Tuong Le

2.2k citations

55 papers · 1.6k · h-index 23

Impact in

Information Systems top 1%
- Data Mining Algorithms and Applications
Computational Theory and Mathematics top 1%
- Rough Sets and Fuzzy Logic

Papers in

Information Systems 35
- Data Mining Algorithms and Applications 30
Artificial Intelligence 30
- Imbalanced Data Classification Techniques 20
- Algorithms and Data Compression 3

Co-authors: Bay Vo (22 shared papers)Sung Wook Baik (12 shared papers)Minh Thanh Vo (11 shared papers)Bay Vo (8 shared papers)Eenjun Hwang (2 shared papers)Seungmin Rho (2 shared papers)Mi Young Lee (4 shared papers)Tzung‐Pei Hong (6 shared papers)
Journals: Applied Intelligence (9 papers)IEEE Access (5 papers)Expert Systems with Applications (5 papers)Computers, materials & continua/Computers, materials & continua (Print) (3 papers)Symmetry (3 papers)
Partner nations: Vietnam South Korea Taiwan

In The Last Decade

Tuong Le

55 papers receiving 1.5k citations

Peers

Replace Suat Özdemi̇r with:

Suat Özdemi̇r Türkiye

Kuo‐Ching Ying Taiwan

Dae-Ki Kang South Korea

Magdalene Marinaki Greece

Korhan Cengiz Türkiye

Mohammad Bagher Dowlatshahi Iran

Piero P. Bonissone United States

S. Geetha India

Ömer Kaan Baykan Türkiye

Leilei Chang China

Tuong Le relative to Suat Özdemi̇r Türkiye Suat Özdemi̇r's profile →

Citations per field

00.5×2×3×4×4.5×

Suat Özdemi̇r · 1×

×1.6 611/383

IS

×4.5 383/85

CTM

×1.2 674/583

AI

×1.7 207/121

SP

×4.5 147/33

ACCOU

Citations per year

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Countries citing papers authored by Tuong Le

Since Specialization

Citations

This map shows the geographic impact of Tuong Le's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Tuong Le with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Tuong Le more than expected).

Fields of papers citing papers by Tuong Le

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Tuong Le. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Tuong Le. The network helps show where Tuong Le may publish in the future.

Co-authors

The 25 scholars most cited alongside Tuong Le, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.

Border = papers with Tuong Le Line = papers co-authored together Tuong Le links everyone, so they are left out of the graph.

All Works

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20 of 20 papers shown

Showing the 20 most-cited of 55 papers — load more, or switch the sort, to bring in the rest.

#	Work
1	Improving Electric Energy Consumption Prediction Using CNN and Bi-LSTM Applied Sciences ·Tuong Le, Minh Thanh Vo, Bay Vo, Eenjun Hwang, Seungmin Rho, Sung Wook Baik	2019	208
2	A Novel Framework for Trash Classification Using Deep Transfer Learning IEEE Access ·(unknown), (unknown), Minh Thanh Vo, Tuong Le	2019	147
3	A Cluster-Based Boosting Algorithm for Bankruptcy Prediction in a Highly Imbalanced Dataset Symmetry ·Tuong Le, Lê Hoàng Sơn, Minh Thanh Vo, Mi Young Lee, Sung Wook Baik	2018	71
4	Multiple Electric Energy Consumption Forecasting Using a Cluster-Based Strategy for Transfer Learning in Smart Building Sensors ·Tuong Le, Minh Thanh Vo, Tung Kieu, Eenjun Hwang, Seungmin Rho, Sung Wook Baik	2020	65
5	Mining frequent itemsets using the N-list and subsume concepts International Journal of Machine Learning and Cybernetics ·Bay Vo, Tuong Le, Frans Coenen, Tzung‐Pei Hong	2014	64
6	Oversampling Techniques for Bankruptcy Prediction: Novel Features from a Transaction Dataset Symmetry ·Tuong Le, Mi Young Lee, Jun Park, Sung Wook Baik	2018	63
7	A fast and accurate approach for bankruptcy forecasting using squared logistics loss with GPU-based extreme gradient boosting Information Sciences ·Tuong Le, Bay Vo, Hamido Fujita, Ngoc Thanh Nguyên, Sung Wook Baik	2019	60
8	A Hybrid Approach Using Oversampling Technique and Cost‐Sensitive Learning for Bankruptcy Prediction Complexity ·Tuong Le, Minh Thanh Vo, Bay Vo, Mi Young Lee, Sung Wook Baik	2019	60
9	An N-list-based algorithm for mining frequent closed patterns Expert Systems with Applications ·Tuong Le, Bay Vo	2015	48
10	Optimization strategies of neural networks for impact damage classification of RC panels in a small dataset Applied Soft Computing ·Quoc Hoan Doan, Tuong Le, Duc‐Kien Thai	2021	47
11	MEI: An efficient algorithm for mining erasable itemsets Engineering Applications of Artificial Intelligence ·Tuong Le, Bay Vo	2013	46
12	A novel approach for mining maximal frequent patterns Expert Systems with Applications ·Bay Vo, Sang Pham, Tuong Le, Zhi‐Hong Deng	2016	43
13	Crime rate detection using social media of different crime locations and Twitter part-of-speech tagger with Brown clustering Journal of Intelligent & Fuzzy Systems ·Thanh Vo, Rohit Sharma, Raghvendra Kumar, Lê Hoàng Sơn, Binh Thai Pham, Dieu Tien Bui, Ishaani Priyadarshini, Manash Sarkar, Tuong Le	2020	35
14	An efficient and effective algorithm for mining top-rank-k frequent patterns Expert Systems with Applications ·Quyen Huynh-Thi-Le, Tuong Le, Bay Vo, Bac Le	2014	35
15	An artificial neural network based approach to fault diagnosis and classification of fluid power systems Proceedings of the Institution of Mechanical Engineers Part I Journal of Systems and Control Engineering ·Tuong Le, J. Watton, Duc Truong Pham	1997	34
16	Brent Oil Price Prediction Using Bi-LSTM Network Intelligent Automation & Soft Computing ·Anh H. Vo, Trang Nguyen, Tuong Le	2020	32
17	Fault classification of fluid power systems using a dynamics feature extraction technique and neural networks Proceedings of the Institution of Mechanical Engineers Part I Journal of Systems and Control Engineering ·Tuong Le, J. Watton, Duc Truong Pham	1998	32
18	An Efficient Algorithm for Mining Erasable Itemsets Using the Difference of NC-Sets Tuong Le, Bay Vo, Frans Coenen	2013	31
19	Mining top- k co-occurrence items with sequential pattern Expert Systems with Applications ·Tung Kieu, Bay Vo, Tuong Le, Zhi‐Hong Deng, Bac Le	2017	29
20	Efficient algorithms for mining top-rank- k erasable patterns using pruning strategies and the subsume concept Engineering Applications of Artificial Intelligence ·Tuong Le, Bay Vo, Sung Wook Baik	2017	28

About Tuong Le

Tuong Le is a scholar working on Information Systems, Artificial Intelligence, Computational Theory and Mathematics, Signal Processing and Electrical and Electronic Engineering, having authored 55 papers that have together received 1.6k indexed citations. Recurring topics across this work include Data Mining Algorithms and Applications (30 papers), Rough Sets and Fuzzy Logic (26 papers), Imbalanced Data Classification Techniques (20 papers), Data Management and Algorithms (9 papers), Financial Distress and Bankruptcy Prediction (6 papers), Energy Load and Power Forecasting (4 papers), Algorithms and Data Compression (3 papers) and Air Quality Monitoring and Forecasting (3 papers). The work is most often cited by research in Information Systems (611 citations), Computational Theory and Mathematics (383 citations), Artificial Intelligence (674 citations), Signal Processing (207 citations) and Accounting (147 citations). Tuong Le has collaborated with scholars based in Vietnam, South Korea and Taiwan. Frequent co-authors include Bay Vo, Sung Wook Baik, Minh Thanh Vo, Bay Vo, Eenjun Hwang, Seungmin Rho, Mi Young Lee, Tzung‐Pei Hong, Frans Coenen and Bac Le. Their work appears in journals such as Applied Intelligence, IEEE Access, Expert Systems with Applications, Computers, materials & continua/Computers, materials & continua (Print) and Symmetry.

Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.

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