An analytical dynamic scaling of supply voltage and body bias exploiting memory stall time variation. - Jungsoo Kim; Younghoon Lee; Sungjoo Yoo; Chong-Min Kyung (ASP-DAC, 2010)

Flexible inverted-L antenna with parasitic elements for E-note. - Hojin Kim; Seonhyeon Lee; Younghoon Lee; Sangseok Lee (APCC, 2011)

Energy-Aware Video Encoding for Image Quality Improvement in Battery-Operated Surveillance Camera. - Younghoon Lee; Jungsoo Kim; Chong-Min Kyung (IEEE Trans. Very Large Scale Integr. Syst., 2012)

A security model for IPTV with one-time password and Conditional Access System for smart mobile platform. - Manhyun Chung; Younghoon Lee; Taeshik Shon; Jongsub Moon (Telecommun. Syst., 2013)

De-noising documents with a novelty detection method utilizing class vectors. - Younghoon Lee; Sungzoon Cho; Jinhae Choi (Intell. Data Anal., 2018)

Applying convolution filter to matrix of word-clustering based document representation. - Younghoon Lee; Jinbae Im; Sungzoon Cho; Jinhae Choi (Neurocomputing, 2018)

Smartphone user segmentation based on app usage sequence with neural networks. - Younghoon Lee; Inbeom Park; Sungzoon Cho; Jinhae Choi (Telematics Informatics, 2018)

Smartphone help contents re-organization considering user specification via conditional GAN. - Younghoon Lee; Sungzoon Cho; Jinhae Choi (Int. J. Hum. Comput. Stud., 2019)

Extraction of Product Evaluation Factors with a Convolutional Neural Network and Transfer Learning. - Younghoon Lee; Minki Chung; Sungzoon Cho; Jinhae Choi (Neural Process. Lett., 2019)

Document representation based on probabilistic word clustering in customer-voice classification. - Younghoon Lee; Seokmin Song; Sungzoon Cho; Jinhae Choi (Pattern Anal. Appl., 2019)

App usage prediction for dual display device via two-phase sequence modeling. - Younghoon Lee; Sungzoon Cho; Jinhae Choi (Pervasive Mob. Comput., 2019)

Serendipity adjustable application recommendation via joint disentangled recurrent variational auto-encoder. - Younghoon Lee (Electron. Commer. Res. Appl., 2020)

Abnormal Usage Sequence Detection for Identification of User Needs via Recurrent Neural Network Semantic Variational Autoencoder. - Younghoon Lee; Sungzoon Cho (Int. J. Hum. Comput. Interact., 2020)

Design of Semantic-Based Colorization of Graphical User Interface Through Conditional Generative Adversarial Nets. - Younghoon Lee; Sungzoon Cho (Int. J. Hum. Comput. Interact., 2020)

Extraction and prioritization of product attributes using an explainable neural network. - Younghoon Lee; Jungmin Park; Sungzoon Cho (Pattern Anal. Appl., 2020)

Ionic spiderwebs. - Younghoon Lee; Won Jun Song; Yeonsu Jung; Hyunjae Yoo; Man-Yong Kim; Ho-Young Kim; Jeong-Yun Sun (Sci. Robotics, 2020)

Semi-Supervised Learning Based on Auto-generated Lexicon Using XAI in Sentiment Analysis. - Hohyun Hwang; Younghoon Lee (RANLP, 2021)

Determining user needs through abnormality detection and heterogeneous embedding of usage sequence. - Younghoon Lee; Sungzoon Cho; Jinhae Choi (Electron. Commer. Res., 2021)

Content Prioritization Based on Usage Pattern Analysis. - Jonghwan Park; Younghoon Lee (Int. J. Hum. Comput. Interact., 2021)

Usability Problem Identification Based on Explainable Neural Network in Asynchronous Testing Environment. - Hohyun Hwang; Younghoon Lee (Interact. Comput., 2021)

User segmentation via interpretable user representation and relative similarity-based segmentation method. - Younghoon Lee; Sungzoon Cho (Multim. Syst., 2021)

Systematic Homonym Detection and Replacement Based on Contextual Word Embedding. - Younghoon Lee (Neural Process. Lett., 2021)

Extraction of Competitive Factors in a Competitor Analysis Using an Explainable Neural Network. - Younghoon Lee (Neural Process. Lett., 2021)

Identifying Competitive Attributes Based on an Ensemble of Explainable Artificial Intelligence. - Younghoon Lee (Bus. Inf. Syst. Eng., 2022)

Explainable Artificial Intelligence-Based Competitive Factor Identification. - Juhee Han; Younghoon Lee (ACM Trans. Knowl. Discov. Data, 2022)

Explainability-Based Mix-Up Approach for Text Data Augmentation. - Soonki Kwon; Younghoon Lee (ACM Trans. Knowl. Discov. Data, 2023)

Laser-assisted failure recovery for dielectric elastomer actuators in aerial robots. - Suhan Kim; Yi Hsuan Hsiao; Younghoon Lee; Weikun Zhu; Zhijian Ren; Farnaz Niroui; Yufeng Chen 003 (Sci. Robotics, 2023)

Mixing Approach for Text Data Augmentation Based on an Ensemble of Explainable Artificial Intelligence Methods. - Jinyi Yu; Jinhae Choi; Younghoon Lee (Neural Process. Lett., 2023)

Oriented-tooth recognition using a five-axis object-detection approach. - Jonghwan Park; Younghoon Lee (Appl. Intell., 2023)

Systematical Randomness Assignment for the Level of Manipulation in Text Augmentation. - Youhoo Cha; Younghoon Lee (ICMLA, 2024)

Soft modularized robotic arm for safe human-robot interaction based on visual and proprioceptive feedback. - Subyeong Ku; Byung-Hyun Song; Taejun Park; Younghoon Lee; Yong-Lae Park (Int. J. Robotics Res., 2024)

Prompt2Rec: Prompt based user and item re-characterizing method for recommendation. - Seonjin Hwang; Younghoon Lee (Inf. Sci., 2024)

Advanced sentence-embedding method considering token importance based on explainable artificial intelligence and text summarization model. - Yuho Cha; Younghoon Lee (Neurocomputing, 2024)

Opinion convergence-based sentiment prediction of image advertisement. - Younghoon Lee (Int. J. Multim. Inf. Retr., 2024)

Enhancing abstractive summarization of implicit datasets with contrastive attention. - Soonki Kwon; Younghoon Lee (Neural Comput. Appl., 2024)

Key attribute generation from review texts based on in-context learning for recommender systems. - Jungmin Park; Younghoon Lee (Appl. Intell., 2024)

Advanced pseudo-labeling approach in mixing-based text data augmentation method. - Jungmin Park; Younghoon Lee (Pattern Anal. Appl., 2024)

User Opinion-Focused Abstractive Summarization Using Explainable Artificial Intelligence. - Hyunho Lee; Younghoon Lee (ACM Trans. Intell. Syst. Technol., 2024)

Novel augmentation techniques using diffusion models for green wall plant health classification. - Minseok Yoon; Younghoon Lee (Comput. Biol. Medicine, 2025)

IntentRec: Incorporating latent user intent via contrastive alignment for sequential recommendation. - Seonjin Hwang; Younghoon Lee (Electron. Commer. Res. Appl., 2025)

Regulating the level of manipulation in text augmentation with systematic adjustment and advanced sentence embedding. - Yuho Cha; Younghoon Lee (Neural Comput. Appl., 2025)

Enhancing plant health classification via diffusion model-based data augmentation. - Younghoon Lee (Multim. Syst., 2025)

Improving the Summarization Effectiveness of Abstractive Datasets through Contrastive Learning. - Junho Shin; Younghoon Lee (ACM Trans. Intell. Syst. Technol., 2025)

Text augmentation method with adjustable manipulation intensity based on in-context learning. - Yuho Cha; Younghoon Lee (Knowl. Inf. Syst., 2025)