Kernel Type Research Articles

Effect of interpolation kernels and grid refinement on two way-coupled point-particle simulations

The predictive capability of two way–coupled point-particle Euler–Lagrange model in accurately capturing particle-flow interactions under grid refinement, wherein the particle size can be comparable to the grid size, is systematically evaluated. Two situations are considered, (i) uniform flow over a stationary particle, and (ii) decaying isotropic turbulence laden with Kolmogorov-scale particles. Particle-fluid interactions are modeled using only the standard drag law, typical of large density-ratio systems. A zonal, advection-diffusion-reaction (Zonal-ADR) model is used to obtain the undisturbed fluid velocity needed in the drag closure. Two main types of interpolation kernels, grid-based and particle size–based, are employed. The effect of interpolation kernels on capturing the particle-fluid interactions, kinetic energy, dissipation rate, and particle acceleration statistics are evaluated in detail. It is shown that the interpolation kernels whose width scales with the particle size perform significantly better under grid refinement than kernels whose width scales with the grid size. Convergence with respect to spatial resolution is obtained with the particle size–based kernels with and without correcting for the self-disturbance effect. While the use of particle size–based interpolation kernels provide spatial convergence and perform better than kernels that scale based on grid size, small differences can still be seen in the converged results with and without correcting for the particle self-disturbance. Such differences indicate the need for self-disturbance correction to obtain the best results, especially when the particles are larger than the grid size.

An ensemble learning approach for resampling forgery detection using Markov process

Resampling is an extremely well-known technique performed for Image forgery detection. It includes the changes in the content of a picture in terms of rotation, stretching/zooming, and shrinking, to frame a forged picture that is a localized forgery in comparison to the original picture. With the wrong intention, resampling forgery has been increased day by day, and its negative impact has been increased in criminology, law enforcement, forensics, research etc. Accordingly, the interest in the algorithm of image resampling forgery detection is significantly developed in image forensics. In this paper, a novel image resampling forgery detection technique has been proposed. In the proposed technique, two types of Markov feature with spatial and Discrete Cosine Transform domains have been extracted to recognize the resampling operation. The spatial domain gives the information for the distribution of the pixels and DCT gives the edge information. Further, these Markov features are consolidated. Due to high dimensionality hard thresholding technique is used for reducing the dimensionality. Then, these Markov features are applied to the set of models of different classifiers. With the utilization of classifiers, weighted majority voting values have been calculated during the ensemble classification. Unlike the other techniques, these weighted voting boundaries have been consequently balanced during the training process until the best accuracy has been obtained. However, it is very difficult to get best accuracy so for getting best accuracy this research needs to do lots of iterations and trained the dataset. For the comparative study very few research has been found for this resampling forgery technique with different interpolation techniques and classifier. Still, comparison has been done with some latest research work. The comparative analysis shows that the proposed ensemble learning-based algorithm provides the best outcomes with the accuracy of 99.12% for bicubic, 98.89% for bilinear, and 98.23% for lanczos3 kernel with considerably less complexity and high speed in comparison to prior techniques which are using single support vector machine for classification. Moreover, the proposed algorithm also detects a very low probability of error of 0.44% and detects the type of interpolation kernel, size of the forgery, and the type of resampling, whether it is up sampling and down sampling, using Graphical User Interface which has not been detected previously with multiple forgery detection.

PEMODELAN PRODUK DOMESTIK BRUTO DI INDONESIA DENGAN PENDEKATAN SEMIPARAMETRIK POLINOMIAL LOKAL DILENGKAPI GUI-R

Gross domestic product is a measuring tool for a country's economy that needs to be known so that the country is able to consider decisions taken regarding future economic policies. The local polynomial semiparametric method that combines parametric regression and local polynomial nonparametric can be one way of predicting a country's GDP. This method is used because in GDP modeling there is one independent variable that has a linear relationship while the other variables have a pattern that tends to cluster. The modeling aims to obtain a semiparametric local polynomial model on GDP in Indonesia with the influence of coal export volume as a parametric independent variable and world oil prices as a nonparametric independent variable from the first quarter of 2005 to the second quarter of 2021 which is equipped with a GUI to simplify calculations. Based on experiments on several types of kernels, bandwidth and model degrees, the best model is local polynomial semiparametric model with Gaussian kernel weighting at degree 2 which has the smallest GCV. This model also has an R-Square value of 89.2% where the value of GDP is strongly influenced by world oil prices and coal export volumes together. The forecasting ability of this best model is said to be good because it has a MAPE of 17.127%.

Sedimentation of a Fine Aerosol in the Acoustic Fieldand with the Electrostatic Charge of Particles

Finding an efficient way to eliminate fine dust (particle diameter of 1–15 μm) from a room can be a challenging problem. Acoustic radiation emitters are widely used to accelerate particle coagulation and sedimentation. In this study, we propose one more method for depositing harmful particles – dispersion of electrostatically charged particles. These particles attract uncharged particles from the air and accelerate coagulation. The paper is devoted to a comparison of methods for the acoustic and electrostatic sedimentation of aerosols. The mathematical model for the coagulation of aerosols on the basis of Smoluchowski’s equation is proposed in the options corresponding to acoustic and electrostatic coagulation. A number of conclusions about the most effective conditions of sedimentation were made on the basis of the analysis of a kernel type of Smoluchowski’s integral equation. The results of the experiments on acoustic and electrostatic sedimentation of the model aerosol media (coal dust) are given. The results of the calculations according to the mathematical model of coagulation taking into account the proposed mechanisms for the sedimentation of aerosols in the acoustic field and electrostatic charging of particles are given.

Kernel estimates as general concept for the measuring of pedestrian density

The standard definition of pedestrian density produces scattered values, hence, many approaches have been developed to improve the features of the estimated density. This paper provides a review of generally applied methods and presents a general framework based on various kernels that bring desired properties of density estimates (e.g. continuity) and incorporate ordinarily used methods. The developed kernel concept considers each pedestrian as a source of density distribution, parametrised by the kernel type (e.g. Gauss, cone) and kernel size. The quantitative parametric study performed on experimental data illustrates that parametrisation brings desired features, for instance, a conic kernel with a base radius in m produces smooth values that retain trend features. The correspondence between kernel and non-kernel methods (namely Voronoi diagram and customised inverse distance to the nearest pedestrian) is achievable for a wide range of kernel parameter. Thereby the generality of the concept is supported.

Modelling point mass balance for the glaciers of the Central European Alps using machine learning techniques

Abstract. Glacier mass balance is typically estimated using a range of in situ measurements, remote sensing measurements, and physical and temperature index modelling techniques. With improved data collection and access to large datasets, data-driven techniques have recently gained prominence in modelling natural processes. The most common data-driven techniques used today are linear regression models and, to some extent, non-linear machine learning models such as artificial neural networks. However, the entire host of capabilities of machine learning modelling has not been applied to glacier mass balance modelling. This study used monthly meteorological data from ERA5-Land to drive four machine learning models: random forest (ensemble tree type), gradient-boosted regressor (ensemble tree type), support vector machine (kernel type), and artificial neural networks (neural type). We also use ordinary least squares linear regression as a baseline model against which to compare the performance of the machine learning models. Further, we assess the requirement of data for each of the models and the requirement for hyperparameter tuning. Finally, the importance of each meteorological variable in the mass balance estimation for each of the models is estimated using permutation importance. All machine learning models outperform the linear regression model. The neural network model depicted a low bias, suggesting the possibility of enhanced results in the event of biased input data. However, the ensemble tree-based models, random forest and gradient-boosted regressor, outperformed all other models in terms of the evaluation metrics and interpretability of the meteorological variables. The gradient-boosted regression model depicted the best coefficient of determination value of 0.713 and a root mean squared error of 1.071 m w.e. The feature importance values associated with all machine learning models suggested a high importance of meteorological variables associated with ablation. This is in line with predominantly negative mass balance observations. We conclude that machine learning techniques are promising in estimating glacier mass balance and can incorporate information from more significant meteorological variables as opposed to a simplified set of variables used in temperature index models.

Morpho-genetic characterization of black-husked small-grain aromatic rice landrace Kalojira of West Bengal

The morpho-genetic characterization of Kalojira, a black-husked non-Basmati type scented rice landrace, was done at Bidhan Chandra Krishi Viswavidyalaya (BCKV), Kalyani, West Bengal, India during kharif (wet) season of 2013, 2014 for DUS test, followed by 2019 and 2020 for confirmation of recorded traits, and molecular study was conducted at Bose Institute, Kolkata. The long-statured plants (scale 7, 140-150 cm height) had less foliage and stiff culm with late heading (119±4 days) and late maturity (scale 7, 151±4 days). The bi-sexual flower of the genotype consisted of six yellow-coloured plump anthers and an ovary with white-coloured feathery stigma. The short-sized matured grain (4.9 mm) had black coloured lemma-palea without awn, purple sterile lemma and low test weight (13.1 g). The white-coloured short-bold type kernels (length 3.7 mm and width 2.1 mm) had low amylose content (18.0%), medium gelatinization temperature (alkali spreading value 3.1) and medium-strong aroma (score 2.4). The DNA-based study using 23 simple sequence repeat (SSR) markers revealed that four markers (RM 149, RM 339, RM 341 and RM 569) made greater genetic distances (276 vs. 247 bp, 180 vs. 143 bp, 141 vs. 175 bp, and 194 vs. 168 bp, respectively) between Kalojira and IR 36 in the investigation.

Performance analysis of support vector machine for early identification of citrus diseases

Early citrus disease detection is necessary for optimum citrus productivity. But detecting a citrus disease at an early stage requires expert views or laboratory tests. But getting an expert view of all time is impossible for rural farmers. The present study aimed to create a low-cost, intelligent, affordable citrus disease classification system. This study offered a Support Vector Machine (SVM) based smart classification method for categorizing various citrus diseases. Citrus photos were subjected to a variety of image processing techniques to categorize the diseases using SVM and the kernel. Prior to classification, the images were segmented and the hue channel threshold value was used to differentiate the diseased area from the remaining portion of the image. The segmented image’s color and grey domains were used to extract 13 different texture and color features. This study outlined three different SVM kernel types- Linear, Gaussian, and Polynomial, and evaluated their accuracy and confusion matrix performances. The Radial Based Function with a polynomial kernel derived from the SVM outperformed the SVM's linear and Gaussian kernel.

Germplasm collection – valuable resources of variability for plant and ear traits in maize breeding

Maize is one of the most important crops, both worldwide and in Romania, and preserving the diversity of the biological material used in the breeding of this plant is of particular importance. The Agricultural Research and Development Station (ARDS) Turda, Romania, inbred lines collection includes both its own genotypes and some obtained as a result of germplasm exchanges with other institutions in the country or abroad. In the present study, 575 lines created at Turda were analysed regarding some traits of the plant (plant height, ear height, total number of leaves, number of tassel branches) and the ear (ear length and weight, number of kernel rows and number of kernels/row). The biological material used in this study shows a great diversity: a medium or high variability coefficient was identified for several of the traits analysed (number of tassel branches, ear height, ear height). Frequency histograms were made for the studied lines, for plant and ear traits. A great variability also was observed in the colours of the anther and silk, but also for kernel type and colour and cob colour.

Physico-chemical characterization of shea butter from Kaima, Western Nigeria

The characterization of Shea butter is important to enable it meet the standard required for certification. This study aimed at evaluating the quality and physico-chemical characteristics of extracted Shea oil obtained from different types of shea kernels harvested in Kaima local government area of Kwara state with the view to assist local and international producers on the ideal shea kernel for production. The Shea kernels obtained were classified into three; intact and whole (Shea kernel in good condition, microbial and pest-infested Shea kernels and wounded Shea kernels). Moisture content, color, specific gravity (density), acid value, peroxide value, Saponification value, unsaponifiable matter, Anisidine value, iodine value, total lipid content (oil content), free fatty acids profile (using the PORIM test method 2004) were carried out on each sample.Our results showed significant differences (p< 0.0001) between the various Shea butters with the microbial and pest-infested Shea butter having the highest contribution in lowering the quality of the Shea butter. Study revealed that intact and whole Shea kernel butter had good physico-chemical characteristics when compared to the wounded and microbial and pest-infested Shea kernel butter. Free fatty acid (7.13±0.00 %, 10.66±0.03 %, 13.51±0.20 %), Acid value (14.25±0.00 mg/g ,21.32±0.07 mg/g27.01±0.40 mg/g), Peroxide value (6.06±00meq/kg, 15.19±0.03meq/kg, 31.47±0.17meq/kg), PAnisidine value( 18.37±0.07, 15.50±0.00, 13.47±0.17), Total oxidation value (24.26±0.00, 30.24±0.10, 44.08±0.17), Saponification value (227.49±4.46 mg, 164.99±0.00 mg, 181.99±5.00 mg), Unsaponifiable matter (4.51±0.00%, 4.67±0.02 %, 3.06±0.00 %), Iodine value (9.04±0.05 g, 8.74±0.02 g, 8.42±0.01 g), Moisture content (1.00±0.44 %, 0.50±0.29%, 1.00±0.29 %) respectively. This study recommends that Shea butter processors should sort the kernels into categories and process them accordingly to yield different grades for local and international market

Analytical chemistry kernel library for spectroscopic profiling data

The kernel is a powerful mathematical tool in spectroscopic profiling data analysis. This paper gives a theoretical and systematic formulation for various kernel types. An open-source Python toolkit (ackl, “analytical chemistry kernel library”) is developed accordingly. Highlights of the toolkit include: (1) It designs a unified API and implements totally 31 kernel types (e.g., linear, poly, Gaussian, Matern, Cauchy, Sorensen, wavelet, Fejér, etc.), which is by far the most comprehensive kernel library. (2) It provides a tailored hyper-parameter optimization mechanism for each kernel, which suits the spectroscopic profiling data properties. (3) It designs a set of 16 evaluation metrics (e.g., classification accuracy, F1 score, MANOVA test statistic, Kolmogorov-Smirnov test statistic, Cohen effect size, Fisher's discriminant ratio, computational cost, etc.) to compare different kernels in discriminative tasks. Finally, we conducted spectroscopic profiling case studies using this tool and summarized a general guideline for kernel selection.

Implementation of n-gram Methodology to Analyze Sentiment Reviews for Indonesian Chips Purchases in Shopee E-Marketplace

Chips are a well-known product among Small and Medium Enterprises (SMEs). In order to enhance the quality of chips as an SME product, sentiment analysis is a crucial step. In this research, sentiment analysis of chip purchases on the Shopee E-marketplace was conducted using the Natural Language Processing (NLP) method, utilizing the N-Gram Model and Term Frequent-Inverse Document Frequency (TF-IDF) as feature extraction techniques, and the Support Vector Machine (SVM) algorithm for sentiment classification. The objective of this research is to identify the most suitable feature extraction model and optimal SVM kernel type from the options of Linear, Polynomial degree, Gaussian RBF, and Sigmoid kernels. Results from the experiments indicate that the TF-IDF and unigram feature extraction techniques offer the best performance for SVM classification when utilizing the Linear kernel. By labeling the dataset, it was observed that using a lexicon-based approach for sentiment classification resulted in 84.31% of the total reviews being positive. The words "price", "cheap" and "quality" in unigram have the highest weights above 0.040. In the unigram model, linear kernel accuracy and precision performance values are 88.4% and 87.3%. At the same time, the recall performance values is 88.4%. The results of the F1-Score assessment matrix from Unigram were 86.9%, Bigram was 78.5% and Trigram was 77.4%. Ultimately, the unigram model combined with a linear kernel in the SVM algorithm demonstrates strong potential for application in the development of various systems focused on detecting user reviews in the Indonesian language on the Shopee E-Marketplace.

Optimization of Kernel Type and Sharpness Level Improves Objective and Subjective Image Quality for High-Pitch Photon Counting Coronary CT Angiography.

(1) Background: Photon-counting detector (PCD) CT offers a wide variety of kernels and sharpness levels for image reconstruction. The aim of this retrospective study was to determine optimal settings for coronary CT angiography (CCTA). (2) Methods: Thirty patients (eight female, mean age 63 ± 13 years) underwent PCD-CCTA in a high-pitch mode. Images were reconstructed using three different kernels and four sharpness levels (Br36/40/44/48, Bv36/40/44/48, and Qr36/40/44/48). To analyze objective image quality, the attenuation, image noise, contrast-to-noise ratio (CNR), and vessel sharpness were quantified in proximal and distal coronaries. For subjective image quality, two blinded readers assessed image noise, visually sharp reproduction of coronaries, and the overall image quality using a five-point Likert scale. (3) Results: Attenuation, image noise, CNR, and vessel sharpness significantly differed across kernels (all p < 0.001), with the Br-kernel reaching the highest attenuation. With increasing kernel sharpness, image noise and vessel sharpness increased, whereas CNR continuously decreased. Reconstruction with Br-kernel generally had the highest CNR (Br > Bv > Qr), except Bv-kernel had a superior CNR at sharpness level 40. Bv-kernel had significantly higher vessel sharpness than Br- and Qr-kernel (p < 0.001). Subjective image quality was rated best for kernels Bv40 and Bv36, followed by Br36 and Qr36. (4) Conclusion: Reconstructions with kernel Bv40 are beneficial to achieve optimal image quality in spectral high-pitch CCTA using PCD-CT.

Morphological characterization of maize genotypes in Sylhet

Maize plays an important role both as food and feed throughout the world. For morphological characterization and evaluation of yield attributes of maize genotypes an experiment was carried out at the experimental field of the Department of Crop Botany and Tea Production Technology, Sylhet Agricultural University (SAU). The experimental materials were seven exotic maize genotypes viz.ZM 0001, ZM 0002, ZM 0003, ZM 0004, ZM 0005, ZM 0006ZM 0007 along with BARI Maize 6 as control. The experiment was laid out in a Randomized Complete Block Design (RCBD) with three replications. Significant variations were observed among eight maize genotypes for most of the plant characters studied. The tallest plant was found in ZM 0005(246.5 cm). The highest number of leaves was produced in ZM 0001 (14.01). The longest and widest leaf was recorded in ZM 0004 (94.67 cm) and BARI Maize 6 (10.32 cm), respectively. The longest internode was found in ZM 0004 and ZM 0005 (21.61 cm). The genotype ZM 0004 was the earliest in respect of tasseling and silking. Ear height was highest in ZM 0005 (143.8 cm). The genotype ZM 0004 matured the earliest (109.3 days) while BARI Maize 6 matured the last (126 days). No variation was found for ear shape but in terms of kernel color, kernel type, kernel row arrangement and shape of upper surface of the kernel there were great variations among the genotypes. BARI Maize 6 produced the largest kernel with the highest thousand kernel weight. Grain yield plant-1 t ha-1 was highest in BARI Maize 6 which was closely followed by ZM 0005 and ZM 0004 among the studied genotypes. A correlation study between the yield and yield contributing characters showed that ear length, ear diameter, number of kernels row-1, kernel length, kernel width, kernel thickness, thousand kernel weights, and the weight of kernels ear-1, prolificacy index and weight of kernels plant-1 had a significant positive correlation with grain yield. Vol. 10, No. 1, April 2023: 21-32

Feasibility of applying graph theory to diagnosing generalized anxiety disorder using machine learning models

The aim of this study was to investigate whether the alterations of topological properties can facilitate the diagnosis of generalized anxiety disorder (GAD). Twenty first-episode drug-naive Chinese individuals with GAD and twenty age-sex-education-matched healthy controls (HCs) were included in the primary training set, and the results of which were validated using nineteen drug-free patients with GAD and nineteen unmatched HCs. Two 3 T scanners were used to acquire T1, diffusion tensor, and resting-state functional images. Topological properties were altered in the functional cerebral networks among patients with GAD, but not in the structural networks. Using the nodal topological properties in the anti-correlated functional networks, machine learning models distinguished drug-naive GADs from their matched HCs independent of the type of kernels and the amount of features. Although the models built with drug-naive GADs failed to distinguish drug-free GADs from HCs, the features selected for those models could be used to build new models for distinguishing drug-free GADs from HCs. Our findings suggested that it is feasible to utilize the topological characteristics of brain network to facilitate the diagnosis of GAD. However, further research with decent sample sizes, multimodal features, and improved modeling methods are needed to build more robust models.

Non-destructive characterization of advanced nuclear fuel materials using neutron imaging

Attenuation-based neutron computed tomography (CT) has been used to non-destructively characterize the uncoated tristructural-isotropic (TRISO) nuclear fuel kernels in this work. Particularly, the effect of two different types of carbon blacks (Raven 3500 and Mogul L) on the internal gelation process of UO3-C kernels has been investigated. With 3D reconstructed kernel volumes and digital imaging processing techniques, heterogenous density distributions are mapped in both types of kernels. It is found that the kernels produced with Mogul L are ∼ 20 % denser and ∼ 10 % larger (in equivalent diameter) than the Raven 3500 kernels. Furthermore, less neutron attenuating regions, which are most likely to be carbon agglomerates as scanning electron microscopy (SEM) and energy-dispersive X-ray spectroscopy (EDS) results show, are observed in the Mogul L kernels. The size distribution of such carbon agglomerates (ranges from 50 μm to 850 μm with a peak at ∼ 200 μm) has been determined by analyzing the CT data. Furthermore, multiple metrics, including equivalent diameter, surface area, volume, sphericity, have been extracted to evaluate the fuel kernels. This work demonstrates that neutron imaging is an excellent, nondestructive tool to efficiently characterize, understand, and explore fuel materials for nuclear material research and development.

A symmetric adaptive visibility graph classification method of orthogonal signals for automatic modulation classification

AbstractVisibility graph methods allow time series to mine non‐Euclidean spatial features of sequences by using graph neural network algorithms. Unlike the traditional fixed‐rule‐based univariate time series visibility graph methods, a symmetric adaptive visibility graph method is proposed using orthogonal signals, a method applicable to in‐phase and quadrature (I/Q) orthogonal signals for adaptive graph mapping for radio modulated signals in automatic modulation classification tasks. The method directly models the intra‐channel and inter‐channel graph relations of I/Q signals using two different types of convolutional kernels. It captures non‐Euclidean spatial feature information of I/Q signals using a graph neural network combining graph sampling aggregation and graph differentiable pooling as a feature extractor. Extensive experimental results on two benchmark datasets and a simulated dataset containing channel fading show that the proposed Quadrature Signal Symmetric Adaptive Visibility Graph (QSSAVG) method in this paper outperforms the benchmark method in terms of classification accuracy and is also more robust against channel fading and noise variations.

Prediction of Student Decisions in Choosing the Type of Bank Using Support Vector Machine (SVM)

A bank is an intermediate financial institution authorized to take deposits, lend money, and issue promissory notes or banknotes. In the present day, every adult must have at least one bank account. Additionally, bank services range from regular and hajj savings to large-scale loans. Students, one of the bank’s customers, usually utilize services confined to savings to preserve pocket money received from their parents and ordinary transactions like transfers and payments. Several factors, including the atmosphere, administrative fees, and the accessibility of ATMs and bank branch offices, impact students’ decisions about where to save money. It prevents the bank from predicting which services must be enhanced to encourage customers, particularly students, to select the bank. Therefore, prediction is required to ascertain the students’ choice of bank. This study employed data mining and the Support Vector Machine (LibSVM) algorithm. The quantity of data impacted the outcomes of the SVM classification. In addition, kernel types, k-fold values, and sampling techniques also influenced classification accuracy. LibSVM with a kernel type of RBF, a k-fold of 8, and shuffled sampling classified 200 data with an accuracy of 68.40%.

Uniform and [formula omitted] convergences for nonparametric continuous time regressions with semiparametric applications

We obtain uniform and Lp convergence rates of kernel type nonparametric estimators for the instantaneous conditional mean and variance functions of continuous time regressions, where the regressor is assumed to be a general recurrent diffusion. Our asymptotics are developed under a general set-up, with a shrinking sampling interval and an increasing time span, and without the stationarity assumption. Based on our convergence results, we develop a semiparametric inferential procedure for continuous time predictive regressions. In particular, a robust semiparametric likelihood ratio test for linear predictability is proposed, with its limit distribution established. We also apply our convergence results to obtain the asymptotics of a semiparametric maximum likelihood estimator of the drift of recurrent diffusions. In our simulation study, we examine the finite sample performance of our robust test against several existing tests in the literature. An empirical illustration is presented to test the predictability of the excess returns of two major stock indices using the commonly used dividend–price ratio and earnings–price ratio as the predictor.

Kernel Estimation of the Extropy Function under α-Mixing Dependent Data

Shannon developed the idea of entropy in 1948, which relates to the measure of uncertainty associated with a random variable X. The contribution of the extropy function as a dual complement of entropy is one of the key modern results based on Shannon’s work. In order to develop the inferential aspects of the extropy function, this paper proposes a non-parametric kernel type estimator as a new method of measuring uncertainty. Here, the observations are exhibiting α-mixing dependence. Asymptotic properties of the estimator are proved under appropriate regularity conditions. For comparison’s sake, a simple non-parametric estimator is proposed, and in this respect, the performance of the estimator is investigated using a Monte Carlo simulation study based on mean-squared error and using two real-life data.