Digital Commons Network^™

Enhanced Heart Rate Prediction Model Using Damped Least-Squares Algorithm, Angela An, Mohammad Al-Fawa’Reh, James Jin Kang

Research outputs 2022 to 2026

Monitoring a patient’s vital signs is considered one of the most challenging problems in telehealth systems, especially when patients reside in remote locations. Companies now use IoT devices such as wearable devices to participate in telehealth systems. However, the steady adoption of wearables can result in a significant increase in the volume of data being collected and transmitted. As these devices run on limited battery power, they can run out of power quickly due to the high processing requirements of the device for data collection and transmission. Given the importance of medical data, it is imperative that all transmitted data …

Facial Re-Enactment, Speech Synthesis And The Rise Of The Deepfake, Nicholas Gardiner

Theses : Honours

Emergent technologies in the fields of audio speech synthesis and video facial manipulation have the potential to drastically impact our societal patterns of multimedia consumption. At a time when social media and internet culture is plagued by misinformation, propaganda and “fake news”, their latent misuse represents a possible looming threat to fragile systems of information sharing and social democratic discourse. It has thus become increasingly recognised in both academic and mainstream journalism that the ramifications of these tools must be examined to determine what they are and how their widespread availability can be managed.

This research project seeks to examine …

Influence Of Neural Network Training Parameters On Short-Term Wind Forecasting, Adel Brka, Yasir M. Al-Abdeli, Ganesh Kothapalli

Research outputs 2014 to 2021

This paper investigates factors which can affect the accuracy of short-term wind speed prediction when done over long periods spanning different seasons. Two types of neural networks (NNs) are used to forecast power generated via specific horizontal axis wind turbines. Meteorological data used are for a specific Western Australian location. Results reveal that seasonal variations affect the prediction accuracy of the wind resource, but the magnitude of this influence strongly depends on the details of the NN deployed. Factors investigated include the span of the time series needed to initially train the networks, the temporal resolution of these data, the …

An Investigation Into The Use Of Neural Networks For The Prediction Of The Stock Exchange Of Thailand, Suchira Chaigusin

Theses: Doctorates and Masters

Stock markets are affected by many interrelated factors such as economics and politics at both national and international levels. Predicting stock indices and determining the set of relevant factors for making accurate predictions are complicated tasks. Neural networks are one of the popular approaches used for research on stock market forecast. This study developed neural networks to predict the movement direction of the next trading day of the Stock Exchange of Thailand (SET) index. The SET has yet to be studied extensively and research focused on the SET will contribute to understanding its unique characteristics and will lead to identifying …

Firearm Identification With Hierarchical Neural Networks By Analyzing The Firing Pin Images Retrieved From Cartridge Cases, Dongguang Li

Research outputs pre 2011

When a gun is fired, characteristic markings on the cartridge and projectile of a bullet are produced. Over thirty different features can be distinguished from observing these marks, which in combination produce a "fingerprint" for identification of a firearm. ln this paper, through the use of hierarchial neural networks a firearm identification system based on cartridge case images is proposed. We focus on the cartridge case identification of rim-fire mechanism. Experiments show that the model proposed has high performance and robustness by integrating two levels Self- Organizing Feature Map (SOFM) neural networks and the decision-making strategy. This model will also …

A Generalised Feedforward Neural Network Architecture And Its Applications To Classification And Regression, Ganesh Arulampalam

Theses: Doctorates and Masters

Shunting inhibition is a powerful computational mechanism that plays an important role in sensory neural information processing systems. It has been extensively used to model some important visual and cognitive functions. It equips neurons with a gain control mechanism that allows them to operate as adaptive non-linear filters. Shunting Inhibitory Artificial Neural Networks (SIANNs) are biologically inspired networks where the basic synaptic computations are based on shunting inhibition. SIANNs were designed to solve difficult machine learning problems by exploiting the inherent non-linearity mediated by shunting inhibition. The aim was to develop powerful, trainable networks, with non-linear decision surfaces, for classification …

Contrast Enhancement Of Ultrasound Images Using Shunting Inhibitory Cellular Neural Networks, Murali M. Gogineni

Theses: Doctorates and Masters

Evolving from neuro-biological insights, neural network technology gives a computer system an amazing capacity to actually generate decisions dynamically. However, as the amount of data to be processed increases, there is a demand for developing new types of networks such as Cellular Neural Networks (CNN), to ease the computational burden without compromising the outcomes. The objective of this thesis is to research the capability of Shunting Inhibitory Cellular Neural Networks (SICNN) to solve the clarity problems in ultrasound imaging. In this thesis, we begin by reviewing a number of traditional enhancement techniques and measures. Since the entire work of this …

An Adaptive Hierarchical Fuzzy Logic System For Modelling And Prediction Of Financial Systems, Mark Kingham

Theses: Doctorates and Masters

In this thesis, an intelligent fuzzy logic system using genetic algorithms for the prediction and modelling of interest rates is developed. The proposed system uses a Hierarchical Fuzzy Logic system in which a genetic algorithm is used as a training method for learning the fuzzy rules knowledge bases. A fuzzy logic system is developed to model and predict three month quarterly interest rate fluctuations. The system is further trained to model and predict interest rates for six month and one year periods. The proposed system is developed with first two, three, then four and finally five hierarchical knowledge bases to …

A Model Of Visual Recognition Implemented Using Neural Networks, Vincent C. Phillips

Theses: Doctorates and Masters

The ability to recognise and classify objects in the environment is an important property of biological vision. It is highly desirable that artificial vision systems also have this ability. This thesis documents research into the use of artificial neural networks to implement a prototype model of visual object recognition. The prototype model, describing a computtional architecture, is derived from relevant physiological and psychological data, and attempts to resolve the use of structural decomposition and invariant feature detection. To validate the research a partial implementation of the model has been constructed using multiple neural networks. A linear feed-forward network performs pre-procesing …

Applications Of Fuzzy Counterpropagation Neural Networks To Non-Linear Function Approximation And Background Noise Elimination, I. M. Wiryana

Theses: Doctorates and Masters

An adaptive filter which can operate in an unknown environment by performing a learning mechanism that is suitable for the speech enhancement process. This research develops a novel ANN model which incorporates the fuzzy set approach and which can perform a non-linear function approximation. The model is used as the basic structure of an adaptive filter. The learning capability of ANN is expected to be able to reduce the development time and cost of the designing adaptive filters based on fuzzy set approach. A combination of both techniques may result in a learnable system that can tackle the vagueness problem …

An Examination And Analysis Of The Boltzmann Machine, Its Mean Field Theory Approximation, And Learning Algorithm, Vincent Clive Phillips

Theses : Honours

It is currently believed that artificial neural network models may form the basis for inte1ligent computational devices. The Boltzmann Machine belongs to the class of recursive artificial neural networks and uses a supervised learning algorithm to learn the mapping between input vectors and desired outputs. This study examines the parameters that influence the performance of the Boltzmann Machine learning algorithm. Improving the performance of the algorithm through the use of a naïve mean field theory approximation is also examined. The study was initiated to examine the hypothesis that the Boltzmann Machine learning algorithm, when used with the mean field approximation, …