In our study, we introduce a novel hybrid ensemble model that synergistically combines
LSTM, BiLSTM, CNN, GRU, and GloVe embeddings for the classification of gene mutations …

In the rapidly evolving domain of machine learning, ensuring model generalizability remains
a quintessential challenge. Overfitting, where a model exhibits superior performance on …

Natural Language Processing (NLP) has emerged as a crucial technology for understanding
and generating human language, playing an essential role in tasks such as machine …

Abstract Continual Test-Time Adaptation (CTTA) aims to adapt a pretrained model to ever-
changing environments during the test time under continuous domain shifts. Most existing …

This paper introduces the innovative concept of the Cubixel—a three-dimensional
representation of the traditional pixel—alongside the derived metric, Volume of the Void …

This research evaluates the performance of two Transformer models, the Vision Transformer
(ViT) and Swin Transformer, in the analysis of thoracic X-ray images. The study's objective is …

Image stability is very important in a time when digital image communication is essential to
many fields. Modern online dangers are often too complicated for old security methods to …

This study examines various machine learning models to predict customer responses in the
auto insurance industry. We focus on metrics like accuracy, precision, recall, and F1-score …

The integration of two foundational mathematical structures, the sine wave and Gaussian
distribution, has led to the formulation of a new model known as the Gaussian-Modulated …

The early detection and classification of skin cancer are pivotal in improving patient
outcomes and reducing healthcare burdens. However, traditional deep learning models in …