R. Uday Kiran

Michael Alexander Riegler

Appl. Intell., January, 2024

PAMI: An Open-Source Python Library for Pattern Mining.

[BibT_eX]

[DOI]

J. Mach. Learn. Res., 2024

ICDAR 24: Intelligent Cross-Data Analysis and Retrieval.

[BibT_eX]

[DOI]

Minh-Son Dao

Proceedings of the 2024 International Conference on Multimedia Retrieval, 2024

A Novel Multi-task Single-Step Traffic Congestion Forecasting Framework for Large-Scale Road Networks.

[BibT_eX]

[DOI]

Kazuki Tejima

Deepika Saxena

Proceedings of the Advances and Trends in Artificial Intelligence. Theory and Applications, 2024

Towards Addressing an Open Problem in Coupled Matrix Tensor Factorization for Satellite Imagery Data Using Human-in-Loop.

[BibT_eX]

[DOI]

Proceedings of the Advances and Trends in Artificial Intelligence. Theory and Applications, 2024

A Novel Multi-scale Spatiotemporal Graph Neural Network for Epidemic Prediction.

[BibT_eX]

[DOI]

Proceedings of the Database and Expert Systems Applications, 2024

2023

A fundamental approach to discover closed periodic-frequent patterns in very large temporal databases.

[BibT_eX]

[DOI]

Appl. Intell., November, 2023

Efficient mining of top-k high utility itemsets through genetic algorithms.

[BibT_eX]

[DOI]

José María Luna

Sebastián Ventura

Inf. Sci., May, 2023

HDSHUI-miner: a novel algorithm for discovering spatial high-utility itemsets in high-dimensional spatiotemporal databases.

[BibT_eX]

[DOI]

Pamalla Veena

Minh-Son Dao

Sai Chithra Bommisetti

Appl. Intell., April, 2023

Training Performance Indications for Amateur Athletes Based on Nutrition and Activity Lifelogs.

[BibT_eX]

[DOI]

Algorithms, January, 2023

Deep Learning-Based Spatiotemporal Fusion of Unmanned Aerial Vehicle and Satellite Reflectance Images for Crop Monitoring.

[BibT_eX]

[DOI]

Juan Xiao

Ashwani Kumar Aggarwal

Vaibhav Katiyar

Ram Avtar

IEEE Access, 2023

Mining Periodic-Frequent Patterns in Irregular Dense Temporal Databases Using Set Complements.

[BibT_eX]

[DOI]

IEEE Access, 2023

k-PFPMiner: Top-k Periodic Frequent Patterns in Big Temporal Databases.

[BibT_eX]

[DOI]

IEEE Access, 2023

Finding Stable Periodic-Frequent Itemsets in Big Columnar Databases.

[BibT_eX]

[DOI]

IEEE Access, 2023

Towards Efficient Discovery of Spatially Interesting Patterns in Geo-referenced Sequential Databases.

[BibT_eX]

[DOI]

Shota Suzuki

Proceedings of the 35th International Conference on Scientific and Statistical Database Management, 2023

Discovering Geo-referenced Frequent Patterns in Uncertain Geo-referenced Transactional Databases.

[BibT_eX]

[DOI]

Proceedings of the Advances in Knowledge Discovery and Data Mining, 2023

A Novel Explainable Link Forecasting Framework for Temporal Knowledge Graphs Using Time-Relaxed Cyclic and Acyclic Rules.

[BibT_eX]

[DOI]

Abinash Maharana

Proceedings of the Advances in Knowledge Discovery and Data Mining, 2023

A Novel Parallel Spatiotemporal Image Fusion Method for Predicting High-Resolution Satellite Images.

[BibT_eX]

[DOI]

Ram Avtar

Yoshiko Ogawa

Makiko Ohtake

Proceedings of the Advances and Trends in Artificial Intelligence. Theory and Applications, 2023

Efficient Parallel Mining of High-utility Itemsets on Multicore Processors.

[BibT_eX]

[DOI]

Proceedings of the 39th IEEE International Conference on Data Engineering, 2023

Discovering Fuzzy Partial Periodic Patterns in Quantitative Irregular Multiple Time Series.

[BibT_eX]

[DOI]

Proceedings of the IEEE International Conference on Fuzzy Systems, 2023

Discovering Top-K Partial Periodic Patterns in Big Temporal Databases.

[BibT_eX]

[DOI]

Palla Likhitha

Proceedings of the Database and Expert Systems Applications, 2023

2022

Online Judge System: Requirements, Architecture, and Experiences.

[BibT_eX]

[DOI]

Taku Matsumoto

Int. J. Softw. Eng. Knowl. Eng., 2022

TSPIN: mining top-k stable periodic patterns.

[BibT_eX]

[DOI]

Appl. Intell., 2022

Educational Data Mining to Support Programming Learning Using Problem-Solving Data.

[BibT_eX]

[DOI]

IEEE Access, 2022

Towards QoE Management for Post-Pandemic Online Learning : Invited Paper.

[BibT_eX]

[DOI]

Proceedings of the 14th International Conference on Knowledge and Systems Engineering, 2022

Towards Efficient Discovery of Stable Periodic Patterns in Big Columnar Temporal Databases.

[BibT_eX]

[DOI]

Hong N. Dao

Palla Likhitha

Incheon Paik

Proceedings of the Advances and Trends in Artificial Intelligence. Theory and Practices in Artificial Intelligence, 2022

A Spatiotemporal Image Fusion Method for Predicting High-Resolution Satellite Images.

[BibT_eX]

[DOI]

Proceedings of the Advances and Trends in Artificial Intelligence. Theory and Practices in Artificial Intelligence, 2022

UPFP-growth++: An Efficient Algorithm to Find Periodic-Frequent Patterns in Uncertain Temporal Databases.

[BibT_eX]

[DOI]

Proceedings of the Neural Information Processing - 29th International Conference, 2022

Towards developing energy efficient algorithms to discover partial periodic patterns in big temporal databases.

[BibT_eX]

[DOI]

Palla Likhitha

Proceedings of the 30th International Conference on Advances in Geographic Information Systems, 2022

Discovering Fuzzy Geo-referenced Periodic-Frequent Patterns in Geo-referenced Time Series Databases.

[BibT_eX]

[DOI]

Proceedings of the IEEE International Conference on Fuzzy Systems, 2022

Discovering Geo-referenced Periodic-Frequent Patterns in Geo-referenced Time Series Databases.

[BibT_eX]

[DOI]

Proceedings of the 9th IEEE International Conference on Data Science and Advanced Analytics, 2022

Towards Efficient Discovery of Periodic-Frequent Patterns in Dense Temporal Databases Using Complements.

[BibT_eX]

[DOI]

Proceedings of the Database and Expert Systems Applications, 2022

Effective and Robust Boundary-Based Outlier Detection Using Generative Adversarial Networks.

[BibT_eX]

[DOI]

Proceedings of the Database and Expert Systems Applications, 2022

A Novel Null-Invariant Temporal Measure to Discover Partial Periodic Patterns in Non-uniform Temporal Databases.

[BibT_eX]

[DOI]

Proceedings of the Database Systems for Advanced Applications, 2022

A Novel GPU-Accelerated Algorithm to Discover Periodic-Frequent Patterns in Temporal Databases.

[BibT_eX]

[DOI]

Proceedings of the IEEE International Conference on Big Data, 2022

Discovering Top-k Periodic-Frequent Patterns in Very Large Temporal Databases.

[BibT_eX]

[DOI]

Proceedings of the Big Data Analytics - 10th International Conference, 2022

Towards Efficient Discovery of Partial Periodic Patterns in Columnar Temporal Databases.

[BibT_eX]

[DOI]

Proceedings of the Intelligent Information and Database Systems - 14th Asian Conference, 2022

2021

Mining local periodic patterns in a discrete sequence.

[BibT_eX]

[DOI]

Inf. Sci., 2021

Impact of Practical Skills on Academic Performance: A Data-Driven Analysis.

[BibT_eX]

[DOI]

IEEE Access, 2021

Challenges and Exit Strategies for Adapting Interactive Online Education Amid the Pandemic and its Aftermath.

[BibT_eX]

[DOI]

Proceedings of the 2021 IEEE International Conference on Engineering, 2021

A Stacked Bidirectional LSTM Model for Classifying Source Codes Built in MPLs.

[BibT_eX]

[DOI]

Raihan Kabir

Proceedings of the Machine Learning and Principles and Practice of Knowledge Discovery in Databases, 2021

Learning Probabilistic Latent Structure for Outlier Detection from Multi-view Data.

[BibT_eX]

[DOI]

Proceedings of the Advances in Knowledge Discovery and Data Mining, 2021

Discovering Knowledge Hidden in Raster Images using RasterMiner.

[BibT_eX]

[DOI]

Proceedings of the ICDAR@ICMR 2021: Proceedings of the 2021 Workshop on Intelligent Cross-Data Analysis and Retrieval, 2021

A Unified Framework to Discover Partial Periodic-Frequent Patterns in Row and Columnar Temporal Databases.

[BibT_eX]

[DOI]

Proceedings of the 2021 International Conference on Data Mining, 2021

Fruit classification using deep feature maps in the presence of deceptive similar classes.

[BibT_eX]

[DOI]

Mohit Dandekar

Narinder Singh Punn

Sanjay Kumar Sonbhadra

Sonali Agarwal

Proceedings of the International Joint Conference on Neural Networks, 2021

Online Automatic Assessment System for Program Code: Architecture and Experiences.

[BibT_eX]

[DOI]

Proceedings of the Advances and Trends in Artificial Intelligence. From Theory to Practice, 2021

Towards Efficient Discovery of Periodic-Frequent Patterns in Columnar Temporal Databases.

[BibT_eX]

[DOI]

Proceedings of the Advances and Trends in Artificial Intelligence. Artificial Intelligence Practices, 2021

A Novel Rule-Based Online Judge Recommender System to Promote Computer Programming Education.

[BibT_eX]

[DOI]

Keita Nakamura

Proceedings of the Advances and Trends in Artificial Intelligence. From Theory to Practice, 2021

IMAGE-2-AQI: Aware of the Surrounding Air Qualification by a Few Images.

[BibT_eX]

[DOI]

Minh-Son Dao

Proceedings of the Advances and Trends in Artificial Intelligence. From Theory to Practice, 2021

Discovering Spatial High Utility Itemsets in High-Dimensional Spatiotemporal Databases.

[BibT_eX]

[DOI]

Sai Chithra Bommisetty

Proceedings of the Advances and Trends in Artificial Intelligence. Artificial Intelligence Practices, 2021

Discovering Periodic-Frequent Patterns in Uncertain Temporal Databases.

[BibT_eX]

[DOI]

Proceedings of the Neural Information Processing - 28th International Conference, 2021

Discovering Fuzzy Frequent Spatial Patterns in Large Quantitative Spatiotemporal databases.

[BibT_eX]

[DOI]

Pamalla Veena

Sai Chithra Bommisetty

Sonali Agarwal

Proceedings of the 30th IEEE International Conference on Fuzzy Systems, 2021

A Novel Parameter-Free Energy Efficient Fuzzy Nearest Neighbor Classifier for Time Series Data.

[BibT_eX]

[DOI]

Proceedings of the 30th IEEE International Conference on Fuzzy Systems, 2021

Efficient Discovery of Partial Periodic-Frequent Patterns in Temporal Databases.

[BibT_eX]

[DOI]

Proceedings of the Database and Expert Systems Applications, 2021

Discovering Top-k Spatial High Utility Itemsets in Very Large Quantitative Spatiotemporal databases.

[BibT_eX]

[DOI]

Proceedings of the 2021 IEEE International Conference on Big Data (Big Data), 2021

Discovering Maximal Partial Periodic Patterns in Very Large Temporal Databases.

[BibT_eX]

[DOI]

Proceedings of the 2021 IEEE International Conference on Big Data (Big Data), 2021

Improving the Awareness of Sustainable Smart Cities by Analyzing Lifelog Images and IoT Air Pollution Data.

[BibT_eX]

[DOI]

Proceedings of the 2021 IEEE International Conference on Big Data (Big Data), 2021

Discovering Relative High Utility Itemsets in Very Large Transactional Databases Using Null-Invariant Measure.

[BibT_eX]

[DOI]

Pradeep Pallikila

José María Luna

Proceedings of the 2021 IEEE International Conference on Big Data (Big Data), 2021

2020

Mining cost-effective patterns in event logs.

[BibT_eX]

[DOI]

Knowl. Based Syst., 2020

Discovering rare correlated periodic patterns in multiple sequences.

[BibT_eX]

[DOI]

Data Knowl. Eng., 2020

Efficient Discovery of Weighted Frequent Neighborhood Itemsets in Very Large Spatiotemporal Databases.

[BibT_eX]

[DOI]

IEEE Access, 2020

Parallel Mining of Partial Periodic Itemsets in Big Data.

[BibT_eX]

[DOI]

Proceedings of the Trends in Artificial Intelligence Theory and Applications. Artificial Intelligence Practices, 2020

Insights From Urban Sensing Data: From Chaos to Predicted Congestion Patterns.

[BibT_eX]

[DOI]

Proceedings of the 20th International Conference on Data Mining Workshops, 2020

Discovering Frequent Spatial Patterns in Very Large Spatiotemporal Databases.

[BibT_eX]

[DOI]

Sourabh Shrivastava

Proceedings of the SIGSPATIAL '20: 28th International Conference on Advances in Geographic Information Systems, 2020

Discovering Fuzzy Periodic-Frequent Patterns in Quantitative Temporal Databases.

[BibT_eX]

[DOI]

Proceedings of the 29th IEEE International Conference on Fuzzy Systems, 2020

Discovering Maximal Periodic-Frequent Patterns in Very Large Temporal Databases.

[BibT_eX]

[DOI]

Proceedings of the 7th IEEE International Conference on Data Science and Advanced Analytics, 2020

Discovering Closed Periodic-Frequent Patterns in Very Large Temporal Databases.

[BibT_eX]

[DOI]

Proceedings of the 2020 IEEE International Conference on Big Data (IEEE BigData 2020), 2020

Distributed Mining of Spatial High Utility Itemsets in Very Large Spatiotemporal Databases using Spark In-Memory Computing Architecture.

[BibT_eX]

[DOI]

Proceedings of the 2020 IEEE International Conference on Big Data (IEEE BigData 2020), 2020

Fusion-3DCNN-max3P: A dynamic system for discovering patterns of predicted congestion.

[BibT_eX]

[DOI]

Proceedings of the 2020 IEEE International Conference on Big Data (IEEE BigData 2020), 2020

2019

Efficient algorithms to identify periodic patterns in multiple sequences.

[BibT_eX]

[DOI]

Inf. Sci., 2019

Discovering Spatial High Utility Itemsets in Spatiotemporal Databases.

[BibT_eX]

[DOI]

Proceedings of the 31st International Conference on Scientific and Statistical Database Management, 2019

Efficiently Finding High Utility-Frequent Itemsets Using Cutoff and Suffix Utility.

[BibT_eX]

[DOI]

T. Yashwanth Reddy

Proceedings of the Advances in Knowledge Discovery and Data Mining, 2019

Discovering Stable Periodic-Frequent Patterns in Transactional Data.

[BibT_eX]

[DOI]

Peng Yang

Jerry Chun-Wei Lin

Proceedings of the Advances and Trends in Artificial Intelligence. From Theory to Practice, 2019

Discovering Periodic Patterns in Irregular Time Series.

[BibT_eX]

[DOI]

C. Saideep

Proceedings of the 2019 International Conference on Data Mining Workshops, 2019

Discovering Spatial Weighted Frequent Itemsets in Spatiotemporal Databases.

[BibT_eX]

[DOI]

Proceedings of the 2019 International Conference on Data Mining Workshops, 2019

Discovering Partial Periodic High Utility Itemsets in Temporal Databases.

[BibT_eX]

[DOI]

Proceedings of the Database and Expert Systems Applications, 2019

Discovering Partial Periodic Spatial Patterns in Spatiotemporal Databases.

[BibT_eX]

[DOI]

Proceedings of the 2019 IEEE International Conference on Big Data (IEEE BigData), 2019

Discovering Spatial High Utility Frequent Itemsets in Spatiotemporal Databases.

[BibT_eX]

[DOI]

Proceedings of the Big Data Analytics - 7th International Conference, 2019

TKG: Efficient Mining of Top-K Frequent Subgraphs.

[BibT_eX]

[DOI]

Proceedings of the Big Data Analytics - 7th International Conference, 2019

2018

Discovering Periodic-Correlated Patterns in Temporal Databases.

[BibT_eX]

[DOI]

Trans. Large Scale Data Knowl. Centered Syst., 2018

A Story Coherence based Neural Network Model for Predicting Story Ending.

[BibT_eX]

[DOI]

Proceedings of the Companion of the The Web Conference 2018 on The Web Conference 2018, 2018

Novel Data Segmentation Techniques for Efficient Discovery of Correlated Patterns Using Parallel Algorithms.

[BibT_eX]

[DOI]

Proceedings of the Big Data Analytics and Knowledge Discovery, 2018

Discovering Periodic Patterns Common to Multiple Sequences.

[BibT_eX]

[DOI]

Proceedings of the Big Data Analytics and Knowledge Discovery, 2018

Efficient Discovery of Weighted Frequent Itemsets in Very Large Transactional Databases: A Re-visit.

[BibT_eX]

[DOI]

Proceedings of the IEEE International Conference on Big Data (IEEE BigData 2018), 2018

2017

Discovering partial periodic-frequent patterns in a transactional database.

[BibT_eX]

[DOI]

J. Syst. Softw., 2017

Discovering Partial Periodic Itemsets in Temporal Databases.

[BibT_eX]

[DOI]

Proceedings of the 29th International Conference on Scientific and Statistical Database Management, 2017

Discovering Periodic Patterns in Non-uniform Temporal Databases.

[BibT_eX]

[DOI]

J. N. Venkatesh

Proceedings of the Advances in Knowledge Discovery and Data Mining, 2017

An Efficient Map-Reduce Framework to Mine Periodic Frequent Patterns.

[BibT_eX]

[DOI]

Proceedings of the Big Data Analytics and Knowledge Discovery, 2017

2016

Efficient discovery of periodic-frequent patterns in very large databases.

[BibT_eX]

[DOI]

J. Syst. Softw., 2016

Memory efficient mining of periodic-frequent patterns in transactional databases.

[BibT_eX]

[DOI]

Proceedings of the 2016 IEEE Symposium Series on Computational Intelligence, 2016

Discovering Periodic-Frequent Patterns in Transactional Databases Using All-Confidence and Periodic-All-Confidence.

[BibT_eX]

[DOI]

Proceedings of the Database and Expert Systems Applications, 2016

2015

Mining coverage patterns from transactional databases.

[BibT_eX]

[DOI]

J. Intell. Inf. Syst., 2015

Efficient discovery of correlated patterns using multiple minimum all-confidence thresholds.

[BibT_eX]

[DOI]

J. Intell. Inf. Syst., 2015

Discovering Recurring Patterns in Time Series.

[BibT_eX]

[DOI]

Proceedings of the 18th International Conference on Extending Database Technology, 2015

Discovering Chronic-Frequent Patterns in Transactional Databases.

[BibT_eX]

[DOI]

Proceedings of the Databases in Networked Information Systems, 2015

Towards Scale-out Capability on Social Graphs.

[BibT_eX]

[DOI]

Proceedings of the 24th ACM International Conference on Information and Knowledge Management, 2015

Finding Periodic Patterns in Big Data.

[BibT_eX]

[DOI]

Proceedings of the Big Data Analytics - 4th International Conference, 2015

2014

Novel Techniques to Reduce Search Space in Periodic-Frequent Pattern Mining.

[BibT_eX]

[DOI]

Proceedings of the Database Systems for Advanced Applications, 2014

2013

Towards efficient discovery of coverage patterns in transactional databases.

[BibT_eX]

[DOI]

Proceedings of the Conference on Scientific and Statistical Database Management, 2013

Mining Correlated Patterns with Multiple Minimum All-Confidence Thresholds.

[BibT_eX]

[DOI]

Proceedings of the Trends and Applications in Knowledge Discovery and Data Mining, 2013

Towards Addressing the Coverage Problem in Association Rule-Based Recommender Systems.

[BibT_eX]

[DOI]

Proceedings of the Database and Expert Systems Applications, 2013

Discovering Quasi-Periodic-Frequent Patterns in Transactional Databases.

[BibT_eX]

[DOI]

Proceedings of the Big Data Analytics - Second International Conference, 2013

An Improved Neighborhood-Restricted Association Rule-based Recommender System.

[BibT_eX]

[DOI]

Proceedings of the Twenty-Fourth Australasian Database Conference, 2013

2012

Temporality-based user interface design approaches for desktop and small screen environment.

[BibT_eX]

[DOI]

Mittapally Kumara Swamy

M. Venugopal Reddy

Int. J. Comput. Sci. Eng., 2012

Discovering Coverage Patterns for Banner Advertisement Placement.

[BibT_eX]

[DOI]

Proceedings of the Advances in Knowledge Discovery and Data Mining, 2012

Efficient Discovery of Correlated Patterns in Transactional Databases Using Items' Support Intervals.

[BibT_eX]

[DOI]

Proceedings of the Database and Expert Systems Applications, 2012

A robust neural network classifier to model the compressive strength of high performance concrete using feature subset selection.

[BibT_eX]

[DOI]

M. Venu

R. Kiranmai

Proceedings of the 5th ACM COMPUTE Conference: Intelligent & scalable system technologies, 2012

Towards Efficient Discovery of Frequent Patterns with Relative Support.

[BibT_eX]

[DOI]

Proceedings of the 18th International Conference on Management of Data, 2012

2011

Coverage patterns for efficient banner advertisement placement.

[BibT_eX]

[DOI]

Bhargav Sripada

Proceedings of the 20th International Conference on World Wide Web, 2011

An Efficient Approach to Mine Periodic-Frequent Patterns in Transactional Databases.

[BibT_eX]

[DOI]

Akshat Surana

Proceedings of the New Frontiers in Applied Data Mining, 2011

Novel techniques to reduce search space in multiple minimum supports-based frequent pattern mining algorithms.

[BibT_eX]

[DOI]

Proceedings of the EDBT 2011, 2011

Improving the Performance of Recommender System by Exploiting the Categories of Products.

[BibT_eX]

[DOI]

Mohak Sharma

Thirumalaisamy Ragunathan

Proceedings of the Databases in Networked Information Systems - 7th International Workshop, 2011

An Alternative Interestingness Measure for Mining Periodic-Frequent Patterns.

[BibT_eX]

[DOI]

Proceedings of the Database Systems for Advanced Applications, 2011

Discovering Diverse-Frequent Patterns in Transactional Databases.

[BibT_eX]

[DOI]

Somya Srivastava

Proceedings of the 17th International Conference on Management of Data, 2011

2010

Analysing dynamics of crop problems by applying text analysis methods on farm advisory data of eSaguTM.

[BibT_eX]

[DOI]

Mittapally Kumara Swamy

G. Syamasundar Reddy

Int. J. Comput. Sci. Eng., 2010

Interface Tailoring by Exploiting Temporality of Attributes for Small Screens.

[BibT_eX]

[DOI]

Mittapally Kumara Swamy

M. Venugopal Reddy

Proceedings of the Databases in Networked Information Systems, 6th International Workshop, 2010

Towards Efficient Mining of Periodic-Frequent Patterns in Transactional Databases.

[BibT_eX]

[DOI]

Proceedings of the Database and Expert Systems Applications, 21th International Conference, 2010

Mining Rare Association Rules in the Datasets with Widely Varying Items' Frequencies.

[BibT_eX]

[DOI]

Proceedings of the Database Systems for Advanced Applications, 2010

Mining periodic-frequent patterns with maximum items' support constraints.

[BibT_eX]

[DOI]

Proceedings of the 3rd Bangalore Annual Compute Conference, Compute 2010, 2010

Selecting a Right Interestingness Measure for Rare Association Rules.

[BibT_eX]

[DOI]

Akshat Surana

Proceedings of the 16th International Conference on Management of Data, 2010

An Efficient Approach to Mine Rare Association Rules Using Maximum Items' Support Constraints.

[BibT_eX]

[DOI]

Proceedings of the Data Security and Security Data, 2010

2009

An Improved Frequent Pattern-growth Approach to Discover Rare Association Rules.

[BibT_eX]

Proceedings of the KDIR 2009 - Proceedings of the International Conference on Knowledge Discovery and Information Retrieval, Funchal, 2009

Mining Rare Periodic-Frequent Patterns Using Multiple Minimum Supports.

[BibT_eX]

Proceedings of the 15th International Conference on Management of Data, 2009

An improved multiple minimum support based approach to mine rare association rules.

[BibT_eX]

[DOI]

Proceedings of the IEEE Symposium on Computational Intelligence and Data Mining, 2009

2007

Understanding the Dynamics of Crop Problems by Analyzing Farm Advisory Data in eSagu TM .

[BibT_eX]

[DOI]