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.
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Item Computing Connection Number-Based Indices for Graphs Derived from Metal Organic(Library Information Services, CUI Lahore, 2023) Muhammad Adeel Arshad; CIIT/FA21-RMT-068/LHR; Dr. Hafiz Muhammad Afzal SiddiquiMetal-organic networks consist of metals and organic ligands, forming their two distinctive components. In the realm of Mathematical Chemistry, metals are elements that exhibit metallic bonding and possess a propensity to readily form positive ions. Ligands, on the other hand, encompass neutral molecules or ions that attach to the central atoms or ions of metals, forming bonds. The recent surge in the significance of distance-based topological indices has led to their widespread use in exploring the structure-property relationship among molecules. Given their importance, this thesis focuses specifically on distance-based topological indices. It delves into Metal-organic Networks, examining their characteristics and properties. The thesis also involves the computation of several connection-based Zagreb indices to gain insights into the Metal-organic Networks. By emphasizing the role of distance-based topological indices, this study aims to contribute to our understanding of Metal-organic Networks and their structural features. The examination of these indices offers valuable information regarding connectivity and complexity, facilitating further research and potential applications in fields such as materials science, catalysis, and drug discovery. .Item A Study of Triglyceride by Using Topological Indices(Library Information Services, CUI Lahore, 2023) Ali Ahmad; FA20-RMT-007; Dr. Hafiz Muhammad Afzal SiddiquiA topological graph index, also called a molecular descriptor, is a mathematical formula that can be applied to any graph which provides the information about its chemical properties. From this index, it is possible to analyse mathematical values and further investigate some physicochemical properties of a molecule. Therefore, it is an efficient method in avoiding expensive and time-consuming laboratory experiments. In this thesis, we study some degree based topological indices of triglyceride its line and paraline graphs.Item Evaluation of Connection Number-Based Indices of Networks Derived from Triglycerides(Library Information Services, CUI Lahore, 2023) Hudaa Zaidi; FA21-RMT-036; Dr. Hafiz Muhammad Afzal SiddiquiGraph theory serves as the fundamental framework for chemical informatics, utilizing topological indices to generate chemical structures and establish connections between real numbers and molecular graphs. In 1974, Gutman and Trinajstic introduced connection-based Zagreb indices as molecular descriptors to analyze the topological properties of chemical compounds. These indices were subsequently named Zagreb indices. Building on this research, Ali et al. further explored the applicability and properties of Zagreb indices in theoretical chemistry and molecular modeling. This thesis specifically focuses on the computation of connection-based number indices for triglycerides, as well as their line and para-line graphs. By studying these indices, the aim is to gain insights into the topological characteristics of triglyceride structures and their corresponding graph representations. This research contributes to the broader field of chemical informatics, providing valuable information for the analysis and modeling of complex molecules. .Item Locating and Fault-Tolerant Locating Numbers of Certain Structure.(Library Information Services, CUI Lahore, 2022) Khadija Mazhar; SP21-RMT-022/; Dr. Hafiz Muhammad Afzal SiddiquiA branch of mathematics called chemical graph theory unifies graph theory and chemistry. Molecules are mathematically modelled using graph theory to reveal the physical characteristics of these chemical compounds. Here chemical structure can be expressed in the form of graph. In chemical graph the vertices represent the compound's atoms, and the edges represent its chemical bonds. For a connected graph ?, a resolving set is a subset of ?(?) that is used to uniquely identify all vertices of graph by means of distance codes in such a way that all the vertices have distinct codes, if the resolving set is minimum that is called basis for ? and its cardinality termed as metric dimension or location number. If all the vertices of the graph are identified by at least one pair of vertices of resolving set, then this set is referring as fault-tolerant resolving set and if this set is minimum then its cardinality is called fault-tolerant metric dimension. This thesis comprised of five main chapters whose details are: first chapter is about introduction, second consist of literature review, chapter 3 and 4 consist of main results in which we study fault-tolerant metric dimension of chemical structure. We show that this parameter is unbounded if we consider n-unit structure. Finally, in chapter 5 we present conclusion of the thesis and some future directions.Item Graph Neural Networks: Bridging Structure and Intelligence(Library Information Services, COMSATS University Islamabad, Lahore Campus, 2025) Fahad Shoaib; SP21-BSM-007; Dr. Hafiz Muhammad Afzal Siddiqui; LHT TP 9880Graph Neural Networks (GNNs) have emerged as a powerful paradigm for learning on non-Euclidean data, bridging the gap between structured graph representations and intel ligent data processing. This thesis explores the theoretical foundations and architectural innovations of GNNs, focusing on their ability to capture complex relational patterns in graph-structured data. We begin by examining the Message Passing Framework, which forms the backbone of most GNN architectures, enabling information propagation across graph nodes. The study then delves into Graph Convolution Operations, highlighting how they generalize traditional convolutions to irregular graph domains. We provide an in-depth analysis of two key GNN architectures: Graph Convolutional Networks (GCNs) and Graph Attention Networks (GATs). GCNs are presented as a foundational model that efficiently aggregates neighborhood information, while GATs are explored for their adaptive atten tion mechanisms that allow for more flexible and expressive feature learning. Through a comparative analysis, we elucidate the strengths and limitations of these architectures, with particular emphasis on citation network analysis, where nodes represent scientific papers and edges represent citations between them. Our experiments on the CORA dataset, com prising 2,708 scientific publications and 5,429 citation links, demonstrate the effectiveness of GNNs in capturing the interdependence between academic papers and their citations.