scholarly journals Identification of Paddy Leaf Diseases using Evolutionary and Machine Learning Methods

2021 ◽  
Vol 12 (2) ◽  
pp. 1672-1686
Author(s):  
Nilam Sachin Patil
Keyword(s):  
Machine Learning ◽  
Early Stage ◽  
Plant Diseases ◽  
Automated System ◽  
Machine Learning Techniques ◽  
Nearest Neighbour ◽  
Agricultural Plant ◽  
Machine Learning Methods ◽  
Cascaded Classifiers ◽  
Learning Techniques

In the field of agriculture, especially paddy plants, there is a demand for research to classify the paddy diseases at early stages. This is feasible if there are automated systems that can assist the farmers to recognize the paddy diseases from the paddy leaf images of the plants. The recognition of agricultural plant diseases by utilizing the image-processing and machine learning techniques can certainly minimize the reliance on the farmers to protect the yield of paddy crops. In this paper, an attempt has been made to pre-process the images to prepare the feature-set for Classifiers and then feature extraction algorithms are used to extract the relevant features from the processed images. The feature-set is then supplied to the classifiers for identification of Paddy Leaf diseases. The usage of cascaded classifiers has been explored to detect the diseases of paddy leaves. An attempt has also been made to use genetic algorithm with nearest neighbour algorithm to identify the diseases of paddy leaves. The proposed automated system can be used on Android , Windows platform and Apple platform for quickly identifying the paddy leaf diseases as the entire implementation has been performed using MATLAB. The proposed automated system can certainly help the farmers to classify the diseased paddy leaves at early stage to protect the crops from further damage.

scholarly journals A Study On Deep Learning And Machine Learning Techniques On Detection Of Parkinson’s Disease

2021 ◽  
Vol 309 ◽  
pp. 01008
Author(s):  
P. Mounika ◽  
S. Govinda Rao
Keyword(s):  
Machine Learning ◽  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Deep Learning ◽  
Early Stage ◽  
Signs And Symptoms ◽  
Machine Learning Techniques ◽  
Learning Approaches ◽  
Machine Learning Methods ◽  
Learning Techniques

Parkinson’s disease (PD) is a sophisticated anxiety malady that impairs movement. Symptoms emerge gradually, initiating with a slight tremor in only one hand occasionally. Tremors are prevalent, although the condition is sometimes associated with stiffness or slowed mobility. In the early degrees of PD, your face can also additionally display very little expression. Your fingers won’t swing while you walk. Your speech can also additionally grow to be gentle or slurred. PD signs and symptoms get worse as your circumstance progresses over time. The goal of this study is to test the efficiency of deep learning and machine learning approaches in order to identify the most accurate strategy for sensing Parkinson’s disease at an early stage. In order to measure the average performance most accurately, we compared deep learning and machine learning methods.

scholarly journals Predictive modeling for peri-implantitis by using machine learning techniques

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Tomoaki Mameno ◽  
Masahiro Wada ◽  
Kazunori Nozaki ◽  
Toshihito Takahashi ◽  
Yoshitaka Tsujioka ◽  
...  
Keyword(s):  
Machine Learning ◽  
Demographic Data ◽  
Risk Indicators ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Machine Learning Methods ◽  
Complex Interactions ◽  
Learning Techniques ◽  
Increased Risk ◽  
Vector Machines

AbstractThe purpose of this retrospective cohort study was to create a model for predicting the onset of peri-implantitis by using machine learning methods and to clarify interactions between risk indicators. This study evaluated 254 implants, 127 with and 127 without peri-implantitis, from among 1408 implants with at least 4 years in function. Demographic data and parameters known to be risk factors for the development of peri-implantitis were analyzed with three models: logistic regression, support vector machines, and random forests (RF). As the results, RF had the highest performance in predicting the onset of peri-implantitis (AUC: 0.71, accuracy: 0.70, precision: 0.72, recall: 0.66, and f1-score: 0.69). The factor that had the most influence on prediction was implant functional time, followed by oral hygiene. In addition, PCR of more than 50% to 60%, smoking more than 3 cigarettes/day, KMW less than 2 mm, and the presence of less than two occlusal supports tended to be associated with an increased risk of peri-implantitis. Moreover, these risk indicators were not independent and had complex effects on each other. The results of this study suggest that peri-implantitis onset was predicted in 70% of cases, by RF which allows consideration of nonlinear relational data with complex interactions.

scholarly journals Comparison of Machine Learning algorithm for COVID-19 Death Risk Prediction

2021 ◽  
Author(s):  
Praveeen Anandhanathan ◽  
Priyanka Gopalan
Keyword(s):  
Machine Learning ◽  
Learning Algorithm ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Nearest Neighbour ◽  
Decision Tree Algorithm ◽  
The Past ◽  
Random Forest Method ◽  
Learning Techniques ◽  
The World

Abstract Coronavirus disease (COVID-19) is spreading across the world. Since at first it has appeared in Wuhan, China in December 2019, it has become a serious issue across the globe. There are no accurate resources to predict and find the disease. So, by knowing the past patients’ records, it could guide the clinicians to fight against the pandemic. Therefore, for the prediction of healthiness from symptoms Machine learning techniques can be implemented. From this we are going to analyse only the symptoms which occurs in every patient. These predictions can help clinicians in the easier manner to cure the patients. Already for prediction of many of the diseases, techniques like SVM (Support vector Machine), Fuzzy k-Means Clustering, Decision Tree algorithm, Random Forest Method, ANN (Artificial Neural Network), KNN (k-Nearest Neighbour), Naïve Bayes, Linear Regression model are used. As we haven’t faced this disease before, we can’t say which technique will give the maximum accuracy. So, we are going to provide an efficient result by comparing all the such algorithms in RStudio.

scholarly journals Heart Disease Prediction using Machine Learning

2020 ◽  
Vol 9 (1) ◽  
pp. 700-704
Keyword(s):  
Machine Learning ◽  
Heart Disease ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Disease Prediction ◽  
Nearest Neighbour ◽  
Decision Tree Classifier ◽  
Support Vector Classifier ◽  
Learning Techniques ◽  
Tree Classifier

Deriving the methodologies to detect heart issues at an earlier stage and intimating the patient to improve their health. To resolve this problem, we will use Machine Learning techniques to predict the incidence at an earlier stage. We have a tendency to use sure parameters like age, sex, height, weight, case history, smoking and alcohol consumption and test like pressure ,cholesterol, diabetes, ECG, ECHO for prediction. In machine learning there are many algorithms which will be used to solve this issue. The algorithms include K-Nearest Neighbour, Support vector classifier, decision tree classifier, logistic regression and Random Forest classifier. Using these parameters and algorithms we need to predict whether or not the patient has heart disease or not and recommend the patient to improve his/her health.

scholarly journals The rise and fall of machine learning methods in biomedical research

F1000Research ◽  
2017 ◽  
Vol 6 ◽  
pp. 2012 ◽  
Author(s):  
Hashem Koohy
Keyword(s):  
Machine Learning ◽  
Biomedical Research ◽  
Life Sciences ◽  
Biological Data ◽  
Research Note ◽  
Machine Learning Techniques ◽  
Learning Methods ◽  
The Past ◽  
Machine Learning Methods ◽  
Learning Techniques

In the era of explosion in biological data, machine learning techniques are becoming more popular in life sciences, including biology and medicine. This research note examines the rise and fall of the most commonly used machine learning techniques in life sciences over the past three decades.

scholarly journals Ecological Interactions and the Netflix Problem

2016 ◽  
Author(s):  
Philippe Desjardins-Proulx ◽  
Idaline Laigle ◽  
Timothée Poisot ◽  
Dominique Gravel
Keyword(s):  
Machine Learning ◽  
Supervised Learning ◽  
Random Forests ◽  
Species Interactions ◽  
Similarity Measures ◽  
Theoretical Models ◽  
Machine Learning Techniques ◽  
Nearest Neighbour ◽  
Ecological Interactions ◽  
Learning Techniques

0AbstractSpecies interactions are a key component of ecosystems but we generally have an incomplete picture of who-eats-who in a given community. Different techniques have been devised to predict species interactions using theoretical models or abundances. Here, we explore the K nearest neighbour approach, with a special emphasis on recommendation, along with other machine learning techniques. Recommenders are algorithms developed for companies like Netflix to predict if a customer would like a product given the preferences of similar customers. These machine learning techniques are well-suited to study binary ecological interactions since they focus on positive-only data. We also explore how the K nearest neighbour approach can be used with both positive and negative information, in which case the goal of the algorithm is to fill missing entries from a matrix (imputation). By removing a prey from a predator, we find that recommenders can guess the missing prey around 50% of the times on the first try, with up to 881 possibilities. Traits do not improve significantly the results for the K nearest neighbour, although a simple test with a supervised learning approach (random forests) show we can predict interactions with high accuracy using only three traits per species. This result shows that binary interactions can be predicted without regard to the ecological community given only three variables: body mass and two variables for the species’ phylogeny. These techniques are complementary, as recommenders can predict interactions in the absence of traits, using only information about other species’ interactions, while supervised learning algorithms such as random forests base their predictions on traits only but do not exploit other species’ interactions. Further work should focus on developing custom similarity measures specialized to ecology to improve the KNN algorithms and using richer data to capture indirect relationships between species.

Supervised Machine Learning for Plants Identification Based on Images of Their Leaves

2020 ◽  
pp. 1314-1330 ◽  
Author(s):  
Mohamed Elhadi Rahmani ◽  
Abdelmalek Amine ◽  
Reda Mohamed Hamou
Keyword(s):  
Machine Learning ◽  
Learning Algorithm ◽  
Texture Feature ◽  
Supervised Machine Learning ◽  
Machine Learning Techniques ◽  
Nearest Neighbour ◽  
Curve Shape ◽  
Distance Curve ◽  
Shape Signature ◽  
Learning Techniques

Botanists study in general the characteristics of leaves to give to each plant a scientific name; such as shape, margin...etc. This paper proposes a comparison of supervised plant identification using different approaches. The identification is done according to three different features extracted from images of leaves: a fine-scale margin feature histogram, a Centroid Contour Distance Curve shape signature and an interior texture feature histogram. First represent each leaf by one feature at a time in, then represent leaves by two features, and each leaf was represented by the three features. After that, the authors classified the obtained vectors using different supervised machine learning techniques; the used techniques are Decision tree, Naïve Bayes, K-nearest neighbour, and neural network. Finally, they evaluated the classification using cross validation. The main goal of this work is studying the influence of representation of leaves' images on the identification of plants, and also studying the use of supervised machine learning algorithm for plant leaves classification.

Early Stage Detection of Heart Failure Using Machine Learning Techniques

2021 ◽  
pp. 75-88
Author(s):  
Zulfikar Alom ◽  
Mohammad Abdul Azim ◽  
Zeyar Aung ◽  
Matloob Khushi ◽  
Josip Car ◽  
...  
Keyword(s):  
Machine Learning ◽  
Heart Failure ◽  
Early Stage ◽  
Machine Learning Techniques ◽  
Learning Techniques

Combining Correlation-Based Feature and Machine Learning for Sensory Evaluation of Saigon Beer

2020 ◽  
Vol 11 (2) ◽  
pp. 71-85
Author(s):  
Nhat-Vinh Lu ◽  
Trong-Nhan Vuong ◽  
Duy-Tai Dinh
Keyword(s):  
Machine Learning ◽  
Sensory Evaluation ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Learning Methods ◽  
Feature Selection Technique ◽  
Machine Learning Methods ◽  
Learning Techniques ◽  
Correlation Based Feature Selection ◽  
Positive Results

Sensory evaluation plays an important role in the food and consumer goods industry. In recent years, the application of machine learning techniques to support food sensory evaluation has become popular. Many different machine learning methods have been applied and produced positive results in this field. In this article, the authors propose a new method to support sensory evaluation on multiple criteria based on the use of a correlation-based feature selection technique, combined with machine learning methods such as linear regression, multilayer perceptron, support vector machine, and random forest. Experimental results are based on considering the correlation between physicochemical components and sensory factors on the Saigon beer dataset.

Predictive Analysis of Diabetes Mellitus Using Machine Learning Techniques

2020 ◽  
Vol 17 (8) ◽  
pp. 3449-3452
Author(s):  
M. S. Roobini ◽  
Y. Sai Satwick ◽  
A. Anil Kumar Reddy ◽  
M. Lakshmi ◽  
D. Deepa ◽  
...  
Keyword(s):  
Diabetes Mellitus ◽  
Machine Learning ◽  
Early Stage ◽  
The Body ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Pima Indians ◽  
Classification Techniques ◽  
Learning Techniques ◽  
Prediction Of Diabetes

In today’s world diabetes is the major health challenges in India. It is a group of a syndrome that results in too much sugar in the blood. It is a protracted condition that affects the way the body mechanizes the blood sugar. Prevention and prediction of diabetes mellitus is increasingly gaining interest in medical sciences. The aim is how to predict at an early stage of diabetes using different machine learning techniques. In this paper basically, we use well-known classification that are Decision tree, K-Nearest Neighbors, Support Vector Machine, and Random forest. These classification techniques used with Pima Indians diabetes dataset. Therefore, we predict diabetes at different stage and analyze the performance of different classification techniques. We Also proposed a conceptual model for the prediction of diabetes mellitus using different machine learning techniques. In this paper we also compare the accuracy of the different machine learning techniques to finding the diabetes mellitus at early stage.

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