scholarly journals Experimental Analysis of Machine Learning Algorithms Based on Agricultural Dataset for Improving Crop Yield Prediction

2019 ◽  
Vol 9 (1) ◽  
pp. 3246-3251
Keyword(s):  
Machine Learning ◽  
Crop Yield ◽  
Machine Learning Algorithms ◽  
Random Tree ◽  
Machine Learning Techniques ◽  
Yield Prediction ◽  
Bayes Net ◽  
Learning Techniques ◽  
Production Area ◽  
Primary Research Study

Agriculture is the primary research study area in India as agriculture is the main source of income for various communities. In classification algorithm for agricultural dataset according to production, area, crop and seasons. Here, four classification algorithms are used with the help of WEKA tool. These algorithms are namely the present scenario, there is a call to renovate the enormous agriculture data into diverse technologies and make them accessible to the farmer for improved decision making. The endeavor of this work is to find out the finest Random Tree, J48, Bayes Net and KStar etc. The captured results revealed that Random tree algorithm performed well in terms of error rate and provides slightly better performance than KStar, Bayes Net and J48 classifiers. In this paper, our objective is to apply machine learning techniques to mine constructive information from the agricultural dataset to improve the crop yield prediction for major crops in Nashik district of Maharashtra.

scholarly journals Clinical Data Analysis for Prediction of Cardiovascular Disease Using Machine Learning Techniques

2022 ◽  
Vol 2022 ◽  
pp. 1-13
Author(s):  
Rajkumar Gangappa Nadakinamani ◽  
A. Reyana ◽  
Sandeep Kautish ◽  
A. S. Vibith ◽  
Yogita Gupta ◽  
...  
Keyword(s):  
Machine Learning ◽  
Cardiovascular Disease ◽  
Cardiac Risk ◽  
Machine Learning Algorithms ◽  
Random Tree ◽  
Machine Learning Techniques ◽  
Learning Technology ◽  
Tree Model ◽  
Learning Techniques ◽  
Machine Learning Model

Cardiovascular disease is difficult to detect due to several risk factors, including high blood pressure, cholesterol, and an abnormal pulse rate. Accurate decision-making and optimal treatment are required to address cardiac risk. As machine learning technology advances, the healthcare industry’s clinical practice is likely to change. As a result, researchers and clinicians must recognize the importance of machine learning techniques. The main objective of this research is to recommend a machine learning-based cardiovascular disease prediction system that is highly accurate. In contrast, modern machine learning algorithms such as REP Tree, M5P Tree, Random Tree, Linear Regression, Naive Bayes, J48, and JRIP are used to classify popular cardiovascular datasets. The proposed CDPS’s performance was evaluated using a variety of metrics to identify the best suitable machine learning model. When it came to predicting cardiovascular disease patients, the Random Tree model performed admirably, with the highest accuracy of 100%, the lowest MAE of 0.0011, the lowest RMSE of 0.0231, and the fastest prediction time of 0.01 seconds.

scholarly journals ML Methods for Crop Yield Prediction and Estimation: An Exploration

2020 ◽  
Vol 9 (3) ◽  
pp. 3506-3508
Keyword(s):  
Machine Learning ◽  
Detailed Analysis ◽  
Crop Yield ◽  
Learning Process ◽  
Machine Learning Techniques ◽  
Yield Prediction ◽  
Yield Estimation ◽  
Data Set ◽  
Yield Optimization ◽  
Learning Techniques

Machine learning Has performed a essential position within the estimation of crop yield for both farmers and consumers of the products. Machine learning techniques learn from data set related to the environment on which the estimations and estimation are to be made and the outcome of the learning process are used by farmers for corrective measures for yield optimization. This paper we explore various ML techniques utilized in crop yield estimation and provide the detailed analysis of accuracy of the techniques.

scholarly journals Crop Yield Prediction Using Machine Learning Techniques

2020 ◽  
pp. 312-315
Author(s):  
Ashwini I. Patil ◽  
Ramesh A. Medar ◽  
Vinod Desai
Keyword(s):  
Machine Learning ◽  
Crop Yield ◽  
Model Performance ◽  
High Accuracy ◽  
Machine Learning Techniques ◽  
Yield Prediction ◽  
The People ◽  
Learning Techniques ◽  
High Production ◽  
Day By Day

Today Indian economy depends upon agriculture. More than 70% of the people in India have taken it as a main occupation, day by day for a particular crop; the formers are not getting proper yield as well as profit due to environmental conditions like soil quality, weather, heavy rainfall, drought, seed damages, fertilizers, pesticides. The farmers not able to produce high production, so taking the historical agricultural data records we can predict the crop yield using machine learning techniques like Linear regression, comparative analysis are done with decision tree, KNN algorithms, using these to achieve the high accuracy and model performance is computed.

Performance Evaluation of Machine Learning Techniques for Mustard Crop Yield Prediction from Soil Analysis

2020 ◽  
Vol 64 (02) ◽  
pp. 394-398
Author(s):  
Vaishali Pandith ◽  
Haneet Kour ◽  
Surjeet Singh ◽  
Jatinder Manhas ◽  
Vinod Sharma
Keyword(s):  
Machine Learning ◽  
Performance Evaluation ◽  
Crop Yield ◽  
Soil Analysis ◽  
Machine Learning Techniques ◽  
Yield Prediction ◽  
Learning Techniques ◽  
Mustard Crop

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.

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.

scholarly journals Feasibility of Machine Learning Algorithms for Predicting the Deformation of Anodic Titanium Films by Modulating Anodization Processes

Materials ◽  
2021 ◽  
Vol 14 (5) ◽  
pp. 1089
Author(s):  
Sung-Hee Kim ◽  
Chanyoung Jeong
Keyword(s):  
Machine Learning ◽  
Learning Algorithms ◽  
Multiclass Classification ◽  
Machine Learning Algorithms ◽  
Machine Learning Techniques ◽  
Smart Manufacturing ◽  
Gradient Boosting ◽  
Experimental Conditions ◽  
Learning Techniques ◽  
Tio2 Nanostructures

This study aims to demonstrate the feasibility of applying eight machine learning algorithms to predict the classification of the surface characteristics of titanium oxide (TiO2) nanostructures with different anodization processes. We produced a total of 100 samples, and we assessed changes in TiO2 nanostructures’ thicknesses by performing anodization. We successfully grew TiO2 films with different thicknesses by one-step anodization in ethylene glycol containing NH4F and H2O at applied voltage differences ranging from 10 V to 100 V at various anodization durations. We found that the thicknesses of TiO2 nanostructures are dependent on anodization voltages under time differences. Therefore, we tested the feasibility of applying machine learning algorithms to predict the deformation of TiO2. As the characteristics of TiO2 changed based on the different experimental conditions, we classified its surface pore structure into two categories and four groups. For the classification based on granularity, we assessed layer creation, roughness, pore creation, and pore height. We applied eight machine learning techniques to predict classification for binary and multiclass classification. For binary classification, random forest and gradient boosting algorithm had relatively high performance. However, all eight algorithms had scores higher than 0.93, which signifies high prediction on estimating the presence of pore. In contrast, decision tree and three ensemble methods had a relatively higher performance for multiclass classification, with an accuracy rate greater than 0.79. The weakest algorithm used was k-nearest neighbors for both binary and multiclass classifications. We believe that these results show that we can apply machine learning techniques to predict surface quality improvement, leading to smart manufacturing technology to better control color appearance, super-hydrophobicity, super-hydrophilicity or batter efficiency.

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