Designing a deep hybridized residual and SE model for MRI image-based brain tumor prediction.

Deep learning techniques have become crucial in the detection of brain tumors but classifying numerous images is time-consuming and error-prone, impacting timely diagnosis. This can hinder the effectiveness of these techniques in detecting brain tumors in a timely manner. To address this limitation, this study introduces a novel brain tumor detection system.

Design and Development of Modified Ensemble Learning with Weighted RBM Features for Enhanced Multi-disease Prediction Model.

In this computer world, huge data are generated in several fields. Statistics in the healthcare engineering provides data about many diseases and corresponding patient's information. These data help to evaluate a huge amount of data for identifying the unknown patterns in the diseases and are also utilized for predicting the disease.

Covid-19 cases prediction using SARIMAX Model by tuning hyperparameter through grid search cross-validation approach.

SARS-Coronavirus was first detected in December 2019, later named COVID-19, and declared a pandemic by the World Health Organization (WHO). As prediction models assist policymakers in making decisions based on expected outcomes. Existing models were only used to anticipate a smaller range of data resulting in irrelevant predictions.

Forecasting COVID-19 Pandemic Using Prophet, ARIMA, and Hybrid Stacked LSTM-GRU Models in India.

Due to the proliferation of COVID-19, the world is in a terrible condition and human life is at risk. The SARS-CoV-2 virus had a significant impact on public health, social issues, and financial issues. Thousands of individuals are infected on a regular basis in India, which is one of the populations most seriously impacted by the pandemic.

A fuzzy rule-based approach for characterization of mammogram masses into BI-RADS shape categories.

We present new geometric shape and margin features for classifying mammogram mass lesions into BI-RADS shape categories: round, oval, lobular and irregular. According to Breast Imaging Reporting and Data System (BIRADS), masses can be differentiated using its shape, size and density, which is how radiologist visualizes the mammograms. Measuring regular and irregular shapes mathematically is found to be a difficult task, since there is no single measure available to differentiate various shapes.