scholarly journals Understanding the Challenges and Opportunities with Big Data Applications over “Smart Healthcare System”

2017 ◽  
Vol 160 (8) ◽  
pp. 23-27 ◽  
Author(s):  
D. Saidulu ◽  
R. Sasikala
Keyword(s):  
Big Data ◽  
Healthcare System ◽  
Smart Healthcare ◽  
Big Data Applications ◽  
Challenges And Opportunities

Big Data Handling for Smart Healthcare System

2021 ◽  
pp. 93-115
Author(s):  
Arnaja Banerjee ◽  
Yashonidhi Srivastava ◽  
Souvik Ganguli
Keyword(s):  
Big Data ◽  
Healthcare System ◽  
Data Handling ◽  
Smart Healthcare

scholarly journals Analysis on E-Healthcare Monitoring System with Iot and Big Patient Data

2021 ◽  
Vol 10 (5) ◽  
pp. 97-102
Author(s):  
Deepa V. ◽  
Rajeswari, K.
Keyword(s):  
Big Data ◽  
Monitoring System ◽  
Healthcare System ◽  
Health Monitoring ◽  
New Technologies ◽  
Patient Data ◽  
Similar Data ◽  
Time Consumption ◽  
Smart Healthcare ◽  
Healthcare Monitoring

Internet of Things (IoT) technology helped the development of healthcare from face-to-face consulting to the telemedicine. Smart healthcare system in IoT environment monitored the patient basic health signs such as heart rate, body temperature, and hospital room condition in real-time applications. The IoT and big data is an important challenge in many fields including smart healthcare systems due to its significance. Big data is employed to analyse the huge volume of data. Big data are significantly used in healthcare technique to determine the normal and abnormal patient condition. The doctors are easily analysed the patient condition in a short time. This system is very easy to design and use. It is employed to enhance the present healthcare system which preserves the lot of lives from death. Healthcare monitoring system in hospitals has experienced large development and portable healthcare monitoring systems with new technologies. Connected healthcare is an essential solution for hospital to record and analyse the patient data and to save money. The clustering and classification methods are used in existing methods. The clustering method is employed to group the similar data. The classification method is utilized to classify the patient data. A lot of healthcare technique was introduced by many researchers ranging from diagnosis to treatment and prevention on efficient e-health monitoring system. But, the accuracy level was not improved and time consumption was not reduced by existing techniques. In order to address these problems, different methods and techniques were reviewed for performing the e-healthcare monitoring system with big data. The machine learning techniques are used for efficient diseased patient health monitoring through the effective performance of feature selection, clustering and patient classification with increase the accuracy and minimum time consumption. The results are is performed using on different factors such as clustering accuracy, clustering time, classification accuracy, classification time, and error rate with respect to number of patient data.

SMARTPHONE’S BIG DATA FOR HEALTHCARE SYSTEM

2017 ◽  
Author(s):  
Mohammed Shukur ◽  
Laith Fliah ◽  
Aram Abdulqadir
Keyword(s):  
Big Data ◽  
Healthcare System

Citizens, Elites, and Social Media Methodological Challenges and Opportunities in the Study of Persuasion and Mobilization

2019 ◽  
pp. 1036-1058
Author(s):  
Philip Habel ◽  
Yannis Theocharis
Keyword(s):  
Social Media ◽  
Big Data ◽  
Supply And Demand ◽  
Political Process ◽  
The Political ◽  
The Novel ◽  
Complete Understanding ◽  
Social Media Data ◽  
Challenges And Opportunities ◽  
Media Data

In the last decade, big data, and social media in particular, have seen increased popularity among citizens, organizations, politicians, and other elites—which in turn has created new and promising avenues for scholars studying long-standing questions of communication flows and influence. Studies of social media play a prominent role in our evolving understanding of the supply and demand sides of the political process, including the novel strategies adopted by elites to persuade and mobilize publics, as well as the ways in which citizens react, interact with elites and others, and utilize platforms to persuade audiences. While recognizing some challenges, this chapter speaks to the myriad of opportunities that social media data afford for evaluating questions of mobilization and persuasion, ultimately bringing us closer to a more complete understanding Lasswell’s (1948) famous maxim: “who, says what, in which channel, to whom, [and] with what effect.”

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