Incorporating VR, AR, and Related Technologies in the Tourism Industry

2020 ◽  
pp. 211-233
Author(s):  
Sandra Maria Correia Loureiro ◽  
Eduardo Moraes Sarmento ◽  
João Ferreira do Rosário
Keyword(s):  
Augmented Reality ◽  
World Economy ◽  
State Of The Art ◽  
Tourism Industry ◽  
The State ◽  
Future Research ◽  
Research Directions ◽  
The World ◽  
Future Research Directions ◽  
Virtual And Augmented Reality

The chapter exposes the importance of tourism in the world economy, gives an overview of what academic and practitioners are doing regarding the use of engagement-facilitating technologies in tourism, and suggests avenues for further research. Authors give insights about the evolution and important of tourism. The chapter presents an overview of the state of the art on the use of engagement-facilitating technologies (mainly virtual and augmented reality) in research. Examples of applications of engagement-facilitating technologies are given. Authors suggest future research directions and present conclusions.

Performance and trends in recent opinion retrieval techniques

2013 ◽  
Vol 30 (1) ◽  
pp. 76-105 ◽  
Author(s):  
Sylvester O. Orimaye ◽  
Saadat M. Alhashmi ◽  
Eu-Gene Siew
Keyword(s):  
State Of The Art ◽  
Performance Comparison ◽  
The State ◽  
Future Research ◽  
Research Directions ◽  
Opinion Retrieval ◽  
Future Research Directions ◽  
And Performance ◽  
Comparison Table ◽  
Art Techniques

AbstractThis paper presents trends and performance of opinion retrieval techniques proposed within the last 8 years. We identify major techniques in opinion retrieval and group them into four popular categories. We describe the state-of-the-art techniques for each category and emphasize on their performance and limitations. We then summarize with a performance comparison table for the techniques on different datasets. Finally, we highlight possible future research directions that can help solve existing challenges in opinion retrieval.

scholarly journals A Survey of the Application of Artifical Intellegence on COVID-19 Diagnosis and Prediction

2021 ◽  
Vol 11 (6) ◽  
pp. 7824-7835
Author(s):  
H. Alalawi ◽  
M. Alsuwat ◽  
H. Alhakami
Keyword(s):  
Machine Learning ◽  
State Of The Art ◽  
Future Research ◽  
Classification Algorithms ◽  
Classification Methods ◽  
Research Directions ◽  
The World ◽  
Modern Methods ◽  
Future Research Directions ◽  
Class Labels

The importance of classification algorithms has increased in recent years. Classification is a branch of supervised learning with the goal of predicting class labels categorical of new cases. Additionally, with Coronavirus (COVID-19) propagation since 2019, the world still faces a great challenge in defeating COVID-19 even with modern methods and technologies. This paper gives an overview of classification algorithms to provide the readers with an understanding of the concept of the state-of-the-art classification algorithms and their applications used in the COVID-19 diagnosis and detection. It also describes some of the research published on classification algorithms, the existing gaps in the research, and future research directions. This article encourages both academics and machine learning learners to further strengthen the basis of classification methods.

The state of the art in semantic relatedness: a framework for comparison

2017 ◽  
Vol 32 ◽  
Author(s):  
Yue Feng ◽  
Ebrahim Bagheri ◽  
Faezeh Ensan ◽  
Jelena Jovanovic
Keyword(s):  
State Of The Art ◽  
Semantic Relatedness ◽  
Knowledge Bases ◽  
The State ◽  
Future Research ◽  
Research Directions ◽  
Knowledge Resources ◽  
Future Research Directions ◽  
The Relationship ◽  
Sr Method

AbstractSemantic relatedness (SR) is a form of measurement that quantitatively identifies the relationship between two words or concepts based on the similarity or closeness of their meaning. In the recent years, there have been noteworthy efforts to compute SR between pairs of words or concepts by exploiting various knowledge resources such as linguistically structured (e.g. WordNet) and collaboratively developed knowledge bases (e.g. Wikipedia), among others. The existing approaches rely on different methods for utilizing these knowledge resources, for instance, methods that depend on the path between two words, or a vector representation of the word descriptions. The purpose of this paper is to review and present the state of the art in SR research through a hierarchical framework. The dimensions of the proposed framework cover three main aspects of SR approaches including the resources they rely on, the computational methods applied on the resources for developing a relatedness metric, and the evaluation models that are used for measuring their effectiveness. We have selected 14 representative SR approaches to be analyzed using our framework. We compare and critically review each of them through the dimensions of our framework, thus, identifying strengths and weaknesses of each approach. In addition, we provide guidelines for researchers and practitioners on how to select the most relevant SR method for their purpose. Finally, based on the comparative analysis of the reviewed relatedness measures, we identify existing challenges and potentially valuable future research directions in this domain.

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