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Presently, the application of machine learning (ML) and data mining (DM) techniques have a vital role in healthcare systems and wisely convert all obtainable data into beneficial knowledge. It is proven from the literature works that a chance of 12% error remains in the diagnosis of the diseases by the medical practitioners. Moreover, for effective disease risk prediction in medical analysis, more emphasis is accorded to the area under the curve (AUC) with accuracy as an evaluation metric. However, the role of the AUC has not been previously characterized notably. In this research article, a novel feature reduction (NFR) model that is aligned with the ML and DM algorithms is proposed to reduce the error rate and further improve the performance. The proposed NFR model comprises of two approaches and uses the AUC in addition to the accuracy to achieve a robust and effective disease risk prediction. The first approach is based on a heuristic process evaluating performance by reducing features with respect to the improvement in the AUC besides the accuracy as evaluation metrics, working to obtain the best subset of highly contributing features in the prediction. The second approach evaluates the accuracy and AUC of all individual features and forms the subsets with the highest accuracies, AUCs, and least difference between them, which are combined in various combinations to achieve the best-reduced set of highly relevant features. For this purpose, the benchmarked public heart datasets of the ML repository of the University of California, Irvine (UCI) are tested; the results are promising. The highest accuracy and AUC achieved with the proposed NFR model are 95.52% and 99.20% with 41.67% feature reduction, respectively. The accuracy is 4.22% higher than recent existing research with a significant improvement of 25% in the performance of the running time of the algorithm.

Electrical Powered Wheelchair (EPW) users may find navigation through indoor and outdoor environments a significant challenge due to their disabilities. Moreover, they may suffer from near-sightedness or cognitive problems that limit their driving experience. Developing a system that can help EPW users to navigate safely by providing visual feedback and further assistance when needed can have a significant impact on the user's wellbeing. This paper presents computer vision systems based on deep learning, with an architecture based on residual blocks that can semantically segment high-resolution images. The systems are modified versions of DeepLab version 3 plus that can process high-resolution input images. Besides, they can simultaneously process images from indoor and outdoor environments, which is challenging due to the difference in data distribution and context. The proposed systems replace the base network with a smaller one and modify the encoder-decoder architecture. Nevertheless, they produce high-quality outputs with fast inference speed compared to the systems with deeper base networks. Two datasets are used to train the semantic segmentation systems: an indoor application-based dataset that has been collected and annotated manually and an outdoor dataset to cover both environments. The user can toggle between the two individual systems depending on the situation. Moreover, we introduce shared systems that automatically use a specific semantic segmentation system depending on the pixels' confidence scores. The annotated output scene is presented to the EPW user, which can aid with the user's independent navigation. Stateof-the-art semantic segmentation techniques are discussed and compared. Results show the ability of the proposed systems to detect objects with sharp edges and high accuracy for indoor and outdoor environments. The developed systems are deployed on a GPU based board and then integrated on an EPW for practical usage and evaluation. The used indoor dataset is made publicly available online.

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