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ORCIDYing Xie 0001
Person information
- affiliation: Kennesaw State University, Department of Information Technology, Kennesaw, GA, USA
- affiliation: University of Louisiana at Lafayette, Center for Advanced Computer studies, Lafayette, LA, USA
Other persons with the same name
- Ying Xie — disambiguation page
- Ying Xie 0002
![[0000-0002-6419-3986]](https://dblp.dagstuhl.de/img/orcid-mark.12x12.png "0000-0002-6419-3986")
— Putian University, School of Information Engineering, Putian, China (and 1 more) - Ying Xie 0003 — University of Texas at Dallas, Naveen Jindal School of Management, Richardson, TX, USA (and 1 more)
2020 – today
- 2025
[i2]Srivatsa Mallapragada, Ying Xie, Varsha Rani Chawan, Zeyad Hailat, Yuanbo Wang:
Multi-Modality Transformer for E-Commerce: Inferring User Purchase Intention to Bridge the Query-Product Gap. CoRR abs/2501.14826 (2025)- 2024
- [j12]Sumit Chakravarty, Arun Kumar, Michael Hales, John David Johnson, Ying Xie:
Machine Learning and Computer Visualization for Monocular Biomechanical Analysis. Wirel. Pers. Commun. 135(4): 2131-2144 (2024) - [c53]Srivatsa Mallapragada, Ying Xie, Varsha Rani Chawan, Zeyad Hailat, Yuanbo Wang:
Multi-Modality Transformer for E-Commerce: Inferring User Purchase Intention to Bridge the Query-Product Gap. IEEE Big Data 2024: 589-598 - [c52]Sahar Yarmohammadtoosky, Ying Xie:
Data-Driven Analysis of Climate Change Impacts on Public Health: A Systematic Review and Future Directions. IEEE Big Data 2024: 4457-4465 - [c51]Qiaomu Li, Ying Xie, Sumit Chakravarty, Dabae Lee:
EduMAS: A Novel LLM-Powered Multi-Agent Framework for Educational Support. IEEE Big Data 2024: 8309-8316 - [c50]Qiaomu Li, Ying Xie, Shaoen Wu:
Chrono Clustering: A Novel Methodology for Dynamic Topic Trend Analysis. IJCNN 2024: 1-6 - 2023
- [j11]Trent Geisler, Herman Ray, Ying Xie:
Finding the Proverbial Needle: Improving Minority Class Identification Under Extreme Class Imbalance. J. Classif. 40(1): 192-212 (2023) - [c49]Shashank Hebbar, Ying Xie, Jiang Zhong:
TreeBERT: Advanced Representation Learning for Relation Extraction. IEEE Big Data 2023: 5960-5966 - [c48]Malik Naik Mohammed, Xinyue Zhang, Maria Valero, Ying Xie:
Poster: AsyncFedKD: Asynchronous Federated Learning with Knowledge Distillation. CHASE 2023: 207-208 - [c47]Ying Xie, Shaoen Wu, Sumit Chakravarty:
AI meets AI: Artificial Intelligence and Academic Integrity - A Survey on Mitigating AI-Assisted Cheating in Computing Education. SIGITE 2023: 79-83 - [e2]Ying Xie, Becky Rutherfoord, Hyesung Park:
The 24th Annual Conference on Information Technology Education, SIGITE 2023, Marietta, GA, USA, October 11-14, 2023. ACM 2023 [contents] - 2022
- [c46]Md. Shafiul Alam, Ying Xie:
Appley: Approximate Shapley Value for Model Explainability in Linear Time. IEEE Big Data 2022: 95-100 - [c45]Garima Thakur, Sitanshu Sekhar Sahu, Ying Xie, Sumit Chakravarty:
Classification of Heart Sound using Tunable Q Wavelet Transform and Machine Learning. IEEE Big Data 2022: 2800-2803 - [c44]Md. Shafiul Alam, Namazbai Ishmakhametov, Ying Xie, Sumit Chakravarty:
An Application of Localized Model Explainability: Identifying Key Disparities in Social Determinants of Health in Food Deserts. IEEE Big Data 2022: 5006-5011 - [c43]Jonathan W. Boardman, Md. Shafiul Alam, Xiao Huang, Ying Xie:
Integrated Gradients is a Nonlinear Generalization of the Industry Standard Approach to Variable Attribution for Credit Risk Models. IEEE Big Data 2022: 5012-5023 - [e1]Shusaku Tsumoto, Yukio Ohsawa, Lei Chen, Dirk Van den Poel, Xiaohua Hu, Yoichi Motomura, Takuya Takagi, Lingfei Wu, Ying Xie, Akihiro Abe, Vijay Raghavan:
IEEE International Conference on Big Data, Big Data 2022, Osaka, Japan, December 17-20, 2022. IEEE 2022, ISBN 978-1-6654-8045-1 [contents] - 2021
- [j10]Linh Le, Ying Xie, Vijay Raghavan:
KNN Loss and Deep KNN. Fundam. Informaticae 182(2): 95-110 (2021) - [c42]Linh Le, Ying Xie, Sumit Chakravarty, Michael Hales, John Johnson, Tu N. Nguyen:
Analysis of Students' Concentration Levels for Online Learning Using Webcam Feeds. IEEE BigData 2021: 5517-5523 - [c41]Sumit Chakravarty, Ying Xie, Linh Le, John Johnson, Michael Hales:
Comparison Between Active and Passive Attention Using EEG Waves and Deep Neural Network. BI 2021: 287-298 - [c40]Shashank Hebbar, Ying Xie:
CovidBERT-Biomedical Relation Extraction for Covid-19. FLAIRS 2021 - 2020
- [j9]Wenchan Jiang, Ming Yang, Ying Xie, Zhigang Li:
Machine Learning-Based Coding Decision Making in H.265/HEVC CTU Division and Intra Prediction. Int. J. Mob. Comput. Multim. Commun. 11(2): 41-60 (2020) - [c39]Linh Le, Ying Xie, Saisangararamaleengam Alagapan, Sumit Chakravarty, Pablo Ordóñez, Michael Hales, John Johnson:
Deep Pose Alignment. IEEE BigData 2020: 5366-5372 - [c38]Kyle Pawlowski, Sumit Chakravarty, Ying Xie, Arjun Kumar Joginipelly:
Action Recognition Using Dynamic Mode Decomposition for Temporal Representation. IEEE BigData 2020: 5381-5386 - [c37]Robert Vanderheyden, Ying Xie:
Mammography Image BI-RADS Classification Using OHPLall. BigDataService 2020: 120-127 - [c36]Meng Han, Lei Li, Ying Xie:
A Novel Framework for Collaborated IT Project with the Consideration of Data Security and Privacy. SIGITE 2020: 345
2010 – 2019
- 2019
- [j8]Linh Le, Ying Xie:
Deep embedding kernel. Neurocomputing 339: 292-302 (2019) - [c35]Bob Vanderheyden, Ying Xie, Mohan Rachumallu:
Net Promoter Sentiment Classifier Using OHPL-ALL. IEEE BigData 2019: 2494-2502 - [c34]Jonathan W. Boardman, Ying Xie:
Radically Simplifying Gated Recurrent Architectures Without Loss of Performance. IEEE BigData 2019: 2615-2623 - [c33]Joel Kamdem Teto, Ying Xie:
Automatically Identifying of animals in the wilderness: Comparative studies between CNN and C-Capsule Network. ICCDA 2019: 128-133 - [c32]Romas James Hada, Miao Jin, Ying Xie, Linh Le:
Link Prediction Based Minimum Cost and Balanced Partition of Large Online Social Networks. NCA 2019: 1-9 - 2018
- [j7]Meng Han, Lei Li, Ying Xie, Jinbao Wang, Zhuojun Duan, Ji Li, Mingyuan Yan:
Cognitive Approach for Location Privacy Protection. IEEE Access 6: 13466-13477 (2018) - [c31]Linh Le, Ying Xie:
Recurrent Embedding Kernel for Predicting Stock Daily Direction. BDCAT 2018: 160-166 - [c30]Bob Vanderheyden, Ying Xie:
Ordinal Hyperplane Loss. IEEE BigData 2018: 2337-2344 - [c29]Linh Le, Ying Xie, Vijay V. Raghavan:
Deep Similarity-Enhanced K Nearest Neighbors. IEEE BigData 2018: 2643-2650 - [c28]Kshitij Bantupalli, Ying Xie:
American Sign Language Recognition using Deep Learning and Computer Vision. IEEE BigData 2018: 4896-4899 - [c27]Vu Linda, Ying Xie, Meng Han:
Designing Visually Interactive Learning Modules to Promote Students' Critical Thinking in Mathematics. FIE 2018: 1-5 - [c26]Meng Han, Zhigang Li, Jing (Selena) He, Dalei Wu, Ying Xie, Asif Iqbal Baba:
A Novel Blockchain-based Education Records Verification Solution. SIGITE 2018: 178-183 - [i1]Linh Le, Ying Xie:
Deep Embedding Kernel. CoRR abs/1804.05806 (2018) - 2017
- [c25]Jing (Selena) He, Ying Xie, Tianyu Du, Shouling Ji, Zhao Li:
Influence Spread in Social Networks with both Positive and Negative Influences. COCOON 2017: 615-629 - [c24]Ying Xie, Linh Le, Jie Hao:
Unsupervised deep kernel for high dimensional data. IJCNN 2017: 294-299 - [c23]Li-li Zhang, Ying Xie, Guoliang Liu:
A sentiment-change-driven event discovery system. WI 2017: 1035-1041 - 2016
- [c22]Linh Le, Jie Hao, Ying Xie, Jennifer L. Priestley:
Deep kernel: learning kernel function from data using deep neural network. BDCAT 2016: 1-7 - [c21]Ying Xie, Pooja Chenna, Jing (Selena) He, Linh Le, Jacey Planteen:
Visualization of big high dimensional data in a three dimensional space. BDCAT 2016: 61-66 - [c20]Tianda Yang, Kai Qian, Dan Chia-Tien Lo, Ying Xie, Yong Shi, Lixin Tao:
Improve the Prediction Accuracy of Naïve Bayes Classifier with Association Rule Mining. BigDataSecurity/HPSC/IDS 2016: 129-133 - [c19]Jing He, Dan Chia-Tien Lo, Ying Xie, Jonathan W. Lartigue:
Integrating Internet of Things (IoT) into STEM undergraduate education: Case study of a modern technology infused courseware for embedded system course. FIE 2016: 1-9 - [c18]Ying Xie, Kai Qian, Jing He:
Multi-dimensional and customizable open-source labware for promoting big data analytical skills in STEM education. FIE 2016: 1-5 - 2015
- [j6]Jing (Selena) He, Shouling Ji, Raheem A. Beyah, Ying Xie, Yingshu Li:
Constructing load-balanced virtual backbones in probabilistic wireless sensor networks via multi-objective genetic algorithm. Trans. Emerg. Telecommun. Technol. 26(2): 147-163 (2015) - [c17]Ying Xie:
KNN++: An Enhanced K-Nearest Neighbor Approach for Classifying Data with Heterogeneous Views. HIS 2015: 13-23 - 2014
- [c16]Ying Xie, Hossain Shahriar, Jing He, Sarah North, Bennett Setzer, Kenneth E. Hoganson:
CS++: expanding CS curriculum via open cybersecurity courseware. ACM Southeast Regional Conference 2014: 24:1-24:7 - 2013
- [c15]Vijay V. Raghavan, Ryan G. Benton, Tom Johnsten, Ying Xie:
Representations for Large-Scale Sequence Data Mining: A Tale of Two Vector Space Models. RSFDGrC 2013: 15-25 - 2012
- [c14]Ying Xie, Vijay V. Raghavan:
A random walk model based approach for quantifying technology emergence and impact for research articles. GrC 2012: 553-555 - [c13]Ying Xie, Jonathan Fisher, Vijay V. Raghavan, Tom Johnsten, Can Akkoç:
Granular Approach for Protein Sequence Analysis. RSCTC 2012: 414-421 - 2011
- [j5]Raj Singh, Tom Johnsten, Vijay V. Raghavan, Ying Xie:
Algorithms for discovering potentially interesting patterns. Int. J. Granul. Comput. Rough Sets Intell. Syst. 2(2): 107-122 (2011) - [c12]Jayasimha Reddy Katukuri, Ying Xie, Vijay V. Raghavan, Ashish Gupta:
Supervised Link Discovery on Large-Scale Biomedical Concept Networks. BIBM 2011: 562-568 - 2010
- [j4]Jayasimha Reddy Katukuri, Ying Xie, Vijay V. Raghavan:
Biomedical Relationship Extraction from literature based on bio-semantic token subsequences. Int. J. Funct. Informatics Pers. Medicine 3(1): 16-28 (2010) - [c11]Raj Singh, Tom Johnsten, Vijay V. Raghavan, Ying Xie:
Efficient Algorithm for Discovering Potential Interesting Patterns with Closed Itemsets. GrC 2010: 414-419
2000 – 2009
- 2009
- [j3]Ying Xie, Vijay V. Raghavan, Andrew Young:
Hyper-textual language model for web information retrieval. Int. J. Granul. Comput. Rough Sets Intell. Syst. 1(2): 190-202 (2009) - [c10]Jayasimha Reddy Katukuri, Ying Xie, Vijay V. Raghavan:
Biomedical Relationship Extraction from Literature Based on Bio-semantic Token Subsequences. BIBM 2009: 366-370 - [c9]Raj Singh, Tom Johnsten, Vijay V. Raghavan, Ying Xie:
An efficient algorithm for discovering positive and negative patterns. GrC 2009: 507-512 - 2008
- [c8]Ying Xie, Vijay V. Raghavan, Andrew Young:
Hyper-Textual Language Model for Web Information Retrieval. GrC 2008: 68-73 - [p2]Ying Xie, Jayasimha Reddy Katukuri, Vijay V. Raghavan, Tony Presti:
Conceptual Biology Research Supporting Platform: Current Design and Future Directions. Applications of Computational Intelligence in Biology 2008: 307-324 - 2007
- [j2]Ying Xie, Vijay V. Raghavan:
Language-modeling kernel based approach for information retrieval. J. Assoc. Inf. Sci. Technol. 58(14): 2353-2365 (2007) - 2006
- [c7]Ying Xie, Manmathasivaram Nagarajan, Vijay V. Raghavan, Hisham Haddad:
On Novelty Evaluation of Potentially Useful Patterns. DMIN 2006: 211-217 - [c6]Ying Xie, Tom Johnsten, Vijay V. Raghavan, Karthik Ramachandran:
On discovering "potentially useful" patterns from databases. GrC 2006: 494-497 - 2005
- [j1]Ying Xie, Vijay V. Raghavan, Praveen Dhatric, Xiaoquan Zhao:
A new fuzzy clustering algorithm for optimally finding granular prototypes. Int. J. Approx. Reason. 40(1-2): 109-124 (2005) - [c5]Ying Xie, Dheerendranath Mundluru, Vijay V. Raghavan:
Incorporating agent based neural network model for adaptive meta-search. ACM Southeast Regional Conference (1) 2005: 53-58 - [p1]Ying Xie, Vijay V. Raghavan:
A Probabilistic Logic-based Framework for Characterizing Knowledge Discovery in Databases. Foundations of Data Mining and knowledge Discovery 2005: 87-100 - 2004
- [c4]Ying Xie, Vijay V. Raghavan:
GAMInG - A Framework for Generalization of Association Mining via Information Granulation. Rough Sets and Current Trends in Computing 2004: 198-203 - 2003
- [c3]Jitender S. Deogun, Liying Jiang, Ying Xie, Vijay V. Raghavan:
Probability Logic Modeling of Knowledge Discovery in Databases. ISMIS 2003: 402-407 - [c2]Ying Xie, Vijay V. Raghavan:
A Theoretical Framework for Knowledge Discovery in Databases Based on Probabilistic Logic. RSFDGrC 2003: 541-548 - 2002
- [c1]Ying Xie, Vijay V. Raghavan, Xiaoquan Zhao:
3M algorithm: finding an optimal fuzzy cluster scheme for proximity data. FUZZ-IEEE 2002: 627-632
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