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

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

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

A Battery Voltage Level Monitoring System for Telecommunication Towers

This research article was published by Engineering, Technology & Applied Science Research, Vol. 11, No. 6, 2021Voltage fluctuations in batteries form a major challenge the telecommunication towers face. These fluctuations mostly occur due to poor management and the lack of a battery voltage level monitoring system. The current paper presents a battery voltage-level monitoring system to be used in telecommunication towers. The proposed solution is incorporated with a centralized mobile application dashboard for accessing the live data of the installed battery, integrated with voltage-level, current, temperature, fire, and gas sensors. An Arduino Uno microcontroller board is used to process and analyze the collected data from the sensors. The Global Service Message (GSM) module is used to monitor and store data to the cloud. Users are alerted in the case of low voltage, fire, and increase in harmful gases in the tower through Short Message Service (SMS). The experiment was conducted at Ngorongoro and Manyara telecommunication towers. The developed system can be used in accessing battery information remotely while allowing real-time continuous monitoring of battery usage. The proposed battery voltage-level monitoring system contributes to the elimination of battery hazards in towers. Therefore, the proposed battery voltage level monitoring system can be adopted by telecommunication tower engineers for the reduction of voltage fluctuation risks

A Deep Learning Model for Predicting Stock Prices in Tanzania

This research article was published by Engineering, Technology & Applied Science Research in Volume: 13 | Issue: 2 | Pages: 10517-10522 | April 2023 |Stock price prediction models help traders to reduce investment risk and choose the most profitable stocks. Machine learning and deep learning techniques have been applied to develop various models. As there is a lack of literature on efforts to utilize such techniques to predict stock prices in Tanzania, this study attempted to fill this gap. This study selected active stocks from the Dar es Salaam Stock Exchange and developed LSTM and GRU deep learning models to predict the next-day closing prices. The results showed that LSTM had the highest prediction accuracy with an RMSE of 4.7524 and an MAE of 2.4377. This study also aimed to examine whether it is significant to account for the outstanding shares of each stock when developing a joint model for predicting the closing prices of multiple stocks. Experimental results with both models revealed that prediction accuracy improved significantly when the number of outstanding shares of each stock was taken into account. The LSTM model achieved an RMSE of 10.4734 when the outstanding shares were not taken into account and 4.7524 when they were taken into account, showing an improvement of 54.62%. However, GRU achieved an RMSE of 12.4583 when outstanding shares were not taken into account and 8.7162 when they were taken into account, showing an improvement of 30.04%. The best model was implemented in a web-based prototype to make it accessible to stockbrokers and investment advisors

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

Characterisation of Malaria Diagnosis Data in High and Low Endemic Areas of Tanzania

This research article was published by East African Health Research Journal 2022, Volume 6, Number 2Background: 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

A Survey of Machine Learning Approaches and Techniques for Student Dropout Prediction

School dropout is absenteeism from school for no good reason for a continuous number of days. Addressing this challenge requires a thorough understanding of the underlying issues and effective planning for interventions. Over the years machine learning has gained much attention on addressing the problem of students dropout. This is because machine learning techniques can effectively facilitate determination of at-risk students and timely planning for interventions. In order to collect, organize, and synthesize existing knowledge in the field of machine learning on addressing student dropout; literature in academic journals, books and case studies have been surveyed. The survey reveal that, several machine learning algorithms have been proposed in literature. However, most of those algorithms have been developed and tested in developed countries. Hence, developing countries are facing lack of research on the use of machine learning on addressing this problem. Furthermore, many studies focus on addressing student dropout using student level datasets. However, developing countries need to include school level datasets due to the issue of limited resources. Therefore, this paper presents an overview of machine learning in education with the focus on techniques for student dropout prediction. Furthermore, the paper highlights open challenges for future research directions

Development of the RFID Based Library Management and Anti-Theft System:A Case of East African Community (EAC) Region

This research article published by International Journal of Advances in Scientific Research and Engineering,Radio Frequency Identification (RFID) Systems are becoming very useful in our daily life due to its advantages such as reduction of human error, theft prevention, time consuming reduction, the auto identification of targeted objects, business processes automation etc. RFID systems has been applied in library to manage items and library operations. Different approaches have been adopted in library management system in the East African region unfortunately some challenges including theft, pages removal, non-customer satisfaction, high cost of used system etc. are still persisting. To address these challenges, an RFID based library management and anti-theft system has been developed to East African Community (EAC) library. It focused on the use of Ultra High Frequency (UHF) band which enable readers and tags to transmit and receive data at longrange. The developed system facilitates users to borrowand return library items using RFID modules and enable librarians to monitor, record library activities and prevent no issued item to cross the library entrance or exit