School of Physical and Mathematical Sciences
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Item A Dynamic Software Startup Competency Model(2019) Assyne, N.; Wiafe, I.Current literature suggests that the engineering activities of software engineering and software startup engineering differ. Thus, there is a need to elicit competencies specific to software startup engineering. This paper proposes a model that provides the various types of competencies and their respective relevance at the various stages of software startup evolution.Item Modeling Large Insurance Claims Using Extreme Value Theory: A Case Study Of The 37 Military Hospital.(University Of Ghana, 2020) Collins, A.The private health insurance industry is one of the vital components in nation-building. It complements government’s efforts in reducing “out-of-pocket” payment for healthcare services in the country. However, some private health insurance companies face severe insolvency issues due to accumulation of unanticipated huge claim amounts. The Extreme Value Theory (EVT) is a statistical tool proven to help solve or mitigate some of these challenges since it focuses mainly on the behaviour of severe but rare occurrence. In this study, we employ the EVT approaches to model large insurance claims from the 37 Military hospital; and to estimate financial risk indicators such as Value-at-Risk (VaR) and Expected Shortfall (ES) among other extreme quantiles. Conclusions drawn from analysis established that the Weibull class of distributions is more appropriate for the data at hand and for this reason, it is not likely for the 37 Military hospital to submit claim amount exceeding 24,618 cedis for any given day. In addition, private health insurance firms can be assured at a confidence level of 99%, 99.5% and 99.9% that within a day, the hospital is not likely to submit a claim amount exceeding 2,910 cedis, 3,938 cedis and 7,946 cedis respectively. Finally, it was recommended that the NHIA could replicate this study using the claims received by the public health insurance scheme (i.e. NHIS) since it can go a long way to strengthen the financial sustainability of the scheme.Item Aflatoxin Levels In Groundnut And Maize Crops And Risk Assessment Based On Consumption In Ghana(University of Ghana, 2023-06) Opoku, R.B.Aflatoxins are a type of mycotoxins that contribute to about 25% loss of annual crop production worldwide. Significant human exposure to aflatoxins is associated with detrimental health implications. Thus, the extensive presence of aflatoxins in food and feed poses a huge threat to public health in many countries in Africa including Ghana. Staples foods such as groundnut and maize are highly susceptible to aflatoxin contamination. Despite their broad exposure, research on aflatoxins in food and exposure to the population is limited and mostly restricted to exported foods. This exposes millions of people to potentially acute or chronic amounts of aflatoxins. In this work, the concentrations of aflatoxins AFB1, AFB2, AFG1, and AFG2 level in 303 samples, including 165 samples of maize and 138 samples of groundnut, obtained from homes, markets, and storage centres in eight regions of Ghana was carried out. The samples were analyzed by extracting aflatoxin with methanol/water, cleaned up on an immunoaffinity column and analysed using Reverse-Phase High-Performance Liquid Chromatography (RP-HPLC) with fluorescence detection. Based on the data obtained, aflatoxins were quantified in 80.6% of the maize samples with levels ranging from 0.20 to 1129.7 μg/kg. The quantified levels of aflatoxins in groundnut samples ranged from 0.20 to 1242.9 μg/kg, with an occurrence of 73.9% in samples. Total aflatoxins were present in more than 50% of maize and 26% of groundnut samples at concentrations that exceeded the Ghanaian standard of 10 μg/kg. The study suggests that the Ghanaian population may be exposed to aflatoxins at significant concentrations, given that, the values obtained in this study represent some of the highest levels and prevalence recorded in the country. This study provides useful data for policy decision-making on the prioritization of aflatoxin as a significant food safety concern in Ghana.Item Application Of The Rainfall Infiltration Breakthrough (Rib) Model For Groundwater Recharge Estimation In The Birim North District Of Eastern Region, Ghana(University Of Ghana, 2022-07) Antwi, N.D.Evaluation of Groundwater recharge from rainfall is essential for sustainable water resources management, particularly in arid and semi-arid environments. The Birim North District, where majority of the population relies on groundwater due to pollution of surface water sources, has already experienced a drop in groundwater levels attending to the cumulative impacts of human activities and climate change. This project applies the Rainfall Infiltration Breakthrough (RIB) methodology to estimate groundwater recharge in the shallow unconfined, saprolite aquifer system in the Birimian Province in Southwestern Ghana. The Water level fluctuation (WTF) approach was used to estimate groundwater recharge in order to check, augment, and confirm the Rainfall Infiltration Breakthrough (RIB) recharge estimates by comparing such groundwater recharge estimates. The specific yield values acquired from the previous studies were compared to those acquired using the linear regression model as a quality assurance measure. The validity of the analysis, i.e., the association between rainfall and groundwater level, was established as a result of this. The line drawn in the regression model for determining the specific yield corresponded to 0.06, which was close to the value (0.05) obtained from literature. The RIB model estimated local recharge at 2.9 % to 21.4% of mean annual precipitation (MAP). The WTF approach estimated recharge to be between 3.2 % to 22.6 %. The prediction showed that decreased rainfall had no effect on groundwater levels during the simulation period in the climate scenario analysis. However, the ratio of recharge rate to precipitation did not alter considerably; it was somewhat greater than the baseline. Correlation examination of rainfall and observed water level fluctuation (WTF) data at the monthly scale, along with recharge estimates derived from other approaches, indicated that the RIB results based on monthly data were plausible and could thus be utilized as recharge estimates. These findings suggested that using these methods to estimate groundwater recharge provides opportunities for assessing temporal variations in groundwater recharge and thus facilitates groundwater resources management. The method can estimate groundwater recharge in similar regions with adequately long time series of rainfall and groundwater levels. The RIB model is particularly suitable for shallow unconfined aquifers with minimal transmissivity; nonetheless, the RIB model's utility for application in various climatic locations and hydrogeological circumstances needs to be further investigated. These strategies could be tested in the future in catchments that have similar conditions of physiographic and hydrogeologic systems to the current research region.Item An Improved Hair Removal Method For Dermoscopic Images Towards Improving Skin Lesion Detection And Diagnosis(University Of Ghana, 2022-07) Kakrabah, M.N.Clinicians are continually improving their diagnostic abilities in the ever-changing medical field. A proper diagnosis is imperative for further health care delivery decisions. Skin cancers appear as lesions on any part of the skin, although most appearances are on body parts exposed to sunlight. Lesions may be either malignant or benign. The cells in malignant skin lesions can proliferate and spread to other tissues in other body parts. Malignant skin lesions are among the most lethal types of skin cancers, responsible for increased mortality rates. The diagnosis of skin diseases at the preliminary stages has been demonstrated to improve patients’ chances of abidance significantly, while the mortality rate for cases with late detection is on the rise. Computer-Aided Diagnostic systems play a very relevant role in thwarting the prevalence of skin cancer-related deaths by assisting in detecting skin lesions and assisting in diagnosis. Image analysis is enhanced by segmented images, highlighting previously undiscovered features of the original image. When detecting skin diseases from dermoscopic images, segmentation is used to separate the region of interest from the background. Lesions hidden behind elements such as hair, blue-white regions, globules, and an unusual pigment network can only be retrieved with accurate lesion border detection. Consequently, the most important phase is lesion segmentation in the early detection of melanoma. Detection of lesion borders in dermoscopy pictures is difficult because of the presence of noise such as skin lines, air bubbles, hairs and reflections. These are capable of causing significant segmentation mistakes when they are not adequately addressed. The existence of hairs is considered one of the most significant stumbling blocks for most algorithms when it comes to proper segmentation. The similarity of the hair color to the surrounding skin region is one of the main reasons for the difficulty. Improperly addressed hairs often tend to be classified as lesions, resulting in inaccurate segmentation and disease classification. In this thesis, selected hair removal and segmentation algorithms are evaluated and the effects of the hair removal method on the output of segmentation are also evaluated. In analyzing and evaluating a leading method for eliminating hairs in dermoscopic images, the dissertation presents an experimental implementation of a sequence of steps curated based on existing studies to improve segmentation results and justify the impact of these hair removal techniques on the output of image segmentation algorithms. This work uses Blackhat morphological filtering and Otsu’s method for thresholding and image inpainting to achieve segmentation with notable improvements recorded.Item Multiple Bug Detection And Effort Estimation Framework For Open-Source Projects(University Of Ghana, 2021-09) Hara, A.Bug reports are essential in the development and maintenance of software. Bug tracking systems allow testers to submit bug reports which allow for report analysis and assignment of reports to fixers to address them. A given bug x is described as multiple bug when it is reported by more than two bug reporters. It is described as a duplicate bug when it was reported by two reporters. In a given pool of bug reports from a tracking system, estimating the effort required to identify multiple bug is a challenge, and hence the need to conduct this study. Thus, a plausible solution based on an effort estimation framework to detect multiple bugs will reduce the effort software testers spend in analyzing bug reports and also improve software reliability and productivity. Although several studies are attempting to solve the problem, there is the need to introduce an effort estimation framework to detect multiple bugs in software projects, specifically open-source projects. However, the following constraints exist when detecting multiple bugs in open-source projects: - (1) a large number of existing bug reports, and (2) much effort is required when detecting and analyzing multiple bug reports. This study seeks to develop a framework to detect multiple bugs and estimate the effort required in identifying such bugs in open-source projects. This study implements the bugdetector tool, which uses bug information and code features to find similar bugs. It will first extract features from bug information in a bug tracking system, next it locates bug methods in source code and extracts bug method code features. It calculates similarities between each overridden and overload method, and finally, it determines which method may cause potentially related or similar bugs. Empirical analysis was conducted on bug reports from two open-source projects, namely Mozilla Firefox and Eclipse. Thus, empirical analysis was conducted on the extracted bug reports by the bugdetector tool. The analysis was conducted using Deep learning algorithms (LSTM, Bidirectional LSTM and CNN) and conventional machine learning algorithms (SVM and Random Forest). Accuracy, Precision, Recall, and F1-score metrics were used to evaluate the models' performance. Estimating the required effort for identifying multiple bugs was done using a proposed effort estimation metric. Empirical result shows that the deep learning method, namely the Bidirectional LSTM algorithm yielded improved performance for multiple bug detection across the two-studied datasets. Thus, for Mozilla Firefox, the Bidirectional LSTM yielded the best performance accuracy (71.09%), precision (68.30%), and recall (45.7%). For Eclipse, Bidirectional LSTM dominated the best performance about accuracy (82.6%) and F1-score (50.9%). The effort required for detecting multiple bugs on average ranges from 1255.7 to 1383.2 days for the studied Eclipse bug repository, and 1049.8 to 1139.2 days for the studied Mozilla Firefox bug repository. The study concluded that the deep learning method has a better tendency in detecting multiple bugs in open-source projects as compared to the conventional machine learning approach. An effort estimation metric is introduced to compute the effort required to detect multiple bugs in open-source projects. This will assist software testers/fixers to differentiate between severity levels of the detected bugs based on the respective efforts computed. Keywords: Duplicate bugs, Effort estimation, Bug detection, Deep learning, Open-source projects.Item Hydrogeological And Hydrochemical Characterization Of Aquifers In The Akatsi Area, Ghana(University Of Ghana, 2022-05) Kassatchia, I.K.Groundwater remains the most significant source of water supply in the Akatsi area for multiple purposes. The demand for clean water supply is increasing year after year because of the growth of population and urbanisation in the district. However, without proper monitoring, the quality of groundwater is easily compromised by either natural processes or anthropogenic activities. Some of these activities comprise agriculture, improper disposal of domestic waste, and rock water interaction as found in the area. In addition, there is very little existing research work on the sustainability and quality of water resources in the study area. This study aimed to assess the hydrogeological and hydrochemical properties of aquifers underlying the Akatsi and surrounding areas. Then identify the major processes that influence groundwater hydro-chemistry and its suitability for diverse purposes in the study area. Hence, a thorough quality assessment of groundwater resources and characterization of aquifers of the Akatsi area was carried out by employing conventional graphical methods, and multivariate statistical methods, as well as the Cooper Jacob method using pumping test data. Conventional graphical techniques, R-mode Hierarchical Cluster Analysis (HCA), and Principal Component Analysis (PCA) revealed carbonate and silicate minerals weathering coupled with reverse ion exchange as well as the impact of domestic waste and agrochemicals as the key factors that control groundwater chemistry in the Akatsi area. Q-mode HCA combined with Stiff diagrams indicated that recharge zones are characterized by Ca-HCO3 low salinity waters, which evolve through rock-water interactions to Na-K-HCO3 high salinity waters in the discharge zones. Groundwater quality for domestic purposes was assessed using the weighted arithmetic index technique. The calculated values of water quality indices from the data suggest that over 91% of groundwater samples fall within "excellent" and "good" water categories, whereas 8.1% of the samples fall within the "poor" water category. Groundwater quality assessment for irrigation purposes based on the classification of United State Salinity Laboratory (USSL, 1964), Wilcox and Doneen's diagrams suggest groundwater from the study area is of suitable quality for irrigation purposes, but the levels of salinity increase towards the discharge zones, such that some of the boreholes in the discharge zones may not be acceptable for irrigation purposes on the soils of high salinities, which might affect the osmotic potentials of crops.Item Survival Analysis Among Tuberculosis Patients: A Case Study of Adults in Kano State in Nigeria(University Of Ghana, 2022-05) Adamu, I.Tuberculosis (TB) is an infectious disease that has been considered as a signi_- cant risk factor that causes ill health. Globally, it has been found to be among the top 10 causes of death and ranks above HIV/AIDS as a single infectious agent that causes death in patient. Many researches have been documented using semiparametric and non-parametric models to analyze survival data in Nigeria. There is dearth of studies on the use of parametric models on tuberculosis survival data. Parametric models such as Weibull, Exponential, Log-logistic, Gompertz etc have been used in various studies to analyze data and Weibull was mostly found to be suitable. The popular non-parametric and semi-parametric tests used in various studies include the K-M, Log rank and Cox Proportional hazard model. However, necessary diagnostic checks on model _tness and non-violation of assumptions were mostly ignored. This reduces the reliability of result and increase chance of estimation error. This study assessed the parametric and semi-parametric model of survival such as Cox Model, Weibull, Exponential and Gompertz Models. A retrospective cohort analysis was conducted on the tuberculosis patients receiving treatment under the Tuberculosis & Leprosy Control Program in Kano, Nigeria. The risk factors for death were assessed using the Cox proportional hazard model. The risk factors for death were assessed using the Cox proportional hazard model. The parametric models were compared, and the gompertz model was found to be the best _t for the data based on its minimum AIC & log-likelihood value. Among 2,555 the TB cases, the success rate of TB treatment was 97.06% and the mortality rate was 2.94%. Multivariate analysis showed that HIV, Age & Weight were signi_cant factors associated with mortality in TB patients during therapy. The study recommends the use of diagnostic checks such as Martingale, Deviance Residuals in model _tness. Also, comparism of parametric models is recommended in determination of best model that _ts tuberculosis data of patients. Key words: Survival Analysis, Kaplan Meier, Cox Proportional Hazard Model, Parametric Models, Tuberculosis.Item Modelling The Impact Of Political Stability On Cocoa Production(University Of Ghana, 2019-12) Oforiwaa, P.Economic growth and political stability are genuinely interrelated. In Ghana, the Cocoa Production Sector is one of the main boosters of the GDP. This paper used political stability as major intervention on the cocoa production. It sought to estimate and assess the impact of Political Stability as a variation on Cocoa Production in Ghana using Bia and Tiao, intervention analysis model. Time series data on cocoa productions from the department of Monitoring, Research and Evaluation of Ghana COCOBOD spanning from the year 1968 to 2016 was used. The Empirical result indicates that, the pre- intervention period was modeled with ARIMAX process based on which the full intervention model was obtained. The intervention event exists but it has an insignificant impact on cocoa production. The Ljung- Box test and its residual plots were significant. It concluded that the insignificant of political stability on cocoa production means that there is no influence of political appointees on the cocoa production. The study recommends that, the cocoa production sector should be independent of political interference since it’s the back bone of Ghana’s GDP.Item Credit Card Fraud Detection; A Machine Learning Approach(University Of Ghana, 2020-11) Glah, J.In recent times, credit card usage has increased tremendously because it is convenient to use and also saves a lot of time. Credit cards are rectangular plastic cards issued by banks which allow a person to borrow funds from a pre - approved limit to pay for one’s purchases now and pay later. In the same manner, credit card frauds have also been on the increase causing huge sums of financial loss to credit card issuers. Credit card fraud is the use of a credit card by someone who is not the owner of the card and is not allowed to use it. In this study, three classification methods were used to do a deep analysis of credit card transactions history and the fraud detection models built. This study presents and demonstrates the advantages of support vector machine, artificial neural network and the k - nearest neighbor algorithms to the credit cards data for the purpose of reducing the bank’s losses. The results show that the linear support vector machine and k - nearest neighbor approaches outperform artificial neural network in solving the problem under investigation. This study allows for multiple algorithms to be integrated together as modules and their results combined to increase the accuracy of the final results.