IoT and Big Data Technologies for Monitoring and Processing Real-Time Healthcare Data

Kharbouch, Abdelhak; NaitMalek, Y.; Elkhoukhi, Hamza; Bakhouya, Mohamed; Florio, Vincenzo De; Ouadghiri, Moulay Driss El; Latré, Steven; Blondia, Chris

doi:10.4018/ijdst.2019100102

Cited by 14 publications

(10 citation statements)

References 22 publications

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“…However, this information is also stored to get a big picture of the patients" diseases over time. Ultimately, IoT working together with BGD results in improved efficiency, cost-saving and better use of resources [87][88][89][90].…”

Section: Findings and Discussionmentioning

confidence: 99%

Role of Emerging IoT Big Data and Cloud Computing for Real Time Application

Humayun¹

2020

IJACSA

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Although the Internet of things (IoT), cloud computing (CC), and Big Data (BGD) are three different approaches that have evolved independently of each other over time; however, with time, they are becoming increasingly interconnected. The convergence of IoT, CC, and BGD provides new opportunities in various real-time applications, including telecommunication, healthcare, business, education, science, and engineering. Together, these approaches are facing various challenges during data gathering, processing, and management. The focus of this research paper is to pinpoint the emerging trends in IoT, CC, and BGD. The convergence of these approaches and their impact on various real-time applications, benefits, and challenges associated with all these approaches, current industry trends, and future research directions with especial focus on the healthcare domain. The paper also provides a conceptual framework that integrates IoT, CC, and BGD and provides an IoT centric cloud infrastructure using BGD. Finally, this paper summarizes by providing directions for researchers and practitioners about how to leverage the benefits of combining these approaches.

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Section: Findings and Discussionmentioning

confidence: 99%

Role of Emerging IoT Big Data and Cloud Computing for Real Time Application

Humayun¹

2020

IJACSA

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“…The differences between the groups are as large as possible, and the differences within the groups are as small as possible. The difference between clustering and classification is that clustering does not depend on predefined classes and does not require a training set [11,12]; (4) association analysis: means that when the value of two or more data elements is represented and the probability is high, at time, there is a specific relationship, and the relationship law of these data elements can be determined [13]; (5) prediction: learn the law of change from the past data, and create a model, and use this model to predict the type and characteristics of future data, etc. [14]; (6) deviation detection (anomaly analysis): deviation detection is to detect significant changes and deviations between the current status of data, historical records, and standards.…”

Section: Constructionmentioning

confidence: 99%

Construction of an Intelligent Processing Platform for Equestrian Event Information Based on Data Fusion and Data Mining

Zhou

2021

Journal of Sensors

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In the past two years, equestrian sports have become more and more popular with the public. Due to the comprehensive development of equestrian preparations for the 2020 Olympic Games in China, the equestrian sports industry presents an unprecedented favorable development environment in China. This article is aimed at studying the construction of an equestrian event information intelligent processing platform based on data fusion and data mining. This article introduces the relevant theoretical knowledge of data mining and data fusion, including the description of the concept of data mining, the common analysis methods and algorithms of data mining, the basic concepts of data fusion, and the functional structure of data fusion. It discusses various algorithms in cluster analysis and focuses on the analysis of distance measurement and similarity coefficient in cluster analysis. In the experimental part, in order to intelligently process and acquire information, an information intelligent processing platform is constructed based on data fusion and data mining technology. The experimental results of this paper show that the precision rate, recall rate, and F -score of the platform under closed test are much higher than those under open test, and the precision rate is increased by about 7.26%.

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“…In fact, a myriad of sensors can be deployed for gathering contextual data that could be integrated with other data, such as location, weather data, and social media data [42]. The processing and analysis of these times series data allows the development of context-aware applications and services in many applications domains, such as in e-health [43], transportation [44], and energy management [39]. For instance, short-term forecasting of solar power production and utility demand could allow dynamic and predictive control of micro-grid energy systems [45].…”

Section: Related Workmentioning

confidence: 99%

Predicting the Future is Like Completing a Painting: Towards a Novel Method for Time-Series Forecasting

2021

Self Cite

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This article is an introductory work towards a larger research framework relative to Scientific Prediction. It is a mixed between science and philosophy of science, therefore we can talk about Experimental Philosophy of Science. As a first result, we introduce a new forecasting method based on image completion, named Forecasting Method by Image Inpainting (FM2I). In fact, time series forecasting is transformed into fully images-and signal-based processing procedures. After transforming a time series data into its corresponding image, the problem of data forecasting becomes essentially a problem of image inpainting problem, i.e., completing missing data in the image. Extensive experimental evaluation was conducted using the shortest series of the dataset proposed by the well-known M3-competition. Results show that FM2I, despite still being in its infancy, represents an efficient and robust tool for short-term time series forecasting. It has achieved prominent results in terms of accuracy and outperforms the best M3 methods. We have also investigated the effectiveness of the FM2I against the Smyl method, the winner of the M4 competition. Using the same category of shortest series, results show a close accuracy compared to the Smyl method. The FM2I is also able to generate ensemble data forecasts, which contain the best and more accurate forecast compared to existing and considered methods.

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IoT and Big Data Technologies for Monitoring and Processing Real-Time Healthcare Data

Cited by 14 publications

References 22 publications

Role of Emerging IoT Big Data and Cloud Computing for Real Time Application

Role of Emerging IoT Big Data and Cloud Computing for Real Time Application

Construction of an Intelligent Processing Platform for Equestrian Event Information Based on Data Fusion and Data Mining

Predicting the Future is Like Completing a Painting: Towards a Novel Method for Time-Series Forecasting

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