Support Vector Machine Fusion Research Articles

A novel fusion Support Vector Machine integrating weak and sphere models for classification challenges with massive data

The unprecedented growth in data generation has necessitated the adoption of advanced analytical techniques. Support Vector Machine (SVM) is a powerful machine learning tool that has proven invaluable in classifying observations through optimal hyperplane in higher dimensions. Despite their widespread use, SVM models encounter substantial challenges during the learning phase with massive datasets, necessitating strategic modifications. This paper introduces a novel fusion methodology incorporating weak and sphere support vector machine to address classification challenges with massive datasets. Comparative analyses across diverse simulated and benchmark real datasets underscore the efficacy of the proposed methodologies, exhibiting sustained predictive performance. The remarkable efficiency gain is noteworthy, as the learning phase requires only 10% of the computation time compared to conventional SVM approaches.

Word2Vec and SVM Fusion for Advanced Sentiment Analysis on Amazon Reviews

As a matter of fact, sentiment analysis plays a crucial role in different kinds of reviews for online shopping. With this in mind, this study explores the application of Word2Vec and Support Vector Machine (SVM) in sentiment analysis of Amazon reviews. To be specific, initially, this study conducted preprocessing and feature extraction on a large-scale review dataset, revealing deep semantic relationships between words through the Word2Vec model. Subsequently, this study utilized SVM for model training and optimization, achieving efficient and accurate sentiment classification. According to the analysis, preliminary experimental results indicate that this combined method can effectively capture complex patterns and relationships in the text, demonstrating significant advantages in enhancing the accuracy as well as efficiency of sentiment analysis compared to traditional methods. Overall, these results shed light on providing a powerful tool for e-commerce platforms to understand better and analyze consumer opinions and feelings as well as guiding further exploration.

Sentiment analysis for Arabic call center notes using machine learning techniques

<p>Call centers handle thousands of incoming calls daily, encompassing a diverse array of categories including product inquiries, complaints, and more. Within these conversations, customers articulate their opinions and interests in the products and services offered. Effectively categorizing and analyzing these calls holds immense importance for organizations, offering a window into their strengths, weaknesses, and gauging customer satisfaction and needs. This paper introduces an innovative approach to extract customer sentiments through an advanced sentiment analysis technique. Leveraging two distinct yet synergistic algorithms—Support Vector Machine (SVM) and Neural Networks (NNs)—on the Kaggle machine-learning platform, our method discerns the polarity of each note, classifying them as positive, negative, or neutral. To enhance the quality of our analysis, we employed Natural Language Processing (NLP) and a range of preprocessing tools, including tokenization. The dataset comprises three thousand notes from various telecommunication companies, authored during real call center interactions. These notes form the basis of a specialized corpus, notable for being composed in the Jordanian dialect. Rigorous training and testing procedures were conducted using this corpus. The results are notable: our proposed algorithms displayed strong performance metrics. SVM yielded a commendable accuracy rate of 66%, while NNs excelled, boasting an impressive accuracy rate of 99.21%. These achievements are substantiated by comprehensive confusion matrices. In conclusion, our research provides a novel and robust framework for customer sentiment analysis in call centers, underpinned by the fusion of SVM and NNs. This technique promises valuable insights into customer feedback, facilitating informed decision-making for businesses seeking to enhance their services and products.</p>

Drought stress identification of tomato plant using multi-features of hyperspectral imaging and subsample fusion.

Drought stress (DS) is one of the most frequently occurring stresses in tomato plants. Detecting tomato plant DS is vital for optimizing irrigation and improving fruit quality. In this study, a DS identification method using the multi-features of hyperspectral imaging (HSI) and subsample fusion was proposed. First, the HSI images were measured under imaging condition with supplemental blue lights, and the reflectance spectra were extracted from the HSI images of young and mature leaves at different DS levels (well-watered, reduced-watered, and deficient-watered treatment). The effective wavelengths (EWs) were screened by the genetic algorithm. Second, the reference image was determined by ReliefF, and the first four reflectance images of EWs that are weakly correlated with the reference image and mutually irrelevant were obtained using Pearson's correlation analysis. The reflectance image set (RIS) was determined by evaluating the superposition effect of reflectance images on identification. The spectra of EWs and the image features extracted from the RIS by LeNet-5 were adopted to construct DS identification models based on support vector machine (SVM), random forest, and dense convolutional network. Third, the subsample fusion integrating the spectra and image features of young and mature leaves was used to improve the identification further. The results showed that supplemental blue lights can effectively remove the high-frequency noise and obtain high-quality HSI images. The positive effect of the combination of spectra of EWs and image features for DS identification proved that RIS contains feature information pointing to DS. Global optimal classification performance was achieved by SVM and subsample fusion, with a classification accuracy of 95.90% and 95.78% for calibration and prediction sets, respectively. Overall, the proposed method can provide an accurate and reliable analysis for tomato plant DS and is hoped to be applied to other crop stresses.

Cow identification in free-stall barns based on an improved Mask R-CNN and an SVM

• A method for individual identification of cows in unconstrained barn was developed. • The extracted and selected features have strong adaptability to pattern deformation. • A method involving fusion of Mask R-CNN and SVM was used to identify cows. • To reduce the running time and number of model parameters, Mask R-CNN was modified. The identification of individual dairy cows is an important prerequisite for dairy cow behaviour analysis and disease detection. Computer vision-based cow recognition is a noncontact and stress-free approach. In a free environment in a barn, due to changes in camera position and angle, recorded cow patterns are often deformed, making individual cow identification difficult. For cows in an unconstrained barn environment, this paper proposes a method for individual cow identification. First, a top-view image of a cow is obtained, and an improved Mask R-CNN is used to segment this image and extract the shape features of the cow’s back. Then, a Fisher approach is used to select the best feature subset, and a support vector machine (SVM) classifier is applied to identify individual cows. To verify the effectiveness of the target detection algorithm, the proposed method is compared with the traditional Mask R-CNN model, and the precision, recall, F1 score, average run time per image and average precision of the improved Mask R-CNN model are 98.21%, 96.48%, 97.34%, 1.02 s, and 97.39%, respectively. An SVM classifier trained based on the obtained shape features is used for individual cow identification. The proposed method achieves a 98.67% cow identification accuracy based on a dataset containing top-view images of 48 cows. The results demonstrate the effectiveness of the proposed cow identification method and its significant potential for use in precision dairy cow management.

Ensemble Learning Approach for Subject-Independent P300 Speller.

P300 speller is a brain-computer interface (BCI) speller system, used for enabling human with different paralyzing disorders, such as amyotrophic lateral sclerosis (ALS), to communicate with the outer world by processing electroencephalography (EEG) signals. Different people have different latency and amplitude of the P300 event-related potential (ERP) component, which is used as the main feature for detecting the target character. In order to achieve robust results for different subjects using generic training (GT), the ensemble learning classifiers are proposed based on linear discriminant analysis (LDA), support vector machine (SVM), k-nearest neighbors (kNN), and convolutional neural network (CNN). The proposed models are trained using data from healthy subjects and tested on both healthy subjects and ALS patients. The results show that the fusion of LDA, kNN and SVM provides the most accurate results, achieving the accuracy of 99% for healthy subjects and about 85% for ALS patients.

Deep support vector machine for PolSAR image classification

ABSTRACT The main problem posed by Polarimetric Synthetic Aperture Radar (PolSAR) image classification in remote sensing is the ability to develop classifiers that can substantially discern the different classes inherent in natural and man-made targets. Emphasis has shifted from the use of conventional classifiers to modern non-parametric classifiers such as the Artificial Neural Network (ANN) and Support Vector Machine (SVM), and most recently the hybrid Deep Neural Network (DNN) which is a fusion of Deep Learning (DL) and ANN. This research therefore presents the novel application of Deep Support Vector Machine (DSVM), which is a fusion of DL and SVM to PolSAR image classification. Two PolSAR images of Flevoland region in the Netherlands and Winnipeg in Canada are used as test beds for the experiment. The Lee filter is used to filter the images to suppress the speckle noise in the images. The Pauli decomposition is applied to decompose the images into , , polarimetric channels. Then, the Gray Level Co-occurrence Matrix (GLCM) texture feature for , , are extracted based on correlation, contrast, energy, and homogeneity statistics, using GLCM directions 0°, 45°, 90°, and 135° with an offset distance of 60. To enhance the efficiency of the model 8, 16, 32, 64, 128, and 256 quantization levels are explored. The DSVM classifier is implemented with four kernel functions: Exponential Radial Basis Function (ERBF), Gaussian Radial Basis Function (GRBF), neural, and polynomial. The first set of results is a comparison of the DSVM and SVM. The result of Flevoland image for ERBF, GRBF, neural, and polynomial kernels are 99.17 (73.39), 99.32 (74.62), 98.64 (71.28), and 99.34 (77.21), respectively; for the Winnipeg image for ERBF, GRBF, neural, and polynomial kernels are 98.65 (72.68), 98.67 (73.54), 98.27 (70.15), and 99.46 (75.03), respectively. The second set of results is a comparison of DSVM, SVM, DNN, Gaussian Mixture Model (GMM), K Nearest Neighbour (KNN), and K Means (KM) classifiers; the results for Flevoland image for DSVM, SVM, DNN, GMM, KNN, and KM are 99.12, 74.13, 96.29, 75.06, 75.85, and 21.43, respectively, while the results for Winnipeg image for DSVM, SVM, DNN, GMM, KNN, and KM are 98.76, 72.85, 95.64, 73.20, 73.91, and 25.60, respectively. Since the Kappa coefficient is presumed to be a more accurate measure for accuracy estimation, it is used to evaluate the performances of all the models. The computed Kappa coefficients for of DSVM, SVM, DNN, GMM, KNN, and KM for Flevoland are 92.45, 70.71, 88.76, 68.59, 68.62, and 18.89, respectively; while the computed Kappa coefficients for DSVM, SVM, DNN, GMM, KNN, and KM for Winnepeg are 92.45, 70.71, 88.76, 68.59, 68.62, and 18.89, respectively. Based on the metrics used to evaluate the performances of the experiments; the results show that the DSVM outperformed the other classifiers. The high accuracy obtained with the DSVM shows it is a state-of-the-art algorithm for PolSAR image classification and a significant progress in the latest of DL applications.

Deep belief network and support vector machine fusion for distributed denial of service and economical denial of service attack detection in cloud

SummaryCloud computing is a progressive technology that offers computing resources as Internet‐based services, revolutionized information, and communication technologies. From an economic standpoint, this transformation is beneficial because it allows them to streamline technology infrastructure and capital costs. However, economical denial of service (EDoS) potential is a crucial impediment to cloud computing success. Several improved ways to detect EDoS and distributed denial of service (DDoS) attacks in the cloud have been presented; nevertheless, these approaches still result in a considerable reduction in detection accuracy when employed in a cloud setting. Because selecting relevant features and precise classifiers for attack detection is a challenge. We recommend using an EDoS and DDoS attack identification framework in the cloud based on optimized deep learning techniques for higher detection accuracy. The experimental results reveal True Positive Rate (TPR) varies from 98.9% to 99.8% when using deep belief network with support vector machine as a learning mechanism, while True Negative Rate (TNR) from 99.6% to 99.9%. TPR and TNR were found to have average values of 99.32% and 99.67%, respectively. At 1600 requests/s, the maximum accuracy achieved and the overall accuracy of the proposed strategy was 99.78%.

Quantitative structure\u2013activity relationship models for genotoxicity prediction based on combination evaluation strategies for toxicological alternative experiments

Mutagenicity exerts adverse effects on humans. Conventional methods cannot simultaneously predict the toxicity of a large number of compounds. Most mutagenicity prediction models are based on a single experimental type and lack other experimental combination data as support, resulting in limited application scope and predictive ability. In this study, we partitioned data from GENE-TOX, CPDB, and Chemical Carcinogenesis Research Information System according to the weight-of-evidence method for modelling. In our data set, in vivo and in vitro experiments in groups as well as prokaryotic and eukaryotic cell experiments were included in accordance with the ICH guideline. We compared the two experimental combinations mentioned in the weight-of-evidence method and reintegrated the experimental data into three groups. Nine sub-models and three fusion models were established using random forest (RF), support vector machine (SVM), and back propagation (BP) neural network algorithms. When fusing base models under the same algorithm according to the ensemble rules, all models showed excellent predictive performance. The RF, SVM, and BP fusion models reached a prediction accuracy rate of 83.4%, 80.5%, 79.0% respectively. The area under the curve (AUC) reached 0.853, 0.897, 0.865 respectively. Therefore, the established fusion QSAR models can serve as an early warning system for mutagenicity of compounds.

SVM-PCA Based Handwritten Devanagari Digit Character Recognition

Objective: The blended fusion of Support Vector Machine (SVM) and Principal Component Analysis (PCA) have been widely used in recognizing handwritten digit characters of Devanagari script. The feature information from the character is extracted using its skeleton structure which optimally reduce data dimensionality using PCA. There is ample information available on handwritten charac-ter recognition on Indian and Non-Indian scripts but very few article emphasized on recognition of Devanagari scripts. Therefore, this paper presents an efficient handwritten Devanagari character recognition system based on block based feature extraction and PCA-SVM classifier. Methods: We have collected samples of handwritten Devanagari characters from different handwritten experts for classification. Results: For experimental work, total of 100 images having Devanagari digit characters been used for the purpose of training and testing. The proposed system achieves a maximum recognition accuracy of 96.6 % and 96.5% for 5 & 10 fold validations with 70% training and 30% testing data using block based feature and SVM classifier having different kernels. Conclusion: The obtained results achieve maximum accuracy using SVM classifier for digit character recognition. In future deep learning networks will be considered for accuracy enhancement and precision.

Speech emotion recognition based on SVM and KNN classifications fusion

Recognizing the sense of speech is one of the most active research topics in speech processing and in human-computer interaction programs. Despite a wide range of studies in this scope, there is still a long gap among the natural feelings of humans and the perception of the computer. In general, a sensory recognition system from speech can be divided into three main sections: attribute extraction, feature selection, and classification. In this paper, features of fundamental frequency (FEZ) (F0), energy (E), zero-crossing rate (ZCR), fourier parameter (FP), and various combinations of them are extracted from the data vector, Then, the principal component analysis (PCA) algorithm is used to reduce the number of features. To evaluate the system performance. The fusion of each emotional state will be performed later using support vector machine (SVM), K-nearest neighbor (KNN), In terms of comparison, similar experiments have been performed on the emotional speech of the German language, English language, and significant results were obtained by these comparisons.

A Novel Pornographic Visual Content Classifier based on Sensitive Object Detection

With the increasing amount of pornography being uploaded on the Internet today, arises the need to detect and block such pornographic websites, especially in Eastern cultural countries. Studying pornographic images and videos, show that explicit sensitive objects are typically one of the main charac-teristics portraying the unique aspect of pornography content. This paper proposed a classification method on pornographic visual content, which involved detecting sensitive objects using object detection algorithms. Initially, an object detection model is used to identify sensitive objects on visual content. The detection results are then used as high-level features combined with two other high-level features including skin body and human presence information. These high-level features finally are fed into a fusion Support Vector Machine (SVM) model, thus draw the eventual decision. Based on 800 videos from the NDPI-800 dataset and the 50.000 manually collected images, the evaluation results show that our proposed approach achieved 94.06% and 94.88% in Accuracy respectively, which can be compared with the cutting-edge pornographic classification methods. In addition, a pornographic alerting and blocking extension is developed for Google Chrome to prove the proposed architecture’s effectiveness and capability. Working with 200 websites, the extension achieved an outstanding result, which is 99.50% Accuracy in classification.

An Intelligent Diagnosis Method of Brain MRI Tumor Segmentation Using Deep Convolutional Neural Network and SVM Algorithm.

Among the currently proposed brain segmentation methods, brain tumor segmentation methods based on traditional image processing and machine learning are not ideal enough. Therefore, deep learning-based brain segmentation methods are widely used. In the brain tumor segmentation method based on deep learning, the convolutional network model has a good brain segmentation effect. The deep convolutional network model has the problems of a large number of parameters and large loss of information in the encoding and decoding process. This paper proposes a deep convolutional neural network fusion support vector machine algorithm (DCNN-F-SVM). The proposed brain tumor segmentation model is mainly divided into three stages. In the first stage, a deep convolutional neural network is trained to learn the mapping from image space to tumor marker space. In the second stage, the predicted labels obtained from the deep convolutional neural network training are input into the integrated support vector machine classifier together with the test images. In the third stage, a deep convolutional neural network and an integrated support vector machine are connected in series to train a deep classifier. Run each model on the BraTS dataset and the self-made dataset to segment brain tumors. The segmentation results show that the performance of the proposed model is significantly better than the deep convolutional neural network and the integrated SVM classifier.

Emotional State Recognition from Peripheral Physiological Signals Using Fused Nonlinear Features and Team-Collaboration Identification Strategy.

Emotion recognition realizing human inner perception has a very important application prospect in human-computer interaction. In order to improve the accuracy of emotion recognition, a novel method combining fused nonlinear features and team-collaboration identification strategy was proposed for emotion recognition using physiological signals. Four nonlinear features, namely approximate entropy (ApEn), sample entropy (SaEn), fuzzy entropy (FuEn) and wavelet packet entropy (WpEn) are employed to reflect emotional states deeply with each type of physiological signal. Then the features of different physiological signals are fused to represent the emotional states from multiple perspectives. Each classifier has its own advantages and disadvantages. In order to make full use of the advantages of other classifiers and avoid the limitation of single classifier, the team-collaboration model is built and the team-collaboration decision-making mechanism is designed according to the proposed team-collaboration identification strategy which is based on the fusion of support vector machine (SVM), decision tree (DT) and extreme learning machine (ELM). Through analysis, SVM is selected as the main classifier with DT and ELM as auxiliary classifiers. According to the designed decision-making mechanism, the proposed team-collaboration identification strategy can effectively employ different classification methods to make decision based on the characteristics of the samples through SVM classification. For samples which are easy to be identified by SVM, SVM directly determines the identification results, whereas SVM-DT-ELM collaboratively determines the identification results, which can effectively utilize the characteristics of each classifier and improve the classification accuracy. The effectiveness and universality of the proposed method are verified by Augsburg database and database for emotion analysis using physiological (DEAP) signals. The experimental results uniformly indicated that the proposed method combining fused nonlinear features and team-collaboration identification strategy presents better performance than the existing methods.

A LiDAR-Aided Multibaseline PolInSAR Method for Forest Height Estimation: With Emphasis on Dual-Baseline Selection

Polarimetric synthetic aperture radar interferometry (PolInSAR) and light detection and ranging (LiDAR) have their own respective advantages and disadvantages in extracting large-scale forest height. In this letter, we present an advanced approach to obtain forest canopy height by combining these two strategies. More specifically, the novelty of the proposed method focuses on a dual-baseline selection from multibaseline PolInSAR data, which ensures the robust performance of the forest height inversion by effectively improving the estimation of volume-only coherence. The dual-baseline selection can be regarded as a supervised classification problem. We consider support vector machine (SVM) as an appropriate classifier, and a small amount of sparse LiDAR samples within the coverage of the PolInSAR data (less than 1%) are chosen to assist with the training of the dual-baseline combination classification, which can be met by the current spaceborne LiDAR missions. Finally, we validate the proposed approach by airborne P-band synthetic aperture radar (SAR) data acquired by the F-SAR system and LiDAR data acquired by the National Aeronautics and Space Administration (NASA) Land, Vegetation, and Ice Sensor (LVIS) during the 2016 AfriSAR campaign. The estimation accuracy of the proposed method [ $R^{2} = 0.73$ , root-mean-square error (RMSE) = 3.17 m] is 25.24% higher than that of the existing SVM fusion approach devoted to single-baseline selection ( $R^{2} = 0.59$ , RMSE = 4.24 m).

Enhanced Super-Resolution Mapping of Urban Floods Based on the Fusion of Support Vector Machine and General Regression Neural Network

Super-resolution mapping of urban flood (SMUF) is one of the hotspots in remote sensing and urban environment research. In this letter, a new SMUF method based on the fusion of support vector machine and general regression neural network (FSVMGRNN) was proposed to achieve enhanced performance. An SVM-SMUF algorithm was developed and a fusion criterion was formulated. Then, the FSVMGRNN-SMUF algorithm was developed. The results of FSVMGRNN-SMUF were evaluated using Landsat 8 OLI imagery of two representative cities in China. FSVMGRNN-SMUF yielded the most accurate SMUF results among the five SMUF methods according to visual comparisons and quantitative comparisons. The mapping accuracy of FSVMGRNN-SMUF related to the kernel functions was also analyzed and discussed. The results of this letter will help to boost practical applications of median-low resolution remote sensing images in urban flooding mapping, and to strengthen the means for monitoring and assessing urban flooding disasters.

Sparse and heuristic support vector machine for binary classifier and regressor fusion

Single-hidden layer feedforward networks (SLFNs) are always viewed as classical methods for binary classification and regression. There are several variant types of SLFNs, such as support vector machines (SVM), extreme learning machines (ELM), etc. It is an open problem for SLFNs to obtain a powerful feature mapper with a simple network structure. In this paper, we propose a framework called sparse and heuristic SVM (SH-SVM) to fuse different SLFNs from the aspect of feature mapping to obtain powerful feature mapping capability and improve the generalization performance. By fusing different SLFNs, the SH-SVM benefits from the learning capabilities of each model. As an example, the fusion of SVM and ELM is studied in detail. Then with the sparse representation method, a compact SLFN is obtained and the most powerful hidden nodes are selected. Furthermore, an efficient method for solving the sparse representation problem in SH-SVM is proposed. Experiments on 25 data sets with eight methods show that SH-SVM has satisfactory results with a compact network structure.

Hydraulic system fault diagnosis method based on a multi‐feature fusion support vector machine

Real-time fault prevention and diagnosis in hydraulic systems is one of the challenges in engineering applications. In order to solve the problems of feature extraction and pattern recognition of different types of fault signals such as leakage, blockage, and cavitation in the hydraulic system, this paper presents a fault diagnosis method based on a multi-feature fusion support vector machine. This method first extracts the typical features of the fault signal from the time domain, frequency domain, and time−frequency domain. Then use the information entropy to calculate the weight of each feature, select the feature with the larger weight to participate in the stock index diagnosis. Finally, a multi-classifier based on the support vector machine is used to realise the fault diagnosis of the hydraulic system. The experimental results show that this method can achieve high accuracy in the hydraulic system fault diagnosis when the Gaussian radial basis function is selected. Also, compared with the commonly used improved back-projection neural network fault diagnosis method, this method has more excellent diagnostic performance.

Automatic measurement of the traffic sign with digital segmentation and recognition

Traffic sign detection assists in driving by acquiring the temporal and spatial information of the potential signs for road awareness and safety. The purpose of conducting research on this topic is introduced to a novel and less complex algorithm that works for traffic signs identification, accurately. Initially, the authors estimate the global threshold value using the correlational property of the given image. In order to get red and blue traffic signs, a segmentation algorithm is developed using estimated threshold and morphological operations followed by an enhancement procedure, the net outcome of which is provided the greater number of potential signs. Moreover, remaining regions are filtered in terms of statistical measures using the non-potential regions. Furthermore, detection is performed on the basis of histogram of oriented gradient features by employing the support vector machine (SVM)–K -nearest neighbour (KNN) classifier. The denoising approach with the weighted fusion of KNN and SVM is used in order to improve the performance of the proposed algorithm by reducing the false positive. A recognition phase is performed on the GTSRB data set in order to formulate the feature vector. The proposed method performed the significant recognition with an accuracy rate of 99.32%. It is quite comparable to the existing state-of-the-art techniques.

A Hybrid Fault Diagnosis Approach for Rotating Machinery with the Fusion of Entropy-Based Feature Extraction and SVM Optimized by a Chaos Quantum Sine Cosine Algorithm.

As crucial equipment during industrial manufacture, the health status of rotating machinery affects the production efficiency and device safety. Hence, it is of great significance to diagnose rotating machinery faults, which can contribute to guarantee the running stability and plan for maintenance, thus promoting production efficiency and economic benefits. For this purpose, a hybrid fault diagnosis model with entropy-based feature extraction and SVM optimized by a chaos quantum sine cosine algorithm (CQSCA) is developed in this research. Firstly, the state-of-the-art variational mode decomposition (VMD) is utilized to decompose the vibration signals into sets of components, during which process the preset parameter K is confirmed with the central frequency observation method. Subsequently, the permutation entropy values of all components are computed to constitute the feature vectors corresponding to different kind of signals. Later, the newly developed sine cosine algorithm (SCA) is employed and improved with chaotic initialization by a Duffing system and quantum technique to optimize the support vector machine (SVM) model, with which the fault pattern is recognized. Additionally, the availability of the optimized SVM with CQSCA was revealed in pattern recognition experiments. Finally, the proposed hybrid fault diagnosis approach was employed for engineering applications as well as contrastive analysis. The comparative results show that the proposed method achieved the best training accuracy 99.5% and best testing accuracy 97.89%. Furthermore, it can be concluded from the boxplots of different diagnosis methods that the stability and precision of the proposed method is superior to those of others.