School of Computing and Informatics

INTERNET OF THINGS CYBER SECURITY ASSESSMENT MODEL AND METRICS

2021-09-01T00:00:00Z

INTERNET OF THINGS CYBER SECURITY ASSESSMENT MODEL AND METRICS Matovu, Davis ABSTRACT In Uganda, there is a general lack of a specific model and appropriate metrics for evaluating IoT cyber security. To provide an informed basis for decision-making by policymakers, industry 6 participants, and the public, a model and metrics in the domains of IoT cyber security readiness, intensity, and adoption are necessary. Previous cyber security research efforts have concentrated the general computer security. However, in the recent past, mobile devices and IoT based devises and networks are on the rise, giving rise to the emerging problem of IoT cyber security. In the recent years, the use of mobile devices and IoT-based devices and networks has increased, resulting in the emergence of the IoT cyber security problem. However, establishing IoT cyber security is difficult due to IoTs' intelligence, connectivity, sensing, and energy characteristics, which must be carefully analyzed if IoT cyber security is to be maintained. This thesis, which is based on a combination of qualitative and quantitative research, addresses the IoT cyber security metrics challenge in Uganda by establishing a model and metrics to assess the level of IoT cyber security in the domains of readiness, intensity, and acceptance. The research was based on the Technology Acceptance Model (TAM) and the Diffusion of Innovations (DOI) theory with the Socio-Technical Systems Theory (STS) providing the underpinning theoretical underpinning.The researcher utilised methodology triangulation involving a questionnaire in each of the research domains and structured interviews. In order to address the research objectives, and answer the research question the researcher firstly identified metrics that lead to increased IoT cyber security readiness, intensity, and adoption in Uganda. The thesis then presented a model, and an IoT cyber security metric, the IoT cyber security Assessment Index (ICSAM) that can be used to assess the state of IoT cyber security in Uganda, and other developing countries based on three sub-indices namely, IoT cyber security readiness (ICSR), IoT cyber security intensity (ICSI), and IoT cyber security adoption (ICSA), respectively across nine (9) constructs. These constructs were found to significantly explain the variation of the respective sub-indices in studies related to each of the research objectives. This thesis proposes an IoT cyber security specific model, and composite IoT cyber security assessment metric across the three domains of the IoT cyber security eco-system, namely readiness, intensity, and adoption designed for assessing IoT cyber security in Uganda, and other developing countries. Currently, general cyber security models and metrics are used to estimate the state of IoT cyber security. Using the delphi method of validation using subject matter experts. The results appropriately validated the ICSAM model. The ICSAM computation algorithm can be easily automated, and the sub-index and construct weights varied to reflect the priorities of a particular decision modeler to suit a given developing country’s special requirements. The major limitation of the study was that the findings and the 7 implication of the study were based on the information received from the respondents in Kampala and Wakiso Districts due to the constraints of finance and time. However, because IoT technology users are dispersed across the country, this left a lot of information untapped. The study recommends further studies focused on developing a model for the implementation of IoT technologies in Uganda. Doctor of Philosophy i Information Technology

AN INTEGRATED MODEL FOR E-HEALTH IMPLEMENTATION IN KENYA

2023-09-01T00:00:00Z

AN INTEGRATED MODEL FOR E-HEALTH IMPLEMENTATION IN KENYA Shirandula, Ayub Hussein numerous challenges which have forced developing countries to focus on digital interventions known as e-health Technologies. Kenya, as a developing country, is not left behind regarding the challenges of e-health. With the vision of efficient implementation and use of e-health, there is need to develop a model that will enable the improvement of effective e-health. The purpose of the study was to investigate the factors influencing adoption and implementation of e-health in Kenya. The objectives of the study were to determine the current status of e-health implementation in Kenya, determining critical factors that affects the implementation of e-health Technologies in health sector in Kenya and to develop a model for e-health implementation in Kenya. The Study used Normalization Process Theory (NPT), Actor Network Theory (ANT) and Technology Organization and Environmental Framework (TOE) to underpin the study. Data was collected using structured questionnaire and interview of key informants. The quantitative data was then coded and analyzed using both descriptive and inferential statistics and the qualitative data was analyzed using thematic analysis. Pragmatist research philosophy was adopted given the multiple realities and the fact that it fitted well with deductive and inductive approaches. The study population included 1243 healthcare workers from which a sample of 303 respondents were obtained using stratified sampling. The findings of the research indicated that social factors were significant predictor of e-health implementation where (p=0.005<0.05), organizational factors were significant predictor of e-health implementation where (p=0.002<0.05), technological factors were significant predictor of e-health implementation where (p=0.000<0.05) and environmental factors were significant predictor of e-health implementation where (p=0.048<0.05). From the outcomes of this study, a model of e-health implementation was realized to guide the process of effective e-health development and used. The study concluded that there was need for various stakeholders to reflect on organizational, social and environmental relationship and interaction with technical aspect as technological factor was the major factor that affect e-health implementation in Kenya. The model developed is a basis upon which future implementation of e-health can be based. It was recommended that for effective implementation, there is need for a well-defined implementation plan for e-health. The study further recommends for research on e-health adoption.

ALOCAL DIRECTIONAL TERNARY PATTERN TEXTURE DESCRIPTOR FOR MAMMOGRAPHIC BREAST CANCER CLASSIFICATION

2020-10-11T00:00:00Z

ALOCAL DIRECTIONAL TERNARY PATTERN TEXTURE DESCRIPTOR FOR MAMMOGRAPHIC BREAST CANCER CLASSIFICATION MWADULO, MARY WALOWE Breast cancer is a top killer illness for women globally, but early and effective screening can increase their survival rate. Mammography is the tool used by a radiologist to screen for breast cancer, however,a radiologist is susceptible to human observer variability, and therefore, reading and interpretation of mammography test results depend on the expertise of the radiologist administeringthe test. To improvethe reading and interpretation accuracy of the test, researchers’ developed computer-aided extraction descriptors thatextract discriminant features.These descriptors include the Local Binary Patterns (LBP), the Local Ternary Patterns (LTP), and the Local Directional Patterns (LDP), however,they have not yet yielded satisfactory results in differentiating breast cancer tumor types. The LBP descriptor is inadequately dependable in capturing breast cancer discriminant features because it is easily affected by noise. TheLTP descriptor uses a fixed threshold value for all images in a dataset, making it not invariant to pixel value transformation. It is also not practically easy to select an optimum threshold value in real applicationdomains. The LDPdescriptor relies on top k significant directional responses and ignores the remaining 8-kdirectional responses. Disregarding the remaining directional responses reduces the computation efficiency since each pixel in an image carries subtle information. Given the limitations identified among the mentioned local texture descriptors, developing an effective texture descriptor becomes a viable and challenging research problem. Therefore, this study seeks to develop an improved local texture descriptor that considers all directional responses and applies an adaptive threshold in encoding image gradient. The new Local Directional Ternary Pattern (LDTP) texture descriptor calculates the absolute difference between the value of the center pixel and the values of its local neighboring pixels for a 3x3 image region. To get edge responses in eight directions, the absolute differences are convolved with a kirsch mask, then the pixels are transformed into zeros and ones using mini-max normalization. We then passed the normalized values through a soft-max function to get the probability of an edge ina certain direction. Then, two threshold values are calculated and used to split the probability space into three parts for -1, 0, +1 bits to generate a ternary pattern. The resultant Local Directional Ternary Pattern (LDTP) code is then split into a positive and negative LDTP code. Histograms of negative and positive LDTP encoded images are fused to get texture features. We validated the LDTP texture descriptor on the Mammographic Image Analysis Society (MIAS) breast cancer dataset using Support Vector Machine (SVM) and Artificial Neural Network (ANN) classifiers for normal/abnormal and benign/malignant classes. When the LDTP texture descriptor was compared against LDP, LTP, and other existing texture descriptors, it showed robustness and reliability in encoding an image gradient. The highest classification accuracy was attained by the SVM classifier, with 97.32% and 93.93% for normal/abnormal and benign/malignant classes, respectively.

MACHINE LEARNING MODEL FOR PREDICTION OF STUDENTS’ ACADEMIC PERFORMANCE, KENYA

2020-10-05T00:00:00Z

MACHINE LEARNING MODEL FOR PREDICTION OF STUDENTS’ ACADEMIC PERFORMANCE, KENYA Musau, Obadiah Matolo Prediction of students’ academic performance with high accuracy is useful in many ways in academic institutions. Institutions would like to know which students are likely to have low academic achievements or need assistance in order to finish their studies. Successful students’ academic performance prediction at an early stage in learning depends on many factors. Machine learning techniques can be utilized to predict students’ future academic performance. The primary objective of this study was to develop a machine learning model for prediction of students’ academic performance. To achieve this objective, the study was guided by the following theoretical and empirical objectives: 1. To analyse existing studies on students’ academic performance prediction, 2. To find out the most significant factors that affect students’ academic performance, 3. To develop a model for students’ academic performance prediction in Kenya and, 4. To validate the students’ academic performance prediction model. Student data was collected from 1720 former secondary school students currently enrolled in tertiary institutions using questionnaires. The data included students’ academic performance, demographic features, social features and school related features. Naïve Bayes, Decision Trees and Neural Networks were used to predict students’ final examination grade. The performance of the prediction models was validated using 10-fold cross-validation method. J48 Decision Tree prediction model achieved 85.9 % prediction accuracy, Naïve Bayes prediction model achieved 78.96% prediction accuracy and Neural Networks Multi Perceptron prediction model achieved the lowest prediction accuracy of 73.73%. This work will help educational institutions, school managements, government ministries, parents, donors and other education stakeholders to predict students’ performance and identify nonperforming student that need assistance to finish their studies.