M.Phil / MS

Permanent URI for this collectionhttps://repository.cuilahore.edu.pk/handle/123456789/52

This collection archives the complete set of theses produced by students of the COMSATS University Islamabad, Lahore Campus.

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Author: search.filters.author.Muhammad BilalSubject: search.filters.subject.SP22

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    Connection Numbers on Carbon Nanocone Line Graph and Various Chemical Lin
    (Library Information Services COMSATS University Lahore Campus, 2023-03-13) Muhammad Bilal; Dr.Imran Zulfiqar Cheem; SP22-RMT-024; LHR TP 8730
    In this study, we delve into the mathematical exploration of the Connection Number in carbon nanocone line graphs and diverse chemical line graphs within the realm of Graph Theory. The theoretical framework is developed based on the principles of connectivity and network structures. These structures are represented as graphs, where atoms con stitute nodes and chemical bonds form edges. The novel metric, Connection Number, is introduced to quantify connectivity within these unique molecular configurations. Em ploying graph invariants and specialized connectivity indices, the study characterizes and compares the structural features of carbon nanocones and various chemical line graphs. Advanced graph algorithms are utilized to unveil the intricate relationship be tween Connection Number and structural stability, offering insights into the impact of connectivity on molecular properties and reactivity. The analysis extends to identifying recurring structural motifs and patterns, contributing to a systematic classification of connectivity profiles. Beyond theoretical considerations, the study explores practical applications in nanotechnology, materials science, and chemical engineering. This con cise investigation harnesses the power of graph theory to deepen our understanding of molecular connectivity in carbon nanocones and diverse chemical line graphs, paving the way for innovative advancements in nanoscience and materials engineering
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    On Cluster Analysis of Some Portfolio Optimization
    (Library Information Services, COMSATS University Islamabad, Lahore Campus, 2023) Muhammad Bilal; CIIT/SP22-RMT-009/LHR; Dr. Sarfraz Ahmad; LHR TP 8716
    The classic mean-variance portfolio optimization approach is criticized in large part for its propensity to overstate estimate error. An estimated inaccuracy of a few percent can skew the entire package. The Black-Litterman technique (Bayesian method) and the resampling methodaretwocommonapproachestosolvingthisproblem. Amorerecentapproachtothe issue’s solution is the clustering method. By clustering, we initially combine the stocks that have a strong correlation and handle the group as a single stock. Following the grouping of the stocks, we will have a few stock clusters. For these clusters, we do the standard mean-variance portfolio optimization. By using the clustering approach, the influence of estimating error may be minimized and the portfolio’s stability can be increased. In this project, we’ll examine how it functions and run experiments to see if clustering techniques enhance the portfolio’s performance and stabilities
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