scholarly journals Machine Learning Technique to Improve an Impedance Matching Characteristic of a Bent Monopole Antenna

2021 ◽  
Vol 11 (22) ◽  
pp. 10829
Author(s):  
Jaeyul Choo ◽  
Pho Thi Ha Anh ◽  
Yong-Hwa Kim
Keyword(s):  
Machine Learning ◽  
Mean Squared Error ◽  
Impedance Matching ◽  
Learning Model ◽  
Monopole Antenna ◽  
Machine Learning Technique ◽  
Squared Error ◽  
Machine Learning Model ◽  
Learning Technique ◽  
The Mean

We designed the wire monopole antenna bent at three points by applying a machine learning technique to achieve a good impedance matching characteristic. After performing the deep neural network (DNN)-based training, we validated our machine learning model by evaluating mean squared error and R-squared score. Considering the mean squared error of about zero and R-squared score of about one, the performance prediction by the resulting machine learning model showed a high accuracy compared with that by the numerical electromagnetic simulation. Finally, we interpreted the operating principle of the antennas with a good impedance matching characteristic by analyzing equivalent circuits corresponding to their structures. The accomplished works in this research provide us with the possibility to use the machine learning technique in the antenna design.

scholarly journals Modeling of Surfactant-Enhanced Drying of Poly(styrene)-p-xylene Polymeric Coatings Using Machine Learning Technique

Coatings ◽  
2021 ◽  
Vol 11 (12) ◽  
pp. 1529
Author(s):  
Raj Kumar Arya ◽  
Jyoti Sharma ◽  
Rahul Shrivastava ◽  
Devyani Thapliyal ◽  
George D. Verros
Keyword(s):  
Machine Learning ◽  
Mean Squared Error ◽  
Regression Tree ◽  
Maximum Error ◽  
Polymeric Coatings ◽  
Tree Model ◽  
Machine Learning Technique ◽  
Squared Error ◽  
Learning Technique ◽  
Predicted Values

In this work, a machine learning technique based on a regression tree model was used to model the surfactant enhanced drying of poly(styrene)-p-xylene coatings. The predictions of the developed model based on regression trees are in excellent agreement with the experimental data. A total of 16,258 samples were obtained through experimentation. These samples were separated into two parts: 12,960 samples were used for the training of the regression tree, and the remaining 3298 samples were used to test the tree’s prediction accuracy. MATLAB software was used to grow the regression tree. The mean squared error between the model-predicted values and actual outputs was calculated to be 8.8415 × 10−6. This model has good generalizing ability; predicts weight loss for given values of time, thickness, and triphenyl phosphate; and has a maximum error of 1%. It is robust and for this system, can be used for any composition and thickness for this system, which will drastically reduce the need for further experimentations to explain diffusion and drying.

scholarly journals Prediction of Heart Disease using Machine Learning

2020 ◽  
pp. 150-166
Author(s):  
Ankit Singh
Keyword(s):  
Machine Learning ◽  
Heart Disease ◽  
Mean Squared Error ◽  
Classification Algorithm ◽  
Support Vector ◽  
Machine Learning Technique ◽  
Machine Learning Classification ◽  
Squared Error ◽  
Clinical Data Analysis ◽  
Learning Technique

Cardiovascular Disease is the leading cause of death (Approximately, 17 million people every year) in the all the area of the world. Prediction of heart disease is the critical challenge in the area of the clinical data analysis. The objective of paper is to build the model for predicting the Heart Disease using various machine learning classification algorithm. Classification is a powerful machine learning technique that is commonly used for prediction. Some of the classification algorithm are Logistic Regression, Support Vector Machine, Naïve Bayes, Decision Tree, Random Forest Classifier, KNN. This paper investigate which algorithm is used for the improving the accuracy in the prediction of heart disease. And, a comparative analysis on the accuracy and mean squared error is to done for predicting the best model. The result of the study indicates that KNN algorithm is effective in predicting the model with the accuracy of the 85.71% and having a very low mean squared error.

scholarly journals Detection and Measure Carstensz Glacier Area Changes using Machine Learning Technique

2020 ◽  
Vol 9 (3) ◽  
pp. 1397-1404
Keyword(s):  
Machine Learning ◽  
Satellite Data ◽  
Learning Model ◽  
Glacier Change ◽  
Glacier Area ◽  
Machine Learning Technique ◽  
Manual Delineation ◽  
Machine Learning Model ◽  
Learning Technique ◽  
Normalized Difference Snow Index

Using satellite data for acquiring glacier outlines has become more popular in the last decade. Glacier change assessment is the main goal for deriving glacier outlines. It's important to make the best method to generate the glacier outline as there most of the glacier outline is made with manual delineation and spectral thresholding. This research used a machine learning model to deriving the glacier pixels from satellite data. The model trained using more than 80 thousand of a glacier and non-glacier pixels. The model that trained has been proved to able classified a glacier pixel with more than 99% accuracy in one of the best experiments. The NDSI (Normalized Difference Snow Index) proved to be the key feature to classifying glaciers and shown to be one the best combination with NDSI + GLCM + TIFF (Band 4). This model hopefully can be further expanded and installed directly in satellite so we can instantly make a glacier outline without any manual delineation or spectral thresholding needed

scholarly journals Object detection and used car price predicting analysis system (ucpas) using machine learning technique

2021 ◽  
Vol 5 (S2) ◽  
Author(s):  
Anu Yadav ◽  
Ela Kumar ◽  
Piyush Kumar Yadav
Keyword(s):  
Machine Learning ◽  
Object Detection ◽  
Research Area ◽  
Machine Learning Technique ◽  
Car Detection ◽  
Machine Learning Model ◽  
Implementation Techniques ◽  
Learning Technique ◽  
Used Car ◽  
Analysis System

The highly interesting research area that noticed in the last few years is object detection and find out the prediction based on the features that can be benefited to consumers and the industry. In this paper, we understand the concept of object detection like the car detection, to look into the price of a second-hand car using automatic machine learning methods. We also understand the concept of object detection categories. Nowadays, the most challenging task is to determine what is the listed price of a used car on the market, Possibility of various factors that can drive a used car price. The main objective of this paper is to develop machine learning models which make it possible to accurately predict the price of a second-hand car according to its parameter or characteristics. In this paper, implementation techniques and evaluation methods are used on a Car dataset consisting of the selling prices of various models of  car across different cities of India. The outcome of this experiment shows that clustering with linear regression and Random Forest model yield the best accuracy outcome. The machine learning model produces a satisfactory result within a short duration of time compared to the aforementioned self.

scholarly journals Sugarcane Yield Mapping Using High-Resolution Imagery Data and Machine Learning Technique

2021 ◽  
Vol 13 (2) ◽  
pp. 232
Author(s):  
Tatiana Fernanda Canata ◽  
Marcelo Chan Fu Wei ◽  
Leonardo Felipe Maldaner ◽  
José Paulo Molin
Keyword(s):  
Machine Learning ◽  
Precision Agriculture ◽  
Mean Squared Error ◽  
Machine Learning Technique ◽  
Yield Mapping ◽  
Essential Information ◽  
Spectral Bands ◽  
Learning Technique ◽  
Sugarcane Yield ◽  
Imagery Data

Yield maps provide essential information to guide precision agriculture (PA) practices. Yet, on-board yield monitoring for sugarcane can be challenging. At the same time, orbital images have been widely used for indirect crop yield estimation for many crops like wheat, corn, and rice, but not for sugarcane. Due to this, the objective of this study is to explore the potential of multi-temporal imagery data as an alternative for sugarcane yield mapping. The study was based on developing predictive sugarcane yield models integrating time-series orbital imaging and a machine learning technique. A commercial sugarcane site was selected, and Sentinel-2 images were acquired from the beginning of the ratoon sprouting until harvesting of two consecutive cropping seasons. The predictive yield models RF (Random forest) and MLR (Multiple Linear Regression) were developed using orbital images and yield maps generated by a commercial sensor-system on harvesting. Original yield data were filtered and interpolated with the same spatial resolution of the orbital images. The entire dataset was divided into training and testing datasets. Spectral bands, especially the near-infrared at tillering crop stage showed greater contribution to predicting sugarcane yield than the use of derived spectral vegetation indices. The Root Mean Squared Error (RMSE) obtained for the RF regression based on multiple spectral bands was 4.63 Mg ha−1 with an R2 of 0.70 for the testing dataset. Overall, the RF regression had better performance than the MLR to predict sugarcane yield.

scholarly journals A New Ensenble Learning based Optimal Prediction Model for Cardiovascular Diseases

2021 ◽  
Vol 309 ◽  
pp. 01007
Author(s):  
B. Srınıvasa Rao
Keyword(s):  
Machine Learning ◽  
Cardiovascular Diseases ◽  
Present Model ◽  
Research Work ◽  
Learning Model ◽  
Ensemble Technique ◽  
Machine Learning Model ◽  
Learning Technique ◽  
Traditional Approaches ◽  
Optimal Machine

The present paperreports an optimal machine learning model for an effective prediction of cardiovascular diseases that uses the ensemble learning technique. The present research work gives an insight about the coherent way of combining Naive Bayes and Random Forest algorithm using ensemble technique. It also discusses how the present model is different from other traditional approaches. The present experimental results manifest that the present optimal machine learning model is more efficient than the other models.

Beatmungsassoziierte Pneumonie: Elektronische Nase und künstliche Intelligenz als Potenzial für die Zukunft

2020 ◽  
Vol 8 (5) ◽  
pp. 254-255
Author(s):  
Johannes Knoch
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Intensive Care Unit ◽  
Intensive Care ◽  
Predictive Value ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Machine Learning Technique ◽  
Learning Technique ◽  
The Mean

Background: Ventilator-associated pneumonia (VAP) is a significant cause of mortality in the intensive care unit. Early diagnosis of VAP is important to provide appropriate treatment and reduce mortality. Developing a noninvasive and highly accurate diagnostic method is important. The invention of electronic sensors has been applied to analyze the volatile organic compounds in breath to detect VAP using a machine learning technique. However, the process of building an algorithm is usually unclear and prevents physicians from applying the artificial intelligence technique in clinical practice. Clear processes of model building and assessing accuracy are warranted. The objective of this study was to develop a breath test for VAP with a standardized protocol for a machine learning technique. Methods: We conducted a case-control study. This study enrolled subjects in an intensive care unit of a hospital in southern Taiwan from February 2017 to June 2019. We recruited patients with VAP as the case group and ventilated patients without pneumonia as the control group. We collected exhaled breath and analyzed the electric resistance changes of 32 sensor arrays of an electronic nose. We split the data into a set for training algorithms and a set for testing. We applied eight machine learning algorithms to build prediction models, improving model performance and providing an estimated diagnostic accuracy. Results: A total of 33 cases and 26 controls were used in the final analysis. Using eight machine learning algorithms, the mean accuracy in the testing set was 0.81 ± 0.04, the sensitivity was 0.79 ± 0.08, the specificity was 0.83 ± 0.00, the positive predictive value was 0.85 ± 0.02, the negative predictive value was 0.77 ± 0.06, and the area under the receiver operator characteristic curves was 0.85 ± 0.04. The mean kappa value in the testing set was 0.62 ± 0.08, which suggested good agreement. Conclusions: There was good accuracy in detecting VAP by sensor array and machine learning techniques. Artificial intelligence has the potential to assist the physician in making a clinical diagnosis. Clear protocols for data processing and the modeling procedure needed to increase generalizability.

scholarly journals Bitcoin Price Prediction using SVM and ARIMA Model

2021 ◽  
pp. 1094-1096
Author(s):  
Gausiya Momin ◽  
Trupti Ingle ◽  
Vaishnavi Mirajkar ◽  
A. A. Magar
Keyword(s):  
Machine Learning ◽  
Regression Models ◽  
Mean Squared Error ◽  
Learning Algorithms ◽  
Arima Model ◽  
Machine Learning Algorithms ◽  
Price Prediction ◽  
Squared Error ◽  
Minute Interval ◽  
The Mean

Bitcoin is the most profitable in the cryptocurrency market. However, the prices of Bitcoin have highly fluctuated which makes them very difficult to predict. This research aims to discover the most efficient accuracy model to predict Bitcoin prices from various machine learning algorithms. Using one-minute interval trading data on the exchange website name is bit stamp from January 1, 2012, to January 8, 2018, some different regression models with sci-kit- learn and Keras libraries had experimented. The best results showed that the Mean Squared Error (MSE) was as low as 0.00002 and the R-Square (R2) was as high as 99.2 Percentage.

Sign in / Sign up

Export Citation Format

Share Document