A Comparative Performance Evaluation of Machine Learning Algorithms for Fingerprinting Based Localization in DM-MIMO Wireless Systems Relying on Big Data Techniques

Machine Learning is part of Artificial Intelligence that has the ability to make future forecastings based on the previous experience. Methods has been proposed to construct models including machine learning algorithms such as Neural Networks (NN), Support Vector Machines (SVM) and Deep Learning. This paper presents a comparative performance of Machine Learning algorithms for cryptocurrency forecasting. Specifically, this paper concentrates on forecasting of time series data. SVM has several advantages over the other models in forecasting, and previous research revealed that SVM provides a result that is almost or close to actual result yet also improve the accuracy of the result itself. However, recent research has showed that due to small range of samples and data manipulation by inadequate evidence and professional analyzers, overall status and accuracy rate of the forecasting needs to be improved in further studies. Thus, advanced research on the accuracy rate of the forecasted price has to be done.

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Performance Evaluation of Different Machine Learning Algorithms in Prediction of Pandemic in Early Stages

GEDRAG & ORGANISATIE REVIEW ◽

10.37896/gor34.01/041 ◽

2021 ◽

Vol 34 (01) ◽

Author(s):

Hemalatha S ◽

◽

Nivedha S ◽

Dr. M Sangeetha ◽

Dr. K. E Kannammal ◽

...

Keyword(s):

Machine Learning ◽

Performance Evaluation ◽

Learning Algorithms ◽

Machine Learning Algorithms ◽

Early Stages

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Machine Learning Algorithms for Big Data Analytics

Computational Methods and Data Engineering - Advances in Intelligent Systems and Computing ◽

10.1007/978-981-15-6876-3_27 ◽

2020 ◽

pp. 359-367

Author(s):

Kumar Rahul ◽

Rohitash Kumar Banyal ◽

Puneet Goswami ◽

Vijay Kumar

Keyword(s):

Machine Learning ◽

Big Data ◽

Data Analytics ◽

Learning Algorithms ◽

Big Data Analytics ◽

Machine Learning Algorithms

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Machine Learning Algorithms for Big Data

Big Data Analytics: Systems, Algorithms, Applications ◽

10.1007/978-981-15-0094-7_6 ◽

2019 ◽

pp. 195-215

Author(s):

C.S.R. Prabhu ◽

Aneesh Sreevallabh Chivukula ◽

Aditya Mogadala ◽

Rohit Ghosh ◽

L.M. Jenila Livingston

Keyword(s):

Machine Learning ◽

Big Data ◽

Learning Algorithms ◽

Machine Learning Algorithms

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Essentiality of Machine Learning Algorithms for Big Data Computation

Advances in Data Mining and Database Management - Managing and Processing Big Data in Cloud Computing ◽

10.4018/978-1-4666-9767-6.ch011 ◽

2016 ◽

pp. 156-167

Author(s):

Manjunath Thimmasandra Narayanapppa ◽

T. P. Puneeth Kumar ◽

Ravindra S. Hegadi

Keyword(s):

Machine Learning ◽

Big Data ◽

Large Scale ◽

Learning Algorithms ◽

Big Data Analytics ◽

Machine Learning Algorithms ◽

Real Time Analysis ◽

Large Scale Data ◽

Computational Environment ◽

Large Scale Data Processing

Recent technological advancements have led to generation of huge volume of data from distinctive domains (scientific sensors, health care, user-generated data, finical companies and internet and supply chain systems) over the past decade. To capture the meaning of this emerging trend the term big data was coined. In addition to its huge volume, big data also exhibits several unique characteristics as compared with traditional data. For instance, big data is generally unstructured and require more real-time analysis. This development calls for new system platforms for data acquisition, storage, transmission and large-scale data processing mechanisms. In recent years analytics industries interest expanding towards the big data analytics to uncover potentials concealed in big data, such as hidden patterns or unknown correlations. The main goal of this chapter is to explore the importance of machine learning algorithms and computational environment including hardware and software that is required to perform analytics on big data.

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