Cardiovascular Diseases (CVDs) have emerged as a significant physiological condition, being a primary contributor to mortality. Timely and precise diagnosis of heart disease is crucial to safeguard patients from additional harm. Recent studies show that the usage of data driven approaches, such as Deep Learning (DL) and Machine Learning (ML) techniques, in the field of medical science is highly useful in accurately diagnosing heart disease in less time. However, statistical learning and traditional ML approaches require feature engineering to generate robust and effective features from data, which are then used in the prediction models. In the case of large complex data, both processes pose many challenges. Whereas, DL techniques are capable of learning features automatically from the data and are effective at handling large and intricate datasets while outperforming the ML models. This study focuses on the accurate prediction of CVDs, considering the patient’s health and socio-economic conditions while mitigating the challenges presented by imbalanced data. The Adaptive Synthetic Sampling Technique is used for data balancing, while the Point Biserial Correlation Coefficient is used as a feature selection technique. In this study, two DL models, Ensemble based Cardiovascular Disease Detection Network (EnsCVDD-Net) and Blending based Cardiovascular Disease Detection Network (BlCVDD-Net), are proposed for accurate prediction and classification of CVDs. EnsCVDD-Net is made by applying an ensemble technique to LeNet and Gated Recurrent Unit (GRU), and BlCVDD-Net is made by blending LeNet, GRU and Multilayer Perceptron. SHapley Additive exPlanations is used to provide a clear understanding of the influence different factors have on CVD diagnosis. The network’s performance is evaluated on the basis of various performance metrics. The results indicate that the EnsCVDD-Net outperforms all base models with 88% accuracy, 88% F1-score, 91% precision, 85% recall, and 777s execution time. Similarly, with 91% accuracy, 91% F1-score, 96% precision, 86% recall, and 247s execution time, BlCVDD-Net outperforms the state-of-the-art DL models. To validate the model’s results, 10-Fold Cross Validation is employed. An eXplainable Artificial Intelligence technique, SHapley Additive exPlanation is employed to know the features contribution in model’s predictions.

Energy storage devices such as batteries hold great importance for society, owing to their high energy density, environmental benignity and low cost. However, critical issues related to their performance and safety still need to be resolved. The periodic table of elements is pivotal to chemistry, physics, biology and engineering and represents a remarkable scientific breakthrough that sheds light on the fundamental laws of nature. Here, we provide an overview of the role of the most prominent elements, including s-block, p-block, transition and inner-transition metals, as electrode materials for lithium-ion battery systems regarding their perspective applications and fundamental properties. We also outline hybrid materials, such as MXenes, transition metal oxides, alloys and graphene oxide. Finally, the challenges and prospects of each element and their derivatives and hybrids for future battery systems are discussed, which may provide guidance towards green, low-cost, versatile and sustainable energy storage devices.