scholarly journals Machine learning model for rice yield prediction using KNN regression.

2021 ◽  
Author(s):  
Akhil Wilson ◽  
Raji Sukumar ◽  
N. Hemalatha
Keyword(s):  
Machine Learning ◽  
Rice Yield ◽  
Support Vector ◽  
Yield Prediction ◽  
Challenging Problem ◽  
Random Forest Regression ◽  
Machine Learning Model ◽  
Smart Farming ◽  
Regression Algorithms ◽  
The One

Abstract The prediction of agriculture yield is the one of the challenging problem in smart farming, we have predicted the yield of rice in the state of Kerala, India with the help of Machine Learning by considering the soil properties, micro climatic condition and area of the rice. Here we have used Decision Tree Regression, Random Forest Regression, Linear Regression, K Nearest Neighbour Regression, Xgboost Regression and Support Vector Regression algorithms in order to predict the rice yield. From the experiments we got KNN regression to be the best with 98.77% accuracy.

scholarly journals Computational prediction model for pepper yield prediction using support vector regression.

2021 ◽  
Author(s):  
Akhil Wilson ◽  
N. Hemalatha ◽  
Raji Sukumar
Keyword(s):  
Machine Learning ◽  
Support Vector Regression ◽  
Climatic Condition ◽  
Research Work ◽  
Computational Prediction ◽  
Support Vector ◽  
Yield Prediction ◽  
Challenging Problem ◽  
Regression Algorithms ◽  
The One

Abstract The yield prediction is the one of the challenging problem in agriculture. Here in this research work we have predicted the yield of Pepper in the state of Kerala, India. With the help of Machine Learning and by considering the soil properties, micro climatic condition and area of the Pepper we have predicted the yield. Here we have used Linear Regression and Support Vector Regression algorithms in order to predict the pepper yield. Experimental results gave best accuracy of 97.685% for Support Vector Regression.

A Novel Amino Acid Sequence-based Computational Approach to Predicting Cell-penetrating Peptides

2019 ◽  
Vol 15 (3) ◽  
pp. 206-211 ◽  
Author(s):  
Jihui Tang ◽  
Jie Ning ◽  
Xiaoyan Liu ◽  
Baoming Wu ◽  
Rongfeng Hu
Keyword(s):  
Machine Learning ◽  
Amino Acid ◽  
Amino Acid Position ◽  
Cell Penetrating Peptides ◽  
Support Vector ◽  
Cell Penetration ◽  
Drug Candidates ◽  
Machine Learning Model ◽  
Cell Penetrating ◽  
Novel Method

<P>Introduction: Machine Learning is a useful tool for the prediction of cell-penetration compounds as drug candidates. </P><P> Materials and Methods: In this study, we developed a novel method for predicting Cell-Penetrating Peptides (CPPs) membrane penetrating capability. For this, we used orthogonal encoding to encode amino acid and each amino acid position as one variable. Then a software of IBM spss modeler and a dataset including 533 CPPs, were used for model screening. </P><P> Results: The results indicated that the machine learning model of Support Vector Machine (SVM) was suitable for predicting membrane penetrating capability. For improvement, the three CPPs with the most longer lengths were used to predict CPPs. The penetration capability can be predicted with an accuracy of close to 95%. </P><P> Conclusion: All the results indicated that by using amino acid position as a variable can be a perspective method for predicting CPPs membrane penetrating capability.</P>

scholarly journals Systematic Framework to Predict Early-Stage Liver Carcinoma Using Hybrid of Feature Selection Techniques and Regression Techniques

Complexity ◽  
2022 ◽  
Vol 2022 ◽  
pp. 1-11
Author(s):  
Marium Mehmood ◽  
Nasser Alshammari ◽  
Saad Awadh Alanazi ◽  
Fahad Ahmad
Keyword(s):  
Feature Selection ◽  
Random Forest ◽  
Liver Diseases ◽  
Early Stage ◽  
Support Vector ◽  
Liver Carcinoma ◽  
Random Forest Regression ◽  
Soft Computing Techniques ◽  
Regression Algorithms ◽  
Regression Techniques

The liver is the human body’s mandatory organ, but detecting liver disease at an early stage is very difficult due to the hiddenness of symptoms. Liver diseases may cause loss of energy or weakness when some irregularities in the working of the liver get visible. Cancer is one of the most common diseases of the liver and also the most fatal of all. Uncontrolled growth of harmful cells is developed inside the liver. If diagnosed late, it may cause death. Treatment of liver diseases at an early stage is, therefore, an important issue as is designing a model to diagnose early disease. Firstly, an appropriate feature should be identified which plays a more significant part in the detection of liver cancer at an early stage. Therefore, it is essential to extract some essential features from thousands of unwanted features. So, these features will be mined using data mining and soft computing techniques. These techniques give optimized results that will be helpful in disease diagnosis at an early stage. In these techniques, we use feature selection methods to reduce the dataset’s feature, which include Filter, Wrapper, and Embedded methods. Different Regression algorithms are then applied to these methods individually to evaluate the result. Regression algorithms include Linear Regression, Ridge Regression, LASSO Regression, Support Vector Regression, Decision Tree Regression, Multilayer Perceptron Regression, and Random Forest Regression. Based on the accuracy and error rates generated by these Regression algorithms, we have evaluated our results. The result shows that Random Forest Regression with the Wrapper Method from all the deployed Regression techniques is the best and gives the highest R2-Score of 0.8923 and lowest MSE of 0.0618.

scholarly journals Diabetes Prediction Using Machine Learning

2020 ◽  
pp. 294-305
Author(s):  
KM Jyoti Rani
Keyword(s):  
Machine Learning ◽  
Blood Glucose ◽  
Data Science ◽  
International Diabetes Federation ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
High Blood Glucose ◽  
Diabetes Prediction ◽  
The One ◽  
Prediction Of Diabetes

Diabetes is a chronic disease with the potential to cause a worldwide health care crisis. According to International Diabetes Federation 382 million people are living with diabetes across the whole world. By 2035, this will be doubled as 592 million. Diabetes is a disease caused due to the increase level of blood glucose. This high blood glucose produces the symptoms of frequent urination, increased thirst, and increased hunger. Diabetes is a one of the leading cause of blindness, kidney failure, amputations, heart failure and stroke. When we eat, our body turns food into sugars, or glucose. At that point, our pancreas is supposed to release insulin. Insulin serves as a key to open our cells, to allow the glucose to enter and allow us to use the glucose for energy. But with diabetes, this system does not work. Type 1 and type 2 diabetes are the most common forms of the disease, but there are also other kinds, such as gestational diabetes, which occurs during pregnancy, as well as other forms. Machine learning is an emerging scientific field in data science dealing with the ways in which machines learn from experience. The aim of this project is to develop a system which can perform early prediction of diabetes for a patient with a higher accuracy by combining the results of different machine learning techniques. The algorithms like K nearest neighbour, Logistic Regression, Random forest, Support vector machine and Decision tree are used. The accuracy of the model using each of the algorithms is calculated. Then the one with a good accuracy is taken as the model for predicting the diabetes.

Machine Learning-Based Subjective Quality Estimation for Video Streaming Over Wireless Networks

2019 ◽  
pp. 235-254
Author(s):  
Monalisa Ghosh ◽  
Chetna Singhal
Keyword(s):  
Machine Learning ◽  
Wireless Networks ◽  
Video Streaming ◽  
Video Transmission ◽  
Video Quality ◽  
Subjective Quality ◽  
Perceptual Quality ◽  
Support Vector ◽  
Machine Learning Model

Video streaming services top the internet traffic surging forward a competitive environment to impart best quality of experience (QoE) to the users. The standard codecs utilized in video transmission systems eliminate the spatiotemporal redundancies in order to decrease the bandwidth requirement. This may adversely affect the perceptual quality of videos. To rate a video quality both subjective and objective parameters can be used. So, it is essential to construct frameworks which will measure integrity of video just like humans. This chapter focuses on application of machine learning to evaluate the QoE without requiring human efforts with higher accuracy of 86% and 91% employing the linear and support vector regression respectively. Machine learning model is developed to forecast the subjective quality of H.264 videos obtained after streaming through wireless networks from the subjective scores.

Opinion Mining

2019 ◽  
pp. 66-82 ◽  
Author(s):  
Vijender Kumar Solanki ◽  
Nguyen Ha Huy Cuong ◽  
Zonghyu (Joan) Lu
Keyword(s):  
Machine Learning ◽  
Information Technology ◽  
Sentiment Analysis ◽  
Programming Languages ◽  
Opinion Mining ◽  
Support Vector ◽  
Emerging Trends ◽  
The One ◽  
The Mind ◽  
Technology Domain

The machine learning is the emerging research domain, from which number of emerging trends are available, among them opinion mining is the one technology attraction through which the we could get analysis of the interested domain or we can say about the review from the customer towards any product or we can say any upcoming trending information. These two are the emerging words and we can say it's the buzz word in the information technology. As you will see that its widely use by the corporate sector to uplift the business next level. Before two decade you will not read any words e.g., Opinion mining or Sentiment analysis, but in the last two decade these words have given a new life to information technology domain as well as to the business. The important question which runs in the mind is why use sentiment analysis or opinion mining. The information technology has given number of new programming languages, new innovation and within that the data mining has given this trends to the users. The chapter is covering the three major concept's which comes under the machine learning e.g., Decision tree, Bayesian network and Support vector machine. The chapter is describing the basic inputs, and how it helps in supporting stakeholders by adopting these technologies.

scholarly journals MEWS++: Enhancing the Prediction of Clinical Deterioration in Admitted Patients through a Machine Learning Model

2020 ◽  
Vol 9 (2) ◽  
pp. 343 ◽  
Author(s):  
Arash Kia ◽  
Prem Timsina ◽  
Himanshu N. Joshi ◽  
Eyal Klang ◽  
Rohit R. Gupta ◽  
...  
Keyword(s):  
Machine Learning ◽  
At Risk ◽  
Area Under The Curve ◽  
Learning Model ◽  
Clinical Deterioration ◽  
Early Warning Score ◽  
Support Vector ◽  
Adult Age ◽  
Machine Learning Model ◽  
Patients At Risk

Early detection of patients at risk for clinical deterioration is crucial for timely intervention. Traditional detection systems rely on a limited set of variables and are unable to predict the time of decline. We describe a machine learning model called MEWS++ that enables the identification of patients at risk of escalation of care or death six hours prior to the event. A retrospective single-center cohort study was conducted from July 2011 to July 2017 of adult (age > 18) inpatients excluding psychiatric, parturient, and hospice patients. Three machine learning models were trained and tested: random forest (RF), linear support vector machine, and logistic regression. We compared the models’ performance to the traditional Modified Early Warning Score (MEWS) using sensitivity, specificity, and Area Under the Curve for Receiver Operating Characteristic (AUC-ROC) and Precision-Recall curves (AUC-PR). The primary outcome was escalation of care from a floor bed to an intensive care or step-down unit, or death, within 6 h. A total of 96,645 patients with 157,984 hospital encounters and 244,343 bed movements were included. Overall rate of escalation or death was 3.4%. The RF model had the best performance with sensitivity 81.6%, specificity 75.5%, AUC-ROC of 0.85, and AUC-PR of 0.37. Compared to traditional MEWS, sensitivity increased 37%, specificity increased 11%, and AUC-ROC increased 14%. This study found that using machine learning and readily available clinical data, clinical deterioration or death can be predicted 6 h prior to the event. The model we developed can warn of patient deterioration hours before the event, thus helping make timely clinical decisions.

scholarly journals Machine learning methods for soil moisture prediction in vineyards using digital images

2020 ◽  
Vol 167 ◽  
pp. 02004
Author(s):  
Chantal Saad Hajjar ◽  
Celine Hajjar ◽  
Michel Esta ◽  
Yolla Ghorra Chamoun
Keyword(s):  
Machine Learning ◽  
Soil Moisture ◽  
Regression Models ◽  
Digital Images ◽  
Pearson Correlation ◽  
Support Vector ◽  
Learning Methods ◽  
Nonlinear Regression Models ◽  
Machine Learning Methods ◽  
The One

In this paper, we propose to estimate the moisture of vineyard soils from digital photography using machine learning methods. Two nonlinear regression models are implemented: a multilayer perceptron (MLP) and a support vector regression (SVR). Pixels coded with RGB colour model extracted from soil digital images along with the associated known soil moisture levels are used to train both models in order to predict moisture content from newly acquired images. The study is conducted on samples of six soil types collected from Chateau Kefraya terroirs in Lebanon. Both methods succeeded in forecasting moisture giving high correlation values between the measured moisture and the predicted moisture when tested on unknown data. However, the method based on SVR outperformed the one based on MLP yielding Pearson correlation coefficient values ranging from 0.89 to 0.99. Moreover, it is a simple and noninvasive method that can be adopted easily to detect vineyards soil moisture.

scholarly journals Distributed collaborative prediction: Results of the machine learning contest

2017 ◽  
Vol 36 (3) ◽  
pp. 267-269 ◽  
Author(s):  
Matt Hall ◽  
Brendon Hall
Keyword(s):  
Machine Learning ◽  
Leading Edge ◽  
Support Vector ◽  
Small Data ◽  
Machine Model ◽  
Data Set ◽  
Facies Classification ◽  
Machine Learning Model ◽  
Wireline Logs ◽  
Naive Approach

The Geophysical Tutorial in the October issue of The Leading Edge was the first we've done on the topic of machine learning. Brendon Hall's article ( Hall, 2016 ) showed readers how to take a small data set — wireline logs and geologic facies data from nine wells in the Hugoton natural gas and helium field of southwest Kansas ( Dubois et al., 2007 ) — and predict the facies in two wells for which the facies data were not available. The article demonstrated with 25 lines of code how to explore the data set, then create, train and test a machine learning model for facies classification, and finally visualize the results. The workflow took a deliberately naive approach using a support vector machine model. It achieved a sort of baseline accuracy rate — a first-order prediction, if you will — of 0.42. That might sound low, but it's not untypical for a naive approach to this kind of problem. For comparison, random draws from the facies distribution score 0.16, which is therefore the true baseline.

scholarly journals Agrocompanion: A Smart Farming Approach Based on Iot and Machine Learning

2020 ◽  
Vol 9 (12) ◽  
pp. 254-262
Keyword(s):  
Machine Learning ◽  
Soil Moisture ◽  
Random Forest ◽  
Weather Conditions ◽  
The Internet ◽  
Machine Learning Model ◽  
Smart Farming ◽  
Water Tanks ◽  
And Control ◽  
The Internet Of Things

Agriculture is one of the cardinal sectors of the Indian Economy. The proposed system offers a methodology to efficiently monitor and control various attributes that affect crop growth and production. The system also uses machine learning along with the Internet of Things (IoT) to predict the crop yield. Various weather conditions such as temperature, humidity, and soil moisture are monitored in real-time using IoT sensors. IoT is also used to regulate the water level in the water tanks, which helps in reducing the wastage of water resources. A machine learning model is developed to predict the yield of the crop based on parameters taken from these sensors. The model uses Random Forest Regressor and gives an accuracy of 87.5%. Such a system provides a simple and efficient way to maintain and monitor the health of the crop.

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