Analysis and Recommendation of Outdoor Activities for Smart City Users Based on Real-Time Contextual Data
Smart cities leverage advanced technologies to enhance urban living through the real-time collection, processing, and analysis of contextual information. The potential to improve residents’ outdoor experiences in these cities increases dramatically as smart technologies are integrated into urban env...
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World Scientific Publishing
2024-01-01
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| Online Access: | https://www.worldscientific.com/doi/10.1142/S2972370124500041 |
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| author | S. R. Mani Sekhar D. M. Mushtaq Ahmed G. M. Siddesh |
| author_facet | S. R. Mani Sekhar D. M. Mushtaq Ahmed G. M. Siddesh |
| author_sort | S. R. Mani Sekhar |
| collection | DOAJ |
| description | Smart cities leverage advanced technologies to enhance urban living through the real-time collection, processing, and analysis of contextual information. The potential to improve residents’ outdoor experiences in these cities increases dramatically as smart technologies are integrated into urban environments, making them more interconnected. It helps to explore the pivotal role of real-time data in optimizing various aspects of city management, focusing on key domains such as traffic, public transportation, emergency response, waste management, and environmental monitoring. A variety of datasets, such as those on the weather, air quality, traffic patterns, event schedules, and user activity patterns, are gathered and analyzed as part of the methodology. This data are processed and interpreted using machine learning algorithms, which find correlations, trends, and patterns that affect outdoor activities. Suggestions for appropriate outdoor activities can be generated in real time based on contextual information, past behavior, and user preferences. This model addresses the dynamic and context-aware nature of Smart Cities by proposing a novel framework for real-time contextual information prediction and personalized outdoor activity suggestions for users. Leveraging the vast amount of data generated by Smart City infrastructure, this study integrates advanced data analysis techniques with deep learning models to enhance the urban living experience. A variety of datasets, such as those on the weather, air quality, traffic patterns, event schedules, and user activity patterns, are gathered and analyzed as part of the methodology. This data are processed and interpreted using machine learning algorithms, which find correlations, trends, and patterns that affect outdoor activities. Suggestions for appropriate outdoor activities can be generated in real time based on contextual information, past behavior, and user preferences. The framework begins by collecting and processing diverse datasets from sensors, Internet of Things (IoT) devices, and other urban sources to create a comprehensive understanding of the current city context. Deep learning models, such as recurrent neural networks (RNNs) and convolutional neural networks (CNNs), are employed to analyze this data and predict real-time contextual information, including weather conditions, traffic patterns, and social events. It contributes to the growing field of Smart Cities by introducing a scalable and adaptable framework that harnesses the power of deep learning to improve urban living. The result shows that the proposed air pollution model predicted 96.06700 PM2.5 concentration levels, subsequently the temperature model predicted 14.06800∘C. The integration of real-time contextual information prediction and personalized outdoor activity suggestions showcases the potential for creating more engaging and user-centric Smart City ecosystems. This research attempts to provide personalized recommendations that are in line with users’ preferences, the state of the environment at the time, and other pertinent contextual factors by utilizing data from multiple sources, including IoT devices, mobile applications, and environmental sensors. |
| format | Article |
| id | doaj-art-8d23c938daef4b708cfb0ab13e511653 |
| institution | Kabale University |
| issn | 2972-3701 |
| language | English |
| publishDate | 2024-01-01 |
| publisher | World Scientific Publishing |
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| series | Computing Open |
| spelling | doaj-art-8d23c938daef4b708cfb0ab13e5116532025-02-04T03:24:11ZengWorld Scientific PublishingComputing Open2972-37012024-01-010210.1142/S2972370124500041Analysis and Recommendation of Outdoor Activities for Smart City Users Based on Real-Time Contextual DataS. R. Mani Sekhar0D. M. Mushtaq Ahmed1G. M. Siddesh2Department of Information Science and Engineering, M. S. Ramaiah Institute of Technology, Bangalore, Karnataka, IndiaDepartment of Information Science and Engineering, M. S. Ramaiah Institute of Technology, Bangalore, Karnataka, IndiaDepartment of Artificial Intelligence and Data Science, M. S. Ramaiah Institute of Technology, Bangalore, Karnataka, IndiaSmart cities leverage advanced technologies to enhance urban living through the real-time collection, processing, and analysis of contextual information. The potential to improve residents’ outdoor experiences in these cities increases dramatically as smart technologies are integrated into urban environments, making them more interconnected. It helps to explore the pivotal role of real-time data in optimizing various aspects of city management, focusing on key domains such as traffic, public transportation, emergency response, waste management, and environmental monitoring. A variety of datasets, such as those on the weather, air quality, traffic patterns, event schedules, and user activity patterns, are gathered and analyzed as part of the methodology. This data are processed and interpreted using machine learning algorithms, which find correlations, trends, and patterns that affect outdoor activities. Suggestions for appropriate outdoor activities can be generated in real time based on contextual information, past behavior, and user preferences. This model addresses the dynamic and context-aware nature of Smart Cities by proposing a novel framework for real-time contextual information prediction and personalized outdoor activity suggestions for users. Leveraging the vast amount of data generated by Smart City infrastructure, this study integrates advanced data analysis techniques with deep learning models to enhance the urban living experience. A variety of datasets, such as those on the weather, air quality, traffic patterns, event schedules, and user activity patterns, are gathered and analyzed as part of the methodology. This data are processed and interpreted using machine learning algorithms, which find correlations, trends, and patterns that affect outdoor activities. Suggestions for appropriate outdoor activities can be generated in real time based on contextual information, past behavior, and user preferences. The framework begins by collecting and processing diverse datasets from sensors, Internet of Things (IoT) devices, and other urban sources to create a comprehensive understanding of the current city context. Deep learning models, such as recurrent neural networks (RNNs) and convolutional neural networks (CNNs), are employed to analyze this data and predict real-time contextual information, including weather conditions, traffic patterns, and social events. It contributes to the growing field of Smart Cities by introducing a scalable and adaptable framework that harnesses the power of deep learning to improve urban living. The result shows that the proposed air pollution model predicted 96.06700 PM2.5 concentration levels, subsequently the temperature model predicted 14.06800∘C. The integration of real-time contextual information prediction and personalized outdoor activity suggestions showcases the potential for creating more engaging and user-centric Smart City ecosystems. This research attempts to provide personalized recommendations that are in line with users’ preferences, the state of the environment at the time, and other pertinent contextual factors by utilizing data from multiple sources, including IoT devices, mobile applications, and environmental sensors.https://www.worldscientific.com/doi/10.1142/S2972370124500041Deep learningartificial intelligencesmart cityoutdoor activitiesair pollutiontemperature |
| spellingShingle | S. R. Mani Sekhar D. M. Mushtaq Ahmed G. M. Siddesh Analysis and Recommendation of Outdoor Activities for Smart City Users Based on Real-Time Contextual Data Computing Open Deep learning artificial intelligence smart city outdoor activities air pollution temperature |
| title | Analysis and Recommendation of Outdoor Activities for Smart City Users Based on Real-Time Contextual Data |
| title_full | Analysis and Recommendation of Outdoor Activities for Smart City Users Based on Real-Time Contextual Data |
| title_fullStr | Analysis and Recommendation of Outdoor Activities for Smart City Users Based on Real-Time Contextual Data |
| title_full_unstemmed | Analysis and Recommendation of Outdoor Activities for Smart City Users Based on Real-Time Contextual Data |
| title_short | Analysis and Recommendation of Outdoor Activities for Smart City Users Based on Real-Time Contextual Data |
| title_sort | analysis and recommendation of outdoor activities for smart city users based on real time contextual data |
| topic | Deep learning artificial intelligence smart city outdoor activities air pollution temperature |
| url | https://www.worldscientific.com/doi/10.1142/S2972370124500041 |
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