Optimizing Skin Cancer Classifiers By Applying Multiplicative Weight Update Into A Mobile Application

In this paper, we use a dataset from the Substance Abuse and Mental Health Data Archive to analyze the impacts of child-mentor relationships on child mental health. Previous studies have come to similar conclusions: positive parent-student and teacher-student relationships often lead to signs of positive mental health in children. To expand upon these past findings, we used different measures of mental health and relationship qualities to create a predictive model. We conducted classification and coefficient weight analyses to see how strong of an impact different variables representing indicators of healthy relationships had on different aspects of child mental health. This information was used to create predictions of future cases. Like previous studies, we found that there was a general pattern that showed that good child-mentor relationships, defined specifically by the frequency of praise and fights, had an overall positive impact on child mental health, specifically when it came to symptoms of depression. Furthermore, parents seemed to have stronger impacts on child mental health than teachers did. Limitations include possibly biased respondents who may not be representative of the greater population, as well as the specificity of the variables that were chosen. Going forward, further steps to analyze different datasets and deepen the scope of the research will be helpful in finding patterns and developing more detailed conclusions.

Not all tumors in the brain are cancerous, so irregular cell growths can often be determined as benign and are ignored completely. However, many of these low-danger cell growths can develop into higher-danger cancerous tumors. Identifying these low-danger tumors early is key to preventing cancer in the future.

Current methods for determining the presence and type of brain tumor in a given patient’s MRI scan can oftentimes be inefficient and are prone for error. By using a machine learning algorithm, the error in these classifications is reduced significantly, and the process is made much more efficient.