Mrs. M. Anbu

https://doi.org/10.64823/ijter.2605011

SQL Injection (SQLi) attacks continue to pose a significant threat to modern web applications, enabling attackers to manipulate database queries and gain unauthorized access to sensitive information. Despite advancements in secure development practices, many applications remain vulnerable due to improper input validation, insecure coding techniques, and evolving attack methods. This research focuses on the detection and prevention of SQL injection attacks in contemporary web environments.

Alzheimer's Disease (AD) is a progressive neurodegenerative disorder affecting ~55 million people globally. Early diagnosis at the Mild Cognitive Impairment (MCI) stage is critical for timely intervention. Functional MRI (fMRI) and Positron Emission Tomography (PET) scans provide complementary information — fMRI captures functional connectivity between brain regions, while PET reveals metabolic activity (e.g., amyloid-β and tau protein accumulation). However, the high dimensionality, complexity, and non-linear nature of these multimodal neuroimaging datasets pose significant challenges for classical deep learning models, which often require massive training data and suffer from overfitting in high-dimensional spaces. Quantum computing offers a paradigm shift: quantum Hilbert spaces grow exponentially with qubit count, enabling richer feature representations through quantum entanglement and superposition.