Browsing by Author "Jonathan, Joan"

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Conceptualizing digital leadership characteristics for successful digital transformation: the case of Tanzania
(Research Gate, 2021-10) Magesa, Mawazo Mwita; Jonathan, Joan
The objective of this study was to examine the attributes of a compelling leader to lead Digital Transformation in a formal organization. The study conceptualized a digital leader with 26 characteristics grouped into 5 roles. Sample respondents were drawn from some organizations in Tanzania and a self-reported questionnaire was used for data collection. Preliminary analysis involved examining inter-correlation among leadership attributes, dropping 3 out of 26. Exploratory factor analysis of 23 items produced 7 factors which were grouped into 5 roles while dropping 2 factors with one item each. Only 4 factors and 13 items qualified for confirmatory factor analysis which provided better fit for the sample data. The validity check showed that the digital leadership construct somehow converges and the four factors were different from one another. It is implied that good digital leader is anticipated to foster economic growth, promote innovation and entrepreneurship, and improve service deliveries.
Prediction of factors influencing rats tuberculosis detection performance using data mining techniques
(Uppsala Univers1tet, 2019) Jonathan, Joan
This thesis aimed to predict the factors that influence rats TB detection performance using data mining techniques. A rats TB detection performance dataset was given from APOPO TB training and research center in Morogoro. Tanzania. After data preprocessing, the size of the dataset was 471,133 rats TB detection performance observations and a sample size of 4 female rats. However, in the analysis, only 200,000 data observations were used. Based on the CRISP- DM methodology, this thesis used R language as a data mining tool to analyze the given data. To build the predictive model the classification technique was used to predict the influencing factors and classify rats using a decision tree, random forest, and naive Bayes algorithms. The built predictive models were validated with the same test data to check their classification prediction accuracy and to find the best. The results pinpoint that the random forest is the best predictive model with an accuracy of 78.82%. However, the accuracy differences are negligible. When considering the predictive model accuracy (78.78%) and speed (3 seconds) of the decision tree, it is the best predictive model since it has less building time compared to the random forest (154 seconds). Moreover, the results manifest that age is the most significant influencing factor, and rats of ages between 3.1 to 6 years portrayed potentiality in detection performance. The other predicted factors are Session_Completion_Time, Session_Start_Timc, and Av_Weight_Pcr_Ycar. These results are useful as a reference to rats TB trainers and researchers in rats TB and Information Systems. Further research using other data mining techniques and tools is valuable.
Visual analytics of tuberculosis detection rat Performance
(Online Journal of Public Health Informatics, 2021) Jonathan, Joan; Sanga, Camilius; Mwita, Magesa; Mgode, Georgies
The diagnosis of tuberculosis (TB) disease remains a global challenge, and the need for innovative diagnostic approaches is inevitable. Trained African giant pouched rats are the scent TB detection technology for operational research. The adoption of this technology is beneficial to countries with a high TB burden due to its cost-effectiveness and speed than microscopy. However, rats with some factors perform better. Thus, more insights on factors that may affect performance is important to increase rats’ TB detection performance. This paper intends to provide understanding on the factors that influence rats TB detection performance using visual analytics approach. Visual analytics provide insight of data through the combination of computational predictive models and interactive visualizations. Three algorithms such as Decision tree, Random Forest and Naive Bayes were used to predict the factors that influence rats TB detection performance. Hence, our study found that age is the most significant factor, and rats of ages between 3.1 to 6 years portrayed potentiality. The algorithms were validated using the same test data to check their prediction accuracy. The accuracy check showed that the random forest outperforms with an accuracy of 78.82% than the two. However, their accuracies difference is small. The study findings may help rats TB trainers, researchers in rats TB and Information systems, and decision makers to improve detection performance. This study recommends further research that incorporates gender factors and a large sample size.
The YEESI Lab Dataset (1.0)
(Zenodo., 2022) Fue, Kadeghe; Barakabitze, Alcardo; Geofrey, Anna; Lebalwa, Bertha; Lyimo, Neema; Mwaipaja, Faraja; Jonathan, Joan; Mbacho, Susan; Sanga, Camilius; Rains, Glen
The Tanzanian agriculture industry faces a great challenge caused by pests and diseases threatening food security. Pests such as tomato leaf miners, aphids, fall armyworms (FAW), and bean leaf miners devastate crops. Also, diseases such as maize streak virus, early blight, Powdery mildew, Leaf spot, Rusty brown leaf, foliar disease, Bacterial Wilt, Blossom end rot, Flower abortion, Leaf Curl and Black rot have caused the crop failure that leads to yield reduction. So, precisely and accurately detecting such pests and diseases to improve agriculture productivity in the country is paramount. However, manual detection is cumbersome, time-consuming and costly. So, automating the procedure using machine vision technologies is necessary for sustainable prosperous agriculture. Therefore, this dataset presents the first Tanzanian agricultural classification dataset that contains 7992 healthy and unhealthy crops images (maize, beans, green peppers, onions, okra, watermelons, sunflowers, African eggplants, tomatoes, Chinese cabbage, hot peppers, wheat, leaf kale and cabbage). Images were collected in real-world conditions in Morogoro, Tanzania, in August and September 2022, using smartphones and professional GoPro Hero 9 cameras. The dataset is called YEESI Dataset. It is used as Open Data. The authors expect this dataset to revolutionize applications of Artificial Intelligence (AI) in agriculture for evaluating classification models related to crop pests, diseases and weed problems from open data.