Final Year Projects (FYPs) - Undergraduates
Permanent URI for this collectionhttps://repository.cuilahore.edu.pk/handle/123456789/53
This collection archives the complete set of theses produced by students of the COMSATS University Islamabad, Lahore Campus.
Browse
2 results
Search Results
Item Fractal geometry(Library Information Services, COMSATS University Islamabad, Lahore Campus, 2025) Madiha Iftikhar; SP21-BSM-015; Dr. Adeel Farooq; LHR TP 9884Fractal geometry explores the complex, self-replicating structures found in mathematical, natural, and computational systems. Originating from the foundational work of Benoˆıt B. Mandelbrot, fractals represent shapes exhibiting self-similarity across scales and fractional dimensions. This study delves into key mathematical constructs like the Cantor set, Koch curve, Mandelbrot set, and Julia sets, showcasing their recursive nature and infinite com plexity. The research emphasizes the significance of fractals in modeling natural phenom ena, including coastlines, mountain ranges, and biological patterns, and highlights their transformative applications in computer graphics, medical imaging, and economic analy sis. The iterative methodologies and Hausdorff dimension analysis offer a framework to describe and simulate intricate, real-world patterns. By bridging theoretical and practical realms, fractal geometry not only enriches mathematical theory but also provides innova tive tools for scientific and artistic endeavorsItem Machine Learning: Modern Techniques and Mathematical Approach to Neural Networks(Library Information Services, COMSATS University Islamabad, Lahore Campus, 2025) Sardar Abdul Wahab CH (FA20-BSM-012) : Abdul Raheem (SP20-BSM-003); Dr. Adeel Farooq; LHR TP 9911This thesis explores the mathematical foundations of machine learning algorithms, providing a comprehensive exploration of various key techniques and models. It covers fundamental concepts and methodologies in regression, classification, support vector machines (SVM), decision trees, neural networks, and perceptrons. Each of these algorithms is analyzed in terms of their mathematical underpinnings, operational mechanisms, and practical applications. The study aims to elucidate the core principles that drive these algorithms, offering insights into their theoretical and practical aspects. By understanding the mathematics behind these models, this research contributes to a deeper appreciation and effective utilization of machine learning techniques in solving complex real-world problems.