Abstract Approaches to evaluate comments based on whether they increase code comprehensibility for software maintenance tasks are important, but largely missing. We proposeCommentfor automated classification and quality evaluation of code comments of C codebases based on how they can help to understand existing code. We conduct surveys and document developers' perceptions on the type of comments that prove useful to maintaining software in the form of comment categories. A total of 20,206 comments have been collected from open‐sourceGithubprojects and annotated with assistance from industry experts. We develop features to semantically analyze comments to locate concepts related to categories of usefulness. Additionally, features based on code and comment correlation are designed to infer whether the comment is also consistent and not superfluous. Using neural networks, comments are classified asuseful,partially useful, andnot usefulwith precision and recall scores of 86.27% and 86.42%, respectively. The proposed framework for comment quality evaluation incorporates industry practices and adds significant value to companies wanting to formulate better code commenting strategies. Furthermore, large codebases can be de‐cluttered by removing comments not helpful in maintaining code.

Remote health monitoring system with Clinical Decision Support System as a key component could potentially quicken the response of medical specialists to critical health emergencies experienced by their patients. A monitoring system, specifically designed for cardiac care with ECG signal analysis as the core diagnostic technique, could play a vital role in early detection of a wide range of cardiac ailments, from a simple arrhythmia to life threatening conditions such as Myocardial Infarction. The system, that we have developed consists of three major components viz., (a) Mobile Gateway, deployed on patient's mobile device, that receives 12-Lead ECG signals from any ECG sensor (b) remote server component that hosts algorithms for accurate annotation and analysis of the ECG signal and (c) Point of Care Device of the doctor to receive a diagnostic report from the server based on the analysis of ECG signals. In the present work our focus has been towards developing a system capable of detecting critical cardiac events well in advance using an advanced remote monitoring system. A system of this kind is expected to have applications ranging from tracking wellness/fitness to detection of symptoms leading to fatal cardiac events.

Detection of cardiac abnormalities through annotation and modeling based on 3-Lead ECG data has been reported in literature very extensively. However, we realize that analysis based on 12-Lead, dimension resolved, ECG data is vital for accurate detection of critical cardiac events such as Myocardial Infarction (MI), Ischemia, Bundle Branch Blocks, Pericarditis etc. and understanding of underlying conditions leading to it. In this work we present an approach to first annotate 12-Lead ECG data and further analyze them with the objective of classifying them broadly into normal or abnormal (MI) conditions. The algorithm that we have developed enables the representation of all 12-Lead ECG data in form of a critical feature set which are then subjected to clinically established rule set to detect cardiac abnormality. With a preliminary implementation of this methodology, the classification results seem encouraging leaving immense scope for further enhancements towards developing a robust Cardiac Decision Support System.