Thomas Best

As cyber threats grow in complexity and volume, traditional methods of threat detection and risk management are proving less effective. Artificial Intelligence (AI) has emerged as a transformative tool in addressing these challenges by providing enhanced capabilities to identify, assess, and mitigate cyber risks in real time. AI-driven solutions le...

The rapid evolution of artificial intelligence (AI) is significantly transforming cybersecurity, offering both new defense mechanisms and emerging threats. AI-driven security solutions enhance threat detection, automate responses, and predict vulnerabilities, making cyber defense more robust and proactive. Machine learning algorithms analyze vast a...

In today's rapidly evolving digital landscape, traditional perimeter-based security models are increasingly inadequate in defending against sophisticated cyberattacks. Zero Trust Security and Cloud Security have emerged as modern approaches to addressing these challenges, focusing on continuous verification and securing distributed environments. Ze...

This study explores the integration of eye tracking and machine learning to advance AI-driven cognitive modeling, enabling the development of personalized user insights. By capturing gaze patterns and applying predictive analytics, the research aims to understand user attention, decision-making processes, and cognitive load in real time. Machine le...

The integration of real-time semantic segmentation with adaptive deep learning models in augmented reality (AR) applications represents a significant leap in enhancing user experiences. This approach enables dynamic interaction with the real world by providing precise object recognition, context awareness, and real-time adaptability. By incorporati...

Hyperparameter optimization is a critical task in the development of deep learning models, especially when scaling to big data environments. Traditional grid search or random search methods are often inefficient, particularly for complex models and large datasets. This paper explores the use of reinforcement learning (RL) for hyperparameter optimiz...

This study presents a comparative analysis of various stock price prediction models, specifically focusing on Generalized Linear Models (GLM), Ridge Regression, Lasso Regression, Elastic Net, and Random Forest. As financial markets become increasingly complex, accurate forecasting of stock prices is critical for investors and financial analysts. Ea...

This case study investigates the forecasting of Netflix stock prices using various regression and machine learning models, aimed at enhancing predictive accuracy in a dynamic financial environment. As one of the leading streaming services globally, Netflix's stock performance is influenced by numerous factors, including subscriber growth, content i...