scholarly journals Supervised Methods Applied to the Construction of a Vision System for the Classification of Cocoa Beans in the Cut-Test

2019 ◽  
Author(s):  
Felipe Antunes ◽  
Anne Canuto ◽  
Benjamin Bedregal ◽  
Eduardo Palmeira ◽  
Iaslan Silva
Keyword(s):  
System Performance ◽  
Vision System ◽  
Attribute Selection ◽  
Supervised Machine Learning ◽  
Classification Algorithms ◽  
Cocoa Beans ◽  
Machine Learning Methods ◽  
Supervised Methods ◽  
Classification Tasks

Supervised machine learning methods, also known as classification algorithms, have been widely used in the literature for many classification tasks. In this context, some aspects of these algorithms, as the used attributes used and the form they were built, have a direct impact in the system performance. Therefore, in this paper, we evaluate the application of classification algorithms, along with attribute selection, to propose an improved version of a vision system that performs the classification of cocoa beans. The main aim of this investigation is to improve the performance of a cocoa classification system that aims at helping farmers to classify the different cocoa beans based on images of these beans.

scholarly journals Seeing It All: Evaluating Supervised Machine Learning Methods for the Classification of Diverse Otariid Behaviours

PLoS ONE ◽  
2016 ◽  
Vol 11 (12) ◽  
pp. e0166898 ◽  
Author(s):  
Monique A. Ladds ◽  
Adam P. Thompson ◽  
David J. Slip ◽  
David P. Hocking ◽  
Robert G. Harcourt
Keyword(s):  
Machine Learning ◽  
Supervised Machine Learning ◽  
Learning Methods ◽  
Machine Learning Methods

scholarly journals Application of Supervised Machine-Learning Methods for Attesting Provenance in Catalan Traditional Pottery Industry

Minerals ◽  
2019 ◽  
Vol 10 (1) ◽  
pp. 8 ◽  
Author(s):  
Anna Anglisano ◽  
Lluís Casas ◽  
Marc Anglisano ◽  
Ignasi Queralt
Keyword(s):  
Machine Learning ◽  
Chemical Composition ◽  
Raw Materials ◽  
Supervised Machine Learning ◽  
Pottery Production ◽  
Learning Methods ◽  
Pottery Industry ◽  
Machine Learning Methods ◽  
Supervised Methods ◽  
Site Discrimination

The traditional pottery industry was an important activity in Catalonia (NE Spain) up to the 20th century. However, nowadays only few workshops persist in small villages were the activity is promoted as a touristic attraction. The preservation and promotion of traditional pottery in Catalonia is part of an ongoing strategy of tourism diversification that is revitalizing the sector. The production of authenticable local pottery handicrafts aims at attracting cultivated and high-purchasing power tourists. The present paper inspects several approaches to set up a scientific protocol based on the chemical composition of both raw materials and pottery. These could be used to develop a seal of quality and provenance to regulate the sector. Six Catalan villages with a renowned tradition of local pottery production have been selected. The chemical composition of their clays and the corresponding fired products has been obtained by Energy dispersive X-ray fluorescence (EDXRF). Using the obtained geochemical dataset, a number of unsupervised and supervised machine learning methods have been applied to test their applicability to define geochemical fingerprints that could allow inter-site discrimination. The unsupervised approach fails to distinguish samples from different provenances. These methods are only roughly able to divide the different provenances in two large groups defined by their different SiO2 and CaCO3 concentrations. In contrast, almost all the tested supervised methods allow inter-site discrimination with accuracy levels above 80%, and accuracies above 85% were obtained using a meta-model combining all the predictive supervised methods. The obtained results can be taken as encouraging and demonstrative of the potential of the supervised approach as a way to define geochemical fingerprints to track or attest the provenance of samples.

scholarly journals An Innovative Method for Predicting and Classifying Inadequate Accuracy in Heart Disease by Using Decision Tree with K-Nearest Neighbors Algorithm

2021 ◽  
Vol 36 (1) ◽  
pp. 609-615
Author(s):  
Mandhapati Rajesh ◽  
Dr.K. Malathi
Keyword(s):  
Machine Learning ◽  
Heart Disease ◽  
Decision Tree ◽  
Supervised Machine Learning ◽  
Machine Learning Techniques ◽  
Accuracy Rate ◽  
K Nearest Neighbors ◽  
Machine Learning Methods ◽  
Learning Techniques

Aim: Predicting the Heartdiseases using medical parameters of cardiac patients to get a good accuracy rate using machine learning methods like innovative Decision Tree (DT) algorithm. Materials and Methods: Supervised Machine learning Techniques with innovative Decision Tree (N = 20) and K Nearest Neighbour (KNN) (N = 20) are performed with five different datasets at each time to record five samples. Results: The Decision Tree is used to predict heart disease with the help of various medical conditions, the accuracy is achieved for DT is 98% and KNN is 72.2%. The two algorithms Decision Tree and KNN are statistically insignificant (=.737) with the independent sample T-Test value (p<0.005) with a confidence level of 95%. Conclusion: Prediction and classification of heart disease significantly seem to be better in DT than KNN.

Opinion Mining with SentiWordNet

2010 ◽  
pp. 266-286 ◽  
Author(s):  
Bruno Ohana ◽  
Brendan Tierney
Keyword(s):  
Opinion Mining ◽  
Sentiment Classification ◽  
The Novel ◽  
Data Set ◽  
Learning Classifier ◽  
Novel Approach ◽  
Supervised Methods ◽  
Classification Tasks ◽  
Film Reviews

Opinion Mining is an emerging field of research concerned with applying computational methods to the treatment of subjectivity in text, with a number of applications in fields such as recommendation systems, contextual advertising and business intelligence. In this chapter the authors survey the area of opinion mining and discuss the SentiWordNet lexicon of sentiment information for terms derived from WordNet. Furthermore, the results of their research in applying this lexicon to sentiment classification of film reviews along with a novel approach that leverages opinion lexicons to build a data set of features used as input to a supervised learning classifier are also presented. The results obtained are in line with other experiments based on manually built opinion lexicons with further improvements obtained by using the novel approach, and are indicative that lexicons built using semi supervised methods such as SentiWordNet can be an important resource in sentiment classification tasks. Considerations on future improvements are also presented based on a detailed analysis of classification results.

scholarly journals Artificial neural network classification of asteroids in the M1:2 mean-motion resonance with Mars

2021 ◽  
Vol 504 (1) ◽  
pp. 692-700
Author(s):  
V Carruba ◽  
S Aljbaae ◽  
R C Domingos ◽  
W Barletta
Keyword(s):  
Supervised Machine Learning ◽  
Machine Learning Methods ◽  
Mean Motion ◽  
Artificial Neural ◽  
Astronomical Images ◽  
Asteroid Dynamics ◽  
First Time ◽  
Asteroid Families ◽  
Mean Motion Resonance

ABSTRACT Artificial neural networks (ANNs) have been successfully used in the last years to identify patterns in astronomical images. The use of ANN in the field of asteroid dynamics has been, however, so far somewhat limited. In this work, we used for the first time ANN for the purpose of automatically identifying the behaviour of asteroid orbits affected by the M1:2 mean-motion resonance with Mars. Our model was able to perform well above 85 per cent levels for identifying images of asteroid resonant arguments in term of standard metrics like accuracy, precision, and recall, allowing to identify the orbital type of all numbered asteroids in the region. Using supervised machine learning methods, optimized through the use of genetic algorithms, we also predicted the orbital status of all multi-opposition asteroids in the area. We confirm that the M1:2 resonance mainly affects the orbits of the Massalia, Nysa, and Vesta asteroid families.

scholarly journals An evaluation of machine learning classifiers for next-generation, continuous-ethogram smart trackers

2021 ◽  
Vol 9 (1) ◽  
Author(s):  
Hui Yu ◽  
Jian Deng ◽  
Ran Nathan ◽  
Max Kröschel ◽  
Sasha Pekarsky ◽  
...  
Keyword(s):  
Machine Learning ◽  
Feature Reduction ◽  
Supervised Machine Learning ◽  
Support Vector ◽  
Learning Methods ◽  
Machine Learning Methods ◽  
Behavioural Research ◽  
Intermittent Sampling

Abstract Background Our understanding of movement patterns and behaviours of wildlife has advanced greatly through the use of improved tracking technologies, including application of accelerometry (ACC) across a wide range of taxa. However, most ACC studies either use intermittent sampling that hinders continuity or continuous data logging relying on tracker retrieval for data downloading which is not applicable for long term study. To allow long-term, fine-scale behavioural research, we evaluated a range of machine learning methods for their suitability for continuous on-board classification of ACC data into behaviour categories prior to data transmission. Methods We tested six supervised machine learning methods, including linear discriminant analysis (LDA), decision tree (DT), support vector machine (SVM), artificial neural network (ANN), random forest (RF) and extreme gradient boosting (XGBoost) to classify behaviour using ACC data from three bird species (white stork Ciconia ciconia, griffon vulture Gyps fulvus and common crane Grus grus) and two mammals (dairy cow Bos taurus and roe deer Capreolus capreolus). Results Using a range of quality criteria, SVM, ANN, RF and XGBoost performed well in determining behaviour from ACC data and their good performance appeared little affected when greatly reducing the number of input features for model training. On-board runtime and storage-requirement tests showed that notably ANN, RF and XGBoost would make suitable on-board classifiers. Conclusions Our identification of using feature reduction in combination with ANN, RF and XGBoost as suitable methods for on-board behavioural classification of continuous ACC data has considerable potential to benefit movement ecology and behavioural research, wildlife conservation and livestock husbandry.

scholarly journals For Acute Pancreatitis using Supervised Machine Learning Algorithms

2020 ◽  
pp. 242-244
Keyword(s):  
Machine Learning ◽  
Acute Pancreatitis ◽  
Machine Learning Algorithms ◽  
Supervised Machine Learning ◽  
Classification Algorithms ◽  
Reliable Prediction ◽  
Test Dataset ◽  
Machine Learning Methods ◽  
Diverse Perspective ◽  
Breathing Problem

The data present in healthcare industry is very huge and delicate which requires to be managed watchfully. There are multiple fatal diseases which grow rapidly all over the world pancreatitis is one among them. Medical professionals want a reliable prediction system to diagnose Pancreatitis. Getting useful information out of the data which has been examined using diverse perspective and various machine learning methods and grouping the required information is a bit difficult task. When various data mining methods are applied on a huge and accessible data which will definitely provide us with the required information to the users. Pancreatitis contributes to Infection, Kidney failure, Breathing problem, Diabetes, Malnutrition, Pancreatic cancer. So, mining the Pancreatitis data in efficient way is a crucial concern. An outcome feature has to be predicted using a dataset where the outcome may contain only two constants that is either 1 or 0. 0 refers to the sufferer having Acute Pancreatitis and 1 refers to the sufferer may have chronic pancreatitis. Thus, an outcome feature with exemplary accuracy has to be predicted using the test dataset and classification algorithms. In order to realize this data is very necessary and then diverse classification techniques can be experimented. Then a finest model can be preferred which gives the maximum accuracy among all others.

scholarly journals Classification of Date Fruits into Genetic Varieties Using Image Analysis

2021 ◽  
Vol 2021 ◽  
pp. 1-13
Author(s):  
Murat Koklu ◽  
Ramazan Kursun ◽  
Yavuz Selim Taspinar ◽  
Ilkay Cinar
Keyword(s):  
Machine Learning ◽  
Vision System ◽  
Great Effort ◽  
Learning Methods ◽  
External Appearance ◽  
Machine Learning Methods ◽  
Date Fruit ◽  
Artificial Neural Network Ann ◽  
Processing Techniques

A great number of fruits are grown around the world, each of which has various types. The factors that determine the type of fruit are the external appearance features such as color, length, diameter, and shape. The external appearance of the fruits is a major determinant of the fruit type. Determining the variety of fruits by looking at their external appearance may necessitate expertise, which is time-consuming and requires great effort. The aim of this study is to classify the types of date fruit, that are, Barhee, Deglet Nour, Sukkary, Rotab Mozafati, Ruthana, Safawi, and Sagai by using three different machine learning methods. In accordance with this purpose, 898 images of seven different date fruit types were obtained via the computer vision system (CVS). Through image processing techniques, a total of 34 features, including morphological features, shape, and color, were extracted from these images. First, models were developed by using the logistic regression (LR) and artificial neural network (ANN) methods, which are among the machine learning methods. Performance results achieved with these methods are 91.0% and 92.2%, respectively. Then, with the stacking model created by combining these models, the performance result was increased to 92.8%. It has been concluded that machine learning methods can be applied successfully for the classification of date fruit types.

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