Introduction Nonlinear dynamics and chaos theory have become increasingly important fields of study in various disciplines such as physics, biology, economics, and engineering. These fields focus on understanding complex systems that exhibit unpredictable behavior despite…
Read MoreIntroduction: Advanced number theory and cryptography are two closely related fields that have become increasingly important in today’s digital age. Number theory is the branch of mathematics that deals with the properties and relationships of…
Read MoreIntroduction Algebraic topology is a branch of mathematics that uses algebraic methods to study topological spaces. It provides a way to understand the shape of spaces by associating algebraic invariants to them. These invariants can…
Read MoreThis project focuses on developing and implementing a machine learning algorithm that predicts stock prices by utilizing mathematical models and historical data analysis. By leveraging advanced algorithms, the system aims to provide accurate and reliable…
Read MoreThis project explores the application of chaos theory and fractals in financial markets through a mathematical lens. By using advanced mathematical tools, the study aims to model the intricate and unpredictable nature of financial systems,…
Read MoreThis project investigates the use of algebraic graph theory in enhancing network security through a computational study. By analyzing the relationships between graphs and network structures, the study aims to develop insights and tools for…
Read MoreThis project thesis explores the chaotic behavior in dynamical systems such as the Logistic Map and Lorenz System and their applications in cryptography. A computational study is conducted to analyze the unpredictability and randomness of…
Read MoreThis project thesis explores the utilization of Topological Data Analysis (TDA) in the fields of machine learning and data science. TDA is a mathematical framework that enables the analysis of shapes and structures in data.…
Read MoreThe project thesis focuses on developing deep learning models for predicting stock market trends by analyzing time series data. By utilizing advanced algorithms such as Long Short-Term Memory (LSTM) networks and Convolutional Neural Networks (CNN),…
Read MoreThis project thesis aims to explore the various applications of differential equations in modeling real-world phenomena. It will analyze how differential equations are used to describe complex systems and predict future behavior. By studying a…
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