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There are lots of work done in a mobile ad-hoc network for enhancing its security still there are some issues regarding security. In this paper, we study about the mobile network and various techniques by which network prevents attacks. In our proposed work we apply the trust method which calculates by dempsters shafer theory, after trust calculation we apply support vector machine to classify nodes...
Brain computer interface is used for human and machine learning analysis. This paper represents the EEG datasets that are built with different cognitive task such as left, right, back and front imaginary movement with eye open. We have used different feature extraction method to classify these EEG signal using Support Vector Machine (SVM), k-Nearest Neighbor (k-NN) and Artificial Neural Network (ANN)...
Based on thermal acoustic data from the body tissue in upper arm that has been produced through thermal acoustic tomography method, classification system for the data has been built as a support of decision making about physiological abnormality. The advantages of the system built in this research is able to detect physiological abnormalities in the body tissues without the need for surgery (non-invasive...
This paper presents a proposal for the use of the Hybrid Fuzzy Inference System algorithm (HyFIS) as solar intensity forecast mechanism. Fuzzy Inference Systems (FIS) are used to solve regression problems in various contexts. The HyFIS is a method based on FIS with the particular advantage of combining fuzzy concepts with Artificial Neural Networks (ANN), thus optimizing the learning process. This...
This work explores the problem of exercise quality measurement since it is essential for effective management of diseases like cerebral palsy (CP). This work examines the assessment of quality of large amplitude movement (LAM) exercises designed to treat CP in an automated fashion. Exercise data was collected by trained participants to generate ideal examples to use as a positive samples for machine...
Artificial neural networks (ANNs) have rarely been used in the field of medical education, especially in the prediction of learning performances. This study aims to evaluate the potential application of ANN models for predicting learning performances, in comparison with multivariate logistic regression models. The predictor variables included demographics, high-school backgrounds, first-year grade-point...
High accuracy of lane changing prediction is beneficial to driver assistant system and fully autonomous cars. This paper proposes a lane changing prediction model based on combined method of Supporting Vector Machine (SVM) and Artificial Neural Network (ANN) at highway lane drops. The vehicle trajectory data are from Next Generation Simulation (NGSIM) data set on U.S. Highway 101 and Interstate 80...
Artificial intelligent models (AIMs) have been successfully adopted in hydrological forecasting in a plenty of literatures. However, the comprehensive comparison of their applicability in particular short-term (i.e. hourly) water level prediction under heavy rainfall events was rarely discussed. Therefore, in this study, the artificial neural networks (ANN), support vector machine (SVM) and adaptive...
This work investigates radar signal classification and source identification using three classification models: Neural Networks (NN), Support Vector Machines (SVM) and Random Forests (RF). The available large dataset consists of pulse train characteristics such as signal frequencies, type of modulation, pulse repetition intervals, scanning type, scan period, etc., represented as a mixture of continuous,...
A biometric person authentication system using brain waves or Electroencephalogram (EEG) signals recorded using a minimum number of channels ranging from 2 to 6 is presented. The task for EEG recording consists of simple motor imagery movements that the subject has to imagine. The system uses an effective time-frequency based feature extraction method using the short-time Fourier transform (STFT)...
Prediction of stock market is a challenging task that has attracted researchers in various fields including the computational intelligence and finance. Since stock market data sets are intrinsically large, nonlinear and time-varying, it is extremely difficult to design models for forecasting the future directions with an acceptable accuracy. In this paper, an integrative and intelligent machine learning...
Gaussian mixture models (GMM) remain popular in pattern classification applications due to their well understood Bayesian framework and the availability of good training algorithms such as the expectation maximization (EM) algorithm. EM is a non-discriminative training algorithm. The performance of a GMM trained with the EM algorithm can often fall short of other discriminative pattern classification...
Cataclysmic variable (CV) stars are binary stars that consist of two components: a white dwarf primary, and a mass transferring secondary. Due to the relative faint of cataclysmic variable and a large number of irregular changes, it is not easy to get valuable data and important research results on observation. But they have significant meaning on the subsequent research of these spectra. In general,...
Oil spill has been a crucial hazard to the coastal environment. A major difficulty of Synthetic Aperture Radar (SAR) based oil-spill detection algorithms is the classification between mineral oil and biogenic look-alikes. Polarimetric SAR features provides helpful information in disguising mineral oil and its look-alikes. In this study, we focused on the extraction and selection of fully polarimetric...
Anti-Malware industry faces the challenge of evaluating huge amount of data for potential malicious contents. This is due to the fact that hackers introduce polymorphism to the existing malicious groups/classes. Effective feature extraction and classification of malware data is necessary to tackle such issues. In this paper, we visualize viruses in an image as they capture minor changes while retaining...
Kidney plays an important role in human bodies. It maintains homeostasis and removes some harmful substance by making and ejecting urine. Renal cell carcinoma, especially clear cell renal cell carcinoma (ccRCC), is the most common type of kidney disease that accounts for 2∼3% of human malignancies. Early diagnosis and accurate classification of ccRCC is an important factor to decrease the motility...
Accurate trajectory tracking of a robotic manipulator has always been the main functioning unit in modern industrial environment. For this, to overcome the problems faced by the conventional classical control schemes like PID, SMC etc. [1], this paper mainly proposes three controllers. To overcome the chattering problem in SMC, signum function is replaced with the saturation function. Further for...
Due to the huge increase in the size of the data it becomes troublesome to perform efficient analysis using the current traditional techniques. Big data put forward a lot of challenges due to its several characteristics like volume, velocity, variety, variability, value and complexity. Today there is not only a necessity for efficient data mining techniques to process large volume of data but in addition...
Because it is able to significantly improve the process, the research issue of determination of process faults has attracted considerable attention. Although some statistical decomposition methods may provide the possible solutions, the mathematical difficulty could confine the applications. As a consequence, this study proposes the soft computing approaches to determine the source of a process fault...
Extreme Learning Machine (ELM) is a fast and efficient classifier with single hidden layer feed-forward neural networks. In this paper, the ELM is employed to classify the EEG signals in BCI system, the BCI competition datasets are used to test, the mutual information and classify accuracy are considered as evaluation criteria. Compare with the LDA and SVM, the ELM method could obtain more mutual...
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