An active learning enhanced data programming (ActDP) framework for ECG time series
This work proposes a novel framework titled active learning enhanced data programming (ActDP). It uses a combination of DP and AL for ECG beat classification using single-lead data. ECG beat classification is pivotal in cardiology and healthcare applications for diagnosing a broad spectrum of heart conditions and arrhythmias.
To establish the usefulness of this proposed ActDP framework, the experiments have been conducted using the MIT-BIH dataset with 94,224 ECG beats. DP assigns a
probabilistic label to each ECG beat using nine novel polar labelling functions and a generative model in this work. Further, AL improves the result of DP by replacing the
labels for sampled ECG beats of a generative model with ground truth. Subsequently, a discriminative model is trained on these labels for each iteration.To know more, follow the link.
Supervised Model for Peri-Urban Area Demarcation in Hyderabad, India
The peripheries of metropolitans are undergoing rapid socio-spatial changes due to economic transformations, especially in the global south, leading to the emergence of peri-urban areas. However, there are limited studies analyzing peri-urban expansion in the past decade in countries like India. This could be attributed to the absence of socio-economic datasets that are generally derived from census-related exercises.In this context, this study introduces a machine-learning (ML)-based model to demarcate peri-urban areas around Hyderabad, India. Our model integrates multiple spatial parameters and domain knowledge-driven thresholds using a radial basis function support vector machine (RBF-SVM). Notably, the findings reveal a remarkable 107.96% increase in peri-urban regions around Hyderabad between 2013 and 2020. Furthermore, this study performs a robustness analysis, assessing the model’s …To know more, follow the link.