Individual Learning Models Research Articles

Game Theory-Based Dynamic Weighted Ensemble for Retinal Disease Classification

An automated retinal disease detection system has long been in existence and it provides a safe, no-contact and cost-effective solution for detecting this disease. This paper presents a game theory-based dynamic weighted ensemble of a feature extraction-based machine learning model and a deep transfer learning model for automatic retinal disease detection. The feature extraction-based machine learning model uses Gaussian kernel-based fuzzy rough sets for reduction of features, and XGBoost classifier for the classification. The transfer learning model uses VGG16 or ResNet50 or Inception-ResNet-v2. A novel ensemble classifier based on the game theory approach is proposed for the fusion of the outputs of the transfer learning model and the XGBoost classifier model. The ensemble approach significantly improves the accuracy of retinal disease prediction and results in an excellent performance when compared to the individual deep learning and feature-based models.

Retrieving soil moisture from grape growing areas using multi-feature and stacking-based ensemble learning modeling

Soil moisture (SM) is an essential parameter for crop growth and development, and temporal and spatial variation in SM in agricultural fields varies by crop type due to corresponding crop growing characteristics and cultivating patterns. Few studies have performed SM retrieval in grape growing areas, and SM estimation using only spectral reflectance (SR) or derived drought indices may not be wholly accurate. In this study, seven features based on evapotranspiration (ET), land surface temperature (LST), and SR were derived from Moderate Resolution Imaging Spectroradiometer (MODIS) data (8-day temporal resolution and 500 m spatial resolution) and integrated with topography feature to determine SM during the main growth stages of grape in the eastern foothills of the Helan Mountains from 2009 to 2018. A total of 25-period models covering April–October (8-day temporal resolution) were constructed. Finally, the models were achieved to retrieve SM, and the spatiotemporal distribution pattern was analyzed. The stacking ensemble algorithm can integrate multiple models for better retrieval accuracy and overcome the limitations of individual machine learning models. We also compared the SM estimation accuracy of three single machine learning models (Category boosting, random forest, and gradient boosting decision tree) with the ensemble model using the stacking algorithm. The results indicated that the stacking-based ensemble model could retrieve SM more accurately and stably than any individual machine learning algorithm, and the stacking-based ensemble model showed good applicability during the main growth stages of grape. The average coefficient of determination (R2) and root mean square error (RMSE) of the 25-period multi-feature stacking-based models were 0.7504 and 0.0245 m3/m3. Overall, the application results showed that spring and summer droughts were more severe than autumn droughts in the study area during the main growth stages of grape. Our findings indicate that using multiple features and the stacking-based ensemble model could improve SM estimation accuracy in grape growing areas.

An interpretable ensemble learning method to predict the compressive strength of concrete

Without rigorous testing, it is challenging to predict the compressive strength of concrete directly from the mix ratio. This study explored an optimal method for predicting the compressive strength of concrete based on various ensemble machine learning methods. The database was collected from real projects, using the Mahalanobis distance to verify database quality. Firstly, the background of ensemble machine learning techniques was presented typically represented by random forest (RF), the adaptive boosting algorithm (AdaBoost), the gradient boosting regression tree (GBRT), extreme gradient boosting(XGBoost), the light gradient boosting machine (LightGBM), the categorical boosting algorithm (CatBoost). Secondly, the K-fold cross-validation (K-fold CV) and Tree-structured Parzen Estimator (TPE) algorithm were applied to find the optimal hyperparameter combinations for the models. Then, the models were compared with the individual machine learning models by using four evaluation indexes, including the root mean squared error (RMSE), mean absolute error (MAE), mean absolute percentage error (MAPE), and the coefficient of determination (R2), results showed that the LightGBM model achieved the best comprehensive performance. The SHapley Additive exPlanations (SHAP) approach was used to produce both global and local explanations for the predictions of the LightGBM model. And a graphical user interface (GUI) was developed to offer users a useful tool. Finally, a conditional generative adversarial network (cGAN) was applied to address the issue of database scarcity or the lack of data for a specific concrete strength grade in the database.

Transferable Shape Estimation of Soft Pneumatic Actuators Based on Active Vibroacoustic Sensing

A soft pneumatic actuator (SPA) is one of the most prominent components in a soft robotic system. The sensing of SPAs is challenging owing to their elasticity and deformability. Sensors for specific factors are required to successfully sense an SPA. A flexible sensor is an important component for sensing the conditions of SPAs, such as the shape and deformation due to contact events during applications. Developing versatile sensors with high flexibility and tolerability for SPAs is challenging. Data-driven sensing approaches involve individual machine learning models for different actuators. In contrast, it is an enormous advantage to have versatile sensors as hardware and machine learning models as software employed on various actuators. Therefore, we propose a transferable shape estimation method based on active vibro-acoustic sensing to achieve tolerability and increase versatility. We created easily transferable sensing devices for SPAs. In addition, we employed a data-driven approach that utilizes a simple transfer learning technique on a two-dimensional convolutional neural network model. We confirm the feasibility and versatility of the proposed method through evaluation experiments. A transferable estimation method was used on SPAs to estimate the bending angle and length under various sensing and environmental conditions, and the average errors were less than 3.5 <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$^\circ$</tex-math></inline-formula> and 2.1 mm, respectively.

The Effect of Cooperative Learning Model Type of Team Assisted Indivisualization Assisted LKPD on Mathematics Learning Outcomes

The purpose of the study was to determine the students' mathematics learning outcomes by using a cooperative learning model of team assisted individualization type assisted by hots-based worksheets; and the effect of cooperative learning model type team assisted individualization assisted LKPD based hots on students' mathematics learning outcomes. The method used is an experiment with sampling determined by taking the available classes without doing random sampling. The experimental class and control class were determined by lottery, the results of the draw determined that class V SD Mallengkeri I was the control class and class V was SD Mallengkeri II as the experimental class, namely the class that was given treatment. The data analysis technique in this study used descriptive statistical analysis. As a requirement, test requirements were carried out, namely normality test and homogeneity test using Kolmogorov-Smirnov and Levene's test. After that, the hypothesis is tested using the Paired sample test. The results showed that the mean of the experimental class was 81, 88 while the mean of the control class was 68, 88. Based on data analysis with the Paired sample test. which was carried out at the 95% confidence level, the results were 0.000 &lt;0.05, which means H0 is rejected. To find out the difference in student learning outcomes and understanding between the experimental class and the control class, the N-gain test was carried out using the N-Gain formula. Based on the calculation results obtained, for the experimental class of 0.58 which shows an increase in understanding in the medium category. Based on these data, it can be concluded that the use of the Team Assisted Individualization learning model has an effect on the learning outcomes of fifth graders at SD Cluster III Tamalate Makassar

PENGARUH MODEL PEMBELAJARAN KOOPERATIF TIPE TEAM ASSISTED INDIVIDUALIZATION (TAI) TERHADAP PEMAHAMAN KONSEP MATEMATIS PESERTA DIDIK KELAS XI IPS SMAN 1 IV NAGARI BAYANG UTARA KABUPATEN PESISIR SELATAN

The tipe of risearch used is a quas experimental with a researh desig using Non-equivalen Posttest Only Control Group Desig. academik year. Samples were taken using the Simple Rndom Sampling technique, so that clas XI IPS 1 was chosen as the experimental class and XI IPS 2 as the control clas. The instrument used is in the form of a final test of the ability to understand mathematicall concepts in the form of essay questions. The data obtained were analyzed by normality test, homogenety test and t tes. Basid on the analisis of the final test, it was obtained that P-value = 0. Because the P-Value is les than = 0.05, it’s mean that thire is an influenc from the aplication of the Tem Assisted Individualization (TAI) learning model on the abiliity to understan mathematical conceps of students, so that the ability to understand mathematical concepts of students who learn with the Team Asisted Individualization (TAI) learning model is better than the ability to understand mathematicallconcepts of students wh’so learn with the drect’s learning mode’sl.. Keywordsi– Cooperative, TAI, Mathematicall concepts understanding.

Machine learning techniques for cross-sectional equity returns’ prediction

We compare the performance of the linear regression model, which is the current standard in science and practice for cross-sectional stock return forecasting, with that of machine learning methods, i.e., penalized linear models, support vector regression, random forests, gradient boosted trees and neural networks. Our analysis is based on monthly data on nearly 12,000 individual stocks from 16 European economies over almost 30 years from 1990 to 2019. We find that the prediction of stock returns can be decisively improved through machine learning methods. The outperformance of individual (combined) machine learning models over the benchmark model is approximately 0.6% (0.7%) per month for the full cross-section of stocks. Furthermore, we find no model breakdowns, which suggests that investors do not incur additional risk from using machine learning methods compared to the traditional benchmark approach. Additionally, the superior performance of machine learning models is not due to substantially higher portfolio turnover. Further analyses suggest that machine learning models generate their added value particularly in bear markets when the average investor tends to lose money. Our results indicate that future research and practice should make more intensive use of machine learning techniques with respect to stock return prediction.

MENINGKATKAN KEMAMPUAN MENULIS PUISI BALI MODERN DENGAN MENERAPKAN MODEL PEMBELAJARAN KOOPERATIF TIPE TEAM ASSISTED INDIVIDUALIZATION (TAI) PADA SISWA KELAS VII H SMP DHARMA WIWEKA DENPASAR TAHUN PELAJARAN 2021/2022”

The ability to write modern Balinese poetry in class VII H SMP Dharma Wiweka Denpasar is still low. To overcome this, the researcher offers to apply the Team Assisted Individualization type of cooperative learning model. The formulation of the problem in this study are: (1) Can the application of the Team Assisted Individualization (TAI) learning model improve the ability to write modern Balinese poetry for students of class VII H SMP Dharma Wiweka Denpasar ? (2) How are the students of class VII H SMP Dharma Wiweka Denpasar responding to the application of the Team Assisted Individualization (TAI) learning model? The application of the Team Assisted Individualization (TAI) learning model can improve the ability to write modern Balinese poetry in class VII H SMP Dharma Wiweka Denpasar . This can be seen from the test results of students in the first cycle obtaining an average score of 76.19 with a total percentage of 71.79%. In the second cycle there was an increase with an average score of 83.14 with a total percentage of 89.74%, besides that an increase also occurred in student responses with an average score of 12.43 in the first cycle to 16.76 in the second cycle from a maximum score of 20 with a very positive category. It is recommended that Balinese language teachers apply the Team Assisted Individualization (TAI) learning model in learning to write modern Balinese poetry, students are advised to improve their abilities by practicing more in writing modern Balinese poetry, for the school the Team Assisted Individualization (TAI) learning model can be used as a learning model. material for consideration to be applied to other lessons. Keywords: Team Assisted Individualization (TAI) model, writing modern Balinese poetry.

Multi-head self-attention bidirectional gated recurrent unit for end-to-end remaining useful life prediction of mechanical equipment

In order to reduce error accumulation caused by multistep modeling and achieve a generally accurate model, this paper proposes an end-to-end remaining useful life (RUL) prediction model based on a multi-head self-attention bidirectional gated recurrent unit (BiGRU). Taking multivariable samples with long time series as the model input and multistep RUL values as the model output, the BiGRU model is constructed for continuous prediction of RUL. In addition, single-head self-attention models are applied for time series and variables of samples before or after the BiGRU, which can be fused into a multi-head attention BiGRU. Aeroengines and rolling bearings are selected to testify the effectiveness of the proposed method from the system level and component level respectively. The results show that the proposed method can achieve end-to-end RUL prediction efficiently and accurately. Compared with single-head models and individual deep learning models, the prediction mean square error of the proposed method is reduced by 20%–70%.

Collaborative deep learning model for tooth segmentation and identification using panoramic radiographs

Panoramic radiographs are an integral part of effective dental treatment planning, supporting dentists in identifying impacted teeth, infections, malignancies, and other dental issues. However, screening for anomalies solely based on a dentist's assessment may result in diagnostic inconsistency, posing difficulties in developing a successful treatment plan. Recent advancements in deep learning-based segmentation and object detection algorithms have enabled the provision of predictable and practical identification to assist in the evaluation of a patient's mineralized oral health, enabling dentists to construct a more successful treatment plan. However, there has been a lack of efforts to develop collaborative models that enhance learning performance by leveraging individual models. The article describes a novel technique for enabling collaborative learning by incorporating tooth segmentation and identification models created independently from panoramic radiographs. This collaborative technique permits the aggregation of tooth segmentation and identification to produce enhanced results by recognizing and numbering existing teeth (up to 32 teeth). The experimental findings indicate that the proposed collaborative model is significantly more effective than individual learning models (e.g., 98.77% vs. 96% and 98.44% vs.91% for tooth segmentation and recognition, respectively). Additionally, our models outperform the state-of-the-art segmentation and identification research. We demonstrated the effectiveness of collaborative learning in detecting and segmenting teeth in a variety of complex situations, including healthy dentition, missing teeth, orthodontic treatment in progress, and dentition with dental implants.

Comparison of Mathematics Learning Outcomes with Student Team Achievement Divisions and Team Assisted Individualization Model

The purpose of this study was to determine the difference between student learning outcomes in mathematics using the Student Team Achievement Divisions (STAD) learning model and the Team Assisted Individualization (TAI) learning model. The research method is descriptive quantitative with a quasi-experimental approach, and a two-group posttest only control design. Samples were taken by means of convenience sampling. The data collection technique is a test instrument by looking at the learning outcomes. The results and findings, based on the results of hypothesis testing using the t-test, obtained that t count is greater than t table of 3.179&gt; 1.995 with a significance level of 0.05. These findings are supported by the calculation of learning outcomes for experimental class I using the Student Team Achievement Divisions method with the average value is 70.01, this shows a higher value than the experimental class II which uses the Team Assisted Individualization method which averages 66.90. The conclusion is that there are differences in the learning outcomes of students who are taught using the Student Team Achievement Divisions model with students who are taught using the Team Assisted Individualization learning model. The Student Team Achievement Divisions learning model produces higher learning outcomes and is recommended to be used in teaching all mathematics materials at all levels

아파트 대량 평가를 위한 기계 학습 알고리즘과 스태킹 앙상블 기법의 응용

Purpose The goal is to find an optimal data-driven model for apartment price appraisal system. We compares the predictive power of several machine learning algorithms and the ensemble model by using 17,779 apartment transaction data in Gangnam from 2006 to 2017. Methods We use three tree-based machine learning techniques, random forest, Adaboost, and Catboost. Also, an ensemble model is developed through the stacking technique using the above algorithm. 5-fold cross validation is used as model validation techniques. Results By applying the algorithm, it was found that the ensemble model constructed from stacking method performs better than individual machine learning models. Conclusion The results illustrate that the quality of mass appraisals or apartment price indices can be improved by using the ensemble model.

PENINGKATAN HASIL BELAJAR PRAKTIKUM FISIKA ‎DENGAN MODEL PEMBELAJARAN KOOPERATIF TIPE TEAM ‎ASSISTED INDIVIDUALIZATION (TAI)‎

This classroom action research was conducted with the aim of knowing the improvement of students' physics practicum learning outcomes on light wave material using the Team Assisted Individualization (TAI) type of cooperative learning model. The research subjects were students of class XI IPA.6 MAN 2 Kota Padang in the 2020/2021 academic year. The research was carried out in two cycles, each cycle consisting of planning, implementation, observation, and reflection stages. Cycle I was carried out on the practical material of parallel plan glass refraction and the second cycle on the practical material of the diffraction grating. Students' practicum activity data is extracted with Student Activity Observation Sheets, teacher practicum learning management data is extracted with Learning Management Observation Sheets, while students' practicum learning outcomes data are excavated using Formative Tests in the form of practice. The results showed that: (1) Cycle I involving students with high initial abilities obtained an average learning outcome of 78.56 and a percentage of completeness 41%; (2) Cycle II involving students with high initial abilities and being given material and skill reinforcement obtained an average learning outcome of 87.78 and a 95% completeness percentage; (3) The learning outcomes of students' physics practicum in the second cycle have increased from the first cycle, both on average and the percentage of completeness. In conclusion, the Team Assisted Individualization (TAI) learning model can improve students' physics practicum learning outcomes.

Stacking ensemble based fault diagnosis approach for improved operation of photovoltaic arrays

ABSTRACT The operation of photovoltaic (PV) systems, like any other system, in fault free environment ensures maximum performance. Hence, accurate and timely fault identification of PV system demands greater importance. Present investigation proposes a stacking ensemble-based PV array fault diagnosis method, which is integrated with automatic feature engineering and selection technique, handling of imbalanced dataset for unbiased classification. The proposed ensemble utilizes decision tree (DT), random forest (RF), extra trees (EXT), extreme gradient boosting machine (XGBoost) as base learners, and light gradient boosting machine (LightGBM) as both base and meta-learner. To validate the proposed technique, a test system of 4.8kWp capacity has been built using the MATLAB/Simulink environment incorporating one-year real-time irradiance and module temperature data. Irradiance, module temperature, voltage, current, and power are collected as primary raw data which are then concocted using the autofeat python library for automatic feature engineering and selection. Subsequently, the proposed fault diagnosis strategy is built in python 3.8.5 using the engineered and class label balanced dataset, prepared using the synthetic minority over-sampling technique (SMOTE). Results demonstrate promising performance of the proposed ensemble technique with 97.04% accuracy in classifying the different faults in the PV array, which accounts for approximately 1–3% improvement over other individual machine learning (ML) models.

An ensemble fault diagnosis method for rotating machinery based on wavelet packet transform and convolutional neural networks

Deep learning has made fascinating achievements in fault diagnosis of rotating machinery. However, the individual deep learning models generally have poor performances under variable operating conditions or noise environment. Additionally, they are prone to overfitting when dealing with unbalanced fault data. Therefore, an ensemble method based on wavelet packet transform (WPT) and convolutional neural networks (CNNs) is presented for rotating machinery fault diagnosis. First, the raw signals are transformed into multiple wavelet packet coefficients with local information and a reconstructed signal with global information through WPT. Then, these signals are separately fed into the corresponding CNN models for diagnosis. Finally, the diagnosis results of multiple CNNs are combined into a more stable and accurate diagnosis result through the improved weighted voting strategy. The proposed method is applied to the fault diagnosis of the motor bearing and the CNC machine tool spindle bearing. Compared with the other traditional diagnosis methods, the results show that the proposed method achieves a better diagnostic performance.

The Application of The Teams Assisted Individualization (TAI) Learning Model to Improve Learning Outcome of The Starter Motor Electrical System in VHS

The objectives of the study were: to determine (1) the application of the TAI (Team Assisted Individualization) learning model to the Motor Starter Electrical System learning in SMK; (2) whether there is an increase in learning outcomes and the percentage of student graduation in the Motor Stater Electrical System learning with the application of the TAI (Team Assisted Individualization) learning model in SMK; (3) how much is the increase in learning outcomes and the percentage of student graduation in the Motor Stater Electrical System learning after the application of the TAI (Team Assisted Individualization) learning model in SMK. The type of research used is Classroom Action Research. The subjects of this study were students of SMK. Data was collected using observation methods and test instruments. After the data was obtained, it was analyzed using descriptive analysis techniques. From the results of the research, it shows that: (1) The application of learning using a Team Assisted Individualization (TAI) cooperative model is carried out in the planning, action and closing stages. (2) The implementation of learning using the Team Assisted Individualization (TAI) type cooperative model can improve student learning outcomes, (3) in the initial conditions the percentage of student graduation is 33.33% with an average score of 60.55 to 58.33% in the first cycle with an average value of 69.02 and 94.44% in the second cycle with an average value of 78.47. So there is an increase in the average value of student learning outcomes by 9.45 and there is an increase in the percentage of student graduation by 36.11%.

META-LEARNING FOR WETLAND CLASSIFICATION USING A COMBINATION OF SENTINEL-1 AND SENTINEL-2 IMAGERY

Abstract. In wetland mapping, a lot of uncertainty is related to the task of selecting an appropriate classification approach. Although the individual models are available and well-established in the literature for the classification task, the combination approaches have become popular recently. Hence, selecting an appropriate method is challenging, whether an individual approach or combination. In this work, a meta-learning study is performed to prove that combining the result of individual machine learning models could be better than using the best single model. This study investigates the applicability of the meta-learning method for wetland classification. We will first explore the importance of extracted features for each model. Then, the essential features are fed to the model with the well-tuned hyper-parameters. Finally, the voting classifier as a meta-learning approach is adopted to improve the classification result. The classification map of the study area reached the highest accuracy (Overall Accuracy = 93.9% and Kappa = 0.92) when the proposed ensemble classifier was employed. The results show the superiority of a combination of methods over simple model selection approaches. The results of this study can provide new insights for researchers to find new combination strategies to improve the classification results.

Development and validation of an ensemble artificial intelligence model for comprehensive imaging quality check to classify body parts and contrast enhancement

BackgroundDespite the dramatic increase in the use of medical imaging in various therapeutic fields of clinical trials, the first step of image quality check (image QC), which aims to check whether images are uploaded appropriately according to the predefined rules, is still performed manually by image analysts, which requires a lot of manpower and time.MethodsIn this retrospective study, 1669 computed tomography (CT) images with five specific anatomical locations were collected from Asan Medical Center and Kangdong Sacred Heart Hospital. To generate the ground truth, two radiologists reviewed the anatomical locations and presence of contrast enhancement using the collected data. The individual deep learning model is developed through InceptionResNetv2 and transfer learning, and we propose Image Quality Check-Net (Image QC-Net), an ensemble AI model that utilizes it. To evaluate their clinical effectiveness, the overall accuracy and time spent on image quality check of a conventional model and ImageQC-net were compared.ResultsImageQC-net body part classification showed excellent performance in both internal (precision, 100%; recall, 100% accuracy, 100%) and external verification sets (precision, 99.8%; recovery rate, 99.8%, accuracy, 99.8%). In addition, contrast enhancement classification performance achieved 100% precision, recall, and accuracy in the internal verification set and achieved (precision, 100%; recall, 100%; accuracy 100%) in the external dataset. In the case of clinical effects, the reduction of time by checking the quality of artificial intelligence (AI) support by analysts 1 and 2 (49.7% and 48.3%, respectively) was statistically significant (p < 0.001).ConclusionsComprehensive AI techniques to identify body parts and contrast enhancement on CT images are highly accurate and can significantly reduce the time spent on image quality checks.

A moderated-mediation model of individual learning and commitment: evidence from healthcare industry in India (part II)

PurposeThis study aims to empirically examine the relationship between individual learning, organizational learning and employee commitment in the context of health-care industry.Design/methodology/approachA survey instrument was distributed, and data was collected from 346 employees in the health-care industry in the Southern part of India. Hayes’s PROCESS macros were used to test the mediation, moderated moderated-mediation hypotheses.FindingsThe results reveal organizational learning as a mediator in the relationship between individual learning and continuance commitment, normative commitment and affective commitment moderate the relationship between organizational learning and continuance commitment and three-way interaction between organizational learning, normative commitment and affective commitment to influence continuance commitment of employees.Research limitations/implicationsAs with any survey-based research, the present study suffers from the problems associated with self-report measures: common method bias and social desirability bias. However, the authors attempted to minimize these limitations by following appropriate statistical techniques.Practical implicationsThe study suggests that managers in work organizations need to promote a climate for enhancing learning so that employees remain committed to completing their job and contribute to organizational effectiveness. The results highlight the importance of all three dimensions of organizational commitment: affective, normative and continuance.Originality/valueThis study provides new insights in understanding complex interactions between three dimensions of commitment in contributing to organizational performance. To the best of the authors’ knowledge, the multilayered model showing three-way interactions between three dimensions of organizational commitment is the first of its kind and is a novel idea.

Mixture of relevance vector regression experts for reservoir properties prediction

One of the most indispensable works in the oil and gas exploration and exploitation is reservoir properties forecast. We develop a reservoir properties prediction method based on the mixture of relevance vector regression (RVR) experts. For reservoir properties prediction, an individual machine learning model is probably insufficient with limited training data. The mixture of experts can decompose a complicated problem of reservoir properties prediction into several relatively simple sub-problems by incorporating multiple learning models, where each model processes specific parts of the data. In the proposed method, RVR has been chosen as the expert because it results in sparser regressors and determines hyperparameters automatically. It can also project data into a high-dimensional space through kernel functions to resolve the non-linear problem and avoid the curse of dimensionality. At first, a mixture of RVR experts model is trained on the well data samples. The input features are elastic properties and the output is a reservoir property. Then, the learning model is applied to test dataset and the corresponding reservoir properties are generated. The proposed method is applied to two field data, in which the learning model is obtained by training on the well data and is tested on the well and seismic data, respectively. Compared with an individual RVR expert, some metrics, such as mean absolute derivation (MAD), root mean square error (RMSE), coefficient of determination (R2) and Akaike information criterion (AIC) etc., are effectively improved. The successful implementation of the method demonstrates its feasibility, certifying the superiority of the new method in the aspect of likelihood of fit and accuracy again.