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In this paper, two generalized class such as ratio to exponential and the difference type of estimators have been proposed for estimating the finite population mean by utilizing information on higher order moments of an auxiliary variable in the two traditional situations of non-response. The expressions of biases and mean squared errors of the estimators are derived up to first order of approximation. The efficiency comparisons of the proposed methods over other contemporary estimators are carried out on real data sets. A simulation study is also performed to support the present study. The performance of the proposed methods are better than the others existing methods. Results are interpreted and suitable recommendations are made.

The transformation method can be used to increase the efficiency of the variable of interest for estimating population mean. Two classes of combined population mean estimators utilizing transformation on an auxiliary variable and on both an auxiliary variable and a study variable have been proposed under double sampling. The formulas of the biases and mean square errors of the proposed estimators are obtained. Simulation studies and an application to fine particulate matter 2.5 and nitrogen dioxide pollution data in Chiang Rai, Thailand have been investigated to assess performance of the proposed estimators compared to other existing estimators. Under certain conditions, the results indicate that the proposed combined estimators perform much better than other existing estimators.

This research article addresses an efficient separate and combined class of estimators for the population mean estimation based on stratified random sampling (StRS). The first order approximated expressions of bias and mean square error of the proposed separate and combined class of estimators are obtained. A comparative study is conducted to determine the efficiency conditions in which the suggested class of estimators outperforms the contemporary estimators. These efficiency conditions are examined through an extensive simulation study by employing a hypothetically drawn symmetrical and asymmetrical populations. The simulation results have shown that the suggested class of estimators is more effective than the other available estimators. In addition, an application of the proposed methods is also presented by examining a real data set. [ABSTRACT FROM AUTHOR]

It is well known that ranked set sampling (RSS) is more efficient than simple random sampling (SRS). Furthermore, the presence of missing data vitiates the conventional results. Only a minuscule amount of work has been conducted under RSS with missing data. This paper makes a modest attempt to provide some efficient difference- and ratio-type imputation methods in the presence of missing values under RSS. The envisaged imputation methods are demonstrated to provide better results than the existing imputation methods. The theoretical results are enhanced by a computational analysis using real and hypothetically generated symmetric (Normal) and asymmetric (Gamma and Weibull) populations. The computational results show that the proposed imputation method outperforms the existing imputation methods in terms of its higher percent relative efficiency. Additionally, the impact of skewness and kurtosis on the efficiency of the suggested imputation methods has also been calculated. [ABSTRACT FROM AUTHOR]

Ranked set sampling (RSS) has been proven an efficient alternative to simple random sampling (SRS). The use of auxiliary information also helps to improve the efficiency of the estimation procedures. Therefore, to accomplish higher efficiency and discuss the optimality issues, we proffer some optimal classes of estimators under RSS by employing multi-auxiliary information. It is seen that the ordinary mean estimator, traditional regression, and ratio estimators are the subsets of the proffered estimators. The expressions of the bias and mean square error are reported. An analytical comparison under some optimality conditions points out the ascendancy of the proffered classes of estimators over all reviewed works. The theoretical results have been furnished with computational study by employing some artificial and natural populations. The computational results show that the proffered estimators outperform the conventional estimators reviewed in this study. Furthermore, apposite advices are suggested to the survey persons. [ABSTRACT FROM AUTHOR]

In this study, we introduce a mixture binary Randomized Response Technique (RRT) model by combining the elements of the Greenberg Unrelated Question model and the Warner Indirect Question model. We also account for untruthful responding in the proposed model. A unified measure of model efficiency and respondent privacy is also presented. Finally, we present a simulation study to validate the theoretical findings. [ABSTRACT FROM AUTHOR]

In the current investigation, we have developed the efficient predictive estimator for finite population mean using auxiliary variable in case of post stratification. Up to the first order of approximation, the expressions for bias and mean square error (MSE) are derived for the proposed estimator. This also reveals the constant's ideal value, which reduces the MSE of the developed estimator. The developed estimator performs better than the existing estimators. Numerical study is also carried out by using the real data sets. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Engineering (17264073) is the property of Republic of Iraq Ministry of Higher Education & Scientific Research (MOHESR) and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Journal of Economics & Administrative Sciences is the property of Republic of Iraq Ministry of Higher Education & Scientific Research (MOHESR) and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Journal of Economics & Administrative Sciences is the property of Republic of Iraq Ministry of Higher Education & Scientific Research (MOHESR) and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Since the number of acquired images and their size have the tendency to increase, their lossy compression is widely applied for their storage, transfer, and dissemination. Simultaneously with providing a relatively large compression ratio, lossy compression produces distortions that are inevitably introduced and have to be controlled. The properties of these distortions depend on several factors such as image properties, the coder used, and a parameter that controls compression, which is different for particular coders. Then, one has to set a parameter that controls compression individually for an image to be compressed to provide image quality appropriate for a given application, and it is often desirable to do this quickly. Iterative procedures are usually not fast enough, and therefore fast and accurate procedures for providing a desired quality are needed. In the paper, such a procedure for two coders based on discrete cosine transform is proposed. This procedure is based on a prediction of mean square errors for a given quantization step using a simple analysis of image complexity (local activity in blocks). The statistical and spatial–spectral characteristics of distortions introduced by DCT-based coders are analyzed, and it is shown that they depend on the quantization step and local content. Generalizing the data for sets of grayscale test images and quantization step values, it is shown that the MSE can be easily predicted. These predictions are accurate enough and can be used to set the quantization step properly, as verified by experiments performed using more than 300 remote sensing and conventional optical images. The proposed approach is applicable to the lossy compression of grayscale images and the component-wise compression of multichannel data. [ABSTRACT FROM AUTHOR]

This study presents an advanced computational Levenberg–Marquardt backpropagation (LMB) neural network for the novel third order (NTO) pantograph Emden–Fowler system (PEFS), i.e., (NTO-PEFS) together with its two forms. The designed novel NTO-PEFS is achieved using the pantograph system and standard form of the Emden–Fowler system. The detail of each form of the NTO-PEFS based on the singular points, pantographs and shape factors is also provided. The numerical performance using the LMB neural network is tested for three different variants of the model and obtained results will be compared through the designed dataset based exact solutions. To assess the approximate solutions of the NTO-PEFS for both forms of each example, the process of testing, authentication and training are implemented to reduce the mean square error (MSE) based on the LMB. One can find the values based absolute error are close to 10–04 to 10–08 for each problem to solve the NTO-PEFS using the stochastic computing paradigms. The relative studies and performance investigations for the error histograms, regression, correlation and MSE enhance the effectiveness as well as the exactness of the designed LMB neural network scheme. [ABSTRACT FROM AUTHOR]

This paper explores the properties of the gap of representative points (RPs) in the sense of minimum mean square error for various univariate statistical distributions. We illustrate the relationship between RPs and doubly truncated mean residual life (DMRL) as well as mean residual life (MRL), which are widely used in survival analysis. The limiting behavior of the gap between the largest two RPs is discussed. In addition, an upper bound of the optimal MSE is given when the univariate random variable X has a domain on finite interval. In simulation studies, the performance of RPs for various distributions is assessed in terms of moment estimation and resampling technique. A brief discussion about the relationship between the tail of the distribution and the gap of RPs is also given. [ABSTRACT FROM AUTHOR]

In this communication, a fractional order design and numerical form of the solutions are presented for numerical simulations of heterogeneous mosquito model. The use of the fractional order derivatives is exploited to observe more accurate and exhaustive performances of the numerical simulation of the model. The novel design of the fractional order heterogeneous mosquito differential system is analyzed with stochastic solver based on the internet of things (IoT) technologies, represented with four categories i.e., normal individuals, people with reflex behavior, panic behavior and controlled behavior based differential system. The solutions of the novel design of the fractional order system are presented by using the stochastic paradigm of artificial neural network (ANN) procedures along with the Scaled Conjugate Gradient (SCG), i.e., ANN-SCG, for learning of weights. In ANN-SCG implementation, the data statistics are picked as 78% for training, 11% for both authorization and testing samples to approximate the solutions. The accuracy of the ANN-SCG technique is seen by correlation of the determined outcomes and the information base on Adams-Bashforth-Moulton method based standard solutions. To achieve the capacity, legitimacy, consistent quality, fitness, and accuracy of the ANN-SCG strategy, the reproductions-based error histograms (EHs), MSE, regression, and state transitions (STs) are used for extensive experimentations. [ABSTRACT FROM AUTHOR]

In this article, a molecularly imprinted polymer-solid phase extraction (SPE)-liquid chromatography method was developed to isolate toxic bentazon in surface water. The molecularly imprinted polymer (MIP) consisted of methacrylic acid as a functional monomer, ethylene glycol dimethacrylate as a crosslinking monomer, and α,α′-azoisobutyronitrile as an initiator for polymer preparation. To evaluate the applicability of the imprinted polymer as a selective sorbent, general parameters, such as pH, amount of loading solvents, washing solution, eluent, and time, were optimized following a step-by-step approach. Under the optimum conditions, the linear range was between 0.05 and 1.0 µg/L. The standard deviation of 2.2% and the method detection limit of 0.05 µg/L were obtained. The recoveries up to approximately 97.0% from spiked surface water samples could be obtained. The observed outcomes confirmed the suitability of the artificial neural network model as a tool for mean square error of bentazon on MIP-SPE (0.018) and non-imprinted polymer-SPE (0.029) selectivity and permeability. The proposed molecularly imprinted polymer-solid phase extraction-liquid chromatography method could be applied to the direct determination of toxic bentazon in water samples. [ABSTRACT FROM AUTHOR]

The study's major goal is to design a superior creative distribution by using the Kumaraswamy-G family of distributions to power Frechet distribution. The Kumaraswamy power Frechet distribution (KPFD) with four parameters is the full name of the revolutionary model. The distribution's probability density function may take numerous forms and graphs and can be used to describe complicated data sets efficiently. Several features of the new distribution are obtained, including dependability, hazard rate, quantile, and moments. The estimation of the unknown parameters of KPFD are provided using the KPFD maximum likelihood estimation technique. Furthermore, a study was performed using the Monte Carlo simulation approach to test estimator accuracy regarding average bias (AB) and mean square error (MSE). Last but not least, two genuine data sets are supplied to compare the proposed model to existing models. [ABSTRACT FROM AUTHOR]

In the presence of outliers in the data set, the utilization of robust regression tools for mean estimation is a widely established practice in survey sampling with single auxiliary variable. Recently, with the aid of some non-conventional location measures and traditional Ordinary Least Square (OLS), proposed a class of mean estimators using information on two supplementary variates under a simple random sampling framework. The utilization of non-traditional measures of location, especially in the presence of outliers, performed better than existing conventional estimators. In this study, a new class of estimators of mean utilizing quantile regression is proposed. The general forms of Mean Square Error (MSE) and Minimum Mean Square Error (MMSE) are also derived. The theoretical findings are being reinforced by different real-life data sets and simulation study. [ABSTRACT FROM AUTHOR]

Statistical models are vital in data analysis, and researchers are always on the search for potential or the latest statistical models to fit data sets in a variety of domains. To create an improved statistical model, we used a T-X transformation and the Gumbel Type-II model in this investigation. The research examined a simulation evaluation to assess the efficacy of the parameters. To show the application of the T-X approach for producing new distributions titled the new and improved Gumbel Type-II (NIGT-II) distribution, two actual data sets were used. The data sets reveal that the NIGT-II distribution sounds nicer than the Gumbel Type-II distribution. [ABSTRACT FROM AUTHOR]

In this work, we presented a new model called the alpha power inverse power Burr-Hatke distribution (APIPBHD). It provides several greater advantages in fitting a variety of different types of data. Estimates of the model parameters are provided and based on traditional research methods. We established the superiority of the proposed distribution by utilizing the importance and adaptability of the APIPBHD compared to other well-known distributions. The real data set includes 63 observations, all of which were manufactured to approximate the strengths of glass fibers to highlight the relevance and flexibility of the provided technique. We proved our superiority using one set of real data. Finally, major findings and conclusions are recorded at the end of the paper. Also, we added future work on the upcoming research depending on the proposed model. [ABSTRACT FROM AUTHOR]

Introduction: Chiang Mai’s air pollution has risen to number one in the world for the highest level of fine particulate matter which further exacerbates the damage to human health. Fine particulate matter can enter the human body and blood circulation, destroying organ systems, increasing the risk for chronic disease and cancer, despite not having smoking habits or other morbidities. The Thai government must sort out this issue before it is too late as the whole nation’s health is at risk due to excessive dust levels higher than standard guidelines. Collection of pollution data can help us to come up with solutions and prevent it from turning into a hazardous situation. Unfortunately, pollution data are missing and need to be dealt with before analysis to obtain accurate results. Materials and methods: A new method of imputation for estimating population mean based on a transformed variable has been suggested under simple random sampling without replacement and the uniform nonresponse mechanism. The bias and mean square error of the proposed estimator are investigated up to the first order of approximation. The performance of the proposed estimator is studied via applications to air pollution data in Chiang Mai, Thailand. Results: The proposed estimator shows the best performance, giving the least bias and mean square error for all levels of sampling fractions. For the results from application the estimated value of sulfur dioxide from Particulate Matter 2.5 (PM2.5), the Percentage Relative Efficiency (PRE) is higher than all existing estimators by at least 16%. For the estimated PM2.5 from PM10 the PRE is higher than all existing estimators by at least 1600%, an extremely significant difference exhibiting similarity to real values. Conclusion: The proposed imputation technique based on the transformed auxiliary variable can be helpful for imputing missing values and improving the efficiency of the estimators.

Recently, the need for more capacity in wireless networks has motivated this current research towards the creation of standards and algorithms that selectively take advantage of space. The development of smart antenna arrays and related beamforming algorithms has received a lot of attention. Cell Planning is an important process in networking, which is used to ensure coverage and avoid interference. Also, cell planning plays a vital role in the placement of base stations in a network. The communication between the base station (BS) and mobile station can happen either using a single antenna or an array of antenna elements. In the case of using a single antenna, if the Electromagnetic (EM) wave has low SNR, then BS cannot decode the data and drops the signal. Conversely, when an array is used, a signal with low SNR also falls on the base station, and due to multiple delayed copies of the same signal, the data gets decoded successfully. With Space Division Multiple Access (SDMA), the frequencies allotted for mobile communication are reused to provide channel access to multiple users at the same time preserving the allowable reuse distance in network architecture, thus increasing the channel capacity and facilitating multiple users separated by a distance at the same time with frequency reuse. The smart antenna system at the base station performs the transceiver function. The transmission phase uses the output from the reception i.e., the detected user direction radiates a beam towards the desired user for communication to narrow the beam. The proposed 6T Beamformer method is a six-tap-based system with three taps having fixed step sizes and the other three having variable step sizes. With the execution of each tap or module, better convergence and quality of service are achieved. In the result analysis, the proposed method is compared with existing high-performing algorithms like LMS, Griffiths, and VSSLMS against Mean Square Error (MSE) to show that it converges faster at the 9th iteration which is better than others in all the probabilities. ABSTRAK: Dewasa ini, keperluan terhadap lebih kapasiti dalam rangkaian tanpa wayar menjadi motivasi kepada kajian terkini dalam membentu piawai dan algoritma yang menjimatkan ruang. Pembangunan tata susun antena pintar dan algoritma pembentukan pancaran telah mendapat perhatian ramai. Merancang sel adalah proses penting dalam jaringan, bagi memastikan liputan terhasil dan mengelak dari gangguan. Juga, merancang sel memainkan peranan penting dalam menempatkan tapak stesen dalam rangkaian. Komunikasi antara stesen pusat (BS) dan stesen bergerak dapat berlaku samada menggunakan antena tunggal atau elemen tata susunan antena. Dalam kes antena tunggal, jika gelombang Elektromagnetik (EM) mempunyai SNR rendah, BS tidak dapat menafsirkan kod data dan signal akan terabai. Sebaliknya, apabila susun atur digunakan, signal dengan gelombang SNR rendah akan terus ke stesen pusat dan disebabkan beberapa gelombang sama yang tertunda, data dapat ditafsir dengan sempurna. Melalui Capaian Pelbagai Pembahagi Ruang (SDMA), frekuensi yang ditimbulkan bagi komunikasi bergerak telah diguna balik bagi menyediakan kemasukan saluran kepada pelbagai pengguna pada waktu sama memelihara jarak guna balik yang dibenarkan dalam binaan rangkaian, oleh itu menambah kapasiti saluran dan membantu gandaan pengguna yang dipisahkan oleh jarak dengan kekerapan guna balik pada masa sama. Sistem antena pintar di stesen pusat pula menjalankan fungsi pemancar. Fasa pemancaran ini menggunakan pengeluaran dari penerima iaitu, pengguna yang dikesan dari arah pancaran, akan memancarkan gelombang kepada pengguna yang memerlukan komunikasi, ini dapat mengecilkan jarak pancaran. Kaedah yang dicadangkan ini menghasil pancaran 6T iaitu sistem berdasarkan-enam-tap di mana tiga tap mempunyai saiz langkah yang tetap dan tiga lagi mempunyai saiz langkah berubah. Dengan pelaksanaan ini setiap tap atau modul mempunyai penumpuan yang lebih baik dan servis yang berkualiti terhasil. Dapatan kajian menunjukkan, kaedah yang dicadangkan dapat dibandingkan dengan algoritma berprestasi tinggi sedia ada seperti LMS, Griffiths, dan VSSLMS berbanding min kuasa dua ralat (MSE) bagi menunjukkan ia tertumpu lebih laju pada iterasi ke 9, iaitu lebih baik daripada ke semua kebarangkalian.

The current study presents the numerical solutions of the Wolbachia invasive model (WIM) using the neural network Levenberg-Marquardt (NN-LM) backpropagation technique. The dynamics of the Wolbachia model is categorized into four classes, namely Wolbachia-uninfected aquatic mosquitoes (An∗), Wolbachia-uninfected adult female mosquitoes (Fn∗), Wolbachia-infected aquatic mosquitoes (Aw∗), and Wolbachia-infected adult female mosquitoes (Fw∗). A reference dataset for the proposed NN-LM technique is created by solving the Wolbachia model using the Runge-Kutta (RK) numerical method. The reference dataset is used for validation, training, and testing of the proposed NN-LM technique for three different cases. The obtained numerical results from the proposed neural network technique are compared with the results obtained from the RK method for accuracy, correctness, and efficiency of the designed methodology. The validation of the proposed solution methodology is checked through the mean square error (MSE), error histograms, error plots, regression plots, and fitness plots.

Ridge regression is one of the most popular shrinkage estimation methods for linear models. Ridge regression effectively estimates regression coefficients in the presence of high-dimensional regressors. Recently, a generalized ridge estimator was suggested that involved generalizing the uniform shrinkage of ridge regression to non-uniform shrinkage; this was shown to perform well in sparse and high-dimensional linear models. In this paper, we introduce our newly developed R package “g.ridge” (first version published on 7 December 2023) that implements both the ridge estimator and generalized ridge estimator. The package is equipped with generalized cross-validation for the automatic estimation of shrinkage parameters. The package also includes a convenient tool for generating a design matrix. By simulations, we test the performance of the R package under sparse and high-dimensional settings with normal and skew-normal error distributions. From the simulation results, we conclude that the generalized ridge estimator is superior to the benchmark ridge estimator based on the R package “glmnet”. Hence the generalized ridge estimator may be the most recommended estimator for sparse and high-dimensional models. We demonstrate the package using intracerebral hemorrhage data.

Imputation methods deal with item nonresponse to solve the missing data problem. A new imputation method and corresponding point estimators for population mean have been proposed under two situations: using the response rate and the constant that gives the minimum mean square error for the estimator. The biases and mean square errors of the proposed estimators are derived. The performance of this method is compared with some existing methods via simulations and an application to fine particulate matter data. The results show that the proposed estimator, which uses the optimum value of a constant, performs the best. It performs the second best when using the response rate in the estimator, which is free of known parameters. The estimated fine particulate matter in Kanchana Phisek Road in Bangkok using the best method is equivalent to 42.22 micrograms per cubic meter with a mean square error of 0.34 micrograms per cubic meter squared.

Transmission lines are generally subjected to faults, so it is advantageous to determine these faults as quickly as possible. This study uses an Artificial Neural Network technique to locate a fault as soon as it happens on the Doukan-Erbil of 132kv double Transmission lines network. CYME 7.1-Programming/Simulink utilized simulation to model the suggested network. A multilayer perceptron feed-forward artificial neural network with a back propagation learning algorithm is used for the intelligence locator's training, testing, assessment, and validation. Voltages and currents were applied as inputs during the neural network's training. The pre-fault and post-fault values determined the scaled values. The neural network's performance was evaluated, and tests were run. Line-to-ground faults were examined. The study demonstrates how effective, rapid, and precise this method is at locating faults. The neural network's performance was examined, and tests were run on it. The overall performance of the mean square error in the trained network execution was 0.11792 at 35 epochs. The correlation coefficient at the entire target was 0.99987 percent of an error on the Doukan-Erbil double transmission lines.

In order to predict the risk level of tunnel collapse and reduce the disaster accidents caused by tunnel collapse, a tunnel collapse risk prediction model based on artificial bee colony(ABC) optimization support vector machine regression ( SVR) was established. Firstly, considering engineering geology, hydrometeorology, design factors and construction factors, 13 main influencing factors were selected to establish the tunnel collapse risk index system. Then, the artificial bee colony algorithm was introduced to optimize the kernel parameter C and penalty parameter g of SVR, the defect of low stability of traditional SVR was solved, and the accuracy of the model was improved. In order to verify the performance of the model, the evaluation parameters of correlation coefficient (R²), mean square error (MSE) and root mean square error (RMSE) were compared and analyzed. Finally, taking a water supply project in northern Xinjiang as the research object, the tunnel collapse risk test samples were predicted, and the ABC-SVR, PSO-SVR, GA-SVR and SVR models were compared and analyzed respectively. The results show that the prediction results of ABC-SVR are 100%, PSO-SVR are 83.3%, GA-SVR and SVR are 66.67% . The prediction results of ABC-SVR are more consistent with the actual engineering results, which can provide a scientific decision-making basis for tunnel collapse risk assessment. [ABSTRACT FROM AUTHOR]

The efficiency of the traditional ratio estimator decreases with the presence of outliers, inliers or when the underlying distribution is not normal. To improve the efficiency, we propose new robust ratio estimators utilizing the Lloyd's robust estimator and modified maximum likelihood estimator (MMLE), and provide their theoretical properties. We calculate the mean square error and relative efficiency of the proposed estimators and compare its performance with some other existing traditional estimators via a simulation study under various contamination or misspecification models. Further, we evaluate the exact value of the mean square error and to compare the performance of the proposed estimators using the numerical illustrations. [ABSTRACT FROM AUTHOR]

The essence of this paper is to propose some efficient combined and separate classes of estimators for estimating population mean Y ¯ under stratified simple random sampling. The bias and mean square error of the proposed classes of estimators are obtained. The proposed estimators are theoretically justified over the conventional mean estimator, classical ratio and regression estimators, Kadilar and Cingi estimators, Shabbir and Gupta estimators, Singh and Vishwakarma estimators, Koyuncu and Kadilar estimators, Singh and Solanki estimator, Yadav et al. estimator, Solanki and Singh estimator and Shahzad et al. estimator. The theoretical findings are extended with a simulation study accomplished over some artificially generated symmetric and asymmetric populations. [ABSTRACT FROM AUTHOR]

This paper presents some improved estimators of population mean using auxiliary variables in Ranked Set Sampling. We have derived the expressions for bias and mean square errors up to the first order of approximation and shown that the proposed estimators under optimum conditions are more efficient than other estimators taken in this paper. In an attempt to verify the efficiencies of proposed estimators, theoretical results are supported by empirical study and simulation study for which we have considered two populations. [ABSTRACT FROM AUTHOR]

In this article, attention is focused on the convex combination estimator, β ¯ = A β ̂ + (I − A) β ̂ R. We have proved that the matrix valued risk of the new convex matrix estimator cannot exceed the individual matrix valued risk of β ̂ and β ̂ R. [ABSTRACT FROM AUTHOR]

The performance of a population mean estimator can be improved by transformation techniques using a subset of the population data. An improved family of estimators for population mean has been proposed under double sampling using a transformed auxiliary variable. The biases and mean square errors of the proposed family of estimators up to the first order of approximation have been investigated. Simulation studies and an application to fine particulate matter in Chiang Rai, Thailand, are used to study the efficiency of the proposed estimators. The results from an application to air pollution in Chiang Rai showed that the proposed estimators gave smaller biases, by at least a half of the existing ones, and gave at least four times less mean square error than the existing ones. [ABSTRACT FROM AUTHOR]

The problem of biasness and availability of auxiliary variable for the estimating population mean is a big concern, both can be handled by proposing unbiased estimators in the absence of auxiliary variable. So in this paper unbiased exponential type estimators of population mean have been proposed. The estimators are proposed in the absence of the instrumental variable called the auxiliary variable by taking the advantage of the population and the sample median of the study variable. To about the first order approximation, the theoretical formulations of the bias and mean square error (MSE) are obtained. The circumstances in which the suggested estimators have the lowest mean squared error values when compared to the existing estimators were also deduced. In comparison to the currently used estimators, it was discovered that the suggested estimators of population mean had the lowest MSE, hence highest efficiency. Also least influence from the data's influential observations when it came to accurately calculating the population mean for skewed data. The theoretical findings of the paper are validated by the numerical study. [ABSTRACT FROM AUTHOR]

Developing models that can accurately predict the soil bulk density using other soil parameters is of great importance given the arduous task of determining the soil bulk density. We conducted the factorial experiment based on the randomized complete block design with five replications to determine the factors that affect the soil bulk density (BD) in three types of soil texture: loam, sandy loan, and loamy sand. Artificial neural network (ANN) and adaptive neuro-fuzzy inference system (ANFIS) were applied to predict and model the soil bulk density using several independent parameters that affect it, including the soil cone index (CI), moisture content (MC), and electrical conductivity (EC). The analysis indicated that the developed ANN using the Bayesian tuning algorithm with R2 = 0.93 is the most suitable model compared to other models that were created. We also performed the soil bulk density modeling using the three effective parameters of soil by ANFIS (adaptive neuro-fuzzy inference system) applying the hybrid method. The coefficient of determination for the ANFIS model was 0.988 (R2 = 0.98), which indicates the correct choice of the parameters affecting the soil bulk density. The comparison between the artificial neural network models and the neuro-fuzzy model developed in this study shows the complete advantage of ANFIS systems in predicting the soil bulk density as supported by the statistical parameters The results showed that ANN and ANFIS are highly capable to predict the soil bulk density in agricultural lands. [ABSTRACT FROM AUTHOR]

Stock prediction is one of the most important issues on which the investor relies in building his investment decisions and the financial literature has relied heavily on predicting future events because of its exceptional importance in financial work, after which profit or loss is determined, and since money dealers are eager to profit, the researchers have devoted techniques to forecast as providing the tools to achieve this. The choice of the proper model of time series data affects the precision of the predictions, and stock market data is typically random and turbulent for various industries. To obtain forecast models of stock market data that can accurately portray reality and obtain future forecasts, these models must take all data considerations from linear and none linear trends, different influences, and other data factors, hence the research problem of obtaining a method that gives predictions of Iraq's stock market indicators that are accurate and reliable in stock analysis. In this paper, two models were proposed to predict the Iraqi stock markets index through the use of artificial neural networks (ANN) and a long short-term memory (LSTM) algorithm where Iraqi stock market data were used from 2017 to 2021 and good results were achieved in the prediction where the long short-term memory (LSTM) algorithm reached a mean square error (MSE) rate of as little as 0.0016 while the artificial neural network (ANN) algorithm reached error rate 0.0055. [ABSTRACT FROM AUTHOR]

This paper considers some efficient classes of estimators for the estimation of population mean using known population proportion. The usual mean estimator, classical ratio, and regression estimators suggested by Naik and Gupta (1996) and Abd-Elfattah et al. (2010) estimators are identified as the members of the suggested class of estimators. The expressions of bias and mean square errors are derived up to first-order approximation. The proposed estimators were put to test against various other competing estimators till date. It has been found both theoretically and empirically that the suggested classes of estimators dominate the existing estimators. [ABSTRACT FROM AUTHOR]

In this paper, we propose a weighted link-specific (WLS) approach that establishes a unified statistical inference framework for high-dimensional Poisson and Gamma regression. We regress the parameter deviations as well as the initial estimation errors and utilize the resulting regression coefficients as correction weights to reduce the total mean square error (MSE). We also develop the asymptotic normality of the correction estimates under sparse and non-sparse conditions and construct associated confidence intervals (CIs) to verify the robustness of the new method. Finally, numerical simulations and empirical analysis show that the WLS method is extensive and effective. [ABSTRACT FROM AUTHOR]

Deep learning has been successfully applied to video anomaly detection. However, the way that deep network learn spatio-temporal features autonomously will ignore the specificity of different pattern features. Therefore, this paper focuses on how to efficiently learn deep appearance feature, introduces the idea of learning appearance information by predicting future frame, and proposes dual stream conditional generative adversarial network fusion for video abnormal behavior detection. The video frame and its corresponding optical flow image are transferred to the conditional generative adversarial network to learn the motion feature representation. In addition, inputting the video frame and its corresponding future frame to the network to generate the appearance representation complementary to motion feature. The model is only trained with normal events, therefore it is not able to generate abnormal events accurately. During the test, for the foreground moving targets, the images generated by the model are compared with the ground truth to obtain a two-stream anomaly probability distribution model based on the mean square error used to achieve the purpose of region anomaly detection. Experiments on the public datasets show that the proposed method can effectively detect and locate abnormal behaviors in the video. [ABSTRACT FROM AUTHOR]

This paper addresses the issue of estimating the population variance of a study character in the joint presence of random non-response and measurement errors and its application for estimating variations in biological data. Additional information on two highly positively correlated auxiliary variables has been incorporated to develop a general class of estimators under stratified two-phase sampling scheme. Its properties, in terms of bias and mean square error, have been examined. Optimum strata weights have been determined by employing suitable calibration techniques. Simulations using artificial data, as well as real data involving the variation in prostate specific antigen in different age groups when information about prostrate cancer volume and prostate weight is available, demonstrate the performance of the proposed class of estimators with respect to a contemporary estimator. Relevant R codes have been provided as Appendix. [ABSTRACT FROM AUTHOR]

The Vehicular Ad-hoc Network (VANET) is an innovative technology that allows vehicles to connect with neighboring roadside structures to deliver intelligent transportation applications. To deliver safe communication among vehicles, a reliable routing approach is required. Due to the excessive mobility and frequent variation in network topology, establishing a reliable routing for VANETs takes a lot of work. In VANETs, transmission links are extremely susceptible to interruption; as a result, the routing efficiency of these constantly evolving networks requires special attention. To promote reliable routing in VANETs, we propose a novel context-aware reliable routing protocol that integrates k-means clustering and support vector machine (SVM) in this paper. The k-means clustering divides the routes into two clusters named GOOD and BAD. The cluster with high mean square error (MSE) is labelled as BAD, and the cluster with low MSE is labelled as GOOD. After training the routing data with SVM, the performance of each route from source to target is improved in terms of Packet Delivery Ratio (PDR), throughput, and End to End Delay (E2E). The proposed protocol will achieve improved routing efficiency with these changes. [ABSTRACT FROM AUTHOR]

The present research suggests a new method for optimum parameters identification of the unknown parameters of the proton-exchange membrane fuel cells (PEMFCs). The technique is defined by proposing an improved bio-inspired technique, which is developed Horse Optimization Algorithm which is used for optimum model identification of PEMFCs. The target here is to provide higher confirmation of the empirical outputted data points and the model outputted voltage. It is done by minimizing the mean square error of the two output values. To specify the efficiency of the proposed technique, this has been executed to a 250 W PEM fuel cell stack model from the literature and the achievements have been put in comparison with several latest techniques to indicate the efficiency of the method technique. Simulation results showed that the proposed technique with 1.2512 MSE delivers the maximum confirmation against the comparative techniques that have been utilized as an effective method for the PEMFC model identification. [ABSTRACT FROM AUTHOR]

This manuscript considers the problem of estimating the population mean of the study variable y using information on two auxiliary attributes under simple random sampling without replacement (SRSWOR) scheme. We have defined a class of estimators for population mean based on two auxiliary attributes. We have studied the properties the suggested class of estimators up to first order of approximation. The regions of preferences are obtained under which the proposed class of estimators is more than some existing estimators including Haq and Shabir (2014) estimator. The optimum conditions are obtained at which the proposed class of estimators has the minimum mean squared error. This study is well supported through two numerical data sets. [ABSTRACT FROM AUTHOR]

Phasor Measurement Units (PMUs) provide Global Positioning System (GPS) time-stamped synchronized measurements of voltage and current with the phase angle of the system at certain points along with the grid system. Those synchronized data measurements are extracted in the form of amplitude and phase from various locations of the power grid to monitor and control the power system condition. A PMU device is a crucial part of the power equipment in terms of the cost and operative point of view. However, such ongoing development and improvement to PMUs' principal work are essential to the network operators to enhance the grid quality and the operating expenses. This paper introduces a proposed method that led to lowcost and less complex techniques to optimize the performance of PMU using Second-Order Kalman Filter. It is based on the Asyncrhophasor technique resulting in a phase error minimization when receiving the signal from an access point or from the main access point. The MATLAB model has been created to implement the proposed method in the presence of Gaussian and non-Gaussian. The results have shown the proposed method which is Second-Order Kalman Filter outperforms the existing model. The results were tested usingMean Square Error (MSE). The proposed Second-Order Kalman Filter method has been replaced with a synchronization unit into thePMUstructure to clarify the significance of the proposed new PMU. [ABSTRACT FROM AUTHOR]

Free fatty acid (FFA) optimization consequent to transesterification of high viscous rubber seed oil by solid base catalyst studied is presented in this work. Presence of more FFA in the feedstock leads to soap formation in the case of base catalysis and hence, pre-treatment is required to reduce the high acid value of raw rubber seed oil. FFA of raw rubber seed oil is reduced to 1.64% by acid catalyzed esterification and the FFA of esterified oil is minimized to 0.115% after biodiesel formation using fluorite (CaF2) derived from the calcination of fluorspar. Response surface methodology (RSM) equipped with central composite design (CCD) is used for FFA optimization. From the RSM analysis, the FFA of synthesized biodiesel is expressed as a quadratic model having a coefficient of determination, R2of value 0.8179. From the 3D-surface plots and 2D contour plots obtained from RSM analysis, it is observed that reaction time is the most significant process parameter that influences FFA optimization. Artificial Neural Network (ANN) modeling with the Levenberg-Marquardt algorithm is used for model network training in rubber seed oil FFA optimization. Mean square error (MSE) of 7.04 × 10−4 and coefficient of determination R2 value of 0.993 was observed at the best validation performance of 0.0019395 at epoch 5. On comparing the coefficient of determination of R2 values observed for both RSM and ANN, it is observed that the value obtained from ANN model fits the data well with minimum error. [ABSTRACT FROM AUTHOR]

The high accurate detection of blood glucose level (BGL) is very important for non-invasive monitoring of diabetes mellitus. In this work, the optoacoustic (OA) quantitative in vitro detection of diabetes mellitus involving the comprehensive impacts of multiple factors (irradiation energy, concentration, temperature, flow rate and vessel depth) was firstly studied. To achieve this aim, a set of OA in vitro detection system of blood glucose with the comprehensive influence of five factors was constructed. The real-time OA signals of 625 rabbit whole blood were obtained at the characteristic wavelength of 750 nm, as well as peak-to-peak values (PPVs). Results show that the accurate detection of BGL was very difficult due to the complicated OA signals. To accurately predict the BGL under the comprehensive impacts of five factors, wavelet neural network (WNN) was employed to train BGL of 500 training set blood. The mean square error (MSE) of BGL for 125 testing set blood was 6.5782 mmol/L. To decrease the MSE, WNN optimized by quantum particle swarm optimization (QPSO), i.e., QPSO-WNN algorithm was utilized. The MSE of BGL based on QPSO-WNN was 0.37485 mmol/L, which was superior to 0.48005 mmol/L of PSO-WNN. Particularly, to further decrease MSE, a novel nonlinear dynamic shrinkage coefficient (DSC) strategy was proposed, and compared with other four kinds of DSC strategies and the fixed one. With the optimal parameters, the MSE of BGL was decreased to 0.3088 mmol/L. Comparison results of seven algorithms and research works demonstrate that OA technology combined with QPSO-WNN algorithm and the novel nonlinear DSC strategy has excellent performance in the quantitative detection of diabetes mellitus involving in the comprehensive impacts. [ABSTRACT FROM AUTHOR]

In the present study, we explore the problem of estimation of finite population variance in simple random sampling (without replacement) by utilizing information of two auxiliary variables. A ratio cum exponential estimator has been proposed and its properties are studied to the first degree of approximation. To demonstrate the efficiency, members of the proposed estimator as well as other existing estimators are compared to the usual unbiased estimator. To study the performance, a simulation study is undertaken for both real and artificial population using R software. The suggested estimator is found to be more efficient than other existing estimators in terms of having minimum MSE. [ABSTRACT FROM AUTHOR]

In this paper, an attempt has been made to study the estimation of finite population variance in simple random sampling without replacement (SRSWOR) in presence of non-response by proposing two estimators. The two estimators (dual to ratio and ratio cum dual to ratio type) have been proposed on the basis of availability and non-availability of auxiliary information. The properties such as bias and mean square error of the proposed estimators have also been studied up to first order of approximation. The proposed estimators are compared with some existing estimators and are also mutually compared. An empirical study, based on both vegetable crop data and simulated data, has been performed to find out the best estimator. It has been seen that out of the two proposed estimators, the ratio cum dual to ratio type estimator performed better than dual to ratio estimator. [ABSTRACT FROM AUTHOR]

In the present paper, we have considered separate ratio estimator using calibration approach for the population mean in the presence of non-response using stratified random sampling. We have used the calibration approach to improve the precision of estimate of the population mean by using auxiliary character. In this study, we have used two constraints to find the calibration estimator. In order to determine the variance or mean square error of the suggested estimator, we use Taylor’s linearization method. In order to assess the efficiency of the proposed calibration estimator, we also conduct a simulation study. On the basis of simulation study, it has been observed that the proposed estimator is more efficient than the Dykes et al. (2015) and Khare (2013) estimator. [ABSTRACT FROM AUTHOR]

This study addresses impulse response identification and acoustic noise cancellation problems using recursive blind source separation (BSS) techniques based sparse adaptive filtering algorithms. The two-channel adaptive filtering feedback algorithms have been proposed to resolve two problems of noise reduction and speech enhancement when the acoustical mixing system is characterized by dispersive impulse responses. In this paper, three recent NLMS-based sparse adaptive filtering algorithms are implemented on two-channel feedback BSS structures. To evaluate their convergence speed property, we use system mismatch and segmental mean square error criteria. We also use the segmental signal-to-noise ratio and cepstral distance criteria to validate the performance of the presented algorithms in noise reduction and speech enhancement properties. We also have tested these sparse recursive versions with real-life speech signals in various noisy conditions. The obtained results show the good performances of these algorithms compared with the non-sparse versions. [ABSTRACT FROM AUTHOR]