Big Data Analytics Processes in Industrial Internet of Things Systems: Sensing and Computing Technologies, Machine Learning Techniques, and Autonomous Decision-Making Algorithms

In the age of start-ups and technical research, the demand for high-end computing power and loads of space is ever increasing. Machine learning techniques have become an inseparable part of the big data analytics. Setting up one’s own infrastructure to deal with all this vastness is usually not feasible due to high expenses and lack of desired expertise. As a solution to this problem, this paper proposes a system for Big-Data Analytics and Machine Learning based on Hadoop and Spark frameworks that also supports Operating System (OS) Rental Services. Machine Learning (ML) services provide option to use both existing inbuilt popular models or create one’s own model. OS Rental services provide users with high end infrastructure on their low-end devices on rent. The entire implementation has been made open source for ease of access and facilitating extensibility.

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REVIEW OF MACHINE LEARNING TECHNIQUES FOR VOLUMINOUS INFORMATION MANAGEMENT

Journal of Soft Computing Paradigm - September 2019 ◽

10.36548/jscp.2019.2.005 ◽

2019 ◽

Vol 2019 (2) ◽

pp. 103-112

Author(s):

Dr. Pasumpon pandian

Keyword(s):

Machine Learning ◽

Big Data ◽

Data Analytics ◽

Learning Algorithms ◽

Big Data Analytics ◽

Machine Learning Algorithms ◽

Machine Learning Techniques ◽

Learning Techniques ◽

Technological Growth ◽

Rapid Pace

The recent technological growth at a rapid pace has paved way for the big data that denotes to the exponential growth of the information’s. The big data analytics are the trending concepts that have emerged as the promising technology that offers more enhanced perceptions from the huge set of the data that have been produced from the diverse areas. The review in the paper proceeds with the methods of the big-data-analytics and the machine-learning in handling, the huge set of data flow. The overview of the utilization of the machine-learning algorithms in the analytics of high voluminous data would provide with the deeper and the richer analysis of the huge set of information gathered to extract the valuable and turn it into actionable information’s. The paper is to review the part of machine-learning algorithms in the analytics of high voluminous data

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Industrial Big Data Analytics for Cognitive Internet of Things: Wireless Sensor Networks, Smart Computing Algorithms, and Machine Learning Techniques

Analysis and Metaphysics ◽

10.22381/am1820193 ◽

2019 ◽

Vol 18 (0) ◽

pp. 23 ◽

Cited By ~ 7

Keyword(s):

Machine Learning ◽

Big Data ◽

Data Analytics ◽

Big Data Analytics ◽

Machine Learning Techniques ◽

Wireless Sensor ◽

Learning Techniques ◽

Industrial Big Data ◽

Smart Computing ◽

Cognitive Internet Of Things

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Credit Rating Forecasting Using Machine Learning Techniques

Advances in Data Mining and Database Management - Managerial Perspectives on Intelligent Big Data Analytics ◽

10.4018/978-1-5225-7277-0.ch010 ◽

2019 ◽

pp. 180-198 ◽

Cited By ~ 1

Author(s):

Mark Wallis ◽

Kuldeep Kumar ◽

Adrian Gepp

Keyword(s):

Machine Learning ◽

Big Data ◽

Data Analytics ◽

Credit Ratings ◽

Relative Effectiveness ◽

Big Data Analytics ◽

Parametric Models ◽

Machine Learning Techniques ◽

Learning Techniques ◽

Missing Variables

Credit ratings are an important metric for business managers and a contributor to economic growth. Forecasting such ratings might be a suitable application of big data analytics. As machine learning is one of the foundations of intelligent big data analytics, this chapter presents a comparative analysis of traditional statistical models and popular machine learning models for the prediction of Moody's long-term corporate debt ratings. Machine learning techniques such as artificial neural networks, support vector machines, and random forests generally outperformed their traditional counterparts in terms of both overall accuracy and the Kappa statistic. The parametric models may be hindered by missing variables and restrictive assumptions about the underlying distributions in the data. This chapter reveals the relative effectiveness of non-parametric big data analytics to model a complex process that frequently arises in business, specifically determining credit ratings.

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An understanding of machine learning techniques in big data analytics: a survey

International Journal of Engineering & Technology ◽

10.14419/ijet.v7i2.33.15471 ◽

2018 ◽

Vol 7 (3.3) ◽

pp. 666

Author(s):

S Josephine Isabella ◽

Sujatha Srinivasan

Keyword(s):

Machine Learning ◽

Big Data ◽

Online Education ◽

Public Management ◽

Data Analytics ◽

Big Data Analytics ◽

Machine Learning Techniques ◽

Modern World ◽

Learning Techniques ◽

Tools And Techniques

Big data is a Firing Term in the recent era of the modern world, due to the information exploita-tion; there is an enormous amount of data produced. Big data is a powerful momentum of infor-mation and communication technology field due to the effect of growing data in healthcare, IOT, cloud computing, online education, online businesses, and public management. The produced data is not only large but also complex. Big data has a large amount of unstructured data so that there is a need to develop advanced tools and techniques for handling big data. Machine Learning is a prominent area of Artificial Intelligence. It makes the system to make intelligent resolutions by giving the knowledge to achieve the goals. This study reviews the various challenges and innovative ideas for big data analytics with machine learning in different fields over the past ten years. This paper mainly organized to identify the research projects based on the discussions over machine learning techniques for big data analytics and provide suggestions to develop the new projects.

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