scholarly journals Machine Learning Methods of Regression for Plasmonic Nanoantenna Glucose Sensing

Sensors ◽  
2021 ◽  
Vol 22 (1) ◽  
pp. 7
Author(s):  
Emilio Corcione ◽  
Diana Pfezer ◽  
Mario Hentschel ◽  
Harald Giessen ◽  
Cristina Tarín
Keyword(s):  
Machine Learning ◽  
Measurement Techniques ◽  
Gaussian Process Regression ◽  
Glucose Sensing ◽  
Sensor Data ◽  
Glucose Measurement ◽  
Sensor Calibration ◽  
Learning Methods ◽  
Machine Learning Methods ◽  
Major Interest

The measurement and quantification of glucose concentrations is a field of major interest, whether motivated by potential clinical applications or as a prime example of biosensing in basic research. In recent years, optical sensing methods have emerged as promising glucose measurement techniques in the literature, with surface-enhanced infrared absorption (SEIRA) spectroscopy combining the sensitivity of plasmonic systems and the specificity of standard infrared spectroscopy. The challenge addressed in this paper is to determine the best method to estimate the glucose concentration in aqueous solutions in the presence of fructose from the measured reflectance spectra. This is referred to as the inverse problem of sensing and usually solved via linear regression. Here, instead, several advanced machine learning regression algorithms are proposed and compared, while the sensor data are subject to a pre-processing routine aiming to isolate key patterns from which to extract the relevant information. The most accurate and reliable predictions were finally made by a Gaussian process regression model which improves by more than 60% on previous approaches. Our findings give insight into the applicability of machine learning methods of regression for sensor calibration and explore the limitations of SEIRA glucose sensing.

Towards Behaviour Recognition with Unlabelled Sensor Data

2013 ◽  
pp. 86-110
Author(s):  
Sook-Ling Chua ◽  
Stephen Marsland ◽  
Hans W. Guesgen
Keyword(s):  
Machine Learning ◽  
Data Mining ◽  
Inverse Problem ◽  
Sensor Data ◽  
Training Set ◽  
Learning Methods ◽  
Machine Learning Methods ◽  
Using Data ◽  
Symbolic Approach ◽  
Behaviour Recognition

The problem of behaviour recognition based on data from sensors is essentially an inverse problem: given a set of sensor observations, identify the sequence of behaviours that gave rise to them. In a smart home, the behaviours are likely to be the standard human behaviours of living, and the observations will depend upon the sensors that the house is equipped with. There are two main approaches to identifying behaviours from the sensor stream. One is to use a symbolic approach, which explicitly models the recognition process. Another is to use a sub-symbolic approach to behaviour recognition, which is the focus in this chapter, using data mining and machine learning methods. While there have been many machine learning methods of identifying behaviours from the sensor stream, they have generally relied upon a labelled dataset, where a person has manually identified their behaviour at each time. This is particularly tedious to do, resulting in relatively small datasets, and is also prone to significant errors as people do not pinpoint the end of one behaviour and commencement of the next correctly. In this chapter, the authors consider methods to deal with unlabelled sensor data for behaviour recognition, and investigate their use. They then consider whether they are best used in isolation, or should be used as preprocessing to provide a training set for a supervised method.

scholarly journals Forecasting Natural Gas Spot Prices with Machine Learning

Energies ◽  
2021 ◽  
Vol 14 (18) ◽  
pp. 5782
Author(s):  
Dimitrios Mouchtaris ◽  
Emmanouil Sofianos ◽  
Periklis Gogas ◽  
Theophilos Papadimitriou
Keyword(s):  
Machine Learning ◽  
Natural Gas ◽  
Gaussian Process Regression ◽  
Time Frame ◽  
Spot Price ◽  
Support Vector ◽  
Learning Methods ◽  
Spot Prices ◽  
Explanatory Variables ◽  
Machine Learning Methods

The ability to accurately forecast the spot price of natural gas benefits stakeholders and is a valuable tool for all market participants in the competitive gas market. In this paper, we attempt to forecast the natural gas spot price 1, 3, 5, and 10 days ahead using machine learning methods: support vector machines (SVM), regression trees, linear regression, Gaussian process regression (GPR), and ensemble of trees. These models are trained with a set of 21 explanatory variables in a 5-fold cross-validation scheme with 90% of the dataset used for training and the remaining 10% used for testing the out-of-sample generalization ability. The results show that these machine learning methods all have different forecasting accuracy for every time frame when it comes to forecasting natural gas spot prices. However, the bagged trees (belonging to the ensemble of trees method) and the linear SVM models have superior forecasting performance compared to the rest of the models.

scholarly journals Modeling Pan Evaporation Using Gaussian Process Regression K-Nearest Neighbors Random Forest and Support Vector Machines; Comparative Analysis

Atmosphere ◽  
2020 ◽  
Vol 11 (1) ◽  
pp. 66 ◽  
Author(s):  
Sevda Shabani ◽  
Saeed Samadianfard ◽  
Mohammad Taghi Sattari ◽  
Amir Mosavi ◽  
Shahaboddin Shamshirband ◽  
...  
Keyword(s):  
Machine Learning ◽  
Random Forest ◽  
Gaussian Process ◽  
Gaussian Process Regression ◽  
Nearest Neighbors ◽  
Support Vector ◽  
Pan Evaporation ◽  
Learning Methods ◽  
K Nearest Neighbors ◽  
Machine Learning Methods

Evaporation is a very important process; it is one of the most critical factors in agricultural, hydrological, and meteorological studies. Due to the interactions of multiple climatic factors, evaporation is considered as a complex and nonlinear phenomenon to model. Thus, machine learning methods have gained popularity in this realm. In the present study, four machine learning methods of Gaussian Process Regression (GPR), K-Nearest Neighbors (KNN), Random Forest (RF) and Support Vector Regression (SVR) were used to predict the pan evaporation (PE). Meteorological data including PE, temperature (T), relative humidity (RH), wind speed (W), and sunny hours (S) collected from 2011 through 2017. The accuracy of the studied methods was determined using the statistical indices of Root Mean Squared Error (RMSE), correlation coefficient (R) and Mean Absolute Error (MAE). Furthermore, the Taylor charts utilized for evaluating the accuracy of the mentioned models. The results of this study showed that at Gonbad-e Kavus, Gorgan and Bandar Torkman stations, GPR with RMSE of 1.521 mm/day, 1.244 mm/day, and 1.254 mm/day, KNN with RMSE of 1.991 mm/day, 1.775 mm/day, and 1.577 mm/day, RF with RMSE of 1.614 mm/day, 1.337 mm/day, and 1.316 mm/day, and SVR with RMSE of 1.55 mm/day, 1.262 mm/day, and 1.275 mm/day had more appropriate performances in estimating PE values. It was found that GPR for Gonbad-e Kavus Station with input parameters of T, W and S and GPR for Gorgan and Bandar Torkmen stations with input parameters of T, RH, W and S had the most accurate predictions and were proposed for precise estimation of PE. The findings of the current study indicated that the PE values may be accurately estimated with few easily measured meteorological parameters.

scholarly journals Generating Artificial Sensor Data for the Comparison of Unsupervised Machine Learning Methods

Sensors ◽  
2021 ◽  
Vol 21 (7) ◽  
pp. 2397
Author(s):  
Bernd Zimmering ◽  
Oliver Niggemann ◽  
Constanze Hasterok ◽  
Erik Pfannstiel ◽  
Dario Ramming ◽  
...  
Keyword(s):  
Machine Learning ◽  
Short Term Memory ◽  
Sensor Data ◽  
Support Vector ◽  
Neural Net ◽  
Generation Process ◽  
Self Organizing Map ◽  
Data Generation ◽  
Learning Methods ◽  
Machine Learning Methods

In the field of Cyber-Physical Systems (CPS), there is a large number of machine learning methods, and their intrinsic hyper-parameters are hugely varied. Since no agreed-on datasets for CPS exist, developers of new algorithms are forced to define their own benchmarks. This leads to a large number of algorithms each claiming benefits over other approaches but lacking a fair comparison. To tackle this problem, this paper defines a novel model for a generation process of data, similar to that found in CPS. The model is based on well-understood system theory and allows many datasets with different characteristics in terms of complexity to be generated. The data will pave the way for a comparison of selected machine learning methods in the exemplary field of unsupervised learning. Based on the synthetic CPS data, the data generation process is evaluated by analyzing the performance of the methods of the Self-Organizing Map, One-Class Support Vector Machine and Long Short-Term Memory Neural Net in anomaly detection.

scholarly journals Descriptor selection for predicting interfacial thermal resistance by machine learning methods

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Xiaojuan Tian ◽  
Mingguang Chen
Keyword(s):  
Machine Learning ◽  
Decision Tree ◽  
Thermal Resistance ◽  
Thermal Management ◽  
Material Selection ◽  
Gaussian Process Regression ◽  
Interfacial Thermal Resistance ◽  
Learning Methods ◽  
Machine Learning Methods ◽  
Descriptor Selection

AbstractInterfacial thermal resistance (ITR) is a critical property for the performance of nanostructured devices where phonon mean free paths are larger than the characteristic length scales. The affordable, accurate and reliable prediction of ITR is essential for material selection in thermal management. In this work, the state-of-the-art machine learning methods were employed to realize this. Descriptor selection was conducted to build robust models and provide guidelines on determining the most important characteristics for targets. Firstly, decision tree (DT) was adopted to calculate the descriptor importances. And descriptor subsets with topX highest importances were chosen (topX-DT, X = 20, 15, 10, 5) to build models. To verify the transferability of the descriptors picked by decision tree, models based on kernel ridge regression, Gaussian process regression and K-nearest neighbors were also evaluated. Afterwards, univariate selection (UV) was utilized to sort descriptors. Finally, the top5 common descriptors selected by DT and UV were used to build concise models. The performance of these refined models is comparable to models using all descriptors, which indicates the high accuracy and reliability of these selection methods. Our strategy results in concise machine learning models for a fast prediction of ITR for thermal management applications.

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