Bøker av Pooja Rani

Machine learning-based heart disease diagnosis is a rapidly evolving area of research that aims to improve the accuracy and efficiency of cardiovascular disease diagnosis using artificial intelligence (AI) algorithms. The use of machine learning models trained on medical imaging and electronic health record (EHR) data has shown promising results in predicting and diagnosing heart disease, as well as identifying risk factors and potential treatments. Predictive models can extract relevant features from EHR data and medical images to identify patterns and predict future outcomes. The use of deep learning algorithms and expert systems can further improve the sensitivity, specificity, and accuracy of heart disease diagnosis. The potential benefits of machine learning-based heart disease diagnosis include improving clinical decision-making, providing personalized treatment plans, and reducing healthcare costs. Additionally, machine learning-based diagnosis has the potential to improve the speed and accuracy of diagnosis, leading to improved patient outcomes. Overall, machine learning-based heart disease diagnosis is an important area of research with significant potential for improving cardiovascular health and patient care.

High-quality code comments support developers in software maintenance and program comprehension tasks. However, the semi-structured nature of comments, several conventions to write comments, and the lack of quality assessment tools for all aspects of comments make comment evaluation and maintenance a non-trivial problem. To understand the specification of high-quality comments to build effective assessment tools, we emphasize on acquiring a multi-perspective view of the comments, which can be approached by analyzing (1) the academic support for comment quality assessment, (2) developer commenting practices across languages, and (3) developer concerns about comments.To help researchers and developers in building comment quality assessment tools, we contribute: (i) a systematic literature review (SLR) of ten years (2010-2020) of research on assessing comment quality, (ii) a taxonomy of comment quality attributes, (iii) an empirically validated taxonomy of class comment information types (CITs) from three programming languages, (iv) a multi-programming-language approach to automatically identify the CITs, and (v) an empirically validated taxonomy of comment convention-related questions and recommendation from various Q&A forums.Our contributions provide various kinds of empirical evidence of thedeveloper's interest in reducing efforts in the software documentation process, of the limited support developers get in automatically assessing comment quality, and of the challenges they face in writing high-quality comments.