ScholarX: KIM, JAEKWANG

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KIM, JAEKWANG (김재광)

Computing and Informatics (Convergence)

Research Area: 인공지능, 추천/예측 시스템, 지능시스템, Artificial Intelligence, Recommendation & Prediction System, Intelligent System

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Google:2||Convolution:2||Feature extraction:2||Convolutional neural networks:2||Twitter:2||Correlation:2||Debiasing:2||Chronological cohesive unit:1||Classification:1||Collaborative filtering:1||Companies:1||Computational modeling:1||Computer Vision:1||Computer vision:1||Contour tracing:1||Credit cards:1||Cube Rover:1||Cuberover:1||Data models:1||Data structure:1||Dataset Bias:1||Decoding:1||Deep learning:1||Delayed Model Update:1||Depth completion:1||ECG:1||Earthquakes:1||Echocardiography:1||Effective Field (EF):1||Evidence theory:1||Fewshot learning:1||Field interaction strengths:1||Fractals:1||Fuzzy logic:1||Generative model:1||Genetic programming:1||Gesture recognition:1||Global information:1||Graph data:1||HMM classification:1||High frequency:1||Highorder feature interactions:1||History:1||Hough transformation:1||Human interaction recognition:1||Image Classification:1||Image Translation:1||Image color analysis:1||Image edge detection:1||Image restoration:1||Image superresolution:1||Imbalanced Data:1||Internet:1||Iron:1||LLMs:1||LVEF:1||LVSD:1||Laser radar:1||Linklink connection:1||Low power embedded PC:1||Market research:1||Metadata:1||MicroRover:1||Model Retraining:1||Multimodal:1||Multisensory integration:1||NLP:1||New drug development:1||Physical / chemical structure:1||Predictive models:1||Probabilistic logic:1||Propagation:1||Query:1||Reweighting:1||Recommendation:1||Recommender Systems:1||Recommender systems:1||SMOTE:1||SVM Classifier:1||Search engines:1||Selfattention:1||Sentence structure:1||Seq2Seq:1||Sequential log:1||Simulation:1||Sparse matrices:1||Subscription:1||Support vector machines:1||TV programme consumption pattern:1||TV viewers today:1||TV viewing records:1||Termweighting:1||Timefrequency analysis:1||Topological data:1||Toxicity prediction:1||Unmanned Rover:1||Videos:1||Web pages:1||barycentric kernel:1||bidirectional LSTM:1||broadcasting contents:1||chemicalreaction optimization:1||class imbalance:1||collaborative filtering:1||comprehensive programming channels:1||concentration space:1||content consumption concentration calculation:1||contextual information:1||convolution neural network:1||credit card payment data:1||customer satisfaction:1||dam inflow:1||deep belief network:1||deep learning:1||differentiable ranking metric:1||digital television:1||dimensionality reduction:1||document ranking:1||dynamic guided filtering:1||edge:1||explainable recommendation:1||fuzzy logic:1||hand detection and tracking:1||highorder connectivity:1||hybrid oversampling:1||image structure:1||imbalanced data:1||interactive television:1||knowledge graph:1||learning to rank:1||log analysis:1||machine learning:1||merchant recommendation:1||microblogs:1||multiaffinity matrix:1||multimodal:1||multimedia computing:1||neural gas:1||oversampling:1||personalized recommender system:1||potential customer:1||predictive models:1||quadratic discriminant analysis:1||recommender systems:1||smart TV programme:1||smart TV programs:1||spatial propagation:1||television:1||television broadcasting:1||texture:1||thermal locomotion mapping:1||time weighted model:1||topK recommendation:1||ubiquitous computing:1||web context:1||독성 예측:1||상태 벡터 머신 분류기:1||시뮬레이션:1||신약 개발:1||Artificial Intelligence:1||하이브리드 예측:1||Association rules:1||Autonomous Driving:1||Bayesian optimization:1||Blog:1||CNN (Convolutional Neural Network):1||CTR prediction:1||

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(03063) 25-2, SUNGKYUNKWAN-RO, JONGNO-GU, SEOUL, KOREA samsunglib@skku.edu

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