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Full-Text Articles in Computer Engineering
Application Of Supervised Machine Learning To Predict The Mortality Risk In Elderly Using Biomarkers, Priyanka Sonkar
Application Of Supervised Machine Learning To Predict The Mortality Risk In Elderly Using Biomarkers, Priyanka Sonkar
Dissertations
The idea of long-term survival amongst older individuals has been a major medical and social concern. A wide range of biomarkers have been identified to prospectively predict disability, morbidity, and mortality outcomes in older adult populations. The machine learning techniques applied with clinically relevant biomarkers provide new ways of understanding diseases and solutions to tackle challenges to the health of the aging population. This paper describes two supervised machine learning techniques, Logistic Regression (LR) and Support Vector Machine (SVM) which are used in the prediction of the mortality in elderly people. LR is one of the traditionally used predictive modeling …
A Comparison Of Supervised Machine Learning Classification Techniques And Theory-Driven Approaches For The Prediction Of Subjective Mental Workload, Dmitrii Gmyzin
Dissertations
In the modern world of technological progress, systems and interfaces are becoming more and more complex. As a consequence, it is a crucial to design the human-computer interaction in the most optimal way to improve the user experience. The construct of Mental Workload is a valid metric that can be used for such a goal. Among the different ways of measuring Mental Workload, self-reporting procedures are the most adopted for their ease of use and application. This research is focused on the application of Machine Learning as an alternative to theory-driven approaches for Mental Workload measurement. In particular, the study …
Physical Human Activity Recognition Using Machine Learning Algorithms, Haritha Vellampalli
Physical Human Activity Recognition Using Machine Learning Algorithms, Haritha Vellampalli
Dissertations
With the rise in ubiquitous computing, the desire to make everyday lives smarter and easier with technology is on the increase. Human activity recognition (HAR) is the outcome of a similar motive. HAR enables a wide range of pervasive computing applications by recognizing the activity performed by a user. In order to contribute to the multi facet applications that HAR is capable to offer, predicting the right activity is of utmost importance. Simplest of the issues as the use of incorrect data manipulation or utilizing a wrong algorithm to perform prediction can hinder the performance of a HAR system. This …