Faculty of Computing

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Subject: search.filters.subject.Prediction

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    CHOLERA PREDICTION MODEL USING FEATURE CLUSTERING BAYESIAN TECHNIQUE
    (School of Mathematics and Computing, Kampala International University, 2021-12-31) Ya’u Nuhu; Yusuf Musa Malgwi; Abdullahi, Adamu Garba; Usman Muhammad Bala
    Cholera is one the most deadly disease that is mostly caused due by poor sanitation or and drinking contaminated water or food with a bacterium called Vibrio Cholera. Many researchers have provided a solution to prevent the outbreak of cholera using various methods, the recent ones are using machine learning techniques and some mathematical methods such as mathematical epidemiological, spatial statistics, and based on association rule mining using the nonstandard distribution dataset to mention a few. These few methods are mostly used in predicting cholera outbreaks but have some limitations, such as using fewer features for prediction, waiting until certain cases are reported before getting data, based on Rainfall, based on the flowing speed of rivers, wind direction, and flood, etc. in this research a more comprehensive cholera features would be used in predicting an outbreak before it occurs based on the existing secondary dataset using The Naïve Bayesian Classification technique. The proposed model has more features and is not dependent on certain events to occur before predicting any outbreak. Python programming was used in implementing the proposed model. An accuracy of 99% was achieved and it shows it is better than the previous models used in predicting cholera outbreaks.
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    Analysis of Prostate Cancer DNA Sequences Using Bi-direction Long Short Term Memory Model
    (IEEE, 2021-02-02) Yusuf Aleshinloye Abass; Steve Adeshina; Nwojo Nnana Agwu; Moussa Mahamat Boukar
    Machine and deep learning-based models are the emerging techniques in addressing prediction problems in biomedical data analysis. DNA sequence prediction is a critical problem that requires huge attention in the biomedical domain. These techniques have been shown to provide better accurate results when compared to the conventional regression-based models. Prediction of the gene sequence that leads to cancerous diseases such as prostate cancer is very crucial. Identifying the most important features in a gene sequence is one of the most challenging tasks and extracting the components of the gene sequence that can give an insight into the kind of mutation in the gene is very important, it will lead to effective drug design and promote the new concept of personalized medicine. In this work we have extracted the exons in the various prostate gene sequence that was used in the experiment, we built a bi-LSTM model using a k-mer encoding for the DNA sequence and one- hot encoding for the class label. The bi-LSTM model was evaluated on different classification metrics. Our experimental results show that the model prediction offers a training accuracy and validation accuracy of 95 percent and 91 percent respectively.