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

A Deep Learning-based Mobile Application for Segmenting Tuta Absoluta’s Damage on Tomato Plants

With the advances in technology, computer vision applications using deep learning methods like Convolutional Neural Networks (CNNs) have been extensively applied in agriculture. Deploying these CNN models on mobile phones is beneficial in making them accessible to everyone, especially farmers and agricultural extension officers. This paper aims to automate the detection of damages caused by a devastating tomato pest known as Tuta Absoluta. To accomplish this objective, a CNN segmentation model trained on a tomato leaf image dataset is deployed on a smartphone application for early and real-time diagnosis of the pest and effective management at early tomato growth stages. The application can precisely detect and segment the shapes of Tuta Absoluta-infected areas on tomato leaves with a minimum confidence of 70% in 5 seconds only.</jats:p

The Usability Testing of SSAAT, a Bioinformatic Web Application for DNA Analysis at a Nucleotide Level

Sanger sequencing remains the cornerstone method for Deoxyribonucleic Acid (DNA) sequencing due to its high accuracy in targeting smaller genomic regions in a larger number of samples. The analysis of Sanger sequence DNA data requires powerful and intelligent software tools. Most of the preferred tools are proprietary licensed tools that offer a user-friendly interface and have many features, however, their affordability, especially to individual scientists or students, is limited. On the other hand, a few free and open-source licensed tools are available but have limited features. This study focuses on the usability testing of the developed Sanger Sequence Automatic Analysis Tool (SSAAT), a free and open-source web tool for Sanger sequence analysis. Usability tests were conducted with potential users and the results demonstrate that the participants were able to use the tool easily and accomplish the test tasks at the given time. Moreover, the participants were excited with the easy-to-use interface and agreed that most users could use the tool with no need for technical assistance. However, the participants also identified some issues that require more development effort.</jats:p

A Battery Voltage Level Monitoring System for Telecommunication Towers

Voltage 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.</jats:p

A Web-based Data Visualization Tool Regarding School Dropouts and User Asssesment

Data visualization is important for understanding the enormous amount of data generated daily. The education domain generates and owns huge amounts of data. Presentation of these data in a way that gives users quick and meaningful insights is very important. One of the biggest challenges in education is school dropouts, which is observed from basic education levels to colleges and universities. This paper presents a web-based data visualization tool for school dropouts in Tanzania targeting primary and secondary schools, together with the users’ feedback regarding the developed tool. We collected data from the United Republic of Tanzania Government Open Data Portal and the President’s Office - Regional Administration and Local Government (PO-RALG). Python was then used to preprocess the data, and finally, with JavaScript, a web-based tool was developed for data visualization. User acceptance testing was conducted and the majority agreed that data visualization is very helpful for quickly understanding data, reporting, and decision making. It was also noted that the developed tool could be useful not only in the education domain but it could also be adopted by other departments and organizations of the government.</jats:p

Mobile-based Deep Learning Models for Banana Disease Detection

In Tanzania, smallholder farmers contribute significantly to banana production and Kagera, Mbeya, and Arusha are among the leading regions. However, pests and diseases are a threat to food security. Early detection of banana diseases is important to identify the diseases before too much damage is done on the plants. In this paper, a tool for early detection of banana diseases by using a deep learning approach is proposed. Five deep learning architectures, namely Vgg16, Resnet18, Resnet50, Resnet152 and InceptionV3 were used to develop models for banana disease detection, achieving all high accuracies, varying from 95.41% for InceptionV3 to 99.2% for Resnet152. InceptionV3 was selected for mobile deployment because it demands much less memory. The developed tool was capable of detecting diseases with a confidence of 99% of the captured leaves from the real environment. This tool will help smallholder farmers conduct early detection of banana diseases and improve their productivity.</jats:p

Usability Testing of a Web Portal for Ornamental Plants and Flowers in Arusha, Tanzania

The Information and Communication Technology (ICT) adoption has steadily advanced in the horticulture sector in Tanzania. This study aims to test and give feedback regarding the usability of a developed web portal prototype for ornamental plants and flowers in Arusha City. The stakeholders are botanists and researchers who search for plant and flower information (taxonomy), small scale farmers, herbalists, and the Arusha City Council. The assessments were organized and conducted in groups where each stakeholder was given access to the web portal for 2 days. Questionnaires were distributed to get feedback from each participant (a total of 48 participants). Multicriteria satisfaction analysis was chosen to measure user satisfaction measuring usability factors such as service quality, technical quality, information quality, and system quality. The overall obtained results had a mean score above 3.5 (70%) on a five-point scale Likert scale analysis.</jats:p