RBF Support Vector Machine Research Articles

A Hybrid ROS-SVM Model for Detecting Target Multiple Drug Types

Misleading in determining the decision to use the target drug will be fatal, even to death. This study examines five pharmacological targets designated as types A, B, C, X, and Y. Early detection of misleading drug targeting will reduce the risk of death. This study aims to develop hybrid random oversampling techniques (ROS) and support vector machine (SVM) methods. The use of the oversampling technique in this study aims to balance classes in the dataset; due to the data collection in each class, there is a relatively large gap. This study applies five schemes to see which combination of models produces the highest accuracy. This study also uses five types of SVM kernels, linear, polynomial, gaussian, RBF, and sigmoid, combined with the ROS oversampling technique. Our proposed model combines the ROS oversampling technique with a linear SVM kernel. We evaluated the proposed model and resulted in an accuracy of 97% and compared it with several experiments, including the ROS technique with a sigmoid kernel which only resulted in 50% accuracy. It can be seen from the results obtained that the linear kernel is very adaptive to data types in the form of numeric and nominal compared to other kernels. The method proposed in this study can be applied to other medical problems. Future research can be carried out using a combination of other sampling techniques with deep learning-based methods on this issue.

Diagnosis of Pulmonary Edema and Covid-19 from CT slices using Squirrel Search Algorithm, Support Vector Machine and Back Propagation Neural Network

A Computer Aided Diagnosis (CAD) framework to diagnose Pulmonary Edema (PE) and covid-19 from the chest Computed Tomography (CT) slices were developed and implemented in this work. The lung tissues have been segmented using Otsu’s thresholding method. The Regions of Interest (ROI) considered in this work were edema lesions and covid-19 lesions. For each ROI, the edema lesions and covid-19 lesions were elucidated by an expert radiologist, followed by texture and shape extraction. The extracted features were stored as feature vectors. The feature vectors were split into train and test set in the ratio of 80 : 20. A wrapper based feature selection approach using Squirrel Search Algorithm (SSA) with the Support Vector Machine (SVM) classifier’s accuracy as the fitness function was used to select the optimal features. The selected features were trained using the Back Propagation Neural Network (BPNN) classifier. This framework was tested on a real-time PE and covid-19 dataset. The BPNN classifier’s accuracy with SSA yielded 88.02%, whereas, without SSA it yielded 83.80%. Statistical analysis, namely Wilcoxon’s test, Kendall’s Rank Correlation Coefficient test and Mann Whitney U test were performed, which indicates that the proposed method has a significant impact on the accuracy, sensitivity and specificity of the novel dataset considered. Comparative experimentations of the proposed system with existing benchmark ML classifiers, namely Cat Boost, Ada Boost, XGBoost, RBF SVM, Poly SVM, Sigmoid SVM and Linear SVM classifiers demonstrate that the proposed system outperforms the benchmark classifiers’ results.

Coal Gangue Classification Based on the Feature Extraction of the Volume Visual Perception ExM-SVM

Computer-vision-based separation methods for coal gangue face challenges due to the harsh environmental conditions in the mines, leading to the reduction of separation accuracy. So, rather than purely depending on the image features to distinguish the coal gangue, it is meaningful to utilize fixed coal characteristics like density. This study achieves the classification of coal and gangue based on their mass, volume, and weight. A dataset of volume, weight and 3_side images is collected. By using 3_side images of coal gangue, the visual perception value of the volume is extracted (ExM) to represent the volume of the object. A Support Vector Machine (SVM) classifier receives (ExM) and the weight to perform the coal gangue classification. The proposed system eliminates computer vision problems like light intensity, dust, and heterogeneous coal sources. The proposed model was tested with a collected dataset and achieved high recognition accuracy (KNN 100%, Linear SVM 100%, RBF SVM 100%, Gaussian Process 100%, Decision Tree 98%, Random Forest 100%, MLP 100%, AdaBosst 100%, Naive Bayes 98%, and QDA 99%). A cross-validation test has been done to verify the generalization ability. The results also demonstrate high classification accuracy (KNN 96%, Linear SVM 100%, RBF SVM 96%, Gaussian Process 96%, Decision Tree 99%, Random Forest 99%, MLP 100%, AdaBosst 99%, Naive Bayes 99%, and QDA 99%). The results show the high ability of the proposed technique ExM-SVM in coal gangue classification tasks.

Brain Computer Interface for Emotion Recognition Based on EEG Signal

This paper presents an emotion recognition system based on electroencephalography (EEG) signals. This system helps medical practitioners to analyse the mental health of an individual. Eight healthy volunteers/ subjects had participated in this experiment. A specific feeling is evoked using particular songs and videos that are collected to present before the subjects. Total 6 emotions namely neutral, happy, sad, disgust, fear and motivate are captured and analysed. Data is classified using eighteen statistical features. The sampling rate is 1200Hz. Signals are filtered using pre-processing techniques. Frequency, time and timefrequency domain features are extracted. An array of 10 classifiers is used including Decision Tree, Random Forest, Optimised Random Forest, Logistic regression, Support Vector Machine (SVM) Polynomial, SVM Sigmoid, SVM RBF, K-Nearest Neighbours, Gaussian NB, Gradient Boosting Classifier. Accuracy, recall, precision, and F1 score are employed as performance metrics. The accuracy obtained for SVM classifier was 79.34%.

Prediction of Student Study Period Based on Admission Pathways Using Support Vector Machine Algorithm

In Indonesia, the quality of a university is measured based on the accreditation by BAN-PT (National Accreditation Board for Higher Education). BAN-PT possesses several main standards in measuring the quality of a university, one of which is students and graduates. The accuracy of the student study period is a crucial issue because it is the basis for the effectiveness of a university. Prediction is a process of systematically estimating something most likely to happen in the future based on past and present information to minimize the error (difference between something that happens and the forecast results). One technique used to make predictions is data mining. Universitas Muhammadiyah Yogyakarta (UMY), as one of the best private universities in Indonesia, must maintain the quality of its students. Student admission at UMY is an internal selection carried out through several methods: student achievement and academic ability tests. The Support Vector Machine (SVM) method is part of the prediction method. Analysis of the SVM prediction utilized the historical data from alumni of the Faculty of Law of UMY in the graduation year of 2015-2019. The application of SVM has provided better accuracy, precision, and recall results. The best kernel accuracy level was the SVM RBF kernel with an optimum C value of 10 and a gamma value of 0.4 with an accuracy of 96.00%.

Application of Machine Learning Algorithms for Sustainable Business Management Based on Macro-Economic Data: Supervised Learning Techniques Approach

Macroeconomic indicators are the key to success in the development of any country and are very much important for the overall economy of any country in the world. In the past, researchers used the traditional methods of regression for estimating macroeconomic variables. However, the advent of efficient machine learning (ML) methods has led to the improvement of intelligent mechanisms for solving time series forecasting problems of various economies around the globe. This study focuses on forecasting the data of the inflation rate and the exchange rate of Pakistan from January 1989 to December 2020. In this study, we used different ML algorithms like k-nearest neighbor (KNN), polynomial regression, artificial neural networks (ANNs), and support vector machine (SVM). The data set was split into two sets: the training set consisted of data from January 1989 to December 2018 for the training of machine algorithms, and the remaining data from January 2019 to December 2020 were used as a test set for ML testing. To find the accuracy of the algorithms used in the study, we used root mean square error (RMSE) and mean absolute error (MAE). The experimental results showed that ANNs archives the least RMSE and MAE compared to all the other algorithms used in the study. While using the ML method for analyzing and forecasting inflation rates based on error prediction, the test set showed that the polynomial regression (degree 1) and ANN methods outperformed SVM and KNN. However, on the other hand, forecasting the exchange rate, SVM RBF outperformed KNN, polynomial regression, and ANNs.

InSiNet: a deep convolutional approach to skin cancer detection and segmentation

Cancer is among the common causes of death around the world. Skin cancer is one of the most lethal types of cancer. Early diagnosis and treatment are vital in skin cancer. In addition to traditional methods, method such as deep learning is frequently used to diagnose and classify the disease. Expert experience plays a major role in diagnosing skin cancer. Therefore, for more reliable results in the diagnosis of skin lesions, deep learning algorithms can help in the correct diagnosis. In this study, we propose InSiNet, a deep learning-based convolutional neural network to detect benign and malignant lesions. The performance of the method is tested on International Skin Imaging Collaboration HAM10000 images (ISIC 2018), ISIC 2019, and ISIC 2020, under the same conditions. The computation time and accuracy comparison analysis was performed between the proposed algorithm and other machine learning techniques (GoogleNet, DenseNet-201, ResNet152V2, EfficientNetB0, RBF-support vector machine, logistic regression, and random forest). The results show that the developed InSiNet architecture outperforms the other methods achieving an accuracy of 94.59%, 91.89%, and 90.54% in ISIC 2018, 2019, and 2020 datasets, respectively. Since the deep learning algorithms eliminate the human factor during diagnosis, they can give reliable results in addition to traditional methods.

Differentiation between Epileptic and Psychogenic Nonepileptic Seizures in Electroencephalogram Using Wavelets and Support-Vector Machines

ABSTRACT The number of people suffering from epilepsy in the world exceeds 50 million and around 20% of this group refers to patients who have psychogenic nonepileptic seizures (PNES). Unlike epilepsy, PNES requires psychological treatment. When not correctly diagnosed, these patients can be submitted to a treatment based on antiepileptic drugs besides specific procedures for epilepsy. In this work, we propose the identification of patients with PNES from those with epilepsy using electroencephalogram (EEG) signals. Discrete Wavelet Transform (DWT) decomposition and a Support-Vector Machine (SVM) classifier were employed. Common types of wavelet families and SVM kernels were combined and compared. The results obtained for accuracy, sensitivity, and specificity are equal to 100% for the set of configuration parameters composed of windows encompassing whole seizures, wavelet Coiflet 1, and SVM kernel sigmoid or RBF. The proposed method is efficient and feasible to be applied to new patients admitted in a hospital center, even without having their previous EEG signals already collected. The main advantages of the proposed work are not requiring the use of accelerometer nor electromyographic signals, not being patient specific and outperforming other works results.

Comparison of Random Forest, Support Vector Machines, and Neural Networks for Post-Disaster Forest Species Mapping of the Krkonoše/Karkonosze Transboundary Biosphere Reserve

Mountain forests are exposed to extreme conditions (e.g., strong winds and intense solar radiation) and various types of damage by insects such as bark beetles, which makes them very sensitive to climatic changes. Therefore, continuous monitoring is crucial, and remote-sensing techniques allow the monitoring of transboundary areas where a common policy is needed to protect and monitor the environment. In this study, we used Sentinel-2 and Landsat 8 open data to assess the forest stands classification of the UNESCO Krkonoše/Karkonosze Transboundary Biosphere Reserve, which is undergoing dynamic changes in recovering woodland vegetation due to an ecological disaster that led to damage and death of a large portion of the forests. Currently, in this protected area, dry big trunks and branches coexist with naturally occurring young forests. This heterogeneity generates mixes, which hinders the automation of classification. Thus, we used three machine learning algorithms—Random Forest (RF), Support Vector Machine (SVM), and Artificial Neural Network (ANN)—to classify dominant tree species (birch, beech, larch and spruce). The best results were obtained for the SVM RBF classifier, which offered an average median F1-score that oscillated around 67.2–91.5% depending on the species. The obtained maps, which were based on multispectral satellite images, were also compared with classifications made for the same area on the basis of hyperspectral APEX imagery (288 spectral bands with three-meter resolution), indicating high convergence in the recognition of woody species.

Comparing supervised and semi-supervised Machine Learning Models on Diagnosing Breast Cancer

BackgroundBreast cancer disease is the most common cancer in US women and the second cause of cancer death among women. ObjectivesTo compare and evaluate the performance and accuracy of the key supervised and semi-supervised machine learning algorithms for breast cancer prediction. Materials and methodsWe have used nine machine learning classification algorithms for supervised (SL) and semi-supervised learning (SSL): 1) Logistic regression; 2) Gaussian Naive Bayes; 3) Linear Support vector machine; 4) RBF Support vector machine; 5) Decision Tree; 6) Random Forest; 7) Xgboost; 8) Gradient Boosting; 9) KNN. The Wisconsin Diagnosis Cancer dataset was used to train and test these models. To ensure the robustness of the model, we have applied K-fold cross-validation and optimized hyperparameters. We have evaluated and compared the models using accuracy, precision, recall, F1-score, and ROC curves. ResultsThe results of all models are inspiring using both SL and SSL. The SSL has high accuracy (90%–98%) with just half of the training data. The KNN model for the SL and logistic regression for the SSL achieved the highest accuracy of 98% ConclusionThe accuracies of SSL algorithms are very close to the SL algorithms. The accuracies of all models are in the range of 91–98%. SSL is a promising and competitive approach to solve the problem. Using a small sample of labeled and low computational power, the SSL is fully capable of replacing SL algorithms in diagnosing tumor type.

Gas outburst prediction model using rough set and support vector machine

This paper is concerned with the problem of gas outburst prediction in coal mine working face. To predict the gas outburst accurately, this paper uses the rough set theory (RS) and support vector machine (SVM) to establish the prediction model. Firstly, based on the analysis of influencing factors of gas outburst, 10 factors including coal thickness variations, geological structures and gas change are selected as the influencing factors. By using the attribute reduction algorithm to eliminate redundant information, the gas outburst influencing factors as input to the prediction model are reduced from 10 to 6 in decision table. Secondly, by applying the particle swarm optimization (PSO) algorithm to optimize penalty parameter and kernel function of SVM and improve the generalization performance of model, the nonlinear relationship between main influencing factors and intensity of gas outburst is established. Finally, 60 sets of data of Jiulishan Coal Mine in Henan are used as training and testing samples to verify the proposed prediction model, and the discriminant results is compared with that of RBF model and SVM model. The results show that the prediction accuracy of the proposed model is 93%, which is improved compared with the other two models. The RS-PSOSVM model can reduce data redundancy, avoid the model to fall into the local extremum, and can predict the risk level of gas outburst effectively.

Klasifikasi Status Calon Pendonor Darah Menggunakan Algoritma Support Vector Machine dan Kernel RBF

To donate blood, generally must qualify the requirements, including physically and mentally healthy, aged 17-65 years, minimum body weight of 45kg, Hb levels 12.5g% - 17.0g%, upper tension (systole) 100-170mmHg, under tension (diastole) 70-100mmHg, 36.6-37.5 degrees Celsius body temperature, never had hemophilia, pulse range 50-100 times/minute, and the timescales 3 months after a previous blood donors. The problem that arises is the small number of officers who often have difficulty recording donor data on the form sheets. Thus allowing unwanted error occurred when registering the identity or the results of the initial examination of prospective donors. Based on these problems, the classification of potential donors is needed as a step to determine the status of potential donors, whether the prospective donors are accepted as donors or rejected based on predetermined requirements. According to this research that using primary data which obtained from The Indonesian Red Cross Subang Regency, the data are using a plenty 50 of the data has accepting and 50 of the data has rejected. Afterwards required to doing continued analysis toward a method capability based on age, Hb level, body weight, sistolik dan diastolik, that using SVM (Support Vector Machine) algorithm.and using Sequential method for training phase in SVM and Kernel RBF for calculating the dot product value. The algotirhm accuration reached 90% / excellent categories with the value gamma = 0.5, C = 1, epsilon = 0.001.

Ensemble learning approach based on stacking for unmanned surface vehicle's dynamics

The 3 degrees of freedom (DOF) model, including surge motion, sway motion and yaw motion, with differential thruster is proposed to describe the unmanned surface vehicle (USV)'s dynamics. The experiment is carried out in the Qing-huai river and the data obtained from different zigzag trajectories is filtered by gaussian filtering method. The base learners, Backpropagation (BPNN), Support vector machine (SVM) with RBF kernel and SVM with Linear kernel, are selected to identify the dynamic model of USV. To guarantee that base learners have a strong robustness and generalization ability, the initial weights in BPNN and hyper-parameters in SVM are optimized by cross validation (CV), genetic algorithm (GA), particle swarm optimization (PSO) and cuckoo search algorithm (CS) method. The results show that the CS method has a better optimal capacity in predicting the USV's dynamics. These methods on prediction USV's dynamics have their own advantages and disadvantages. Furthermore, The Ensemble learning (EL) method, called stacking, integrating these base learners is firstly proposed to identify the USV's dynamics. The results demonstrate that the EL method has more accuracy in identifying the dynamic models than base-learners.

Retinal Fundus Image Research for Diagnosis of Diabetic Maculopathy

Fundus images are valuable resources in diagnosis of retinal diseases. This paper proposes a computer-aided method based on various feature extraction techniques and support vector machines (SVM) for detection and classification of diabetic maculopathy (DM). DM, defined as retinopathy within one disc diameter of the centre of the macula, is a major cause of sight loss in diabetes. Here, we bring out a new approach to detect DM based on retinal fundus image features. During the first stage the input image is enhanced and the optic disc is masked to determine the presence of regions of foveal neighborhood. The second stage, deals with various feature extraction technique based on transform, shape and texture features. Extracted features are further categorized as healthy or affected images. Here we go for classification task using the RBF Support Vector Machine (SVM) classification, the techniques have been tested on retinal databases and these are compared with trained phase to categorize Healthy and DM images. This method can detect DM with a level accuracy on par with human retinal specialists

Cervical cancer prognosis using genetic algorithm and adaptive boosting approach

Cervical cancer is fourth main causes of death in women. Cervix is the main origin of cervical cancer. The idea of this research is to explore and propose an efficient and improved prediction method of cervical cancer. Earlier detection and prediction methods/test were very complex, tedious and requires medical and pathological expertise. In this paper, Machine learning approach is used for prediction and detection of cervical cancer. Integrated approach of Genetic Algorithm and Adaptive Boosting is used for performance evaluation for prediction of disease. Genetic algorithm is used as attribute selector to decrease the number of attributes. This not only declines the computational cost but also reduces the number of parameters for diagnosis. Adaptive Boosting is used to improve the performance of classifiers. C 4.5 Decision Tree and Support Vector Machine (SVM) are proposed for prediction of disease. Initially 32 attributes are used for prediction of cervical cancer. The numbers of attributes are reduced with genetic algorithm and further performance enhancement is proposed with adaptive boosting technique. With proposed integrated approach of genetic algorithm and adaptive boosting the improved accuracy lies between 94.17%-94.69%, sensitivity 97.36%-98.90%, specificity 93.37%-94.72% and precision 93%-95.17% for Support Vector Machine Radial Bias Function (SVM RBF), SVM Linear and Decision Tree.

Analysis and classification of malignancy in pancreatic magnetic resonance images using neural network techniques

AbstractComputer‐aided diagnosis (CAD) is a computerized way of detecting tumors in MR images. Magnetic resonance imaging (MRI) has been generally used in the diagnosis and detection of pancreatic tumors. In a medical imaging system, soft tissue contrast and noninvasiveness are clear preferences of MRI. Inaccurate detection of tumor and long time consumption are the disadvantages of MRI. Computerized classifiers can greatly renew the diagnosis activity, in terms of both accuracy and time necessity by normal and abnormal images, automatically. This article presents an intelligent, automatic, accurate, and robust method to classify human pancreas MRI images as normal or abnormal in terms of pancreatic tumor. It represents the response of artificial neural network (ANN) and support vector machine (SVM) techniques for pancreatic tumor classification. For this, we extract features from MR images of pancreas using the GLCM method and select the best features using JAFER algorithm. These features are analyzed by five classification techniques: ANN BP, ANN RBF, SVM Linear, SVM Poly, and SVM RBF. We compare the results with benchmark data set of MR brain images. The analytical outcome presents that the two best features used to classify the MR images using ANN BP technique have 98% classification accuracy.

RETRACTED ARTICLE: Lung nodule malignancy classification in chest computed tomography images using transfer learning and convolutional neural networks

Lung cancer accounts for more than 1.5 million deaths worldwide, and it corresponded to 26% of all deaths due to cancer in 2017. However, lung computer-aided diagnosis systems developed to identify lung cancer at early stages are increasing survival rates. This study explores the performance of deep transfer learning from non-medical images on lung nodule malignancy classification tasks in order to improve such systems. Initially, the 1018 chest computed tomography (CT) examinations and medical annotations from the LIDC/IDRI were processed. Then, several convolutional neural networks (VGG16, VGG19, MobileNet, Xception, InceptionV3, ResNet50, InceptionResNetV2, DenseNet169, DenseNet201, NASNetMobile and NASNetLarge) were built, trained on the ImageNet dataset, converted into feature extractors and applied on the LIDC/IDRI nodule images. Following this, each set of deep features was submitted to 10-fold cross-validations with naive Bayes, multilayer perceptron, support vector machine (SVM), K-nearest neighbors KNN and random forest classifiers. Finally, the evaluation metrics accuracy (ACC), area under the curve (AUC), true positive rate (TPR), precision (PPV) and F1-score of each cross-validation average result were computed and compared. The results showed that the deep feature extractor based on the ResNet50 and the SVM RBF classifier, achieved an AUC metric of 93.1% (the highest value not only among the evaluated combinations, but also among the related works in the literature evaluated), a TPR of 85.38%, an ACC of 88.41%, a PPV of 73.48% and an F1-score of 78.83%. Based on these results, deep transfer learning proves to be a relevant strategy to extract representative features from lung nodule CT images.

Pemodelan Support Vector Machine Data Tidak Seimbang Keberhasilan Studi Mahasiswa Magister IPB

Bogor Agricultural University Postgraduate School (SPs-IPB) can maintain its reputation by applying a more selective admissions system. This research predicts the success of student using Support Vector Machine (SVM) modeling by considering the characteristics and educational background of the students. But there is an imbalance of data class. SVM modeling on unbalanced data produces poor performance with a sensitivity value of 0.00%. Unbalanced data handling using Sythetic Minority Oversampling Technique (SMOTE) succeeded in improving SVM classification performance in classifying unsuccessful students. Based on accuracy, sensitivity, and specificity with the default cut off, the exact type of SVM to model student success is SVM RBF. When using the optimum cut-off value from each type of SVM, the sensitivity value can be improved again. SVM RBF still gives the best result when using cut off 0.6. The final model that will be used to predict the success of the SPs-IPB student is obtained from SVM RBF modeling with cut off 0.6 using the entire data that has been through the SMOTE stage.

Object-based forest gaps classification using airborne LiDAR data

Object-based classification differentiates forest gaps from canopies at large regional scale by using remote sensing data. To study the segmentation and classification processes of object-based forest gaps classification at a regional scale, we sampled a natural secondary forest in northeast China at Maoershan Experimental Forest Farm. Airborne light detection and ranging (LiDAR; 3.7 points/m2) data were collected as the original data source and the canopy height model (CHM) and topographic dataset were extracted from the LiDAR data. The accuracy of object-based forest gaps classification depends on previous segmentation. Thus our first step was to define 10 different scale parameters in CHM image segmentation. After image segmentation, the machine learning classification method was used to classify three kinds of object classes, namely, forest gaps, tree canopies, and others. The common support vector machine (SVM) classifier with the radial basis function kernel (RBF) was first adopted to test the effect of classification features (vegetation height features and some typical topographic features) on forest gap classification. Then the different classifiers (KNN, Bayes, decision tree, and SVM with linear kernel) were further adopted to compare the effect of classifiers on machine learning forest gaps classification. Segmentation accuracy and classification accuracy were evaluated by using Moller’s method and confusion metrics, respectively. The scale parameter had a significant effect on object-based forest gap segmentation and classification. Classification accuracies at different scales revealed that there were two optimal scales (10 and 20) that provided similar accuracy, with the scale of 10 yielding slightly greater accuracy than 20. The accuracy of the classification by using combination of height features and SVM classifier with linear kernel was 91% at the optimal scale parameter of 10, and it was highest comparing with other classification classifiers, such as SVM RBF (90%), Decision Tree (90%), Bayes (90%), or KNN (87%). The classifiers had no significant effect on forest gap classification, but the fewer parameters in the classifier equation and higher speed of operation probably lead to a higher accuracy of final classifications. Our results confirm that object-based classification can extract forest gaps at a large regional scale with appropriate classification features and classifiers using LiDAR data. We note, however, that final satisfaction of forest gap classification depends on the determination of optimal scale (s) of segmentation.

Identification of four class emotion from Indonesian spoken language using acoustic and lexical features

One of the important aspects in human to human communication is to understand emotion of each party. Recently, interactions between human and computer continues to develop, especially affective interaction where emotion recognition is one of its important components. This paper presents our extended works on emotion recognition of Indonesian spoken language to identify four main class of emotions: Happy, Sad, Angry, and Contentment using combination of acoustic/prosodic features and lexical features. We construct emotion speech corpus from Indonesia television talk show where the situations are as close as possible to the natural situation. After constructing the emotion speech corpus, the acoustic/prosodic and lexical features are extracted to train the emotion model. We employ some machine learning algorithms such as Support Vector Machine (SVM), Naive Bayes, and Random Forest to get the best model. The experiment result of testing data shows that the best model has an F-measure score of 0.447 by using only the acoustic/prosodic feature and F-measure score of 0.488 by using both acoustic/prosodic and lexical features to recognize four class emotion using the SVM RBF Kernel.