행사 상세 정보 :

데이터사이언스대학원 비교과프로그램의 일환으로 국외대학(인도 바라나스 힌두 대학) 석학과의 머신러닝 분야 연구과정 공유 및 기술교류 세미나를 진행하오니

관심있는 분들의 많은 참여 부탁드립니다. (특강 운영: 데이터사이언스대학원 성진택 교수)

[주제] Data Transformation and Discovery: Exploring Dimensional Reduction, Affective Computing and Content-Based Retrieval

[연사] Manoj Kumar Singh (Professor) / Sunnel Kumar, Ruchilekha(Ph.D candidate)

[일시] 2023. 11. 13. (월) 오후 3시 ~ 5시

[장소] 도서관 별관 203호 강의실

[참석대상] 해당 주제에 관심있는 누구나, 특히 박사후연구원 채용을 희망하는 학과 내 교원 참석 추천

[참석방법] 포스터 내 QR코드 스캔 ▶ 구글 폼 작성 https://forms.gle/GrgJsp2NQUtZRzXy9 (작성 제출: 11월 12일 오후 11시까지)

[참석문의] 데이터사이언스대학원 오희숙 팀장 / 062-530-5773

★ Abstract ★

[#SESSION 1: Dimensional Reduction Method: A Trace Optimization and Eigen Problem]

This research gives an overview of the eigenvalue problems encountered in areas of data mining that are related to dimension reduction.

Given some input high-dimensional data, the goal of dimension reduction is to map them to a low dimensional space such that certail properties of the initial data are preserved.

Optimizing the above properties among the reduced data can be typically posed as a trace optimization problem

that leads to an eigenvalue problem.

[#SESSION 2: Content-Based Image and Video Retrieval in Machine Learning Framework]

This research is to design an efficient framework for image and video retrieval by leveraging machine learning, with a specific emphasis on

utilizing deep Convolutional Neural Network(CNN) features.

This research entails an in-depth exploration of various feature extraction methods, dimension reduction techniques,

and fusion approaches for enhancing image and video retrieval tasks.

The aim is to optimize and enhance the retrieval process through advanced machine learning techniques,

ultimately leading to improve performance and accuracy in image and video retrieval systems.

[#SESSION 3: Affective Computing Framework for Emotion Recognition using EEG Signals]

This research is to develop an affective framework for emotion recognition using physiological signals, mainly focusing on EEG signals.

Here, we have explored different features, dimensionality reduction, and classification algorithms to investigate human emotional state.

Apart from this, we have also performed empirical study of brain connections established in response to emotions.