An automated detection process for Eukaryotic ribonucleic acid (RNA) Polymerase II Promoter is presented in this dissertation. We employ an artificial neural network (ANN) in conjunction with features that were selected using an information-theoretic approach. Firstly an introduction is given where the problem is described briefly. Some background is given about the biological and genetic principles involved in DNA, RNA and Promoter detection. The automation process is described with each step given in detail. This includes the data information gathering, feature generation, and the full ANN process. The ANN section of the project is split up in a generation process, a training section as well as a testing section. Lastly the final detection program was tested and compared to other promoter detection systems. An improvement of at least 10% in positive prediction value (PPV) in comparison with current state-of-the-art solutions was obtained. Note: A Companion CD should accompany this report that contains all the program code and some of the source data that was used in this project. All the references to “Companion CD”, reference number [18] are references to these programs.acquisition process, how the different samples were split into different sets and statistical. / Dissertation (MEng (Computer Engineering))--University of Pretoria, 2007. / Electrical, Electronic and Computer Engineering / unrestricted

High blood pressure is a major public health issue. However, there are many physical and non-physical factors that affect the measurement of blood pressure (BP) over very short time spans. Therefore, it is very difficult to write a mathematical equation which includes all relevant factors needed to estimate accurate BP values. As a result, a possible solution to overcome these limitations is the use of an artificial neural network (ANN). The aim of this research is to design and implement a new ANN approach, which correlates the arterial pulse waveform shape to BP values, for estimation of BP in a single heartbeat. To test the feasibility of this approach, a pilot study was performed on an arterial pulse waveform dataset obtained from 11 patients with normal BP and 11 patients with hypertension. It was found that the proposed method can accurately estimate BP in single heartbeats and satisfy the requirements of the ANSI/AAMI standard for non-invasive measurement of BP.

Falls are a leading cause of injury and accidental injury death worldwide. Fall-risk prevention techniques exist but fall-risk identification can be difficult. While clinical assessment tools are the standard for identifying fall risk, wearable-sensors and machine learning could improve outcomes with automated and efficient techniques. Machine learning research has focused on older adults. Since people with lower limb amputations have greater falling and injury risk than the elderly, research is needed to evaluate these approaches with the amputee population. In this thesis, random forest and fully connected feedforward artificial neural network (ANN) machine learning models were developed and optimized for fall-risk identification in amputee populations, using smartphone sensor data (phone at posterior pelvis) from 89 people with various levels of lower-limb amputation who completed a 6-minute walk test (6MWT). The best model was a random forest with 500 trees, using turn data and a feature set selected using correlation-based feature selection (81.3% accuracy, 57.2% sensitivity, 94.9% specificity, 0.59 Matthews correlation coefficient, 0.83 F1 score). After extensive ANN optimization with the best ranked 50 features from an Extra Trees Classifier, the best ANN model achieved 69.7% accuracy, 53.1% sensitivity, 78.9% specificity, 0.33 Matthews correlation coefficient, and 0.62 F1 score. Features from a single smartphone during a 6MWT can be used with random forest machine learning for fall-risk classification in lower limb amputees. Model performance was similarly effective or better than the Timed Up and Go and Four Square Step Test. This model could be used clinically to identify fall-risk individuals during a 6MWT, thereby finding people who were not intended for fall screening. Since model specificity was very high, the risk of accidentally misclassifying people who are a no fall-risk individual is quite low, and few people would incorrectly be entered into fall mitigation programs based on the test outcomes.

Without improved water management the global population could be facing serious water shortages. River flow discharge rates are one factor that could contribute to improving water management, being able to predict a forecasted river flow value would provide support in the management of water resources. This research investigates the use of an artificial neural network (ANN) to create a model that predicts river flow gauge values. The Driel Barrage monitoring station on the Thukela river in South Africa was used as a case study. The research makes use of data from the Department of Water and Sanitation (DWS) and weather forecast data from the European Center For Medium- Range Forecasts (ECMWF) to train the predictive model. An evaluation of the ANN model identified that the model is highly sensitive to selected weather parameters and is sensitive to the initial weights used in the ANN. These were overcome using an ANN ensemble and selective scenarios to identify the best weather parameters to use as input into the ANN model. Five weather parameters and a correlation coefficient cut-off value produced the most accurate prediction by the ANN. The research found that ANNs can be used for predicting river flow gauge values but to improve the results a greater ensemble, additional data and different ANN structures may create a better performing model. For the ANN model to be used in practice the research needs to be extended to evaluate the whole catchment area and a range of rivers in South Africa. / Dissertation (MSc)--University of Pretoria, 2017. / Geography, Geoinformatics and Meteorology / MSc / Unrestricted

Prediction of stock prices is an important financial problem that is receiving increased attention in the field of artificial intelligence. Many different neural network and hybrid models for obtaining accurate prediction results have been proposed during the last few years in an attempt to outperform the traditional linear and nonlinear approaches. This study evaluates the performance of three different hybrid neural network models used for one-day stock close price prediction; a pre-processed evolutionary Levenberg-Marquardt neural network, Bayesian regularized artificial neural network and neural network with technical- and fractal analysis. It was also determined which of the three outperformed the others. The performance evaluation and comparison of the models are done using statistical error measures for accuracy; mean square error, symmetric mean absolute percentage error and point of change in direction. The results indicate good performance values for the Bayesian regularized artificial neural network, and varied performance for the others. Using the Friedman test, one model clearly is different in its performance relative to the others, probably the above mentioned model. The results for two of the models showed a large standard deviation of the error measurements which indicates that the results are not entirely reliable.

Yes / Corneal images can be acquired using confocal microscopes which provide detailed images of the different layers inside the cornea. Most corneal problems and diseases occur in one or more of the main corneal layers: the epithelium, stroma and endothelium. Consequently, for automatically extracting clinical information associated with corneal diseases, or evaluating the normal cornea, it is important also to be able to automatically recognise these layers easily. Artificial intelligence (AI) approaches can provide improved accuracy over the conventional processing techniques and save a useful amount of time over the manual analysis time required by clinical experts. Artificial neural networks (ANN) and adaptive neuro fuzzy inference systems (ANFIS), are powerful AI techniques, which have the capability to accurately classify the main layers of the cornea. The use of an ANFIS approach to analyse corneal layers is described for the first time in this paper, and statistical features have been also employed in the identification of the corneal abnormality. An ANN approach is then added to form a combined committee machine with improved performance which achieves an accuracy of 100% for some classes in the processed data sets. Three normal data sets of whole corneas, comprising a total of 356 images, and seven abnormal corneal images associated with diseases have been investigated in the proposed system. The resulting system is able to pre-process (quality enhancement, noise removal), classify (whole data sets, not just samples of the images as mentioned in the previous studies), and identify abnormalities in the analysed data sets. The system output is visually mapped and the main corneal layers are displayed. 3D volume visualisation for the processed corneal images as well as for each individual corneal cell is also achieved through this system. Corneal clinicians have verified and approved the clinical usefulness of the developed system especially in terms of underpinning the expertise of ophthalmologists and its applicability in patient care.

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