scholarly journals Evaluation of Several Machine Learning Models for Field Canal Improvement Project Cost Prediction

Complexity ◽  
2021 ◽  
Vol 2021 ◽  
pp. 1-12
Author(s):  
Saadi Shartooh Sharqi ◽  
Aayush Bhattarai
Keyword(s):  
Machine Learning ◽  
Civil Engineering ◽  
Partial Least Square ◽  
Least Square ◽  
Coefficient Of Determination ◽  
Percentage Error ◽  
Cost Prediction ◽  
Engineering Project ◽  
Project Cost ◽  
Improvement Project

Project cost prediction is one of the key elements in the civil engineering activities development. Project cost is a highly sensitive component to diverse parameters and hence it is associated with complex trends that make it difficult to be predicted and fully understood. Due to the massive advancement of soft computing (SC) and Internet of things (IoT), the main research objective of the current study was initiative. Several machine learning (ML) models including extreme learning machine (ELM), multivariate adaptive regression spline (MARS), and partial least square regression (PLS) were adopted to predict field canal cost. Several essential predictors were used to develop the prediction network “the learning process” including the total length of the PVC pipeline, served area, geographical zone, construction year, and cost and duration of field canal improvement projects (FCIP) construction. Data were collected from the open source published literature. The modeling results evidenced the potential of the applied SC models in predicting the FCIP cost. In numerical magnitude evaluation, MARS model indicated the least value for the root mean square error (RMSE = 27422.7), mean absolute error (MAE = 19761.8), and mean absolute percentage error (MAPE = 0.05454) with Nash–Sutcliffe efficiency (NSE = 0.94), agreement index (MD = 0.89), and coefficient of determination (R2 = 0.94), with best precision of prediction using all predictors, except geographical zone parameter in which less influence on the cost construction is presented. In general, the research outcome gave an informative primary cost initiative for cost civil engineering project.

scholarly journals Machine Learning and Machine Vision Accelerate 3D Printed Orodispersible Film Development

Pharmaceutics ◽  
2021 ◽  
Vol 13 (12) ◽  
pp. 2187
Author(s):  
Colm S. O’Reilly ◽  
Moe Elbadawi ◽  
Neel Desai ◽  
Simon Gaisford ◽  
Abdul W. Basit ◽  
...  
Keyword(s):  
Machine Learning ◽  
3D Printing ◽  
Machine Vision ◽  
Near Infrared ◽  
Partial Least Square ◽  
Least Square ◽  
Coefficient Of Determination ◽  
Linear Discriminant ◽  
On Demand

Orodispersible films (ODFs) are an attractive delivery system for a myriad of clinical applications and possess both large economical and clinical rewards. However, the manufacturing of ODFs does not adhere to contemporary paradigms of personalised, on-demand medicine, nor sustainable manufacturing. To address these shortcomings, both three-dimensional (3D) printing and machine learning (ML) were employed to provide on-demand manufacturing and quality control checks of ODFs. Direct ink writing (DIW) was able to fabricate complex ODF shapes, with thicknesses of less than 100 µm. ML algorithms were explored to classify the ODFs according to their active ingredient, by using their near-infrared (NIR) spectrums. A supervised model of linear discriminant analysis was found to provide 100% accuracy in classifying ODFs. A subsequent partial least square algorithm was applied to verify the dose, where a coefficient of determination of 0.96, 0.99 and 0.98 was obtained for ODFs of paracetamol, caffeine, and theophylline, respectively. Therefore, it was concluded that the combination of 3D printing, NIR and ML can result in a rapid production and verification of ODFs. Additionally, a machine vision tool was used to automate the in vitro testing. These collective digital technologies demonstrate the potential to automate the ODF workflow.

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 Histamine Control in Raw and Processed Tuna: A Rapid Tool Based on NIR Spectroscopy

Foods ◽  
2021 ◽  
Vol 10 (4) ◽  
pp. 885
Author(s):  
Sergio Ghidini ◽  
Luca Maria Chiesa ◽  
Sara Panseri ◽  
Maria Olga Varrà ◽  
Adriana Ianieri ◽  
...  
Keyword(s):  
Root Mean Square ◽  
Near Infrared ◽  
Nir Spectroscopy ◽  
Partial Least Square ◽  
Least Square ◽  
Partial Least Square Regression ◽  
Coefficient Of Determination ◽  
Mean Square ◽  
Tuna Fish ◽  
Mean Square Errors

The present study was designed to investigate whether near infrared (NIR) spectroscopy with minimal sample processing could be a suitable technique to rapidly measure histamine levels in raw and processed tuna fish. Calibration models based on orthogonal partial least square regression (OPLSR) were built to predict histamine in the range 10–1000 mg kg−1 using the 1000–2500 nm NIR spectra of artificially-contaminated fish. The two models were then validated using a new set of naturally contaminated samples in which histamine content was determined by conventional high-performance liquid chromatography (HPLC) analysis. As for calibration results, coefficient of determination (r2) > 0.98, root mean square of estimation (RMSEE) ≤ 5 mg kg−1 and root mean square of cross-validation (RMSECV) ≤ 6 mg kg−1 were achieved. Both models were optimal also in the validation stage, showing r2 values > 0.97, root mean square errors of prediction (RMSEP) ≤ 10 mg kg−1 and relative range error (RER) ≥ 25, with better results showed by the model for processed fish. The promising results achieved suggest NIR spectroscopy as an implemental analytical solution in fish industries and markets to effectively determine histamine amounts.

scholarly journals Machine Learning for Sensorless Temperature Estimation of a BLDC Motor

Sensors ◽  
2021 ◽  
Vol 21 (14) ◽  
pp. 4655
Author(s):  
Dariusz Czerwinski ◽  
Jakub Gęca ◽  
Krzysztof Kolano
Keyword(s):  
Machine Learning ◽  
Temperature Measurement ◽  
Stochastic Gradient Descent ◽  
Estimation Accuracy ◽  
Coefficient Of Determination ◽  
Percentage Error ◽  
Support Vector ◽  
Bldc Motor ◽  
Temperature Estimation ◽  
Motor Operation

In this article, the authors propose two models for BLDC motor winding temperature estimation using machine learning methods. For the purposes of the research, measurements were made for over 160 h of motor operation, and then, they were preprocessed. The algorithms of linear regression, ElasticNet, stochastic gradient descent regressor, support vector machines, decision trees, and AdaBoost were used for predictive modeling. The ability of the models to generalize was achieved by hyperparameter tuning with the use of cross-validation. The conducted research led to promising results of the winding temperature estimation accuracy. In the case of sensorless temperature prediction (model 1), the mean absolute percentage error MAPE was below 4.5% and the coefficient of determination R2 was above 0.909. In addition, the extension of the model with the temperature measurement on the casing (model 2) allowed reducing the error value to about 1% and increasing R2 to 0.990. The results obtained for the first proposed model show that the overheating protection of the motor can be ensured without direct temperature measurement. In addition, the introduction of a simple casing temperature measurement system allows for an estimation with accuracy suitable for compensating the motor output torque changes related to temperature.

scholarly journals Compréhension du microclimat urbain lyonnais par l’intégration de prédicteurs complémentaires à différentes échelles dans des modèles de régression

Climatologie ◽  
2020 ◽  
Vol 17 ◽  
pp. 2
Author(s):  
Lucille Alonso ◽  
Florent Renard
Keyword(s):  
Machine Learning ◽  
Partial Least Square ◽  
Least Square ◽  
Light Detection And Ranging ◽  
Landsat 8 ◽  
Light Detection ◽  
Changement Climatique ◽  
Milieu Urbain ◽  
Changements Climatiques

Le changement climatique est un phénomène majeur actuel générant de multiples conséquences. En milieu urbain, il exacerbe celui de l’îlot de chaleur urbain. Ces deux manifestations climatiques engendrent des conséquences sur la santé des habitants et sur la sensation d’inconfort thermique ressenti en milieu urbain. Ainsi, il est nécessaire d’estimer au mieux la température de l’air en tout point d’un territoire, notamment face à la rationalisation actuelle du réseau de stations météorologiques fixes de Météo France. La connaissance spatialisée de la température de l’air est de plus en plus demandée pour alimenter des modèles quantitatifs liés à un large éventail de domaines, tels que l’hydrologie, l’écologie ou les études sur les changements climatiques. Cette étude se propose ainsi de modéliser la température de l’air, mesurée durant 4 campagnes mobiles réalisées durant les mois d’été, entre 2016 et 2019, dans Lyon par temps clair, à l’aide de modèle de régressions à partir de 33 variables explicatives issues de données traditionnellement utilisées, de données issues de la télédétection par une acquisition LiDAR (Light Detection And Ranging) ou satellitaire Landsat 8. Trois types de régression statistique ont été expérimentés, la régression partial least square, la régression linéaire multiple et enfin, une méthode de machine learning, la forêt aléatoire de classification et de régression. Par exemple, pour la journée du 30 août 2016, la régression linéaire multiple a expliqué 89% de la variance pour les journées d’étude, avec un RMSE moyen de seulement 0,23°C. Des variables comme la température de surface, le NDVI ou encore le MNDWI impactent fortement le modèle d’estimation.

scholarly journals Influence factor of consumers interest on using E-money

2019 ◽  
Vol 3 (2) ◽  
pp. 176-186
Author(s):  
Ni Wayan Mentari ◽  
I Nyoman Djinar Setiawina ◽  
I Made Kembar Sri Budhi ◽  
I Wayan Sudirman
Keyword(s):  
Influence Factor ◽  
Consumer Attitudes ◽  
Structural Equations ◽  
Partial Least Square ◽  
Least Square ◽  
Coefficient Of Determination ◽  
Consumer Knowledge ◽  
Perceived Benefits ◽  
Consumer Interest ◽  
The Relationship

The objectives of this study was to determine the factors that influence consumer interest in using e-money in Badung and Denpasar City in Bali. This study uses the analysis of SEM structural equations with alternative Partial Least Square (PLS). Consumer attitudes mediate the influence of the relationship between perceived benefits and perceived ease of consumer interest in using e-money, the attitude of consumers in using e-money does not mediate the effect of the relationship between customer knowledge on consumer interest in using e-money. The coefficient of determination R-square for attitude variables is 0.502, which means that the variable attitude of consumers in using e-money can be explained by the variable perception of benefits, perceived convenience and consumer knowledge by 50.2 percent, or in other words, every variant of consumer attitudes e-money will be explained by the variable perception of benefits, perceived ease and consumers knowledge by 50.2 percent, the rest explained by other variables outside the model by 49.8 percent.

scholarly journals Application of FTIR Spectroscopy and Chemometrics for Halal Authentication of Beef Meatball Adulterated with Dog Meat

2018 ◽  
Vol 18 (2) ◽  
pp. 376 ◽  
Author(s):  
Wiranti Sri Rahayu ◽  
Abdul Rohman ◽  
Sudibyo Martono ◽  
Sudjadi Sudjadi
Keyword(s):  
Ftir Spectroscopy ◽  
Reliable Method ◽  
Cross Validation ◽  
Partial Least Square ◽  
Least Square ◽  
Coefficient Of Determination ◽  
Calibration Model ◽  
Mean Square ◽  
Folch Method ◽  
Halal Authentication

Beef meatball is one of the favorite meat-based food products among Indonesian community. Currently, beef is very expensive in Indonesian market compared to other common meat types such as chicken and lamb. This situation has intrigued some unethical meatball producers to replace or adulterate beef with lower priced-meat like dog meat. The objective of this study was to evaluate the capability of FTIR spectroscopy combined with chemometrics for identification and quantification of dog meat (DM) in beef meatball (BM). Meatball samples were prepared by adding DM into BM ingredients in the range of 0–100% wt/wt and were subjected to extraction using Folch method. Lipid extracts obtained from the samples were scanned using FTIR spectrophotometer at 4000–650 cm-1. Partial least square (PLS) calibration was used to quantify DM in the meatball. The results showed that combined frequency regions of 1782–1623 cm-1 and 1485-659 cm-1 using detrending treatment gave optimum prediction of DM in BM. Coefficient of determination (R2) for correlation between the actual value of DM and FTIR predicted value was 0.993 in calibration model and 0.995 in validation model. The root mean square error of calibration (RMSEC) and standard error of cross validation (SECV) were 1.63% and 2.68%, respectively. FTIR spectroscopy combined with multivariate analysis can serve as an accurate and reliable method for analysis of DM in meatball.

scholarly journals Authentication of Patin Fish Oil (Pangasius micronemus) using FTIR Spectroscopy Combined with Chemometrics

2020 ◽  
pp. 23
Author(s):  
Anggita Rosiana Putri ◽  
Abdul Rohman ◽  
Sugeng Riyanto ◽  
Widiastuti Setyaningsih
Keyword(s):  
Root Mean Square Error ◽  
Fish Oil ◽  
Ftir Spectroscopy ◽  
Mean Square Error ◽  
Root Mean Square ◽  
Partial Least Square ◽  
Least Square ◽  
Partial Least Square Regression ◽  
Coefficient Of Determination ◽  
Mean Square

Authentication of Patin fish oil (MIP) is essential to prevent adulteration practice, to ensure quality, nutritional value, and product safety. The purpose of this study is to apply the FTIR spectroscopy combined with chemometrics for MIP authentication. The chemometrics method consists of principal component regression (PCR) and partial least square regression (PLSR). PCR and PLSR were used for multivariate calibration, while for grouping the samples using discriminant analysis (DA) method. In this study, corn oil (MJ) was used as an adulterate. Twenty-one mixed samples of MIP and MJ were prepared with the adulterate concentration range of 0-50%. The best authentication model was obtained using the PLSR technique using the first derivative of FTIR spectra at a wavelength of 650-3432 cm-1. The coefficient of determination (R2) for calibration and validation was obtained 0.9995 and 1.0000, respectively. The value of root mean square error of calibration (RMSEC) and root mean square error of prediction (RMSEP) were 0.397 and 0.189. This study found that the DA method can group the samples with an accuracy of 99.92%.

scholarly journals The Effects of Social Capital on Performance of Coffee-Based Agribusiness Smes in Tabanan Regency, Bali Province, Indonesia

2019 ◽  
Vol 6 (6) ◽  
pp. 5513-5520
Author(s):  
Ida Ayu Komang Juniasih ◽  
Dr. I Wayan Widnyana ◽  
I Gusti Agung Ayu Ambarawati ◽  
Dwi Putra Darmawan
Keyword(s):  
Social Capital ◽  
Small And Medium Enterprises ◽  
Local Area ◽  
Sem Analysis ◽  
Partial Least Square ◽  
Least Square ◽  
Coefficient Of Determination ◽  
Business Success ◽  
P Values ◽  
Significance Level

Small and Medium Enterprises (SMEs) is one of the businesses that have an important role in the country's economy. The potential of these SMEs needs to get serious attention from the local government in order to increase the potential of the local area in supporting the economy. Several SMEs have been developed in Tabanan Regency considering local potential-based agribusiness, namely coffee processing agribusiness, especially Robusta coffee. The development of SMEs does not only require financial capital but also requires social capital. The purpose of this study was to analyze the effect of social capital on the performance of coffee-based agribusinessSMEs in Tabanan Regency, Bali Province.The social capital in thisstudy includes trust, norms and networks, Sampling was taken by using Solvin technique from the members of 16 SMEs, counting to 73 respondents. The location of the study was conducted by purposive sampling. The data used were qualitative and quantitative data and analyzed by using Partial Least Square (PLS) - SEM analysis.             The result shows that the construct of trust had an effect on the performance of SMEs of coffee-based agribusinesses of 0.482 (48.2 per cent) with the level of significance of p values < 0.05. The construct of norms affected the performance of SMEsby 0.326 (32.6 per cent)with the significance level of p values < 0.05. The network construct influenced the performance of SMEsby 0.287 or 28.7 per cent with the significance level (p values < 0.05). The results of combined analysis show that social capital consisting of trust, norms, and networks on the performance of coffee-based agribusiness SMEshad a coefficient of determination (R-square) of 0.448, reflecting the effect is categorized moderate.In this study it shows that social capital consisting of trust, norms, and networks had a positive and significant effect on the performance of coffee-based agribusinessSMEs in Tabanan Regency. There needs to be strengthening of social capital from both SME players and government officials for business developmenttogether with other capital to achieve business success.

scholarly journals Impacts of multicollinearity on CAPT modalities: An heterogeneous machine learning framework for computer-assisted French phoneme pronunciation training

PLoS ONE ◽  
2021 ◽  
Vol 16 (10) ◽  
pp. e0257901
Author(s):  
Yanjing Bi ◽  
Chao Li ◽  
Yannick Benezeth ◽  
Fan Yang
Keyword(s):  
Machine Learning ◽  
Neural Networks ◽  
Support Vector Machines ◽  
Partial Least Square ◽  
Least Square ◽  
Support Vector ◽  
Computer Assisted ◽  
Long Distance ◽  
Relationship Analysis ◽  
Vector Machines

Phoneme pronunciations are usually considered as basic skills for learning a foreign language. Practicing the pronunciations in a computer-assisted way is helpful in a self-directed or long-distance learning environment. Recent researches indicate that machine learning is a promising method to build high-performance computer-assisted pronunciation training modalities. Many data-driven classifying models, such as support vector machines, back-propagation networks, deep neural networks and convolutional neural networks, are increasingly widely used for it. Yet, the acoustic waveforms of phoneme are essentially modulated from the base vibrations of vocal cords, and this fact somehow makes the predictors collinear, distorting the classifying models. A commonly-used solution to address this issue is to suppressing the collinearity of predictors via partial least square regressing algorithm. It allows to obtain high-quality predictor weighting results via predictor relationship analysis. However, as a linear regressor, the classifiers of this type possess very simple topology structures, constraining the universality of the regressors. For this issue, this paper presents an heterogeneous phoneme recognition framework which can further benefit the phoneme pronunciation diagnostic tasks by combining the partial least square with support vector machines. A French phoneme data set containing 4830 samples is established for the evaluation experiments. The experiments of this paper demonstrates that the new method improves the accuracy performance of the phoneme classifiers by 0.21 − 8.47% comparing to state-of-the-arts with different data training data density.

Sign in / Sign up

Export Citation Format

Share Document