Future smart cities: requirements, emerging technologies, applications, challenges, and future aspects
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TLDR
In this article , a survey on analyzing future technologies and requirements for future smart cities is presented, where the authors provide extensive research to identify and inspect the latest technology advancements, the foundation of the upcoming robust era, such as deep learning (DL), machine learning (ML), internet of things (IoT), mobile computing, big data, blockchain, sixth generation (6G) networks, WiFi-7, industry 5.0, robotic systems, heating ventilation, and air conditioning (HVAC), digital forensic, industrial control systems, connected and automated vehicles (CAVs), electric vehicles, product recycling, flying cars, pantry backup, calamity backup and vital integration of cybersecurity to keep the user concerns secured.About:
This article is published in Cities.The article was published on 2022-10-01 and is currently open access. It has received 80 citations till now. The article focuses on the topics: Backup & Smart city.read more
Citations
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Blockchain for Modern Applications: A Survey
TL;DR: This paper reviews the use of blockchain in several interesting fields, namely: finance, healthcare, information systems, wireless networks, Internet of Things, smart grids, governmental services, and military/defense, and identifies the challenges to overcome.
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Metaverse for Healthcare: A Survey on Potential Applications, Challenges and Future Directions
TL;DR: A comprehensive review of the Metaverse for healthcare, emphasizing on the state of the art, the enabling technologies to adopt the metaverse for health care, the potential applications, and the related projects are discussed in this article .
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Stakeholder-inclusive multi-criteria development of smart cities
TL;DR: Considering the flexibility and robustness of the Frank aggregation method, a new framework for smart city evaluation by integrating the Frank operator with q-ROFS was investigated in this article , where the authors presented q-rung orthopair fuzzy Frank weighted averaging/geometric operators.
Journal ArticleDOI
Ensemble deep learning for brain tumor detection
Shtwai Alsubai,Habib Ullah Khan,Abdullah Alqahtani,Mohemmed Sha,Sidra Abbas,Uzma Ghulam Mohammad +5 more
TL;DR: A hybrid deep learning model Convolutional Neural Network-Long Short Term Memory (CNN-LSTM) for classifying and predicting brain tumors through Magnetic Resonance Images (MRI) is proposed and experiment on an MRI brain image dataset.
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A Survey of Explainable Artificial Intelligence for Smart Cities
Abdul Rehman Javed,Waqas Ahmed,Sharnil Pandya,Praveen Kumar Reddy Maddikunta,Mamoun Alazab,Thippa Reddy Gadekallu +5 more
TL;DR: In this paper , the authors present the key to enabling explainable Artificial Intelligence (XAI) technologies for smart cities in detail and discuss the use cases, challenges, applications, possible alternative solutions and current and future research enhancements.
References
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Applications of Deep Reinforcement Learning in Communications and Networking: A Survey
Nguyen Cong Luong,Dinh Thai Hoang,Shimin Gong,Dusit Niyato,Ping Wang,Ying-Chang Liang,Dong In Kim +6 more
TL;DR: This paper presents a comprehensive literature review on applications of deep reinforcement learning (DRL) in communications and networking, and presents applications of DRL for traffic routing, resource sharing, and data collection.
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Smart Factory of Industry 4.0: Key Technologies, Application Case, and Challenges
TL;DR: A hierarchical architecture of the smart factory was proposed first, and then the key technologies were analyzed from the aspects of the physical resource layer, the network layer, and the data application layer, which showed that the overall equipment effectiveness of the equipment is significantly improved.
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Machine Learning: Algorithms, Real-World Applications and Research Directions
TL;DR: In this paper, the authors present a comprehensive view on these machine learning algorithms that can be applied to enhance the intelligence and the capabilities of an application and highlight the challenges and potential research directions based on their study.
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A Survey of Machine and Deep Learning Methods for Internet of Things (IoT) Security
TL;DR: A comprehensive survey of ML methods and recent advances in DL methods that can be used to develop enhanced security methods for IoT systems and presents the opportunities, advantages and shortcomings of each method.
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Are lodging customers ready to go green? An examination of attitudes, demographics, and eco-friendly intentions
TL;DR: In this article, the authors attempted to answer the following research questions: (1) Do eco-friendly attitudes affect hotel customers' environmentally friendly intentions to visit a green hotel, to spread word-of-mouth about green hotels, and to pay more for a Green hotel?; (2) If so, which facet of attitudes has the greatest impact?; and (3) How do their expressed intentions differ across gender, age, education, and household income?