scholarly journals Use of Machine Learning and Wearable Sensors to Predict Energetics and Kinematics of Cutting Maneuvers

Sensors ◽  
2019 ◽  
Vol 19 (14) ◽  
pp. 3094 ◽  
Author(s):  
Zago ◽  
Sforza ◽  
Dolci ◽  
Tarabini ◽  
Galli
Keyword(s):  
Machine Learning ◽  
Neural Networks ◽  
Support Vector Regression ◽  
Mean Absolute Error ◽  
Inertial Sensor ◽  
Absolute Error ◽  
Mechanical Work ◽  
Supervised Machine Learning ◽  
Support Vector ◽  
Turn Direction

Changes of directions and cutting maneuvers, including 180-degree turns, are common locomotor actions in team sports, implying high mechanical load. While the mechanics and neurophysiology of turns have been extensively studied in laboratory conditions, modern inertial measurement units allow us to monitor athletes directly on the field. In this study, we applied four supervised machine learning techniques (linear regression, support vector regression/machine, boosted decision trees and artificial neural networks) to predict turn direction, speed (before/after turn) and the related positive/negative mechanical work. Reference values were computed using an optical motion capture system. We collected data from 13 elite female soccer players performing a shuttle run test, wearing a six-axes inertial sensor at the pelvis level. A set of 18 features (predictors) were obtained from accelerometers, gyroscopes and barometer readings. Turn direction classification returned good results (accuracy >98.4%) with all methods. Support vector regression and neural networks obtained the best performance in the estimation of positive/negative mechanical work (coefficient of determination R2 = 0.42–0.43, mean absolute error = 1.14–1.41 J) and running speed before/after the turns (R2 = 0.66–0.69, mean absolute error = 0.15–018 m/s). Although models can be extended to different angles, we showed that meaningful information on turn kinematics and energetics can be obtained from inertial units with a data-driven approach.

scholarly journals COVID-19 Prediction Applying Supervised Machine Learning Algorithms with Comparative Analysis Using WEKA

Algorithms ◽  
2021 ◽  
Vol 14 (7) ◽  
pp. 201
Author(s):  
Charlyn Nayve Villavicencio ◽  
Julio Jerison Escudero Macrohon ◽  
Xavier Alphonse Inbaraj ◽  
Jyh-Horng Jeng ◽  
Jer-Guang Hsieh
Keyword(s):  
Machine Learning ◽  
Support Vector Machine ◽  
Mean Absolute Error ◽  
Learning Algorithms ◽  
Absolute Error ◽  
Machine Learning Algorithms ◽  
Supervised Machine Learning ◽  
Support Vector ◽  
K Nearest Neighbors ◽  
Use Of Technology

Early diagnosis is crucial to prevent the development of a disease that may cause danger to human lives. COVID-19, which is a contagious disease that has mutated into several variants, has become a global pandemic that demands to be diagnosed as soon as possible. With the use of technology, available information concerning COVID-19 increases each day, and extracting useful information from massive data can be done through data mining. In this study, authors utilized several supervised machine learning algorithms in building a model to analyze and predict the presence of COVID-19 using the COVID-19 Symptoms and Presence dataset from Kaggle. J48 Decision Tree, Random Forest, Support Vector Machine, K-Nearest Neighbors and Naïve Bayes algorithms were applied through WEKA machine learning software. Each model’s performance was evaluated using 10-fold cross validation and compared according to major accuracy measures, correctly or incorrectly classified instances, kappa, mean absolute error, and time taken to build the model. The results show that Support Vector Machine using Pearson VII universal kernel outweighs other algorithms by attaining 98.81% accuracy and a mean absolute error of 0.012.

scholarly journals Previsão de casos de dengue através de Machine Learning e Deep Learning: uma revisão sistemática

2021 ◽  
Vol 10 (11) ◽  
pp. e33101119347
Author(s):  
Ewethon Dyego de Araujo Batista ◽  
Wellington Candeia de Araújo ◽  
Romeryto Vieira Lira ◽  
Laryssa Izabel de Araujo Batista
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Short Term Memory ◽  
Mean Absolute Error ◽  
Absolute Error ◽  
Support Vector ◽  
Short Term ◽  
Term Memory ◽  
Long Short Term Memory ◽  
De Medicina

Introdução: a dengue é uma arbovirose causada pelo vírus DENV e transmitida para o homem através do mosquito Aedes aegypti. Atualmente, não existe uma vacina eficaz para combater todas as sorologias do vírus. Diante disso, o combate à doença se volta para medidas preventivas contra a proliferação do mosquito. Os pesquisadores estão utilizando Machine Learning (ML) e Deep Learning (DL) como ferramentas para prever casos de dengue e ajudar os governantes nesse combate. Objetivo: identificar quais técnicas e abordagens de ML e de DL estão sendo utilizadas na previsão de dengue. Métodos: revisão sistemática realizada nas bases das áreas de Medicina e de Computação com intuito de responder as perguntas de pesquisa: é possível realizar previsões de casos de dengue através de técnicas de ML e de DL, quais técnicas são utilizadas, onde os estudos estão sendo realizados, como e quais dados estão sendo utilizados? Resultados: após realizar as buscas, aplicar os critérios de inclusão, exclusão e leitura aprofundada, 14 artigos foram aprovados. As técnicas Random Forest (RF), Support Vector Regression (SVR), e Long Short-Term Memory (LSTM) estão presentes em 85% dos trabalhos. Em relação aos dados, na maioria, foram utilizados 10 anos de dados históricos da doença e informações climáticas. Por fim, a técnica Root Mean Absolute Error (RMSE) foi a preferida para mensurar o erro. Conclusão: a revisão evidenciou a viabilidade da utilização de técnicas de ML e de DL para a previsão de casos de dengue, com baixa taxa de erro e validada através de técnicas estatísticas.

scholarly journals Predicting Daily Returns of Global Stocks Indices: Neural Networks vs Support Vector Machines

2019 ◽  
pp. 1-13 ◽  
Author(s):  
Jasleen Kaur ◽  
Khushdeep Dharni
Keyword(s):  
Data Mining ◽  
Neural Networks ◽  
Support Vector Machines ◽  
Mean Square Error ◽  
Mean Absolute Error ◽  
Absolute Error ◽  
Support Vector ◽  
Mean Square ◽  
Data Mining Techniques ◽  
Vector Machines

Uniqueness in economies and stock markets has given rise to an interesting domain of exploring data mining techniques across global indices. Previously, very few studies have attempted to compare the performance of data mining techniques in diverse markets. The current study adds to the understanding regarding the variations in performance of data mining techniques across the global stock indices. We compared the performance of Neural Networks and Support Vector Machines using accuracy measures Mean Absolute Error (MAE) and R­­­­oot Mean Square Error (RMSE) across seven major stock markets. For prediction purpose, technical analysis has been employed on selected indicators based on daily values of indices spanning a period of 12 years. We created 196 data sets spanning different time periods for model building such as 1 year, 2 years, 3 years, 4 years, 6 years and 12 years for selected seven stock indices. Based on prediction models built using Neural Networks and Support Vector Machines, the findings of the study indicate there is a significant difference, both for MAE and RMSE, across the selected global indices. Also, Mean Absolute Error and Root Mean Square Error of models built using NN were greater than Mean Absolute Error and Root Mean Square Error of models built using SVM.

scholarly journals Predicting and Analysing the Behaviour of COVID-19

2021 ◽  
pp. 40-46
Author(s):  
Gaurav Singh ◽  
Shivam Rai ◽  
Himanshu Mishra ◽  
Manoj Kumar
Keyword(s):  
Machine Learning ◽  
Polynomial Regression ◽  
Mean Squared Error ◽  
Absolute Error ◽  
Machine Learning Algorithms ◽  
Supervised Machine Learning ◽  
Systems Science ◽  
Data Repository ◽  
Support Vector ◽  
Squared Error

The prime objective of this work is to predicting and analysing the Covid-19 pandemic around the world using Machine Learning algorithms like Polynomial Regression, Support Vector Machine and Ridge Regression. And furthermore, assess and compare the performance of the varied regression algorithms as far as parameters like R squared, Mean Absolute Error, Mean Squared Error and Root Mean Squared Error. In this work, we have used the dataset available on Covid-19 Data Repository by the Center for Systems Science and Engineering (CSSE) at John Hopkins University. We have analyzed the covid19 cases from 22/1/2020 till now. We applied a supervised machine learning prediction model to forecast the possible confirmed cases for the next ten days.

PEMODELAN PREDIKTIF KONSUMSI ENERGI BANGUNAN GEDUNG KOMERSIAL DENGAN ALGORITMA SUPPORT VECTOR MACHINE

2018 ◽  
Vol 14 (2) ◽  
pp. 225
Author(s):  
Indriyanti Indriyanti ◽  
Agus Subekti
Keyword(s):  
Machine Learning ◽  
Support Vector Machine ◽  
Root Mean Square Error ◽  
Mean Square Error ◽  
Root Mean Square ◽  
Mean Absolute Error ◽  
Absolute Error ◽  
Support Vector ◽  
Mean Square

Konsumsi energi bangunan yang semakin meningkat mendorong para peneliti untuk membangun sebuah model prediksi dengan menerapkan metode machine learning, namun masih belum diketahui model yang paling akurat. Model prediktif untuk konsumsi energi bangunan komersial penting untuk konservasi energi. Dengan menggunakan model yang tepat, kita dapat membuat desain bangunan yang lebih efisien dalam penggunaan energi. Dalam tulisan ini, kami mengusulkan model prediktif berdasarkan metode pembelajaran mesin untuk mendapatkan model terbaik dalam memprediksi total konsumsi energi. Algoritma yang digunakan yaitu SMOreg dan LibSVM dari kelas Support Vector Machine, kemudian untuk evaluasi model berdasarkan nilai Mean Absolute Error dan Root Mean Square Error. Dengan menggunakan dataset publik yang tersedia, kami mengembangkan model berdasarkan pada mesin vektor pendukung untuk regresi. Hasil pengujian kedua algoritma tersebut diketahui bahwa algoritma SMOreg memiliki akurasi lebih baik karena memiliki nilai MAE dan RMSE sebesar 4,70 dan 10,15, sedangkan untuk model LibSVM memiliki nilai MAE dan RMSE sebesar 9,37 dan 14,45. Kami mengusulkan metode berdasarkan algoritma SMOreg karena kinerjanya lebih baik.

Machine Learning Techniques for Estimating Amount of Coolant Required in Shipping of Temperature Sensitive Products

2020 ◽  
Vol 10 (10) ◽  
pp. 67-70
Author(s):  
Divya Choudhary ◽  
Siripong Malasri
Keyword(s):  
Machine Learning ◽  
Neural Networks ◽  
Absolute Error ◽  
Machine Learning Algorithms ◽  
Machine Learning Techniques ◽  
Threshold Temperature ◽  
Support Vector ◽  
Temperature Sensitive ◽  
Learning Techniques ◽  
The Neural Networks

This paper implements and compares machine learning algorithms to predict the amount of coolant required during transportation of temperature sensitive products. The machine learning models use trip duration, product threshold temperature and ambient temperature as the independent variables to predict the weight of gel packs need to keep the temperature of the product below its threshold temperature value. The weight of the gel packs can be translated to number of gel packs required. Regression using Neural Networks, Support Vector Regression, Gradient Boosted Regression and Elastic Net Regression are compared. The Neural Networks based model performs the best in terms of its mean absolute error value and r-squared values. A Neural Network model is then deployed on as webservice to score allowing for client application to make rest calls to estimate gel pack weights

scholarly journals Application of Machine Learning in the Control of Metal Melting Production Process

2020 ◽  
Vol 10 (17) ◽  
pp. 6048 ◽  
Author(s):  
Nedeljko Dučić ◽  
Aleksandar Jovičić ◽  
Srećko Manasijević ◽  
Radomir Radiša ◽  
Žarko Ćojbašić ◽  
...  
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Cast Iron ◽  
Production Process ◽  
Support Vector Regression ◽  
White Cast Iron ◽  
Supervised Machine Learning ◽  
Support Vector ◽  
The Neural Network ◽  
Metal Melting

This paper presents the application of machine learning in the control of the metal melting process. Metal melting is a dynamic production process characterized by nonlinear relations between process parameters. In this particular case, the subject of research is the production of white cast iron. Two supervised machine learning algorithms have been applied: the neural network and the support vector regression. The goal of their application is the prediction of the amount of alloying additives in order to obtain the desired chemical composition of white cast iron. The neural network model provided better results than the support vector regression model in the training and testing phases, which qualifies it to be used in the control of the white cast iron production.

scholarly journals Determination of Water Depth in Ports Using Satellite Data Based on Machine Learning Algorithms

Energies ◽  
2021 ◽  
Vol 14 (9) ◽  
pp. 2486
Author(s):  
Vanesa Mateo-Pérez ◽  
Marina Corral-Bobadilla ◽  
Francisco Ortega-Fernández ◽  
Vicente Rodríguez-Montequín
Keyword(s):  
Machine Learning ◽  
Water Depth ◽  
Satellite Data ◽  
Learning Algorithm ◽  
Mean Absolute Error ◽  
Absolute Error ◽  
Machine Learning Algorithms ◽  
Support Vector ◽  
Single Beam

One of the fundamental maintenance tasks of ports is the periodic dredging of them. This is necessary to guarantee a minimum draft that will enable ships to access ports safely. The determination of bathymetries is the instrument that determines the need for dredging and permits an analysis of the behavior of the port bottom over time, in order to achieve adequate water depth. Satellite data processing to predict environmental parameters is used increasingly. Based on satellite data and using different machine learning algorithm techniques, this study has sought to estimate the seabed in ports, taking into account the fact that the port areas are strongly anthropized areas. The algorithms that were used were Support Vector Machine (SVM), Random Forest (RF) and the Multi-Adaptive Regression Splines (MARS). The study was carried out in the ports of Candás and Luarca in the Principality of Asturias. In order to validate the results obtained, data was acquired in situ by using a single beam provided. The results show that this type of methodology can be used to estimate coastal bathymetry. However, when deciding which system was best, priority was given to simplicity and robustness. The results of the SVM and RF algorithms outperform those of the MARS. RF performs better in Candás with a mean absolute error (MAE) of 0.27 cm, whereas SVM performs better in Luarca with a mean absolute error of 0.37 cm. It is suggested that this approach is suitable as a simpler and more cost-effective rough resolution alternative, for estimating the depth of turbid water in ports, than single-beam sonar, which is labor-intensive and polluting.

scholarly journals Performance Evolution of Different Machine Learning Algorithms for Prediction of Liver Disease

2019 ◽  
Vol 9 (2) ◽  
pp. 1115-1122
Keyword(s):  
Machine Learning ◽  
Mean Absolute Error ◽  
Absolute Error ◽  
Machine Learning Algorithms ◽  
Support Vector ◽  
Nearest Neighbour ◽  
Mean Square ◽  
Vector Machines ◽  
Point Of No Return ◽  
Liver Infection

Liver malady is an overall medical issue that is related with different inconveniences and high mortality. It is of basic significance that illness be recognized before such huge numbers of these lives can be spared. The phases of liver ailment are a significant viewpoint for focused treatment. It is a terribly troublesome undertaking for therapeutic analysts to foresee the disease inside the beginning times on account of sensitive manifestations. Generally the side effects become evident once it's past the point of no return. To beat this issue, we have liver infection forecast. Liver sickness might be distinguished with incalculable order systems, and these have been classified the utilization forecast of a number highlights and classifier blends. In this investigation, we applied five sort of classifiers that is Naïve Bayes, logistic regression, support vector machines, Random Forest, K Nearest Neighbour for the examination of liver malady. The classification exhibitions are assessed with 5 distinctive by and large execution measurements, i.e., precision, kappa, Mean absolute error (MAE), Root mean square error (RMSE), and F measures. The objective of this query work is to foresee liver infection with different machine learning and pick most efficient algorithm.

scholarly journals Air Pollution Prediction using Machine Learning Algorithms

2019 ◽  
Vol 8 (6S3) ◽  
pp. 160-164
Keyword(s):  
Machine Learning ◽  
Air Pollution ◽  
Support Vector Regression ◽  
Air Pollutants ◽  
Absolute Error ◽  
Machine Learning Algorithms ◽  
Support Vector ◽  
Time Interval ◽  
K Nearest Neighbors ◽  
Data Set

Air pollution has a serious impact on human health. It occurs because of natural and man-made factors. The major contribution of this research is that it provides a comparison between different methodologies and techniques of mathematical and machine learning models. The process began with integrating data from different sources at different time interval. The preprocessing phase resulted in two different datasets: one-hour and five-minute datasets. Next, we established a forecasting model for particulate matter PM2.5, which is one of the most prevalent air pollutants and its concentration affects air quality. Additionally, we completed a multivariate analysis to predict the PM2.5 value and check the effects of other air pollutants, traffic, and weather. The algorithms used are support vector regression, k-nearest neighbors and decision tree models. The results showed that for the one-hour data set, of the three algorithms, support vector regression has the least root-mean-square error (RMSE) and also lowest value in mean absolute error (MAE). Alternatively, for the five-minute dataset, we found that the auto-regression model showed the least RMSE and MAE; however, this model only predicts short-term PM2.5.

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