Design and implementation a network mobile application for plants shopping center using QR code

Nasir, Saja; Al-Qaraawi, Salih M.; Croock, Muayad Sadik

doi:10.11591/ijece.v10i6.pp5940-5950

Cited by 7 publications

(5 citation statements)

References 10 publications

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“…Looking at the scientific papers that belong to this parameter, we were able to find several topics that capture various mobile application services. These services include integration of the Android platform and GPS services in guiding tourists to their preferred sites [29], augmented reality for historical tourism using mobile and smart devices [30,31], the use of Mobile Applications for tracking tourist travel experiences [247], determining different types of tourists mobility and providing location-based services [20,248], and implementing QR systems for shopping centers [249].…”

Section: Mobile Applicationsmentioning

confidence: 99%

City 4.0: Urban Planning and Development in the Age of Digital Transformation

2024

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His research interestsare in design-build, project delivery systems, construction management, sustainable buildings, and built environment, particularly for older people. His research received funding from nationally competitive grant schemes and findings of these projects have been published in internationally leading high impact journals and presented at mainstream international conferences. Bo Xia is a co-director of the City 4.0 Lab located at the School of Architecture and Built Environment,

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Section: Mobile Applicationsmentioning

confidence: 99%

City 4.0: Urban Planning and Development in the Age of Digital Transformation

2024

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Section: Mobile Applicationsmentioning

confidence: 99%

Smarter Sustainable Tourism: Data-Driven Multi-Perspective Parameter Discovery for Autonomous Design and Operations

2023

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Global natural and manmade events are exposing the fragility of the tourism industry and its impact on the global economy. Prior to the COVID-19 pandemic, tourism contributed 10.3% to the global GDP and employed 333 million people but saw a significant decline due to the pandemic. Sustainable and smart tourism requires collaboration from all stakeholders and a comprehensive understanding of global and local issues to drive responsible and innovative growth in the sector. This paper presents an approach for leveraging big data and deep learning to discover holistic, multi-perspective (e.g., local, cultural, national, and international), and objective information on a subject. Specifically, we develop a machine learning pipeline to extract parameters from the academic literature and public opinions on Twitter, providing a unique and comprehensive view of the industry from both academic and public perspectives. The academic-view dataset was created from the Scopus database and contains 156,759 research articles from 2000 to 2022, which were modelled to identify 33 distinct parameters in 4 categories: Tourism Types, Planning, Challenges, and Media and Technologies. A Twitter dataset of 485,813 tweets was collected over 18 months from March 2021 to August 2022 to showcase the public perception of tourism in Saudi Arabia, which was modelled to reveal 13 parameters categorized into two broader sets: Tourist Attractions and Tourism Services. The paper also presents a comprehensive knowledge structure and literature review of the tourism sector based on over 250 research articles. Discovering system parameters are required to embed autonomous capabilities in systems and for decision-making and problem-solving during system design and operations. The work presented in this paper has significant theoretical and practical implications in that it improves AI-based information discovery by extending the use of scientific literature, Twitter, and other sources for autonomous, holistic, dynamic optimizations of systems, promoting novel research in the tourism sector and contributing to the development of smart and sustainable societies.

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“…Looking at the scientific papers that belong to this parameter we were able to find several topics that capture various mobile application services. These services include integration of the Android platform and GPS services in guiding tourists to their preferred sites [18], augmented reality for historical tourism using mobile and smart devices [19], [20], the use of Mobile Applications for tracking tourist travel experiences [241], determining different types of tourists mobility and providing location-based services [9], [242], and implementing QR systems for shopping centers [243].…”

Section: Mobile Applicationsmentioning

confidence: 99%

Smarter Sustainable Tourism: Data-Driven Multi-Perspective Parameter Discovery for Autonomous Design and Operations

Alsahafi¹,

Alzahrani²,

Mehmood³

2023

Preprint

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The Global natural and manmade events are exposing the fragility of the tourism industry and its impact on the global economy. Prior to the COVID-19 pandemic, tourism contributed 10.3% to the global GDP and employed 333 million people but saw a significant decline due to the pandemic. Sustainable and smart tourism requires collaboration from all stakeholders and a comprehensive understanding of global and local issues to drive responsible and innovative growth in the sector. This paper presents an approach for leveraging big data and deep learning to dis-cover holistic, multi-perspective (e.g., local, cultural, national, and international) and objective information on a subject. Specifically, we develop a machine learning pipeline to extract parameters from academic literature and public opinions on Twitter, providing a unique and comprehensive view of the industry from both academic and public perspectives. The academic-view dataset was created from the Scopus database and contains 156,759 research articles from 2000 to 2022, which were modelled to identify 33 distinct parameters in 4 categories: Tourism Types, Planning, Challenges, and Media &amp; Technologies. A Twitter dataset of 485,813 tweets was collected over 18 months starting March 2021 to August 2022 to showcase public perception of tourism in Saudi Arabia, which was modelled to reveal 13 parameters categorized into two broader sets: Tourist Attractions and Tourism Services. Discovering system parameters are re-quired to embed autonomous capabilities in systems and for decision-making and problem-solving during system design and operations. The proposed approach improves AI-based information discovery by extending the use of scientific literature, Twitter, and other sources for autonomous, dynamic optimizations of systems, promoting novel research in the tourism sector and contributing to the development of smart and sustainable societies. The paper also presents a comprehensive knowledge structure and literature review of the tourism sector based on over 250 research articles.

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Design and implementation a network mobile application for plants shopping center using QR code

Cited by 7 publications

References 10 publications

City 4.0: Urban Planning and Development in the Age of Digital Transformation

City 4.0: Urban Planning and Development in the Age of Digital Transformation

Smarter Sustainable Tourism: Data-Driven Multi-Perspective Parameter Discovery for Autonomous Design and Operations

Smarter Sustainable Tourism: Data-Driven Multi-Perspective Parameter Discovery for Autonomous Design and Operations

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