scholarly journals Deep Learning-Based Question Answering System for Intelligent Humanoid Robot

2020 ◽  
Author(s):  
Widodo Budiharto ◽  
Vincent Andreas ◽  
Alexander Agung Santoso Gunawan
Keyword(s):  
Big Data ◽  
Deep Learning ◽  
Humanoid Robot ◽  
Question Answering ◽  
Training Dataset ◽  
Learning Approach ◽  
Exact Match ◽  
Learning Capability ◽  
Question Answering Systems ◽  
Self Learning

Abstract The development of intelligent Humanoid Robot focuses on question answering systems to be able to interact with people is very rare. In this research, we would like to propose a Humanoid Robot with the self-learning capability for accepting and giving a response from people based on Deep Learning and big data from the internet. This kind of robot can be used widely in hotels, universities and public services. The Humanoid Robot should consider the style of questions and conclude the answer through conversation between robot and user. In our scenario, the robot will detect the user’s face and accept commands from the user to do an action, where the question from the user will be processed using deep learning, and the result will be compared with knowledge on the system. We proposed our deep learning approach, based on use GRU/LSTM, CNN and BiDAF with big data SQuAD as training dataset. Our experiment indicates that using GRU/LSTM encoder with BiDAF gives higher Exact Match and F1 Score, than CNN with the BiDAF model.

scholarly journals Deep learning-based question answering system for intelligent humanoid robot

2020 ◽  
Vol 7 (1) ◽  
Author(s):  
Widodo Budiharto ◽  
Vincent Andreas ◽  
Alexander Agung Santoso Gunawan
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Knowledge Base ◽  
Humanoid Robot ◽  
Question Answering ◽  
The Self ◽  
Learning Approach ◽  
Learning Capability ◽  
Question Answering Systems ◽  
Self Learning

Abstract Background The development of Intelligent Humanoid Robot focuses on question answering systems that can interact with people is very limited. In this research, we would like to propose an Intelligent Humanoid Robot with the self-learning capability for accepting and giving responses from people based on Deep Learning and Big Data knowledge base. This kind of robot can be used widely in hotels, universities, and public services. The Humanoid Robot should consider the style of questions and conclude the answer through conversation between robot and user. In our scenario, the robot will detect the user’s face and accept commands from the user to do an action. Findings The question from the user will be processed using deep learning, and the result will be compared to the knowledge base on the system. We proposed our Deep Learning approach, based on Recurrent Neural Network (RNN) encoder, Convolution Neural Network (CNN) encoder, with Bidirectional Attention Flow (BiDAF). Conclusions Our evaluation indicates that using RNN based encoder with BiDAF gives a higher score, than CNN encoder with the BiDAF. Based on our experiment, our model get 82.43% F1 score and the RNN based encoder will give a higher EM/F1 score than using the CNN encoder.

scholarly journals Deep Learning-Based Question Answering System for Intelligent Humanoid Robot

2020 ◽  
Author(s):  
Widodo Budiharto ◽  
Vincent Andreas ◽  
Alexander Agung Santoso Gunawan
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Knowledge Base ◽  
Humanoid Robot ◽  
Question Answering ◽  
The Self ◽  
Learning Approach ◽  
Learning Capability ◽  
Question Answering Systems ◽  
Self Learning

Abstract Background- The development of Intelligent Humanoid Robot focuses on question answering systems that can interact with people is very limited. In this research, we would like to propose an Intelligent Humanoid Robot with the self-learning capability for accepting and giving responses from people based on Deep Learning and Big Data knowledge base. This kind of robot can be used widely in hotels, universities, and public services. The Humanoid Robot should consider the style of questions and conclude the answer through conversation between robot and user. In our scenario, the robot will detect the user’s face and accept commands from the user to do an action. Findings- The question from the user will be processed using deep learning, and the result will be compared to the knowledge base on the system. We proposed our Deep Learning approach, based on Recurrent Neural Network (RNN) encoder, Convolution Neural Network (CNN) encoder, with Bidirectional Attention Flow (BiDAF). Conclusions- Our evaluation indicates that using RNN based encoder with BiDAF gives a higher score, than CNN encoder with the BiDAF. Based on our experiment, our model get 82.43% F1 score and the RNN based encoder will give a higher EM / F1 score than using the CNN encoder.

BUILD KNOWLEDGE GRAPH FROM HETEROGENEOUS DOCUMENTS

2021 ◽  
Vol 47 (05) ◽  
Author(s):  
NGUYỄN CHÍ HIẾU
Keyword(s):  
Information Retrieval ◽  
Deep Learning ◽  
Natural Language Processing ◽  
Natural Language ◽  
Language Processing ◽  
Question Answering ◽  
Semantic Analysis ◽  
Knowledge Graph ◽  
Question Answering Systems ◽  
Knowledge Graphs

Knowledge Graphs are applied in many fields such as search engines, semantic analysis, and question answering in recent years. However, there are many obstacles for building knowledge graphs as methodologies, data and tools. This paper introduces a novel methodology to build knowledge graph from heterogeneous documents.  We use the methodologies of Natural Language Processing and deep learning to build this graph. The knowledge graph can use in Question answering systems and Information retrieval especially in Computing domain

scholarly journals Recent Trends in Deep Learning Based Open-Domain Textual Question Answering Systems

IEEE Access ◽  
2020 ◽  
Vol 8 ◽  
pp. 94341-94356
Author(s):  
Zhen Huang ◽  
Shiyi Xu ◽  
Minghao Hu ◽  
Xinyi Wang ◽  
Jinyan Qiu ◽  
...  
Keyword(s):  
Deep Learning ◽  
Question Answering ◽  
Open Domain ◽  
Question Answering Systems ◽  
Recent Trends

scholarly journals REVIEW ON DEEP LEARNING APPROACH FOR BRAIN TUMOR GLIOMA ANALYSIS

2021 ◽  
Vol 9 (1) ◽  
pp. 395-408
Author(s):  
Mrs. Disha Sushant Wankhede, Dr. Selvarani Rangasamy
Keyword(s):  
Deep Learning ◽  
Brain Tumor ◽  
Early Diagnosis ◽  
Image Coding ◽  
Learning Approach ◽  
Sample Processing ◽  
Processing Efficiency ◽  
Learning Capability ◽  
Tumor Patient ◽  
Mri Diagnosis

Brain tumor diagnosis has evolved as a very critical need in current medical diagnosis. Early diagnosis of tumor detection is an important need for the primitive treatment of brain tumor patient increasing the survival rate of patient. MRI diagnosis of brain tumor for cancer treatment is a large processing due to volumetric content of scan sample. The processing of clinical data is large and consumes a high processing time. Hence, the need of early diagnosis and proper segmentation of brain tumor region is in need. This paper outlines a review on the developments of MRI sample processing for early diagnosis for brain tumor glioma diagnosis using deep learning approach. The advantage of learning capability and finer processing efficiency has gained an advantage in MRI image processing, which enable a better processing efficiency and accuracy in early diagnosis. Deep learning approach has shown a benefit of image coding based on selective features and state of art processing in diagnosis. The evaluation objective of the MRI sample processing has shown a better accuracy than the comparative existing approaches.  The recent trends, the advantages and limitation of the existing approach for MRI diagnosis is outlined.

Sign in / Sign up

Export Citation Format

Share Document