Geometrical features for premature ventricular contraction recognition with analytic hierarchy process based machine learning algorithms selection

2019 ◽  
Vol 169 ◽  
pp. 59-69 ◽  
Author(s):  
Bruno Rodrigues de Oliveira ◽  
Caio Cesar Enside de Abreu ◽  
Marco Aparecido Queiroz Duarte ◽  
Jozue Vieira Filho
Keyword(s):  
Machine Learning ◽  
Analytic Hierarchy Process ◽  
Learning Algorithms ◽  
Premature Ventricular Contraction ◽  
Machine Learning Algorithms ◽  
Ventricular Contraction ◽  
Analytic Hierarchy ◽  
Geometrical Features ◽  
Hierarchy Process

scholarly journals A Review : Classification of Food Grains and Quality Prediction

2020 ◽  
pp. 34-37
Author(s):  
Mahalaxmi P P ◽  
Kavita D. Hanabaratti
Keyword(s):  
Machine Learning ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Automated System ◽  
Quality Prediction ◽  
Food Grains ◽  
Geometrical Features ◽  
Image Procession

This review paper discuss about recent techniques and methods used for grain classification and grading. Grains are important source of nutrients and they play important role in healthy diet. The production of grains across worldwide each year is in terms of hundreds of millions. The common method to classify these hugely produced grains is manual which is mind-numbing and not accurate. So the automated system is required which can classify the verities and predict the quality (i.e. grade A, grade B) of grain fast and accurate. As machine learning had done most of the difficult things easy, many machine learning algorithms can be used which can easily classify and predict the quality of grains. The system uses colour and geometrical features like size and area of grains as attributes for classification and quality prediction. Here, several image procession methods and machine learning algorithms are reviewed.

Ideological and political teaching model using fuzzy analytic hierarchy process based on machine learning and artificial intelligence

2020 ◽  
pp. 1-13
Author(s):  
Yuxia Wang
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Analytic Hierarchy Process ◽  
Teaching Quality ◽  
Ideological And Political Education ◽  
Fuzzy Analytic Hierarchy Process ◽  
Political Education ◽  
Current Status ◽  
Analytic Hierarchy ◽  
Hierarchy Process

Ideological and political education plays an important role in supporting social talent input. However, the current evaluation effect of ideological and political education is difficult to quantify. Therefore, in order to improve the evaluation effect of ideological and political education, based on artificial intelligence algorithms, this study combines machine learning ideas and the current status of ideological and political education to build a fuzzy analytic hierarchy process model of the of ideological and political teaching quality based on machine learning and artificial intelligence. Moreover, this study uses a three-tier structure to build a model network structure, and based on the characteristics of fuzzy evaluation, this study uses the expert system to conduct data management, operation and control of model evaluation, and build a corresponding database to update the data in real time. In addition, in order to verify the effect of the model, this study sets simulation experiments to analyze the model performance. From the point of view of running effect and running speed, this research model meets the actual needs of the system, so it can be applied to the evaluation process of ideological and political teaching quality in colleges and universities.

scholarly journals K-Nearest Neighbor with K-Fold Cross Validation and Analytic Hierarchy Process on Data Classification

2021 ◽  
Vol 2 (1) ◽  
Author(s):  
Zoelkarnain Rinanda Tembusai ◽  
Herman Mawengkang ◽  
Muhammad Zarlis
Keyword(s):  
Machine Learning ◽  
Feature Selection ◽  
Analytic Hierarchy Process ◽  
Cross Validation ◽  
Nearest Neighbor ◽  
K Nearest Neighbor ◽  
Analytic Hierarchy ◽  
Machine Learning Model ◽  
Hierarchy Process ◽  
Fold Cross Validation

This study analyzes the performance of the k-Nearest Neighbor method with the k-Fold Cross Validation algorithm as an evaluation model and the Analytic Hierarchy Process method as feature selection for the data classification process in order to obtain the best level of accuracy and machine learning model. The best test results are in fold-3, which is getting an accuracy rate of 95%. Evaluation of the k-Nearest Neighbor model with k-Fold Cross Validation can get a good machine learning model and the Analytic Hierarchy Process as a feature selection also gets optimal results and can reduce the performance of the k-Nearest Neighbor method because it only uses features that have been selected based on the level of importance for decision making.

Forecasting US movies box office performances in Turkey using machine learning algorithms

2020 ◽  
Vol 39 (5) ◽  
pp. 6579-6590
Author(s):  
Sandy Çağlıyor ◽  
Başar Öztayşi ◽  
Selime Sezgin
Keyword(s):  
Machine Learning ◽  
Global Economy ◽  
Learning Algorithms ◽  
Forecast Model ◽  
Machine Learning Algorithms ◽  
Gradient Boosting ◽  
High Stakes ◽  
Box Office ◽  
Industry Forecast ◽  
The Impact

The motion picture industry is one of the largest industries worldwide and has significant importance in the global economy. Considering the high stakes and high risks in the industry, forecast models and decision support systems are gaining importance. Several attempts have been made to estimate the theatrical performance of a movie before or at the early stages of its release. Nevertheless, these models are mostly used for predicting domestic performances and the industry still struggles to predict box office performances in overseas markets. In this study, the aim is to design a forecast model using different machine learning algorithms to estimate the theatrical success of US movies in Turkey. From various sources, a dataset of 1559 movies is constructed. Firstly, independent variables are grouped as pre-release, distributor type, and international distribution based on their characteristic. The number of attendances is discretized into three classes. Four popular machine learning algorithms, artificial neural networks, decision tree regression and gradient boosting tree and random forest are employed, and the impact of each group is observed by compared by the performance models. Then the number of target classes is increased into five and eight and results are compared with the previously developed models in the literature.

Intelligent system of English composition scoring model based on improved machine learning algorithm

2020 ◽  
pp. 1-11
Author(s):  
Jie Liu ◽  
Lin Lin ◽  
Xiufang Liang
Keyword(s):  
Machine Learning ◽  
Evaluation System ◽  
Intelligent System ◽  
Learning Algorithm ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Assessment System ◽  
English Composition ◽  
Region Extraction ◽  
Constraint Model

The online English teaching system has certain requirements for the intelligent scoring system, and the most difficult stage of intelligent scoring in the English test is to score the English composition through the intelligent model. In order to improve the intelligence of English composition scoring, based on machine learning algorithms, this study combines intelligent image recognition technology to improve machine learning algorithms, and proposes an improved MSER-based character candidate region extraction algorithm and a convolutional neural network-based pseudo-character region filtering algorithm. In addition, in order to verify whether the algorithm model proposed in this paper meets the requirements of the group text, that is, to verify the feasibility of the algorithm, the performance of the model proposed in this study is analyzed through design experiments. Moreover, the basic conditions for composition scoring are input into the model as a constraint model. The research results show that the algorithm proposed in this paper has a certain practical effect, and it can be applied to the English assessment system and the online assessment system of the homework evaluation system algorithm system.

Sign in / Sign up

Export Citation Format

Share Document