Polynomial Regression Algorithm Research Articles

Automated group constant parameterization for low sample sizes using different Machine learning approaches

This paper deals with group constant parameterization, a necessary step to utilize the results of assembly-level neutronics calculations at the full-core level. The focus is on low sample size problems when the commonly used linear interpolation approach is inadequate, a typical situation of using Monte Carlo codes for group constant generation. This work presents a newly developed code package for automated group constant parameterization. It implements several machine learning regression models − including a novel polynomial regression algorithm − performs hyperparameter optimization and selects the best model based on a detailed evaluation. The applicability of the new code package is demonstrated in a case study for a VVER-1200 fuel assembly covering both normal operation and transient conditions. In this example, the novel polynomial regression model provides a 73 pcm average error in kinf that leads to reactivity coefficients well within the desired precision.

YOUNG’S MODULUS OF CALCIUM-ALUMINO-SILICATE GLASSES: INSIGHT FROM MACHINE LEARNING

Modern technologies require the development of new materials with exceptional properties. Machine Learning (ML) and Deep Learning (DL) techniques have become important tools for discovering new materials and predicting the properties of specific materials, such as glasses. In this paper, we used ML and DL techniques to predict the Young's modulus E of Calcium-Alumino-Silicate (CAS) glasses based on their chemical composition. We evaluated four different algorithms, including Polynomial Regression (PR), Random Forest (RF), K-Nearest Neighbors (KNN), and Multi-Layer Perceptron Regressor (MLPRegressor). We found that the PR algorithm provides excellent predictions without Cross-Validation (CV), while the MLPRegressor yields the best performance when CV is implemented.

Prediction Of Cryptocurrency Prices Using Lstm, Svm And Polynomial Regression

The rapid development of information technology, especially the Internet, has facilitated users with a quick and easy way to seek information. With these convenience offered by internet services, many individuals who initially invested in gold and precious metals are now shifting into digital investments in form of cryptocurrencies. However, investments in crypto coins are filled with uncertainties and fluctuation in daily basis. This risk posed as significant challenges for coin investors that could result in substantial investment losses. The uncertainty of the value of these crypto coins is a critical issue in the field of coin investment. Forecasting, is one of the methods used to predict the future value of these crypto coins. By utilizing the models of Long Short Term Memory, Support Vector Machine, and Polynomial Regression algorithm for forecasting, a performance comparison is conducted to determine which algorithm model is most suitable for predicting crypto currency prices. The mean square error is employed as a benchmark for the comparison. By applying those three constructed algorithm models, the Support Vector Machine uses a linear kernel to produce the smallest mean square error compared to the Long Short Term Memory and Polynomial Regression algorithm models, with a mean square error value of 0.02.

Hardness Prediction of WC-Co Cemented Carbide Based on Machine Learning Model

The hardness of cemented carbides is a fundamental property that plays a significant role in their design, preparation, and application evaluation. This study aims to identify the critical factors affecting the hardness of WC-Co cemented carbides and develop a high-throughput predictive model for hardness. A dataset consisting raw material composition, sintering parameters and characterization results of cemented carbides was constructed in which the hardness of cemented carbide was set as the target variable. By analyzing the pearson correlation coefficient, shapley additive explanations(SHAP) results, WC grain size and Co content were determined to be the key characteristics influencing the hardness of cemented carbide. Subsequently, machine learning models such as support vector regression (SVR), polynomial regression (PR), gradient boosting decision tree (GBDT), and random forest (RF) were optimized to construct prediction models for hardness. Evaluations using 10-fold cross-validation demonstrated that the GBDT algorithm model exhibits the highest accuracy and strong generalization ability, making it most suitable for predicting and analyzing the hardness of cemented carbides. Based on predictions from GBDT algorithm model, PR algorithm model was established to achieve high-precision interpretable prediction of the hardness of cemented carbides. As a result, a quantitative relationship between hardness and Co content and WC grain size were obtained, which show that reducing grain size and Co content is the key to obtain high hardness of cemented carbide. This research provides a data-driven method for accurately and efficiently predicting cemented carbide properties, offering valuable insights for the design and development of high-performance cemented carbide materials.

Impact characterization on thin structures using machine learning approaches

Machine learning algorithms are trained and compared to identify and to characterise the impact on typical aerospace panels of different geometry. Experimental activities are conducted to build a proper impacts’ dataset. Polynomial regression algorithm and artificial neural network are applied and optimised to panels without stringer to test their capability to identify the impacts. Subsequently, the algorithms are applied to panels reinforced with stringers that represent a significant increase of complexity in terms of dynamic features of the system to test: the focus is not only on the impact position’s detection but also on the event’s severity. After the identification of the best algorithm, the corresponding machine learning model is deployed on an ARM processor mini-computer, implementing an impact detection system, able to be installed on board an aerial vehicle, making it a smart aircraft equipped with an artificial intelligence decision-making system.

Prediction of band gap and optimum electrical parameters of a thin homojunction perovskite solar cell based on FA1−xCsxSnyPb1−yI3 through a combination of SCAPS-1D and machine learning based modelling

This paper presents a machine learning (ML) model developed to predict the band gap and optimum electrical parameters of a thin homojunction perovskite solar cell (PSC) based on FA1−xCsxSnyPb1−yI3. For different cesium (x) and tin (y) compositions, band gaps are tabulated, and electrical parameters such as open circuit voltage, short circuit current, and power conversion efficiency are generated in standard test condition (STC) using the SCAPS-1D platform. The data obtained are used to build two sub-models wherein first Model 1 uses the composition to predict the band gap and second Model 2 uses the predicted band gap to predict the electrical parameters using a polynomial regression algorithm. The sub-models are then connected in series. Train and test scores, correlation (r), and root mean square error (RMSE) are calculated to investigate the accuracy of the models while the cross-validation score checks the stability and the overfitting of the models. The final, best performance model’s metrics–R2, r, and RMSE, are 0.99, 0.99, and 0.09 respectively for Model 1 and 0.97, 0.99, and 0.28 for Model 2. These are achieved for a fourth order polynomial. In addition, we realize that the homojunction PSC based on FACsSnPbI3 offers a high efficiency of 17.17% for a band gap of around 1.44 eV. We also reveal that the presence of tin induces lattice contraction, affects the stability, and changes the configuration of the crystal structure from cubic to octahedral tilting when tin composition increases.

Enhancement method for non-uniform scattering images of turbid underwater based on neural network

Due to the influence of light absorption and scattering in the water medium, underwater images often appear with different degrees of blur, color distortion, and so on. The physical model-based method needs to establish image degradation processes and improve image quality by assuming and estimating complex parameters, which limits the generalization ability and practical application effect of these methods. To solve the above problems, this paper proposes an image enhancement method of non-uniform scattering in turbid underwater based on neural network. First, the high-frequency and low-frequency components of underwater images were obtained through the difference of gaussian (DOG) and bilateral filtering (BF), respectively. Then, for the high-frequency component, pulse coupled neural networks (PCNN) to improve the edge details of the image. For low-frequency components, it is carried out in two steps: an adaptive automatic white balance and polynomial regression (AAWBPR) algorithm is proposed, which can effectively eliminate the color bias under different color temperatures and improve the brightness of images; the adaptive color and contrast enhancement (ACCE) method is used to further enhance the color and contrast of images. Finally, Decom-Net and the improved U-Net method are used to reduce the errors caused by the non-uniform scattering effect, weaken the noise interference, and enhance the overall effect of images. Extensive experiments show that the proposed method can effectively improve the quality of turbid underwater non-uniform scattering images, enhance the contrast of images, reduce the complexity of parameters, and has good generalization performance and practical application effects.

Estimation of Solar PV Plant output using Polynomial Regression and ANN Algorithm

The paper proposes the use of Artificial Neural Networks (ANN) and Polynomial Regression (PR) algorithms for estimating the output of a Solar Photovoltaic (PV) Plant. The study compares the performance of these two methods, using a dataset of weather parameters and PV Plant output. The results show that both the methods can effectively estimate the PV Plant output, with the ANN model having a slightly higher accuracy than the polynomial regression model as ANNs can learn the nonlinear relationship between input stochastic parameters and output in a better manner. The study highlights the potential of machine learning techniques for optimizing the performance of solar PV plants and improving their efficiency.

Salary Prediction Model for Non-Academic Staff Using Polynomial Regression Technique

The idea of regression has increased rapidly and significantly in the machine-learning domain. This paper builds a salary prediction model to predict a justifiable salary of an employee commensurate to the increase or decrease in exchange rate using polynomial regression techniques of degree 2 in Jupyter Notebook on Annaconda Navigator tool. Predicting a feasible salary for an employee by the employer is a challenging task since every employee has a high goal and hope as the standard of leaving increases without a corresponding increase in salary. This model uses a salary dataset from Taraba State University Jalingo, Nigeria in building and training the model and exchange rate dataset for the prediction of employee salary. The result of the research shows that since the distribution of the dataset was non-linear and the major feature significant in determining employee’s salary from the in-salary dataset was grade level and exchange rate, this fully confirmed the use of polynomial regression algorithm. The research has immensely contributed to the knowledge and understanding of regression techniques. The researcher recommended other machine learning algorithms explored with various salary datasets and the potential applicability of machine learning fully incorporated in the financial department on the large dataset for better performance. The model performance was evaluated using R2 scores accuracy and the value of 97.2% realized, indicating how well the data points fit the line of regression and unseen dataset in the developed model.

Half-Space Albedo Problem for Triplet Anlı-Güngör Scattering

Introduction The Anlı-Güngör (AG) scattering function which is written in triplet form is applied to the half-space albedo problem. The aim of the study is to compare the results of albedo between the triplet AG scattering and quadratic AG scattering. Methods The analytical calculations are performed by HN method. Since the HN method is based on the usage of the Case eigenfunctions, they should be derived for the interested in this study. The tabulated results are calculated for varying the secondary neutron number and the scattering parameter. Albedo values for the triplet anisotropic scattering are convergent. A special code is used to get the numerical values in Mathematica with high precision This code holds all numerical values and the results of all algebraic process as a rational number. Thus, the round-off errors in machine language are minimized. A second and completely different numerical process is performed with the Polynomial Regression (PR) and the Artificial Neural Network (ANN). The application of the PR and ANN algorithms are studied with the calculated albedo results in this study. This application is only data mining without performing any analytical calculation. Discussion The expectation is to get bigger albedo values than the quadratic AG scattering. But the results are surprising that the triplet and the quadratic albedo results are close to each other. The result of ANN is more successful than PR results. Moreover, the intermediate values that are not in the data set can also be successfully calculated.

Spectra Reconstruction for Human Facial Color from RGB Images via Clusters in 3D Uniform CIELab* and Its Subordinate Color Space

Previous research has demonstrated the potential to reconstruct human facial skin spectra based on the responses of RGB cameras to achieve high-fidelity color reproduction of human facial skin in various industrial applications. Nonetheless, the level of precision is still expected to improve. Inspired by the asymmetricity of human facial skin color in the CIELab* color space, we propose a practical framework, HPCAPR, for skin facial reflectance reconstruction based on calibrated datasets which reconstruct the facial spectra in subsets derived from clustering techniques in several spectrometric and colorimetric spaces, i.e., the spectral reflectance space, Principal Component (PC) space, CIELab*, and its three 2D subordinate color spaces, La*, Lb*, and ab*. The spectra reconstruction algorithm is optimized by combining state-of-art algorithms and thoroughly scanning the parameters. The results show that the hybrid of PCA and RGB polynomial regression algorithm with 3PCs plus 1st-order polynomial extension gives the best results. The performance can be improved substantially by operating the spectral reconstruction framework within the subset classified in the La* color subspace. Comparing with not conducting the clustering technique, it attains values of 25.2% and 57.1% for the median and maximum errors for the best cluster, respectively; for the worst, the maximum error was reduced by 42.2%.

Modeling walkability by remote sensing as latent walking speed extracted from multiple digital trail maps

Coordinating and managing teams searching for missing persons in wilderness areas is challenging. Local terrain characteristics and environmental conditions strongly influence how searchers accomplish their search tasks. When making decisions, searchers consult various maps of the area. In this paper we proposed a methodology for mapping characteristics of the area that influence user behavior when walking the area, and define a walkability model of the terrain. We define walkability as a measure of how fast a person can walk through terrain. The observed walking speed depends on factors such as the fitness and motivation of a person walking through the terrain, as well as on assistive features and the configuration of the terrain. In our method, walkability is predicted only as a feature of terrain configuration. We used singular value decomposition (SVD) to transform datasets to extract latent features of the terrain and users from multiple Global Positioning System (GPS) trails. We define the walkability measure as a latent component of walking speed, which is a function of terrain features. Finally, we use a polynomial regression algorithm to build a model for predicting terrain walkability based on remote sensing imagery from the Sentinel-2 mission. The application of the proposed model is demonstrated in the Kozjak mountain region in the Republic of Croatia.

Prediction of Rotor Slot Size Variations in Induction Motor Using Polynomial Chirplet Transform and Regression Algorithms

For constant monitoring of rotor slot in induction motor, average rotor slot variation (ARSV) prediction is proposed. The rotor slots expand due to thermal stress and high intensity magnetic flux. The magnetic flux with high intensity is created in rotor lamination sheet, because of stretching and curving magnetic flux. The surface of rotor slot exhibits thermal stress due to over current which in turn generates heat and transfers the heat into the rotor lamination surface. The magnetic stress-based rotor slot size variation is never monitored or measured before. In the proposed ARSV method, multimodal sensor signals such as Giant magnetoresistance, temperature, current, vibration and voltage acquire magnetic stress and temperature stress of rotor. The acquired signals are analyzed through polynomial chirplet transform to obtain energy band values of signal. The energy band values and microscopic camera image-based rotor slot variation values are used for ARSV prediction. The prediction of ARSV is computed using polynomial regression (PR) method. From experimental results, it is observed that ARSV is greater than 5% from the normal size which leads to extreme damage to the motor through vibration, sparking and harmonics. The prediction accuracy of ARSV is about 94.6% when compared to manual measurement of slots in rotor. Moreover, the prediction of ARSV avoids the major faults, namely eccentricity, overload, under voltage, unbalance voltage, induced rotor slot, rotor crack and rotor burn.

Linear Regression Machine Learning Algorithms for Estimating Reference Evapotranspiration Using Limited Climate Data

A linear regression machine learning model to estimate the reference evapotranspiration based on temperature data for South Korea is developed in this study. FAO56 Penman–Monteith (FAO56 P–M) reference evapotranspiration calculated with meteorological data (1981–2021) obtained from sixty-two meteorological stations nationwide is used as the label. All study datasets provide daily, monthly, or annual values based on the average temperature, daily temperature difference, and extraterrestrial radiation. Multiple linear regression (MLR) and polynomial regression (PR) are applied as machine learning algorithms, and twelve models are tested using the training data. The results of the performance evaluation of the period from 2017 to 2021 show that the polynomial regression algorithm that learns the amount of extraterrestrial radiation achieves the best performance (the minimum root-mean-square errors of 0.72 mm/day, 11.3 mm/month, and 40.5 mm/year for daily, monthly, and annual scale, respectively). Compared to temperature-based empirical equations, such as Hargreaves, Blaney–Criddle, and Thornthwaite, the model trained using the polynomial regression algorithm achieves the highest coefficient of determination and lowest error with the reference evapotranspiration of the FAO56 Penman–Monteith equation when using all meteorological data. Thus, the proposed method is more effective than the empirical equations under the condition of insufficient meteorological data when estimating reference evapotranspiration.

Optimization of Pesticides Spray on Crops in Agriculture using Machine Learning.

Pesticides are chemicals used to eradicate pests. Not only are they used for plant protection and livestock in agriculture, but they are also used in public areas to kill mosquitoes, cockroaches, and other pests. Approximately 95% of the pesticides produced are only used in agriculture for crop protection. Every country wants to increase crop production. To protect their crops from pests, farmers must use pesticides. Exposure to pesticides is increasing day by day, whether occupationally or environmentally. This has resulted in an increase in crop production, but it has numerous adverse effects on human health, animal health, and the environment. Farmers repeatedly use the same pesticides on their crops, which is detrimental to human health and the environment. In this research, according to authors, the repetition of pesticides in agriculture is controlled using adjuvant and machine learning algorithms. An adjuvant is a chemical agent that is inserted within the pesticide product for enhanced pesticide performance. By utilizing an algorithm for machine learning, it is no longer necessary to repeatedly spray the same pesticide over the entire crop field in order to determine which sections of the crop field still require repeated pesticide spraying. In this research, the authors predict that 72.5% of insecticides are used in India. Logical regression classification, polynomial regression, and K-nearest neighbor algorithm (KNN) are applied to detect this required field.

Groundwater level prediction with machine learning for the Vidisha district, a semi-arid region of Central India

Groundwater is one of the most important freshwater resources, particularly in dry and semi-arid regions with low annual precipitation and frequent droughts. For the planning and the management of water resources, this is very essential to generate a soft computing method that can predict or forecast groundwater table (GWT). The different machine and deep learning algorithms are evaluated in this research work to predict the groundwater table using season, monthly rainfall, and evapotranspiration rate, which are further compared. The performance evaluation of the different algorithms was done using five different statistical parameters, i.e., mean absolute error (MAE), mean squared error (MSE), root-mean-square error (RMSE), R2−score, and Nash-Sutcliffe Efficiency (NSE). From the results of this study, it is concluded that the Multivariate polynomial regression algorithm is the most efficient for predicting the groundwater table with the minimum mean absolute error in the range of 0.63–0.85 and R2-score of 0.82–0.88.

Joint parameter estimation and decoding in a distributed receiver

SummaryThis paper presents an algorithm for iterative joint channel parameter (carrier phase, Doppler shift, and Doppler rate) estimation and decoding of transmission over channels affected by Doppler shift and Doppler rate using a distributed receiver. This algorithm is derived by applying the sum‐product algorithm (SPA) to a factor graph representing the joint a posteriori distribution of the information symbols and channel parameters given the channel output. In this paper, we present two methods for dealing with intractable messages of the SPA. In the first approach, we use particle filtering with sequential importance sampling for the estimation of the unknown parameters. We also propose a method for fine‐tuning of particles for improved convergence. In the second approach, we approximate our model with a random walk phase model, followed by a phase tracking algorithm and polynomial regression algorithm to estimate the unknown parameters. We derive the Weighted Bayesian Cramer‐Rao Bounds for joint carrier phase, Doppler shift, and Doppler rate estimation, which take into account the prior distribution of the estimation parameters and are accurate lower bounds for all considered signal‐to‐noise ratio values. Numerical results (of bit error rate and the mean‐square error of parameter estimation) suggest that phase tracking with the random walk model slightly outperforms particle filtering. However, particle filtering has a lower computational cost than the random walk model‐based method.

Adsorptive removal of toxic malachite green from its aqueous solution by Bambusa vulgaris leaves and its acid-treated form: DFT, MPR and GA modeling

In the current investigation, bamboo leaves (BL), H2SO4 treated bamboo leaves (SBL), and H3PO4 treated bamboo leaves (PBL) were utilized as biosorbents for the elimination of Malachite Green (MG) from the synthetic medium. The adsorption study was executed under the variation of different experimental conditions like pH, biosorbent dosage, contact time, and the temperature on the MG. Langmuir isotherm fit the adsorption data with maximum monolayer adsorption capacity, 151.735 mg/g at 298 K − 168.339 mg/g at 318 K for PBL. The data fitted the pseudo-2nd -order kinetic model for all three adsorbents with a correlation coefficient greater than 0.99, indicating that chemisorption controls adsorption. The Dubinin-Radushkevich isotherm indicated that the mean sorption energy (E) is a physisorption mechanism. So, the process involves physical sorption as well as chemical sorption processes with negligible sorption energy. The FTIR, Raman Spectra, and 13C NMR results show that different functional groups of cellulose, hemicellulose and lignin are involved in MG elimination. The theoretical studies were applied for a deeper understanding of the adsorption mechanism of MG using semi-empirical quantum chemical calculations. The DFT Calculations yielded valuable information about quantum chemical properties, like EHOMO, ELUMO, electrophilicity, chemical reactivity, dipole moment, and hardness for adsorbent/adsorbate components and the binding energy from adsorbent/adsorbate interactions. Multiple polynomial regression and genetic algorithm also have been successfully applied to predict the removal percentage of malachite green.

A real-time collaborative machine learning based weather forecasting system with multiple predictor locations

Weather forecasting is an important application in meteorology and has been one of the most scientifically and technologically challenging problems around the world. As the drastic effects of climate change continue to unfold, localised short term weather prediction with high accuracy has become more important than ever. In this paper, a collaborative machine learning-based real-time weather forecasting system has been proposed whereby data from several locations are used to predict the weather for a specific location. In this work, five machine learning algorithms have been used and tests have been performed in four different locations in Mauritius to predict weather parameters such as Temperature, Wind Speed, Wind Direction, Pressure, Humidity, and Cloudiness. The weather data were collected using the OpenWeather API from a mobile as well as a desktop edge device. The data were stored as a JSON file in both the IBM Cloudant database and a local MySQL database. Analytics were performed on both a local server that captures the incoming data from the edge device and via a servlet deployed on the IBM cloud platform. Five machine learning algorithms namely Multiple Linear Regression (MLP), Multiple Polynomial Regression (MPR), K-Nearest Neighbours (KNN), Multilayer Perceptron (MLP) and Convolutional Neural Network (CNN) were tested using both collaborative and non-collaborative methods. The experiments showed that the collaborative regression schemes achieved 5% lower Mean Absolute Percentage Error (MAPE) than non-collaborative ones and the Multiple Polynomial Regression (MLR) algorithm outperformed all the other algorithms with errors ranging from 0.009% to 9% for the different weather parameters. In general, the results showed that collaborative based weather forecasting with multiple predictor locations can potentially increase the accuracy of the predictions in machine learning algorithms.

Ensemble Machine Learning for Predicting Viscosity of Nanoparticle-Surfactant-Stabilized CO2 Foam

Summary This paper investigates the computational behaviors of simple-to-use, relatively fast, and versatile machine learning (ML) methods to predict apparent viscosity, a key rheological property of nanoparticle-surfactant-stabilized CO2 foam in unconventional reservoir fracturing. The first novelty of our study is the investigation of the predictive performance of ML approaches as viable alternatives for predicting the apparent viscosity of NP-Surf-CO2 foams. The predictive and computational performance of five nonlinear ML algorithms were first compared. Support vector regression (SVR), K-nearest neighbors (KNN), classification and regression trees (CART), feed-forward multilayer perceptron neural network (MLPNN), and multivariate polynomial regression (MPR) algorithms were used to create models. Temperature, foam quality, pressure, salinity, shear rate, nanoparticle size, nanoparticle concentration, and surfactant concentration were identified as relevant input parameters using principal component analysis (PCA). A data set containing 329 experimental data records was used in the study. In building the models, 80% of the data set was used for training and 20% of the data set for testing. Another unique aspect of this research is the examination of diverse ensemble learning techniques for improving computational performance. We developed meta-models of the generated models by implementing various ensemble learning algorithms (bagging, boosting, and stacking). This was done to explore and compare the computational and predictive performance enhancements of the base models (if any). To determine the relative significance of the input parameters on prediction accuracy, we used permutation feature importance (PFI). We also investigated how the SVR model made its predictions by utilizing the SHapely Additive exPlanations (SHAP) technique to quantify the influence of each input parameter on prediction. This work’s application of the SHAP approach in the interpretation of ML findings in predicting apparent viscosity is also novel. On the test data, the SVR model in this work had the best predictive performance of the single models, with an R2 of 0.979, root mean squared error (RMSE) of 0.885 cp, and mean absolute error (MAE) of 0.320 cp. Blending, a variant of the stacking ensemble technique, significantly improved this performance. With an R2 of 1.0, RMSE of 0.094 cp, and MAE of 0.087 cp, an SVR-based meta-model ensembled with blending outperformed all single and ensemble models in predicting apparent viscosity. However, in terms of computational time, the blended SVR-based meta-model did not outperform any of its constituent models. PCA and PFI ranked temperature as the most important factor in predicting the apparent viscosity of NP-Surf-CO2 foams. The ML approach used in this study provides a comprehensive understanding of the nonlinear relationship between the investigated factors and apparent viscosity. The workflow can be used to evaluate the apparent viscosity of NP-Surf-CO2 foam fracturing fluid efficiently and effectively.