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 system, and decision makers to improve detection performance. This study recommends further research that incorporates gender factors and a large sample size.
The widc applieahility of Neural Networks to solve various real life problem is well understood. This paper proposes Mechanism for Antomated Neural network based Transport system with LEarning (MAKTLE), an automated transport system with considerable advantages over previous attempts in this field. The system uses a multi-layer feed-forward nenrnl network with hack propagation learning. In addition, the deylgn of MANTLE inrolvec the convergcnce of n plethora of technologies like n Global Positioning System (GPS), a Gcyraphic Information System (GIs), and laser ranging. MANTLE eau guide 8 mobile agent through n hostile and unfamlllar domain after being trained by a human user with domain expertise. One of the many areas In which MANTLE scores agninct the competition b lhat the system Is completely doilwin independent and incurs substantially less processor overhead. MANTLE thus provides more functionality even though it requires a lot less input as compared to other attempts in this field. This reduction in the size of the input vector translates into more efficient and faster processing. Another of MANTLE'S hallmark features is its ability to negotiate tums and implenient lane-changing maneuvers with a view to overtaking obstacles. It does thi. hy employing a novel technique, Selective Net Masking. A simulation of MAhTLE's neural network wns performcd an a variety of network topologies, and the hest network selected.
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