Enhanced data clustering and classification using auto-associative neural networks and self organizing maps

This thesis presents a number of investigations leading to introduction of novel applications of intelligent algorithms in the fields of informatics and analytics. This research aims to develop novel methodologies to reduce dimensions and clustering of highly non-linear multidimensional data. Improving the performance of existing methodologies has been based on two fundamental approaches. The first is to look into making novel structural re-arrangements by hybridisation of conventional intelligent algorithms which are Auto-Associative Neural Networks (AANN) and Self Organizing Maps (SOM) for data clustering improvement. The second is to enhance data clustering and classification performance by introducing novel fundamental algorithmic changes known as M3-SOM in the data processing and training procedure of conventional SOM. Both approaches are tested, benchmarked and analysed using three datasets which are Iris Flowers, Italian Olive Oils and Wine through case studies for dimension reduction, clustering and classification of complex and non-linear data. The study on AANN alone shows that this non-linear algorithm is able to efficiently reduce dimensions of the three datasets. This paves the way towards structurally hybridising AANN as dimension reduction method with SOM as clustering method (AANNSOM) for data clustering enhancement. This hybrid AANNSOM is then introduced and applied to cluster Iris Flowers, Italian Olive Oils and Wine datasets. The hybrid methodology proves to be able to improve data clustering accuracy, reduce quantisation errors and decrease computational time when compared to SOM in all case studies. However, the topographic errors showed inconsistency throughout the studies and it is still difficult for both AANNSOM and SOM to provide additional inherent information of the datasets such as the exact position of a data in a cluster. Therefore, M3-SOM, a novel methodology based on SOM training algorithm is proposed, developed and studied on the same datasets. M3-SOM was able to improve data clustering and classification accuracy for all three case studies when compared to conventional SOM. It is also able to obtain inherent information about the position of one data or "sub-cluster" towards other data or sub-cluster within the same class in Iris Flowers and Wine datasets. Nevertheless, it faces difficulties in achieving the same level of performance when clustering Italian Olive Oils data due to high number of data classes. However, it can be concluded that both methodologies have been able to improve data clustering and classification performance as well as to discover inherent information inside multidimensional data

Classification for Driver’s Distraction and Drowsiness Through Eye Closeness Using Receiver Operating Curve (ROC)

In Malaysia, driver inattention and drowsiness becomes one of the causes of road accidents which sometime could lead to fatal ones. From the data provided by Malaysian Police Force, Polis Di Raja Malaysia or PDRM in 2016, deaths from road accidents increased from 6,706 in 2015 to 7,512 in 2016. Some accidents were caused by human factor such as driver's inattention and drowsiness. This problem motivates many parties to look for better solution in dealing with this human factor. Some of the car manufacturers have introduced to their certain models of car with an assistant system to oversee driver’s condition. The assistant system is in fact part of the main safety system known as Advanced Driver Assistance Systems (ADAS). The kind of system has been developed to strengthen vehicle systems for safety and conducive driving. The system has been contemplated to congregate accurate input, rapid processing data, precisely predict context, and respond in real time. In addition to that, suitable method is also needed to detect and classify driver drowsiness and inattention using computer vision as the latter become more and more important in any intelligent system development. In this paper, the proposed system introduces a method to classify drowsiness into three different classes of eye state; open, semi close and close. The classification has been done by using feature extraction method, percentage of eye closure (PERCLOS) technique and Support Vector Machine (SVM) classifier. The performances of the methods have been then measured and represented by using confusion matrix and ROC performance graph. The results have show that the proposed system has been able to achieve high performance of distraction and drowsiness detection according to driver's eye closeness level

Multi-type Noise Removal in Lead Frame Image Using Enhanced Hybrid Median Filter

Image filtering technique plays a very important role in digital image processing. It is one of the major steps in image enhancement and restoration. This filtering technique can remove noise and preserve the details of the image for feature extraction processes. However, filtering process can still be considered as a huge challenge for image filtering technique. Common noises in the image such as Salt & Pepper, Gaussian, Speckle, and Poisson Noise are still posing problems in filtering process where the quality and the originality of the images can be degraded and disturbed. Meanwhile, a single filtering technique is usually fit to deal with only certain specific noise. This paper presents an enhanced Hybrid Median Filter (H6F) technique to improve image filtering process. The technique involves 3x3 sub-mask and determination of new pixel value from the median value of the three steps which are the median calculation of ‘+’-neighbours, median calculation of all sub-masks and selection of centre pixel value. The H6F technique has been computed on lead frame inspection system. The results have shown that the technique has been able to remove multi-type of noises efficiently and produce exceptionally low Mean-Square Error (MSE) while consuming the acceptable amount of execution time when compared to other filtering techniques

Comparative Study of Machine Learning Algorithms and Correlation Between Input Parameters

The availability of big data and computing power have triggered a big success in Artificial Intelligence (AI) field. Machine Learning (ML) becomes major highlights in AI due to the ability of self-improved as it is fed with more data. Therefore, Machine Learning is suitable to be applied in financial industry especially in detecting financial fraud which is one of the main challenges in financial system. In this paper, 15 different types of supervised machine learning algorithms are studied in order to find the highest accuracy that should be able to detect credit card fraudulent transactions. The best algorithm among these algorithms is then further used and studied to find the correlation between the input variables and the accuracy of the results produced. The results have shown that Multilayer Perceptron (MLP) produced the highest accuracy among the 15 other algorithms with 98% accuracy of detection. Besides that, the input parameters also play an important role in determining the accuracy of the results. Based on the result, when input parameter known as ‘V4’ decreased, the recorded accuracy has increased to 99.17%

Improved salient object detection via boundary components affinity

Referring to the existing model that considers the image boundary as the image background, the model is still not able to produce an optimum detection. This paper is introducing the combination features at the boundary known as boundary components affinity that is capable to produce an optimum measure on the image background. It consists of contrast, spatial location, force interaction and boundary ratio that contribute to a novel boundary connectivity measure. The integrated features are capable to produce clearer background with minimum unwanted foreground patches compared to the ground truth. The extracted boundary features are integrated as the boundary components affinity. These features were used for measuring the image background through its boundary connectivity to obtain the final salient object detection. Using the verified datasets, the performance of the proposed model was measured and compared with the 4 state-of-art models. In addition, the model performance was tested on the close contrast images. The detection performance was compared and analysed based on the precision, recall, true positive rate, false positive rate, F Measure and Mean Absolute Error (MAE). The model had successfully reduced the MAE by maximum of 9.4%

Enhanced feature selections of Adaboost training for face detection using genetic algorithm

A wide variety of face detection techniques have been proposed over the past decades. Generally, a large number of features are required to be selected for training purposes. Often some of these features are irrelevant and do not contribute directly to the face detection techniques. This creates unnecessary computation and usage of large memory space. In this thesis, features search space has been enlarged by enriching it with seven additional new feature types. With these new feature types and larger search space, Genetic Algorithm (GA) is used within the Adaboost framework, to find sets of features which can provide a better cascade of boosted classifiers with a shorter training time. This technique is referred to as GABoost for this training part of a face detection system. The GA carries out an evolutionary search to select features which results in a higher number of feature types and sets selected in less time. Experiments on a set of images from BioID face database proved that by using GA to search on a large number of feature types and sets, the proposed technique referred to as GABoost was able to obtain the cascades of boosted classifiers for the face detection system that can give higher detection rates (94.25%), lower false positive rates (55.94%) and less training time (6.68 hours)

Enhanced feature selections of Adaboost training for face detection using genetic algorithm (GABoost)

Various face detection techniques has been proposed over the past decade. Generally, a large number of features are required to be selected for training purposes of face detection system. Often some of these features are irrelevant and does not contribute directly to the face detection algorithm. This creates unnecessary computation and usage of large memory space. In this paper we propose to enlarge the features search space by enriching it with more types of features. With an additional seven new feature types, we show how Genetic Algorithm (GA) can be used, within the Adaboost framework, to find sets of features which can provide better classifiers with a shorter training time. The technique is referred as GABoost for our face detection system. The GA carries out an evolutionary search over possible features search space which results in a higher number of feature types and sets selected in lesser time. Experiments on a set of images from BioID database proved that by using GA to search on large number of feature types and sets, GABoost is able to obtain cascade of boosted classifiers for a face detection system that can give higher detection rates, lower false positive rates and less training time

Enhancing feature selection for face detection using genetic algorithm

Generally, a large number of features are required to be selected for training purposes of face detection system. Often some of these features are irrelevant and does not contribute directly to the face detection algorithm. This creates unnecessary computation and usage of large memory space. In this paper we propose to enlarge the features search space by enriching it with more types of features. With an additional seven new feature types, we show how Genetic Algorithm (GA) can be used, within the Adaboost framework, to find sets of features which can provide better classifiers with a shorter training time

Evolutionary feature selections for face detection system

Various face detection techniques has been proposed over the past decade. Generally, a large number of features are required to be selected for training purposes of face detection system. Often some of these features are irrelevant and does not contribute directly to the face detection algorithm. This creates unnecessary computation and usage of large memory space. In this paper we propose to enlarge the features search space by enriching it with more types of features. With an additional seven new feature types, we show how Genetic Algorithm (GA) can be used, within the Adaboost framework, to find sets of features which can provide better classifiers with a shorter training time. The technique is referred as GABoost for our face detection system. The GA carries out an evolutionary search over possible features search space which results in a higher number of feature types and sets selected in lesser time. Experiments on a set of images from BioID database proved that by using GA to search on large number of feature types and sets, GABoost is able to obtain cascade of boosted classifiers for a face detection system that can give higher detection rates, lower false positive rates and less training time