An Integrated Mobile Application for Enhancing Management of Nutrition Information in Arusha Tanzania

Based on the fact that management of nutrition information is still a problem in many developing countries including Tanzania and nutrition information is only verbally provided without emphasis, this study proposes mobile application for enhancing management of nutrition information. The paper discusses the implementation of an integrated mobile application for enhancing management of nutrition information based on literature review and interviews, which were conducted in Arusha region for the collection of key information and details required for designing the mobile application. In this application, PHP technique has been used to build the application logic and MySQL technology for developing the back-end database. Using XML and Java, we have built an application interface that provides easy interactive view

Gender Differences in Rural Off-farm Employment Participation in Tanzania: Is Spatial Mobility an Issue?

This paper investigates gender differences in spatial mobility with respect to participation in off-farm employment in rural Tanzania. The mobility issue arises because the recent increase in women participation in off-farm employment is likely to saturate the local labor market/off-farm  opportunities and dampen the rural wages/profit among women if it is not accompanied by increased geographical mobility. The results show that, despite the recent increase in their participation, women do not have significant geographical mobility, thus tends to operate more locally as compared to men. The results of decompositions of gender differences in participation in off-farm employment show that a substantial portion of the gender differences is not explained by individuals' endowments. However, policy interventions that could narrow the education gap between male and female are likely to narrow the existing gender gap. Likewise, policies that increases access to water (reduce time needed to collects water) have the potential of reducing the observed gender differences. Since geographical mobility among women is likely to be dictated by cultural factors that tend to have policy inertia, in the short run, there is need to create diversified off-farm opportunities for women within the rural areas in order to reduce unnecessary competition among them.Key words: Gender, off-farm employment, geographical mobility, rural areas, Tanzania

Combining Clinical Symptoms and Patient Features for Malaria Diagnosis: Machine Learning Approach

This research article published by Taylor & Francis Online, 2022Presumptive 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

Data driven approach for predicting student dropout in secondary schools

A Thesis Submitted in Fulfillment of the Requirements for the Degree of Doctor of Philosophy in Information and Communication Science and Engineering of the Nelson Mandela African Institution of Science and TechnologyStudent dropout is among the challenges that face most schools in developing countries particularly in Africa. In Tanzania alone, student dropout in secondary schools is pronounced to be around 36%. In addressing the student dropout problem, a thorough understanding of the fundamental factors that cause the student dropout is essential. Several researchers have identified and proposed causes, methods and strategies that will help to reduce or stop the student dropout problem, however, most of the proposed solutions didn’t show promising results and the students dropout trend continue to increase over time. This study focused on developing a data driven approach that will help to identify and predict students who are at risk of dropping out of school in order to facilitate an intervention program as an active measure in eliminating the problem of dropout in Tanzania. In doing so, (a) 122 research articles were examined, (b) 4 focus group discussions and 2 round table surveys with 38 respondents from 5 districts (Arusha, Mbeya, Kisarawe, Rufiji and Nzega) were conducted, and (c) 3 datasets from Tanzania and India were used in order to identify factors that contribute significantly to student dropout problem, disclose the best classifier from the commonly used classifiers (Logistic Regression, Random Forest, K-nearest Neighbor and Multilayer Perceptron) and assessing the data balancing techniques for predictive performance of the model. Results revealed that, most of the respondents mentioned students’ gender, age, parent’s income, number of qualified teachers and remoteness as the main contributing factors to the students’ dropout problem in secondary schools. Furthermore, results from the examined articles indicated that, most studies conducted in developing countries focused on the social aspects of student dropout, and a paltry mentioned the use of other approaches such as machine learning. Nevertheless, results from data driven approach development shows that the Logistic Regression and Multilayer perceptron achieved the highest performance when over-sampling technique was employed. Also, the hyper parameter tuning improved the algorithm's performance compared to its baseline settings, and stacking of the classifiers improved the overall predictive performance of the developed approach. The study, therefore, recommends the developed approach to be considered by relevant authorities in identifying and predicting students at risk of dropping out for early intervention, planning and informative decisions making on addressing the student dropout problem

Data Balancing Techniques for Predicting Student Dropout Using Machine Learning

This research article was published MDPI, 2023Predicting student dropout is a challenging problem in the education sector. This is due to an imbalance in student dropout data, mainly because the number of registered students is always higher than the number of dropout students. Developing a model without taking the data imbalance issue into account may lead to an ungeneralized model. In this study, different data balancing techniques were applied to improve prediction accuracy in the minority class while maintaining a satisfactory overall classification performance. Random Over Sampling, Random Under Sampling, Synthetic Minority Over Sampling, SMOTE with Edited Nearest Neighbor and SMOTE with Tomek links were tested, along with three popular classification models: Logistic Regression, Random Forest, and Multi-Layer Perceptron. Publicly accessible datasets from Tanzania and India were used to evaluate the effectiveness of balancing techniques and prediction models. The results indicate that SMOTE with Edited Nearest Neighbor achieved the best classification performance on the 10-fold holdout sample. Furthermore, Logistic Regression correctly classified the largest number of dropout students (57348 for the Uwezo dataset and 13430 for the India dataset) using the confusion matrix as the evaluation matrix. The applications of these models allow for the precise prediction of at-risk students and the reduction of dropout rates

Sources and hydro-geochemical characteristics of lake Duluti waters, Tanzania

A Dissertation Submitted in Partial Fulfilment of the Requirements for the Degree of Master’s in Hydrology and Water Resources Engineering of the Nelson Mandela African Institution of Science and TechnologyWater chemistry, and stable isotopes of oxygen and hydrogen ( 18O and 2H), were used collectively to characterize and quantify Lake Duluti (L. Duluti) – groundwater interaction. Physico-chemical parameters including temperature, pH, electrical conductivity, dissolved oxygen, total dissolved solids, alkalinity, major cations and anions were used to determine chemical characteristics of the lake and to assess its relationship with groundwater sources. Furthermore, water balance approaches were used to estimate surrounding ground-water exchange with L. Duluti and addressing the role of groundwater on lake hydrological system. Physico-chemical parameters results showed abundance of major cations and anions in the lake water in the following order Na+ >Ca2+>K + >Mg2+ and HCO3 - >Cl- >F- >SO4 2- >NO3 - respectivelly. Water in L. Duluti was found to be of predominantly Na-HCO3 type while that from springs and boreholes was of the Ca-HCO3 and Ca-Na-HCO3-types, respectively. Isotopic results indicated that evaporation-induced isotopic enrichment prevailed in L. Duluti and contributed significantly to water loss from the lake. The isotopic composition of oxygen (δ 18O) of lake water averaged sigma notation(‰) of 6.1 ‰ while that of well/boreles and spings averaged -1.2 ‰ and -2.1 ‰ respectively. Similarly, the isotopic composition of hydrogen (δD) of lake water averaged 24.2 ‰ while that of well/boreles and spings averaged -12.9 ‰ and -12.2 ‰ respectively. Stable isotope calculations suggested that L. Duluti loses water to the aquifer and it is more recharged by the groundwater relative to precipitation and surface runoff. Groundwater inflow to the lake is approximately 2 430 960 meter cubic per year (m 3 /yr) while lake water discharge to groundwater is 2 902 620 m 3 /yr. The lake is recharged through precipitation by 612 000 m 3 /yr. Hence, groundwater plays a major role in the hydrological system of L. Duluti. Based on these findings from the study, there is more groundwater outflow than inflow, hence citing of boreholes in the area should be properly done so as to maintain the state of the lake or groundwater aquifers. In the long run, pumping water from the lake may also introduce more groundwater inflows and less outflows. The findings in this research are of assistance to policy makers and management personnel to make use of the information provided for better management of the lake water. The information will also enable the Arusha water supply and sewerage authority to know the hydrological state of L. Duluti

Informing aerial total counts with demographic models: population growth of Serengeti elephants not explained purely by demography

Conservation management is strongly shaped by the interpretation of population trends. In the Serengeti ecosystem, Tanzania, aerial total counts indicate a striking increase in elephant abundance compared to all previous censuses. We developed a simple age-structured population model to guide interpretation of this reported increase, focusing on three possible causes: (1) in situ population growth, (2) immigration from Kenya, and (3) differences in counting methodologies over time. No single cause, nor the combination of two causes, adequately explained the observed population growth. Under the assumptions of maximum in situ growth and detection bias of 12.7% in previous censuses, conservative estimates of immigration from Kenya were between 250 and 1,450 individuals. Our results highlight the value of considering demography when drawing conclusions about the causes of population trends. The issues we illustrate apply to other species that have undergone dramatic changes in abundance, as well as many elephant populations

Midwives’ perceptions on using a fetoscope and Doppler for fetal heart rate assessments during labor: a qualitative study in rural Tanzania

Background: The Doppler is thought to be more comfortable and effective compared to the fetoscope for assessing the fetal heart rate (FHR) during labor. However, in a rural Tanzanian hospital, midwives who had easy access to both devices mostly used fetoscope. This study explored midwives’ perception of factors influencing their preference for using either a Pinard fetoscope or a FreePlay wind-up Doppler for intermittent FHR monitoring. Methods: Midwives who had worked for at least 6 months in the labor ward were recruited. Focus group discussion (FGD) was used to collect data. Five FGDs were conducted between December 2015 and February 2016. Qualitative content analysis was employed using NVivo 11.0. Results: Three main themes emerged as factors perceived by midwives as influencing their preference; 1) Sufficient training and experience with using a device; Midwives had been using fetoscopes since their midwifery training, and they had vast experience using it. The Doppler was recently introduced in the maternity ward, and midwives had insufficient training in how to use it. 2) Ability of the device to produce reliable measurements; Using a fetoscope, one must listen for the heartbeat, count using a watch, and calculate, the Doppler provides both a display and sound of the FHR. Fetoscope measurements are prone to human errors, and Doppler measurements are prone to instrumental errors. 3) Convenience of use and comfort of a device; Fetoscopes do not need charging, and while it is possible to “personalize/hide” the measurements, and may be painful for mothers. Dopplers need charging and do not cause pain, but provide limited privacy. Conclusion: Midwives’ preferences of FHR monitoring devices are influenced by the level of device training, experience with using a device, reliable measurements, and convenience and comfort during use. Fetoscopes and Dopplers should be equally available during midwifery training and in clinical practice

A Machine Learning Model for detecting Covid-19 Misinformation in Swahili Language

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

Machine learning model for predicting fetal nutritional status

Malnutrition tends to be one of the most important reasons for child mortality in Tanzania and other developing countries, in most cases during the first five years of life. This research was conducted todevelop machine learning model for predicting fetal nutritional status. Several machine learning techniques such as AdaBoost, Logistic Regression, Support Vector Machine, Random Forest, Naive Bayes, Decision Tree, K-nearest neighbor and Stochastic Gradient Descent, were used to categorize the children in the test dataset as "malnourished" or "nourished". The accuracy, sensitivity, and specificity of these algorithms' prediction abilities were comparedusing performance measures such as accuracy, sensitivity, and specificity. Results show that malnutrition status can be predicted using Random Forest machine learning technique which was about 98% and brings positive impact to the society. The study findings indicated a need for more attention on nutrition to expected mothers and children under five to be well administered with the government and the society at large by putting relevance to the suggestion that cooperation between government organizations, academia, and industry is necessary to provide sufficient infrastructure support for the future society