scholarly journals A Study on Machine Learning in Big Data

2017 ◽  
Vol 10 (3) ◽  
pp. 660-663
Author(s):  
L. Dhanapriya ◽  
Dr. S. MANJU
Keyword(s):  
Machine Learning ◽  
Big Data ◽  
Large Data ◽  
Machine Learning Algorithms ◽  
Large Data Sets ◽  
Machine Learning Techniques ◽  
Data Sets ◽  
Huge Data ◽  
Learning Techniques ◽  
Market Needs

In the recent development of IT technology, the capacity of data has surpassed the zettabyte, and improving the efficiency of business is done by increasing the ability of predictive through an efficient analysis on these data which has emerged as an issue in the current society. Now the market needs for methods that are capable of extracting valuable information from large data sets. Recently big data is becoming the focus of attention, and using any of the machine learning techniques to extract the valuable information from the huge data of complex structures has become a concern yet an urgent problem to resolve. The aim of this work is to provide a better understanding of this Machine Learning technique for discovering interesting patterns and introduces some machine learning algorithms to explore the developing trend.

Deep Learning Approaches for Sentiment Analysis Challenges and Future Issues

2022 ◽  
pp. 27-50
Author(s):  
Rajalaxmi Prabhu B. ◽  
Seema S.
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Model Building ◽  
Large Data ◽  
Machine Learning Algorithms ◽  
Large Data Sets ◽  
Data Sets ◽  
Learning Approaches ◽  
Learning Techniques ◽  
Important Challenge

A lot of user-generated data is available these days from huge platforms, blogs, websites, and other review sites. These data are usually unstructured. Analyzing sentiments from these data automatically is considered an important challenge. Several machine learning algorithms are implemented to check the opinions from large data sets. A lot of research has been undergone in understanding machine learning approaches to analyze sentiments. Machine learning mainly depends on the data required for model building, and hence, suitable feature exactions techniques also need to be carried. In this chapter, several deep learning approaches, its challenges, and future issues will be addressed. Deep learning techniques are considered important in predicting the sentiments of users. This chapter aims to analyze the deep-learning techniques for predicting sentiments and understanding the importance of several approaches for mining opinions and determining sentiment polarity.

scholarly journals Machine learning in diachronic corpus phonology: mining verse data to infer trajectories in English phonotactics

2018 ◽  
Vol 3 ◽  
Author(s):  
Andreas Baumann
Keyword(s):  
Machine Learning ◽  
Middle English ◽  
Large Data ◽  
Large Data Sets ◽  
Machine Learning Techniques ◽  
Data Sets ◽  
Powerful Method ◽  
K Nearest Neighbors ◽  
Learning Techniques ◽  
Standard Techniques

Machine learning is a powerful method when working with large data sets such as diachronic corpora. However, as opposed to standard techniques from inferential statistics like regression modeling, machine learning is less commonly used among phonological corpus linguists. This paper discusses three different machine learning techniques (K nearest neighbors classifiers; Naïve Bayes classifiers; artificial neural networks) and how they can be applied to diachronic corpus data to address specific phonological questions. To illustrate the methodology, I investigate Middle English schwa deletion and when and how it potentially triggered reduction of final /mb/ clusters in English.

Analytics

2019 ◽  
pp. 170-179
Author(s):  
Mark E. Frisse ◽  
Karl E. Misulis
Keyword(s):  
Machine Learning ◽  
Quality Improvement ◽  
Cost Management ◽  
Large Data ◽  
Clinical Informatics ◽  
Large Data Sets ◽  
Machine Learning Techniques ◽  
Data Sets ◽  
Learning Techniques ◽  
Clinical Action

Healthcare analytics is a subject important to all informatics professionals, from providers to payers to regulators. The analysis of clinical and administrative data is essential to quality improvement, cost management, and research. With the advent of large data sets and sophisticated machine learning techniques, options are growing. Often, the weakness of an analytic approach is more due to a failure to ask a question that leads to clinical action or an inability to answer a question because the data available are not sufficient in either quality or quantity to address the primary concerns. Effective clinical informatics professionals focus on questions for which the data are sufficient and where answers can yield to improved actions.

Big Data-Based Spectrum Sensing for Cognitive Radio Networks Using Artificial Intelligence

2020 ◽  
pp. 146-159 ◽  
Author(s):  
Suriya Murugan ◽  
Sumithra M. G.
Keyword(s):  
Machine Learning ◽  
Big Data ◽  
Cognitive Radio ◽  
Spectrum Sensing ◽  
Machine Learning Algorithms ◽  
Machine Learning Techniques ◽  
Spectrum Utilization ◽  
Candidate Solution ◽  
Learning Techniques ◽  
Efficient Data

Cognitive radio has emerged as a promising candidate solution to improve spectrum utilization in next generation wireless networks. Spectrum sensing is one of the main challenges encountered by cognitive radio and the application of big data is a powerful way to solve various problems. However, for the increasingly tense spectrum resources, the prediction of cognitive radio based on big data is an inevitable trend. The signal data from various sources is analyzed using the big data cognitive radio framework and efficient data analytics can be performed using different types of machine learning techniques. This chapter analyses the process of spectrum sensing in cognitive radio, the challenges to process spectrum data and need for dynamic machine learning algorithms in decision making process.

What is Machine Learning? A Primer for the Epidemiologist

2019 ◽  
Author(s):  
Qifang Bi ◽  
Katherine E Goodman ◽  
Joshua Kaminsky ◽  
Justin Lessler
Keyword(s):  
Machine Learning ◽  
Big Data ◽  
Computer Science ◽  
Research Methods ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Machine Learning Techniques ◽  
Epidemiologic Research ◽  
Learning Techniques ◽  
Applications Of Machine Learning

Abstract Machine learning is a branch of computer science that has the potential to transform epidemiologic sciences. Amid a growing focus on “Big Data,” it offers epidemiologists new tools to tackle problems for which classical methods are not well-suited. In order to critically evaluate the value of integrating machine learning algorithms and existing methods, however, it is essential to address language and technical barriers between the two fields that can make it difficult for epidemiologists to read and assess machine learning studies. Here, we provide an overview of the concepts and terminology used in machine learning literature, which encompasses a diverse set of tools with goals ranging from prediction to classification to clustering. We provide a brief introduction to 5 common machine learning algorithms and 4 ensemble-based approaches. We then summarize epidemiologic applications of machine learning techniques in the published literature. We recommend approaches to incorporate machine learning in epidemiologic research and discuss opportunities and challenges for integrating machine learning and existing epidemiologic research methods.

scholarly journals Implementation of Supervised Learning towards Optimizing Queries in Database Systems

2019 ◽  
Vol 9 (2) ◽  
pp. 1182-1187
Keyword(s):  
Machine Learning ◽  
Supervised Learning ◽  
Student Loans ◽  
Large Data ◽  
Database Systems ◽  
Large Data Sets ◽  
Data Sets ◽  
Human Intervention ◽  
Huge Data ◽  
Future Direction

Machine learning is a technology which with accumulated data provides better decisions towards future applications. It is also the scientific study of algorithms implemented efficiently to perform a specific task without using explicit instructions. It may also be viewed as a subset of artificial intelligence in which it may be linked with the ability to automatically learn and improve from experience without being explicitly programmed. Its primary intention is to allow the computers learn automatically and produce more accurate results in order to identify profitable opportunities. Combining machine learning with AI and cognitive technologies can make it even more effective in processing large volumes human intervention or assistance and adjust actions accordingly. It may enable analyzing the huge data of information. It may also be linked to algorithm driven study towards improving the performance of the tasks. In such scenario, the techniques can be applied to judge and predict large data sets. The paper concerns the mechanism of supervised learning in the database systems, which would be self driven as well as secure. Also the citation of an organization dealing with student loans has been presented. The paper ends discussion, future direction and conclusion.

scholarly journals Relevant Independent Variables on MOBA Video Games to Train Machine Learning Algorithms

2021 ◽  
Author(s):  
Juan Guillermo López Guzmán ◽  
Cesar Julio Bustacara Medina
Keyword(s):  
Machine Learning ◽  
Video Games ◽  
Machine Learning Algorithms ◽  
Machine Learning Techniques ◽  
Multidimensional Data ◽  
Data Sets ◽  
Network Architectures ◽  
Independent Variables ◽  
Learning Techniques ◽  
Multidimensional Data Sets

Popularity of Multiplayer Online Battle Arena (MOBA) video games has grown considerably, its popularity as well as the complexity of their playability, have attracted the attention in recent years of researchers from various areas of knowledge and in particular how they have resorted to different machine learning techniques. The papers reviewed mainly look for patterns in multidimensional data sets. Furthermore, these previous researches do not present a way to select the independent variables (predictors) to train the models. For this reason, this paper proposes a list of variables based on the techniques used and the objectives of the research. It allows to provide a set of variables to find patterns applied in MOBA videogames. In order to get the mentioned list, the consulted works were grouped by the used machine learning techniques, ranging from rule-based systems to complex neural network architectures. Also, a grouping technique is applied based on the objective of each research proposed.

scholarly journals A distributed big data library extending Java 8

2017 ◽  
Vol 7 (1.1) ◽  
pp. 237
Author(s):  
MD. A R Quadri ◽  
B. Sruthi ◽  
A. D. SriRam ◽  
B. Lavanya
Keyword(s):  
Big Data ◽  
Distributed Computing ◽  
Programming Model ◽  
Large Data ◽  
Large Data Sets ◽  
Data Sets ◽  
Distributed Environment ◽  
Multiple Systems ◽  
Huge Data ◽  
Distributed Streams

Java is one of the finest language for big data because of its write once and run anywhere nature. The new release of java 8 introduced few strategies like lambda expressions and streams which are helpful for parallel computing. Though these new strategies helps in extracting, sorting and filtering data from collections and arrays, still there are problems with it. Streams cannot properly process with the large data sets like big data. Also, there are few problems associated while executing in distributed environment. The new streams introduced in java are restricted to computations inside the single system there is no method for distributed computing over multiple systems. And streams store data in their memory and therefore cannot support huge data sets. Now, this paper cope with java 8 behalf of massive data and deed in distributed environment by providing extensions to the Programming model with distributed streams. The distributed computing of large data programming models may be consummated by introducing distributed stream frameworks.

scholarly journals A system for analyzing large data sets using machine learning algorithms

2021 ◽  
pp. 142
Author(s):  
Sergey Pronin ◽  
Mykhailo Miroshnichenko
Keyword(s):  
Machine Learning ◽  
Learning Algorithms ◽  
Large Data ◽  
Machine Learning Algorithms ◽  
Large Data Sets ◽  
Data Sets

A system for analyzing large data sets using machine learning algorithms

scholarly journals REVIEW OF MACHINE LEARNING TECHNIQUES FOR VOLUMINOUS INFORMATION MANAGEMENT

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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