Lane changing prediction at highway lane drops using support vector machine and artificial neural network classifiers

Surface electromyogram (sEMG) signals are widely used to control the myoelectric prosthetic arm for amputees. In this study, the authors investigated the usefulness of discrete wavelet transform (DWT) features from multiple levels of approximation and detail coefficients obtained from sEMG signals. DWT is used for de-noising as well as feature extraction in this study and further tested using Support Vector Machine (SVM) classifier and Artificial Neural Network (ANN) Classifier. The performance of SVM classifier is compared with ANN classifier. The classification accuracy of SVM classifiers was found better as compared to ANN in term of speed and robustness. In the first section of paper the authors presented the introduction related to the study. The experimental set-up for recording surface EMG signal is presented in section 2. In section 3 the experimental results are depicted and in section 4 the results are concluded.

This article presents a sufficient comparison of two types of advanced non-parametric classifiers implemented in remote sensing for land cover classification. A SPOT-5 HRG image of Yanqing County, Beijing, China, was used, in which agriculture and forest dominate land use. Artificial neural networks (ANNs), including the adaptive backpropagation (ABP) algorithm, Levenberg–Marquardt (LM) algorithm, Quasi-Newton (QN) algorithm and radial basis function (RBF) were carefully tested. The LM–ANN and RBF–ANN, which outperform the other two, were selected to make a detailed comparison with support vector machines (SVMs). The experiments show that those well-trained ANNs and SVMs have no significant difference in classification accuracy, but the SVM usually performs slightly better. Analysis of the effect of the training set size highlights that the SVM classifier has great tolerance on a small training set and avoids the problem of insufficient training of ANN classifiers. The testing also illustrates that the ANNs and SVMs can vary greatly with regard to training time. The LM–ANN can converge very quickly but not in a stable manner. By contrast, the training of RBF–ANN and SVM classifiers is fast and can be repeatable.

“Artificial neural networks (ANNs)” and support vector networks have been widely used for the purpose of analysis of spatial remote sensory images. Attributes selection plays a major role in reduction of computational time for model processing and improvement in performance accuracy. In this paper, we present rough set-based artificial neural network (RS-ANN) and support vector machine classification (RS-SVM) methods for the classification of hyperspectral images. In both of these techniques, rough set (RS) is used as a feature selection mathematical tool. Further, this diminished dimensionality data is carried to artificial neural network (ANN) and support vector machine (SVM) classifiers correspondingly. The proposed procedure uses spatial information and implements the strategy for efficient processing of the image, and with utilization of the “graphics processing units (GPUs)” successfully implemented with improved efficiency. The experimental analysis achieved efficient outcome that the proposed procedures perform with improved accuracy, improved efficiency with a good amount of reduction in the execution and computing time, and enhancing the accuracy in the categorizing of spatial images in comparison with other conventional strategies.

Traffic Sign Recognition (TSR) system is a significant component of Intelligent Transport System (ITS) as traffic signs assist the drivers to drive more safely and efficiently. This paper represents a new approach for TSR system where detection of traffic sign is carried out using fuzzy rules based color segmentation method and recognition is accomplished using Speeded Up Robust Features(SURF) descriptor, trained by artificial neural network (ANN) classifier. In the detection step, the region of interest (sign area) is segmented using a set of fuzzy rules depending on the hue and saturation values of each pixel in the HSV color space, post processed to filter unwanted region. Finally the recognition of the traffic sign is implemented using ANN classifier upon the training of SURF features descriptor. The proposed system simulated on offline road scene images captured under different illumination conditions. The detection algorithm shows a high robustness and the recognition rate is quite satisfactory. The performance of the ANN model is illustrated in terms of cross entropy, confusion matrix and receiver operating characteristic (ROC) curves. Also, performances of some classifier such as Support Vector Machine (SVM), Decision Trees, Ensembles Learners (Adaboost) and K-Nearest Neighbor (KNN) classifier are assessed with ANN approach. The simulation results illustrate that recognition using ANN model is higher than classifiers stated above.

Comparative studies between response surface methodology (RSM) and artificial neural network (ANN) methods to find the effects of electrospinning parameters on the porosity of nanofiber mats is described. The four important electrospinning parameters studied included solution concentration (wt.%), applied voltage (kV), spinning distance (cm) and volume flow rate (mL/h). It was found that the applied voltage and solution concentration are the two critical parameters affecting the porosity of the nanofiber mats. The two approaches were compared for their modeling and optimization capabilities with the modeling capability of RSM showing superiority over ANN, having comparatively lower values of errors. The mean relative error for the RSM and ANN models were 1.97% and 2.62% and the root mean square errors (RMSE) were 1.50 and 1.95, respectively. The superiority of the RSM-based approach is due to its high prediction accuracy and the ability to compute the combined effects of the electrospinning factors on the porosity of the nanofiber mats.

Parkinson's disease (PD) is a neurological disorder which is progressive in nature. Although there is no cure to this disease, symptomatic treatments are available. These treatments can slow the progressive development of the symptoms. Medications can treat some of the symptoms of the PD up to a great extent that in turn may help the patients to live a normal life. Besides these medications, the patients can also be provided with various therapies based on the types of their symptoms. But for providing any treatment, detection of its symptoms at an early stage is very important. This can reduce its future complexities considerably. Early diagnosis along with proper medications may treat the symptoms of PD significantly. This motivates to propose a new and effective methodology for detection and analysis of PD. In this work, an approach has been proposed for identification of PD patients by using Electroencephalogram (EEG) signals. Here, the EEG signals of normal persons and PD patients are processed in three stages. First, the raw EEG signals are pre-processed for removal of noises and artefacts present. Out of various techniques, Wavelet transform is used for this purpose. In the MATLAB environment, de-noising can be executed by using the in-built functions. Performances of the de-noising techniques are examined with the performance parameters namely Root Mean Square Error (RMSE) as well as Signal to Noise Ratio (SNR). In the second stage, statistical features are extracted from the pre-processed EEG signals. In this work, five statistical features are considered for performing the classification. In the final stage, the extracted features are classified using Artificial Neural Network (ANN) and Support Vector Machine (SVM) techniques. ANN is an efficient classifier that predicts the human brain's working manners. SVM on the other hand has been proven as one of the most prevailing classification algorithms that gives highly accurate and robust results. This is a novel approach of analyzing the performances of the classification techniques by evaluating the best performing feature. In both the classifiers accuracy, precision, sensitivity and specificity are calculated from the confusion matrix evaluated from the values of the statistical features. In ANN, results using six different training algorithms at different hidden layers are calculated and compared. This proves the training algorithm Levenberg-Marquardt back-propagation with hidden layer 20 as the best combination for performing the classification. From the results it is seen that both ANN and SVM classifiers provide significant classification accuracies of 94.7% and 96.5% respectively. Amongst the five considered features, Mean performs the best in terms of classification accuracy.

It is still difficult to distinguish Parkinson's disease from parkinsonism brought on by other disorders at an early stage. To process images from dopamine transporter single-photon emission computed tomography, we used an artificial neural network (ANN) (DAT-SPECT). Training and test sets of abnormal DAT-SPECT images of individuals with Parkinson's disease and parkinsonism brought on by other disorders were created. To distinguish Parkinson's disease from parkinsonism brought on by other disorders, the striatal regions of the images were segmented using an active contour model and used as the data for transfer learning on a pre-trained ANN. For comparison, a support vector machine was trained using semi-quantitative measurement parameters such as the specific binding ratio and asymmetry index. The ANN classifier's predictive accuracy (86%) was higher than the support vector machine classifier's (68%). The ANN classifier had a sensitivity and specificity of 81.8% and 88.6%, respectively, in predicting Parkinson's disease. The ANN classifier performed better than traditional biomarkers at distinguishing between Parkinson's disease and parkinsonism brought on by other disorders. At an early stage of the parkinsonian disorders, ANN can be a promising biomarker, particularly when it concentrates on the putamen.

Heat rate of a combined cycle power plant (CCPP) is a parameter that is typically used to assess how efficient a power plant is. In this paper, the CCPP heat rate was predicted using an artificial neural network (ANN) method to support maintenance people in monitoring the efficiency of the CCPP. The ANN method used fuel gas heat input (P1), CO2 percentage (P2), and power output (P3) as input parameters. Approximately 4322 actual operation data are generated from the digital control system (DCS) in a year. These data were used for ANN training and prediction. Seven parameter variations were developed to find the best parameter variation to predict heat rate. The model with one input parameter predicted heat rate with regression R2 values of 0.925, 0.005, and 0.995 for P1, P2, and P3. Combining two parameters as inputs increased accuracy with regression R2 values of 0.970, 0.994, and 0.984 for P1 + P2, P1 + P3, and P2 + P3, respectively. The ANN model that utilized three parameters as input data had the best prediction heat rate data with a regression R2 value of 0.995.

Detecting the different blends of diesel and biodiesel fuels using electronic nose machine coupled ANN and RSM methods

Regression prediction of hydrogen enriched compressed natural gas (HCNG) engine performance based on improved particle swarm optimization back propagation neural network method (IMPSO-BPNN)

Human action recognition from 2D videos is a demanding area due to its broad applications. Many methods have been proposed by the researchers for recognizing human actions. The improved accuracy in identifying human actions is desirable. This paper presents an improved method of human action recognition using support vector machine (SVM) classifier. This paper proposes a novel feature descriptor constructed by fusing the various investigated features. The handcrafted features such as scale invariant feature transform (SIFT) features, speed up robust features (SURF), histogram of oriented gradient (HOG) features and local binary pattern (LBP) features are obtained on online 2D action videos. The proposed method is tested on different action datasets having both static and dynamically varying backgrounds. The proposed method achieves shows best recognition rates on both static and dynamically varying backgrounds. The datasets considered for the experimentation are KTH, Weizmann, UCF101, UCF sports actions, MSR action and HMDB51.The performance of the proposed feature fusion model with SVM classifier is compared with the individual features with SVM. The fusion method showed best results. The efficiency of the classifier is also tested by comparing with the other state of the art classifiers such as k-nearest neighbors (KNN), artificial neural network (ANN) and Adaboost classifier. The method achieved an average of 94.41% recognition rate.

Interstitial lung disease (ILD) is complicated group of pulmonary disorders. High Resolution Computed Tomography (HRCT) considered to be best imaging technique for analysis of different pulmonary disorders. HRCT findings can be categorised in several patterns viz. Consolidation, Emphysema, Ground Glass Opacity, Nodular, Normal etc. based on their texture like appearance. Clinician often find it difficult to diagnosis these pattern because of their complex nature. In such scenario computer-aided diagnosis system could help clinician to identify patterns. Several approaches had been proposed for classification of ILD patterns. This includes computation of textural feature and training /testing of classifier such as artificial neural network (ANN), support vector machine (SVM) etc. In this paper, wavelet features are calculated from two different ILD database, publically available MedGIFT ILD database and private ILD database, followed by performance evaluation of ANN and SVM classifiers in terms of average accuracy. It is found that average classification accuracy by SVM is greater than ANN where trained and tested on same database. Investigation continued further to test variation in accuracy of classifier when training and testing is performed with alternate database and training and testing of classifier with database formed by merging samples from same class from two individual databases. The average classification accuracy drops when two independent databases used for training and testing respectively. There is significant improvement in average accuracy when classifiers are trained and tested with merged database. It infers dependency of classification accuracy on training data. It is observed that SVM outperforms ANN when same database is used for training and testing.

Diesel oil is known to be one of the major petroleum products that can pollute water and soil. Soil pollution caused by petroleum hydrocarbons has substantially impacted the environment, especially in the Middle East. In this study, modeling and optimization of hexadecane removal from soil was performed using two pure cultures of Acinetobacter and Acromobacter and consortium culture of both bacterial species using artificial neural network (ANN) method. Then the best ANN structure was proposed based on mean square error (MSE) as well as correlation coefficient (R) for pure cultures of Acinetobacter and Acromobacter as well as their consortium. The results showed that the correlations between the actual data and the data predicted by ANN (R2) in Acromobacter, Acinetobacter and consortium of both cultures were 0.50, 0.47 and 0.63, respectively. Despite the low correlation between the experimental data and the data predicted by the ANN, the correlation coefficient and the precision of ANN for the consortium was higher. As a result, ANN had desirable precision to predict hexadecan removal by the cobsertium culture of Ochromobater and Acintobacter.

Skin disease is prevalent in tropical climates, developing countries, people with poor hygiene, and polluted areas. It is a kind of disease that is visible to human eyes which makes carriers susceptible to shame and may cause people to keep distance from them. Even though it is visible to human eyes, people are unaware of what kind of skin disease they have, and with this, people would go to dermatological clinics to have their skin checked, and to have a diagnosis. In line with this, the proponents created a system that detects and classifies skin diseases, particularly acne vulgaris, atopic dermatitis, keratosis pilaris (Chicken Skin), psoriasis, leprosy, and warts. This research has used different pre-processing and segmentation algorithms to successfully extract features (texture, edge, and color). The extracted features were contained as a feature vector and were used to train the Support Vector Machine classifier and the Artificial Neural Network classifier. For the Support Vector Machine (SVM), the model peaked an average of 93.55% and 93.33% for precision and recall, respectively, and for the Artificial Neural Network (ANN) classifier, it peaked 96.55% for precision and 100% for recall.

In this study, seismic events in Kula district (Manisa, Turkey) and its vicinity have been investigated and then natural and artificial seismic activities are discriminated. Total of 77 digital vertical component velocity seismograms of seismic activities with M L ≤3.5 magnitude from seismic activity catalogs between 2009 to 2014 recorded by Manisa Kula (KULA) broadband station operated by Bogazici University, Kandilli Observatory and Earthquake Resarch Institute Regional Earthquake- Tsunami Monitoring Center (RETMC) were used in this study. The maximum S-wave and maximum P-wave amplitude ratio (Ratio) of vertical component velocity seismograms and power ratio for (1 and 12 sec.) (Complexity-C) and total signal duration (Duration) of the waveform were calculated. The earthquakes and the quarry blasts have been discriminated using linear discriminant function (LDF) and Back Propagation-Feed Forward Neural Networks (BPNNs) that is one of the learning algorithms at the artificial neural networks (ANNs) methods taking correlation between these parameters into consideration. 39 (51%) of the 77 seismic activities were identified as quarry blasts and 38 (49%) of them as earthquakes LDF and ANNs methods have been applied together for the first time for Ratio-C, Ratio-logS and Ratio-duration parameter pairs with the data of Manisa and surroundings, and earthquakes and quarry blasts have been distinguished from each other. LDF and ANNs methods were compared for each pair of parameters. Both of two methods are successful but the ANNs method has higher accuracy percentage values than LDF method when there is sufficient number of data. The accuracy percentages are different for a pair of Ratio versus C, for a pair of Ratio versus logS and for a pair of Ratio versus duration, respectively.