Merged Ontology and SVM-Based Information Extraction and Recommendation System for Social Robots
Farman Ali,Daehan Kwak,Pervez Khan,Shaker Hassan A. Ei-Sappagh,S. M. Riazul Islam,Daeyoung Park,Kyung Sup Kwak +6 more
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A merged ontology and support vector machine (SVM)-based information extraction and recommendation system that suggests items with a positive polarity term to the disabled user, and is highly productive when analyzing retrieved information, and provides accurate recommendations.Abstract:
The recent technology of human voice capture and interpretation has spawned the social robot to convey information and to provide recommendations. This technology helps people obtain information about a particular topic after giving an oral query to a humanoid robot. However, most of the search engines are keyword-matching mechanism-based, and the existing full-text query search engines are inadequate at retrieving relevant information from various oral queries. With only predefined words and sentence-based recommendations, a social robot may not suggest the correct items, if items retrieved along with the information are not predefined. In addition, the available conventional ontology-based systems cannot extract precise data from webpages to show the correct results. In this regard, we propose a merged ontology and support vector machine (SVM)-based information extraction and recommendation system. In the proposed system, when a humanoid robot receives an oral query from a disabled user, the oral query changes into a full-text query, the system mines the full-text query to extract the disabled user’s needs, and then converts the query into the correct format for a search engine. The proposed system downloads a collection of information about items (city features, diabetes drugs, and hotel features). The SVM identifies the relevant information on the item and removes anything irrelevant. Merged ontology-based sentiment analysis is then employed to find the polarity of the item for recommendation. The system suggests items with a positive polarity term to the disabled user. The intelligent model and merged ontology were designed by employing Java and Protege Web Ontology Language 2 software, respectively. Experimentation results show that the proposed system is highly productive when analyzing retrieved information, and provides accurate recommendations.read more
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Journal ArticleDOI
An intelligent healthcare monitoring framework using wearable sensors and social networking data
Farman Ali,Shaker El-Sappagh,Shaker El-Sappagh,S. M. Riazul Islam,Amjad Ali,Muhammad Attique,Muhammad Imran,Kyung Sup Kwak +7 more
TL;DR: A novel healthcare monitoring framework based on the cloud environment and a big data analytics engine is proposed to precisely store and analyze healthcare data, and to improve the classification accuracy.
Journal ArticleDOI
Type-2 fuzzy ontology–aided recommendation systems for IoT–based healthcare
Farman Ali,S. M. Riazul Islam,Daehan Kwak,Pervez Khan,Niamat Ullah,Sang-Jo Yoo,Kyung Sup Kwak +6 more
TL;DR: A type-2 fuzzy ontology–aided recommendation systems for IoT-based healthcare to efficiently monitor the patient's body while recommending diets with specific foods and drugs and the experimental results show that the proposed system is efficient for patient risk factors extraction and diabetes prescriptions.
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Transportation sentiment analysis using word embedding and ontology-based topic modeling
Farman Ali,Daehan Kwak,Pervez Khan,Shaker El-Sappagh,Shaker El-Sappagh,Amjad Ali,Amjad Ali,Sana Ullah,Sana Ullah,Kyehyun Kim,Kyung Sup Kwak +10 more
TL;DR: This work proposes an ontology and latent Dirichlet allocation (OLDA)-based topic modeling and word embedding approach for sentiment classification, which achieves accuracy of 93%, which shows that the proposed approach is effective for sentiment Classification.
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Deep Correlation Mining Based on Hierarchical Hybrid Networks for Heterogeneous Big Data Recommendations
TL;DR: A hierarchical hybrid network (HHN) model is constructed to describe multitype relationships among different entities, and a series of measures are defined to quantify the internal correlations within one specific layer or external correlations between different layers.
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Fuzzy Ontology and LSTM-Based Text Mining: A Transportation Network Monitoring System for Assisting Travel
TL;DR: A novel fuzzy ontology-based semantic knowledge with Word2vec model is proposed to improve the task of transportation features extraction and text classification using the Bi-directional Long Short-Term Memory (Bi-LSTM) approach.
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