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.

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.

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.

Fog Computing Realization for Big Data Analytics

2019 ◽  
pp. 259-290 ◽  
Author(s):  
Farhad Mehdipour ◽  
Bahman Javadi ◽  
Aniket Mahanti ◽  
Guillermo Ramirez-Prado
Keyword(s):  
Big Data ◽  
Data Analytics ◽  
Fog Computing ◽  
Big Data Analytics

IoT Big Data Architectures, Approaches, and Challenges

2021 ◽  
pp. 227-250
Author(s):  
David Sarabia-Jácome ◽  
Regel Gonzalez-Usach ◽  
Carlos E. Palau
Keyword(s):  
Big Data ◽  
Data Analytics ◽  
Fog Computing ◽  
Big Data Analytics ◽  
Late Response ◽  
Reference Architecture ◽  
Comprehensive Overview ◽  
High Bandwidth ◽  
Iot Devices ◽  
Application Developers

The internet of things (IoT) generates large amounts of data that are sent to the cloud to be stored, processed, and analyzed to extract useful information. However, the cloud-based big data analytics approach is not completely appropriate for the analysis of IoT data sources, and presents some issues and limitations, such as inherent delay, late response, and high bandwidth occupancy. Fog computing emerges as a possible solution to address these cloud limitations by extending cloud computing capabilities at the network edge (i.e., gateways, switches), close to the IoT devices. This chapter presents a comprehensive overview of IoT big data analytics architectures, approaches, and solutions. Particularly, the fog-cloud reference architecture is proposed as the best approach for performing big data analytics in IoT ecosystems. Moreover, the benefits of the fog-cloud approach are analyzed in two IoT application case studies. Finally, fog-cloud open research challenges are described, providing some guidelines to researchers and application developers to address fog-cloud limitations.

A Survey of Fog Computing-Based Healthcare Big Data Analytics and Its Security

2021 ◽  
Vol 12 (2) ◽  
pp. 53-72
Author(s):  
Rojalina Priyadarshini ◽  
Rabindra Kumar Barik ◽  
Harish Chandra Dubey ◽  
Brojo Kishore Mishra
Keyword(s):  
Big Data ◽  
Data Processing ◽  
Fog Computing ◽  
Big Data Analytics ◽  
Machine Intelligence ◽  
Future Research ◽  
Data Generation ◽  
Local Data ◽  
Research Areas ◽  
Computing Paradigm

Growing use of wearables within internet of things (IoT) creates ever-increasing multi-modal data from various smart health applications. The enormous volume of data generation creates new challenges in transmission, storage, and processing. There were challenges such as communication latency and data security associated with processing medical big data in cloud backend. Fog computing (FC) is an emerging distributed computing paradigm that solved these problems by leveraging local data processing, storage, filtering, and machine intelligence within an intermediate fog layer that resides between cloud and wearables devices. This paper focuses on doing survey on two major aspects of deploying fog computing for smart and connected health. Firstly, the role of machine learning-based edge intelligence in fog layer for data processing is investigated. A comprehensive analysis is provided during the survey, highlighting the strength and improvements in the existing literature. The paper ends with some open challenges and future research areas in the domain of fog-based healthcare.

Fog Computing for Big Data Analytics in IoT Aided Smart Grid Networks

2020 ◽  
Vol 114 (4) ◽  
pp. 3395-3418
Author(s):  
Md. Muzakkir Hussain ◽  
M. M. Sufyan Beg ◽  
Mohammad Saad Alam
Keyword(s):  
Big Data ◽  
Smart Grid ◽  
Data Analytics ◽  
Fog Computing ◽  
Big Data Analytics ◽  
Grid Networks
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