4 research outputs found

    Ensemble learning using multi-objective optimisation for arabic handwritten words

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    Arabic handwriting recognition is a dynamic and stimulating field of study within pattern recognition. This system plays quite a significant part in today's global environment. It is a widespread and computationally costly function due to cursive writing, a massive number of words, and writing style. Based on the literature, the existing features lack data supportive techniques and building geometric features. Most ensemble learning approaches are based on the assumption of linear combination, which is not valid due to differences in data types. Also, the existing approaches of classifier generation do not support decision-making for selecting the most suitable classifier, and it requires enabling multi-objective optimisation to handle these differences in data types. In this thesis, new type of feature for handwriting using Segments Interpolation (SI) to find the best fitting line in each of the windows with a model for finding the best operating point window size for SI features. Multi-Objective Ensemble Oriented (MOEO) formulated to control the classifier topology and provide feedback support for changing the classifiers' topology and weights based on the extension of Non-dominated Sorting Genetic Algorithm (NSGA-II). It is designated as the Random Subset based Parents Selection (RSPS-NSGA-II) to handle neurons and accuracy. Evaluation metrics from two perspectives classification and Multiobjective optimization. The experimental design based on two subsets of the IFN/ENIT database. The first one consists of 10 classes (C10) and 22 classes (C22). The features were tested with Support Vector Machine (SVM) and Extreme Learning Machine (ELM). This work improved due to the SI feature. SI shows a significant result with SVM with 88.53% for C22. RSPS for C10 at k=2 achieved 91% accuracy with fewer neurons than NSGA-II, and for C22 at k=10, accuracy has been increased 81% compared to NSGA-II 78%. Future work may consider introducing more features to the system, applying them to other languages, and integrating it with sequence learning for more accuracy

    FEATURE EXTRACTION AND CLASSIFICATION THROUGH ENTROPY MEASURES

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    Entropy is a universal concept that represents the uncertainty of a series of random events. The notion \u201centropy" is differently understood in different disciplines. In physics, it represents the thermodynamical state variable; in statistics it measures the degree of disorder. On the other hand, in computer science, it is used as a powerful tool for measuring the regularity (or complexity) in signals or time series. In this work, we have studied entropy based features in the context of signal processing. The purpose of feature extraction is to select the relevant features from an entity. The type of features depends on the signal characteristics and classification purpose. Many real world signals are nonlinear and nonstationary and they contain information that cannot be described by time and frequency domain parameters, instead they might be described well by entropy. However, in practice, estimation of entropy suffers from some limitations and is highly dependent on series length. To reduce this dependence, we have proposed parametric estimation of various entropy indices and have derived analytical expressions (when possible) as well. Then we have studied the feasibility of parametric estimations of entropy measures on both synthetic and real signals. The entropy based features have been finally employed for classification problems related to clinical applications, activity recognition, and handwritten character recognition. Thus, from a methodological point of view our study deals with feature extraction, machine learning, and classification methods. The different versions of entropy measures are found in the literature for signals analysis. Among them, approximate entropy (ApEn), sample entropy (SampEn) followed by corrected conditional entropy (CcEn) are mostly used for physiological signals analysis. Recently, entropy features are used also for image segmentation. A related measure of entropy is Lempel-Ziv complexity (LZC), which measures the complexity of a time-series, signal, or sequences. The estimation of LZC also relies on the series length. In particular, in this study, analytical expressions have been derived for ApEn, SampEn, and CcEn of an auto-regressive (AR) models. It should be mentioned that AR models have been employed for maximum entropy spectral estimation since many years. The feasibility of parametric estimates of these entropy measures have been studied on both synthetic series and real data. In feasibility study, the agreement between numeral estimates of entropy and estimates obtained through a certain number of realizations of the AR model using Montecarlo simulations has been observed. This agreement or disagreement provides information about nonlinearity, nonstationarity, or nonGaussinaity presents in the series. In some classification problems, the probability of agreement or disagreement have been proved as one of the most relevant features. VII After feasibility study of the parametric entropy estimates, the entropy and related measures have been applied in heart rate and arterial blood pressure variability analysis. The use of entropy and related features have been proved more relevant in developing sleep classification, handwritten character recognition, and physical activity recognition systems. The novel methods for feature extraction researched in this thesis give a good classification or recognition accuracy, in many cases superior to the features reported in the literature of concerned application domains, even with less computational costs

    Multiresolution Methods in Face Recognition

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