Modeling urban evolution by identifying spatiotemporal patterns and applying methods of artificial intelligence.Case study: Athens, Greece.

While during the past decades, urban areas experience constant slow population growth, the spatial patterns they form, by means of their limits and borders, are rapidly changing in a complex way. Furthermore, urban areas continue to expand to the expense of "rural” intensifying urban sprawl. The main aim of this paper is the definition of the evolution of urban areas and more specifically, the specification of an urban model, which deals simultaneously with the modification of population and building use patterns. Classical theories define city geographic border, with the Aristotelian division of 0 or 1 and are called fiat geographic boundaries. But the edge of a city and the urbanization "degree" is something not easily distinguishable. Actually, the line that city ends and rural starts is vague. In this respect a synthetic spatio - temporal methodology is described which, through the adaptation of different computational methods aims to assist planners and decision makers to gain an insight in urban - rural transition. Fuzzy Logic and Neural Networks are recruited to provide a precise image of spatial entities, further exploited in a twofold way. First for analysis and interpretation of up - to - date urban evolution and second, for the formulation of a robust spatial simulation model, the theoretical background of which is that the spatial contiguity between members of the same or different groups is one of the key factors in their evolution. The paper finally presents the results of the model application in the prefecture of Attica in Greece, unveiling the role of the Athens Metropolitan Area to its current and future evolution, by illustrating maps of urban growth dynamics.urban growth; urban dynamics; neural networks; fuzzy logic; Greece; Athens

Neural networks using homotopy continuation methods

http://www.worldcat.org/oclc/2527077

The forecasting of transmission network loads

The forecasting of Eskom transmission electrical network demands is a complex task. The lack of historical data on some of the network components complicates this task even further. In this dissertation a model is suggested which will address all the requirements of the transmission system expansion engineers in terms of future loads and market trends. Suggestions are made with respect to ways of overcoming the lack of historical data, especially on the point loads, which is a key factor in modelling the electrical networks. A brief overview of the transmission electrical network layout is included to provide a better understanding of what is required from such a forecast. Lastly, some theory on multiple regression, neural networks and qualitative forecasting techniques is included, which will be of value for further model developments.ComputingM. Sc. (Operations Research

Cerebellar models of associative memory: Three papers from IEEE COMPCON spring 1989

Three papers are presented on the following topics: (1) a cerebellar-model associative memory as a generalized random-access memory; (2) theories of the cerebellum - two early models of associative memory; and (3) intelligent network management and functional cerebellum synthesis

Statistical modelling by neural networks

In this thesis the two disciplines of Statistics and Artificial Neural Networks are combined into an integrated study of a data set of a weather modification Experiment. An extensive literature study on artificial neural network methodology has revealed the strongly interdisciplinary nature of the research and the applications in this field. An artificial neural networks are becoming increasingly popular with data analysts, statisticians are becoming more involved in the field. A recursive algoritlun is developed to optimize the number of hidden nodes in a feedforward artificial neural network to demonstrate how existing statistical techniques such as nonlinear regression and the likelihood-ratio test can be applied in innovative ways to develop and refine neural network methodology. This pruning algorithm is an original contribution to the field of artificial neural network methodology that simplifies the process of architecture selection, thereby reducing the number of training sessions that is needed to find a model that fits the data adequately. [n addition, a statistical model to classify weather modification data is developed using both a feedforward multilayer perceptron artificial neural network and a discriminant analysis. The two models are compared and the effectiveness of applying an artificial neural network model to a relatively small data set assessed. The formulation of the problem, the approach that has been followed to solve it and the novel modelling application all combine to make an original contribution to the interdisciplinary fields of Statistics and Artificial Neural Networks as well as to the discipline of meteorology.Mathematical SciencesD. Phil. (Statistics

Social determinants and child survival in Nigeria in the era of Sustainable Development Goals: Progress, challenges, and opportunities

Introduction: Like in many low- and middle-income settings, childhood mortality remains a big challenge in Nigeria—being the second largest contributor to under-five mortality globally, after India. Currently, there is little local evidence to guide policymakers in Nigeria to tailor appropriate social interventions to make the Sustainable Development Goal (SDG) targets of child survival (SDG-3), gender equality (SDG-5), and social inclusiveness (SDG-10) achievable by 2030. In addition, lack of methodological rigor and theoretical foundations of child survival research in Nigeria limit their use for proper planning of child health services. Aims: The basis of this thesis is to understand the complex issues relating to child survival and recommend new approaches to guide policymakers on interventions that will improve child survival in Nigeria. The overarching goal of this thesis is to address the methodological and theoretical shortcomings identified in the previous studies conducted in Nigeria. Using robust interdisciplinary analytic techniques, this thesis assessed the following specific objectives. Objective 1: (a) Compare predictive abilities of the most used conventional statistical time-series methods—ARIMA and Holt-Winters exponential smoothing models, with artificial intelligence technique such as group method of data handling (GMDH)-type artificial neural network (ANN), and (b) estimate the age- and sex-specific mortality trends in child-related SDG indicators (i.e., neonatal and under-five mortality rates) over the 1960s-2017 period, and estimate the expected annual reduction rates needed to achieve the SDG-3 targets by projecting rates from 2018 to 2030. Objective 2: (a) Identify the social determinants of age-specific childhood (0-59 months) mortalities, which are disaggregated into neonatal mortality (0-27 days), post-neonatal mortality (1-11 months) and child mortality (12-59 months), and (b) estimate the within- and between-community variations of mortality among under-five children in Nigeria. Objective 3: Identify the critical pathways through which social factors (at maternal, household, community levels) determine neonatal, infant, and under-five mortalities in Nigeria. Objective 4: (a) Determine patterns and determinants of geographical clustering of neonatal mortality at the state and regional levels in Nigeria, (b) assess gender inequity for neonatal mortality between urban and rural communities across the regions in Nigeria, and (c) measure gaps in SDG-3 target for neonatal mortality at the state and regional levels in Nigeria. Methods: This thesis is a quantitative study which used two secondary datasets—aggregated historical childhood mortality data from 1960s to 2017 (objective 1), and the latest (2016/2017) Nigeria Multiple Indicator Cluster Survey (MICS) for 36 states and Federal Capital Territory (FCT) in Nigeria (objectives 2-4). To minimize recall bias, analysis was limited to a weighted nationally representative sample of 30,960 live births delivered within five years before the survey. The selection of relevant social determinants of child survival was primarily informed by Mosley-Chen framework. The candidate variables were layered across child, maternal, household, and community-levels. The analytic approaches include artificial intelligence technique (i.e., group method of data handling (GMDH)-type artificial neural network, and multilayer perceptron (MLP) neural network), autoregressive integrated moving average (ARIMA), Holt-Winters exponential smoothing models, spatial cluster analysis, hierarchical path analysis with time-to-event outcome, and multilevel multinomial regression. Results: Progress towards achieving SDG targets – Nigeria is not likely to achieve SDG targets for child survival and, within, gender equity by 2030 at the current annual reduction rates (ARR) under-five mortality rate (U5MR): 1.2%, and neonatal mortality rate (NMR): 2.0%. If the current trend continues, U5MR will begin to increase by 2028. Also, at the end of SDG-era, female deaths will be higher than male deaths (80.9 vs. 62.6 deaths per 1000 live births). To make child-related SDG targets achievable by 2030, Nigeria needs to reduce annual U5MR by 9 times and annual NMR by 4 times the current rate of decrease. Social determinants of childhood mortality – At each stage of early childhood development, there are different factors relating to survival outcomes. Surprisingly, attendance of skilled health providers during delivery was associated with an increased neonatal mortality risk, although its effect disappeared during post-neonatal and toddler/pre-school stages. The observed association requires cautious interpretation because of unavailability of variables on quality of care in MICS dataset to assess how skilled birth delivery impacts child survival in Nigeria. However, there is a possibility of under-reporting under-five mortalities at the community level. Also, it could indicate a functioning referral system that sends the high-risk deliveries to health facilities to a greater extent. There is a large variation (39%) of under-five mortalities across the Nigerian communities, which is accounted for by maternal-level factors (i.e., maternal education, contraceptive use, maternal wealth, parity, death of previous children and quality of perinatal care). Pathways to childhood mortality – Region and area of residence (urban/rural), infrastructural development, maternal education, contraceptive use, marital status, and maternal age at birth were found to operate indirectly on neonatal, infant and under-five survival. Female children, singleton, children whose mothers delivered at least two years apart and aged 20-34 years survived much longer. Specifically, women from Northern areas of Nigeria were less likely to reside in urban cities and towns than those in the Southern areas. This, in turn, limited their access to social infrastructure and acted as a barrier to maternal education. Without adequate education, women were less likely to use contraceptive methods. Women with no history of contraceptive use were more likely to have childbirths closer together (less than two-year gap), which in turn, negatively impacted child survival. Regional inequities in childhood mortality – There was significant state-level clustering of NMR in Nigeria. The states with higher neonatal mortality rates were majorly clustered in the North-West and North-Central regions, and states with lower neonatal mortality rates were clustered in the South-South and South-East regions. Gender inequity was worse in the rural areas of Northern Nigeria, while it was worse in the urban areas of Southern Nigeria. NMR was disproportionately higher among females in urban areas (except North-West and South-West regions). Conversely, male neonates had higher mortality risks in the rural areas for all the regions. Conclusions: This thesis provides more refined age- and sex-specific mortality estimates for Nigeria. At the current rates, Nigeria will not meet SDG targets for child survival. In addition, this thesis identifies the critical intervention pathways to child survival in Nigeria during the SDG-era. The new estimates may be used to improve the design and accelerate the implementation of child health programmes to attain the SDG targets. Also, it is important for stakeholders to implement more impactful policies that promote maternal education and improve living conditions of women (especially in the rural areas). To address gender inequities, gender-sensitive policies, and community mobilization against gender-based discrimination towards girl-child should be implemented. Further research is required to assess the quality of skilled birth attendants in Nigeria

Complex Neural Networks for Audio

Audio is represented in two mathematically equivalent ways: the real-valued time domain (i.e., waveform) and the complex-valued frequency domain (i.e., spectrum). There are advantages to the frequency-domain representation, e.g., the human auditory system is known to process sound in the frequency-domain. Furthermore, linear time-invariant systems are convolved with sources in the time-domain, whereas they may be factorized in the frequency-domain. Neural networks have become rather useful when applied to audio tasks such as machine listening and audio synthesis, which are related by their dependencies on high quality acoustic models. They ideally encapsulate fine-scale temporal structure, such as that encoded in the phase of frequency-domain audio, yet there are no authoritative deep learning methods for complex audio. This manuscript is dedicated to addressing the shortcoming. Chapter 2 motivates complex networks by their affinity with complex-domain audio, while Chapter 3 contributes methods for building and optimizing complex networks. We show that the naive implementation of Adam optimization is incorrect for complex random variables and show that selection of input and output representation has a significant impact on the performance of a complex network. Experimental results with novel complex neural architectures are provided in the second half of this manuscript. Chapter 4 introduces a complex model for binaural audio source localization. We show that, like humans, the complex model can generalize to different anatomical filters, which is important in the context of machine listening. The complex model\u27s performance is better than that of the real-valued models, as well as real- and complex-valued baselines. Chapter 5 proposes a two-stage method for speech enhancement. In the first stage, a complex-valued stochastic autoencoder projects complex vectors to a discrete space. In the second stage, long-term temporal dependencies are modeled in the discrete space. The autoencoder raises the performance ceiling for state of the art speech enhancement, but the dynamic enhancement model does not outperform other baselines. We discuss areas for improvement and note that the complex Adam optimizer improves training convergence over the naive implementation