3D Virtual Reality Implementation of Tourist Attractions Based on the Deep Belief Neural Network

In today’s society, information technology is widely used, and virtual reality technology, as one of the emerging frontier technologies, has entered a stage of rapid development. Virtual reality is the use of computer technology to simulate the real-life environment into a virtual simulation environment, with the help of special equipment to realize the natural interaction between users and technical environment, in which the tourism industry is the most widely used. In order to realize 3D virtual reality of tourist attractions and improve users’ immersive experience in the process of interaction, the deep belief neural network is introduced to realize the target recognition and reconstruction in virtual reality. The results show that the algorithm has excellent performance in target recognition and target reconstruction, and deep belief networks improve the accuracy by 0.57% and 0.81% and the accuracy by 0.21% and 2.06%, respectively, compared with the current optimal algorithm in target recognition of 12 and 20 view regular projection images. Compared with the current optimal algorithm, deep belief networks are reduced by 0.2%, 3.7%, and 0.6%, respectively. The accuracy index was increased by 2%, 0.1%, and 0.1%, respectively. The above results show that the proposed algorithm based on the deep belief neural network can realize 3D virtual reality of complex scenes such as tourist attractions according to its excellent performance.

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APPLYING MODERN MEDIA TECHNOLOGIES TO PRESERVE THE CULTURAL SPACE OF HISTORICAL CITIES USING THE CASE OF ST. PETERSBURG: METHODICAL APPROACHES

Services in Russia and abroad ◽

10.22412/1995-042x-11-7-2 ◽

2018 ◽

pp. 17-31

Author(s):

Анна Антонова ◽

Anna Antonova ◽

Александра РАДУШИНСКАЯ ◽

Aleksandra RADUShINSKAYa ◽

Ольга ШАРАПОВА ◽

...

Keyword(s):

Virtual Reality ◽

Tourism Industry ◽

Virtual Space ◽

Tourist Attractions ◽

Media Technologies ◽

Managerial Decisions ◽

Tourist Flows ◽

Multimedia Technologies ◽

Historic City ◽

Historic Cities

The article is dedicated to the questions of using modern multimedia technologies for popularization of historical cities as tourist attractions from and for pre- serving cultural heritage by partially transferring the influence of mass tourist flows from the real world to the virtual reality. The authors formulate the main theses of the "new normality", reflecting the specifics of the current stage of economic development and characterizing the environment in which managerial decisions are currently being taken. The au- thors also highlight the main trends and challenges of this "new normality" influencing the development of tourism industry and, particularly, tourism in historical cities. The article notes that technical capabilities and marketing needs of the target audience of travelers should be taken into account. The authors classify modern, technically accessible technologies of augmented and virtual reality and their possibilities in the organization of tourism in historic cities. The article shows the role of storytelling technologies in the forming the audiences of potential tourists and the loyalty of attendees, who visit the historic city more than once. The results of online surveys conducted on a sufficiently wide sample substantiate the relevance of the approaches proposed by the authors. The material also shows the ways of using the augmented reality tools in historical cities. The authors raise the issues of the safe organization of virtual space in the historic cities and propose options for approbation of the developed approach in St. Petersburg.

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Human Action Recognition in Smart Cultural Tourism Based on Fusion Techniques of Virtual Reality and SOM Neural Network

Computational Intelligence and Neuroscience ◽

10.1155/2021/3495203 ◽

2021 ◽

Vol 2021 ◽

pp. 1-12

Author(s):

Zaosheng Ma

Keyword(s):

Neural Network ◽

Virtual Reality ◽

Action Recognition ◽

Recognition Rate ◽

Human Action Recognition ◽

Cultural Tourism ◽

Human Action ◽

Human Motion ◽

Tourism Industry ◽

Som Neural Network

Smart cultural tourism is the development trend of the future tourism industry. Virtual reality is an important tool to realize smart tourism. The reality of virtual reality mainly comes from human-computer interaction, which is closely related to human action recognition technology. Therefore, the research takes human action recognition as the research direction, uses a self-organizing mapping network (SOM) neural network to extract the key frame of action video, combines it with multi-feature vector method to recognize human action, and compares the recognition rate and user satisfaction of different recognition methods. The results show that the recognition rate of multi-feature voting human action recognition algorithm based on SOM neural network is 93.68% on UT-Kinect action, 59.06% on MSRDailyActivity3D, and the overall action recognition time is only 3.59 s. Within six months, the total profit of human-computer interactive virtual reality tourism project with SOM neural network multi-eigenvector as the core algorithm reached 422,000 yuan, and 88% of users expressed satisfaction after use. It shows that the proposed method has a good recognition rate and can give users effective feedback in time. It is hoped that this research has a certain reference value in promoting the development of human motion recognition technology.

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A Novel Hybrid Model for Predicting Traffic Flow via Improved Ensemble Learning Combined with Deep Belief Networks

Mathematical Problems in Engineering ◽

10.1155/2021/7328056 ◽

2021 ◽

Vol 2021 ◽

pp. 1-16

Author(s):

Yikang Rui ◽

Wenqi Lu ◽

Ziwei Yi ◽

Renfei Wu ◽

Bin Ran

Keyword(s):

Neural Network ◽

Traffic Flow ◽

Ensemble Learning ◽

Hybrid Model ◽

Urban Traffic ◽

Combination Method ◽

Belief Networks ◽

Deep Belief Networks ◽

Short Term ◽

Traffic State

The intelligent transportation system (ITS) plays an irreplaceable role in alleviating urban traffic congestion and realizing sustainable urban development. Accurate and efficient short-term traffic state forecasting is a significant issue in ITS. This study proposes a novel hybrid model (ELM-IBF) to predict the traffic state on urban expressways by taking advantage of both deep learning models and ensemble learning framework. First, a developed bagging framework is introduced to combine several deep belief networks (DBNs) that are utilized to capture the complicated temporal characteristic of traffic flow. Then, a novel combination method named improved Bayesian fusion (IBF) is proposed to replace the averaging method in the bagging framework since it can better fuse the prediction results of the component DBNs by assigning the reasonable weights to DBNs at each prediction time interval. Finally, the proposed hybrid model is validated with ground-truth traffic flow data captured by the remote traffic microwave sensors installed on the multiple road sections of 2nd Ring Road in Beijing. The experimental results illustrate that the ELM-IBF method can effectively capture sharp fluctuations in the traffic flow. Compared with several benchmark models (e.g., artificial neural network, long short-term memory neural network, and DBN), the ELM-IBF model reveals better performance in forecasting single-step-ahead traffic volume and speed. Additionally, it is proved that the ELM-IBF model is capable of providing stable and high-quality results in multistep-ahead traffic flow prediction.

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Neural Network Data Processing Technology Based on Deep Belief Networks

Communications in Computer and Information Science - Open Semantic Technologies for Intelligent System ◽

10.1007/978-3-030-60447-9_15 ◽

2020 ◽

pp. 234-244

Author(s):

Viktor V. Krasnoproshin ◽

Vadim V. Matskevich

Keyword(s):

Neural Network ◽

Data Processing ◽

Processing Technology ◽

Network Data ◽

Belief Networks ◽

Deep Belief Networks

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Competitive Deep-Belief Networks for Underwater Acoustic Target Recognition

Sensors ◽

10.3390/s18040952 ◽

2018 ◽

Vol 18 (4) ◽

pp. 952 ◽

Cited By ~ 21

Author(s):

◽

Keyword(s):

Target Recognition ◽

Belief Networks ◽

Deep Belief Networks ◽

Underwater Acoustic ◽

Acoustic Target

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Research on the Integration and Development of Culture and Tourism Based on Wireless Communication and Virtual Reality Technology

Wireless Communications and Mobile Computing ◽

10.1155/2021/8322092 ◽

2021 ◽

Vol 2021 ◽

pp. 1-6

Author(s):

HeChi Gan ◽

YaoGuang Li ◽

YaNan Song

Keyword(s):

Virtual Reality ◽

Wireless Communication ◽

Industrial Development ◽

Current Trend ◽

Modern Technology ◽

Tourism Industry ◽

Virtual Reality Technology ◽

Tourist Attractions ◽

Scenic Spot ◽

The Times

The interconnection of all things and industrial integration is the current trend of the times. Among them, the interconnection of all things is the demand of informationization of the times, and the industrial integration is the demand of industrial development. The interconnection of all things is realized based on wireless communication technology. It is necessary to combine the development of the tourist area and the surrounding culture. The relationship between tourist attractions and culture needs to be fully and effectively developed. In order to fully explore the advantages of the cooperation between the two, it is necessary to combine modern technology to package the tour process of each characteristic culture of the scenic spot. Virtual reality is a modern technology that can combine culture and tourism. Wireless communication and VR technology are applied to the development of integration of culture and tourism. The process of tourism will promote profound changes in the tourism model. Under the demand for informatization in various industries, the tourism industry is gradually developing in this direction. The integration of culture and tourism will also be driven by informatization and technology. This paper analyzes the current situation of culture and tourism, summarizes the problems existing in the current process of integration of culture and tourism, and finally puts forward targeted solutions. Mainly in the process of the integration and development of tourism and cultural industries, the traditional culture and scenic spots are the basic factors, combined with wireless communication and virtual reality technology, to develop a tourism industry with technological characteristics of the new era.

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Face Recognition Based on Fractal Code and Deep Belief Networks

Journal of Information Technology Research ◽

10.4018/jitr.2021100107 ◽

2021 ◽

Vol 14 (4) ◽

pp. 82-93

Author(s):

Mohamed Benouis

Keyword(s):

Neural Network ◽

Face Recognition ◽

Probabilistic Neural Network ◽

Belief Networks ◽

Human Face ◽

Structural Components ◽

Deep Belief Networks ◽

Space Reduction ◽

Face Images ◽

Nearest Neighbours

An enhanced algorithm to recognize the human face using bi-dimensional fractal codes and deep belief networks is presented in this work. The proposed method is experimentally robust against variations in the appearance of human face images, despite different disturbances affecting the measurements and the acquisition process such as occlusion, changes in lighting, pose, and expression or the presence or absence of structural components. That is mainly based on fractal codes (IFS) and bi-dimensional subspaces for features extraction and space reduction, combined with a deep belief network (DBN) classifier. The evaluation is performed through comparisons using probabilistic neural network (PNN) and nearest neighbours (KNN) approaches on three well-known databases (FERET, ORL, and FEI). The results suggest the effectiveness and robustness of the proposed approach.

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Speech/Music Classification Enhancement for 3GPP2 SMV Codec Based on Deep Belief Networks

IEICE Transactions on Fundamentals of Electronics Communications and Computer Sciences ◽

10.1587/transfun.e97.a.661 ◽

2014 ◽

Vol E97.A (2) ◽

pp. 661-664 ◽

Cited By ~ 2

Author(s):

Ji-Hyun SONG ◽

Hong-Sub AN ◽

Sangmin LEE

Keyword(s):

Belief Networks ◽

Deep Belief Networks ◽

Music Classification

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Influence of Primary Auditory Cortex in the Characterization of Autism Spectrum in Young Adults using Brain Connectivity Parameters and Deep Belief Networks: An fMRI Study

Current Medical Imaging Formerly Current Medical Imaging Reviews ◽

10.2174/1573405615666191111142039 ◽

2020 ◽

Vol 16 (9) ◽

pp. 1059-1073

Author(s):

Vidhusha Srinivasan ◽

N. Udayakumar ◽

Kavitha Anandan

Keyword(s):

Functional Connectivity ◽

Auditory Cortex ◽

Language Processing ◽

Brain Connectivity ◽

Primary Auditory Cortex ◽

Autism Spectrum ◽

Belief Networks ◽

Deep Belief Networks ◽

High Functioning ◽

Low Functioning

Background: The spectrum of autism encompasses High Functioning Autism (HFA) and Low Functioning Autism (LFA). Brain mapping studies have revealed that autism individuals have overlaps in brain behavioural characteristics. Generally, high functioning individuals are known to exhibit higher intelligence and better language processing abilities. However, specific mechanisms associated with their functional capabilities are still under research. Objective: This work addresses the overlapping phenomenon present in autism spectrum through functional connectivity patterns along with brain connectivity parameters and distinguishes the classes using deep belief networks. Methods: The task-based functional Magnetic Resonance Images (fMRI) of both high and low functioning autistic groups were acquired from ABIDE database, for 58 low functioning against 43 high functioning individuals while they were involved in a defined language processing task. The language processing regions of the brain, along with Default Mode Network (DMN) have been considered for the analysis. The functional connectivity maps have been plotted through graph theory procedures. Brain connectivity parameters such as Granger Causality (GC) and Phase Slope Index (PSI) have been calculated for the individual groups. These parameters have been fed to Deep Belief Networks (DBN) to classify the subjects under consideration as either LFA or HFA. Results: Results showed increased functional connectivity in high functioning subjects. It was found that the additional interaction of the Primary Auditory Cortex lying in the temporal lobe, with other regions of interest complimented their enhanced connectivity. Results were validated using DBN measuring the classification accuracy of 85.85% for high functioning and 81.71% for the low functioning group. Conclusion: Since it is known that autism involves enhanced, but imbalanced components of intelligence, the reason behind the supremacy of high functioning group in language processing and region responsible for enhanced connectivity has been recognized. Therefore, this work that suggests the effect of Primary Auditory Cortex in characterizing the dominance of language processing in high functioning young adults seems to be highly significant in discriminating different groups in autism spectrum.

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