Statistical analysis of machine learning techniques for predicting powdery mildew disease in tomato plants

2021 ◽  
Vol 9 (1) ◽  
pp. 24
Author(s):  
Anshul Bhatia ◽  
Anuradha Chug ◽  
Amit Prakash Singh
Keyword(s):  
Machine Learning ◽  
Statistical Analysis ◽  
Powdery Mildew ◽  
Machine Learning Techniques ◽  
Tomato Plants ◽  
Learning Techniques ◽  
Powdery Mildew Disease

Value Analysis and Prediction Through Machine Learning Techniques for Popular Basketball Brands

2022 ◽  
pp. 323-345
Author(s):  
Jason Michaud
Keyword(s):  
Machine Learning ◽  
Statistical Analysis ◽  
Stock Price ◽  
Machine Learning Techniques ◽  
Learning Models ◽  
Value Analysis ◽  
Learning Techniques ◽  
The World ◽  
Machine Learning Models

For popular sports brands such as Nike, Adidas, and Puma, value often depends upon the performance of star athletes and the success of professional leagues. These leagues and players are watched closely by many around the world, and exposure to a brand may ultimately cause someone to buy a product. This can be explored statistically, and the interconnectedness of brands, athletes, and the sport of basketball are covered in this chapter. Specifically, data about the NBA and Google Ngrams data are explored in relation to the stock price of these various sports brands. This is done through both statistical analysis and machine learning models. Ultimately, it was concluded that these factors do influence the stock price of Nike, Adidas, and Puma. This conclusion is supported by the machine learning models where this diverse dataset was utilized to accurately predict the stock price of sports brands.

scholarly journals CHAOTIC PROCESSES FORECASTING BASED ON PRECEDENTIAL DATA ANALYSIS

2020 ◽  
Vol 55 ◽  
pp. 85-90
Author(s):  
Andrey V. Makshanov ◽  
◽  
Alexander A. Musaev ◽  
Keyword(s):  
Machine Learning ◽  
Data Analysis ◽  
Statistical Analysis ◽  
Nonlinear Equations ◽  
Machine Learning Techniques ◽  
Traditional Methods ◽  
Unstable Systems ◽  
Bifurcation Points ◽  
Learning Techniques

The problem of predicting of chaotic processes – which are typical for evolution of parameters of unstable systems – is considered. Examples of such systems are gas, hydro, and thermodynamic media whose state dynamics are described by a system of open nonlinear equations. In that case, the traditional methods of extrapolation approach are ineffective. The multiplicity of bifurcation points leads to the fact that even minor perturbations can radically change the dynamics of the observed process. In this regard, the article presents precedential forecasting algorithms. The proposed method is based on a combination of statistical analysis and machine learning techniques.

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