Abstract:Computer aided classification of HEp-2 cell based indirect immunofluorescence (IIF) images is a recommended procedure for standardising autoimmune disease diagnostics. In this work a novel feature, the thresholded local binary count (TLBC) has been proposed to classify IIF images into one among six classes. The TLBC is rotational invariant and is insensitive to pixel quantization noise. It characterizes the local binary gray scale pixel information in an image. The proposed feature along with global features such as area, entropy, illumination level and mean intensity, when classified using a support vector machine gave an accuracy of 86%. This feature could help in improving the diagnostics of autoimmune diseases which is highly clinically significant.
<p>This paper introduces a framework aimed at improving the lifestyle of individuals engaged in health self-management programs by providing them with valuable and actionable insights derived from their personal health data. The framework is designed to autonomously discover, optimize, and deliver these insights to the participants. To evaluate the effectiveness of the framework, an experiment was conducted where participants were provided with insights related to their sleep and physical activity. The results demonstrate that the proposed framework, which incorporates feedback-driven optimization, effectively recommends insights that align closely with the behavior and preferences of the participants. These findings highlight the potential of the framework to enhance the impact of health self-management programs.</p>
<p>This paper introduces a framework aimed at improving the lifestyle of individuals engaged in health self-management programs by providing them with valuable and actionable insights derived from their personal health data. The framework is designed to autonomously discover, optimize, and deliver these insights to the participants. To evaluate the effectiveness of the framework, an experiment was conducted where participants were provided with insights related to their sleep and physical activity. The results demonstrate that the proposed framework, which incorporates feedback-driven optimization, effectively recommends insights that align closely with the behavior and preferences of the participants. These findings highlight the potential of the framework to enhance the impact of health self-management programs.</p>
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