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MicroRNAs (miRNAs) are small Ribonucleic Acid (RNA) molecules ~18-22 nucleotides (nt) in length that regulates gene expression in animals, plants and viruses. Due to its small size and occurrence in different development stages of organisms, the experimental identification of miRNAs becomes difficult, and computational approaches are being developed in order to precede and guide biological experiments...
This paper introduces a novel approach which uses a Hidden Markov Model (HMM) based Fuzzy Inference System (FIS) for prediction of systems that are non deterministic, dynamical and chaotic in nature. The HMM is used for shape based batch creation of training data which is then processed one batch at a time by a FIS. The Membership functions and Rule Base of the FIS are tweaked to predict the correct...
Recent library digitization projects attempt to provide large collections of printed material from varying sources in a searchable format. The scanned documents are typically processed using Optical Character Recognition (OCR), which typically introduces errors in the text. This paper proposes a technique for correction of OCR degraded text that is independent of character-level OCR errors, and hence...
Non-negative matrix factorization is an important method helpful in the analysis of high dimensional datasets. It has a number of applications including pattern recognition, data clustering, information retrieval or computer security. One its significant drawback lies in its computational complexity. In this paper, we introduce a new method allowing fast approximate transformation from input space...
This article presents two classifiers based on machine learning methods, aiming to detect physiologic anomalies considering Poincaré plots of heart rate variability. It was developed a preprocessing procedure to encoding the plots, based on the Cellular Features Extraction Method. Simulation of different classifiers, artificial neural networks and support vector machine, has been performed and the...
Using well-established techniques of Genetic Programming (GP), we automatically optimize image feature filters over several inputs and within transformation images, improving the Automatic Construction of Tree-Structural Image Transformation (ACTIT) system. Our objective is to also produce optimal solutions in substantially less computation time than require for generating features of ACTIT. We improved...
Since the outset of the deregulation of international financial markets in the 1980s, the frequency of currency crises has increased. Solely in the 1990s, five global storms of financial turmoil, also including collapses of the currency, have occurred. To date, crisis forecasting and monitoring of financial stability is still at a preliminary stage. This paper explores whether the application of the...
One of the major issues concerning the Artificial Neural Networks (ANNs) design is a proper adjustment of the weights of the network. There have been a number of studies comparing the performance of evolutionary and gradient based ANNs learning. But the results of the studies, sometime conflicting to each other although the same and standard dataset development had been used. Motivated by this finding,...
The extraction of temporal information from text documents is becoming increasingly important in many applications such as natural language processing, information retrieval, question answering, etc. Indeed, the temporal dimension plays a key role on most of these systems, promoting better performance. Our goal is the definition of a temporal document representation, incorporating the time dimension...
This paper proposes an improved Hierarchical Multi-label Classification (HMC) method for solving the gene function prediction. The HMC task is transferred into a series of binary SVM classification tasks. By introducing the hierarchy constraint into learning procedures, two measures with incorporating prior information are implemented to improve the HMC performance. Firstly, for imbalanced functional...
According to some biological observations, generating output variability is one of the characteristics expected from a memory model. In this paper a BAM inspired chaotic model is used to mimic this functionality of the brain. Chaos gives the potential to create deterministic variability and control its degree of uncertainty. Using some time series generated by the trained network, largest lyapunov...
Support Vector Machines (SVMs) ensembles have been widely used to improve classification accuracy in complicated pattern recognition tasks. In this work we propose to apply an ensemble of SVMs coupled with feature-subset selection methods to aleviate the curse of dimensionality associated with expression-based classification of DNA microarray data. We compare the single SVM classifier to SVM ensembles...
In some machine learning applications using soft labels is more useful and informative than crisp labels. Soft labels indicate the degree of membership of the training data to the given classes. Often only a small number of labeled data is available while unlabeled data is abundant. Therefore, it is important to make use of unlabeled data. In this paper we propose an approach for Fuzzy-Input Fuzzy-Output...
Support Vector Machine (SVM) is one of the most popular tools for solving general classification and regression problems because of its high predicting accuracy. However, the training phase of nonlinear kernel based SVM algorithm is a computationally expensive task, especially for large datasets. In this paper, we propose an intelligent system to solve large classification problems based on parallel...
The problem of object detection in image and video has been treated by a large number of researchers. Many design factors degrade the reliability of the problem solutions, such as manual modeling of the object, manual features selection, handcrafting architecture, and learning algorithm selection. Here, a generalized object detection and localization system is presented. It has the ability to learn...
We propose a classification model for the cognitive level of question items in examinations based on Bloom's taxonomy. The model implements the artificial neural network approach, which is trained using the scaled conjugate gradient learning algorithm. Several data preprocessing techniques such as word extraction, stop word removal, stemming, and vector representation are applied to a feature set...
This paper presents a model of a supervised machine learning approach for classification of a dataset. The model extracts a set of patterns common in a single class from the training dataset according to the rules of the pattern-based subspace clustering technique. These extracted patterns are used to classify the objects of that class in the testing dataset. The user-defined threshold dependence...
In BCI research community, support vector machine (SVM) is an effective method for motor imagery (MI)-based electroencephalographic (EEG) classification. However, the computation of decision function during SVM classification stage for a new EEG trial is time-consuming due to the large number of support vectors (SV). This paper proposes a new method to reduce the number of support vectors so that...
In order to improve the classifier performance in semantic image annotation, we propose a novel method which adopts learning vector quantization (LVQ) technique to optimize low level feature data extracted from given image. Some representative vectors are selected with LVQ to train support vector machine (SVM) classifier instead of using all feature data. Performance is compared between the methods...
Statistically-based parsers for large corpora, in particular the Penn Tree Bank (PTB), typically have not used all the linguistic information encoded in the annotated trees on which they are trained. In particular, they have not in general used information that records the effects of derivations, such as empty categories and the representation of displaced phrases, as is the case with passive, topicalization,...
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