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In Face recognition, a combination of neural network (NN), known as an ensemble of neural network, often outperforms individual ones. This paper is aiming to present a support vector machines (SVM)-ensemble-based efficient face recognition system. The training samples are randomly chosen by means of bootstrap technique to train the different SVM independently. These SVM's are combined together to...
The typical method of entering a password for user authentication is vulnerable to hacking; therefore, various security technologies using bio-signals, such as iris scan, electrocardiography, electromyography (EMG), and fingerprint recognition, are being developed. In this research, an authentication algorithm using an EMG signal is proposed to supplement the weakness of personal certification techniques...
An algorithm that can predict the review rating of a potential business with only existing information about the location and business categories would be an invaluable tool in making investment decisions. Utilizing the Yelp business dataset, we built a model, that can do as such, by classifying whether a potential business belongs to a positively-reviewed class (star ratings greater than or equal...
A variety of methods exists for electroencephalographic (EEG) signals classification. In this paper, we briefly review selected methods developed for such a purpose. First, a short description of the EEG signal characteristics is provided. Then, a comparison between the selected EEG signal classification methods, based on the overview of research studies on this topic, is presented. Examples of methods...
Convolutional neural network (CNN) is more and more important in pattern recognition. In this work, we adopt label relations and long short-term memory (LSTM) to develop an accurate CNN-based scene classification algorithm. Traditional scene classification algorithms assume that labels are mutually exclusive. However, this is not reasonable when an image has a variety of objects and hence has multiple...
The class imbalance problem occurs when instances in one class are more than that in another. It has been reported to severely hinder classification performance of many traditional classification algorithms and many researchers have paid a great deal of attention to this field. Different kinds of methods have been pro-posed to solve the problem these years, such as resampling methods, integrated learning...
Prediction in the stock market is challenging and complicated for investors. Many researches have performed to sense the future market movements. In the stock market, social media data have high impact today than ever. In this work, various prediction algorithms are analyzed to build a prediction model. The prediction model will be based on monthly prediction and daily prediction to forecast the next...
The Robot based landmine detection problem is a multiphase problem in which one element is the classification of landmines and clutter. To design an efficient and effective classification model requires considering factors such as the failure to detect a landmine, detection time and the high amount of false alarms that occur due of improper classification. In the absence of an extensive analysis on...
Parkinson’s disease (PD) is a progressive neurodegenerative motor system disorder. Early diagnosis of PD is important to control the symptoms appropriately. Recent voice and speech recognition techniques provide alternative solutions for PD screening. In this paper, an optimal support vector machine (SVM) based on bacterial foraging optimization (BFO) was established to predict PD effectively. The...
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...
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...
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...
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...
The purpose of this paper is to propose a modular integrating algorithm. This algorithm can let the program detect multiple arrhythmias and is very easy to add more diseases detection algorithm. Also, it can save the repeated calculations in multiple algorithms. By a real test of the program, the result is that the computing time of the integrating algorithm is 46.86% less than the sum of the computing...
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,...
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