scholarly journals Blood Glucose Level Regression for Smartphone PPG Signals Using Machine Learning

2021 ◽  
Vol 11 (2) ◽  
pp. 618
Author(s):  
Tanvir Tazul Islam ◽  
Md Sajid Ahmed ◽  
Md Hassanuzzaman ◽  
Syed Athar Bin Amir ◽  
Tanzilur Rahman
Keyword(s):  
Blood Glucose ◽  
Blood Glucose Level ◽  
Glucose Level ◽  
Quantitative Estimation ◽  
Partial Least Square ◽  
Least Square ◽  
Partial Least Square Regression ◽  
Support Vector ◽  
Frequency Noise ◽  
Optical Noise

Diabetes is a chronic illness that affects millions of people worldwide and requires regular monitoring of a patient’s blood glucose level. Currently, blood glucose is monitored by a minimally invasive process where a small droplet of blood is extracted and passed to a glucometer—however, this process is uncomfortable for the patient. In this paper, a smartphone video-based noninvasive technique is proposed for the quantitative estimation of glucose levels in the blood. The videos are collected steadily from the tip of the subject’s finger using smartphone cameras and subsequently converted into a Photoplethysmography (PPG) signal. A Gaussian filter is applied on top of the Asymmetric Least Square (ALS) method to remove high-frequency noise, optical noise, and motion interference from the raw PPG signal. These preprocessed signals are then used for extracting signal features such as systolic and diastolic peaks, the time differences between consecutive peaks (DelT), first derivative, and second derivative peaks. Finally, the features are fed into Principal Component Regression (PCR), Partial Least Square Regression (PLS), Support Vector Regression (SVR) and Random Forest Regression (RFR) models for the prediction of glucose level. Out of the four statistical learning techniques used, the PLS model, when applied to an unbiased dataset, has the lowest standard error of prediction (SEP) at 17.02 mg/dL.

scholarly journals Monitoring the Foliar Nutrients Status of Mango Using Spectroscopy-Based Spectral Indices and PLSR-Combined Machine Learning Models

2021 ◽  
Vol 13 (4) ◽  
pp. 641
Author(s):  
Gopal Ramdas Mahajan ◽  
Bappa Das ◽  
Dayesh Murgaokar ◽  
Ittai Herrmann ◽  
Katja Berger ◽  
...  
Keyword(s):  
Machine Learning ◽  
Remote Sensing ◽  
Partial Least Square ◽  
Least Square ◽  
Partial Least Square Regression ◽  
Support Vector ◽  
Spectral Indices ◽  
Learning Models ◽  
Leaf Nutrients ◽  
Machine Learning Models

Conventional methods of plant nutrient estimation for nutrient management need a huge number of leaf or tissue samples and extensive chemical analysis, which is time-consuming and expensive. Remote sensing is a viable tool to estimate the plant’s nutritional status to determine the appropriate amounts of fertilizer inputs. The aim of the study was to use remote sensing to characterize the foliar nutrient status of mango through the development of spectral indices, multivariate analysis, chemometrics, and machine learning modeling of the spectral data. A spectral database within the 350–1050 nm wavelength range of the leaf samples and leaf nutrients were analyzed for the development of spectral indices and multivariate model development. The normalized difference and ratio spectral indices and multivariate models–partial least square regression (PLSR), principal component regression, and support vector regression (SVR) were ineffective in predicting any of the leaf nutrients. An approach of using PLSR-combined machine learning models was found to be the best to predict most of the nutrients. Based on the independent validation performance and summed ranks, the best performing models were cubist (R2 ≥ 0.91, the ratio of performance to deviation (RPD) ≥ 3.3, and the ratio of performance to interquartile distance (RPIQ) ≥ 3.71) for nitrogen, phosphorus, potassium, and zinc, SVR (R2 ≥ 0.88, RPD ≥ 2.73, RPIQ ≥ 3.31) for calcium, iron, copper, boron, and elastic net (R2 ≥ 0.95, RPD ≥ 4.47, RPIQ ≥ 6.11) for magnesium and sulfur. The results of the study revealed the potential of using hyperspectral remote sensing data for non-destructive estimation of mango leaf macro- and micro-nutrients. The developed approach is suggested to be employed within operational retrieval workflows for precision management of mango orchard nutrients.

scholarly journals Least Square Support Vector Regression-Based Model for Whiteness Index of Cotton Fabric Prediction

2021 ◽  
Author(s):  
Wan Sieng Yeo
Keyword(s):  
Support Vector Regression ◽  
Cotton Fabric ◽  
Predictive Modelling ◽  
Partial Least Square ◽  
Least Square ◽  
Partial Least Square Regression ◽  
Support Vector ◽  
Bleaching Process ◽  
Whiteness Index ◽  
Least Square Regression

Abstract The textile bleaching process uses a hydrogen peroxide (H2O2) solution in alkali pH associated with high temperature is the commonly used bleaching procedure in cotton fabric manufacture. The purpose of the bleaching process is to remove the natural colour from cotton to obtain a permanent white colour before dyeing or shape matching. Normally, the visual ratings of whiteness on the cotton are measured by the whiteness index (WI). Notice that lesser research study is focusing on chemical predictive modelling of the WI of cotton fabric than its experimental study. Predictive analytics using predictive modelling can forecast the outcomes that can lead to better-informed cotton quality assurance and control decisions. Up to date, limited study applying least square support vector regression (LSSVR) based model in the textile domain. Hence, the present study was aimed to develop the LSSVR-based model, namely multi-output LSSVR (MLSSVR) using bleaching process variables to predict the WI of cotton. The predictive accuracy of the MLSSVR model is measured by root mean square error (RMSE), mean absolute error (MAE), and the coefficient of determination (R2), and its results are compared with other regression models including partial least square regression, predictive fuzzy model, locally weighted partial least square regression and locally weighted kernel partial least square regression. The results indicate that the MLSSVR model performed better than other models in predicting the WI as it has 60–1209% lower values of RMSE and MAE as well as it provided the highest R2 values which are up to 0.9985.

scholarly journals Estimation of strawberry firmness using hyperspectral imaging: a comparison of regression models

2021 ◽  
Author(s):  
Binu Devassy ◽  
Sony George
Keyword(s):  
Hyperspectral Imaging ◽  
Regression Models ◽  
Partial Least Square ◽  
Least Square ◽  
Partial Least Square Regression ◽  
Support Vector ◽  
Regression Techniques ◽  
Important Quality ◽  
Non Destructive ◽  
Better Than

Firmness is one of the most important quality measures of strawberries, and is related to other aspects of the fruit, such as flavour, ripeness and internal characteristics. The most popular method for measuring firmness is puncturing with a penetrometer, which is destructive and time-consuming. In the present study, we make an attempt to predict the firmness of strawberries in a fast, non-destructive and non-contact way using hyperspectral imaging (HSI) and data analysis with various regression techniques. The primary goal of this research is to investigate and compare the firmness prediction capability of seven prominent regression techniques. We have performed HSI data acquisition of 150 strawberries and optimised seven regression models using the spectral information to predict strawberry firmness. These models are linear, ridge, lasso, k-neighbours, random forest, support vector and partial least square regression. The res ults show that HSI data with regression models has the potential to predict firmness in a rapid, non-destructive manner. Out of these seven regression models, the k-neighbours regression model outperformed all other methods with a standard error of prediction of 0.14, which is better than that of the state-of-the-art results.

scholarly journals Towards an accurate Ground-Level Ozone Prediction

2018 ◽  
Vol 8 (2) ◽  
pp. 1131
Author(s):  
Eiman Tamah Alshammari
Keyword(s):  
Ground Level ◽  
Absolute Error ◽  
Partial Least Square ◽  
Least Square ◽  
Partial Least Square Regression ◽  
Polynomial Kernel ◽  
Support Vector ◽  
Pls Regression ◽  
Ground Level Ozone ◽  
Least Square Regression

This paper motivation is to find the most accurate technique to predict the ground level ozone at Al Jahra station, Kuwait. The data on the meteorological variables (air temperature, relative humidity, solar radiation, direction and speed of wind) and concentration of seven pollutants of environment (SO2, NO2, NO, CO2, CO, NMHC, and CH4) were applied to forecast the ozone concentration in atmosphere. In this report, three methods (PLS regression, support vector machine (SVM), and multiple least-square regression) were used to predict ground-level ozone. We used Fifteen parameters to evaluate the performance of methods. Multiple least-square regression, partial least square regression (PLS regression), and SVM using linear and radial kernels were the best performers with MAE (mean absolute error) of 9.17x 10-03, 9.72 x 10-03, 9.64 x 10-03, and 9.12 x 10-03, respectively. SVM with polynomial kernel had MAE of 5.46 x 10-02. These results show that these methods could be used to predict ground-level ozone concentrations at Al Jahra station in Kuwait.

scholarly journals The Use of Remote Sensing to Determine Nitrogen Status in Perennial Ryegrass (Lolium perenne L.) for Seed Production

Nitrogen ◽  
2021 ◽  
Vol 2 (2) ◽  
pp. 229-243
Author(s):  
René Gislum ◽  
Stamatios Thomopoulos ◽  
Jacob Glerup Gyldengren ◽  
Anders Krogh Mortensen ◽  
Birte Boelt
Keyword(s):  
Remote Sensing ◽  
Nitrogen Nutrition ◽  
Vegetation Indices ◽  
Partial Least Square ◽  
Least Square ◽  
Partial Least Square Regression ◽  
Yield Potential ◽  
Support Vector ◽  
N Application ◽  
Grass Seed

Sufficient nitrogen (N) supply is decisive to achieve high grass seed yields while overfertilization will lead to negative environmental impact. From the literature, estimation of N rates taking into account the crop’s N status and its yield potential, seems promising for attaining high yields and averting adverse environmental impacts. This study aimed at an evaluation of remote sensing to predict final seed yield, N traits of the grass seed crop and the usability of nitrogen nutrition index (NNI) to measure additional N requirement. It included four years’ data and eight N application rates and strategies. Several reflectance measurements were made and used for the calculation of 18 vegetation indices. The predictions were made using partial least square regression and support vector machine. Three different yield responses to N fertilization were noted; one with linear response, one with optimum economic nitrogen (EON) at ~188 kg N ha−1, and one with EON at ~138 kg N ha−1. We conclude that although it is possible to make in-season predictions of NNI, it does not always portray the differences in yield potential; thus, it is challenging to utilize it to optimize N application.

scholarly journals Transferability and scaling of soil total carbon prediction models in Florida

2014 ◽  
Author(s):  
Sabine Grunwald ◽  
Congrong Yu ◽  
Xiong Xiong
Keyword(s):  
Prediction Error ◽  
Near Infrared ◽  
Prediction Models ◽  
Regional Scale ◽  
Partial Least Square ◽  
Least Square ◽  
Partial Least Square Regression ◽  
Total Carbon ◽  
Support Vector ◽  
Domain Iii

The applicability, transfer, and scalability of visible/near-infrared (VNIR)-derived soil models are still poorly understood. The objectives of this study in Florida, U.S. were to: (i) compare three methods to predict soil total carbon (TC) using five fields (local scale) and a pooled (regional scale) VNIR spectral dataset, (ii) assess the model’s transferability among fields, and (iii) evaluate the up- and down-scaling behavior of TC prediction models. A total of 560 TC-spectral sets were modeled by Partial Least Square Regression (PLSR), Support Vector Machine (SVM), and Random Forest. The transferability and up- and down-scaling of models were limited by the following factors: (i) the spectral data domain, (ii) soil attribute domain, (iii) methods that describe the internal model structure of VNIR-TC relationships, and (iv) environmental domain space of attributes that control soil carbon dynamics. All soil logTC models showed excellent performance based on all three methods with R2 > 0.86, bias < 0.01%, root mean square prediction error (RMSE) = 0.09%, residual predication deviation (RPD) > 2.70% , and ratio of prediction error to inter-quartile range (RPIQ) > 4.54. PLSR performed substantially better than SVM to scale and transfer models. Upscaled soil TC models performed somewhat better in terms of model fit (R2), RPD, and RPIQ, whereas downscaled models showed less bias and smaller RMSE based on PLSR. Given the many factors that can impinge on empirically derived soil spectral prediction models, as demonstrated by this study, more focus on the applicability and scaling of them is needed.

scholarly journals Applying Spectral Fractal Dimension to Predict the SPAD Value of Rice Leaves Under Disease Stress

2021 ◽  
Author(s):  
YiFei Cao ◽  
Huanliang Xu ◽  
Jin Song ◽  
Yao Yang ◽  
Xiaohui Hu ◽  
...  
Keyword(s):  
Fractal Dimension ◽  
Prediction Model ◽  
Chlorophyll Content ◽  
Partial Least Square ◽  
Least Square ◽  
Partial Least Square Regression ◽  
Estimation Accuracy ◽  
Support Vector ◽  
Spad Value ◽  
Rice Leaves

Abstract BackgroundThe chlorophyll content is a vital indicator for reflecting the photosynthesis ability of plants and it plays a significant role in monitoring the general health of plants. Since the chlorophyll content and the soil-plant analysis development (SPAD) value are positively correlated, it is feasible to estimate the SPAD value by calculating the vegetation indices (VIs) through hyperspectral images, thereby estimating the chlorophyll content. However, current indices simply adopted few wavelengths of the hyperspectral information, which may decrease the estimation accuracy. Besides, few researches explored the applicability of VIs over plant leaves under disease stress.MethodsIn this study, the SPAD value was estimated by calculating the fractal dimension of hyperspectral curves, ranging from 420 to 950 nm. The correlation between the SPAD value and wavelengths under disease stress was analyzed. In addition, a SPAD prediction model was built upon the combination of selected indices and 4 machine learning methods, including decision tree (DT), partial least square regression (PLSR), support vector regression (SVR), and back propagation neural network (BPNN). The performance of these models was compared through the correlation of determination, root mean square error, and relative error.ResultsThe results suggested that the SPAD value of rice leaves under different disease levels were sensitive to different wavelengths, meaning that the fixed wavelength selection in current indices may achieve poor estimation results. Compared with current VIs, a stronger positive correlation was detected between the SPAD value and our proposal, reaching an average correlation coefficient of 0.8263. For the prediction model, the one built with our proposal and SVR achieved the best performance, reaching R2, RMSE, and RE at 0.8752, 3.7715, and 7.8614%, respectively.ConclusionsThis work provides an in-depth insight for accurately and robustly estimating the SPAD value of rice leaves under disease stress, and our proposal is of great significance for monitoring the chlorophyll content in large-scale fields non-destructively.

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