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Parallel Support Vector Machine (SVM) based on big data has achieved some results in data mining, but due to the complexity of the data itself and a large amount of noisy data, its execution efficiency and classification accuracy in the big data environment are very low. In order to eliminate noise, a noise reduction method based on Noise Cleaning (NC) strategy was proposed, and redundant training samples in big data environments were deleted; Introduce an improved Artificial Fish Swarm Algorithm (IAFSA) to obtain the final Parallel SVM algorithm using mutual information and artificial fish swarm algorithm based on MapReduce (MIAFSA-PSVM) classification model. The results indicate that when compared to CMI-PSVM, the execution time of MIAFSA-PSVM algorithm is higher on the NDC dataset with the largest data size, The SVM parameter optimization algorithm based on MapReduce and cuckoo search (CSSVM-MR) and the particle swarm optimization based parallel support vector machine ensemble algorithm (PSO-PSVM) decreased by 40.1%, 79.3%, and 51.7%, respectively. This indicates that GIESVM-MR and MIAFSA-PSVM have strong adaptability to big data environments and high classification accuracy. [ABSTRACT FROM AUTHOR]

In order to achieve the goal of dynamically adjusting daily passenger flow to effectively control the overall efficiency of the transportation system, this study constructs a real-time monitoring and prediction system for subway passenger flow based on front-end voice processing technology and support vector machine models. The study first conducted a railway passenger flow analysis, and then used a support vector machine model to construct a preliminary prediction system. In order to achieve global optimization, the study also introduced particle swarm optimization algorithm to construct an optimization prediction model based on PSO-SVM. The results show that the proposed PSO-SVM method has undergone 48 iterations of training, and the predicted values closely match the actual passenger flow curve. The maximum RE error is 2%, and the overall prediction error is 98%. The decision coefficient of PSO-SVM is 0.998932. Therefore, this indicates that it has high performance and feasibility in predicting and controlling passenger flow during peak hours of urban rail transit. [ABSTRACT FROM AUTHOR]

The performance-related pay system in colleges and universities is one of the hot spots in the reform of the wage system in colleges and universities in recent years. It is an important measure to improve the quality of education and teaching in colleges and universities and the construction of talent teams. However, there are many problems in the performance-related pay system of colleges and universities, such as unreasonable evaluation indicators, unfair weight distribution, etc., which makes it difficult to accurately reflect the work performance of teachers and staff in the measurement of performance-related pay. Based on this background and trend, this paper conducted an in-depth discussion on the salary system in China's colleges and universities, and used the support vector regression algorithm to study the performance-based salary management of colleges and universities based on linear programming and mathematical statistics. The research showed that with other conditions remaining unchanged, the proportions of primary, intermediate, deputy and senior high school students who were dissatisfied with the system of performance-based pay in colleges and universities were 82%, 57%, 46%, and 9%, respectively. After applying linear programming and mathematical statistics, the proportions of elementary, intermediate, sub-high and ortho-high became 1%, 2%, 5% and 7% respectively. The decline of the first three was particularly obvious, and the teachers with positive high school also dropped by 2%, indicating that linear programming and mathematical statistics were beneficial to the management of performance wages in colleges and universities. [ABSTRACT FROM AUTHOR]

Meiotic recombination has a crucial role in the biological process involving double-strand DNA breaks. Recombination hotspots are regions with a size varying from 1 to 2 kb, which is closely related to the double-strand breaks. With the increasement of both sperm data and population data, it has been demonstrated that computational methods can help us to identify the recombination spots with the advantages of time-saving and cost-saving compared to experimental verification approaches. To obtain better identification performance and investigate the potential role of various DNA sequence-derived features in building computational models, we designed a computational model by extracting features including the position-specific trinucleotide propensity (PSTNP) information, the electron-ion interaction potential (EIIP) values, nucleotide composition (NC) and dinucleotide composition (DNC). Finally, the supporting vector machine (SVM) model was trained by using the 172-dimensional features selected by means of the F -score feature ranking mode, and the accuracy of the predictor reached 98.24% in the jackknife test, which elucidates this model is a potential way for identifying recombination spots. [ABSTRACT FROM AUTHOR]

Finding the head of a traveling wave is challenging because an overhead line-cable hybrid transmission line's wave impedance is not constant. Because of this, it is difficult to measure the hybrid transmission line precisely. This leads to the proposal of a hybrid transmission line fault location algorithm, which combines VMD energy entropy for training support vector machines and traveling wave law graphs. The VMD energy entropy is first used to train SVM to identify the faulty section. The method of traveling waves with a single end is utilized to precisely fix the fault distance within the faulty segment when the type of traveling wave has been identified. The simulation results demonstrate that the method provides precise measurements and is effective for hybrid transmission lines with a range of fault distances and grounding resistances. [ABSTRACT FROM AUTHOR]

Mechanical fault detection has an important influence on production schedule and efficiency. With the development of intelligent technology, more and more intelligent detection technologies are applied to mechanical fault detection. In order to detect mechanical faults more efficiently and accurately, this experiment proposes a production knowledge base model based on genetic algorithm (GA algorithm). The model uses the unique biological genetics principle of genetic algorithm to evolve the interested population, and can conduct spatial search to find the global optimal solution. By comparing the performance of GA algorithm model with other similar detection models, it is found that the model proposed in the experiment has obvious advantages in mechanical fault detection performance. The experimental results show that the maximum accuracy of the GA algorithm is 0.935, 0.074 higher than the support vector machine (SVM) model, 0.118 higher than the linear discriminant analysis (LDA) model, 0.032 higher than the random forest (RF) model, and 0.166 higher than the K nearest neighbor (KNN) model. In addition, the error value of GA algorithm is the lowest among these models, which is 0.028. This proves that the genetic algorithm model has higher diagnostic accuracy and can play an important role in mechanical fault detection. [ABSTRACT FROM AUTHOR]

When using the support vector regression method to predict grain storage temperature, it is challenging to choose the appropriate model parameters. Generally, it is effective to examine the trend of grain storage temperature in different layers after ventilation intervention. To enhance the performance of a support vector machine, it is necessary to choose an appropriate parameter optimization algorithm. The adaptive particle swarm optimization algorithm completes the operation by continuously updating the particles in the spatial domain; after discussing its application principle in detail, the convergence effect is more optimal; and the algorithms are applied to parameter optimization for support vector regression models. After employing the adaptive particle swarm optimization algorithm, the evaluation indicators and experimental prediction results demonstrate that the APSO model has fewer errors, a higher tracking degree, superior generalization performance, and greater prediction accuracy. This is a useful resource for forecasting grain temperature trends. [ABSTRACT FROM AUTHOR]

A modification to the gear tooth surface is crucial for reducing whistling noise generated by gearboxes of electric vehicles. Using the orthogonal test method, this paper studies geometry parameters of gear pair and the effect of load conditions on modification parameters of tooth surface. To achieve this, the paper develops various test models of gear pairs based on MASTA and then corrects the gear profiles according to load distribution state. The influential factors of gear transmission to modification parameters of tooth surface are thereby intuitively analyzed. Finally, a prediction model of the result of gear modification is built, based on test parameters, by method of support regression vector machine, providing a valuable reference to determine the tooth-surface modification parameters of the transmission gear of electric vehicles. [ABSTRACT FROM AUTHOR]

Quantitative investment can manage enterprise assets better to obtain higher revenues. This paper analyzed quantitative investment prediction using machine learning algorithms. First, the support vector machine (SVM) algorithm was introduced, and stock changes were predicted by the SVM algorithm. Then, the feature factors in stock data were extracted by maximum information coefficient (MIC) as the input of the SVM algorithm. Finally, the performance and backtest results of the SVM algorithm was analyzed. It was found that the SVM algorithm had a good performance, and its F1-score was 0.9884, which was better than C4.5 and random forest algorithms. In terms of backtesting, the portfolio built based on the prediction results of the SVM algorithm obtained a higher annualized return rate when the number of stocks was small; when the number of stocks was 10, the portfolio built based on the SVM algorithm had an annualized return rate of 83.67%, a smaller maximum retracement, and a higher Sharpe ratio than the other algorithms, which balanced the risk and return well. The results demonstrate the reliability of the SVM algorithm in predicting quantitative investment, which is beneficial to achieving the optimization of enterprise asset management. [ABSTRACT FROM AUTHOR]

In the context of the explosive growth of online reviews in e-commerce, some merchants began to hire consumers to make fake purchases to increase sales, which caused the difficulty in identifying fake reviews. Therefore, from the perspective of consumers' shopping behavior, a feature recognition method based on comment text, reviewer attribute and reviewer's shopping behavior (referred to as TAB) is proposed to solve the problem of fake reviews caused by fake shopping behavior. Compared with BOW, TF-IDF, N-gram, FSP and other traditional feature extraction methods, experiments were carried out on Naive Bayes, SVM, Logistic Regression, Random Forest, CNN and other classification models. By changing the data dimension, the stability of each false comment recognition model was analyzed. The experimental results show that the TAB feature recognition method has better classification performance on logistic regression and support vector machine models, and the model is relatively stable, and the classification effect is not easily affected by dimension change. [ABSTRACT FROM AUTHOR]

The stability of the economic market is an important factor for the rapid development of the economy, especially for the listed companies, whose financial and economic stability affects the stability of the financial market. It is helpful for the healthy development of enterprises and financial markets to make an accurate early warning of the financial economy of listed enterprises. This paper briefly introduced the support vector machine (SVM) and back-propagation neural network (BPNN) algorithms in the machine learning method. To make up for the defects of the two algorithms, they were combined and applied to the enterprise financial economics early warning. A simulation experiment was carried out on the single SVM algorithm-based, single BPNN algorithm-based, and SVM algorithm and BPNN algorithm combined model with the MATLAB software. The results show that the SVM algorithm and BP algorithm combined model converges faster and has higher precision and recall rate and larger area under the curve (AUC) than the single SVM algorithm-based model and the single BPNN algorithm-based model. [ABSTRACT FROM AUTHOR]

Diabetes is a disease that seriously endangers human health. Early detection and early treatment can reduce the likelihood of complications and mortality. The predictive model can effectively solve the above problems and provide helpful information for the clinic. Based on this, it is proposed to apply the idea of integrated algorithm in DBN algorithm, collect the hospital data by investigating its related factors, clean and process the collected data, and sample and model the processed data multiple times. It is shown that a single DBN classifier is better than support vector machine and logistic regression algorithm. The model established by the integrated deep confidence network has a significant improvement in classification accuracy compared to a single DBN classifier, and solves the unstable classification effect of a single DBN classifier. [ABSTRACT FROM AUTHOR]

OBJECTIVE: With Sina Weibo data as the background, support vector machine (SVM) and k-nearest neighbor (KNN) method are used to predict and analyze the user's micro-blog emotion and related behavior in social network, hoping to obtain rich potential business value. METHODS: First, the API interface of Sina Weibo is utilized to obtain the information of users in Sina Weibo; then, the Excel software is utilized to sort and analyze the extracted data to extract the features of micro- blogs posted by users. Second, SVM and KNN algorithms are utilized to calculate the weighted average and propose a hybrid multi-classifier-based Mixed Classifier Emotion Prediction Model (MCEPM). Finally, through the evaluation criteria, including precision (P), recall rate (R), and harmonic average (F1), the specific experimental results of SVM and KNN weight coefficients are compared with the prediction results of MCEPM. RESULTS: The prediction effect of MCEPM is associated with the weight coefficients of SVM and KNN. If the weight coefficients of SVM and KNN are 0.6 and 0.4, the prediction effect of MCEPM will be optimal. Comprehensive analysis shows that the MCEPM model can balance the prediction results of the positive and negative samples of the two classifiers. CONCLUSION: MCEPM model is superior to other algorithms in micro-blog emotion prediction, which can help enterprises analyze users' product inclination and provide accurate customer service requirements for enterprises. [ABSTRACT FROM AUTHOR]

The metal-metal contact nonlinearity can degrade the performance of communication system, which has become a hot issue in the electrical contact field. The key problem is to establish the relationship between the parameters of rough surface and the real contact area under the preload. The surface roughness parameters are dependent on the metal material and processing technology etc. The real contact area is generally calculated by the finite element method (FEM). However, there exist some difficulties for FEM such as exact model of rough surface and tedious calculation in the engineering. Aimed at this problem, we propose a new method to predict the real contact area of fractal rough surface, and establish the least-square support vector machines (SVM) regression model and optimize the hyper-parameters by the combination of the coupled simulated annealing and grid searching algorithms. The model can predict the real contact area of metal-metal contact for different working conditions, and address the problem to establish the relationship between the parameters of rough surfaces and the real contact area under the preload. The work lays good foundation for analysis of metal-metal contact nonlinearity. [ABSTRACT FROM AUTHOR]

The kernel correlation filtering (KCF) tracking algorithm cannot solve the target tracking mesoscale variation and target loss problem. For this, an improved kernel correlation filtering (IKCF) target tracking algorithm is proposed in this paper. A scale filter is added to the training displacement filter to improve the target scale change problem. In order to solve the problem of target loss, the occlusion processing mechanism is combined, when the target is affected by a small occlusion area, the support vector machine (SVM) is used to train the sample online; when the target is occluded, the re-detection classifier is used for detection. The experimental results show that the tracking accuracy of this method is significantly improved compared with other excellent tracking algorithms. [ABSTRACT FROM AUTHOR]

A fuzzy support vector machine kernel regression decision method of reliability probability distributions is presented aiming at the complexity of reliability probability distributions and disadvantage of the other regression model. The comprehensive decision model of probability distributions is built by the network design and feature extraction of the fuzzy support vector machine algorithm. A example is give for inward stress probability distribution type of a stem structural member by the model, the recognition result is Weibull distribution, the total recognition rate achieves 98.75%. The fuzzy support vector optimized algorithm has strong ability of nonlinear mapping and functional approach, it avoids availably partial minimum and overfitting, and gains high precision by comparing the numerical value of the network output with the numerical value of experiment. [ABSTRACT FROM AUTHOR]

In order to meet the real-time and robustness requirement of driverless cars driving on highway, this paper proposed a lane line identification method based on the Deep Learning. This method first built a lane line image library and then input the pictures of the lane line image for denoising and normalized processing. Secondly, the Lenet – 5 network model was used for classification and recognition, with a recognition rate of 99.4%, and the lane line type was displayed through GUI interface. Finally, this method was compared with the support vector machine and BP neural network, and the results effectively verified that the method can satisfy the requirement of real-time and accuracy of lane line identification. [ABSTRACT FROM AUTHOR]

An dynamic reliability analysis method based on support vector machine and finite element combined with Monte Carlo is presented for the parts in complex external conditions, it is difficult to built a dynamic reliability model including the models of the stress distribution and the joint probability density for the parts operating in an uncertain environment, the support vector machine has a good generalization ability and prediction ability, the integration algorithm based on support vector machine, finite element and Monte Carlo is excellently used to dynamic reliability simulation and calculation for complex and certain system. The method is applied to the reliability analysis of catenary parts in the high-speed electrified railway, the integration algorithm mathematic model of reliability analysis for the catenary suspension system is built, and the system reliability is analyzed by the model. [ABSTRACT FROM AUTHOR]

A vehicle detection method based on the fast extraction of object-oriented candidate window and fused feature of HOG-LBP is proposed for the vehicle detection algorithms based on the single shape feature in the video monitoring of expressway may lead to mistaken inspection and the detection algorithm using the support vector machine (SVM) sliding window is quite time-consuming. Firstly, the vehicle candidate window is quickly extracted based on the binary normalized gradient feature and the background difference, then the histograms of oriented gradients (HOG) feature of the candidate window image and the local binary pattern (LBP) feature are calculated and the feature fusion is carried out, and finally the vehicle detection is taken combing with the SVM classifier. The experimental results show that the fusion of shape and texture features can effectively improve the performance of vehicle detection, and the detection speed of SVM can be raised about 8 times by fast extraction of the candidate window, which can meet the requirements of real time engineering. [ABSTRACT FROM AUTHOR]

Fatigue will affect the normal work and even cause accidents. In order to reduce fatigue's impact on people, we proposes a method for providing a real-time fatigued detection.Specifically, this method comprise the following steps. Firstly of all, we use Active Shape Model (ASM) to detect face, extract the Histogram of Orientation Gradient (HOG) features of eyes and mouth. Secondly, we use Support Vector Machine (SVM) to classify the states and Pose from Orthography and Scaling with Iterations (POSIT) algorithm to estimate the poses of the head. Thirdly, based on the states of face, we obtain a fatigue decision index, wherein a weight of the fatigue decision index is calculated by the Entropy-weighting method. Finally, we apply Bayesian method to evaluate driver's fatigued level based on calculated fatigue decision index. The final mean accuracy of this method is 83.3%. [ABSTRACT FROM AUTHOR]

This paper presented a non-destructive approach for detection of intact and crack eggs using transmission imaging combined with support vector machine classifier. Two hundred brown chicken eggs, including 100 intact and 100 crack eggs were collected as samples. Transmission vision system was developed to capture the sample images. Green color component and edge algorithm based on confidence were then used to transform the color image into edge image for next analysis. Features (mean, variance and third moment) characterizing the differences between crack and intact eggs were extracted through analyzing projection functions as input vectors of the detection model. The detection model in this paper was conducted by support vector machine (SVM). Cracked and Intact eggs could be distinguished by SVM using the statistics parameters. Experimental results showed that the overall identification accuracy in training and test sets were 94% and 93% using 10-fold cross validation approach, respectively. [ABSTRACT FROM AUTHOR]

The behavior prediction of non-linear dynamic system is a challenging problem, especially when the system includes many independent subsystems. The observations from the complex dynamic system are the result of the interaction of multiple dynamic subsystems, which results in a loss of predictability. In this paper, signal separation technique is adopted to separate the observations of complex non-linear dynamic system in order to improve its predictability. And then local support vector regression technique is used to model the separated observations and make prediction. Finally, the prediction results are remixed as the original observation prediction or the behavior prediction of the complex non-linear dynamic system. The experimental results show that the proposed method improves the prediction accuracy substantially, which proves that signal separation can improve the predictability of complex non-linear dynamic system. [ABSTRACT FROM AUTHOR]

Within the field of data mining and machine learning, the K-Nearest Neighbor algorithm is a classic algorithm which simply yet elegantly classifies data based upon its similarity to other data. While it follows that the accuracy increases as more data are provided, handling large sets of data is difficult to process serially. It is therefore ideal to perform these tasks in parallel or distributed mode. In this paper, we proposed a framework for distributed nearest neighbor classification. A custom K-Nearest Neighbor algorithm was developed using Hadoop, an environment for developing and deploying applications in parallel on a cluster. The algorithm was implemented on a cluster then tested for accuracy and time of execution. It was observed that the accuracy depends on the provided k-value and on the data set, which is to be expected for the K-Nearest Neighbor process. The time of execution was found to increase logarithmically as the file size, and thus the amount of data the algorithm must parse, increases exponentially. [ABSTRACT FROM AUTHOR]

The serial genetic algorithms (SGAs) have been widely applied in improving support vector machine (SVM) performance (e.g., classification accuracy), and these hybrid SGA-SVM methods show good capability to detect breast cancer. However, there remain two great challenges: (1) the improvements tend to be at the great cost of time-consuming training; and (2) the SGA-based search may risk the premature convergence to local optima and thereby decrease the quality of the solutions found. The study aimed to investigate the use of parallel genetic algorithms (PGAs) in improving SVM performance, and build an efficient and accurate classifier of detecting breast cancer. A coarse-grained parallel genetic algorithm (CGPGA) was used to select a feature subset and optimize the parameters of SVM simultaneously. This approach (CGPGA-SVM) was then applied to a well characterized breast cancer dataset, consisting of 699 samples (458 benign and 241 malignant samples). In addition, the proposed CGPGA-SVM classier was compared with a range of SVM-based classifiers to understand its performance improvements. Compared with the SGA-SVM classifier, the training time of the CGPGA-SVM classier decreased by 75.77% on a commonly used 4-core CPU; moreover, the classification accuracy and sensitivity of the CGPGA-SVM classifier increased by 0.43% and 1.25%, respectively. [ABSTRACT FROM AUTHOR]

Various methods have been explored to differentiate these electric molten marks including primary molten marks (PMMs, the cause of fires) and secondary molten marks (SMMs, were caused by the fires). However, only a small number of specimens were tested by related methods and none of these methods are reproducible. In order to build an objective and developmental method for analyzing electric molten marks, many molten marks were made under different current and heat treatment temperature and time. And some descriptors were extracted from metallographic images of primary molten marks (PMMs) and secondary molten marks (SMMs) with help of digital image processing method. For purpose of identifying the type of electric molten mark, some judgment models were established with the aid of support vector machine (SVM). There were 380 molten marks from the experiment, 288 of them were PMM and 92 of them were SMM. All of them were used in training model, 5 PMMs and 5 SMMs were used to test these models. Then, these judgment models were selected and optimized by different methods. Thereby, an optimal model was found, the classification accuracy of this model was above 66.2%. Finally, this model was tested by actual fire for the forensic application, the accuracy was 100%. [ABSTRACT FROM AUTHOR]

In order to solve the problem of seismic facies classification, transductive support vector machine is improved. The improvement of transductive support vector machine model is proposed. Now the research of TSVM is only on the elementary stage, the optimization of TSVM is able to be solved theoretically, but it is quite difficult to find the exact solution. Therefore, we try to convert the optimization of TSVM into unconstraint problem, then we could able to construct smooth unconstraint problem with kernel, in order to create an optimized model which is easy to be solved by TSVM. The improvement of transductive support vector machine is applied to the seismic facies classification. This is a new method in the seismic facies classification never used. The experimental data show that the proposed algorithm can improve the seismic facies classification, economic benefit. The main positive feature of improved transductive support vector machine such as the less number of support vectors, there are the smaller training time and testing time and the higher correctly prediction.It has theoretical value and practical significance. [ABSTRACT FROM AUTHOR]

The productivity of steam flooding production wells is determined by numerous factors. In the paper the dominant influence factors of the productivity have been revealed using Gray Relation Analysis (GRA). Based on the Gray Relation Analysis, a model was established for predicting the productivity of steam flooding production wells by Support Vector Machine (SVM), the training algorithm was used to train the model and predict the productivity of test samples. The results show that high precision can be achieved from SVM, the error committed by the model was about ± 10% which is acceptable in field application. The results of SVM were compared with those of Group Method of Data Handling (GMDH) and Backward Propagation (BP) Artificial Neural Network, it indicated that the prediction precision of SVM was obviously higher than that of the two mentioned above. It was demonstrated that the SVM had a good adaptability and practicability for predicting the productivity of steam flooding production wells. [ABSTRACT FROM AUTHOR]

This paper studies pedestrian detection algorithm based on trapezoidal feature of local model, and applied to histogram intersection kernel support vector machine (HIKSVM) for verification. In comparison with traditional asymmetry, rectangle and triangle features, the experimental results from trapezoid feature of local model indicate that it can more effectively describe the pedestrian's postures and promote the accuracy and robustness of pedestrian detection. Experimental results show that the proposed algorithm is robust and accurate against cluttered dynamical background, occlusion and the object deformation, and tested in many pedestrian datasets and achieved good results. [ABSTRACT FROM AUTHOR]

For the support vectors pre-extracting method based on vector projection, the extraction procedure of the bound vector set has a higher computation cost and needs more bound vectors to be selected to include the support vectors. For this problem, we propose a novel bound vectors extracting method by studying the distribution characteristics of vector projection of samples. This method only relies on the projection values of samples and avoids complex computation. Furthermore, a binning method is adopted to determine the borders between different type projection regions more exactly. In this way we can extract fewer bound vectors for training. Experiments show that the proposed method is almost as accurate as standard SVM but is much faster and effective. [ABSTRACT FROM AUTHOR]

The selection of a proper auditor is driven by several factors. Here, we use three data mining classification techniques to predict the auditor choice. The methods used are Decision Trees, Neural Networks and Support Vector Machines. The developed models are compared in term of their performances. The wrapper feature selection technique is used for the Decision Tree model. Two models reveal that the level of debt is a factor that influences the auditor choice decision. This study has implications for auditors, investors, company decision makers and researchers. [ABSTRACT FROM AUTHOR]

Purpose: In this paper we address the demanding diagnostic problem of classifying tumors according to the degree of their malignancy by investigating the efficiency of Support Vector Machines (SVMs) and Probabilistic neural networks (PNN). Material and methods: 129 cases of urinary bladder carcinomas were diagnosed as high or low-risk according to the WHO grading system. Each case was represented by 36 automatically extracted nuclear features. Two different classification designs based on SVMs and PNNs were tested according to their ability in differentiating superficial urinary bladder carcinomas according to the degree of malignancy. Best feature combination for each classification scheme was obtained performing an exhaustive search in feature space and employing the leave-one-out method. Results: Both classification models (SVM and PNN) resulted in a relatively high overall accuracy of 85.3% and 83.7% respectively. Descriptors of nuclear size and chromatin cluster patterns were participated in both best feature vectors that optimized classification performance of the two classifiers. Conclusion: The good performance and consistency of the SVM and PNN models render these techniques viable alternatives in the diagnostic process of assigning urinary bladder tumors grade. [ABSTRACT FROM AUTHOR]