LNTP-MDBN: Big Data Integrated Learning Framework for Heterogeneous Image Set Classification

2019 ◽  
Vol 15 (2) ◽  
pp. 227-236
Author(s):  
D. Franklin Vinod ◽  
V. Vasudevan
Keyword(s):  
Big Data ◽  
Medical Image ◽  
Research Study ◽  
Big Data Analytics ◽  
Learning Framework ◽  
Image Set Classification ◽  
Filtering Technique ◽  
Image Set ◽  
Hidden Patterns ◽  
Global Data

Background: With the explosive growth of global data, the term Big Data describes the enormous size of dataset through the detailed analysis. The big data analytics revealed the hidden patterns and secret correlations among the values. The major challenges in Big data analysis are due to increase of volume, variety, and velocity. The capturing of images with multi-directional views initiates the image set classification which is an attractive research study in the volumetricbased medical image processing. Methods: This paper proposes the Local N-ary Ternary Patterns (LNTP) and Modified Deep Belief Network (MDBN) to alleviate the dimensionality and robustness issues. Initially, the proposed LNTP-MDBN utilizes the filtering technique to identify and remove the dependent and independent noise from the images. Then, the application of smoothening and the normalization techniques on the filtered image improves the intensity of the images. Results: The LNTP-based feature extraction categorizes the heterogeneous images into different categories and extracts the features from each category. Based on the extracted features, the modified DBN classifies the normal and abnormal categories in the image set finally. Conclusion: The comparative analysis of proposed LNTP-MDBN with the existing pattern extraction and DBN learning models regarding classification accuracy and runtime confirms the effectiveness in mining applications.

FogLearn

2019 ◽  
pp. 1225-1241 ◽  
Author(s):  
Rabindra K. Barik ◽  
Rojalina Priyadarshini ◽  
Harishchandra Dubey ◽  
Vinay Kumar ◽  
Kunal Mankodiya
Keyword(s):  
Big Data ◽  
Fog Computing ◽  
Big Data Analytics ◽  
River Basin Management ◽  
Pathological Feature ◽  
Ganga Basin ◽  
Learning Framework ◽  
Geospatial Database ◽  
Patients With Diabetes ◽  
River Ganga

Big data analytics with the cloud computing are one of the emerging area for processing and analytics. Fog computing is the paradigm where fog devices help to reduce latency and increase throughput for assisting at the edge of the client. This article discusses the emergence of fog computing for mining analytics in big data from geospatial and medical health applications. This article proposes and develops a fog computing-based framework, i.e. FogLearn. This is for the application of K-means clustering in Ganga River Basin Management and real-world feature data for detecting diabetes patients suffering from diabetes mellitus. The proposed architecture employs machine learning on a deep learning framework for the analysis of pathological feature data that obtained from smart watches worn by the patients with diabetes and geographical parameters of River Ganga basin geospatial database. The results show that fog computing holds an immense promise for the analysis of medical and geospatial big data.

Semi-supervised learning framework based on statistical analysis for image set classification

2020 ◽  
Vol 107 ◽  
pp. 107500 ◽  
Author(s):  
Wenzhu Yan ◽  
Quansen Sun ◽  
Huaijiang Sun ◽  
Yanmeng Li
Keyword(s):  
Statistical Analysis ◽  
Supervised Learning ◽  
Learning Framework ◽  
Image Set Classification ◽  
Image Set

FogLearn

2018 ◽  
Vol 1 (1) ◽  
pp. 15-34 ◽  
Author(s):  
Rabindra K. Barik ◽  
Rojalina Priyadarshini ◽  
Harishchandra Dubey ◽  
Vinay Kumar ◽  
Kunal Mankodiya
Keyword(s):  
Big Data ◽  
Fog Computing ◽  
Big Data Analytics ◽  
River Basin Management ◽  
Pathological Feature ◽  
Ganga Basin ◽  
Learning Framework ◽  
Geospatial Database ◽  
Patients With Diabetes ◽  
River Ganga

Big data analytics with the cloud computing are one of the emerging area for processing and analytics. Fog computing is the paradigm where fog devices help to reduce latency and increase throughput for assisting at the edge of the client. This article discusses the emergence of fog computing for mining analytics in big data from geospatial and medical health applications. This article proposes and develops a fog computing-based framework, i.e. FogLearn. This is for the application of K-means clustering in Ganga River Basin Management and real-world feature data for detecting diabetes patients suffering from diabetes mellitus. The proposed architecture employs machine learning on a deep learning framework for the analysis of pathological feature data that obtained from smart watches worn by the patients with diabetes and geographical parameters of River Ganga basin geospatial database. The results show that fog computing holds an immense promise for the analysis of medical and geospatial big data.

scholarly journals Which Aspects of Big Data Usage is Creating Information Security Concern?

2018 ◽  
Vol 1 (4) ◽  
Author(s):  
Hira Batool ◽  
DU Rong
Keyword(s):  
Big Data ◽  
Information Security ◽  
Research Study ◽  
Big Data Analytics ◽  
Online Behavior ◽  
Security Issue ◽  
Data Usage ◽  
Theoretical Support ◽  
Proposed Model ◽  
Security Concern

The present research study proposed some of the big data usage perspective for testing either they have role in creating information security concern or not. The researchers first dig out some of the theoretical support for filling the gap regarding big data and information security bridge that was previously noted in the literature. The present researches approached big data analytics manager in the Pakistani banking industries for validating the proposed model. The Data was analyzed by using SPSS Andrews approach due to the nature of the research study. The findings revealed that proposed perspective including perceived benefits, cloud storage and online behavior monitoring should be test in the future studies by proposing their indirect affect in the creation of information security issue. The study brings new aspect in the literature of management regarding big data usage practices.

scholarly journals Cloud Enabled Predictive Big data Analytics Framework for Healthcare

2019 ◽  
Vol 8 (3) ◽  
pp. 3784-3789
Keyword(s):  
Big Data ◽  
Data Analytics ◽  
Predictive Analytics ◽  
Big Data Analytics ◽  
Detailed Examination ◽  
Healthcare Applications ◽  
Large Size ◽  
Architecture Framework ◽  
Framework Model ◽  
Hidden Patterns

Big data is one of the recently emerged domain in today's digital age where everything is being digitized. Very large size of data is produced from various organizations all through the globe .This very large size of data is termed as big data..Conventional databases are not able to handle the challenges with enormous data. Detailed examination of large amounts of data requires a lot of efforts at various levels with the aim of finding knowledge , hidden patterns for decision making. Big data analytics plays an essential character in order to attain predictive analytics in healthcare. Cloud systems can be employed for the storage of big data so that users can be able to access it from anywhere . The objective of this paper is to review the concept of big data , healthcare in the context of big data and cloud computing. It also proposes architecture framework model for predictive big data analytics in healthcare area. It also presents technology for big data analytics. This paper also addresses the use of big data analytics along with specifying healthcare applications

Big Data Analytics for Medical Image Analysis in Screening and Detection of Pancreatic Tumor

2019 ◽  
Author(s):  
K. Sujatha ◽  
R. Shobarani ◽  
J. Veerendra Kumar ◽  
V. Karthikeyan ◽  
Sai Krishna ◽  
...  
Keyword(s):  
Image Analysis ◽  
Big Data ◽  
Data Analytics ◽  
Medical Image ◽  
Pancreatic Tumor ◽  
Medical Image Analysis ◽  
Big Data Analytics

scholarly journals BIG DATA ANALYTICS: A PRIMER

2020 ◽  
Vol 5 (9) ◽  
pp. 44-49
Author(s):  
Matthew Sadiku ◽  
Justin Foreman ◽  
Sarhan Musa
Keyword(s):  
Social Media ◽  
Big Data ◽  
Data Analytics ◽  
Big Data Analytics ◽  
Smart Phones ◽  
The Internet ◽  
Paper Briefly ◽  
Digital Devices ◽  
Hidden Patterns ◽  
Internet And Social Media

The use of digital devices and systems such smart phones, computers, the Internet, and social media has resulted in a massive volume of data which is exponentially increasing daily. Such data is processed using multiple techniques, collectively known as big data analytics. Big data analytics is the process of examining large amounts of data (big data) to uncover hidden patterns, correlations, and other insights. Analyzing big data enables organizations and businesses to make better and faster decisions. This paper briefly presents the fundamental concepts of big data analytics and its tools.

Machine learning framework for predicting reliability of solder joints

2019 ◽  
Vol 32 (2) ◽  
pp. 82-92 ◽  
Author(s):  
Sung Yi ◽  
Robert Jones
Keyword(s):  
Machine Learning ◽  
Big Data ◽  
Data Analytics ◽  
Failure Modes ◽  
Solder Joints ◽  
Big Data Analytics ◽  
Content Type ◽  
Learning Framework ◽  
Surface Finishes ◽  
Reliability Of Solder Joints

Purpose This paper aims to present a machine learning framework for using big data analytics to predict the reliability of solder joints. The purpose of this study is to accurately predict the reliability of solder joints by using big data analytics. Design/methodology/approach A machine learning framework for using big data analytics is proposed to predict the reliability of solder joints accurately. Findings A machine learning framework for predicting the life of solder joints accurately has been developed in this study. To validate its accuracy and efficiency, it is applied to predict the long-term reliability of lead-free Sn96.5Ag3.0Cu0.5 (SAC305) for three commonly used surface finishes such OSP, ENIG and IAg. The obtained results show that the predicted failure based on the machine learning method is much more accurate than the Weibull method. In addition, solder ball/bump joint failure modes are identified based on various solder joint failures reported in the literature. Originality/value The ability to predict thermal fatigue life accurately is extremely valuable to the industry because it saves time and cost for product development and optimization.

scholarly journals Big Data: Definition, Architecture & Applications

2020 ◽  
Vol 4 (1) ◽  
Author(s):  
Humam Khalid Yaseen ◽  
Ahmed Mahdi Obaid
Keyword(s):  
Big Data ◽  
Data Analytics ◽  
Complex Structure ◽  
Big Data Analytics ◽  
Massive Data ◽  
Data Sets ◽  
Potential Applications ◽  
Data Definition ◽  
Hidden Patterns ◽  
Different Sources

Big data is a term for massive data sets having large, more varied and complex structure with the difficulties of storing, analyzing and visualizing for further processes or results. The process of research into massive amounts of data to reveal hidden patterns and secret correlations named as big data analytics. These useful informations for companies or organizations with the help of gaining richer and deeper insights and getting an advantage over the competition. For this reason, big data implementations need to be analyzed and executed as accurately as possible. In this paper; Firstly, we will discuss what big data and how it is defined according to different sources; Secondly, what are the characteristics of big data and where should it be used; Thirdly, the architecture of big data is discussed along with the different models of Big data; Fourthly, what are some potential applications of big data and how will it make the job easier for the persisting machines and users; Finally, we will discuss the future of Big data.

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