Intelligent learning style classification model and cross-relational study with intelligence quotient / Megat Syahirul Amin Megat Ali

The electroencephalogram is an effective approach for measuring brainwaves and has been widely used to study mental performance such as learning and intelligence. Conventional assessment methods are exposed to reliability issues which stems from cultural and language barriers. Alternative approach based on electroencephalogram has since been proposed; indicating correlation between resting brainwaves and learning styles. Validity of the findings however, was based on unconfirmed theories. Moreover, a systematic approach for learning style assessment based on brainwaves and advanced modelling technique as yet to be studied. Therefore, this research proposes an intelligent learning style classification model via brainwave features and artificial neural network. Eighty samples from various universities are segregated into four learning style groups based on Kolb's Learning Style Inventory. Twenty samples are identified as Divergers, twenty-two as Assimilators, twenty-one as Convergers and seventeen as Accommodators. Resting electroencephalogram is then recorded from the prefrontal region. Spectral centroid features from theta and alpha bands are then extracted for independent pattern analysis. Meanwhile, k-nearest neighbour is used for feature selection purposes. An intelligent learning style classification model is then constructed using spectral centroid features and multi-layered perceptron network. An independent dataset of fifty samples with varying levels of intelligence is used for a cross-relational mapping by the model. The pattern of features for each learning style group has shown correlation with the Neural Efficiency Hypothesis of intelligence. Subsequently, the fully developed model has attained excellent classification accuracy of 98.8% with mean squared error of 0.07. Moreover, the network has fulfilled all, correlation requirements in classifying learning styles. The cross-relational analysis revealed that brighter individuals are predicted to be either Assimilative or Convergent. Meanwhile, the less brilliant ones are predicted to be either Divergent or Accommodative. Therefore, high level of intelligence is linked to excellent analytical skills, whereas low level of intelligence is associated with reliance on intuition rather than cognitive abilities. Conclusively, this thesis has proven that spectral centroid features from the resting brainwaves are suitable descriptors for characterising learning styles. The systematic approach established by the intelligent model provides an alternative for assessing the behaviour via electroencephalogram. Furthermore, the study has also confirmed that brainwaves from the prefrontal region are adequate for classification of learning styles

Intelligent learning style classification model and crossrelational study with intelligence quotient / Megat Syahirul Amin Megat Ali

The electroencephalogram is an effective approach for measuring brainwaves and has been widely used to study mental performance such as learning and intelligence. Conventional assessment methods are exposed to reliability issues which stems from cultural and language barriers. Alternative approach based on electroencephalogram has since been proposed; indicating correlation between resting brainwaves and learning styles. Validity of the findings however, was based on unconfirmed theories. Moreover, a systematic approach for learning style assessment based on brainwaves and advanced modelling technique as yet to be studied. Therefore, this research proposes an intelligent learning style classification model via brainwave features and artificial neural network. Eighty samples from various universities are segregated into four learning style groups based on Kolb's Learning Style Inventory. Twenty samples are identified as Divergers, twenty-two as Assimilators, twenty-one as Convergers and seventeen as Accommodators. Resting electroencephalogram is then recorded from the prefrontal region. Spectral centroid features from theta and alpha bands are then extracted for independent pattern analysis. Meanwhile, k-nearest neighbour is used for feature selection purposes. An intelligent learning style classification model is then constructed using spectral centroid features and multi-layered perceptron network. An independent dataset of fifty samples with varying levels of intelligence is used for a cross-relational mapping by the model. The pattern of features for each learning style group has shown correlation with the Neural Efficiency Hypothesis of intelligence. Subsequently, the fully developed model has attained excellent classification accuracy of 98.8% with mean squared error of 0.07. Moreover, the network has fulfilled all correlation requirements in classifying learning styles. The cross-relational analysis revealed that brighter individuals are predicted to be either Assimilative or Convergent. Meanwhile, the less brilliant ones are predicted to be either Divergent or Accommodative. Therefore, high level of intelligence is linked to excellent analytical skills, whereas low level of intelligence is associated with reliance on intuition rather than cognitive abilities. Conclusively, this thesis has proven that spectral centroid features from the resting brainwaves are suitable descriptors for characterising learning styles. The systematic approach established by the intelligent model provides an alternative for assessing the behaviour via electroencephalogram. Furthermore, the study has also confirmed that brainwaves from the prefrontal region are adequate for classification of learning styles

Ananas comosus crown image thresholding and crop counting using a colour space transformation scheme

The implementation of unmanned aerial vehicle (UAV) technology having image processing capabilities provides an alternative way to observe pineapple crowns captured from aerial images. In the majority of pineapple plantations, an agricultural officer will physically count the crop yield prior to harvesting the Ananas Comosus, also known as pineapple. This process is particularly evident in large plantation areas to accurately identify pineapple numbers. To alleviate this issue, given it is both time-consuming and arduous, automating the process using image processing is suggested. In this study, the possibilities and comparisons between two techniques associated with an image thresholding scheme known as HSV and L*A*B* colour space schemes were implemented. This was followed by determining the threshold by applying an automatic counting (AC) method to count the crop yield. The results of the study found that by applying colour thresholding for segmentation, it improved the low contrast image due to different heights and illumination levels on the acquired colour image. The images that were acquired using a UAV revealed that the best distance for capturing the images was at the height of three (3) metres above ground level. The results also confirm that the HSV colour space provides a more efficient approach with an average error increment of 47.6% when compared to the L*A*B*colour space

IQ Classification via Brainwave Features: Review on Artificial Intelligence Techniques

Intelligence study is one of keystone to distinguish individual differences in cognitive psychology. Conventional psychometric tests are limited in terms of assessment time, and existence of biasness issues. Apart from that, there is still lack in knowledge to classify IQ based on EEG signals and intelligent signal processing (ISP) technique. ISP purpose is to extract as much information as possible from signal and noise data using learning and/or other smart techniques. Therefore, as a first attempt in classifying IQ feature via scientific approach, it is important to identify a relevant technique with prominent paradigm that is suitable for this area of application. Thus, this article reviews several ISP approaches to provide consolidated source of information. This in particular focuses on prominent paradigm that suitable for pattern classification in biomedical area. The review leads to selection of ANN since it has been widely implemented for pattern classification in biomedical engineering

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

Comparison between Cascade Forward and Multi-Layer Perceptron Neural Networks for NARX Functional Electrical Stimulation (FES)-Based Muscle Model

This paper presents the development and comparison of muscle models based on Functional Electrical Stimulation (FES) stimulation parameters using the Nonlinear Auto-Regressive model with Exogenous Inputs (NARX) using Multi-Layer Perceptron and Cascade Forward Neural Network (CFNN). FES stimulations with varying frequency, pulse width and pulse duration were used to estimate the muscle torque. About 722 data points were used to create muscle model. One Step Ahead (OSA) prediction, correlation tests and residual histogram analysis were performed to validate the model. The optimal Multi-Layer Perceptron (MLP) results were obtained from input lag space of 1, output lag space of 43 and hidden units 30. The MLP selected a total of three terms were selected to construct the final model, which producing a final Mean Square Error (MSE) of 1.1299. The optimal CFNN results were obtained from input lag space of 1, output lag space of 5 and hidden units 20 with similar terms selected. The final MSE produced was 1.0320. The proposed approach managed to approximate the behavior of the system well with unbiased residuals, which CFNN showing 8.66% MSE improvement over MLP with 33.33% less hidden units

Maritime radar: a review on techniques for small vessels detection

Maritime radar is an essential technology for observation and tracking systems in various marine applications. In comparison with terrestrial radar systems, maritime radar faces the challenge of large clutter signals, contributed by sea waves. This problem becomes more critical when the system is detecting relatively small vessels, where the probability of detection is reduced due to small radar cross section (RCS) of the vessels themselves. This paper presents a review of recent techniques in maritime radar, developed to overcome this issue, discussing several aspects such as (i) system topology, (ii) radar waveforms, and (iii) detection algorithms. Considering the recent works in this area, several recommendations for future works are presented to further improve the performance of modern maritime radar detecting small vessels

Application of Long-Short Term Memory for Accurate Biochemical Oxygen Demand Prediction in Rivers through Water Quality Parameters

Evaluating water quality is crucial for preserving the quality of river water. However, the typical technique of getting biochemical oxygen demand (BOD) values via laboratory testing might take several days, delaying the application of real-time measurement to improve water quality. This paper suggests using machine learning to predict BOD values from eight water quality measurements. The BOD rate in the Klang River, Selangor, Malaysia, was estimated using the long short-term memory (LSTM) method. The model was trained using historical data collected from eleven water collection points along the river. The predictive test results indicated that the LSTM model with 8 water parameters as input gave the most accurate predictions compared to the models with 5 and 3 water parameters. The results of this study indicate that machine learning methods can be used to predict BOD levels in real-time. It enables water quality managers to enhance water quality and safeguard human health proactively