Early diagnosis and clinical decision-making depend on accurate brain tumor classification using magnetic resonance imaging (MRI). However, traditional deep learning methods usually rely on centralized medical data, which raises privacy concerns and limits the use of distributed clinical data. This research proposes a privacy-preserving federated learning framework for MRI image-based binary brain tumor classification using a decentralized ResNet-18 architecture that enables collaborative training without sharing raw patient data. To reflect realistic clinical conditions, the framework integrates heterogeneous multi-source datasets in different image formats (PNG and JPG) and evaluates performance under both IID and non-IID settings

A Multiple System Biometric System Based on ECG Data

Precise forecasting of pore pressures is crucial for efficiently planning and drilling oil and gas wells. It reduces expenses and saves time while preventing drilling complications. Since direct measurement of pore pressure in wellbores is costly and time-intensive, the ability to estimate it using empirical or machine learning models is beneficial. The present study aims to predict pore pressure using artificial neural network. The building and testing of artificial neural network are based on the data from five oil fields and several formations. The artificial neural network model is built using a measured dataset consisting of 77 data points of Pore pressure obtained from the modular formation dynamics tester. The input variables

This paper deals with a new Henstock-Kurzweil integral in Banach Space with Bilinear triple n-tuple and integrator function Ψ which depends on multiple points in partition. Finally, exhibit standard results of Generalized Henstock - Kurzweil integral in the theory of integration.

This study was aimed to synthesis Ag2O nanoparticles (N.P.s) utilizing the Ginger plant (Zingiber officinale) extract. As catalysts for pyran derivatives formation employing a three-component coupling reaction among aromatic and aliphatic aldehydes, malononitrile, and dimedone. The nanoparticles exhibit robust catalytic activity with high productivity. Results revealed that Ag2O NPs improved various vital features, like higher yields, reaction time, simple chemical separation, catalytic economic efficiency, and quick process. This study aimed to cyclize heterocyclic compounds to provide new hetero compounds applying nano-oxides obtained from natural (unmanufactured) sources and used in several medical, pharmaceutical, and industrial