most citedA Hybrid Machine Learning Model for Classifying Gene Mutations in Cancer using LSTM, BiLSTM, CNN, GRU, and GloVe

9 citations · 12 across the 3 of their papers we have counts for

collaborators

3 papers

cs.CL2023★ 1 cited

An Ensemble Approach to Question Classification: Integrating Electra Transformer, GloVe, and LSTM

Sanad Aburass, Osama Dorgham, Maha Abu Rumman

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 translatio…

cs.CV2023★ 2 cited

Performance Evaluation of Swin Vision Transformer Model using Gradient Accumulation Optimization Technique

Sanad Aburass, Osama Dorgham

Vision Transformers (ViTs) have emerged as a promising approach for visual recognition tasks, revolutionizing the field by leveraging the power of transformer-based architectures.…

q-bio.QM2023★ 9 cited

A Hybrid Machine Learning Model for Classifying Gene Mutations in Cancer using LSTM, BiLSTM, CNN, GRU, and GloVe

Sanad Aburass, Osama Dorgham, Jamil Al Shaqsi

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 canc…