1,720,994 research outputs found
The Nelson Mandela African Institution of Science and Technology Maize dataset.
No full textThe maize images dataset was created to contribute to the study of maize diseases diagnostics. The images target the diagnostics of Maize Lethal Necrosis (MLN) and Maize Streak Virus (MSV) diseases. We are motivated in developing end-to-end tools to help farmers diagnose diseases and improve maize productivity. The dataset was created to facilitate image classification and object detection tasks
The Nelson Mandela African Institution of Science and Technology Bananas dataset
No full textThe banana images dataset was created to contribute to the study of banana diseases diagnostics. The images target the diagnostics of Black Sigatoka and Fusarium Wilt Race 1 diseases. We are motivated in developing end to end tools to help farmers diagnose diseases and improve banana productivity. The dataset was created to facilitate image classification and object detection tasks
An integrated mobile application for enhancing management of nutrition information in Tanzania
Full text linkA Dissertation Submitted in Partial Fulfillment of the Requirements for the Degree of Master’s in Information and Communication Science and Engineering of the Nelson Mandela African Institution of Science and TechnologyMalnutrition contributes to over one half of the deaths of children under age of five years in developing countries and is the single greatest cause of child mortality in Tanzania. Studies reveal that, the issue of malnutrition is aggravated by lack of nutrition information especially in rural communities. Absence of proper tools makes collection, management and access to nutrition information very difficult. This study improves accessibility of nutrition information by taking advantage of the advanced mobile technologies and develops a system for managing nutrition information. The system was implemented using a mixed approach involving qualitative techniques whereby the requirements and fact finding was done through interviews and literature review. Unified Modelling Language (UML) technique was used to design and model the user requirements and system specification. PHP, MySQL, XML and Java were used to complement the development of this system.
The developed mobile-based nutrition information management system was then integrated with existing Health Centre System and is able to provide a platform that gives mothers instant access to nutritional tips, allow them to interact with nutrition practitioners and help in record keeping. The results demonstrate the potential of using mobile technology for collection and delivering nutrition information in various sectors. In particular, this system could be adopted to improve prenatal and postnatal health in Tanzania and therefore help in bringing down the number of deaths of children under age of five
Data driven approach for predicting student dropout in secondary schools
Full text linkA 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
Going Beyond Counting First Authors in Author Co-citation Analysis
Full text linkThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
An Integrated Mobile Application for Enhancing Management of Nutrition Information in Arusha Tanzania.
No full textResearch Article published International Journal of Computer Science and Information Security, Vol. 13, No. 7, July 2015Based 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
Enhancing Management of Nutrition Information Using Mobile Application: Prenatal and Postnatal Requirements
No full textConference paper published by IST-AfricaMalnutrition contributes to over one half of the deaths of children under
age of five years in developing countries and is the single greatest cause of child
mortality in Tanzania. Investigations reveal that the issue of malnutrition is
aggravated by lack of nutritional information especially in rural communities.
Absence of proper tools makes collection, management and access to nutrition
information very difficult. The aim of this study is to improve accessibility of
nutritional information by taking advantage of the advanced mobile technologies to
integrate a mobile-based information management platform with existing Health
Information Systems. The platform will give mothers instant access to nutritional
tips, allow them to interact with nutrition practitioners and help in record keeping. In
this paper, we present the requirements of a mobile application for managing
prenatal and postnatal nutritional information. The requirements have been
established from interviews with the various stakeholders and literature reviews. The
established requirements become a necessary input towards development of a
complete mobile-based nutrition information management platform, which is to be
integrated with existing health information system
Convolutional Neural Network Deep Learning Model for Early Detection of Streak Virus and Lethal Necrosis in Maize: A Case of Northern-Highlands, Tanzania
Full text linkThis research article was published by Springer Nature Link 2024In the Tanzanian context, maize is the dominant food crop that serves as a significant common and traditional food being grown in about 45% of the country’s farmland. However, its productivity is hindered by diseases that diminutions its quality and quantity. Maize streak virus (MSV) and maize lethal necrosis (MLN) are the two diseases that have been reported by farmers to dominate for ages. These diseases are likely to be cured if early detected. Nevertheless, sophisticated tools for detecting these diseases are still lagging behind the fast pace of technology in developing countries like Tanzania. That being the case, this study aims to fill the gap by investigating the need and development of a deep learning model for early detection of these two diseases. In doing so, a deep learning solution based on Convolution Neural Networks (CNN) has been developed to predict the early occurrence of these diseases in maize leaves. A CNN model was developed from scratch with a total of 1500 datasets belonging to three classes namely; healthy, MLN, and MSV. The developed model attained a validation accuracy of 98.44%. Since the validation accuracy is more than 70% then, this model is reliable and have potential of being adopted in early prediction of MLN and MSV diseases. However, the vision transformer (ViT) model will be developed, and its efficiency be compared with CNN. The model with best results will be deployed in a mobile device, ready for use by farmers in real-life environments
Mask R-CNN Model for Banana Diseases Segmentation
Full text linkThis research article was published by Artificial Intelligence Tools and Applications in Embedded and Mobile Systems 2024Early detection of banana diseases is necessary for developing an effective control plan and minimizing quality and financial losses. Fusarium Wilt Race 1 and Black Sigatoka diseases are among the most harmful banana diseases globally. In this study, we propose a model based on the Mask R-CNN architecture to effectively segment the damage of these two banana diseases. We also include a CNN model for classifying these diseases. We used an image dataset of 6000 banana leaves and stalks collected in the field. In our experiment, Mask R-CNN achieved a mean average precision of 0.04529, while the CNN model achieved an accuracy of 96.75%. The Mask R-CNN model was able to accurately segment areas where the banana leaves and stalk were affected by Black Sigatoka and Fusarium Wilt Race 1 diseases in the image dataset. This model can assist farmers to take the required measures for early control and minimize the harmful effects of these diseases and rescue their yields
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