Amar Bangladesh - a Machine Learning Based Smart Tourist Guidance System

Ghani, Tasfiqul; Jahan, Nusrat; Ridoy, Sadman Hossain; Khan, Abu Talha; Khan, Saif A.; Khan, Mohammad Monirujjaman

doi:10.1109/iementech.2018.8465377

Cited by 8 publications

(2 citation statements)

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“…Techniques such as sentiment analysis [7], which will help in processing and filtering relevant information "in situ" to the tourist. In Bangladesh, there is a tourism application that has machine learning as a principle [8] , an application based on artificial intelligence and continuous data analysis, as for its design, the author proposes an interface based on the UCD, based on a design similar to Google Maps, since, as such, it is recommending intelligent routes.…”

Section: Theoretical Foundationmentioning

confidence: 99%

Development of mobile application prototype for smart tourism based on user-centered design

Reveles-Gómez

Luna-García

Celaya-Padilla

et al. 2021

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<p>In this paper, the implementation of ISO 9241- 210:2010 (Human Centred Design for Interactive Systems) standard for the development of a mobile application is presented in order to strengthen the user experience when using the mobile application in situ. Following the phases that the standard dictates for the development and evaluation of software and hardware in order to obtain a working prototype, and at the end of the process a product. The implementation of the standard allowed to generate an initial prototype validated by real users (tourists), so that, for future work will be carried out using artificial intelligence (AI) techniques and data analysis, these same, will complement this work, resulting in a fully validated and functional application for Smart Tourism. It should be noted that the purpose is to use User-Centered Design (UCD), thus achieving a high-fidelity prototype.</p>

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Section: Theoretical Foundationmentioning

confidence: 99%

Development of mobile application prototype for smart tourism based on user-centered design

Reveles-Gómez

Luna-García

Celaya-Padilla

et al. 2021

View full text Add to dashboard Cite

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“…Simultaneously, the growth in functionality of machine-learning techniques has created an intersection point which may provide benefits in the optimization of distributed systems. Current ML-enhanced applications range from tourist guidance [9] to malware detection [10] to cataract detection [11], and similar approaches may provide substantial benefits in the energy market. In particular, ML approaches directed at implementation in embedded and/or "server-less" or "edge" computing systems [12] make the combination of ML and applications such as voltage optimization a very fertile environment for exploration.…”

Section: Introductionmentioning

confidence: 99%

Dynamic Voltage Optimization Based on In-Band Sensors and Machine Learning

2019

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A feedback-based architecture is presented for the distribution grid which enables the use of Machine Learning (ML) techniques for various applications, including Dynamic Voltage Optimization (DVO) and Demand Response (DR). In this architecture, sensor devices are resident on the distribution grid and therefore have a unique awareness of multiple system parameters. This enables the use of ongoing ML techniques for implementation of critical applications in the Smart Grid. Monitoring devices are placed at the endpoints and monitoring/control devices are placed along the power line on various types of grid-resident systems. Because the devices are grid-resident and interact directly with other devices on the same physical link, applications such as ML-assisted DVO can be targeted with very high confidence.

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