Approximately 2.5 quintillion bytes of data are emitted on a daily basis, and this has brought the world into the era of ''big data.'' Artificial neural networks (ANNs) are known for their effectiveness and efficiency for small datasets, and this era of big data has posed a challenge to the big data analytics using ANN. Recently, much research effort has been devoted to the application of the ANN in big data analytics and is still ongoing, although it is in it is early stages. The purpose of this paper is to summarize recent progress, challenges, and opportunities for future research. This paper presents a concise view of the state of the art, challenges, and future research opportunities regarding the applications of the ANN in big data analytics and reveals that progress has been made in this area. Our review points out the limitations of the previous approaches, the challenges in the ANN approaches in terms of their applications in big data analytics, and several ANN architecture that have not yet been explored in big data analytics and opportunities for future research. We believe that this paper can serve as a yardstick for future progress on the applications of the ANN in big data analytics as well as a starting point for new researchers with an interest in the exploration of the ANN in big data analytics. INDEX TERMS Big data analytics, artificial neural networks, evolutionary neural network, convolutional neural network, dataset. The associate editor coordinating the review of this manuscript and approving it for publication was Shirui Pan. many organizations on an ongoing basis. These datasets are being collected from various sources, including but not limited to the World Wide Web (WWW), social networks and sensor networks [3]. The discovery of knowledge from unstructured data accumulated from the WWW remains a difficult task because the content is suitable for human consumption rather than for machines [4]. Experimental evidence has shown that if big datasets are exploited and managed properly, it can give rise to critical intelligence that can motivate informed decisions and
Recent technological developments and the availability of enormous amounts of real-time data have played a vital role in the expansion, evolution, and success of smart city projects. Smart data can be used in a variety of smart city applications, but difficulties in managing such data are pushing smart cities toward the adoption of data management frameworks. Many studies have brought into focus the importance of these frameworks as they combine data collection, processing, analysis, management, and visualization and provide privacy and security features for different smart city applications, i.e., transportation, to promote a better quality of life. This chapter highlights key components of the data management framework, reviews various smart city applications, and discusses privacy and security challenges associated with smart city data. From the perspective of data frameworks, it is seen that the data used in smart city applications is unstructured coming from heterogeneous sources, i.e., sensors and social media, besides others. Therefore, the collection, processing, analysis, management, and visualization of such data are challenging. To perform these tasks, recent technologies, i.e., Internet of Things (IoT), sensor networks, machine learning, etc., have been used. Moreover, the use of smart data for smart government and governance provides several facilities for the public and business. The smart data is revolutionizing the daily communication of users along with their mode of transportation by introducing Social IoT (SIoT) and autonomous vehicles. Lastly, the challenges related to privacy and security of the data in smart cities that needed to be addressed are highlighted. This chapter will guide academics and enterprises to progress in data management framework and its applications in smart cities in the near future.
The recent proliferation of ubiquitous computing technologies has led to the emergence of urban computing that aims to provide intelligent services to inhabitants of smart cities. Urban computing deals with enormous amounts of data collected from sensors and other sources in a smart city. In this article, we investigated and highlighted the role of urban computing in sustainable smart cities. In addition, a taxonomy was conceived that categorized the existing studies based on urban data, approaches, applications, enabling technologies, and implications. In this context, recent developments were elucidated. To cope with the engendered challenges of smart cities, we outlined some crucial use cases of urban computing. Furthermore, prominent use cases of urban computing in sustainable smart cities (e.g., planning in smart cities, the environment in smart cities, energy consumption in smart cities, transportation in smart cities, government policy in smart cities, and business processes in smart cities) for smart urbanization were also elaborated. Finally, several research challenges (such as cognitive cybersecurity, air quality, the data sparsity problem, data movement, 5G technologies, scaling via the analysis and harvesting of energy, and knowledge versus privacy) and their possible solutions in a new perspective were discussed explicitly.
The spread of COVID-19 across the world continues as efforts are being made from multi-dimension to curtail its spread and provide treatment. The COVID-19 triggered partial and full lockdown across the globe in an effort to prevent its spread. COVID-19 causes serious fatalities with United States of America recording over 3,000 deaths within 24 hours, the highest in the world for a single day. In this paper, we propose a framework integrated with machine learning to curtail the spread of COVID-19 in smart cities. A novel mathematical model is created to show the spread of the COVID-19 in smart cities. The proposed solution framework can generate, capture, store and analyze data using machine learning algorithms to detect, prevent the spread of COVID-19, forecast next epidemic, effective contact tracing, diagnose cases, monitor COVID-19 patient, COVID-19 vaccine development, track potential COVID-19 patients, aid in COVID-19 drug discovery and provide better understanding of the virus in smart cities. The study outlined case studies on the application of machine learning to help in the fight against COVID-19 in hospitals in smart cities across the world. The framework can provide a guide for real world execution in smart cities. The proposed framework has the potential for helping national healthcare systems in curtailing the COVID-19 pandemic in smart cities.
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