5 research outputs found
Learning Style Classification via EEG Sub-band Spectral Centroid Frequency Features
Kolb’s Experiential Learning Theory postulates that in learning, knowledge is created by the learners’ ability to absorb and transform experience. Many studies have previously suggested that at rest, the brain emits signatures that can be associated with cognitive and behavioural patterns. Hence, the study attempts to characterise and classify learning styles from EEG using the spectral centroid frequency features. Initially, learning style of 68 university students has been assessed using Kolb’s Learning Style Inventory. Resting EEG is then recorded from the prefrontal cortex. Next, the EEG is pre-processed and filtered into alpha and theta sub-bands in which the spectral centroid frequencies are computed from the corresponding power spectral densities. The dataset is further enhanced to 160 samples via synthetic EEG. The obtained features are then used as input to the k-nearest neighbour classifier that is incorporated with k-fold cross-validation. Feature classification via k-nearest neighbour has attained five-fold mean training and testing accuracies of 100% and 97.5%, respectively. Hence, results show that the alpha and theta spectral centroid frequencies represent distinct and stable EEG signature to distinguish learning styles from the resting brain.DOI:http://dx.doi.org/10.11591/ijece.v4i6.683
A Self-adaptive Fireworks Algorithm for Classification Problems
his work was supported in part by the National Natural Science Foundation of China under Grants 61403206 and 61771258, in part by the Natural Science Foundation of Jiangsu Province under Grants BK20141005 and BK20160910, in part by the Natural Science Foundation of the Jiangsu Higher Education Institutions of China under Grant 14KJB520025, in part by the Priority Academic Program Development of Jiangsu Higher Education Institutions, in part by the Open Research Fund of Jiangsu Engineering Research Center of Communication and Network Technology, NJUPT, under Grant JSGCZX17001, and in part by the Shaanxi Key Laboratory of Complex System Control and Intelligent Information Processing, Xi’an University of Technology, under Contract SKL2017CP01.Peer reviewedPublisher PD
Development of Features for Recognition of Handwritten Odia Characters
In this thesis, we propose four different schemes for recognition of handwritten atomic Odia characters which includes forty seven alphabets and ten numerals. Odia is the mother tongue of the state of Odisha in the republic of India. Optical character recognition (OCR) for many languages is quite matured and OCR systems are already available in industry standard but, for the Odia language OCR is still a challenging task. Further, the features described for other languages can’t be directly utilized for Odia character recognition for both printed and handwritten text. Thus, the prime thrust has been made to propose features and utilize a classifier to derive a significant recognition accuracy. Due to the non-availability of a handwritten Odia database for validation of the proposed schemes, we have collected samples from individuals to generate a database of large size through a digital note maker. The database consists of a total samples of 17, 100 (150 × 2 × 57) collected from 150 individuals at two different times for 57 characters. This database has been named Odia handwritten character set version 1.0 (OHCS v1.0) and is made available in http://nitrkl.ac.in/Academic/Academic_Centers/Centre_For_Computer_Vision.aspx for the use of researchers. The first scheme divides the contour of each character into thirty segments. Taking the centroid of the character as base point, three primary features length, angle, and chord-to-arc-ratio are extracted from each segment. Thus, there are 30 feature values for each primary attribute and a total of 90 feature points. A back propagation neural network has been employed for the recognition and performance comparisons are made with competent schemes. The second contribution falls in the line of feature reduction of the primary features derived in the earlier contribution. A fuzzy inference system has been employed to generate an aggregated feature vector of size 30 from 90 feature points which represent the most significant features for each character. For recognition, a six-state hidden Markov model (HMM) is employed for each character and as a consequence we have fifty-seven ergodic HMMs with six-states each. An accuracy of 84.5% has been achieved on our dataset. The third contribution involves selection of evidence which are the most informative local shape contour features. A dedicated distance metric namely, far_count is used in computation of the information gain values for possible segments of different lengths that are extracted from whole shape contour of a character. The segment, with highest information gain value is treated as the evidence and mapped to the corresponding class. An evidence dictionary is developed out of these evidence from all classes of characters and is used for testing purpose. An overall testing accuracy rate of 88% is obtained.
The final contribution deals with the development of a hybrid feature derived from discrete wavelet transform (DWT) and discrete cosine transform (DCT). Experimentally it has been observed that a 3-level DWT decomposition with 72 DCT coefficients from each high-frequency components as features gives a testing accuracy of 86% in a neural classifier. The suggested features are studied in isolation and extensive simulations has been carried out along with other existing schemes using the same data set. Further, to study generalization behavior of proposed schemes, they are applied on English and Bangla handwritten datasets. The performance parameters like recognition rate and misclassification rate are computed and compared. Further, as we progress from one contribution to the other, the proposed scheme is compared with the earlier proposed schemes
Advances in Detection and Classification of Underwater Targets using Synthetic Aperture Sonar Imagery
In this PhD thesis, the problem of underwater mine detection and classification using
synthetic aperture sonar (SAS) imagery is considered. The automatic detection and
automatic classification (ADAC) system is applied to images obtained by SAS systems.
The ADAC system contains four steps, namely mine-like object (MLO) detection, image
segmentation, feature extraction, and mine type classification. This thesis focuses
on the last three steps.
In the mine-like object detection step, a template-matching technique based on the a
priori knowledge of mine shapes is applied to scan the sonar imagery for the detection
of MLOs. Regions containing MLOs are called regions of interest (ROI). They are
extracted and forwarded to the subsequent steps, i.e. image segmentation and feature
extraction.
In the image segmentation step, a modified expectation-maximization (EM) approach
is proposed. For the sake of acquiring the shape information of the MLO in the ROI, the
SAS images are segmented into highlights, shadows, and backgrounds. A generalized
mixture model is adopted to approximate the statistics of the image data. In addition,
a Dempster-Shafer theory-based clustering technique is used to consider the spatial
correlation between pixels so that the clutters in background regions can be removed.
Optimal parameter settings for the proposed EM approach are found with the help of
quantitative numerical studies.
In the feature extraction step, features are extracted and will be used as the inputs
for the mine type classification step. Both the geometrical features and the texture
features are applied. However, there are numerous features proposed to describe the
object shape and the texture in the literature.
Due to the curse of dimensionality, it is indispensable to do the feature selection during
the design of an ADAC system. A sophisticated filter method is developed to choose
optimal features for the classification purpose. This filter method utilizes a novel
feature relevance measure that is a combination of the mutual information, the modified
Relief weight, and the Shannon entropy. The selected features demonstrate a higher
generalizability. Compared with other filter methods, the features selected by our
method can lead to superior classification accuracy, and their performance variation
over different classifiers is decreased.
In the mine type classification step, the prediction of the types of MLO is considered. In
order to take advantage of the complementary information among different classifiers, a classifier combination scheme is developed in the framework of the Dempster-Shafer
theory. The outputs of individual classifiers are combined according to this classifier
combination scheme. The resulting classification accuracy is better than those of
individual classifiers.
All of the proposed methods are evaluated using SAS data. Finally, conclusions are
drawn, and some suggestions about future works are proposed as well
Concept Mining: A Conceptual Understanding based Approach
Due to the daily rapid growth of the information, there are
considerable needs to extract and discover valuable knowledge from
data sources such as the World Wide Web. Most of the common
techniques in text mining are based on the statistical analysis of a
term either word or phrase. These techniques consider documents as
bags of words and pay no attention to the meanings of the document
content. In addition, statistical analysis of a term frequency
captures the importance of the term within a document only. However,
two terms can have the same frequency in their documents, but one
term contributes more to the meaning of its sentences than the other
term. Therefore, there is an intensive need for a model that
captures the meaning of linguistic utterances in a formal structure.
The underlying model should indicate terms that capture the
semantics of text. In this case, the model can capture terms that
present the concepts of the sentence, which leads to discover the
topic of the document.
A new concept-based model that analyzes terms on the sentence,
document and corpus levels rather than the traditional analysis of
document only is introduced. The concept-based model can effectively
discriminate between non-important terms with respect to sentence
semantics and terms which hold the concepts that represent the
sentence meaning.
The proposed model consists of concept-based statistical analyzer,
conceptual ontological graph representation, concept extractor and
concept-based similarity measure. The term which contributes to the
sentence semantics is assigned two different weights by the
concept-based statistical analyzer and the conceptual ontological
graph representation. These two weights are combined into a new
weight. The concepts that have maximum combined weights are selected
by the concept extractor. The similarity between documents is
calculated based on a new concept-based similarity measure. The
proposed similarity measure takes full advantage of using the
concept analysis measures on the sentence, document, and corpus
levels in calculating the similarity between documents.
Large sets of experiments using the proposed concept-based model on
different datasets in text clustering, categorization and retrieval
are conducted. The experiments demonstrate extensive comparison
between traditional weighting and the concept-based weighting
obtained by the concept-based model. Experimental results in text
clustering, categorization and retrieval demonstrate the substantial
enhancement of the quality using: (1) concept-based term frequency
(tf), (2) conceptual term frequency (ctf), (3) concept-based
statistical analyzer, (4) conceptual ontological graph, (5)
concept-based combined model.
In text clustering, the evaluation of results is relied on two
quality measures, the F-Measure and the Entropy. In text
categorization, the evaluation of results is relied on three quality
measures, the Micro-averaged F1, the Macro-averaged F1 and the Error
rate. In text retrieval, the evaluation of results relies on three
quality measures, the precision at 10 documents retrieved P(10), the
preference measure (bpref), and the mean uninterpolated average
precision (MAP). All of these quality measures are improved when the
newly developed concept-based model is used to enhance the quality
of the text clustering, categorization and retrieval