Investigation of the factors affecting reverse osmosis membrane performance using machine-learning techniques

2022 ◽  
pp. 107669
Author(s):  
Çağla Odabaşı ◽  
Pelin Dologlu ◽  
Fatih Gülmez ◽  
Gizem Kuşoğlu ◽  
Ömer Çağlar
Keyword(s):  
Machine Learning ◽  
Reverse Osmosis ◽  
Machine Learning Techniques ◽  
Reverse Osmosis Membrane ◽  
Factors Affecting ◽  
Membrane Performance ◽  
Learning Techniques

scholarly journals Performance Forecasting of Share Market using Machine Learning Techniques: A Review

2016 ◽  
Vol 6 (6) ◽  
pp. 3196 ◽  
Author(s):  
Sachin Kamley ◽  
Shailesh Jaloree ◽  
R.S. Thakur
Keyword(s):  
Machine Learning ◽  
Systematic Review ◽  
Share Price ◽  
Machine Learning Techniques ◽  
Factors Affecting ◽  
Price Prediction ◽  
Forecasting Methods ◽  
Learning Techniques ◽  
Enormous Amount ◽  
Performance Forecasting

<p>Forecasting share performance becomes more challenging issue due to the enormous amount of valuable trading data stored in the stock database. Currently, existing forecasting methods are insufficient to analyze the share performance accurately. There are two main reasons for that: First, the study of existing forecasting methods is still insufficient to identify the most suitable methods for share price prediction. Second, the lack of investigations made on the factors affecting the share performance. In this regard, this study presents a systematic review of the last fifteen years on various machine learning techniques in order to analyze share performance accurately. The only objective of this study is to provide an overview of the machine learning techniques that have been used to forecast share performance. This paper also highlights a how the prediction algorithms can be used to identify the most important variables in a share market dataset. Finally, we could have succeeded to analyze share performance effectively. It could bring benefits and impacts to researchers, society, brokers and financial analysts.</p>

Performance Forecasting of Share Market using Machine Learning Techniques: A Review

2016 ◽  
Vol 6 (6) ◽  
pp. 3196 ◽  
Author(s):  
Sachin Kamley ◽  
Shailesh Jaloree ◽  
R.S. Thakur
Keyword(s):  
Machine Learning ◽  
Systematic Review ◽  
Share Price ◽  
Machine Learning Techniques ◽  
Factors Affecting ◽  
Price Prediction ◽  
Forecasting Methods ◽  
Learning Techniques ◽  
Enormous Amount ◽  
Performance Forecasting

<p>Forecasting share performance becomes more challenging issue due to the enormous amount of valuable trading data stored in the stock database. Currently, existing forecasting methods are insufficient to analyze the share performance accurately. There are two main reasons for that: First, the study of existing forecasting methods is still insufficient to identify the most suitable methods for share price prediction. Second, the lack of investigations made on the factors affecting the share performance. In this regard, this study presents a systematic review of the last fifteen years on various machine learning techniques in order to analyze share performance accurately. The only objective of this study is to provide an overview of the machine learning techniques that have been used to forecast share performance. This paper also highlights a how the prediction algorithms can be used to identify the most important variables in a share market dataset. Finally, we could have succeeded to analyze share performance effectively. It could bring benefits and impacts to researchers, society, brokers and financial analysts.</p>

Big Data Is Decision Science

2021 ◽  
pp. 126-150
Author(s):  
Jacques R. J. Bughin ◽  
Michele Cincera ◽  
Dorota Reykowska ◽  
Rafał Ohme
Keyword(s):  
Climate Change ◽  
Machine Learning ◽  
Big Data ◽  
Data Science ◽  
Machine Learning Techniques ◽  
Protective Measures ◽  
Factors Affecting ◽  
Learning Techniques ◽  
Regression Techniques ◽  
A Company

Data science has been proven to be an important asset to support better decision making in a variety of settings, whether it is for a scientist to better predict climate change for a company to better predict sales or for a government to anticipate voting preferences. In this research, the authors leverage random forest (RF) as one of the most effective machine learning techniques using big data to predict vaccine intent in five European countries. The findings support the idea that outside of vaccine features, building adequate perception of the risk of contamination, and securing institutional and peer trust are key nudges to convert skeptics to get vaccinated against COVID-19. What machine learning techniques further add beyond traditional regression techniques is some extra granularity in factors affecting vaccine preferences (twice more factors than logistic regression). Other factors that emerge as predictors of vaccine intent are compliance appetite with non-pharmaceutical protective measures as well as perception of the crisis duration.

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.

Sign in / Sign up

Export Citation Format

Share Document