This work presents a recognition system for Offline Yoruba characters recognition using Freeman chain code and K-Nearest Neighbor (KNN). Most of the Latin word recognition and character recognition have used k-nearest neighbor classifier and other classification algorithms. Research tends to explore the same recognition capability on Yoruba characters recognition. Data were collected from adult indigenous writers and the scanned images were subjected to some level of preprocessing to enhance the quality of the digitized images. Freeman chain code was used to extract the features of THE digitized images and KNN was used to classify the characters based on feature space. The performance of the KNN was compared with other classification algorithms that used Support Vector Machine (SVM) and Bayes classifier for recognition of Yoruba characters. It was observed that the recognition accuracy of the KNN classification algorithm and the Freeman chain code is 87.7%, which outperformed other classifiers used on Yoruba characters.
- Handwritten recognition systems enable automatic recognition of human handwriting, thereby increasing human-computer interaction. Despite enormous efforts in handwritten recognition, little progress has been made due to the variability of human handwriting, which presents numerous difficulties for machines to recognize. It was discovered that while tremendous progress has been made in handwritten recognition of English and Arabic languages, very little work has been done on Yorùbá handwritten characters. Those few works, in turn, made use of HMM, SVM, Bayes theorem, and decision tree algorithms. To integrate and save one of Nigeria's indigenous languages from extinction, as well as to make Yorùbá documents accessible and available in the digital world, this research work was undertaken. The research presents a convolutional recurrent neural network (CRNN) for the recognition of Yorùbá handwritten characters. Data were collected from students of Kwara State University who were literate in Yorùbá. The collected data were subjected to some level of preprocessing such as grayscale, binarization, and normalization in order to remove perturbations introduced during the digitization process. The convolutional recurrent neural network model was trained using the preprocessed images. The evaluation was conducted using the acquired Yorùbá characters, 87,5% 0f the acquired images were used for the training while 12.5% were used to evaluate the developed system. As there is currently no publicly available database of Yorùbá characters for validating Yorùbá recognition systems. The resulting recognition accuracy was 87.2% while the characters with under-dot and diacritic signs have low recognition accuracy.
Aims: This work aim is to develop an enhanced predictive system for Coronary Heart Disease (CHD).
Study Design: Synthetic Minority Oversampling Technique and Random Forest.
Methodology: The Framingham heart disease dataset was used, which was collected from a study in Framingham, Massachusetts, the data was cleaned, normalized, rebalanced. Classifiers such as random forest, artificial neural network, naïve bayes, logistic regression, k-nearest neighbor and support vector machine were used for classification.
Results: Random Forest outperformed other classifiers with an accuracy of 98%, a sensitivity of 99% and a precision of 95.8%. Feature selection was employed for better classification, but no significant improvement was recorded on the performance of the classifier with feature selection. Train test split also performed better that cross validation.
Conclusion: Random Forest is recommended for research in Coronary Heart Disease prediction domain.
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