Smart Health for Smart Cities

The paper presents a recommendation model for developing new smart city and smart health projects. The objective is to provide recommendations to citizens about smart city and smart health startups to improve entrepreneurship and leadership. These recommendations may lead to the country’s advancement and the improvement of national income and reduce unemployment. This work focuses on designing and implementing an approach for processing and analyzing tweets inclosing data related to smart city and smart health startups and providing recommended projects as well as their required skills and competencies. This approach is based on tweets mining through a machine learning method, the Word2Vec algorithm, combined with a recommendation technique conducted via an ontology-based method. This approach allows discovering the relevant startup projects in the context of smart cities and makes links to the needed skills and competencies of users. A system was implemented to validate this approach. The attained performance metrics related to precision, recall, and F-measure are, respectively, 95%, 66%, and 79%, showing that the results are very encouraging.

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A Comprehensive Survey on Sharding in Blockchains

Mobile Information Systems ◽

10.1155/2021/5483243 ◽

2021 ◽

Vol 2021 ◽

pp. 1-22

Author(s):

Jinwen Xi ◽

Shihong Zou ◽

Guoai Xu ◽

Yanhui Guo ◽

Yueming Lu ◽

...

Keyword(s):

Smart Cities ◽

Detailed Comparison ◽

Smart Manufacturing ◽

Low Latency ◽

Communication Overhead ◽

P2p Networks ◽

Blockchain Technology ◽

Three Layer Architecture ◽

Comprehensive Survey ◽

Smart Health

Blockchain technology has been widely used in many fields, such as smart cities, smart health care, and smart manufacturing, due to its anonymity, decentralization, and tamper resistance in peer-to-peer (P2P) networks. However, poor scalability has severely affected the widespread adoption of traditional blockchain technology in high-throughput and low-latency applications. Therefore, based on the three-layer architecture, this study presents a variety of solutions to improve the scalability of the blockchain. As the scale of the network expands, one of the most practical ways to achieve horizontal scalability is sharding, where the network is divided into multiple subnetworks to avoid repeated communication overhead, storage, and calculations. This study provides a systematic and comprehensive introduction to blockchain sharding, along with a detailed comparison and evaluation for primarily considered sharding mechanisms. We also provide the detailed calculations and then analyze the characteristics of existing solutions along with our insights.

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Synergic Deep Learning for Smart Health Diagnosis of COVID-19 for Connected Living and Smart Cities

ACM Transactions on Internet Technology ◽

10.1145/3453168 ◽

2022 ◽

Vol 22 (3) ◽

pp. 1-14

Author(s):

K. Shankar ◽

Eswaran Perumal ◽

Mohamed Elhoseny ◽

Fatma Taher ◽

B. B. Gupta ◽

...

Keyword(s):

Deep Learning ◽

Smart Cities ◽

Imaging Techniques ◽

Input Image ◽

Significant Loss ◽

Deep Convolutional Neural Networks ◽

Smart Health ◽

And Control ◽

Health Diagnosis

COVID-19 pandemic has led to a significant loss of global deaths, economical status, and so on. To prevent and control COVID-19, a range of smart, complex, spatially heterogeneous, control solutions, and strategies have been conducted. Earlier classification of 2019 novel coronavirus disease (COVID-19) is needed to cure and control the disease. It results in a requirement of secondary diagnosis models, since no precise automated toolkits exist. The latest finding attained using radiological imaging techniques highlighted that the images hold noticeable details regarding the COVID-19 virus. The application of recent artificial intelligence (AI) and deep learning (DL) approaches integrated to radiological images finds useful to accurately detect the disease. This article introduces a new synergic deep learning (SDL)-based smart health diagnosis of COVID-19 using Chest X-Ray Images. The SDL makes use of dual deep convolutional neural networks (DCNNs) and involves a mutual learning process from one another. Particularly, the representation of images learned by both DCNNs is provided as the input of a synergic network, which has a fully connected structure and predicts whether the pair of input images come under the identical class. Besides, the proposed SDL model involves a fuzzy bilateral filtering (FBF) model to pre-process the input image. The integration of FBL and SDL resulted in the effective classification of COVID-19. To investigate the classifier outcome of the SDL model, a detailed set of simulations takes place and ensures the effective performance of the FBF-SDL model over the compared methods.

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Smart health: A context-aware health paradigm within smart cities

IEEE Communications Magazine ◽

10.1109/mcom.2014.6871673 ◽

2014 ◽

Vol 52 (8) ◽

pp. 74-81 ◽

Cited By ~ 286

Author(s):

Agusti Solanas ◽

Constantinos Patsakis ◽

Mauro Conti ◽

Ioannis Vlachos ◽

Victoria Ramos ◽

...

Keyword(s):

Smart Cities ◽

Context Aware ◽

Smart Health

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Smart Healthcare Devices for Smart Cities: A Review

Journal of Applied and Emerging Sciences ◽

10.36785/buitems.jaes.398 ◽

2020 ◽

pp. 98-101

Keyword(s):

Health Care ◽

Health Care System ◽

Health Care Professionals ◽

Smart City ◽

Health Care Services ◽

Smart Cities ◽

Care Services ◽

Smart Healthcare ◽

Smart Health ◽

Care System

Smart City has become increasingly important worldwide since the last decade. It is the advanced system for communication among people with smart infrastructure ingrained in the smart city. In the smart city, the infrastructure will track and manage all basic facilities, health care, law implementation, water supply, traffic, and transport. Improvement in smart sensor networks, ubiquitous computing, mobile cloud computing, and intellectual services for the communication of information among the sensors, all these facilities built the base for the smart city. The smart health care system will perform an important part in transforming old cities into smart cities. Telecommunication engineering scientists have prepared smarter health services which are improving the standards of living of the society. These health care services significantly develop the quality of health care services in hospitals and also decrease the burden of health care professionals and paramedical staff. This research article presents the applications of a smart health care system which will benefit everyone in the society by providing easy telecommunication access to health care professionals and patients. This system will also track the patient's health online using wearable and implantable devices.

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Attribute-Based Credentials for Privacy-Aware Smart Health Services in IoT-Based Smart Cities

Computer ◽

10.1109/mc.2018.3011042 ◽

2018 ◽

Vol 51 (7) ◽

pp. 44-53 ◽

Cited By ~ 7

Author(s):

Jose Maria de Fuentes ◽

Lorena Gonzalez-Manzano ◽

Agusti Solanas ◽

Fatbardh Veseli

Keyword(s):

Health Services ◽

Smart Cities ◽

Smart Health

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NORMALIZATION OF HEALTH RECORDS IN THE SERBIAN LANGUAGE WITH THE AIM OF SMART HEALTH SERVICES REALIZATION

Facta Universitatis Series Mathematics and Informatics ◽

10.22190/fumi2003825a ◽

2020 ◽

pp. 825

Author(s):

Aldina R. Avdić ◽

Ulfeta A. Marovac ◽

Dragan S. Janković

Keyword(s):

Medical Records ◽

Smart Cities ◽

Healthcare Services ◽

Health Records ◽

Smart Healthcare ◽

Textual Data ◽

Efficient Data ◽

Electronic Recording ◽

Smart Health ◽

Textual Form

The development of information technology increases its use in various spheres of human activity, including healthcare. Bundles of data and reports are generated and stored in textual form, such as symptoms, medical history, and doctor’s observations of patients' health. Electronic recording of patient data not only facilitates day-to-day work in hospitals, enables more efficient data management and reduces material costs, but can also be used for further processing and to gain knowledge to improve public health. Publicly available health data would contribute to the development of telemedicine, e-health, epidemic control, and smart healthcare within smart cities. This paper describes the importance of textual data normalization for smart healthcare services. An algorithm for normalizing medical data in Serbian is proposed in order to prepare them for further processing (F1-score=0,816), in this case within the smart health framework. By applying this algorithm, in addition to the normalized medical records, corpora of keywords and stop words, which are specific to the medical domain, are also obtained and can be used to improve the results in the normalization of medical textual data.

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Sensors for Context-Aware Smart Healthcare: A Security Perspective

Sensors ◽

10.3390/s21206886 ◽

2021 ◽

Vol 21 (20) ◽

pp. 6886

Author(s):

Edgar Batista ◽

M. Angels Moncusi ◽

Pablo López-Aguilar ◽

Antoni Martínez-Ballesté ◽

Agusti Solanas

Keyword(s):

Communication Networks ◽

Smart Cities ◽

Waiting Times ◽

Added Value ◽

Security And Privacy ◽

Context Aware ◽

Comprehensive Overview ◽

Smart Healthcare ◽

Smart Health ◽

Real Time Information

The advances in the miniaturisation of electronic devices and the deployment of cheaper and faster data networks have propelled environments augmented with contextual and real-time information, such as smart homes and smart cities. These context-aware environments have opened the door to numerous opportunities for providing added-value, accurate and personalised services to citizens. In particular, smart healthcare, regarded as the natural evolution of electronic health and mobile health, contributes to enhance medical services and people’s welfare, while shortening waiting times and decreasing healthcare expenditure. However, the large number, variety and complexity of devices and systems involved in smart health systems involve a number of challenging considerations to be considered, particularly from security and privacy perspectives. To this aim, this article provides a thorough technical review on the deployment of secure smart health services, ranging from the very collection of sensors data (either related to the medical conditions of individuals or to their immediate context), the transmission of these data through wireless communication networks, to the final storage and analysis of such information in the appropriate health information systems. As a result, we provide practitioners with a comprehensive overview of the existing vulnerabilities and solutions in the technical side of smart healthcare.

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