One-dimensional Convolutional Neural Network Research Articles

Authenticity Verification of Alcoholic Beverages in IoT Environment through Fluorescence Hyperspectral Technology and Machine Learning

In the Internet of Things (IoT) environment, verifying the authenticity of beverages is an important issue. Baijiu, a unique Chinese distilled liquor, has gained popularity among people due to its distinctive flavor. In recent years, there has been increasing concern about the adulteration of baijiu. Many studies in this field have attempted to combine chemometrics, electronic nose, Raman spectroscopy, and other detection technologies with machine learning for baijiu detection. However, no research has yet applied the mature detection technology of fluorescence hyperspectral technology to this field. Additionally, existing studies have mainly focused on branded baijiu, without addressing the problem of adulteration in bulk baijiu, which remains a long-standing issue. In this study, a combination of fluorescence hyperspectral and machine learning was used for rapid and nondestructive adulteration detection of bulk liquor in Chengdu. The fluorescence hyperspectral data of three types of samples, namely, original liquor, adulterated liquor with water, and adulterated liquor with industrial alcohol, were obtained, and the raw data were preprocessed using Savitzky–Golay (SG), baseline correction (BL), multivariate scatter correction (MSC), and the combination of Savitzky–Golay and baseline correction (SG-BL). Finally, PCA and MDS are also used to extract spectral data characteristics, respectively. For model selection, three models were chosen: back-propagation artificial neural network (BP-ANN), support vector machine (SVM), and one-dimensional convolutional neural network (1D-CNN). For model evaluation, we choose accuracy, recall, precision, F1-score, memory occupied, and time consumed by the model to evaluate. The experiments show that the combination of fluorescence hyperspectral technology and machine learning can provide a new method, MSC-PCA-SVM, for bulk liquor detection, with a final classification accuracy, recall, precision, and F1-score all reaching 100%, The effectiveness and application potential of this method in the IoT environment have been demonstrated. The results of this study can not only be applied to liquor detection but also have the value of reference for the adulthood and detection of the entire food industry. It is of great significance to standardize the market and protect consumer rights.

A CNN model for early detection of pepper Phytophthora blight using multispectral imaging, integrating spectral and textural information

BackgroundPepper Phytophthora blight is a devastating disease during the growth process of peppers, significantly affecting their yield and quality. Accurate, rapid, and non-destructive early detection of pepper Phytophthora blight is of great importance for pepper production management. This study investigated the possibility of using multispectral imaging combined with machine learning to detect Phytophthora blight in peppers. Peppers were divided into two groups: one group was inoculated with Phytophthora blight, and the other was left untreated as a control. Multispectral images were collected at 0-h samples before inoculation and at 48, 60, 72, and 84 h after inoculation. The supporting software of the multispectral imaging system was used to extract spectral features from 19 wavelengths, and textural features were extracted using a gray-level co-occurrence matrix (GLCM) and a local binary pattern (LBP). The principal component analysis (PCA), successive projection algorithm (SPA), and genetic algorithm (GA) were used for feature selection from the extracted spectral and textural features. Two classification models were established based on effective single spectral features and significant spectral textural fusion features: a partial least squares discriminant analysis (PLS_DA) and one-dimensional convolutional neural network (1D-CNN). A two-dimensional convolutional neural network (2D-CNN) was constructed based on five principal component (PC) coefficients extracted from the spectral data using PCA, weighted, and summed with 19-channel multispectral images to create new PC images.ResultsThe results indicated that the models using PCA for feature selection exhibit relatively stable classification performance. The accuracy of PLS-DA and 1D-CNN based on single spectral features is 82.6% and 83.3%, respectively, at the 48h mark. In contrast, the accuracy of PLS-DA and 1D-CNN based on spectral texture fusion reached 85.9% and 91.3%, respectively, at the same 48h mark. The accuracy of the 2D-CNN based on 5 PC images is 82%.ConclusionsThe research indicates that Phytophthora blight infection can be detected 48 h after inoculation (36 h before visible symptoms). This study provides an effective method for the early detection of Phytophthora blight in peppers.

A detection method of Auricularia auricula mycelium storage period based on near-infrared spectral characteristics and deep learning model

The storage time of Auricularia auricula mycelium was an important factor influencing its growth activity. The rapid detection of storage time had important theoretical significance and practical value for evaluating, cultivating, and scientifically managing Auricularia auricula germplasm resources. Due to the problem of mapping the relationship between the complex near-infrared (NIR) spectral characteristics of Auricularia auricula mycelium and its storage period, it was difficult to characterize using mathematical models accurately. In this paper, a method for detecting the storage period of Auricularia auricula mycelium based on NIR spectral characteristics and deep learning was proposed. First, five different storage periods of Auricularia auricula mycelium were taken as the research object, and the de-trending (DT) algorithm was used to pre-process the spectral data. Then, the Competitive Adaptive Reweighted Sampling (CARS) algorithm was applied to extract the NIR spectral characteristics of the mycelium. The reduction rate of the spectral variables reached 94.64 %. Further, a one-dimensional convolutional neural network (T-CNN) was constructed, and the SGD-Adam algorithm was used to calculate the optimal network parameters. Finally, a method for detecting the storage period of Auricularia auricula mycelium was implemented, achieving a simulation accuracy of 98.33 %. The experimental results revealed that this method outperformed traditional machine learning models such as partial least squares-discriminant analysis (PLS-DA), back propagation (BP), support vector machine (SVM), and k-nearest neighbors (KNN) by an average of 19.56 % in detection accuracy. Additionally, the detection speed was only 0.07 s. This achievement enabled rapid and accurate detection of the storage time of Auricularia auricula mycelium, providing a dependable foundation for identifying the growth activity of Auricularia auricula mycelium. It provided technical support for the scientific management of the edible fungus mycelium, including quality monitoring, evaluation, and screening.

Prediction of wind power ramp events via a self-attention based deep learning approach

With the increasing popularity of wind power generation, the importance of accurate wind power ramp events (WPRE) prediction has been further emphasized. At present, WPRE prediction model based on deep learning shows superior performance, which does not need to fully grasp the operation of wind power generation, but ignores the interdependence mechanism of prediction error along the input characteristics of the neural network. This paper develops a self-attention (SAM) and wavelet transform (WT) based hybrid 1DCNN (one-dimensional convolutional neural network) and LSTM (long short term memory networks) for WPRE forecasting. In the proposed model, WT effectively separates the middle and high frequency noise signals in wind power and SAM can redistribute the neural weights in 1DCNN-LSTM, then 1DCNN-LSTM further extracts the space-time information of effective wind power. Finally, the performance of the forecast model proposed in this paper is tested by using the wind power data from the Belgian ELIA website. The experimental results show that, compared with the other six competition models, the proposed model effectively suppresses the influence of randomness, volatility and uncertainty of wind power on the recognition of ramp events. Furthermore, through quantitative analysis, this paper explains the reason of SAM is superior to traditional AM in terms of their application object and weight control range in the proposed model.

A comparative experimental research on the diagnosis of tooth root cracks in asymmetric spur gear pairs with a one-dimensional convolutional neural network

Gearboxes transfer rotational motion and handle precision functionalities in many fields, including aviation, wind turbines, and industrial services. Their health management is essential to minimize workforce risks, increase the level of safety, and avoid machine breakdowns. From this standpoint, the present experimental research work developed a convolutional neural network-based method for diagnosing different levels of tooth root cracks (25 %-50 %-75 %-100 %) for symmetric (20°/20°) and asymmetric (20°/30°) profiled gear pairs. A series of vibration experiments were performed on a one-stage spur gearbox to achieve this by using a tri-axial accelerometer under variable working loads. The main purpose of this experimental research study is to explore the influence of the tooth profile on spur gears’ vibration responses and whether utilizing an asymmetric tooth profile would positively impact a deep learning algorithm's classification accuracy to add to the enhancements it provides in terms of fatigue life, mesh stiffness, and impact strength. Experimental results revealed that the overall classification accuracy could be increased by 7.712 % by feeding the proposed deep learning model with vibration data measured using test samples with asymmetric teeth.

Mid-infrared methane standoff sensor using a frequency channel attention based convolutional neural network filter

Highly sensitive standoff methane detection is vital for atmospheric science, environmental protection, and production safety. We develop a mid-infrared methane standoff sensor using a cooperative target based on tunable diode laser absorption spectroscopy (TDLAS). To enhance sensor sensitivity, we propose a one-dimensional frequency channel attention-based convolutional neural network (1D-FCACNN) filter, which can effectively denoise the methane absorbance signals and its second harmonic signals. The filter model is adequately trained on a simulated spectrum dataset, which we constructed based on data augmentation. In comparison with reported filtering algorithms, the proposed filter shows the best performance in both measuring modes and evaluation metrics. Real-time measurements show that the measuring accuracy and limit of detection (LOD) of the proposed sensor reach a minimum of 30.40 ppb and 5.04 ppb over a 10-meter optical range, a significant improvement compared to previous reports of methane standoff sensors. The proposed methane standoff sensor proves the feasibility of enhancing the performance of TDLAS gas sensors with the attention mechanism, bringing a new option for high-sensitivity measurements of methane and other atmospheric trace gases.

Deep learning-assisted multispectral imaging for early screening of skin diseases

IntroductionMelanocytic nevi (MN), warts, seborrheic keratoses (SK), and psoriasis are four common types of skin surface lesions that typically require dermatoscopic examination for definitive diagnosis in clinical dermatology settings. This process is labor-intensive and resource-consuming. Traditional methods for diagnosing skin lesions rely heavily on the subjective judgment of dermatologists, leading to issues in diagnostic accuracy and prolonged detection times. ObjectivesThis study aims to introduce a multispectral imaging (MSI)-based method for the early screening and detection of skin surface lesions. By capturing image data at multiple wavelengths, MSI can detect subtle spectral variations in tissues, significantly enhancing the differentiation of various skin conditions. MethodsThe proposed method utilizes a pixel-level mosaic imaging spectrometer to capture multispectral images of lesions, followed by reflectance calibration and standardization. Regions of interest were manually extracted, and the spectral data were subsequently exported for analysis. An improved one-dimensional convolutional neural network is then employed to train and classify the data. ResultsThe new method achieves an accuracy of 96.82 % on the test set, demonstrating its efficacy. ConclusionThis multispectral imaging approach provides a non-contact and non-invasive method for early screening, effectively addressing the subjective identification of lesions by dermatologists and the prolonged detection times associated with conventional methods. It offers enhanced diagnostic accuracy for a variety of skin lesions, suggesting new avenues for dermatological diagnostics.

Forecasting Copper Prices Using Deep Learning: Implications for Energy Sector Economies

Energy is a foundational element of the modern industrial economy. Prices of metals play a crucial role in energy sectors’ revenue evaluations, making them the cornerstone of effective payment management employed by resource policymakers. Copper is one of the most important industrial metals, and plays a vital role in various aspects of today’s economies. Copper is strongly associated with many industries, such as electrical wiring, construction, and equipment manufacturing; therefore, the price of copper has become a significant impact factor on the performance of related energy companies and economies. The accurate prediction of copper prices holds particular significance for market participants and policymakers. This study carried out research to address the gap in copper price forecasting using a one-dimensional convolutional neural network (1D-CNN). The proposed method was implemented and tested using extensive data spanning from November 1991 to May 2023. To assess the performance of the CNN model, standard evaluation metrics, such as the R-value, mean squared error (MSE), root mean squared error (RMSE), and mean absolute error (MAE), were employed. For the prediction of global copper prices, the proposed artificial intelligence algorithm demonstrated high accuracy. Lastly, future global copper prices were predicted up to 2027 by the CNN and compared with forecasts published by the International Monetary Fund and the International Society of Automation. The results show the exceptional performance of the CNN, establishing it as a reliable tool for monitoring copper prices and predicting global copper price volatilities near reality, and as carrying significant implications for policymakers and governments in shaping energy policies and ensuring equitable implementation of energy strategies.

Optimizing starch content prediction in kudzu: Integrating hyperspectral imaging and deep learning with WGAN-GP

Rapid and non-destructive prediction of starch content in kudzu is essential for the food industry. In this work, we present an approach combining hyperspectral imaging (HSI) and deep learning (DL) techniques for predicting kudzu root starch content. Practical constraints such as equipment and experimental conditions limit the quantity of spectral data and labels obtained, which leads to diminish model performance. To address this restriction, we employ Wasserstein generative adversarial networks with gradient penalty (WGAN-GP) to augment spectral and starch content data simultaneously. Through numerous iterations, synthetic data that closely resemble real data is generated, which is validated through comprehensive evaluations using various qualitative and quantitative analysis. Additionally, we establish and compare partial least squares regression (PLSR), support vector regression (SVR) and one-dimensional convolutional neural network (1DCNN) model before and after data augmentation. Experimental results demonstrate that the introduction of synthetic data could improve model performance significantly. Particularly, 1DCNN model exhibits the best performance, achieving correlation coefficients (R2) of 92.97% and 93.43% for starch content in the two types of kudzu roots. Overall, this study not only provides an effective method for rapidly, non-destructively, and accurately determining starch content in kudzu roots, but also addresses the challenge of requiring a large amount of data.

Fault Diagnosis Method for Tractor Transmission System Based on Improved Convolutional Neural Network–Bidirectional Long Short-Term Memory

In response to the problems of limited algorithms and low diagnostic accuracy for fault diagnosis in large tractor transmission systems, as well as the high noise levels in tractor working environments, a defect detection approach for tractor transmission systems is proposed using an enhanced convolutional neural network (CNN) and a bidirectional long short-term memory neural network (BILSTM). This approach uses a one-dimensional convolutional neural network (1DCNN) to create three feature extractors of varying scales, directly extracting feature information from different levels of the raw vibration signals. Simultaneously, in order to enhance the model’s predicted accuracy and learn the data features more effectively, it presents the multi-head attention mechanism (MHA). To overcome the issue of high noise levels in tractor working environments and enhance the model’s robustness, an adaptive soft threshold is introduced. Finally, to recognize and classify faults, the fused feature data are fed into a classifier made up of bidirectional long short-term memory (BILSTM) and fully linked layers. The analytical findings demonstrate that the fault recognition accuracy of the method described in this article is over 98%, and it also has better performance in noisy environments.

SOH estimation of lithium-ion batteries based on multi-feature deep fusion and XGBoost

State of Health (SOH) is a crucial metric for battery management systems, and accurate estimation of battery SOH is essential for the underlying management and maintenance of batteries. Traditional data-driven approaches lack in-depth analysis of health features and fail to exploit the deep-level health information among these features. Therefore, this paper extracts six health features from both measurement and calculation perspectives based on actual measurable charging curve data, and conducts aging analysis of health features from a mechanistic perspective and Spearman correlation analysis from a quantitative perspective. Additionally, a strategy combining deep learning with ensemble learning is proposed. This strategy utilizes one-dimensional convolutional neural networks to deeply integrate multiple original features, extracting fusion features with deep-level health information. Subsequently, XGBoost is employed to learn the fusion features for estimating battery SOH. Finally, the effectiveness and superiority of the proposed method are verified on battery aging datasets from the University of Maryland and our laboratory. Experimental results demonstrate that the RMSE and MAE of the proposed method are both below 1 % and outperform other methods.

Predicting Rail Corrugation Based on Convolutional Neural Networks Using Vehicle's Acceleration Measurements.

This paper presents a deep learning approach for predicting rail corrugation based on on-board rolling-stock vertical acceleration and forward velocity measurements using One-Dimensional Convolutional Neural Networks (CNN-1D). The model's performance is examined in a 1:10 scale railway system at two different forward velocities. During both the training and test stages, the CNN-1D produced results with mean absolute percentage errors of less than 5% for both forward velocities, confirming its ability to reproduce the corrugation profile based on real-time acceleration and forward velocity measurements. Moreover, by using a Gradient-weighted Class Activation Mapping (Grad-CAM) technique, it is shown that the CNN-1D can distinguish various regions, including the transition from damaged to undamaged regions and one-sided or two-sided corrugated regions, while predicting corrugation. In summary, the results of this study reveal the potential of data-driven techniques such as CNN-1D in predicting rails' corrugation using online data from the dynamics of the rolling-stock, which can lead to more reliable and efficient maintenance and repair of railways.

Blood CO Status Classification Using UV-VIS Spectroscopy and PSO-optimized 1D-CNN Model

Rapid and effective blood carbon monoxide (CO) assessment is of great importance, especially in estimating CO-related morbidity and instituting effective preventive measures. The conventional detection methods using CO breath analysis lack sensitivity, while collecting biological fluid samples for CO level measurement is prone to external contamination and expensive for frequent use. This study proposes a one-dimensional convolutional neural network (1D-CNN) consisting of three stacked biconvolutional layers for binary classification of blood CO status using the diffuse reflectance spectroscopy technique. Iterative particle swarm optimization (PSO) has efficiently found the best network parameters to learn important features from the reflectance spectroscopy data. The findings showed good testing accuracy, specificity, and precision of 92.9%, 90%, and 89.7%, respectively, and a high sensitivity of 96.3% in determining abnormal blood CO among smokers using the proposed CNN network. Comparisons with eight existing machine learning and deep learning models revealed the proposed method’s effectiveness in classifying blood CO status while reducing computing time by 8–13 folds. The findings of this work provide new insights that are valuable for researchers in neural network design automation, healthcare management, and skin-related research, specifically for application in nondestructive evaluation and clinical decision-making.

Prediction of Sunspot Number with Hybrid Model Based on 1D-CNN, BiLSTM and Multi-Head Attention Mechanism

Sunspots have a significant impact on human activities. In this study, we aimed to improve solar activity prediction accuracy. To predict the sunspot number based on different aspects, such as extracted features and relationships among data, we developed a hybrid model that includes a one-dimensional convolutional neural network (1D-CNN) for extracting the features of sunspots and bidirectional long short-term memory (BiLSTM) embedded with a multi-head attention mechanism (MHAM) to learn the inner relationships among data and finally predict the sunspot number. We evaluated our model and several existing models according to different evaluation indicators, such as mean absolute error (MAE) and root mean square error (RMSE). Compared with the informer, stacked LSTM, XGBoost-DL, and EMD-LSTM-AM models, the RMSE and MAE of our results were more than 42.5% and 65.1% lower, respectively. The experimental results demonstrate that our model has higher accuracy than other methods.

Application of Attention-Enhanced 1D-CNN Algorithm in Hyperspectral Image and Spectral Fusion Detection of Moisture Content in Orah Mandarin (Citrus reticulata Blanco)

A method fusing spectral and image information with a one-dimensional convolutional neural network(1D-CNN) for the detection of moisture content in Orah mandarin (Citrus reticulata Blanco) was proposed. The 1D-CNN model integrated with three different attention modules (SEAM, ECAM, CBAM) and machine learning models were applied to individual spectrum and fused information by passing the traditional feature extraction stage. Additionally, the dimensionality reduction of hyperspectral images and extraction of one-dimensional color and textural features from the reduced images were performed, thus avoiding the large parameter volumes and efficiency decline inherent in the direct modeling of two-dimensional images. The results indicated that the 1D-CNN model with integrated attention modules exhibited clear advantages over machine learning models in handling multi-source information. The optimal machine learning model was determined to be the random forest (RF) model under the fusion information, with a correlation coefficient (R) of 0.8770 and a root mean square error (RMSE) of 0.0188 on the prediction set. The CBAM-1D-CNN model under the fusion information exhibited the best performance, with an R of 0.9172 and an RMSE of 0.0149 on the prediction set. The 1D-CNN models utilizing fusion information exhibited superior performance compared to single spectrum, and 1D-CNN with the fused information based on SEAM, ECAM, and CBAM respectively improved Rp by 4.54%, 0.18%, and 10.19% compared to the spectrum, with the RMSEP decreased by 11.70%, 14.06%, and 31.02%, respectively. The proposed approach of 1D-CNN integrated attention can obtain excellent regression results by only using one-dimensional data and without feature pre-extracting, reducing the complexity of the models, simplifying the calculation process, and rendering it a promising practical application.

Transfer learning with cross-domain adaptation of vibro-acoustic signals for electric motor mechanical fault detection and diagnosis.

Contact-based vibration measurement techniques involving accelerometers are typically utilized for machine health monitoring and manufacturing end-of-line quality control. Acoustic signals, encompassing sound pressure and particle velocity, carry crucial information about the operational status of underlying equipment and can be utilized for detecting various machine faults. Utilizing one-dimensional Convolution Neural Networks (1D-CNN) based on deep learning for processing raw acoustic time signals has proven to be a valuable approach in detecting machinery faults, offering an effective alternative to using traditional accelerometer signals. However, the success of deep learning methods is heavily dependent on the quantity and availability of data for robust network training. When transitioning to new non-contact type sensor technology, manufacturers may face challenges due to insufficient data for training deep learning models. In such cases, leveraging existing historical accelerometer-based data becomes crucial. Transfer learning emerges as a promising solution, allowing deep learning models trained on a source domain (SD) to be applied to a separate but related target domain (TD). This paper hence introduces a domain adaptation methodology by implementing transfer learning, where the SD 1D-CNN network is trained on accelerometer signals, then the trained model weights are transferred to the TD 1D-CNN network that can process acoustic signals.

Online meta-learning approach for sensor fault diagnosis using limited data

The accurate and timely diagnosis of sensor faults plays a critical role in ensuring the reliability and performance of structural health monitoring (SHM) systems. However, the challenge is detecting, locating, and estimating sensor faults in an online manner using limited training data. To resolve this problem, a novel approach for online sensor fault diagnosis is proposed for SHM. The proposed approach is based on meta-learning, which enables superior model generalization capabilities using limited data. The detection, localization, and estimation of typical sensor faults in an online manner can be achieved efficiently by the proposed approach. First, a one-dimensional convolutional neural network (1D CNN) is designed to detect and locate faulty sensors. The initial model parameters of the 1D CNN are optimized using a model-agnostic meta-learning training strategy. This strategy allows the acquisition of transferable prior knowledge, which can speed up the learning process on new sensor fault detection and localization tasks. The meta-learning strategy also enables efficient and accurate detection and localization of potential faulty sensors with limited data. After detecting and locating the faulty sensors, an online updating algorithm based on a dual Kalman filter is used to estimate the severity of sensor faults and structural states simultaneously. The proposed approach is demonstrated with simulated sensor faults that cover a numerical example and field measurements from the Canton Tower. The results show that the proposed approach is applicable for online sensor fault diagnosis in SHM.

Moving object detection via feature extraction and classification

Abstract Foreground segmentation (FS) plays a fundamental and important role in computer vision, but it remains a challenging task in dynamic backgrounds. The supervised method has achieved good results, but the generalization ability needs to be improved. To address this challenge and improve the performance of FS in dynamic scenarios, a simple yet effective method has been proposed that leverages superpixel features and a one-dimensional convolution neural network (1D-CNN) named SPF-CNN. SPF-CNN involves several steps. First, the coined Iterated Robust CUR (IRCUR) is utilized to obtain candidate foregrounds for an image sequence. Simultaneously, the image sequence is segmented using simple linear iterative clustering. Next, the proposed feature extraction approach is applied to the candidate matrix region corresponding to the superpixel block. Finally, the 1D-CNN is trained using the obtained superpixel features. Experimental results demonstrate the effectiveness of SPF-CNN, which also exhibits strong generalization capabilities. The average F1-score reaches 0.83.

Research on CO concentration detection based on deep learning and TDLAS technology

In order to improve the accuracy of detecting CO and other gases in the industrial field, a CO concentration detection scheme based on tunable diode laser absorption spectroscopy (TDLAS) and the CO absorption spectral line at 1566 nm was proposed. At the outset, after removing the outliers from the original data, the harmonic signal is filtered using a moving average filtering method to enhance the signal-to-noise ratio. Subsequently, a one-dimensional convolutional neural network (1D-CNN) is employed to extract the peak and valley values, as well as the mean values of each segment of harmonic data. In the end, the Long short term memory network (LSTM) model establishes a nonlinear fitting relationship between gas characteristics and concentration, and the attention layer is added to further optimize the network weight parameters. The experimental results indicate that the R2 for the test set is 0.9884, and the RMSE is 156.869. Through Allan deviation analysis, the detection limit reaches 1.9605 ppm at an integration time of 60 s. Compared to the traditional regression model, the accuracy and stability are significantly improved, making it highly suitable for gas detection in the industrial circle.

Prediction of Aerodynamic Forces at the Tip of the Compressor Blades Based on Multi-scale 1DCNN Combined with CBAM

The compressor is a crucial component of aircraft engines, and the blades are the critical factor affecting the performance of the compressor. Based on multi-scale one-dimensional convolution neural network (1DCNN) with Convolutional Block Attention Module (CBAM), a data-driven model is proposed for predicting the aerodynamic characteristics of the blade tips. The model is trained using the Adam with decoupled weight decay (AdamW) optimizer and a staged learning rate scheduling strategy. Due to the distinct aerodynamic pressure distributions on the suction and pressure sides, separate models are constructed in order to reveal the aerodynamic performance of the blade tips accurately. During the model validation, Root Mean Square Error (RMSE) and the coefficient of determination (R2) are taking as evaluation criterions, where high reliability is demonstrated compared to Computational Fluid Dynamics (CFD) results.