Hate-Speech Recognition using Machine Learning

The convergence of quantum computing and genomic data analysis offers a groundbreaking methodology for tackling intricate biological datasets. Traditional computational techniques often struggle with the complexities of high-dimensional genomic information. This study introduces a quantum-enhanced framework for cancer type detection, leveraging the capabilities of Quantum Support Vector Machines (QSVM). The proposed approach showcases significant advancements in efficiency, particularly in training speed and scalability, highlighting the potential of quantum algorithms as valuable extensions to classical methods. The paper also addresses the limitations of current quantum hardware and explores potential directions for future development. Keywords- Machine Learning, Transformer Models, BERT (Bidirectional Encoder Representations from Transformers), Contextual Embeddings, Offensive language Identification