The potential of technology to revolutionize healthcare is exemplified by the synergy between artificial intelligence (AI) and early detection of cardiomegaly, demonstrating the power of proactive intervention in cardiovascular health. This paper presents an innovative approach that leverages advanced AI algorithms, specifically deep learning (DL) technology, for the early detection of cardiomegaly. The methodology consists of five key steps, including data collection, image preprocessing, data augmentation, feature extraction, and classification. Utilizing chest X-ray (CXR) images from the National Institutes of Health (NIH), the study applies rigorous image preprocessing operations, including color transformation and normalization. To enhance model generalization, data augmentation is employed, paving the way for two distinct DL models, a convolutional neural network (CNN) developed from scratch and a pretrained residual network with 50 layers (ResNet50), and adapted to the problem domain. Both models are systematically evaluated with five optimizers, revealing the AdaMax optimizer’s superiority for the CNN model and AdaGrad’s efficacy for the modified ResNet50. The proposed CNN with AdaMax achieves an impressive 99.91% accuracy, outperforming recent techniques in precision, recall, and F1−score . This research underscores the transformative potential of AI in cardiovascular health diagnostics, emphasizing the significance of timely intervention.

The need for energy sources in India has increased abnormally in recent years due to industrial and societal growth. To meet this demand, it was a necessary choice of renewable energy sources (RESs) as a solution to lack of nonrenewable energy sources. Due to the multiplicity of involved factors, selecting the most appropriate RESs is a multiattribute decision making (MADM) problem. There is a large number of work associated with the development of MADM techniques, especially under ambiguous and uncertain conditions. However, the effective embedding of uncertainty and ambiguity and in decision-making remains a difficult challenge, and thus this study introduces a new framework for solving the problem of selecting the most suitable RESs which is based on the neutrosophic set and TODIM (an acronym in Portuguese of interactive and multicriteria decision-making) method. It also reduces human intervention by being systematically applied. First, it transforms the linguistic terms presented into neutrosophic values and implements systematic techniques to compute missing values in the decision matrix using the case-based technique. Second, it calculates the weight of every linguistic variable as well as those of the decision-makers (DMs) and weighted attributes. Furthermore, it creates an aggregated single valued neutrosophic decision matrix for DMs. Finally, it calculates the overall dominance-degree matrix, derives the overall values, and ranks the alternatives. It is applied to select RESs in Karnataka, India, and the obtained results show that wind energy is the most suitable RES for India, with small hydroenergy second most appropriate.