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This paper proposed a method based on Zernike moments to classify the various stages of Alzheimer's Disease(AD) from structural MRIs. The proposed method is benefited from all three orthogonal directions of MRIs i.e. Axial, Sagittal and Coronal images. Three back-propagation algorithms had been used to train the neural network with seven neurons in hidden layer to reach the best accuracy. We experimented...
Emotion recognition from speech signals has abundant applications in daily life. Particularly in speech-based human machine interaction it is used for improving the naturalness. Speech based emotion recognition is done in two steps namely Gender Recognition and Emotion Recognition. Gender Recognition will give the information about the gender (Male or Female) of the speaker and Emotion Recognition...
This paper proposes a hybrid approach, integrating Decision Trees (DT) and Artificial Neural Networks (ANN) for energy price classification in deregulated electricity market. The proposed model does not aim to predict future values of energy prices, but classify and explain the negative Locational Marginal Price (LMP) that are observed in the grid. The negative LMPs are grouped by the K-means technique...
Artificial Neural Networks (ANNs) are human made information processing artifacts, and grown up vast in two-three decade. Neural Networks are highly parallelized dynamic system which accept output response as input and produce output. They have confirmed to be extensively beneficial in solving those problems which cannot be solved by using algorithmic procedures which are considered to be conventional,...
Traffic safety is an important problem for autonomous vehicles. The development of Traffic Sign Recognition (TSR) dedicated to reducing the number of fatalities and the severity of road accidents is an important and an active research area. Recently, most TSR approaches of machine learning and image processing have achieved advanced performance in traditional natural scenes. However, there exists...
This paper presents a predictive model which to predict the trends of stock prices using Data Mining techniques. This research will allow the investor to make a more informed decision to buy and sell stocks, and in the most appropriate period. The predictive concept in this work implies learning historical price patterns, indicators, and behavior; and then predicting the future trends in one, five,...
A challenge is indexing the facial beauty by a machine as same evaluated by human beings. A question arises: Can beauty be learnt by machines? Every individual have different concept of facial beauty. Somebody can be attracted by someone but might not be by another person. In recent past, many psychologists, neurologists and other scientists have done tremendous work in this area. This work presents...
Supervised learning of convolutional neural networks (CNNs) can require very large amounts of labeled data. Labeling thousands or millions of training examples can be extremely time consuming and costly. One direction towards addressing this problem is to create features from unlabeled data. In this paper we propose a new method for training a CNN, with no need for labeled instances. This method for...
Human Epithelial type-2 (HEp-2) cells are used as substrates for the detection of Anti Nuclear Antibodies (ANA) in the Indirect Immunofluorescence (IIF) test to diagnose autoimmune diseases. Pathologists in the laboratory examine the IIF slides to detect and recognize theHEp-2 cell patterns to generate the report. So, the IIF test is subjective and requires objective analysis. This paper introduces...
The main objective of the spatial image classification is to extract information classes from a multiband raster spatial image. The network structure and number of inputs are the key factors in deciding the performance and accuracy of the traditional pixel based image classification techniques like Support Vector Machines (SVM), Artificial Neural Networks (ANN), Fuzzy logic, Decision Trees (DT) and...
Improving bicycle safety is considered as a growing concern for two reasons. First, in the United States in recent years, about 700 cyclists were killed and about 48,000 were injured in bicycle motor vehicle crashes each year. Regarding crash location, from 2008 to 2012 in the United States, more than 30% of cyclist fatalities occurred at intersections. Furthermore, up to 16% of bicycle-related crashes...
This paper presents a comparison of Electroencephalogram (EEG) signals classification for Brain Computer-Interfaces (BCI). At present, it is a challenging task to extract the meaningful EEG signal patterns from a large volume of poor quality data and simultaneously with the presence of artifacts noises. Selection of the effective classification technique of the EEG signals at classification stage...
For cardiologists, the detection of cardiac abnormalities is a very delicate and crucial task for the treatment of a patient's condition. This task that requires electronic systems of medical assistance that is more precise, faster and reliable to help cardiologists to analyze and make the right decisions. These medical assistance systems tend to model the human expertise and perception using signal...
We defined a set of quantifiable features for authorship categorization. We performed our experiments on public domain literature — all books analyzed were obtained in plain text format through Project Gutenberg's online repository of classic books. We tested three machine learning algorithms: Artificial Neural Network, Naïve Bayes Classifier, and Support Vector Machine with our features. We found...
This paper presents a Face Detection System with Expression Recognition using Artificial Neural Networks. It is an automated vision system designed and implemented using MATLAB. The Face Detection with Expression Recognition system accomplishes facial expression recognition through two phases. The captured image is processed first to detect the face, and then the facial expression is recognized. These...
Microgrid comprises of versatile type of alternate energy resources in order to distribute electricity economically, effectively and securely. In this study, the energy demand of Goldwind smart industrial park demand is forecasted and optimized based on daily load, wind and PV generation system using three techniques. PSO-NN, VAR for Forecasting and GA for optimization algorithm. The complete generation...
Support Vector Machine (SVM) is one of widely-used text classification method. Although SVM performs well in practice, SVM encounters two problems: the data distribution is not taken into consideration in the process of classification and its performance is greatly influenced by noises. In view of this, Fuzzy Support Vector Machine based on Manifold Discriminant Analysis (FSVM-MDA) is proposed and...
Nowadays Opinion mining is given more important, since it provides decision makers to estimate the success of a newly proposed techniques, novel ad campaign or novel product launch. In general, supervised methods such as Support Vector Machine (SVM) and Artificial Neural Network (ANN) are used to classify the opinions. In some cases SVM performs better classification and some cases ANN performs better...
Cardiovascular risk prediction is a vital aspect of personalized health care. In this study, retinal vascular function is assessed in asymptomatic participants who are classified into risk groups based on Framingham Risk Score. Feature selection, oversampling and state-of-the-art classification methods are applied to provide a sound individual risk prediction based on Retinal Vessel Analysis (RVA)...
Nowadays, classification tasks are very challenging because data is usually large and imbalanced. They can cause low prediction accuracy and high computation costs. Active Learning is a technique that employs only a small set of data to construct an initial classification model. Then, it iteratively improves the model by incrementally learning from the misclassified examples. In this paper, we aim...
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