A Survey on Medical Diagnosis of Diabetes Using Machine Learning Techniques

Choudhury, Ambika; Gupta, Deepak

doi:10.1007/978-981-13-1280-9_6

Cited by 98 publications

(54 citation statements)

References 13 publications

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“…By adopting the kernel trick and soft penalization, one is able to classify nonlinear separable data including misclassified data points. SVM is one of the keystones of machine learning and has been adopted successfully in several disciplines, see for example the recent reviews by Choudhury and Gupta (2019) Figure 1. The proposed SVM-based MBDO approach compared to a traditional CAD-driven design process.…”

Section: Introductionmentioning

confidence: 99%

Efficient detailed design optimization of topology optimization concepts by using support vector machines and metamodels

Strömberg

2019

Engineering Optimization

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In this article, an approach for metamodel-based design optimization (MBDO) of topology optimization (TO) concepts is proposed by using support vector machines (SVMs) as geometric models of the concepts instead of traditional parametric computer aided design (CAD) models. In such a manner, an efficient approach for the MBDO-driven design of TO-based concepts is obtained. An implicit hypersurface representing the TO-based concept is generated by classifying the TO-solutions of zeros and ones by using the 1-norm SVM of Mangasarian. The implicit SVM-based hypersurfaces are then utilized to set up designs of experiments of nonlinear finite element analyses by morphing the TO-based concepts by using Boolean and blending operations. Finally, MBDO is performed by using an ensemble of metamodels consisting of quadratic regression, Kriging, radial basis function networks, polynomial chaos expansion and support vector regression models. The proposed MBDO framework is demonstrated by minimizing the mass of a three-dimensional design domain with a constraint on the plastic limit load. The performance of the approach is most promising.

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Section: Introductionmentioning

confidence: 99%

Efficient detailed design optimization of topology optimization concepts by using support vector machines and metamodels

Strömberg

2019

Engineering Optimization

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“…This method is easy to implement and effective because it does not require data scaling. However, it can easily fall short to handle non-linear mappings [ 37 ].…”

Section: Approach Descriptionmentioning

confidence: 99%

“…Random forest offers the advantage of handling large amounts of data and does not suffer from overfitting. On the other hand, it is slow to predict and hard to interpret how it performs [ 37 ].…”

Section: Approach Descriptionmentioning

confidence: 99%

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Analysis of Machine Learning Techniques Applied to Sensory Detection of Vehicles in Intelligent Crosswalks

Domínguez

Al-Tam

Sanguino

et al. 2020

Sensors

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Improving road safety through artificial intelligence-based systems is now crucial turning smart cities into a reality. Under this highly relevant and extensive heading, an approach is proposed to improve vehicle detection in smart crosswalks using machine learning models. Contrarily to classic fuzzy classifiers, machine learning models do not require the readjustment of labels that depend on the location of the system and the road conditions. Several machine learning models were trained and tested using real traffic data taken from urban scenarios in both Portugal and Spain. These include random forest, time-series forecasting, multi-layer perceptron, support vector machine, and logistic regression models. A deep reinforcement learning agent, based on a state-of-the-art double-deep recurrent Q-network, is also designed and compared with the machine learning models just mentioned. Results show that the machine learning models can efficiently replace the classic fuzzy classifier.

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“…The hyperplane with the smallest total distance is the best solution. SVR has been used successfully to solve various problems in numerous fields, such as medicine [ 20 ], and has been proven to be a superior prediction model for time-series analysis [ 21 ] and regression analysis [ 22 ]. Research based on ANNs and SVR, which have promising nonlinear adaptability, has widened the application of nonlinear methods.…”

Section: Introductionmentioning

confidence: 99%

Student Enrollment and Teacher Statistics Forecasting Based on Time-Series Analysis

Yang

Chen

et al. 2020

Computational Intelligence and Neuroscience

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Education competitiveness is a key feature of national competitiveness. It is crucial for nations to develop and enhance student and teacher potential to increase national competitiveness. The decreasing population of children has caused a series of social problems in many developed countries, directly affecting education and com.petitiveness in an international environment. In Taiwan, a low birthrate has had a large impact on schools at every level because of a substantial decrease in enrollment and a surplus of teachers. Therefore, close attention must be paid to these trends. In this study, combining a whale optimization algorithm (WOA) and support vector regression (WOASVR) was proposed to determine trends of student and teacher numbers in Taiwan for higher accuracy in time-series forecasting analysis. To select the most suitable support vector kernel parameters, WOA was applied. Data collected from the Ministry of Education datasets of student and teacher numbers between 1991 and 2018 were used to examine the proposed method. Analysis revealed that the numbers of students and teachers decreased annually except in private primary schools. A comparison of the forecasting results obtained from WOASVR and other common models indicated that WOASVR provided the lowest mean absolute percentage error (MAPE) and root mean square error (RMSE) for all analyzed datasets. Forecasting performed using the WOASVR method can provide accurate data for use in developing education policies and responses.

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A Survey on Medical Diagnosis of Diabetes Using Machine Learning Techniques

Cited by 98 publications

References 13 publications

Efficient detailed design optimization of topology optimization concepts by using support vector machines and metamodels

Efficient detailed design optimization of topology optimization concepts by using support vector machines and metamodels

Analysis of Machine Learning Techniques Applied to Sensory Detection of Vehicles in Intelligent Crosswalks

Student Enrollment and Teacher Statistics Forecasting Based on Time-Series Analysis

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