Aryan Singhal

Neuroscience · Paper · July 2024

QViSTA: A Novel Quantum Vision Transformer for Early Multi-Stage Alzheimer's Diagnosis Using Optimized Variational Quantum Circuits

Magnetic resonance imaging (MRI) is widely used by neurologists to detect brain abnormalities such as strokes, tumors, and various forms of dementia, including Alzheimer's disease. However, accurately diagnosing the different stages of Alzheimer's disease remains a challenge, with nearly one in five patients misdiagnosed due to symptom overlap with other conditions. This paper introduces QViSTA, a novel hybrid quantum vision transformer (QViT) model that exploits quantum parallelism to improve early diagnosis and differentiation of Alzheimer's disease stages. By integrating quantum variational circuits (VQCs) with vision transformers (ViTs), QViSTA addresses the data scalability and computational efficiency limitations of classical machine learning models. Using a balanced, multi-class dataset of 40,000 MRI images, QViSTA achieved a validation area under the receiver operating characteristic (AUC) of 87.86% and a test AUC of 86.67%, closely matching the performance of a benchmarked classical ViT while reducing feature space by 3.18%. Early and accurate detection of Alzheimer's disease is critical, as it allows for timely interventions that can significantly improve the quality of life for patients and their caregivers. As more hospitals adopt AI for biomedical imaging, QViSTA's innovative approach could dramatically reduce misdiagnosis rates, improve patient outcomes, and reduce costs.

Social science · Paper · July 2023

Inferring Hate Speech Trends for Contemporary Tweets Using a Novel Machine Learning Approach from Supervised Learning Algorithms

Aryan Singhal

Social media platforms such as Twitter have become ubiquitous in our contemporary society, providing a platform for individuals to express their opinions and engage in discussions on a wide range of topics including those that are neutral and controversial. However, the growing popularity of Twitter has also led to an increase in the prevalence of hate speech, which raises concerns about its impact on individuals and society. This research investigates hate speech trends on Twitter by utilizing supervised machine learning classification, specifically Naive Bayes, and employing Natural Language Processing (NLP) features such as on a range of neutral and controversial topics. The study compares the prevalence of hate speech in these topics and tracks such trends from January 2022 to January 2023. The results show that hate speech was nearly 400% more prevalent in controversial topics than in neutral topics over the course of the year. In addition, this research finds that controversial topics are consistently more vulnerable to hate speech throughout the course of the year when compared to neutral topics. To conduct this study, a Multinomial Naive Bayes classification model was trained on a publicly available Twitter dataset that was specifically labeled for semantic hate speech and achieved an accuracy rate of 94.46%. Ultimately, the higher vulnerability of controversial topics should necessitate policymakers to introduce stricter warnings or frequent policy reminders to platform users. Such changes will foster a respectful and inclusive platform for users, preserving their freedom of expression and encouraging constructive discussions.