In Africa, Uganda is among the countries with a high number of babies (20,000 babies) born with sickle cell, contributing between 6.8% of the children born with sickle cell every year worldwide and approximately 4.5% of the children born with hemoglobinopathies worldwide. It is estimated that by 2050, sickle cell cases will increase by 30% if no intervention is put in place. To facilitate early detection of sickle cell anaemia, medical experts employ machine learning algorithms to detect sickle cell abnormality. Previous research revealed that algorithms for recognizing shape of a sickle cell from blood smear by fractional dimension, cannot detect sickle cells if applied on blood samples containing overlapping red blood cells. In this research, the authors developed an algorithm to detect overlapping red blood cells for sickle cell disease diagnosis. The algorithm uses canny edge and double threshold machine learning techniques and takes overlapping red blood cells images as inputs to detect if these cells are sickle cell anaemic. These images have a scale magnification of (200×, 400×, 650×) pixel taken using a microscope. The algorithm was tested on a total of 1000 digital images and the overall accuracy, sensitivity and specificity were 98.18%, 98.29% and 97.98% respectively.
Passwords are a common measure used in Authentication systems to make sure that the users are who they say they are. The complexity of these Passwords is relied on while ensuring security. However, the role of complexity is limited. Users are forced to write down complex passwords since easy ones are easily guessed. This study aimed at evaluating the uniqueness of typing patterns of password holders so as to strengthen the authentication process beyond matching the string of characters. Using our own dataset, this research experimentally showed that k Nearest Neighbor algorithm using Euclidean distance as the metric, produces sufficient results to distinguish samples and detect whether they are from the same authentic user or from an impostor based on a threshold that was computed. Results obtained indicated that typing patterns are distinct even on simple guessable passwords and that typing pattern biometrics strengthens the authentication process. This research extends work in typing pattern analysis using k Nearest Neighbor machine learning approach to auto detect the password pattern of the authentic and non-authentic users. It also provides an investigation and assessment to the effect of using different k values of the KNN algorithm. Further to this field is the methodology for calculating an optimal threshold value with higher accuracy levels that acted as a basis for rejection or acceptance of a typing sample. Additionally is an introduction of a new feature metric of a combined dataset which is a concatenation of both the dwell and latency timings. A comparison of performance for independent and a combined dataset of the feature metrics was also evaluated.
Several Machine Learning Classification Techniques have been applied in predicting Protein Localization sites of E.coli using a number of techniques. However, research done is limited to no prediction of Localization sites of Proteins on Ecoli s minimal dataset with the most informative features obtained using different feature selection techniques. This study investigated several Machine learning Classification and Feature Selection Techniques as applied on Ecoli s minimal dataset. The implementation of classifiers aided in predicting localization sites of E.coli s minimal subset using its informative features obtained by feature selection techniques. Results were achieved in four parts including; (Data Collection, Cleaning and Preprocessing), Feature selection where the most informative features are selected, Classification where prediction of the localization of proteins is done and then Evaluation of the Classifiers to assess their performance using a number of measures including Accuracy from Cross-validation, and AUROCC to enable in recommending the best Classifier at the end. Among the Classifiers used, Extra Tree Classifier and Gradient Boosting are seen to be the best at performance followed by Random forest as seen from Precision, Recall and F-measure scores. AdaBoost is the worst at 83%.
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