scholarly journals Improving CSI Prediction Accuracy with Deep Echo State Networks in 5G Networks

Sensors ◽  
2020 ◽  
Vol 20 (22) ◽  
pp. 6475
Author(s):  
Tommaso Pecorella ◽  
Romano Fantacci ◽  
Benedetta Picano
Keyword(s):  
Machine Learning ◽  
Prediction Accuracy ◽  
Time Series Prediction ◽  
Machine Learning Techniques ◽  
Percentage Error ◽  
Channel State ◽  
Absolute Deviation ◽  
Combined Application ◽  
Echo State Networks ◽  
Learning Techniques

The forthcoming fifth-generation networks require improvements in cognitive radio intelligence, going towards more smart and aware radio systems. In the emerging radio intelligence approach, the empowerment of cognitive capabilities is performed through the adoption of machine learning techniques. This paper investigates the combined application of the convolutional and recurrent neural networks for the channel state information forecasting, providing a multivariate scalar time series prediction by taking into account the multiple factors dependence of the channel state conditions. Finally, the system performance has been analyzed in terms of prediction accuracy expressed as absolute deviation error and mean percentage error, in comparison with an alternative machine learning method recently proposed in the literature with the aim at solving the same prediction problem.

scholarly journals Application of time series prediction techniques for coastal bridge engineering

2021 ◽  
Vol 2 (1) ◽  
Author(s):  
Enbo Yu ◽  
Huan Wei ◽  
Yan Han ◽  
Peng Hu ◽  
Guoji Xu
Keyword(s):  
Machine Learning ◽  
Time Series ◽  
Prediction Accuracy ◽  
Time Series Prediction ◽  
Data Preprocessing ◽  
Bridge Engineering ◽  
Machine Learning Techniques ◽  
Wave Load ◽  
Learning Techniques ◽  
Extreme Gradient Boosting

AbstractIn this study, three machine learning techniques, the XGBoost (Extreme Gradient Boosting), LSTM (Long Short-Term Memory Networks), and ARIMA (Autoregressive Integrated Moving Average Model), are utilized to deal with the time series prediction tasks for coastal bridge engineering. The performance of these techniques is comparatively demonstrated in three typical cases, the wave-load-on-deck under regular waves, structural displacement under combined wind and wave loads, and wave height variation along with typhoon/hurricane approaching. To enhance the prediction accuracy, a typical data preprocessing method is adopted and an improved prediction framework for the LSTM model after the rolling forecast prediction is proposed. The obtained results show that: (a) When making a prediction on data featured with periodic regularity, both the XGBoost and ARIMA models perform well, and the XGBoost model can make predictions multi-step ahead, (b) The ARIMA model can predict just one step ahead based on aperiodic dataset with limited amplitude more accurately, while the XGBoost and LSTM models can predict multi-step ahead with appropriate data preprocessing, and (c) All the three models can predict the data tendency with model updating over time, but the prediction accuracy of the LSTM model is more favorable. The successful application of these three machine learning techniques can provide guidance to resolve engineering problems with time-history prediction requirements.

Application of Machine Learning Techniques in the Study of the Relevance of Environmental Factors in Prediction of Tropospheric Ozone

2010 ◽  
pp. 278-292
Author(s):  
Juan Gómez-Sanchis ◽  
Emilio Soria-Olivas ◽  
Marcelino Martinez-Sober ◽  
Jose Blasco ◽  
Juan Guerrero ◽  
...  
Keyword(s):  
Machine Learning ◽  
Neural Networks ◽  
Ozone Concentration ◽  
Tropospheric Ozone ◽  
Prediction Accuracy ◽  
Linear Models ◽  
Machine Learning Techniques ◽  
Learning Techniques ◽  
Atmospheric Phenomena ◽  
Atmospheric Concentrations

This work presents a new approach for one of the main problems in the analysis of atmospheric phenomena, the prediction of atmospheric concentrations of different elements. The proposed methodology is more efficient than other classical approaches and is used in this work to predict tropospheric ozone concentration. The relevance of this problem stems from the fact that excessive ozone concentrations may cause several problems related to public health. Previous research by the authors of this work has shown that the classical approach to this problem (linear models) does not achieve satisfactory results in tropospheric ozone concentration prediction. The authors’ approach is based on Machine Learning (ML) techniques, which include algorithms related to neural networks, fuzzy systems and advanced statistical techniques for data processing. In this work, the authors focus on one of the main ML techniques, namely, neural networks. These models demonstrate their suitability for this problem both in terms of prediction accuracy and information extraction.

A Statistical Scrutiny of Three Prominent Machine-Learning Techniques to Forecast Machining Performance Parameters of Inconel 690

2018 ◽  
pp. 104-120
Author(s):  
Binayak Sen ◽  
Uttam Kumar Mandal ◽  
Sankar Prasad Mondal
Keyword(s):  
Machine Learning ◽  
Population Based ◽  
Black Box ◽  
Machine Learning Techniques ◽  
Percentage Error ◽  
Machining Parameters ◽  
Ann Model ◽  
Machining Performance ◽  
Learning Techniques ◽  
Inconel 690

Computational approaches like “Black box” predictive modeling approaches are extensively used technique applied in machine learning operations of today. Considering the latest trends, present study compares capabilities of two different “Black box” predictive model like ANFIS and ANN with a population-based evolutionary algorithm GEP for forecasting machining parameters of Inconel 690 material, machined in a CNC-assisted 3-axis milling machine. The aims of this article are to represent considerable data showing, every techniques performance under the criteria of root mean square error (RSME), Correlational coefficient R and Mean absolute percentage error (MAPE). In this chapter, we vigorously demonstrate that the performance of the GEP model is far superior to ANFIS and ANN model.

scholarly journals Machine Learning-Based Code Auto-Completion Implementation for Firmware Developers

2020 ◽  
Vol 10 (23) ◽  
pp. 8520
Author(s):  
Junghyun Kim ◽  
Kyuman Lee ◽  
Sanghyun Choi
Keyword(s):  
Machine Learning ◽  
Language Processing ◽  
Prediction Accuracy ◽  
Language Model ◽  
Hybrid Approach ◽  
Machine Learning Techniques ◽  
Specific Domain ◽  
Efficient Manner ◽  
Source Codes ◽  
Learning Techniques

With the advent of artificial intelligence, the research paradigm in natural language processing has been transitioned from statistical methods to machine learning-based approaches. One application is to develop a deep learning-based language model that helps software engineers write code faster. Although there have already been many attempts to develop code auto-completion functionality from different research groups, a need to establish an in-house code has been identified for the following reasons: (1) a security-sensitive company (e.g., Samsung Electronics) may not want to utilize commercial tools given that there is a risk of leaked source codes and (2) commercial tools may not be applicable to the specific domain (e.g., SSD firmware development) especially if one needs to predict unique code patterns and style. This research proposes a hybrid approach that harnesses the synergy between machine learning techniques and advanced design methods aiming to develop a code auto-completion framework that helps firmware developers write code in a more efficient manner. The sensitivity analysis results show that the deterministic design results in reducing prediction accuracy as it generates output in some unexpected ways, while the probabilistic design provides a list of reasonable next code elements in which one could select it manually to increase prediction accuracy.

scholarly journals The Effectiveness of Feature Selection Method in Solar Power Prediction

2013 ◽  
Vol 2013 ◽  
pp. 1-9 ◽  
Author(s):  
Md Rahat Hossain ◽  
Amanullah Maung Than Oo ◽  
A. B. M. Shawkat Ali
Keyword(s):  
Machine Learning ◽  
Feature Selection ◽  
Prediction Accuracy ◽  
Solar Power ◽  
Feature Subset Selection ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Selection Methods ◽  
Power Prediction ◽  
Learning Techniques

This paper empirically shows that the effect of applying selected feature subsets on machine learning techniques significantly improves the accuracy for solar power prediction. Experiments are performed using five well-known wrapper feature selection methods to obtain the solar power prediction accuracy of machine learning techniques with selected feature subsets. For all the experiments, the machine learning techniques, namely, least median square (LMS), multilayer perceptron (MLP), and support vector machine (SVM), are used. Afterwards, these results are compared with the solar power prediction accuracy of those same machine leaning techniques (i.e., LMS, MLP, and SVM) but without applying feature selection methods (WAFS). Experiments are carried out using reliable and real life historical meteorological data. The comparison between the results clearly shows that LMS, MLP, and SVM provide better prediction accuracy (i.e., reduced MAE and MASE) with selected feature subsets than without selected feature subsets. Experimental results of this paper facilitate to make a concrete verdict that providing more attention and effort towards the feature subset selection aspect (e.g., selected feature subsets on prediction accuracy which is investigated in this paper) can significantly contribute to improve the accuracy of solar power prediction.

scholarly journals A Comparitive Study of E-Mail Spam Detection using Various Machine Learning Techniques

2021 ◽  
Author(s):  
Simarjeet Kaur ◽  
Meenakshi Bansal ◽  
Ashok Kumar Bathla
Keyword(s):  
Machine Learning ◽  
Prediction Accuracy ◽  
Learning Algorithm ◽  
Machine Learning Algorithms ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Spam Detection ◽  
Learning Techniques ◽  
E Mail ◽  
Email Spam

Due to the rise in the use of messaging and mailing services, spam detection tasks are of much greater importance than before. In such a set of communications, efficient classification is a comparatively onerous job. For an addressee or any email that the user does not want to have in his inbox, spam can be defined as redundant or trash email. After pre-processing and feature extraction, various machine learning algorithms were applied to a Spam base dataset from the UCI Machine Learning repository in order to classify incoming emails into two categories: spam and non-spam. The outcomes of various algorithms have been compared. This paper used random forest, naive bayes, support vector machine (SVM), logistic regression, and the k nearest (KNN) machine learning algorithm to successfully classify email spam messages. The main goal of this study is to improve the prediction accuracy of spam email filters.

scholarly journals Detection of Significant Risk Factors of Heart Diseases by Educing Multimodal Features and Implementing Machine Learning Techniques

2021 ◽  
pp. 621-630
Author(s):  
Kanyifeechukwu Jane Oguine ◽  
Ozioma Collins Oguine ◽  
Chito Frances Ofodum
Keyword(s):  
Machine Learning ◽  
Risk Factors ◽  
Prediction Accuracy ◽  
Heart Diseases ◽  
Significant Risk ◽  
Machine Learning Techniques ◽  
Medical Practitioners ◽  
Learning Techniques ◽  
Multimodal Features ◽  
Concealed Information

One of the major reasons for deaths worldwide is heart diseases and possible detection at an earlier stage will prevent these attacks. Medical practitioners generate data with a wealth of concealed information present, and it’s not used effectively for predictions. For this reason, the research will convert the unused data into a dataset for shaping using different data mining techniques. People die having encountered symptoms that were not taken into considerations. The main objective of this paper is to analyze the most significant risk factors of Heart Diseases of patients by extracting multimodal features and predicting the occurrence of heart diseases using different classification techniques comparatively. This study will help improve the decision-making of medical professionals on the occurrence of heart diseases patients in a bid to enhance early detection by implementing comparatively several machine learning techniques resulting in an improved prediction accuracy using patient records (Multimodal Features).

Machine Learning in Fine Wine Price Prediction

2015 ◽  
Vol 10 (2) ◽  
pp. 151-172 ◽  
Author(s):  
Michelle Yeo ◽  
Tristan Fletcher ◽  
John Shawe-Taylor
Keyword(s):  
Machine Learning ◽  
Gaussian Process ◽  
Predictive Accuracy ◽  
Time Series Prediction ◽  
Feature Learning ◽  
Gaussian Process Regression ◽  
Machine Learning Techniques ◽  
Linear Regression Models ◽  
Price Prediction ◽  
Learning Techniques

AbstractAdvanced machine learning techniques like Gaussian process regression and multi-task learning are novel in the area of wine price prediction; previous research in this area being restricted to parametric linear regression models when predicting wine prices. Using historical price data of the 100 wines in the Liv-Ex 100 index, the main contributions of this paper to the field are, firstly, a clustering of the wines into two distinct clusters based on autocorrelation. Secondly, an implementation of Gaussian process regression on these wines with predictive accuracy surpassing both the trivial and simple ARMA and GARCH time series prediction benchmarks. Lastly, an implementation of an algorithm which performs multi-task feature learning with kernels on the wine returns as an extension to our optimal Gaussian process regression model. Using the optimal covariance kernel from Gaussian process regression, we achieve predictive results which are comparable to that of Gaussian process regression. Altogether, our research suggests that there is potential in using advanced machine learning techniques in wine price prediction. (JEL Classifications: C6, G12)

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