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.

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 Computation of Person Re-Identification using Self-Learning Anomaly Detection Framework in Deep-Learning

2019 ◽  
Vol 9 (1) ◽  
pp. 1106-1109
Keyword(s):  
Deep Learning ◽  
Anomaly Detection ◽  
Data Clustering ◽  
Clustering Algorithm ◽  
Attack Detection ◽  
The Self ◽  
Supervised Machine Learning ◽  
Learning Capability ◽  
Original Dataset ◽  
Self Learning

This paper proposes an application of a self-Learning anomaly detection framework in Deep-learning. In this application, both hybrid unsupervised and supervised machine learning schemes are used. Firstly, it takes metadata of the unsupervised data clustering module (DCM). Data clustering module (DCM) analyses the pattern of the monitoring data and enables the self-learning capability that eliminates the requirement of the prior knowledge of the abnormal network behaviors and also has the potential to detect the unforeseen anomalies. Next, we use the self-learning mechanism that transfer pattern learned by the DCM to a supervised data regression and classification module (DRCM) it’s Complexity is mainly related to scalability of supervised learning module. It is more measurable and less time consuming for online anomalies by avoiding excessively usage of the original dataset. It has a density-based clustering algorithm and deep learning, neural network structure-based DCM and DRCM. We are also using an anti-spoofing-based approach for presentation attack detection (PAD). In these approaches, we are mainly detecting a person reidentify and computing without having any false anomalies.

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

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