A Hybrid Data Analytic Approach to Evaluate the Performance of Stirling Engine using Machine Learning Techniques

2021 ◽  
Author(s):  
A. Sheik Abdullah ◽  
A. Manoj ◽  
S. Selvakumar
Keyword(s):  
Machine Learning ◽  
Stirling Engine ◽  
Analytic Approach ◽  
Machine Learning Techniques ◽  
Learning Techniques ◽  
Hybrid Data ◽  
Data Analytic

scholarly journals Simulating a Stochastic Signal of Urban Water Demand by a Novel Combination of Data Analytic and Machine Learning Techniques

2021 ◽  
Vol 1058 (1) ◽  
pp. 012066
Author(s):  
Salah L. Zubaidi ◽  
Hussein Al-Bugharbee ◽  
Yousif Raad Muhsin ◽  
Sadik Kamel Gharghan ◽  
Khalid Hashim ◽  
...  
Keyword(s):  
Machine Learning ◽  
Water Demand ◽  
Machine Learning Techniques ◽  
Urban Water ◽  
Stochastic Signal ◽  
Urban Water Demand ◽  
Learning Techniques ◽  
Data Analytic

Discovering Knowledge Hidden in Big Data from Machine-Learning Techniques

2017 ◽  
pp. 167-183 ◽  
Author(s):  
Adiraju Prashantha Rao
Keyword(s):  
Machine Learning ◽  
Big Data ◽  
Data Analytics ◽  
Decision Makers ◽  
Machine Learning Techniques ◽  
Human Beings ◽  
Raw Data ◽  
Learning Techniques ◽  
Data Analytic ◽  
Information Growth

As the speed of information growth exceeds in this new century, excessive data is making great troubles to human beings. However, there are so much potential and highly useful values hidden in the huge volume of data. Big Data has drawn huge attention from researchers in information sciences, policy and decision makers in governments and enterprises. Data analytic is the science of examining raw data with the purpose of drawing conclusions about that information. Data analytics is about discovering knowledge from large volumes data and applying it to the business. Machine learning is ideal for exploiting the opportunities hidden in big data. This chapter able to discover and display the patterns buried in the data using machine learning.

Discovering Knowledge Hidden in Big Data From Machine-Learning Techniques

Web Services ◽  
2019 ◽  
pp. 684-700
Author(s):  
Adiraju Prashantha Rao
Keyword(s):  
Machine Learning ◽  
Big Data ◽  
Data Analytics ◽  
Decision Makers ◽  
Machine Learning Techniques ◽  
Human Beings ◽  
Raw Data ◽  
Learning Techniques ◽  
Data Analytic ◽  
Information Growth

As the speed of information growth exceeds in this new century, excessive data is making great troubles to human beings. However, there are so much potential and highly useful values hidden in the huge volume of data. Big Data has drawn huge attention from researchers in information sciences, policy and decision makers in governments and enterprises. Data analytic is the science of examining raw data with the purpose of drawing conclusions about that information. Data analytics is about discovering knowledge from large volumes data and applying it to the business. Machine learning is ideal for exploiting the opportunities hidden in big data. This chapter able to discover and display the patterns buried in the data using machine learning.

Using machine learning techniques to reduce data annotation time

2006 ◽  
Author(s):  
Christopher Schreiner ◽  
Kari Torkkola ◽  
Mike Gardner ◽  
Keshu Zhang
Keyword(s):  
Machine Learning ◽  
Machine Learning Techniques ◽  
Data Annotation ◽  
Learning Techniques

Using Machine Learning Algorithms on Prediction of Stock Price

2020 ◽  
Vol 12 (2) ◽  
pp. 84-99
Author(s):  
Li-Pang Chen
Keyword(s):  
Machine Learning ◽  
Stock Price ◽  
Short Term Memory ◽  
Machine Learning Algorithms ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Short Term ◽  
Learning Techniques ◽  
Historical Database ◽  
Long Short Term Memory

In this paper, we investigate analysis and prediction of the time-dependent data. We focus our attention on four different stocks are selected from Yahoo Finance historical database. To build up models and predict the future stock price, we consider three different machine learning techniques including Long Short-Term Memory (LSTM), Convolutional Neural Networks (CNN) and Support Vector Regression (SVR). By treating close price, open price, daily low, daily high, adjusted close price, and volume of trades as predictors in machine learning methods, it can be shown that the prediction accuracy is improved.

389-P: Ability for Detecting or Predicting Hypoglycemia with the Aid of Machine Learning Techniques: A Meta-analysis

Diabetes ◽  
2020 ◽  
Vol 69 (Supplement 1) ◽  
pp. 389-P
Author(s):  
SATORU KODAMA ◽  
MAYUKO H. YAMADA ◽  
YUTA YAGUCHI ◽  
MASARU KITAZAWA ◽  
MASANORI KANEKO ◽  
...  
Keyword(s):  
Machine Learning ◽  
Meta Analysis ◽  
Machine Learning Techniques ◽  
Learning Techniques

A Comparative Study of Different Machine Learning Algorithms for Disease Prediction

2017 ◽  
Vol 7 (7) ◽  
pp. 172
Author(s):  
Anantvir Singh Romana
Keyword(s):  
Machine Learning ◽  
Subsequent Treatment ◽  
Machine Learning Algorithms ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Disease Prediction ◽  
Classification Problems ◽  
Learning Techniques ◽  
Neural Network Classifiers ◽  
Diagnostic Detection

Accurate diagnostic detection of the disease in a patient is critical and may alter the subsequent treatment and increase the chances of survival rate. Machine learning techniques have been instrumental in disease detection and are currently being used in various classification problems due to their accurate prediction performance. Various techniques may provide different desired accuracies and it is therefore imperative to use the most suitable method which provides the best desired results. This research seeks to provide comparative analysis of Support Vector Machine, Naïve bayes, J48 Decision Tree and neural network classifiers breast cancer and diabetes datsets.

A Brief Survey on Text Classification Using Various Machine Learning Techniques

2018 ◽  
Vol 8 (1) ◽  
pp. 14
Author(s):  
Padmavathi .S ◽  
M. Chidambaram
Keyword(s):  
Machine Learning ◽  
Text Classification ◽  
Fixed Number ◽  
Machine Learning Techniques ◽  
Online Information ◽  
Rule Based ◽  
Learning Techniques ◽  
Machine Learning Approach ◽  
Rule Based Approach

Text classification has grown into more significant in managing and organizing the text data due to tremendous growth of online information. It does classification of documents in to fixed number of predefined categories. Rule based approach and Machine learning approach are the two ways of text classification. In rule based approach, classification of documents is done based on manually defined rules. In Machine learning based approach, classification rules or classifier are defined automatically using example documents. It has higher recall and quick process. This paper shows an investigation on text classification utilizing different machine learning techniques.

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