Accent Classification Using Support Vector Machine and Hidden Markov Model

Near infrared spectroscopy was used in discrimination of intact bovine teeth in terms of animal sex, diet, tooth type and place of origin. Discriminant Analysis (DA) models were developed and tested using a stratified random 70:30 data split to calibration and test sets. Of the discriminant techniques of Soft Independent Modelling of Class Analogy (SIMCA), Partial Least Squares – DA, Artificial Neural Networks (ANN) and Support Vector Machine (SVM), SVM and PLS-DA models performed best in most instances, with pretreatment choice impacting technique success. For test set prediction of animal sex, an accuracy of 95% was achieved using a PLS-DA model, and similar performance achieved using a SVM model. SVM, ANN and SIMCA models predicted three categories of tooth type (deciduous, permanent unerupted and erupted) with similar accuracies (89, 79 and 82%, respectively), however, permanent unerupted teeth were conflated with permanent erupted teeth. Prediction accuracy for ANN, SVM and PLS-DA models were also similar for discrimination of teeth from animals on a grain versus grass diet (PLS-DA 82%). There was considerable variation between models in prediction of geographic origin. Of the seven locations, two were discriminated with true positive rate around 70% using SVM and ANN models. Potential applications of this technology are discussed.

A hybrid support vector machine (SVM) and hidden Markov model (HMM) approach is proposed for designing continuous speech recognition systems. Using novel properties of SVMs and combining them with HMMs one can obtain models that map easily to hardware and lead to more modular and scalable design. The overall architecture of the proposed system is based on the MAP (maximum a posteriori) framework which offers a direct, feedforward recognition model. The SVMs generate smooth estimates of local transition probabilities in the HMM, conditioned on the acoustic inputs. The transition probabilities are then used to estimate the global posterior probabilities of HMM state sequences. A parallel architecture that implements a simple speech recognition model in real-time is presented.

Flooding causes several threats with outcomes which include peril to human and animal life, damage to property, and adversity to agricultural fields. Therefore, flood prediction is of prime importance for reducing loss of life and devastation to property. To model complex nature of hydrologic processes artificial neural network (ANN) tool is effectively being utilized for modelling different nonlinear relationships, and has proved to be an appropriate method for flood prediction. Present study investigated relative accuracy of radial basis function neural network (RBFNN) and support vector machine (SVM) models combined with Firefly Algorithm (FA) in predicting river flood discharge and contrasted with that of regular ANN, RBFNN and SVM models. Monthly river flow data of Silchar and Dholai stations located in Cachar district of Assam, India are utilized for the present study. For assessing model performance, coefficient of determination (R2), mean square error (MSE) and root mean square error (RMSE) were measured. Evaluation of outcomes shows that both RBF-FA (radial basis function - firefly algorithm) and SVM-FA (support vector machine - firefly algorithm) hybrid models give more precise forecasting results than RBFNN, FFBPNN (feed forward back propagation neural network) and SVM models. Yet, it can be observed that SVM-FA model give better forecasting outputs with R2 value 0.9818 than RBF-FA model. Results also reveal that simple SVM model performs marginally better than simple ANN model.

The paper explores the hybrid of SVM and system of Artificial Neural Network as the finest binary classification system for calculating the diabetic nature of people in comparison to Support Vector Machine (SVM). In this research, the sets of all the parameters describing the diabetic nature of a person are taken from the laboratories. This approach is chosen so that a better learning method can be used for various problems. The testing results were found to be in accordance with the accepted results that resemble with the direct diagnosis of a physician. The results of this research shows that this hybrid SVM and Artificial Neural Network (ANN) model is more precise than the SVM model. These results of the hybrid SVM and ANN model suggest that it is very effective for the classification of Diabetic and Non Diabetic nature of a person. This paper highlights the concept of Support Vector Machines and its integration with Artificial Neural Network, the two key characteristics with one being the generalization theory of the SVM model that best describes how to select a hypothesis and functions given by Kernel that introduces the idea of non-linearity without the inclusion of the actual algorithm.

Aggregate stability is a useful soil physical dynamic index of soil resistivity to surface wind and water erosion in all ecosystems, especially, in arid and semi-arid regions. Two machine learning techniques including support vector machines (SVMs) and artificial neural networks (ANNs) were used to develop predictive models for the estimation of geometric mean diameter (GMD) of soil aggregates. An empirical multiple linear regression (MLR) model was also constructed as the benchmark to compare their performances. Furthermore, the influence of feature space dimension reduction using parallel genetic algorithm (PGA) on the prediction accuracy of all investigated techniques was evaluated. The ANN model achieved greater accuracy in GMD prediction as compared to the MLR and SVM models. The obtained ERROR% value in GMD prediction using the ANN model was 6.9%, while it was 15.7 and 10.6% for the MLR and SVM models, respectively. Feature selection using PGA improved the prediction accuracy of all investigated techniques. The coefficient of determination (R2) values between the measured and the predicted GMD values using PGA-based MLR, SVM, and ANN models increased by 20.0, 12.2, and 8.8% in comparison with the proposed MLR, SVM, and ANN models. In conclusion, it appears that the PGA-based ANN model could be considered as an alternative to conventional regression models for the GMD prediction.

Hand gesture recognition for human-computer interaction has become very popular in recent times. The main problem of this technology is how the system can recognize the presence of a gesture in a streaming video. In this paper, we propose a model that can recognize hand motion gesture in avideo stream using Support Vector Machine and Hidden-Markov model. Support Vector Machine has the advantage of generalizing classification. On the other hand, Hidden Markov Model is a statistical model that is capable of modeling Spatial-temporal time series. This system is divided into two main processes. First, this system recognizes hand posture using SMV (static gesture recognition) and generates sequence observation which is used for the second process later. The second process is recognizing dynamic hand gesture with the sequence observation from the static gesture. The implementation shows that static hand gesture recognition achieves average accuracy at 91% using testing dataset. Meanwhile,dynamically isolated hand gesture recognition gets average accuracy at 89% using testing dataset. We also have tested the system with continuous dynamic gesture using video stream. The system can recognize the gesture very well with accuracy of 83%. This achievement shows that the model can be used in human computer-interaction with specific supports such as Vector Machine and Hidden Markov Model parameter.

A machine learning approach for early warning of cyanobacterial bloom outbreaks in a freshwater reservoir

Astrocytoma is the most common neuroepithelial neoplasm, and its grading greatly affects treatment and prognosis. According to relevant factors of astrocytoma, this study developed a support vector machine (SVM) model to predict the astrocytoma grades and compared the SVM prediction with the clinician's diagnostic performance. Patients were recruited from a cohort of astrocytoma patients in our hospital between January 2008 and April 2009. Among all astrocytoma patients, nine had grade I, 25 had grade II, 12 had grade III, and 60 had grade IV astrocytoma. An SVM model was constructed using radial basis kernel. The SVM model was trained with nine magnetic resonance (MR) features and one clinical parameter by fivefold cross-validation and differentiated astrocytomas of grades I-IV at two levels, respectively. The clinician also predicted the grade of astrocytoma. According to the two prediction methods above, the areas under receiving operating characteristics (ROC) curves to discriminate low- and high-grade groups, accuracies of high-grade grouping, overall accuracy, and overall kappa values were compared. For SVM, the overall accuracy was 0.821 and the overall kappa value was 0.679; for clinicians, the overall accuracy was 0.651 and the overall kappa value was 0.466. The diagnostic performance of SVM is significantly better than clinician performance, with the exception of the low-grade group. The SVM model can provide useful information to help clinicians improve diagnostic performance when predicting astrocytoma grade based on MR images.

기업의 부도를 예측하는 것은 회계나 재무 분야에서 중요한 연구주제이다. 지금까지 기업 부도예측을 위해 여러 가지 데이터마이닝 기법들이 적용되었으나 주로 단일 모형을 사용함으로서 복잡한 분류 문제에의 적용에 한계를 갖고 있었다. 본 논문에서는 최근에 각광받고 있는 SVM (support vector machine) 모형들을 결합한 앙상블 SVM 모형 (ensemble SVM model)을 부도예측에 사용하고자 한다. 제안된 앙상블 모형은 v-조각 교차 타당성 (v-fold cross-validation)에 의해 얻어진 여러 가지 모형 중에서 성능이 좋은 상위 k개의 단일 모형으로 구성하고 과반수 투표 방식 (majority voting)을 사용하여 미지의 클래스를 분류한다. 본 논문에서 제안된 앙상블 SVM 모형의 성능을 평가하기 위해 실제 기업의 재무비율 자료와 모의실험자료를 가지고 실험하였고, 실험결과 제안된 앙상블 모형이 여러 가지 평가척도 하에서 단일 SVM 모형들보다 좋은 성능을 보임을 알 수 있었다. Corporate bankruptcy prediction has been an important topic in the accounting and finance field for a long time. Several data mining techniques have been used for bankruptcy prediction. However, there are many limits for application to real classification problem with a single model. This study proposes ensemble SVM (support vector machine) model which assembles different SVM models with each different kernel functions. Our ensemble model is made and evaluated by v-fold cross-validation approach. The k top performing models are recruited into the ensemble. The classification is then carried out using the majority voting opinion of the ensemble. In this paper, we investigate the performance of ensemble SVM classifier in terms of accuracy, error rate, sensitivity, specificity, ROC curve, and AUC to compare with single SVM classifiers based on financial ratios dataset and simulation dataset. The results confirmed the advantages of our method: It is robust while providing good performance.

Global solar irradiation (Hg) is a variable of great importance for different applications. Despite the great advance in the latter, Hg measurements are not readily available in many places. So, several models with different input variables have been developed and used to estimate Hg worldwide. Estimating Hg from models that use air temperature offers an interesting alternative in the absence of measurements. Four groups of models were used in this study: empirical models group (M) with ten models, linear regression models group (RM) with six models, artificial neural network (ANN) models, and support vector machine (SVM) models groups with six models for each one. All the methods take into account different air temperature combinations as input variables (Tmax, Tmin, ΔT, and Tmed). These models were applied to Campo Grande, Brazil, with 10 years data (2005–2017) in order to adjust and evaluate the Hg. The models were evaluated based on four statistical indexes: RMBE, RRMSE, MAPE, and WAI. The models were adjusted and validated using statistical techniques. The results show that the air temperature is an important entry point. Estimates with empirical models perform well (RMBE between 2.84 and 8.68%; RRMSE between 17.78 and 22.93%; MAPE between 17.23 and 23.31%; WAI between 0.85 and 0.90), with errors less than or equal to ANN models (RMBE between 13.94 and 22.67%; RRMSE between 25.37 and 34.95%; MAPE between 29.26 and 49.51%; WAI between 0.51 and 0.81) and SVM (RMBE between 5.80 and 10.15%; RRMSE between 18.87 and 30.34%; MAPE between 19.49 and 42.14%; WAI between 0.55 and 0.884). The regression models had errors with variations similar to the empirical and SVM models. The results show the need to adjust the local coefficients and further studies to further consolidate the models.

Speech emotion recognition as a significant part has become a challenge to artificial emotion. It is particularly difficult to recognize emotion independent of the person concentrating on the speech channel. In the paper, an integrated system of hidden Markov model (HMM) and support vector machine (SVM), which combining advantages on capability to dynamic time warping of HMM and pattern recognition of SVM, has been proposed to implement speaker independent emotion classification. Firstly, all emotions are divided into two groups by SVM. Then, HMMs are used to discriminate emotions from each group. For a more robust estimation, we also combine four HMMs classifiers into a system. The recognition result of the fusion system has been compared with the isolated HMMs using Mandarin database. Experimental results demonstrate that comparing with the method based on only HMMs, the proposed system is more effective and the average recognition rate reaches 76.1% when speaker is independent.

Polyvinyl Chloride (PVC) is a promising sustainable alternative to traditional materials for confining concrete in structural applications due to its corrosion resistance, durability, and cost-effectiveness. The present research is focused on the axial compressive strength of PVC-confined concrete short columns with machine learning models for superior predictive accuracy. A database gathered from FEA simulations was utilized to train the Artificial Neural Network (ANN) and Support Vector Machine (SVM) models, in which the performance of each model was compared with an available empirical formula. The ANN and SVM models could achieve a high predictive accuracy with R² values close to 1.0 and smaller RMSE values than those by traditional empirical approaches. Results have shown that machine-learning models succeed in capturing complex interactions among the parameters, including PVC thickness, column diameter, and concrete compressive strength, providing a versatile and powerful method for strength prediction. These models offer construction engineers a rapid, cost-effective tool for predicting PVC-confined concrete column strengths without extensive physical testing, potentially accelerating the adoption of sustainable materials in structural design. By reducing experimental costs and design time, the approach demonstrates significant practical value for innovative construction technologies.

Classifiers in Automatic Speech Recognition (ASR) aims to improve the generalization ability of the machine learning and improve the recognition accuracy in noisy environments. This paper discusses the classification performance of Hidden Markov Models (HMM) and Support Vector Machines (SVM) applied to a wavelet front end based ASR. The experiments are performed on speaker independent TIMIT database which are trained in a clean environment and later tested in the presence of Additive White Gaussian Noise (AWGN) for various SNR levels using the HTK toolkit and SVM Light software tool. Experiments indicate that for large vocabulary the wavelet front end and the Multiclass SVM classifier with RBF kernel performs better than the conventional HMM classifier.

Promoters are regions in DNA that play important role in the regulation of gene expression. The ability to locate promoters within a section of DNA is known to be a very difficult and important task in DNA analysis. Since experimental techniques to identify promoters are costly and time consuming, in silico methods offer an alternative. In this study, we have developed a tool for identification of s70 promoters in the –10 and –35 regions of sequences from Pseudomonas spp. Promoters were predicted using both Support Vector Machine (SVM) and Hidden Markov Model (HMM) based approaches. SVM performed better when trained using RBF kernel with a cross-validation of 5 and a value of 0.03 for the gamma parameter. The module developed using SVM showed a sensitivity of 78% and a specificity of 80%. The programmes required to process the user input were written using Perl and HTML codes were used to create a user interface. The user interface accepts a query sequence and the processed result will be displayed in a new window. The tool named ‘PROMIT’ (PROMoter Identification Tool), was developed in the Windows platform, has a user friendly interface and works well for sequences from Pseudomonas spp.

In this paper we investigated Artificial Neural Networks (ANN) based Automatic Speech Recognition (ASR) by using limited Arabic vocabulary corpora. These limited Arabic vocabulary subsets are digits and vowels carried by specific carrier words. In addition to this, Hidden Markov Model (HMM) based ASR systems are designed and compared to two ANN based systems, namely Multilayer Perceptron (MLP) and recurrent architectures, by using the same corpora. All systems are isolated word speech recognizers. The ANN based recognition system achieved 99.5% correct digit recognition. On the other hand, the HMM based recognition system achieved 98.1% correct digit recognition. With vowels carrier words, the MLP and recurrent ANN based recognition systems achieved 92.13% and 98.06, respectively, correct vowel recognition; but the HMM based recognition system achieved 91.6% correct vowel recognition.