Browsing by Author "Mkoba, Elizabeth"

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A Loan Application Management System for Efficient Loan Processing: A Case of Muhimbili SACCOS LTD
(Springer Nature, 2024-06-30) Murimi, Luciana; Siebert, Marius; Salira, Godwin; Mkoba, Elizabeth; Ally, Mussa
Major parts of the population in emerging markets are still unbanked. The 2021 Global Findex Database shows that only 52% of Tanzanian adults own a formal financial account. Unbanked individuals cannot access capital to grow their businesses. In Tanzania, Savings and Credit Cooperative Organization Societies (SACCOS) have traditionally provided services and products such as loans and savings tailored to fit the needs of the financially excluded. By doing so, tremendous success has been achieved in attaining financial inclusion. However, inefficient manual business processes still pose a great challenge, hindering SACCOS performance and sustainability. Whereas digital solutions such as web and mobile applications have been widely adopted to improve business processes in various sectors, this adoption has been quite slow in the SACCOS sector. Lack of affordable entry-level solutions has resulted in most SACCOS relying on manual paper-based processes. There is therefore need, for the design and implementation of affordable, entry-level digital solutions. This study presents the implementation of a loan application management system: a case of Muhimbili SACCOS LTD. Qualitative methods of data collection were used in identifying system requirements. An Android tablet-based loan application management system was implemented, allowing loan officers to capture rich information required to determine members’ loan eligibility. Through the application, loan officers can retrieve stored loan applications and generate the required templates needed for further processing. For integration with the Core Banking System (CBS), a schema is generated that can be uploaded to the CBS for further loan processing. Thus, achieving an efficient loan application process.
Characterisation of Malaria Diagnosis Data in High and Low Endemic Areas of Tanzania
(East African Health Research Journal, 2022) Mariki, Martina; Mduma, Neema; Mkoba, Elizabeth
Background: Malaria remains a significant cause of morbidity and mortality, especially in the sub-Saharan African region. Malaria is considered preventable and treatable, but in recent years, it has increased outpatient visits, hospitalisation, and deaths worldwide, reaching a 9% prevalence in Tanzania. With the massive number of patient records in the health facilities, this study aims to understand the key characteristics and trends of malaria diagnostic symptoms, testing and treatment data in Tanzania’s high and low endemic regions. Methods: This study had retrospective and cross-sectional designs. The data were collected from four facilities in two regions in Tanzania,i.e., Morogoro Region (high endemicity) and Kilimanjaro Region (low endemicity). Firstly, malaria patient records were extracted from malaria patients’ files from 2015 to 2018. Data collected include (i) the patient’s demographic information, (ii) the symptoms presented by the patient when consulting a doctor, (iii) the tests taken and results, (iv) diagnosis based on the laboratory results and (v) the treatment provided. Apart from that, we surveyed patients who visited the health facility with malaria-related symptoms to collect extra information such as travel history and the use of malaria control initiatives such as insecticide-treated nets. A descriptive analysis was generated to identify the frequency of responses. Correlation analysis random effects logistic regression was performed to determine the association between malaria-related symptoms and positivity. Significant differences of p < 0.05 (i.e., a Confidence Interval of 95%) were accepted. Results: Of the 2556 records collected, 1527(60%) were from the high endemic area, while 1029(40%) were from the low endemic area. The most observed symptoms were the following: for facilities in high endemic regions was fever followed by headache, vomiting and body pain; for facilities in the low endemic region was high fever, sweating, fatigue and headache. The results showed that males with malaria symptoms had a higher chance of being diagnosed with malaria than females. Most patients with fever had a high probability of being diagnosed with malaria. From the interview, 68% of patients with malaria-related symptoms treated themselves without proper diagnosis. Conclusions: Our data indicate that proper malaria diagnosis is a significant concern. The majority still self-medicate with anti-malaria drugs once they experience any malaria-related symptoms. Therefore, future studies should explore this challenge and investigate the potentiality of using malaria diagnosis records to diagnose the disease.
Combining Clinical Symptoms and Patient Features for Malaria Diagnosis: Machine Learning Approach
(Taylor & Francis online, 2022-01-30) Mariki, Martina; Mkoba, Elizabeth; Mduma, Neema
Presumptive treatment and self-medication for malaria have been used in limited-resource countries. However, these approaches have been considered unreliable due to the unnecessary use of malaria medication. This study aims to demonstrate supervised machine learning models in diagnosing malaria using patient symptoms and demographic features. Malaria diagnosis dataset extracted in two regions of Tanzania: Morogoro and Kilimanjaro. Important features were selected to improve model performance and reduce processing time. Machine learning classifiers with the k-fold cross-validation method were used to train and validate the model. The dataset developed a machine learning model for malaria diagnosis using patient symptoms and demographic features. A malaria diagnosis dataset of 2556 patients’ records with 36 features was used. It was observed that the ranking of features differs among regions and when combined dataset. Significant features were selected, residence area, fever, age, general body malaise, visit date, and headache. Random Forest was the best classifier with an accuracy of 95% in Kilimanjaro, 87% in Morogoro and 82% in the combined dataset. Based on clinical symptoms and demographic features, a regional-specific malaria predictive model was developed to demonstrate relevant machine learning classifiers. Important features are useful in making the disease prediction.
Feature Selection Approach to Improve Malaria Prediction Model’s Performance for High- and Low-Endemic Areas of Tanzania
(Springer Link, 2024-06) Mariki, Martina; Mduma, Neema; Mkoba, Elizabeth
Malaria remains a significant cause of death, especially in sub-Saharan Africa, with about 228 million malaria cases worldwide. Parasitological tests, like microscopic and rapid diagnostic tests (RDT), are the recommended and standard tools for diagnosing malaria. However, clinical diagnosis is advised in areas where parasitological tests for malaria are not readily available. This method is the least expensive and most widely practiced. A clinical diagnosis called presumptive treatment is based on the patient’s signs and symptoms and physical findings at the examination. A malaria diagnosis dataset was extracted from patients’ files from four (4) identified health facilities in Kilimanjaro and Morogoro. These regions were selected to represent the country’s high- (Morogoro) and low-endemic areas (Kilimanjaro). The dataset contained 2556 instances and 36 variables. The random forest classifier, a tree-based, was used to select the most important features for malaria prediction since this classifier was selected for feature selection because it was robust and had high performance. Regional-based features were obtained to facilitate accurate prediction. The feature ranking indicated that fever is universally the most noteworthy feature for predicting malaria, followed by general body malaise, vomiting, and headache. However, these features are ranked differently across the regional datasets. Subsequently, six predictive models, using important features selected by the feature selection method, were used to evaluate the performance of the features. The identified features comply with the malaria diagnosis and treatment guidelines WHO and Tanzania Mainland provided. The compliance is observed to produce a prediction model that will fit in the current healthcare provision system.
A Machine Learning Model for detecting Covid-19 Misinformation in Swahili Language
(Engineering, Technology & Applied Science Research, 2023-06-02) Mlawa, Filbert; Mkoba, Elizabeth; Mduma, Neema
The recorded cases of corona virus (COVID-19) pandemic disease are millions and its mortality rate was maximized during the period from April 2020 to January 2022. Misinformation arose regarding this threat, which spread through social media platforms, and especially Twitter, often spreading confusion, social turmoil, and panic to the public. To identify such misinformation, a machine learning model is needed to detect whether the given information is true (true information) or not (misinformation). The aim of this paper is to present a machine-learning model for detecting COVID-19 misinformation in the Swahili language in tweets. The five machine learning algorithms that were trained for detecting Swahili language misinformation related to COVID-19 are Logistic Regression (LR), Support Vector Machine (SVM), Bagging Ensemble (BE), Multinomial Naïve Bayes (MNB), and Random Forest (RF). The study used the qualitative research method because non-numerical data, i.e. text, were used. Python programming language was used for data analysis due to its powerful libraries such as pandas and numpy. Four metrics were used to evaluate the model performance. The results revealed that SVM achieved the highest accuracy of 83.67% followed by LR with 82.47%. MNB achieved the best precision of 92.00% and in terms of recall and F1-score, RF, and SVM achieved the best results with 84.82% and 81.45%, respectively. This study will enable the public to easily identify Swahili language misinformation related to COVID-19 that is circulated on Twitter social media platform.
An Optimal Smart Tank Juice-level Monitoring System for Beverage Industries: A Case Study of Raha Beverages Company Limited, Arusha, Tanzania
(IEIESPC(IEIE Transactions on Smart Processing and Computing), 2022-06-30) Irankunda, Yvonne; Mkoba, Elizabeth; Mirau, Silas
Poor monitoring of levels in juice tanks is among the challenges that beverage industries face when pumping liquid from one tank to another. This leads to spilling fluids, faulty juice tests, and industrial accidents. To keep track of the liquid level in a tank, various approaches have been used. Existing technologies are costly and not interactive, and the majority do not benefit individuals with physical disabilities when manual monitoring is needed. The purpose of this paper is to present an optimal smart tank juice-level monitoring system that can be used in beverage industries. The system is able to monitor the juice level within a tank and regulate a pump using voice commands via Alexa and the Amazon Echo Dot. The proposed system was tested and validated, with key findings being that the developed prototype prevented overflowing, accidents, and changes in juice flavor during the dilution process. This paper contributes to the body of knowledge for food and beverage industries in that engineers and operators of beverage industries can monitor the level of juice in a tank, as well as enhance communication when pumping juice from one tank to another in real time.
Organisational Culture Attributes Influencing the Adoption of Agile Practices: A Systematic Literature Review
(Journal of Information Systems Engineering and Management, 2022-01-31) Mkoba, Elizabeth; Marnewick, Carl
Organisations have been adopting agile practices to deliver information system projects faster and create business value. Despite its advantages, many organisations battle to successfully adopt agile practices. While there are several challenges for agile adoption, organisational culture has been amongst the challenges on adopting agile practices. The objective of this study was to determine the organisational culture attributes which influence the adoption of agile practices within the organisation. The systematic literature review aimed to explore the organisational culture attributes which influence the adoption of agile practices. The review focused on papers published on organisational culture influencing the adoption of agile practices between January 2015 to December 2020. The search strategy retrieved 204 papers of which nine papers were selected for a detailed analysis. The study revealed five factors of organisational culture that influence the adoption of agile practices in the organization as: management control, team collaboration, market, values and creativity. These factors have a number of organisational culture attributes that influence the adoption of agile practices. This study contributes to the body of knowledge by providing organisational culture attributes which influence the adoption of agile practices. The results of the systematic literature review presented in this study will benefit researchers and practitioners of project management.
Rule-Based Engine for Automatic Allocation of Smallholder Dairy Producers in Preidentified Production Clusters
(Hindawi, 2022-06-30) Mavura, Fatuma; Pandhare, Sanket; Mkoba, Elizabeth; Nyambo, Devotha
Smallholder dairy producers account for around half of all African livestock ventures; nevertheless, they face challenges in producing more milk due to an insufficient framework and infrastructure to maximize their output. Smallholder dairy producers in this scenario use a variety of tactics to boost milk output. However, the attempts need multiple heuristics, time, and financial investment. Furthermore, because of a lack of extension officers, smallholder dairy producers become trapped in failure cycles, unsuccessful attempts, and a diminished motivation to continue farming. Therefore, the interventions were more straightforward as smallholder dairy producers with comparable characteristics grouped. This research aimed to create a rule-based engine that automatically assigns smallholder dairy producers to predefined clusters. About 78 stakeholders were interviewed, including 69 smallholder dairy producers and 9 extension officers from Meru-Arusha, Tanzania. The 10 production features and 6 predefined clusters were adopted from the previous study. Therefore, a rule-based engine used the selected 10 production features. As a result, the rule-based engine automatically assigns the smallholder dairy producers to their respective clusters. Therefore, smallholder dairy producers share their farming skills and experience to increase milk output through these clusters. Furthermore, extension officers in the system provide timely assistance to smallholder dairy producers with farming concerns.
Swahili questions and answers dataset for aflatoxin knowledge domain
(Elsevier, 2025-03-20) Chogo, Pamela; Mkoba, Elizabeth; Kassim, Neema
Aflatoxin contamination is a challenge facing food security, health, and trade in Tanzania and other parts of the world. This contamination affects maize, groundnuts, and other crops and animal products. Once contamination occurs, the contaminated crops and animal products become toxic causing illness or death to humans and animals who consume them. Lack of awareness and knowledge of the contamination is seen to be one of the reasons for its continued occurrence. Various awareness-creation and knowledge-sharing techniques have been used but the situation is still not appealing. For this case, the use of a Natural Language Processing (NLP) chatbot in sharing aflatoxin knowledge is proposed. This is because NLP chatbots have been successful in knowledge sharing in various contexts. This data article presents a Swahili text-based aflatoxin knowledge questions and answers dataset. Data were collected through 7 focus group discussion (FGD) sessions conducted in Arusha, Dodoma, Mtwara, Tabora, Morogoro, and Iringa regions in Tanzania. Respondents for the study were farmers, traders, and consumers of maize and groundnuts. The collected data were processed and analyzed using R qualitative data analysis tool. This allowed the identification of 6 themes with respective questions under each theme. The questions were shared with experts through 9 interview sessions and the experts gave answers to the questions. The set of questions and answers were then translated into Swahili language using google translate and manual verification. Finally, an aflatoxin knowledge dataset containing 221 paired questions and answers organized into 6 knowledge areas Swahili dataset was developed. With this dataset, an NLP-based chatbot that uses Swahili language can be developed. This will be beneficial to farmers, traders, consumers, researchers, and policymakers. They can use it to learn more about aflatoxin and be able to make informed decisions. Moreover, the dataset can be adopted and modified to create NLP chatbots that can share aflatoxin knowledge in other languages apart from Swahili. The dataset also contributes to the availability of Swahili language datasets.
Towards an Artificial Intelligence Readiness Index for Africa
(Springer Nature, 2023-03-18) Baguma, Rehema; Mkoba, Elizabeth; Nahabwe, Monica; Mubangizi, Martin; Amutorine, Morine; Wanyama, Denis
The applications and benefits of Artificial Intelligence (AI) for socio-economic development are immense. AI is projected to contribute approximately USD 15.7 trillion to the global Gross Domestic Product (GDP) by 2030. However, countries need to be prepared to harness such benefits. Hence, assessing the AI readiness of a country is paramount. Africa is currently the only continent without an AI readiness index tailored to its needs. It relies on the existing global indices, which may not accurately measure the progress attained by individual African countries because of the different levels of development and unique context. This paper proposes an AI readiness index for Africa. It starts by exploring what the AI readiness index needs of Africa are, examines the extent to which existing AI readiness indices meet the needs, and then looks at indicators that should constitute the AI readiness index for Africa. The study employed a systematic literature review that aimed to explore the AI readiness needs for Africa and the extent existing indices meet these. The review focused on papers published on the AI readiness index between January 2018 to August 2022. The search strategy retrieved 301 papers, of which seven papers were selected for a detailed analysis. The study revealed that the existing indices partially meet AI readiness needs for Africa. The study also found that AI readiness index dimensions pertinent to Africa’s requirements are: Vision, Governance and Ethics, Digital Capacity, Size of the Technology Sector, Research and Development, Education, Infrastructure, Data Availability, general level of employment, employment in Data Science and AI roles, and Gross Domestic Product-Per Capita Purchasing Power Parity. This study contributes to the knowledge of AI readiness for Africa and globally. The results of this study will benefit governments, researchers, and practitioners of AI and its applications.
Towards an Artificial Intelligence Readiness Index for Africa
(Springer Nature, 2023-03-18) Baguma, Rehema; Mkoba, Elizabeth; Nahabwe, Monica; Mubangizi, Martin; Amutorine, Morine; Wanyama, Denis
The applications and benefits of Artificial Intelligence (AI) for socio-economic development are immense. AI is projected to contribute approximately USD 15.7 trillion to the global Gross Domestic Product (GDP) by 2030. However, countries need to be prepared to harness such benefits. Hence, assessing the AI readiness of a country is paramount. Africa is currently the only continent without an AI readiness index tailored to its needs. It relies on the existing global indices, which may not accurately measure the progress attained by individual African countries because of the different levels of development and unique context. This paper proposes an AI readiness index for Africa. It starts by exploring what the AI readiness index needs of Africa are, examines the extent to which existing AI readiness indices meet the needs, and then looks at indicators that should constitute the AI readiness index for Africa. The study employed a systematic literature review that aimed to explore the AI readiness needs for Africa and the extent existing indices meet these. The review focused on papers published on the AI readiness index between January 2018 to August 2022. The search strategy retrieved 301 papers, of which seven papers were selected for a detailed analysis. The study revealed that the existing indices partially meet AI readiness needs for Africa. The study also found that AI readiness index dimensions pertinent to Africa’s requirements are: Vision, Governance and Ethics, Digital Capacity, Size of the Technology Sector, Research and Development, Education, Infrastructure, Data Availability, general level of employment, employment in Data Science and AI roles, and Gross Domestic Product-Per Capita Purchasing Power Parity. This study contributes to the knowledge of AI readiness for Africa and globally. The results of this study will benefit governments, researchers, and practitioners of AI and its applications.