| 1. |
- Javed, Abdul Rehman, et al.
(författare)
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A Survey of Explainable Artificial Intelligence for Smart Cities
- 2023
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Ingår i: Electronics. - : MDPI. - 2079-9292. ; 12:4
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Tidskriftsartikel (refereegranskat)abstract
- The emergence of Explainable Artificial Intelligence (XAI) has enhanced the lives of humans and envisioned the concept of smart cities using informed actions, enhanced user interpretations and explanations, and firm decision-making processes. The XAI systems can unbox the potential of black-box AI models and describe them explicitly. The study comprehensively surveys the current and future developments in XAI technologies for smart cities. It also highlights the societal, industrial, and technological trends that initiate the drive towards XAI for smart cities. It presents the key to enabling XAI technologies for smart cities in detail. The paper also discusses the concept of XAI for smart cities, various XAI technology use cases, challenges, applications, possible alternative solutions, and current and future research enhancements. Research projects and activities, including standardization efforts toward developing XAI for smart cities, are outlined in detail. The lessons learned from state-of-the-art research are summarized, and various technical challenges are discussed to shed new light on future research possibilities. The presented study on XAI for smart cities is a first-of-its-kind, rigorous, and detailed study to assist future researchers in implementing XAI-driven systems, architectures, and applications for smart cities
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| 2. |
- Pandya, Sharnil, Researcher, 1984-, et al.
(författare)
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COUNTERSAVIOR : AIoMT and IIoT enabled Adaptive Virus Outbreak Discovery Framework for Healthcare Informatics
- 2023
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Ingår i: IEEE Internet of Things Journal. - : IEEE. - 2327-4662 .- 2372-2541. ; 10:4, s. 4202-4212
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Tidskriftsartikel (refereegranskat)abstract
- In the current Pandemic, global issues have caused health issues as well as economic downturns. At the beginning of every novel virus outbreak, lockdown is the best possible weapon to reduce the virus spread and save human life as the medical diagnosis followed by treatment and clinical approval takes significant time. The proposed COUNTERSAVIOR system aims at an Artificial Intelligence of Medical Things (AIoMT), and an edge line computing enabled and Big data analytics supported tracing and tracking approach that consumes GPS spatiotemporal data. COUNTERSAVIOR will be a better scientific tool to handle any virus outbreak. The proposed research discovers the prospect of applying an individual’s mobility to label mobility streams and forecast a virus such as COVID-19 pandemic transmission. The proposed system is the extension of the previously proposed COUNTERACT system. The proposed system can also identify the alternative saviour path concerning the confirmed subject’s cross-path using GPS data to avoid the possibility of infections. In the undertaken study, dynamic meta direct and indirect transmission, meta behaviour, and meta transmission saviour models are presented. In conducted experiments, the machine learning and deep learning methodologies have been used with the recorded historical location data for forecasting the behaviour patterns of confirmed and suspected individuals and a robust comparative analysis is also presented. The proposed system produces a report specifying people that have been exposed to the virus and notifying users about available pandemic saviour paths. In the end, we have represented 3D tracker movements of individuals, 3D contact analysis of COVID-19 and suspected individuals for 24 hours, forecasting and risk classification of COVID-19, suspected and safe individuals.
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| 3. |
- Pandya, Sharnil, Researcher, 1984-, et al.
(författare)
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Federated learning for smart cities : A comprehensive survey
- 2023
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Ingår i: Sustainable Energy Technologies and Assessments. - : Elsevier. - 2213-1388 .- 2213-1396. ; 55
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Tidskriftsartikel (refereegranskat)abstract
- With the advent of new technologies such as the Artificial Intelligence of Things (AIoT), big data, fog computing, and edge computing, smart city applications have suffered from issues, such as leakage of confidential and sensitive information. To envision smart cities, it will be necessary to integrate federated learning (FL) with smart city applications. FL integration with smart city applications can provide privacy preservation and sensitive information protection. In this paper, we present a comprehensive overview of the current and future developments of FL for smart cities. Furthermore, we highlight the societal, industrial, and technological trends driving FL for smart cities. Then, we discuss the concept of FL for smart cities, and numerous FL integrated smart city applications, including smart transportation systems, smart healthcare, smart grid, smart governance, smart disaster management, smart industries, and UAVs for smart city monitoring, as well as alternative solutions and research enhancements for the future. Finally, we outline and analyze various research challenges and prospects for the development of FL for smart cities.
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