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A noticeable increase in drought frequency and severity has been observed across the globe due to climate change, which attracted scientists in development of drought prediction models for mitigation of impacts. Droughts are usually monitored using drought indices (DIs), most of which are probabilistic and therefore, highly stochastic and non-linear. The current research investigated the capability of different versions of relatively well-explored machine learning (ML) models including random forest (RF), minimum probability machine regression (MPMR), M5 Tree (M5tree), extreme learning machine (ELM) and online sequential-ELM (OSELM) in predicting the most widely used DI known as standardized precipitation index (SPI) at multiple month horizons (i.e., 1, 3, 6 and 12). Models were developed using monthly rainfall data for the period of 1949–2013 at four meteorological stations namely, Barisal, Bogra, Faridpur and Mymensingh, each representing a geographical region of Bangladesh which frequently experiences droughts. The model inputs were decided based on correlation statistics and the prediction capability was evaluated using several statistical metrics including mean square error ( MSE ), root mean square error ( RMSE ), mean absolute error ( MAE ), correlation coefficient ( R ), Willmott’s Index of agreement ( WI ), Nash Sutcliffe efficiency ( NSE ), and Legates and McCabe Index ( LM ). The results revealed that the proposed models are reliable and robust in predicting droughts in the region. Comparison of the models revealed ELM as the best model in forecasting droughts with minimal RMSE in the range of 0.07–0.85, 0.08–0.76, 0.062–0.80 and 0.042–0.605 for Barisal, Bogra, Faridpur and Mymensingh, respectively for all the SPI scales except one-month SPI for which the RF showed the best performance with minimal RMSE of 0.57, 0.45, 0.59 and 0.42, respectively.

A fundamental prerequisite for obtaining realistic finite element simulation of machining processes, which has become a key machinability assessment for metals and alloys, is the establishment of a reliable material model. To obtain the constitutive model for wire–arc additive-manufactured ATI 718Plus, Hopkinson pressure bar is used to characterise the flow stress of the alloy over a wide range of temperatures and strain rates. Experiment results show that the deformation behaviours of as-deposited ATI 718Plus superalloy are influenced by the applied strain rate, test temperature and strain. Post-deformation microstructures show localised deformation within the deposit, which is attributable to the heterogeneous distribution of the strengthening precipitates in as-deposited ATI 718Plus. Furthermore, cracks are observed to be preferentially initiated at the brittle eutectic solidification constituents within the localised band. Constitutive models, based on the strain-compensated Arrhenius-type model and the modified Johnson–Cook model, are developed for the deposit based on experimental data. Standard statistical parameters, correlation coefficient ( R ), root-mean-square error ( RMSE ) and average absolute relative error ( AARE ) are used to assess the reliability of the models. The results show that the modified Johnson–Cook model has better reliability in predicting the dynamic flow stress of wire–arc-deposited ATI 718Plus superalloy.

The concept of missing data is important to apply statistical methods on the dataset. Statisticians and researchers may end up to an inaccurate illation about the data if the missing data are not handled properly. Of late, Python and R provide diverse packages for handling missing data. In this study, an imputation algorithm, cumulative linear regression, is proposed. The proposed algorithm depends on the linear regression technique. It differs from the existing methods, in that it cumulates the imputed variables; those variables will be incorporated in the linear regression equation to filling in the missing values in the next incomplete variable. The author performed a comparative study of the proposed method and those packages. The performance was measured in terms of imputation time, root-mean-square error, mean absolute error, and coefficient of determination $\\lpar {\\bi R}^2\\rpar $(R2). On analysing on five datasets with different missing values generated from different mechanisms, it was observed that the performances vary depending on the size, missing percentage, and the missingness mechanism. The results showed that the performance of the proposed method is slightly better.

Signal processing algorithms are crucial for the integrity of information transfer in wireless transceivers, with mean square error (MSE) serving as a pivotal metric for performance assessment. In multiple input multiple output (MIMO) systems, the transmission chain is susceptible to errors regardless of the antenna count, necessitating robust error analysis. This research article presents an evaluation of MSE in the context of MIMO wireless transceivers, focusing on data transmission at physical layer level. Signal processing algorithms, including least squares (LS), minimum mean square error (MMSE), and maximum likelihood (ML) algorithms, are analyzed for their efficacy in mean square error quantification, offering valuable insights for future research. A comprehensive analysis is conducted using training signals to ascertain the MSE, with simulations performed in MATLAB environment. Comparative results demonstrate that MMSE and ML algorithms outperform LS in reducing MSE, attributable to their reliance on probabilistic density functions (PDFs). The findings underscore the potential in error assessment and can aid emerging 5G and 6G wireless systems, which are predicated on advanced technologies such as massive MIMO and millimeter-wave communications. These results may pave the way for further research into optimizing signal fidelity in next-generation wireless communication systems.

This paper reports on the development of an integrated leaf quality inspecting system using near infrared reflectance (NIR) spectroscopy for quick and in situ estimation of total polyphenol (TP) content of fresh tea leaves, which is the most important quality indicator of tea. The integrated system consists of a heating system to dry the fresh tea leaves to the level of 3–4% moisture, a grinding and sieving system fitted with a 250 micron mesh sieve to make fine powder from the dried leaf. Samples thus prepared are transferred to the NIR beam and TP is measured instantaneously. The wavelength region, the number of partial least squares (PLS) component and the choice of preprocessing methods are optimized simultaneously by leave-one-sample out cross-validation during the model calibration. In order to measure polyphenol percentage in situ, the regression model is developed using PLS regression algorithm on NIR spectra of fifty-five samples. The efficacy of the model developed is evaluated by the root mean square error of cross-validation, root mean square error of prediction and correlation coefficient (R2) which are obtained as 0.1722, 0.5162 and 0.95, respectively.

Regression analysis makes up a large part of supervised machine learning, and consists of the prediction of a continuous independent target from a set of other predictor variables. The difference between binary classification and regression is in the target range: in binary classification, the target can have only two values (usually encoded as 0 and 1), while in regression the target can have multiple values. Even if regression analysis has been employed in a huge number of machine learning studies, no consensus has been reached on a single, unified, standard metric to assess the results of the regression itself. Many studies employ the mean square error (MSE) and its rooted variant (RMSE), or the mean absolute error (MAE) and its percentage variant (MAPE). Although useful, these rates share a common drawback: since their values can range between zero and +infinity, a single value of them does not say much about the performance of the regression with respect to the distribution of the ground truth elements. In this study, we focus on two rates that actually generate a high score only if the majority of the elements of a ground truth group has been correctly predicted: the coefficient of determination (also known as R -squared or R 2 ) and the symmetric mean absolute percentage error (SMAPE). After showing their mathematical properties, we report a comparison between R 2 and SMAPE in several use cases and in two real medical scenarios. Our results demonstrate that the coefficient of determination ( R -squared) is more informative and truthful than SMAPE, and does not have the interpretability limitations of MSE, RMSE, MAE and MAPE. We therefore suggest the usage of R -squared as standard metric to evaluate regression analyses in any scientific domain.

This paper describes a new apple classification system based on machine vision and artificial neural network (ANN), which classifies apple in real time on the basis of physical parameters of apple such as size, color and external defects. A specific hardware subsystem has been developed and described for every stage of input and output. The hardware subsystem is interfaced with the software to make the whole system automatic. The purpose of this paper is to automate apple classification. Presently, ANN is used in a wide range of classification applications. We have trained a back-propagation neural network to classify apple. Two sets of variables are used for the training purpose. First set is the independent variable, which is the surface level apple quality parameter. Second set is the dependent variable, which is the quality of the apple. The results of ANN model are discussed; however, the modeling results showed that there is an excellent agreement between the experimental data and predicted values, with a high determination coefficient, very good performance, fewer parameters, shorter calculation time and lower prediction error. The classification accuracy achieved is high, showing that a neural network is capable of making such classification. A low level of errors in classification confirmed that the neural network models are an effective instrument for apple classification. This model might be an alternative method for assessing the quality of apple and provide consumers with a safer food supply.

The Financial Times Stock Exchange (FTSE) Bursa Malaysia KLCI Index is a key component in the development of Malaysia's economic growth and the complexity in terms of identifying the factors that have a substantial impact on the Malaysian stock market has always been a contentious issue. In this study, the macroeconomic factors of exchange rate, interest rate, gold price, consumer price index, money supply M1, M2, and M3, industrial production, and oil price were discussed by using economic LASSO regression and Bayesian Model Averaging (BMA) with monthly average and monthly end time-series data spanning from January 2015 to June 2021, with a total of 78 observations by using the R Studio. The findings demonstrate that month-end data is better suited for stock market prediction than month-average data and that the BMA model is more suitable than the LASSO model, as seen by lower Mean Square Error of Prediction, MSE(P) and Residual Mean Square Error of Prediction, RMSE(P) values. The exchange rate, gold price, and money supply have a negative association with the dependent variables, while the consumer price index has a positive relationship associated with the dependent variables. The consumer price index is the most significant contributing factor, whereas gold price is the least significant. The result depicted that the KLCI index has no significant relationship with the variables interest rate, money supply M2, M1, industrial production index, and oil price. In conclusion, investors could specifically focus on the positive contributor and put lesser attention on improving their portfolio return.

It is well known that the use of prior information in the logistic regression improves the estimates of regression coefficients when multicollinearity presents. This prior information may be in the form of exact or stochastic linear restrictions. In this article, in the presence of stochastic linear restrictions, we propose a new efficient estimator, named Stochastic restricted optimal logistic estimator for the parameters in the logistic regression models when the multicollinearity presents. Further, conditions for the superiority of the new optimal estimator over some existing estimators are derived with respect to the mean square error matrix sense. Moreover, a Monte Carlo simulation study and a real data example are provided to illustrate the performance of the proposed optimal estimator in the scalar mean square error sense.

Power and energy systems around the world are expanding and evolving in tandem with technological advancement. In the current scenario, energy is a crucial requirement for the development for any country. Machine learning (ML) is used as a technology to address the requirement for quicker and more accurate analyses that would support the control and operation of modern power systems. In this paper, analysis is performed using Machine Learning and Deep Learning (DL) models to predict power estimation at a photovoltaic (PV) solar site with the capacity of 79.95 kW, installed in Dhar district, Madhya Pradesh (MP), India. The model's accuracy is evaluated using various statistical parameters, R2 score, Mean Square Error (MAE), Root Means Square Error (RMSE), Mean Square Error (MSE), Mean Absolute Error (MAE), Mean Absolute Percent Error (MAPE) and variance. The proposed method Linear Regression (LR) algorithm shows a maximum R2 score of 0.99994, a small error metric of MAE 0.0091, and an RMSE of 0.121, which indicate the highest accuracy model as compared to other algorithms. Accurate prediction of solar power without irradiance, season-wise (five seasons in India) and month-wise, is also predicted with high accuracy using ten different models of machine learning and one deep learning method, with comparison of its results with the existing work.