What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Operating system
- Computer network
Young-Koo Lee mainly investigates Artificial intelligence, Data mining, Tree, Machine learning and Accelerometer.
He has researched Artificial intelligence in several fields, including Computer vision and Pattern recognition.
His studies deal with areas such as Tree structure and Recommender system as well as Data mining.
His Tree structure research is multidisciplinary, incorporating elements of Trie, Data stream mining and Incremental decision tree, Decision tree learning.
His work carried out in the field of Tree brings together such families of science as Filter bank, Set and Data structure.
His Accelerometer research incorporates elements of Artificial neural network, Activity recognition and Simulation.
His most cited work include:
- Efficient Tree Structures for High Utility Pattern Mining in Incremental Databases (396 citations)
- A Triaxial Accelerometer-Based Physical-Activity Recognition via Augmented-Signal Features and a Hierarchical Recognizer (384 citations)
- Human Activity Recognition via an Accelerometer-Enabled-Smartphone Using Kernel Discriminant Analysis (169 citations)
What are the main themes of his work throughout his whole career to date?
His primary areas of investigation include Artificial intelligence, Data mining, Ubiquitous computing, Wireless sensor network and Computer network.
His Artificial intelligence study combines topics in areas such as Machine learning, Computer vision and Pattern recognition.
As a part of the same scientific family, he mostly works in the field of Data mining, focusing on Tree and, on occasion, K-optimal pattern discovery.
His Ubiquitous computing research focuses on subjects like Computer security, which are linked to Cloud computing.
His research integrates issues of Key management, Scheme, Key distribution in wireless sensor networks and Real-time computing in his study of Wireless sensor network.
The Routing protocol research he does as part of his general Computer network study is frequently linked to other disciplines of science, such as Energy consumption, therefore creating a link between diverse domains of science.
He most often published in these fields:
- Artificial intelligence (26.62%)
- Data mining (19.16%)
- Ubiquitous computing (16.23%)
What were the highlights of his more recent work (between 2011-2021)?
- Artificial intelligence (26.62%)
- Data mining (19.16%)
- Activity recognition (9.09%)
In recent papers he was focusing on the following fields of study:
Young-Koo Lee mainly focuses on Artificial intelligence, Data mining, Activity recognition, Big data and Machine learning.
Artificial intelligence connects with themes related to Pattern recognition in his study.
His study in Data mining is interdisciplinary in nature, drawing from both Trust network, Ranking, Representation, Visual Word and Automatic image annotation.
His Activity recognition research is multidisciplinary, incorporating perspectives in Active learning, Home automation, Speech recognition, Simulation and Computer vision.
His Big data study also includes fields such as
- Scalability and related Trie, Knowledge extraction and Data stream mining,
- Cloud computing which intersects with area such as Access control, Data curation and Search engine indexing,
- Video production, Relevance feedback and Relation most often made with reference to Distributed database.
Young-Koo Lee combines subjects such as SPQR tree and Strength of a graph with his study of Machine learning.
Between 2011 and 2021, his most popular works were:
- Daily life activity tracking application for smart homes using android smartphone (70 citations)
- Single-pass incremental and interactive mining for weighted frequent patterns (61 citations)
- Comprehensive Context Recognizer Based on Multimodal Sensors in a Smartphone (61 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Operating system
- Computer network
His primary areas of study are Data mining, Activity recognition, Home automation, Artificial intelligence and Machine learning.
His study in Data mining is interdisciplinary in nature, drawing from both Social network, Periodic graph, Graph, Epigraph and Dynamic network analysis.
His Activity recognition research is multidisciplinary, incorporating perspectives in Classifier, Random subspace method and Genetic algorithm.
His studies in Home automation integrate themes in fields like Web service, Independent living and Internet privacy.
His research on Artificial intelligence often connects related areas such as Pattern recognition.
His research in Machine learning intersects with topics in Set, Feature extraction and Face.
This overview was generated by a machine learning system which analysed the scientist’s
body of work. If you have any feedback, you can
contact us
here.
