Software Project Risk Management Modeling with Neural Network and Support Vector Machine Approaches

The paper explores the potential of Support Vector Machines (SVM) approach in predicting 28-day compressive strength and slump flow of self-compacting concrete. Total of 80 data collected from the exiting literature were used in present work. To compare the performance of the technique, prediction was also done using a back propagation neural network model. For this data-set, RBF kernel worked well in comparison to polynomial kernel based support vector machines and provide a root mean square error of 4.688 (MPa) (correlation coefficient=0.942) for 28-day compressive strength prediction and a root mean square error of 7.825 cm (correlation coefficient=0.931) for slump flow. Results obtained for RMSE and correlation coefficient suggested a comparable performance by Support Vector Machine approach to neural network approach for both 28-day compressive strength and slump flow prediction.

Rotating Disc Contactor (RDC) columns are one of the extractors that used for liquid-liquid extraction. It has an extensive application in various industries. The performances of these columns indicate that they are more efficient and possess better operational flexibility. However, there is still some improving that researchers can do to enhance the performances. This paper presents Support Vector Machine (SVM) and Neural Network (NN) modeling in prediction of concentration of dispersed phase outlet in RDC column. SVM is an exciting Machine Learning technique that learns by example to sign labels to object and can be used for regression as well as classification purpose, while NN is widely used as an effective approach for handling non-linear data especially in situations where the physical processes are not fully understood. The mean square error (MSE) is calculated to compare the result between the two models. The analysis shows that both SVM and NN modeling can predict the concentration of dispersed phase in RDC column but the SVM approach gives better result than the NN approach. Both modelling systems offer the potential for a more flexible and less error in forecasting. Thus, it can help to save time and reducing cost in conducting experiments.

This paper presents an artificial neural network (ANN) and support vector machine (SVM) approach for locating faults in radial distribution systems. Different from the traditional Fault Section Estimation methods, the proposed approach uses measurements available at the substation, circuit breaker and relay statuses. The data is analyzed using the principal component analysis (PCA) technique and the faults are classified according to the reactances of their path using a combination of support vector classifiers (SVCs) and feedforward neural networks (FFNNs). A practical 52 bus distribution system with loads is considered for studies, and the results presented show that the proposed approach of fault location gives accurate results in terms of the estimated fault location. Practical situations in distribution systems, such as protective devices placed only at the substation, all types of faults, and a wide range of varying short circuit levels, are considered for studies. The results demonstrate the feasibility of applying the proposed method in practical distribution system fault diagnosis.

The recognition of blood disorder through the visual observation is the most challenging job. In the current technological era computer become the most important part of medical science. The haematological disorders of white blood cells (WBC) are really frequent in medical practices. The objective of this research is to design and development of automated identification of Leukemia using microscopic blood smear image database. This proposed scheme uses the most significant steps of image processing like, pre-processing, image segmentation, extraction of features and classification. The Leukemia smear image database is segmented using Otsu image segmentation. The feature extraction extracts the area, perimeter, solidity, orientation, eccentricity, centroid, entropy and energy features. The classification method applied using neural network, Support vector machine and QDA approach. In the neural network the 60% dataset has been passed for the training, 35% towards the testing and remaining 05% is used for the validation. The SVM and QDA classify the dataset for the two groups such as normal and leukaemia. The classification is done on the extracted 08 features of each image. The performance of the neural network is achieving 98.97% with 1.0246 error rate. The support vector machine is shows the 99.35% accuracy with 0.6500 error rate. The QDA classification reported the 99.70% accuracy with 0.300% error rate. From the reported accuracy the QDA and support vector machine proved as dominant as the neural network.

A support vector machine (SVM) approach was presented for predicting the drilling fluid density at high temperature and high pressure (HTHP). It is a universal model for water-based, oil-based, and synthetic drilling fluids. Available experimental data in the literature were used to develop and test this SVM model. Good agreement between SVM predictions and measured drilling fluid density values confirmed that the developed SVM model had good predictive precision and extrapolative features. The SVM model was also compared with the most popular models such as the artificial neural network (ANN) model, empirical correlations, and analytical models. Results showed that the SVM approach outperformed the competing methods for the prediction of drilling fluid density at HTHP.

The problem of conditional simulation of non-Gaussian stochastic processes and fields has gained a significant interest recently because of its applications in many fields, such as wind engineering, ocean engineering, and soil engineering. In this paper, the support vector machines (SVM) approach is developed for the conditional simulation of non-Gaussian stochastic processes and fields. To show the advantages of the presented method, the conditional simulation of non-Gaussian fluctuating wind pressures is carried out by using SVM and artificial neural networks (ANN). SVM considers three kinds of kernel function, such as linear function, Gaussian radial basis function, and exponential radial basis function, whereas ANN employs back-propagation and generalized regression. In machine learning of these artificial intelligences, two ways (interpolation and extrapolation) are employed to train finite non-Gaussian samples. The feasibility and validity of these algorithms are evaluated through the correlation coefficients, root mean square errors, skewness errors, and kurtosis errors between simulated samples and target samples and probability density functions (PDF), power spectral density (PSD) functions, and autocorrelation functions (ACF) of the simulated non-Gaussian fluctuating wind pressures versus their corresponding targets. The results show that the accuracy and effectiveness of SVM with an appropriate kernel function are superior to the back-propagation neural network (BPNN) and generalized regression neural network (GRNN). Furthermore, the advantage of the presented SVM approach is very obvious when the trained non-Gaussian samples are few.

This paper will study insurance data clustering using Support Vector Machine (SVM) approaches. It investigates the optimum condition employing the three most popular kernels of SVM, i.e., linear, polynomial, and radial basis kernel. To explore sum insured datasets, kernel comparisons for Root Mean Square Error (RMSE) and density analysis have been provided. It employs these kernels to classify based on sum insured datasets. The objective of this research is to demonstrate to industrial researchers that data grouping may be accomplished in an organized, error-free, and efficient manner utilizing R programming and the SVM approach. In this study, we check the insurance data for the sum insured with statistical methods in the form of Model Performance Evaluation (MPE), Receiver Operating Characteristics (ROC), Area Under Curve (AUC), partial AUC (pAUC), smoothing, confidence intervals, and thresholds. Then, sum insured data are followed up to classify using SVM kernels. This paper finds new ideas for evaluating insurance data using the SVM approach with multiple kernels. This novel research emphasizes the statistical analysis methods for insurance data and uses the SVM method for more accurate data classification. Finally, it informs that this research is a pure finding, and there has never been any research on this subject. This research was conducted using the sum insured data as a sample from the Office of the Insurance Commission (OIC) in Thailand as an independent insurance institution providing actual data. Doi: 10.28991/ESJ-2022-06-04-014 Full Text: PDF

The present study proposes a novel method to discriminate the sex and species of silkworm pupae using NIR spectroscopy (800–2778 nm). The spectra from 840 silkworm pupae were collected then divided into a calibration set (700) and a test set (140) using the Kennard–Stone (KS) algorithm. The recognition models were built using the radial basis function and neural network (RBF–NN) and support vector machine (SVM) approaches. The species and sex identification results using the RBF–NN and SVM models based on full spectral data achieved a low accuracy of 5% and 33.57%, respectively. To improve the accuracy and decrease the processing time, both principal component analysis (PCA) and linear discriminant analysis (LDA) were used to reduce the data dimensions. The performance of the optimized SVM model (92.14%) was much better than the RBF–NN model (19.29%) based on PCA. Overall, the best discrimination results were obtained using the RBF–NN and SVM models based on LDA, providing an accuracy of 100%. These promising results have shown that the LDA–SVM and LDA–RBF–NN models can accurately recognize the sex and species of silkworm pupae using NIR spectroscopy.

Water quality prediction is an important and widely studied topic since it has significant impact on national or regional ecological and water resources management. Due to water quality indicators series nonlinearity and non-stationary, the accuracy of conventional mostly used methods including regression analysis, ARIMA and neural network has been limited. The use of support vector machine has been shown to be an effective technology to solve classification, prediction problem of nonlinearity and small sample. However, the practicability of SVM is effected due to the difficulty of selecting appropriate SVM parameters. This paper presents a hybrid approach of support vector machine with particle swarm optimization to determine svm free parameters for developing the accuracy of predictions. The approach is applied to predict Heishui river water quality of the Beibei, Chongqing. Traditional ARIAM model and BP neural network are investigated as comparison basis. The experiment results show that the proposed approach can achieve better prediction performance.

The Industry 4.0 revolution is insisting strongly for use of machine learning-based processes and condition monitoring. In this paper, emphasis is given on machine learning-based approach for condition monitoring of shaft misalignment. This work highlights combined approach of artificial neural network and support vector machine for identification and measure of shaft misalignment. The measure of misalignment requires more features to be extracted under variable load conditions. Hence, primary objective is to measure misalignment with a minimum number of extracted features. This is achieved through normalization of vibration signal. An experimental setup is prepared to collect the required vibration signals. The normalized time domain nonstationary signals are given to discrete wavelet transform for features extraction. The extracted features such as detailed coefficient is considered for feature selection viz. Skewness, Kurtosis, Max, Min, Root mean square, and Entropy. The ReliefF algorithm is used to decide best feature on rank basis. The ratio of maximum energy to Shannon entropy is used in wavelet selection. The best feature is used to train machine learning algorithm. The rank-based feature selection has improved classification accuracy of support vector machine. The result obtained with the combined approach are discussed for different misalignment conditions.

In this study, we examined whether age can be predicted on the basis of different anatomical features obtained from a large sample of healthy subjects (n = 3,144). From this sample we obtained different anatomical feature sets: (1) 11 larger brain regions (including cortical volume, thickness, area, subcortical volume, cerebellar volume, etc.), (2) 148 cortical compartmental thickness measures, (3) 148 cortical compartmental area measures, (4) 148 cortical compartmental volume measures, and (5) a combination of the above-mentioned measures. With these anatomical feature sets, we predicted age using 6 statistical techniques (multiple linear regression, ridge regression, neural network, k-nearest neighbourhood, support vector machine, and random forest). We obtained very good age prediction accuracies, with the highest accuracy being R2 = 0.84 (prediction on the basis of a neural network and support vector machine approaches for the entire data set) and the lowest being R2 = 0.40 (prediction on the basis of a k-nearest neighborhood for cortical surface measures). Interestingly, the easy-to-calculate multiple linear regression approach with the 11 large brain compartments resulted in a very good prediction accuracy (R2 = 0.73), whereas the application of the neural network approach for this data set revealed very good age prediction accuracy (R2 = 0.83). Taken together, these results demonstrate that age can be predicted well on the basis of anatomical measures. The neural network approach turned out to be the approach with the best results. In addition, it was evident that good prediction accuracies can be achieved using a small but nevertheless age-representative dataset of brain features. Hum Brain Mapp 38:997-1008, 2017. © 2016 Wiley Periodicals, Inc.

We live in a sphere that has unpredictable and multifaceted landscapes that make the risk arising from several incidences that are omnipresent. Floods and landslides are widespread and recurring hazards occurring at an alarming rate in recent years. The importance of this study is to produce multi-hazard exposure maps for flooding and landslides for the federal State of Salzburg, Austria, using the selected machine learning (ML) approach of support vector machine (SVM) and random forest (RF). Multi-hazard exposure maps were established on thirteen influencing factors for flood and landslides such as elevation, slope, aspect, topographic wetness index (TWI), stream power index (SPI), normalized difference vegetation index (NDVI), geology, lithology, rainfall, land cover, distance to roads, distance to faults, and distance to drainage. We classified the inventory data for flood and landslide into training and validation with the widely used splitting ratio, where 70% of the locations are used for training, and 30% are used for validation. The accuracy assessment of the exposure maps was derived through ROC (receiver operating curve) and R-Index (relative density). RF yielded better results for both flood and landslide exposure with 0.87 for flood and 0.90 for landslides compared to 0.87 for flood and 0.89 for landslides using SVM. However, the multi-hazard exposure map for the State of Salzburg derived through RF and SVM provides the planners and managers to plan better for risk regions affected by both floods and landslides.

Prediction of the acute toxicity of chemical compounds to the fathead minnow by machine learning approaches

Numerical modelling and multi-objective optimization of the novel hydrocyclone for ultra-fine particles classification

The aim of this paper is twofold. First, we present a thorough experimental study to show the superiority of the generalization capability of the support vector machine (SVM) approach in the automatic classification of electrocardiogram (ECG) beats. Second, we propose a novel classification system based on particle swarm optimization (PSO) to improve the generalization performance of the SVM classifier. For this purpose, we have optimized the SVM classifier design by searching for the best value of the parameters that tune its discriminant function, and upstream by looking for the best subset of features that feed the classifier. The experiments were conducted on the basis of ECG data from the Massachusetts Institute of Technology-Beth Israel Hospital (MIT-BIH) arrhythmia database to classify five kinds of abnormal waveforms and normal beats. In particular, they were organized so as to test the sensitivity of the SVM classifier and that of two reference classifiers used for comparison, i.e., the k-nearest neighbor (kNN) classifier and the radial basis function (RBF) neural network classifier, with respect to the curse of dimensionality and the number of available training beats. The obtained results clearly confirm the superiority of the SVM approach as compared to traditional classifiers, and suggest that further substantial improvements in terms of classification accuracy can be achieved by the proposed PSO-SVM classification system. On an average, over three experiments making use of a different total number of training beats (250, 500, and 750, respectively), the PSO-SVM yielded an overall accuracy of 89.72% on 40438 test beats selected from 20 patient records against 85.98%, 83.70%, and 82.34% for the SVM, the kNN, and the RBF classifiers, respectively.