bathala Venus Vikranth Raj, Minh-Son Dao, Koji Zettsu, Sai Chithra Bommisetti: HDSHUI-miner: a novel algorithm for discovering spatial high-utility itemsets in high-dimensional spatiotemporal databases Appl. Intell. 53(8): 8536-8561 (2023)	NA	5.524
23	José María Luna, Rage Uday Kiran, Philippe Fournier-Viger, Sebastián Ventura:Efficient mining of top-k high utility itemsets through genetic algorithms. Inf. Sci. 624: 529-553 (2023)	NA	5.524
22	Efficient Discovery of Partial Periodic Patterns in Large Temporal Databases. Kiran, R.U., Veena, P., Ravikumar, P., ...Kitsuregawa, M., Reddy, P.K. Electronics (Switzerland), 2022, 11(10), 1523	NA	5.524
21	Estimation of the Standardized Precipitation Evapotranspiration Index (SPEI) Using a Multilayer Perceptron Artificial Neural Network Model for Central India Shrivastava, S., Kiran, R.U., Bal, P.K., Singh, K.K.Pure and Applied Geophysics, 2022, 179(4), pp. 1461–1473	NA	5.524
20	Md. Mostafizer Rahman, Yutaka Watanobe, Taku Matsumoto, Rage Uday Kiran, Keita Nakamura:Educational Data Mining to Support Programming Learning Using Problem-Solving Data. IEEE Access 10: 26186-26202 (2022)	NA	5.524
19	Philippe Fournier-Viger, Ying Wang, Peng Yang, Jerry Chun-Wei Lin, Unil Yun, Rage Uday Kiran:TSPIN: mining top-k stable periodic patterns. Appl. Intell. 52(6): 6917-6938 (2022)	NA	5.524
18	Yutaka Watanobe, Md. Mostafizer Rahman, Taku Matsumoto, Rage Uday Kiran, Penugonda Ravikumar:Online Judge System: Requirements, Architecture, and Experiences. Int. J. Softw. Eng. Knowl. Eng. 32(6): 917-946 (2022)	NA	5.524
18	Efficient discovery of periodic-frequent patterns in columnar temporal databases. Ravikumar, P., Likhitha, P., Raj, B.V.V., ...Watanobe, Y., Zettsu, K. Electronics (Switzerland), 2021, 10(12), 1478	NA	5.524
17	Md. Mostafizer Rahman, Yutaka Watanobe, Rage Uday Kiran, Truong Cong Thang, Incheon Paik: Impact of Practical Skills on Academic Performance: A Data-Driven Analysis. IEEE Access 9: 139975-139993 (2021)	NA	5.524
16	Philippe Fournier-Viger, Peng Yang, Rage Uday Kiran, Sebastián Ventura, José María Luna: Mining local periodic patterns in a discrete sequence. Inf. Sci. 544: 519-548 (2021) [EI]	NA	5.524
15	R. Uday Kiran, P. P. C. Reddy, Koji Zettsu, Masashi Toyoda, Masaru Kitsuregawa, P. Krishna Reddy:Efficient Discovery of Weighted Frequent Neighborhood Itemsets in Very Large Spatiotemporal Databases. IEEE Access 8: 27584-27596 (2020) [EI]	NA	4.098
14	Philippe Fournier-Viger, Peng Yang, Zhitian Li, Jerry Chun-Wei Lin, Rage Uday Kiran: Discovering rare correlated periodic patterns in multiple sequences. Data Knowl. Eng. 126: 101733 (2020) [EI]	NA	1.5
13	Philippe Fournier-Viger, Jiaxuan Li, Jerry Chun-Wei Lin, Tin Truong-Chi, R. Uday Kiran:Mining cost-effective patterns in event logs. Knowl. Based Syst. 191: 105241 (2020) [EI]	NA	5.921
12	Philippe Fournier-Viger, Zhitian Li, Jerry Chun-Wei Lin, Rage Uday Kiran, Hamido Fujita:Efficient algorithms to identify periodic patterns in multiple sequences. Inf. Sci. 489: 205-226 (2019) [EI]	NA	5.524
11	R. Uday Kiran, Alampally Anirudh, Chennupati Saideep, Masashi Toyoda, P. Krishna Reddy, Masaru Kitsuregawa: Finding Periodic-Frequent Patterns in Temporal Databases using Periodic Summaries. Data Science and Pattern Recognition, Vol. 3, No. 2, pp. 24-46 (2019)	NA	7.69	-
10	P Fournier-Viger, Peng Yang, Zhitian Li, JCW Lin, R. Uday Kiran: Discovering Rare Correlated Periodic Patterns in Multiple Sequences. To be appeared in the journal of Data and Knowledge Engineering	NA	1.467
9	P Fournier-Viger, JCW Lin, R. Uday Kiran, YS Koh, R Thomas: A survey of sequential pattern mining. Data Science and Pattern Recognition 1 (1), 54-77 (2018)	102	7.69	-
8	J. N. Venkatesh, R. Uday Kiran, P. Krishna Reddy, Masaru Kitsuregawa:Discovering Periodic-Correlated Patterns in Temporal Databases. T. Large-Scale Data- and Knowledge-Centered Systems 38: 146-172 (2018) [EI]	1	NA	-
7	R. Uday Kiran, J. N. Venkatesh, Masashi Toyoda, Masaru Kitsuregawa, P. Krishna Reddy:Discovering partial periodic-frequent patterns in a transactional database. Journal of Systems and Software 125: 170-182 (2017) [EI]	9	2.28
6	R. Uday Kiran, Masaru Kitsuregawa, P. Krishna Reddy:Efficient discovery of periodic-frequent patterns in very large databases. Journal of Systems and Software 112: 110-121 (2016) [EI]	22	2.28
5	R. Uday Kiran, Masaru Kitsuregawa: Efficient discovery of correlated patterns using multiple minimum all-confidence thresholds. J. Intell. Inf. Syst. 45(3): 357-377 (2015) [EI]	5	1.107
4	P. Gowtham Srinivas, P. Krishna Reddy, A. V. Trinath, Bhargav Sripada, R. Uday Kiran(:Mining coverage patterns from transactional databases. J. Intell. Inf. Syst. 45(3): 423-439 (2015) [EI]	13	1.107
3	Wei Guo, R. Uday Kiran, and Sesishi Ninomiya: Illumination invariant segmentation of vegetation for time series wheat images based on decision tree model, Journal of Computers and Electronics in Agriculture, Vol. 96, pp. 58-66, 2013. [EI]	96	2.427
2	M.Kumara Swamy, P. Krishna Reddy, R. Uday Kiran, and M. Venugopal Reddy: Temporality-based User Interface Design Approaches for Desktop and Small Screens. Appeared in International Journal of Computational Science and Engineering (IJCSE), Vol.7, No.1, 2012. [EI]	5	NA	-
1	R. Uday Kiran, P. Krishna Reddy, Mittapally Kumara Swamy, G. Syamasundar Reddy:Analysing dynamics of crop problems by applying text analysis methods on farm advisory data of eSaguTM. IJCSE 5(2): 154-164 (2010) [EI]	3	NA	-

Introduction: Customers purchase items in a supermarket (see Fig. 1a). This purchase data represents big sales data (see Fig. 1b). Useful information that can facilitate the supermarket owners to gain competitive advantage lies hidden in the sales data. Our research investigates novel pattern mining techniques (see Fig. 1c) to uncover customers' purchase patterns (see Fig. 1d) in sales data. The generated information can be beneficial to the managers for various purposes, such as product placements, inventory management, and introduction of new schemes.

Research: Our research aims to discover various types of patterns hidden in sales data. Some of the interesting patterns that we try to discover in sales data are as follows:
(i) Frequent patterns: itemsets that were frequently purchased by the customers
(ii) Rare patterns: Expensive items that were infrequently purchased by the customers
(iii) Periodic-Frequent patterns: itemsets that were regularly purchased by the customers
(iv) )High utility patterns: itemsets that have generated high revenue for the manager

Real-world application: We have applied our models on the Yahoo! Japan retail data and discovered useful information.
(i) We have discovered that many customers were simultaneously purchasing games of Nintendo3Ds and PlayStation. This information is interesting because it disproves the general assumption that people who purchase Nintendo3Ds games may not purchase Playstation games at the same time.
(ii) Many people were purchasing the items, WhitewhineSet and RedWhineSet, in the morning.

Publications and Patents:
(i) R. Uday Kiran, T. Yashwanth Reddy, Philippe Fournier-Viger, Masashi Toyoda, P. Krishna Reddy, Masaru Kitsuregawa: Efficiently Finding High Utility-Frequent Itemsets Using Cutoff and Suffix Utility. PAKDD (2) 2019: 191-203
(ii) R. Uday Kiran, Pradeep Pallikila, José María Luna, Philippe Fournier-Viger, Masashi Toyoda, P. Krishna Reddy: Discovering Relative High Utility Itemsets in Very Large Transactional Databases Using Null-Invariant Measure. IEEE BigData 2021: 252-262

Introduction: Disasters, such as earthquakes and typhoons, are very common in Japan. Recommending proper traffic routes by identifying future congestion patterns is a crucial task to save human life and enhance drivers' experience in autonomous driving. Our research (see Figure 1) primarily aims to develop an explainable AI model to predict future traffic congestion patterns in transportation networks.

Research: Our research aims to discover different types of interesting patterns found in congestion data:
(i) Development of explainable traffic congestion prediction system.
(ii) Discovering the neighboring road segements in which people have regularly faced traffic congestion.
(iii) Finding high congested road segements.
(iv) Designing a data warehouse to store large scale traffic congestion data.

Publications and Patents:
(i) R. Uday Kiran, Sourabh Shrivastava, Philippe Fournier-Viger, Koji Zettsu, Masashi Toyoda, Masaru Kitsuregawa: Discovering Frequent Spatial Patterns in Very Large Spatiotemporal Databases. SIGSPATIAL/GIS 2020: 445-448
(ii) R. Uday Kiran, Sadanori Ito, Minh-Son Dao, Koji Zettsu, Cheng-Wei Wu, Yutaka Watanobe, Incheon Paik, Truong Cong Thang: Distributed Mining of Spatial High Utility Itemsets in Very Large Spatiotemporal Databases using Spark In-Memory Computing Architecture. IEEE BigData 2020: 4724-4733
(iii) R. Uday Kiran, Palla Likhitha, Minh-Son Dao, Koji Zettsu, Ji Zhang: Discovering Periodic-Frequent Patterns in Uncertain Temporal Databases. ICONIP (5) 2021: 710-718

Note: This is a joint collaborative work with NICT X-Data platform.

Introduction: Air pollution is a major cause of cardio-respiratory problems for people living in Japan. To tackle this problem, the Japanese Ministry of Environment has set up the Atmospheric Environmental Regional Observation System (AEROS) constituting of several air pollution measuring sensors (or stations) positioned throughout the Japan. The spatial locations of these sensors is shown in Fig. 1a. The data generated by this sensor network is shown in Fig. 1b. Useful information that can facilitate the ecologists to come up with location-specific pollution control policies lies hidden in this data. Our research aims to transform this big data into a spatiotemporal database (see Fig. 1c) using ETL techniques, apply pattern mining techniques (see Fig. 1c), discover the high polluted areas (see Fig. 1d), and visualize them (see Fig. 1e).

Publications and Patents:
(i) Pamalla Veena, Penugonda Ravikumar, Kundai Kwangwari, R. Uday Kiran, Kazuo Goda, Yutaka Watanobe, Koji Zettsu: Discovering Fuzzy Geo-referenced Periodic-Frequent Patterns in Geo-referenced Time Series Databases. FUZZ-IEEE 2022: 1-8
(ii) Pamalla Veena, Sai Chithra Bommisetty, R. Uday Kiran, Sonali Agarwal, Koji Zettsu: Discovering Fuzzy Frequent Spatial Patterns in Large Quantitative Spatiotemporal databases. FUZZ-IEEE 2021: 1-8
(iii) R. Uday Kiran, Palla Likhitha, Minh-Son Dao, Koji Zettsu, Ji Zhang: Discovering Periodic-Frequent Patterns in Uncertain Temporal Databases. ICONIP (5) 2021: 710-718

Grants