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Brain tumors are still diagnosed and classified based on the results of histopathological examinations of biopsy samples. The existing method requires extra effort from the user, takes too long, and can lead to blunders. These limitations underline the need of employing a fully automated deep learning system for the multi‐classification of brain tumors. In order to facilitate early detection, this study employs a convolutional neural network (CNN) to multi‐classify brain tumors. In this research, we present three distinct CNN models for use in three separate categorization tasks. The first CNN model can correctly categorize brain tumors 99.74% of the time. The second CNN model is 96.27% accurate in differentiating between normal, glioma, meningioma, pituitary, and metastatic brain tumors. The third CNN model successfully distinguishes between Grades II, III, and IV brain tumors 99.18% of the time. The Hybrid Particle Swarm Grey Wolf Optimization (HPSGWO) technique is used to quickly and accurately determine optimal values for all of CNN models most important hyperparameters. An HPSGWO algorithm is used to fine‐tune all the necessary hyperparameters for optimal classification performance. The results are compared with standard existing CNN models across a range of performance measures. The proposed models are trained using publicly available large clinical datasets. To verify their initial multi‐classification of brain tumors, clinicians and radiologists might use the proposed CNN models. [ABSTRACT FROM AUTHOR]

In many parts of the globe, heart disease or cardiovascular disease is a leading cause of mortality. It is a group of conditions that includes heart failure and other cardiac problems. In the USA, there are an estimated 356,000 cases of hospital cardiac arrest every year, 90% of which are fatal, as reported by the American Heart Association (Tsao et al. in Circulation https://doi.org/10.1161/CIR.0000000000001052, 2022). However, the survival rate may be improved by routine monitoring of cardiac function in order to detect the illness at an early stage. Many formerly intractable medical issues have made significant progress because of the combination of computer technology and biomedical research. Automatic prediction is made possible by the examination of previously learned data in machine learning. In this research, we utilize the Physionet Cinc/C Challenge 2016 Phonocardiogram dataset. Classification models like K-nearest neighbour (KNN), support vector machine (SVM), and AlexNet are used with the features retrieved using wavelet scattering transform and continuous wavelet transform. The primary objective of this work is to provide a very effective method for early diagnosis of cardiovascular disorders, which will help clinical professionals better spot heart issues in their patients. Sensitivity, Specificity and other performance metrics are used to assess the effectiveness of the various categorization models. KNN, SVM, and AlexNet classifiers all achieve good accuracy with AlexNet outperforms the former two giving an accuracy of 98.11%, with a sensitivity of 96.20% and a specificity of 98.73%. Our suggested model's output has the potential to contribute significantly to the categorization of cardiac sounds. [ABSTRACT FROM AUTHOR]