Big Data Predictive Analysis for Detection of Prostate Cancer on Cloud-Based Platform

Web Services ◽  
2019 ◽  
pp. 933-952
Author(s):  
Ritesh Anilkumar Gangwal ◽  
Ratnadeep R. Deshmukh ◽  
M. Emmanuel
Keyword(s):  
Prostate Cancer ◽  
Big Data ◽  
Predictive Analytics ◽  
Confusion Matrix ◽  
Big Data Analytics ◽  
Cancer Growth ◽  
Predictive Analysis ◽  
Mri Scans ◽  
Microsoft Azure ◽  
Visual Artifacts

Big data as the name would refer to a subsequently large quantity of data which is being processed. With the advent of social media the data presently available is text, images, audio video. In order to process this data belonging to variety of format led to the concept of Big Data processing. To overcome these challenges of data, big data techniques evolved. Various tools are available for the big data naming MAP Reduce, etc. But to get the taste of Cloud based tool we would be working with the Microsoft Azure. Microsoft Azure is an integrated environment for the Big data analytics along with the SaaS Cloud platform. For the purpose of experiment, the Prostate cancer data is used to perform the predictive analysis for the Cancer growth in the gland. An experiment depending on the segmentation results of Prostate MRI scans is used for the predictive analytics using the SVM. Performance analysis with the ROC, Accuracy and Confusion matrix gives the resultant analysis with the visual artifacts. With the trained model, the proposed experiment can statistically predict the cancer growth.

Big Data Predictive Analysis for Detection of Prostate Cancer on Cloud-Based Platform

2017 ◽  
pp. 259-278
Author(s):  
Ritesh Anilkumar Gangwal ◽  
Ratnadeep R. Deshmukh ◽  
M. Emmanuel
Keyword(s):  
Prostate Cancer ◽  
Big Data ◽  
Predictive Analytics ◽  
Confusion Matrix ◽  
Big Data Analytics ◽  
Cancer Growth ◽  
Predictive Analysis ◽  
Mri Scans ◽  
Microsoft Azure ◽  
Visual Artifacts

Big data as the name would refer to a subsequently large quantity of data which is being processed. With the advent of social media the data presently available is text, images, audio video. In order to process this data belonging to variety of format led to the concept of Big Data processing. To overcome these challenges of data, big data techniques evolved. Various tools are available for the big data naming MAP Reduce, etc. But to get the taste of Cloud based tool we would be working with the Microsoft Azure. Microsoft Azure is an integrated environment for the Big data analytics along with the SaaS Cloud platform. For the purpose of experiment, the Prostate cancer data is used to perform the predictive analysis for the Cancer growth in the gland. An experiment depending on the segmentation results of Prostate MRI scans is used for the predictive analytics using the SVM. Performance analysis with the ROC, Accuracy and Confusion matrix gives the resultant analysis with the visual artifacts. With the trained model, the proposed experiment can statistically predict the cancer growth.

Big Data Analytics in Bioinformatics

Biotechnology ◽  
2019 ◽  
pp. 1967-1984
Author(s):  
Dharmendra Trikamlal Patel
Keyword(s):  
Big Data ◽  
Internet Of Things ◽  
Data Analytics ◽  
Big Data Analytics ◽  
Annual Growth ◽  
Predictive Analysis ◽  
The Internet ◽  
Data Generation ◽  
Intelligent Devices ◽  
The Internet Of Things

Voluminous data are being generated by various means. The Internet of Things (IoT) has emerged recently to group all manmade artificial things around us. Due to intelligent devices, the annual growth of data generation has increased rapidly, and it is expected that by 2020, it will reach more than 40 trillion GB. Data generated through devices are in unstructured form. Traditional techniques of descriptive and predictive analysis are not enough for that. Big Data Analytics have emerged to perform descriptive and predictive analysis on such voluminous data. This chapter first deals with the introduction to Big Data Analytics. Big Data Analytics is very essential in Bioinformatics field as the size of human genome sometimes reaches 200 GB. The chapter next deals with different types of big data in Bioinformatics. The chapter describes several problems and challenges based on big data in Bioinformatics. Finally, the chapter deals with techniques of Big Data Analytics in the Bioinformatics field.

Big Data and Analytics

2020 ◽  
pp. 47-65
Author(s):  
Sheik Abdullah A. ◽  
Priyadharshini P.
Keyword(s):  
Social Media ◽  
Big Data ◽  
Predictive Analytics ◽  
Data Warehousing ◽  
Big Data Analytics ◽  
Structured Data ◽  
Social Media Analytics ◽  
Data Set ◽  
Data Volume ◽  
Tools And Techniques

The term Big Data corresponds to a large dataset which is available in different forms of occurrence. In recent years, most of the organizations generate vast amounts of data in different forms which makes the context of volume, variety, velocity, and veracity. Big Data on the volume aspect is based on data set maintenance. The data volume goes to processing usual a database but cannot be handled by a traditional database. Big Data is stored among structured, unstructured, and semi-structured data. Big Data is used for programming, data warehousing, computational frameworks, quantitative aptitude and statistics, and business knowledge. Upon considering the analytics in the Big Data sector, predictive analytics and social media analytics are widely used for determining the pattern or trend which is about to happen. This chapter mainly deals with the tools and techniques that corresponds to big data analytics of various applications.

High Performance Storage for Big Data Analytics and Visualization

2018 ◽  
pp. 254-275
Author(s):  
Armando Fandango ◽  
William Rivera
Keyword(s):  
Big Data ◽  
High Speed ◽  
High Performance ◽  
File System ◽  
Predictive Analytics ◽  
Big Data Analytics ◽  
File Systems ◽  
Distributed Applications ◽  
System Level ◽  
File Formats

Scientific Big Data being gathered at exascale needs to be stored, retrieved and manipulated. The storage stack for scientific Big Data includes a file system at the system level for physical organization of the data, and a file format and input/output (I/O) system at the application level for logical organization of the data; both of them of high-performance variety for exascale. The high-performance file system is designed with concurrent access, high-speed transmission and fault tolerance characteristics. High-performance file formats and I/O are designed to allow parallel and distributed applications with easy and fast access to Big Data. These specialized file formats make it easier to store and access Big Data for scientific visualization and predictive analytics. This chapter provides a brief review of the characteristics of high-performance file systems such as Lustre and GPFS, and high-performance file formats such as HDF5, NetCDF, MPI-IO, and HDFS.

Predictive Analysis for Digital Marketing Using Big Data

2017 ◽  
pp. 259-283
Author(s):  
Balamurugan Balusamy ◽  
Priya Jha ◽  
Tamizh Arasi ◽  
Malathi Velu
Keyword(s):  
Artificial Intelligence ◽  
Social Media ◽  
Big Data ◽  
Statistical Analysis ◽  
Data Analytics ◽  
Big Data Analytics ◽  
Predictive Analysis ◽  
Cognitive Computing ◽  
Internet Resources ◽  
To Come

Big data analytics in recent years had developed lightning fast applications that deal with predictive analysis of huge volumes of data in domains of finance, health, weather, travel, marketing and more. Business analysts take their decisions using the statistical analysis of the available data pulled in from social media, user surveys, blogs and internet resources. Customer sentiment has to be taken into account for designing, launching and pricing a product to be inducted into the market and the emotions of the consumers changes and is influenced by several tangible and intangible factors. The possibility of using Big data analytics to present data in a quickly viewable format giving different perspectives of the same data is appreciated in the field of finance and health, where the advent of decision support system is possible in all aspects of their working. Cognitive computing and artificial intelligence are making big data analytical algorithms to think more on their own, leading to come out with Big data agents with their own functionalities.

Business Analytics and Big Data

2015 ◽  
pp. 1-11 ◽  
Author(s):  
Dennis T. Kennedy ◽  
Dennis M. Crossen ◽  
Kathryn A. Szabat
Keyword(s):  
Big Data ◽  
Data Analytics ◽  
Quantitative Methods ◽  
Business Processes ◽  
Predictive Analytics ◽  
Big Data Analytics ◽  
Database Systems ◽  
Three Dimensions ◽  
Business Analytics ◽  
The People

Big Data Analytics has changed the way organizations make decisions, manage business processes, and create new products and services. Business analytics is the use of data, information technology, statistical analysis, and quantitative methods and models to support organizational decision making and problem solving. The main categories of business analytics are descriptive analytics, predictive analytics, and prescriptive analytics. Big Data is data that exceeds the processing capacity of conventional database systems and is typically defined by three dimensions known as the Three V's: Volume, Variety, and Velocity. Big Data brings big challenges. Big Data not only has influenced the analytics that are utilized but also has affected technologies and the people who use them. At the same time Big Data brings challenges, it presents opportunities. Those who embrace Big Data and effective Big Data Analytics as a business imperative can gain competitive advantage.

scholarly journals Big Data Analytics For 6G

2020 ◽  
pp. 158-164
Author(s):  
Aarushi Sharma ◽  
Rahul Aggarwal ◽  
H. Srikanth Kamath
Keyword(s):  
Big Data ◽  
Communication System ◽  
Communication Systems ◽  
Data Analytics ◽  
Big Data Analytics ◽  
Predictive Analysis ◽  
Traffic Patterns

The scope of this paper is to analyse traffic demands for 6G communication systems. Since it is important to maintain the sustainability and competitiveness of the communication system, we need to invest in researching about what 6G would be like. I have taken an important possible application of 6G which is Data Analytics. I will be performing Predictive Analysis to predict the traffic patterns based on the traffic of each cell at any given time of the day.

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