Remaining Useful Life (RUL) of a component or a system is defined as the length from the current time to the end of the useful life. Accurate RUL estimation plays a crucial role in Predictive Maintenance applications. Traditional regression methods, both linear and non-linear, have struggled to achieve high accuracy in this domain. While Convolutional Neural Networks (CNNs) have shown improved accuracy, they often overlook the sequential nature of the data, relying instead on features derived from sliding windows. Since RUL prediction inherently involves multivariate time series analysis, robust sequence learning is essential. In this work, we propose a hybrid approach combining Convolutional Neural Networks with Long Short-Term Memory (LSTM) networks for RUL estimation. Although CNN-based LSTM models have been applied to sequence prediction tasks in financial forecasting, this is the first attempt to adopt this approach for RUL estimation in prognostics. In this approach, CNN is first employed to efficiently extract features from the data, followed by LSTM, which uses these extracted features to predict RUL. This method effectively leverages sensor sequence information, uncovering hidden patterns within the data, even under multiple operating conditions and fault scenarios. Our results demonstrate that the hybrid CNN-LSTM model achieves the highest accuracy, offering a superior score compared to the other methods.

COVID-19 is a pandemic that has spread all over the world. With the US now projected at over 6 million cases, and a lot more people are assumed to be exposed and asymptomatic, based on the seroprevalence studies. With the many COVID-19 related datasets that have been collected, AI is helping us fight this virus with applications such as early detection and diagnosis, contact tracing, projection of cases and mortality, development of drugs and vaccines, etc. We invite submission of papers describing timely and innovative research on all aspects of using AI in the fight against COVID.We invite submission of papers describing timely and innovative research on fighting COVID-19 using AI. Some examples that have been delivered in our BIOKDD 2020 workshop (http://home.biokdd.org/biokdd20/program.html) include: (i) bioinformatics (e.g., SARS-CoV-2 study using signature mutations and human leukocyte antigen)(ii) data curation (e.g., COVID-19 knowledge graph and knowledge base, gene signature database, 1Point3Acres CovidNet, COVID-19 literature curation), (iii) deep learning models (e.g., for case projection, COVID-19 detection using chest X-ray), and (iv) statistical methods (e.g., analysis using Bayesian inference and virtual reality). We welcome papers in all aspects of using AI in the fight against COVID-19, such as clinical, epidemiological, data-driven machine learning, statistical research in developing AI for COVID-19, as well as application-oriented papers that make innova...