Emerging Strategies to Big Data Analytics in Healthcare

2021 ◽  
pp. 254-260
Author(s):  
Tanmayee Tushar Parbat ◽  
Rohan Benhal ◽  
Honey Jain ◽  
Dr. Vinayak Musale
Keyword(s):  
Big Data ◽  
Human Services ◽  
Social Insurance ◽  
Insurance Industry ◽  
Big Data Analytics ◽  
Clinical Assessments ◽  
The Social ◽  
Effective Administration ◽  
Clinical Records ◽  
General Wellbeing

Big data is gigantic measures of data that can do some incredible things. It has gotten a subject specifically compelling for as long as two decades in view of a high potential that is covered up in it. Different open and private part ventures create, store, and break down huge information to improve the administrations they give. In the social insurance industry, various hotspots for huge information incorporate emergency clinic records, clinical records of patients, aftereffects of clinical assessments, and gadgets that are a piece of the web of things. Biomedical examination additionally creates a critical bit of enormous information pertinent to open medicinal services. This information requires legitimate administration and examination to determine important data. Something else, looking for an answer by breaking down large information rapidly gets tantamount to finding a needle in the pile. There are different difficulties related with each progression of dealing with huge information which must be outperformed by utilizing very good quality registering answers for huge information investigation. That is the reason, to give significant answers for improving general wellbeing, social insurance suppliers are required to be completely outfitted with proper framework to produce and examine huge information methodically. Effective administration, examination, and understanding of large information can change the game by opening new roads for present day human services. That is exactly why different ventures, including the human services industry, are finding a way to change over this potential into better administrations and budgetary focal points. With a protected mix of biomedical and social insurance information, present day human services associations can upset the clinical treatments and customized medication.

Security in IoT and Big Data With Technical Challenges

2019 ◽  
pp. 197-210 ◽  
Author(s):  
Mamata Rath
Keyword(s):  
Big Data ◽  
Internet Of Things ◽  
Mobile Phones ◽  
Social Insurance ◽  
Insurance Industry ◽  
Big Data Analytics ◽  
Business Analytics ◽  
Security Issues ◽  
The Social ◽  
Massive Data Processing

There are higher rates of mobile devices in current networks that are associated with each other intelligently using internet of things (IoT) domain to make diverse communication in various applications. Therefore, security issues are a major concern to accomplish a protected communication. There are many functions and cryptographic algorithms developed during research which can be utilized as a part of the communication trade among the devices to make secure internet of things networks in an approach to ensure right transmission. Big data is the most demanding concept used in business analytics and massive data processing. It is heard all over the place, particularly in the social insurance industry. Traditionally, the tremendous measure of data produced by the medicinal services industry was put away as printed version. This data has the ability to help an extensive variety of medicinal services and restorative capacities. The digitization of such data is called big data. The chapter features critical security issues in IoT-associated devices both in wireless network and big data analytics along with all other widely used area where IoT environment has been implemented. It also describes security issues in IoT systems particularly when IoT computing devices are used in critical real-time applications such as utilizing IoT frameworks involving information transmission with mobile phones, existing secured systems for enormous information, and individual information security including verification and secured correspondence in IoT.

scholarly journals CONTRIBUTION OF PHARMACIST FOR TRANSFORMING GLOBAL HEALTH CARE SYSTEM: ROLES AND OPPURTUNITIES

2020 ◽  
pp. 11-12
Author(s):  
ZULFKAR LATIEF QADRIE ◽  
SHAHID UD DIN WANI ◽  
SURYA PRAKASH GAUTAM
Keyword(s):  
Human Services ◽  
Social Insurance ◽  
Toxic Substance ◽  
Essential Information ◽  
Drug Store ◽  
The Social ◽  
The World ◽  
Global Health Care ◽  
Clinical Drug ◽  
General Wellbeing

Fifty years ago, the essential job of drug specialists has only been to apportion solutions. Giving clinical drug store administrations at the medical clinics' wards spoke to the development of the calling. Be that as it may, the social insurance framework, around the world, is persistently creating. Today, the drug specialist's unmistakable new jobs have emphatically added to human services and society around the world. Drug specialists are presently accepting more prominent accountability for patients' wellbeing status and advancing the results of medication use. They give human services counsel and oversee interminable ailments. Different orders of social insurance where particular drug specialists are utilized to incorporate oncology offices, irresistible sickness control, general wellbeing, medicate data, toxicology and toxic substance control, atomic medication, and sustenance support. In any case, a fitting determination, instruction, preparing and workforce arranging to speak to an essential for the cutting edge drug store jobs. Specific instruction programs are required. New drug specialists should be appropriately qualified. By and by, rehearsing drug specialists need to adjust the essential information and required aptitudes, therefore, they can build up their training and jobs to address evolving issues.

scholarly journals Cardiovascular Disease Prediction and Classification using Modified Teaching Learning Optimization Method

2020 ◽  
Vol 9 (5) ◽  
pp. 808-813
Keyword(s):  
Data Mining ◽  
Cardiovascular Disease ◽  
Social Insurance ◽  
Insurance Industry ◽  
Optimization Method ◽  
Optimization Techniques ◽  
Prognostic Models ◽  
Features Selection ◽  
Teaching Learning ◽  
Clinical Records

Cardiovascular disease (CVD) is possibly the greatest reason for casualty and death rate among the number of inhabitants on the planet. Projection of cardiopathy is viewed as one of the most crucial subjects in the area of clinical records exploration. The measure of information in the social insurance industry is massive. The Data mining process transforms the huge range of unrefined medical service data into meaningful information that can lead to erudite decision and projection. Some recent investigations have applied data exploratory procedures too in CVD estimation. However, only very few studies have revealed the elements that play crucial role in envisioning CVDs. It is imperative to opt for the combination of correct and significant elements that can enhance the functioning of the forecasting prototypes. This study aims to ascertain meaningful elements and data mining procedures that can enrich the correctness of foretelling CVDs. Prognostic models were formulated employing distinctive blend of features selection modified teaching learning optimization techniques, SVM and boosting classification. Here the proposed strategy gives high precision outcomes with existing classification.

Big Data Analytics in the Healthcare Industry

2020 ◽  
pp. 160-178
Author(s):  
Arulkumar Varatharajan ◽  
Selvan C. ◽  
Vimalkumar Varatharajan
Keyword(s):  
Big Data ◽  
Data Analytics ◽  
Human Services ◽  
Big Data Analytics ◽  
Healthcare Industry ◽  
Multimodal Data ◽  
Design Structure ◽  
Costs Of Treatment ◽  
The Way

Big Data has changed the way we manage, analyze and impact the data information in any industry. A champion among the most promising zones where it will, in general, be associated with takeoff progress is therapeutic medicinal administrations. Administration examinations can diminish costs of treatment, foresee flare-ups of pestilences, keep up a key separation from preventable diseases and improve individual fulfillment overall. The chapter depicts the beginning field of a huge information investigation in human services, talks about the advantages, diagrams a design structure and approach, portrays models revealed in the writing, quickly examines the difficulties, and offers ends. A continuous examination which targets the utilization of tremendous volumes of remedial data information while combining multimodal data information from various sources is discussed. Potential locales of research inside this field which can give noteworthy impact on medicinal administrations movement are in like manner dissected.

scholarly journals Conceptual frameworks for social and cultural Big Data analytics: Answering the epistemological challenge

2019 ◽  
Vol 6 (1) ◽  
pp. 205395171882381 ◽  
Author(s):  
Lucy Resnyansky
Keyword(s):  
Big Data ◽  
Data Analytics ◽  
Big Data Analytics ◽  
Task Demands ◽  
Digital Data ◽  
Conceptual Frameworks ◽  
Social Scientists ◽  
The Social ◽  
Epistemological Challenge ◽  
Social Scientific

This paper aims to contribute to the development of tools to support an analysis of Big Data as manifestations of social processes and human behaviour. Such a task demands both an understanding of the epistemological challenge posed by the Big Data phenomenon and a critical assessment of the offers and promises coming from the area of Big Data analytics. This paper draws upon the critical social and data scientists’ view on Big Data as an epistemological challenge that stems not only from the sheer volume of digital data but, predominantly, from the proliferation of the narrow-technological and the positivist views on data. Adoption of the social-scientific epistemological stance presupposes that digital data was conceptualised as manifestations of the social. In order to answer the epistemological challenge, social scientists need to extend the repertoire of social scientific theories and conceptual frameworks that may inform the analysis of the social in the age of Big Data. However, an ‘epistemological revolution’ discourse on Big Data may hinder the integration of the social scientific knowledge into the Big Data analytics.

scholarly journals Human Behavior Analysis Using Intelligent Big Data Analytics

2021 ◽  
Vol 12 ◽  
Author(s):  
Muhammad Usman Tariq ◽  
Muhammad Babar ◽  
Marc Poulin ◽  
Akmal Saeed Khattak ◽  
Mohammad Dahman Alshehri ◽  
...  
Keyword(s):  
Social Media ◽  
Big Data ◽  
Human Behavior ◽  
Data Analytics ◽  
Data Science ◽  
Big Data Analytics ◽  
Apache Spark ◽  
Social Media Data ◽  
The Social ◽  
Media Data

Intelligent big data analysis is an evolving pattern in the age of big data science and artificial intelligence (AI). Analysis of organized data has been very successful, but analyzing human behavior using social media data becomes challenging. The social media data comprises a vast and unstructured format of data sources that can include likes, comments, tweets, shares, and views. Data analytics of social media data became a challenging task for companies, such as Dailymotion, that have billions of daily users and vast numbers of comments, likes, and views. Social media data is created in a significant amount and at a tremendous pace. There is a very high volume to store, sort, process, and carefully study the data for making possible decisions. This article proposes an architecture using a big data analytics mechanism to efficiently and logically process the huge social media datasets. The proposed architecture is composed of three layers. The main objective of the project is to demonstrate Apache Spark parallel processing and distributed framework technologies with other storage and processing mechanisms. The social media data generated from Dailymotion is used in this article to demonstrate the benefits of this architecture. The project utilized the application programming interface (API) of Dailymotion, allowing it to incorporate functions suitable to fetch and view information. The API key is generated to fetch information of public channel data in the form of text files. Hive storage machinist is utilized with Apache Spark for efficient data processing. The effectiveness of the proposed architecture is also highlighted.

scholarly journals A standards-based ontology and support for Big Data Analytics in the insurance industry

ICT Express ◽  
2017 ◽  
Vol 3 (2) ◽  
pp. 57-61
Author(s):  
Dimitrios A. Koutsomitropoulos ◽  
Aikaterini K. Kalou
Keyword(s):  
Big Data ◽  
Data Analytics ◽  
Insurance Industry ◽  
Big Data Analytics

scholarly journals Proactive Healthcare Monitoring using IOT

2019 ◽  
Vol 8 (2S10) ◽  
pp. 459-464
Keyword(s):  
Internet Of Things ◽  
Human Services ◽  
Social Insurance ◽  
Capacity Utilization ◽  
Data Access ◽  
Physiological Parameters ◽  
Small Scale ◽  
Amount Of Information ◽  
The Social ◽  
Healthcare Monitoring

Internet of Things (IOT) is a figuring technique, where each question is provided with sensors, small scale controllers and handsets for enabling correspondence and is made with fitting convention stacks that encourage them associating with each other and speaking with the clients. In IOT based human services, different circulated gadgets consolidate, examine and impart continuous medicinal information to the cloud, therefore making it conceivable to assemble, store and break down the gigantic amount of information in numerous new structures and enact setting based cautions. This novel information obtaining worldview licenses consistent and present medicinal data access from any associated gadget over the net. As the majority of the gadgets utilized in IOT are limited in battery control, it's best to limit the capacity utilization to fortify the lifetime of the social insurance framework. This work clarifies the execution of an IOT situated In-doctor's facility human services framework utilizing ZigBee work convention. The medicinal services framework execution can sporadically screen the physiological parameters of the In-doctor's facility patients. Hence, IOT spectred gadgets in the meantime upgrade the standard of consideration with customary perception and cut back the estimation of consideration and effectively take part in information combination and investigation of the equivalent.

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