scholarly journals Comparison of Machine Learning Algorithm For Urine Glucose Level Classification Using Side-Polished Fiber Sensor

2020 ◽  
Vol 2 (2) ◽  
pp. 33-39
Author(s):  
Riky Tri Yunardi ◽  
Retna Apsari ◽  
Moh Yasin
Keyword(s):  
Machine Learning ◽  
Neural Networks ◽  
Artificial Neural Networks ◽  
Random Forest ◽  
Cross Validation ◽  
Fiber Sensor ◽  
Urine Glucose ◽  
Glucose Levels ◽  
Artificial Neural ◽  
Side Polished Fiber

Urine glucose levels can be used to determine if glucose levels in the human body are too high, which may be a sign of diabetes. A non-invasive urine glucose classification model was conducted by using of the color of urine after benedict reaction to measure the level of glucose. The aim of this study is to classification urine glucose levels from a side-polished fiber sensor performed by using machine learning algorithms to get the best algorithm performance. By removing the coating and cladding this sensor is made of a polymer optical fiber. The measurement is focused on changes in the cladding refractive index which affects the amount of light transmitted.  The machine learning system has been implemented using the Naïve Bayes Classifier, k-Nearest Neighbor Classifier, Logistic Regression, Random Forest, Artificial Neural Networks and Support Vector Machine. The measurement data on samples were collected from previous studies of a total of 120 urine samples for testing in this study. The results of the experiments performed with k-fold cross validation show that the neural network gets the accuracy results of 96.7%, the value of precision 0.967, recall 0.967, and F1-Measure 0.967. With cross validation leave-one-out, the experimental results show the classification algorithm with the best accuracy value that is at the random forest and artificial neural networks 0.975, precision 0.975, recall 0975, and F1-Measure 0.975. While the ANN algorithm is superior in achieving an accuracy value of 98.6%. Therefore, artificial neural networks are the best method for classifying glucose levels in the human body for fasting and postprandial urine tests.

scholarly journals ALGORITMOS DE APRENDIZAGEM DE MÁQUINA NA MODELAGEM DA DISTRIBUIÇÃO POTENCIAL DE HABITATS DE ESPÉCIES ARBÓREAS

Nativa ◽  
2019 ◽  
Vol 7 (5) ◽  
pp. 600
Author(s):  
Mônica Canaan Carvalho ◽  
Luciano Cavalcante de Jesus França ◽  
Isaira Leite e Lopes ◽  
Laís Almeida Araújo ◽  
José Márcio de Mello ◽  
...  
Keyword(s):  
Machine Learning ◽  
Neural Networks ◽  
Artificial Neural Networks ◽  
Random Forest ◽  
Multilayer Perceptron ◽  
Tree Species ◽  
Potential Distribution ◽  
Minas Gerais ◽  
Artificial Neural ◽  
Eugenia Dysenterica

O estudo teve como objetivo avaliar três métodos de aprendizagem de máquina (árvore de decisão-J48, random forest e redes neurais artificias), na modelagem da distribuição de dez espécies arbóreas mais abundantes em uma sub-bacia do rio São Francisco (MG). Utilizaram-se dados provenientes do Inventário Florestal de Minas, com total de 77 fragmentos amostrados e 2.234 parcelas, nas quais foram computadas a presença/ausência de cada espécie. Empregaram-se 12 variáveis ambientais categóricas procedentes do Zoneamento Ecológico Econômico de Minas Gerais (ZEE/MG), além de variáveis relacionadas ao balanço hídrico do solo (evapotranspiração atual e potencial, aridez e índice alpha). A parametrização dos três algoritmos para as dez espécies selecionadas foi feita com o auxílio do algoritmo cv parameter do software WEKA. Os resultados mostram que os algoritmos testados apresentaram desempenhos estatisticamente iguais em 60% das espécies arbóreas. Os algoritmos random forest e multilayer perceptron foram estatisticamente iguais para a espécie Eugenia dysenterica, sendo superiores ao algoritmo J48. Contudo, o algoritmo random forest foi superior aos demais para as três espécies do gênero Qualea. Conclui-se que o algoritmo random forest apresentou-se como o mais robusto para a modelagem da distribuição potencial de habitat de espécies arbóreas.Palavras-chave: inteligência artificial; árvore de decisão; random forest; redes neurais artificiais. MACHINE LEARNING ALGORITHMS FOR MODELING THE POTENTIAL DISTRIBUTION HABITAT OF TREE SPECIES ABSTRACT: The aim of the present study was to evaluate three methods of machine learning (decision tree-J48, random forest and artificial neural networks) to model the potential habitat distribution of the ten most abundant tree species of the São Francisco river watershed. The presence/absence tree species data were from 77 fragments sampled with 2,234 plots. We used 12 categorical environmental variables from the Economic Ecological Zoning of Minas Gerais (ZEE/MG), as well as variables related to soil water balance (current and potential evapotranspiration, aridity and alpha index). The parameterization of the three algorithms was done with cv parameter algorithm of the WEKA software. The results showed the applied algorithms were statistically similar for 60% of the tree species. The random forest and multilayer perceptron algorithms were statistically similar considering the Eugenia dysenterica and superior to J48 algorithm. However, the random forest algorithm was superior to the other for the three species of Qualea genera. The conclusion is the random forest was the most robust model for the potential distribution habitat of tree species.Keywords: artificial intelligence; decision trees; random forest; artificial neural networks.

scholarly journals A Machine Learning Approach as a Surrogate for a Finite Element Analysis: Status of Research and Application to One Dimensional Systems

Sensors ◽  
2021 ◽  
Vol 21 (5) ◽  
pp. 1654
Author(s):  
Poojitha Vurtur Badarinath ◽  
Maria Chierichetti ◽  
Fatemeh Davoudi Kakhki
Keyword(s):  
Machine Learning ◽  
Neural Networks ◽  
Finite Element Analysis ◽  
Finite Element ◽  
Artificial Neural Networks ◽  
Stress Distribution ◽  
Maintenance Scheduling ◽  
Element Analysis ◽  
One Dimensional ◽  
Artificial Neural

Current maintenance intervals of mechanical systems are scheduled a priori based on the life of the system, resulting in expensive maintenance scheduling, and often undermining the safety of passengers. Going forward, the actual usage of a vehicle will be used to predict stresses in its structure, and therefore, to define a specific maintenance scheduling. Machine learning (ML) algorithms can be used to map a reduced set of data coming from real-time measurements of a structure into a detailed/high-fidelity finite element analysis (FEA) model of the same system. As a result, the FEA-based ML approach will directly estimate the stress distribution over the entire system during operations, thus improving the ability to define ad-hoc, safe, and efficient maintenance procedures. The paper initially presents a review of the current state-of-the-art of ML methods applied to finite elements. A surrogate finite element approach based on ML algorithms is also proposed to estimate the time-varying response of a one-dimensional beam. Several ML regression models, such as decision trees and artificial neural networks, have been developed, and their performance is compared for direct estimation of the stress distribution over a beam structure. The surrogate finite element models based on ML algorithms are able to estimate the response of the beam accurately, with artificial neural networks providing more accurate results.

scholarly journals Quantum enhanced cross-validation for near-optimal neural networks architecture selection

2018 ◽  
Vol 16 (08) ◽  
pp. 1840005 ◽  
Author(s):  
Priscila G. M. dos Santos ◽  
Rodrigo S. Sousa ◽  
Ismael C. S. Araujo ◽  
Adenilton J. da Silva
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Experimental Analysis ◽  
Cross Validation ◽  
Quantum Memory ◽  
Classical Algorithm ◽  
Artificial Neural ◽  
Quantum Speedup

This paper proposes a quantum-classical algorithm to evaluate and select classical artificial neural networks architectures. The proposed algorithm is based on a probabilistic quantum memory (PQM) and the possibility to train artificial neural networks (ANN) in superposition. We obtain an exponential quantum speedup in the evaluation of neural networks. We also verify experimentally through a reduced experimental analysis that the proposed algorithm can be used to select near-optimal neural networks.

Adaptive kinetic structural behavior through machine learning: Optimizing the process of kinematic transformation using artificial neural networks

2015 ◽  
Vol 29 (4) ◽  
pp. 371-391 ◽  
Author(s):  
Odysseas Kontovourkis ◽  
Marios C. Phocas ◽  
Ifigenia Lamprou
Keyword(s):  
Machine Learning ◽  
Neural Networks ◽  
Artificial Neural Networks ◽  
Adaptive Behavior ◽  
Physical Environment ◽  
Transformation Process ◽  
Artificial Neural ◽  
Kinematic Transformation ◽  
Alternative Solutions ◽  
Digital Or

AbstractNowadays, on the basis of significant work carried out, architectural adaption structures are considered to be intelligent entities, able to react to various internal or external influences. Their adaptive behavior can be examined in a digital or physical environment, generating a variety of alternative solutions or structural transformations. These are controlled through different computational approaches, ranging from interactive exploration ones, producing alternative emergent results, to automate optimization ones, resulting in acceptable fitting solutions. This paper examines the adaptive behavior of a kinetic structure, aiming to explore suitable solutions resulting in final appropriate shapes during the transformation process. A machine learning methodology that implements an artificial neural networks algorithm is integrated to the suggested structure. The latter is formed by units articulated together in a sequential composition consisting of primary soft mechanisms and secondary rigid components that are responsible for its reconfiguration and stiffness. A number of case studies that respond to unstructured environments are set as examples, to test the effectiveness of the proposed methodology to be used for handling a large number of input data and to optimize the complex and nonlinear transformation behavior of the kinetic system at the global level, as a result of the units’ local activation that influences nearby units in a chaotic and unpredictable manner.

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