Intelligent Human Computer Interaction 9th International Conference IHCI 2017 Evry France December 11 13 2017 Proceedings 1st Edition Patrick Horain 3030446891 9783030446895
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Product details:
- ISBN 10: 3030446891
- ISBN 13: 9783030446895
- Author: Patrick Horain
This volume constitutes the proceedings of the 11th International Conference on Intelligent Human Computer Interaction, IHCI 2019, held in Allahabad, India, in December 2019. The 25 full papers presented in this volume were carefully reviewed and selected from 73 submissions. The papers are grouped in the following topics: EEG and other biological signal based interactions; natural language, speech and dialogue processing; vision based interactions; assistive living and rehabilitation; and applications of HCI.
Table of contents:
EEG and Other Biological Signal Based Interactions
Classification of Motor Imagery EEG Signal for Navigation of Brain Controlled Drones
Monitoring Post-stroke Motor Rehabilitation Using EEG Analysis
Wavelet Transform Selection Method for Biological Signal Treatment
Fuzzy Inference System for Classification of Electroencephalographic (EEG) Data
Empirical Mode Decomposition Algorithms for Classification of Single-Channel EEG Manifesting McGurk Effect
Natural Language, Speech and Dialogue Processing
Abstractive Text Summarization Using Enhanced Attention Model
Highlighted Word Encoding for Abstractive Text Summarization
Detection of Hate and Offensive Speech in Text
Deep Learning for Hindi Text Classification: A Comparison
A Stacked Ensemble Approach to Bengali Sentiment Analysis
Computing with Words Through Interval Type-2 Fuzzy Sets for Decision Making Environment
RNN Based Language Generation Models for a Hindi Dialogue System
Bengali Handwritten Character Classification Using Transfer Learning on Deep Convolutional Network
Vision Based Interactions
Predicting Body Size Using Mirror Selfies
Retinal Vessel Classification Using the Non-local Retinex Method
Rule Generation of Cataract Patient Data Using Random Forest Algorithm
Yawn Detection for Driver’s Drowsiness Prediction Using Bi-Directional LSTM with CNN Features
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