MARY AKINYEMI

BACKGROUND Natural Language Processing models have wide and growing use in clinical and healthcare domains. Such applications enable scalable, efficient delivery of health information, but they are prone to equity challenges in their effectiveness across demographics and contexts. These models are only as good as the data they are trained on, the type of training, and parameters. Moreover, they are highly sensitive to latent demographic signals such as gender, age, nationality, and native language. Applications with biased components lead to inequitable outcomes. These accessibility challenges are more prevalent in rural regions of the world. OBJECTIVE This paper describes and evaluates a novel active learning approach for incrementally improving the accuracy of a Natural Language Processing (NLP), while optimizing for gender-equitable outcomes in healthcare systems. The approach employs an iterative cyclic model, incorporating data annotation using NLP, human auditing to improve th...

Financial markets play an essential role in developing modern society and enabling the deployment of economic resources. This study focuses on predicting stock prices using deep learning models. In particular, the daily closing prices of two different stocks from the Casablanca Stock Market Viz Bank of Africa and Itissalat Al-Maghrib (IAM) are considered. The datasets were pre-processed and passed through the Long Short-Term Memory (LSTM), Multi-Layer Perceptron (MLP), and Convolutional Neural Networks (CNN) models. The models’ performances were compared based on the performance evaluation metrics, viz: mean squared error (MSE) and root mean squared error (RMSE) and Mean Absolute Error (MAE). The paper proposes a novel hybrid model. The hybrid design of the model improves its predictive power as the results of the Hybrid network performance surpassed all the other models.

Abstract A family of Exponentially Fitted Block Backward Differentiation Formulas (EFBBDFs) whose coefficients depend on a parameter and step-size is developed and implemented on the Black–Scholes partial differential equation (PDE) for the valuation of options on a non-dividend-paying stock. Specific EFBBDFs of order 2 and 4 are applied to solve the PDE after reducing it into a system of ordinary differential equations via the method of lines. The methods are shown to be superior to the well-known Crank–Nicolson method since they are -stable and do not exhibit oscillations usually triggered by discontinuities inherent in the payoff function of financial contracts. We confirmed the accuracy of the methods by initially applying them to a prototype example based on the one-dimensional time-dependent convection–diffusion equation with a known analytical solution. It is demonstrated that the American put can be exercised early by computing the hedging parameter “delta”, which specifies the condition for early exercise of the put option. Although the methods can be used to price all vanilla options, we elect to focus on the put due to its optimality.

With the advent of the SARS-CoV-2 pandemic, Wastewater-Based Epidemiology (WBE) has been applied to track community infection in cities worldwide and has proven succesful as an early warning system for identification of hotspots and changingprevalence of infections (both symptomatic and asymptomatic) at a city or sub-city level. Wastewater is only one of environmental compartments that requires consideration. In this manuscript, we have critically evaluated the knowledge-base and preparedness for building early warning systems in a rapidly urbanising world, with particular attention to Africa, which experiences rapid population growth and urbanisation. We have proposed a Digital Urban Environment Fingerprinting Platform (DUEF) – a new approach in hazard forecasting and early-warning systems for global health risks and an extension to the existing concept of smart cities. The urban environment (especially wastewater) contains a complex mixture of substances including toxic chemicals, infectious biological agents and human excretion products. DUEF assumes that these specific endo- and exogenous residues, anonymously pooled by communities’ wastewater, are indicative of community-wide exposure and the resulting effects. DUEF postulates that the measurement of the substances continuously and anonymously pooled by the receiving environment (sewage, surface water, soils and air), can provide near real-time dynamic information about the quantity and type of physical, biological or chemical stressors to which the surveyed systems are exposed, and can create a risk profile on the potential effects of these exposures. Successful development and utilisation of a DUEF globally requires a tiered approach including: Stage I: network building, capacity building, stakeholder engagement as well as a conceptual model, followed by Stage II: DUEF development, Stage III: implementation, and Stage IV: management and utilization. We have identified four key pillars required for the establishment of a DUEF framework: (1) Environmental fingerprints, (2) Socioeconomic fingerprints, (3) Statistics and modelling and (4) Information systems. This manuscript critically evaluates the current knowledge base within each pillar and provides recommendations for further developments with an aim of laying grounds for successful development of global DUEF platforms.

In the developing nations that are located in the tropical region; there is a growing trend of fire incidence in buildings without adequate development of fire prevention and/or reduction protocol. Thus, this study addresses the growth and spread of fire in multi-storey buildings. The rooms are structured as cells in order to reduce the flame spread from a single fuel item, by heat release, to other neighbouring items or rooms (otherwise known as cells). The philosophy is to reduce the advent of vertical and horizontal fire spread. Thus, the mathematical model for the spread of fire in buildings over a solid fuel surface is therefore developed using the adaptation, development and simulation of cellular automata (CA) discrete model. The von Neumann neighbourhood cell configuration is adopted. Hence, the surface of the fuel is analysed using a regular square array (i.e. cells), while the flame spread is depicted as a series of ignitions of surface elements. In which case, ignition of...

For the first time, a location-scale regression model based on the logarithm of an extended Raleigh Lomax distribution which has the ability to deal and model of any survival data than classical regression model is introduced. We obtain the estimate for the model parameters using the method of maximum likelihood by considering breast cancer data. In addition, normal probability plot of the residual is used to detect the outliers and evaluate model assumptions. We use a real data set to illustrate the performance of the new model, some of its submodels and classical models consider in the study. Also, we perform the statistics AIC, BIC and CAIC to select the most appropriate model among those regression models considered in the study.

This thesis extensively studies the class of Mixture autoregressive (MAR) models in terms of its asymptotic properties and applications to financial risk evaluation. We establish geometric ergodicity of the MAR models and by implication absolute regular and strong-mixing properties of the models. In addition, we also show the consistency and asymptotic normality of the maximum likelihood estimators of the MAR models. We compare the estimates of Value at Risk (VaR) and Expected Shortfall (ES) based on the MAR models to estimates based on a number of other methods, for individual stocks, exchange rates and stock indices. We find that the MAR models consistently perform better than the other models. In addition, tail density forecast performance of individual stocks, stock indices and exchange rate, based on some popular GARCH models are compared to tail forecasts based on MAR models with both Gaussian and Student-t innovations. The MAR models mostly outperform the other models. Confir...

Nonparametric supervised learning algorithms represent a succinct class of supervised learning algorithms where the learning parameters are highly flexible and whose values are directly dependent on the size of the training data. In this paper, we comparatively study the properties of four nonparametric algorithms, k-Nearest Neighbours (k-NNs), Support Vector Machines (SVMs), Decision trees and Random forests. The supervised learning task is a regression estimate of the time lapse in medical insurance reimbursement. Our study is concerned precisely with how well each of the nonparametric regression models fits the training data. We quantify the goodness of fit using the R-squared metric. The results are presented with a focus on the effect of the size of the training data, the feature space dimension and hyperparameter optimization. The findings suggest k-NN’s and SVM’s algorithms as better models in predicting well-defined output labels (i.e. Time lapse in days). However, overall, ...

In the present study, the Directed Acyclic Graph (DAG) was used for the first time to analyze health insurance data. The objective was to find relationships between 89 diagnoses in reference to health insurance data from Nigeria. Unlike regression analysis, a classical method was used to explore relationships between variables, and DAG had the key advantage of establishing the direction of the relationships identified.

Density forecasts have become more popular as real life scenarios require not only a forecast estimate but also the uncertainty associated with such a forecast. The class of mixture autoregressive (MAR) models provide a flexible way to model various features of financial time series and are also suitable for density forecasting. This study forecasted the out-of-sample tail density of Nigerian foreign exchange rates using MAR models with Student-t innovations. The model parameters were estimated using the maximum likelihood method. The forecast results of the MAR model were compared with some competing asymmetric Generalised Autoregressive Conditional Heteroskedastic (GARCH) models. Comparisons were based on the Berkowitz tail test. The test results suggested that the MAR model provided the best out-of-sample tail-density forecasts. The findings support the suggestion that the MAR models are well suited to capture the kind of data dynamics present in financial data and provide a usef...

Geometric ergodicity is very useful in establishing mixing conditions and central limit results for parameter estimates of a model. It also justifies the use of laws of large numbers and forms part of the basis for exploring the asymptotic theory of a model. The class of mixture autoregressive (MAR) models provides a flexible way to model various features of time series data and is well suited for density forecasting. The MAR models are able to capture many stylised properties of real data, such as multimodality, asymmetry and heterogeneity. We show here that the MAR model is geometrically ergodic and by implication satisfies the absolutely regular and strong mixing conditions.

Background: Domiciliary cockroaches are obnoxious pests of significant medical importance. We investigated the prevalence of human intestinal parasites in cockroaches and its attendant public health importance. Methods: Overall, 749 cockroaches (Periplaneta americana, 509, Blattella germanica, 240) caught by trapping from 120 households comprising 3 different housing types in Somolu, Lagos metropolis, southwest Nigeria, in 2015 were screened for human intestinal parasites using standard parasitological techniques. Results: The prevalence of human intestinal parasites in cockroaches was 96.4%. There was no statistically signifi­cant difference (P> 0.05) in parasite prevalences between P. americana (95.7%) and B. germanica (97.9%). Parasite species identified and their prevalence were as follows: Entamoeba histolytica/dispar (44.1%), E. coli (37.8%), Gi­ardia lamblia (18.7%), Cryptosporidium sp. (13.8%), Ascaris lumbricoides (61.3%), Trichuris trichiura (55.8%), hookworms (11.6%), ...