scholarly journals A Machine Learning Approach to Determine Maturity Stages of Tomatoes

2017 ◽  
Vol 10 (3) ◽  
pp. 683-690 ◽  
Author(s):  
Kamalpreet Kaur ◽  
O.P. Guptata
Keyword(s):  
Machine Learning ◽  
Support Vector Machine ◽  
Random Forest ◽  
Fruits And Vegetables ◽  
Support Vector ◽  
Large Set ◽  
Accuracy Score ◽  
Data Set ◽  
Maturity Stages ◽  
Total Data

Maturity checking has become mandatory for the food industries as well as for the farmers so as to ensure that the fruits and vegetables are not diseased and are ripe. However, manual inspection leads to human error, unripe fruits and vegetables may decrease the production [3]. Thus, this study proposes a Tomato Classification system for determining maturity stages of tomato through Machine Learning which involves training of different algorithms like Decision Tree, Logistic Regression, Gradient Boosting, Random Forest, Support Vector Machine, K-NN and XG Boost. This system consists of image collection, feature extraction and training the classifiers on 80% of the total data. Rest 20% of the total data is used for the testing purpose. It is concluded from the results that the performance of the classifier depends on the size and kind of features extracted from the data set. The results are obtained in the form of Learning Curve, Confusion Matrix and Accuracy Score. It is observed that out of seven classifiers, Random Forest is successful with 92.49% accuracy due to its high capability of handling large set of data. Support Vector Machine has shown the least accuracy due to its inability to train large data set.

scholarly journals Heart Disease Prediction and Performance Assessment through Attribute Element Diminution using Machine Learning

2019 ◽  
Vol 8 (11) ◽  
pp. 604-609
Keyword(s):  
Machine Learning ◽  
Support Vector Machine ◽  
Heart Disease ◽  
Random Forest ◽  
Heart Diseases ◽  
Support Vector ◽  
Disease Prediction ◽  
Data Set ◽  
Raw Data ◽  
Reduced Data

In today’s modern world, the human beings are affected with heart disease irrespective of the age. With the advancement of technological growth, predicting the availability of Heart diseases still remains a challenging issue. The difficulty of predicting the heart disease prevails due to the lack of availability of the symptoms. According to World Health Organization, 33% of population died due to heart diseases. For this, the diagnosis of heart diseases is made by complex combination of clinical data. With this overview, we have used Heart Disease Prediction dataset extracted from UCI Machine Learning Repository for predicting the level of heart disease. The prediction of heart disease classes are achieved in four ways. Firstly, the data set is preprocessed with Feature Scaling and Missing Values. Secondly, the raw data set is fitted to classifiers like logistic regression, KNN classifier, Support Vector Machine, Kernel Support Vector Machine, Naive Bayes, Random Forest and Decision Tree classifiers. Third, the raw data set is subjected to dimensionality reduction using Principal Component Analysis to project the dataset with important components. The dimensionality PCA reduced data set is fitted to the above-mentioned classifiers. Fourth, the performance comparison of raw data set and PCA reduced data set is done by analyzing the performance metrics like Precision, Recall, Accuracy and F-score. The implementation is done using python language under Spyder platform with Anaconda Navigator. Experimental results shows that Random forest is found to be effective with the accuracy of 89% without applying PCA, 85% with five component PCA and 86% with seven component PCA.

scholarly journals Estimation of Soil Cohesion Using Machine Learning Method: A Random Forest Approach

2021 ◽  
Vol 2021 ◽  
pp. 1-14
Author(s):  
Hai-Bang Ly ◽  
Thuy-Anh Nguyen ◽  
Binh Thai Pham
Keyword(s):  
Machine Learning ◽  
Random Forest ◽  
Soil Properties ◽  
Clay Content ◽  
Absolute Error ◽  
Experimental Methods ◽  
Liquid Limit ◽  
Support Vector ◽  
Data Set ◽  
Soil Cohesion

Soil cohesion (C) is one of the critical soil properties and is closely related to basic soil properties such as particle size distribution, pore size, and shear strength. Hence, it is mainly determined by experimental methods. However, the experimental methods are often time-consuming and costly. Therefore, developing an alternative approach based on machine learning (ML) techniques to solve this problem is highly recommended. In this study, machine learning models, namely, support vector machine (SVM), Gaussian regression process (GPR), and random forest (RF), were built based on a data set of 145 soil samples collected from the Da Nang-Quang Ngai expressway project, Vietnam. The database also includes six input parameters, that is, clay content, moisture content, liquid limit, plastic limit, specific gravity, and void ratio. The performance of the model was assessed by three statistical criteria, namely, the correlation coefficient (R), mean absolute error (MAE), and root mean square error (RMSE). The results demonstrated that the proposed RF model could accurately predict soil cohesion with high accuracy (R = 0.891) and low error (RMSE = 3.323 and MAE = 2.511), and its predictive capability is better than SVM and GPR. Therefore, the RF model can be used as a cost-effective approach in predicting soil cohesion forces used in the design and inspection of constructions.

Classification study of solvation free energies of organic molecules using machine learning techniques

RSC Advances ◽  
2014 ◽  
Vol 4 (106) ◽  
pp. 61624-61630 ◽  
Author(s):  
N. S. Hari Narayana Moorthy ◽  
Silvia A. Martins ◽  
Sergio F. Sousa ◽  
Maria J. Ramos ◽  
Pedro A. Fernandes
Keyword(s):  
Machine Learning ◽  
Support Vector Machine ◽  
Random Forest ◽  
Organic Molecules ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Classification Models ◽  
Free Energies ◽  
Learning Techniques ◽  
Solvation Free Energies

Classification models to predict the solvation free energies of organic molecules were developed using decision tree, random forest and support vector machine approaches and with MACCS fingerprints, MOE and PaDEL descriptors.

scholarly journals Execution Assessment of Machine Learning Algorithms for Spam Profile Detection on Instagram

2021 ◽  
Vol 10 (3) ◽  
pp. 1889-1894
Keyword(s):  
Machine Learning ◽  
Support Vector Machine ◽  
Random Forest ◽  
Nearest Neighbor ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Support Vector ◽  
Learning Tools ◽  
Learning Models ◽  
K Nearest Neighbor

Witheverypassingsecondsocialnetworkcommunityisgrowingrapidly,becauseofthat,attackershaveshownkeeninterestinthesekindsofplatformsandwanttodistributemischievouscontentsontheseplatforms.Withthefocus on introducing new set of characteristics and features forcounteractivemeasures,agreatdealofstudieshasresearchedthe possibility of lessening the malicious activities on social medianetworks. This research was to highlight features for identifyingspammers on Instagram and additional features were presentedto improve the performance of different machine learning algorithms. Performance of different machine learning algorithmsnamely, Multilayer Perceptron (MLP), Random Forest (RF), K-Nearest Neighbor (KNN) and Support Vector Machine (SVM)were evaluated on machine learning tools named, RapidMinerand WEKA. The results from this research tells us that RandomForest (RF) outperformed all other selected machine learningalgorithmsonbothselectedmachinelearningtools.OverallRandom Forest (RF) provided best results on RapidMiner. Theseresultsareusefulfortheresearcherswhoarekeentobuildmachine learning models to find out the spamming activities onsocialnetworkcommunities.

scholarly journals Application of Natural Language Processing with Supervised Machine Learning Techniques to Predict the Overall Drugs Performance

2020 ◽  
Vol 11 (40) ◽  
pp. 8-23
Author(s):  
Pius MARTHIN ◽  
Duygu İÇEN
Keyword(s):  
Machine Learning ◽  
Support Vector Machine ◽  
Random Forest ◽  
Semantic Analysis ◽  
Classification Tree ◽  
Supervised Machine Learning ◽  
Training Dataset ◽  
Support Vector ◽  
Learning Models ◽  
Machine Learning Models

Online product reviews have become a valuable source of information which facilitate customer decision with respect to a particular product. With the wealthy information regarding user's satisfaction and experiences about a particular drug, pharmaceutical companies make the use of online drug reviews to improve the quality of their products. Machine learning has enabled scientists to train more efficient models which facilitate decision making in various fields. In this manuscript we applied a drug review dataset used by (Gräβer, Kallumadi, Malberg,& Zaunseder, 2018), available freely from machine learning repository website of the University of California Irvine (UCI) to identify best machine learning model which provide a better prediction of the overall drug performance with respect to users' reviews. Apart from several manipulations done to improve model accuracy, all necessary procedures required for text analysis were followed including text cleaning and transformation of texts to numeric format for easy training machine learning models. Prior to modeling, we obtained overall sentiment scores for the reviews. Customer's reviews were summarized and visualized using a bar plot and word cloud to explore the most frequent terms. Due to scalability issues, we were able to use only the sample of the dataset. We randomly sampled 15000 observations from the 161297 training dataset and 10000 observations were randomly sampled from the 53766 testing dataset. Several machine learning models were trained using 10 folds cross-validation performed under stratified random sampling. The trained models include Classification and Regression Trees (CART), classification tree by C5.0, logistic regression (GLM), Multivariate Adaptive Regression Spline (MARS), Support vector machine (SVM) with both radial and linear kernels and a classification tree using random forest (Random Forest). Model selection was done through a comparison of accuracies and computational efficiency. Support vector machine (SVM) with linear kernel was significantly best with an accuracy of 83% compared to the rest. Using only a small portion of the dataset, we managed to attain reasonable accuracy in our models by applying the TF-IDF transformation and Latent Semantic Analysis (LSA) technique to our TDM.

scholarly journals Perbandingan Algoritma Klasifikasi Sentimen Twitter Terhadap Insiden Kebocoran Data Tokopedia

2021 ◽  
Vol 6 (2) ◽  
pp. 120-129
Author(s):  
Nadhif Ikbar Wibowo ◽  
Tri Andika Maulana ◽  
Hamzah Muhammad ◽  
Nur Aini Rakhmawati
Keyword(s):  
Support Vector Machine ◽  
Logistic Regression ◽  
Random Forest ◽  
Supervised Learning ◽  
Support Vector ◽  
Data Set ◽  
Logistic Regression Classifier

Public responses, posted on Twitter reacting to the Tokopedia data leak incident, were used as a data set to compare the performance of three different classifiers, trained using supervised learning modeling, to classify sentiment on the text. All tweets were classified into either positive, negative, or neutral classes. This study compares the performance of Random Forest, Support-Vector Machine, and Logistic Regression classifier. Data was scraped automatically and used to evaluate several models; the SVM-based model has the highest f1-score 0.503583. SVM is the best performing classifier.

scholarly journals Prediction of Collapsibility of Loess of Construction Sites in Xining Based on Machine Learning Methods

2021 ◽  
Author(s):  
Qifei Zhao ◽  
Xiaojun Li ◽  
Yunning Cao ◽  
Zhikun Li ◽  
Jixin Fan
Keyword(s):  
Machine Learning ◽  
Support Vector Machine ◽  
Training Data ◽  
Support Vector ◽  
Engineering Practice ◽  
Burial Depth ◽  
Learning Methods ◽  
Data Set ◽  
Machine Learning Methods ◽  
North East

Abstract Collapsibility of loess is a significant factor affecting engineering construction in loess area, and testing the collapsibility of loess is costly. In this study, A total of 4,256 loess samples are collected from the north, east, west and middle regions of Xining. 70% of the samples are used to generate training data set, and the rest are used to generate verification data set, so as to construct and validate the machine learning models. The most important six factors are selected from thirteen factors by using Grey Relational analysis and multicollinearity analysis: burial depth、water content、specific gravity of soil particles、void rate、geostatic stress and plasticity limit. In order to predict the collapsibility of loess, four machine learning methods: Support Vector Machine (SVM), Random Subspace Based Support Vector Machine (RSSVM), Random Forest (RF) and Naïve Bayes Tree (NBTree), are studied and compared. The receiver operating characteristic (ROC) curve indicators, standard error (SD) and 95% confidence interval (CI) are used to verify and compare the models in different research areas. The results show that: RF model is the most efficient in predicting the collapsibility of loess in Xining, and its AUC average is above 80%, which can be used in engineering practice.

scholarly journals Detecting Real-Time Fall of Elderly People Using Machine Learning

2021 ◽  
Vol 9 (12) ◽  
pp. 1913-1918
Author(s):  
Prathima P
Keyword(s):  
Machine Learning ◽  
Support Vector Machine ◽  
Random Forest ◽  
Elderly People ◽  
Fall Detection ◽  
Machine Learning Algorithms ◽  
Supervised Machine Learning ◽  
Support Vector ◽  
False Alarms ◽  
Severe Injuries

Abstract: Fall is a significant national health issue for the elderly people, generally resulting in severe injuries when the person lies down on the floor over an extended period without any aid after experiencing a great fall. Thus, elders need to be cared very attentively. A supervised-machine learning based fall detection approach with accelerometer, gyroscope is devised. The system can detect falls by grouping different actions as fall or non-fall events and the care taker is alerted immediately as soon as the person falls. The public dataset SisFall with efficient class of features is used to identify fall. The Random Forest (RF) and Support Vector Machine (SVM) machine learning algorithms are employed to detect falls with lesser false alarms. The SVM algorithm obtain a highest accuracy of 99.23% than RF algorithm. Keywords: Fall detection, Machine learning, Supervised classification, Sisfall, Activities of daily living, Wearable sensors, Random Forest, Support Vector Machine

scholarly journals Aspect Category Classification dengan Pendekatan Machine Learning Menggunakan Dataset Bahasa Indonesia

2021 ◽  
Vol 10 (3) ◽  
pp. 229-235
Author(s):  
Syaifulloh Amien Pandega Perdana ◽  
Teguh Bharata Aji ◽  
Ridi Ferdiana
Keyword(s):  
Machine Learning ◽  
Support Vector Machine ◽  
Random Forest ◽  
Sentiment Analysis ◽  
Support Vector ◽  
Term Weighting ◽  
Inverse Document Frequency ◽  
Term Frequency ◽  
Document Frequency ◽  
Bahasa Indonesia

Ulasan pelanggan merupakan opini terhadap kualitas barang atau jasa yang dirasakan konsumen. Ulasan pelanggan mengandung informasi yang berguna bagi konsumen maupun penyedia barang atau jasa. Ketersediaan ulasan pelanggan dalam jumlah besar pada website membutuhkan suatu framework untuk mengekstraksi sentimen secara otomatis. Sebuah ulasan pelanggan sering kali mengandung banyak aspek sehingga Aspect Based Sentiment Analysis (ABSA) harus digunakan untuk mengetahui polaritas masing-masing aspek. Salah satu tugas penting dalam ABSA adalah Aspect Category Detection. Metode machine learning untuk Aspect Category Detection sudah banyak dilakukan pada domain berbahasa Inggris, tetapi pada domain bahasa Indonesia masih sedikit. Makalah ini membandingkan kinerja tiga algoritme machine learning, yaitu Naïve Bayes (NB), Support Vector Machine (SVM), dan Random Forest (RF) pada ulasan pelanggan berbahasa Indonesia menggunakan Term Frequency–Inverse Document Frequency (TF-IDF) sebagai term weighting. Hasil menunjukkan bahwa RF memiliki kinerja paling unggul dibandingkan NB dan SVM pada tiga domain yang berbeda, yaitu restoran, hotel, dan e-commerce, dengan nilai f1-score untuk masing-masing domain adalah 84.3%, 85.7%, dan 89,3%.

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