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 On Fermatean Fuzzy Bipolar Soft Topological Space(Library Information Services, CUI Lahore, 2023) Zainab Zaka; CIIT/FA21-RMT-056/LHR; Dr. Hani ShakerIn the current thesis, on fermatean fuzzy bipolar soft topological space is investigated. A fermatean fuzzy set is converted by using bipolar soft set in topological space. The soft sets are a family of parameterized sets. The concepts of a soft neighborhood of a point, a soft open set, and a soft closed set are introduced. Soft topological space offers variety of topological spaces that are parameterized.The BSS is made with the two SS. One gives negative information while the other one gives us positive information. Two mappings are used to explain the FBSS. In FBS, one mapping is used to approximate fuzziness relative to the degree of positivity while another mapping is used to approximate fuzziness relative to the degree of negativity in the initial universal set objects. To cope with uncertainty in various real-world condition, a reducing mathematical technique called fermatean fuzzy set is being developed. FFS is more flexible than intuitionistic and PFS.The result are evaluated in on FFBSTS and FFBS. We work on some features such as neighborhood, continuity, and others.Item Metric Dimension of Wheel Based Graphs(Library Information Services, COMSATS University Islamabad, Lahore Campus, 2024) Muzammal Ashraf; CIIT/SP21-BSM-037/LHR; Dr. Hani Shaker; LHR TP 9894The concept of metric dimension is essential for solving a variety of structural and opti- mization issues. The smallest size of a set of vertices that allows the original vertex to be uniquely determined by the shortest distances from any vertex to these selected vertices is known as the graph’s metric dimension. This idea finds use in the construction of effective routing algorithms, network navigation, and locating issues. In this study, we investigate the metric dimension of different classes of graphs, such as trees, pathways, cycles, and more intricate structures like grids and hypercubes. We explore methods for computing the metric dimension and present novel approaches to obtain accurate values or constraints for particular graph families. The study sheds light on the behavior of the metric dimen- sion under various graph settings, emphasizing how it varies with respect to graph factors including degree, vertex connectivity, and diameter. Furthermore, we examine the compu- tational difficulty of figuring out the metric dimension and suggest effective strategies for real-world uses. In order to provide more efficient solutions for graph-based problems in practical settings, we seek to optimize the determination of metric dimensions by utilizing recent developments in computational techniques.Item House Price Prediction by using Machine Learning(Library Information Services, COMSATS University Islamabad, Lahore Campus, 2024) Laiba Faisal (CIIT/FA20-BSM-057/LHR), Ameema Umar (CIIT/FA20-BSM-053/LHR); Dr. Hani Shaker; LHR TP 9909The goal of this project is to create a machine learning model that can predict home selling prices by taking into account variables like location, square footage, number of bedrooms and bathrooms, and other relevant characteristics. The model prioritizes optimization for managing missing data in order to produce trustworthy estimates for the real estate market. This study advances real estate predictive modelling by using rigorous analysis and experimentation. It provides practitioners and stakeholders with useful information for making precise cost projections.Item Metric Dimension of Related Wheel Graphs(Library Information Services, COMSATS University Islamabad, Lahore Campus, 2024) Muhammad Zubair Aslam; CIIT/SP21-BSM-033/LHR; Dr. Hani Shaker; LHR TP 9893The metric dimension of a graph is a central concept in graph theory, which deals with de- termining the minimum number of vertices required to uniquely identify every other vertex in the graph through their distances to the selected set of vertices, known as a resolving set. This thesis explores the metric dimension of related wheel graphs, a family of graphs ob- tained by attaching a cycle graph to a central vertex, thus forming a wheel like structure. We examine the metric dimension in both classical and related wheel graphs, where modifica- tions such as additional connections or altered structures are introduced. Through a detailed analysis, we aim to provide bounds on the metric dimension for different classes of related wheel graphs, propose efficient algorithms for finding the resolving set, and compare the metric dimension of these graphs with other well-studied graph families. The results of this study contribute to a deeper understanding of the interplay between graph structure and metric dimension, with potential applications in network design, sensor placement and graph labeling.Item Metric Dimension of Related Wheel Graphs(Library Information Services, COMSATS University Islamabad, Lahore Campus, 2025) Muhammad Zubair Aslam; SP21-BSM-033; Dr. Hani Shaker; LHR TP 9893The metric dimension of a graph is a central concept in graph theory, which deals with de termining the minimum number of vertices required to uniquely identify every other vertex in the graph through their distances to the selected set of vertices, known as a resolving set. This thesis explores the metric dimension of related wheel graphs, a family of graphs ob tained by attaching a cycle graph to a central vertex, thus forming a wheel like structure. We examine the metric dimension in both classical and related wheel graphs, where modifica tions such as additional connections or altered structures are introduced. Through a detailed analysis, we aim to provide bounds on the metric dimension for different classes of related wheel graphs, propose efficient algorithms for finding the resolving set, and compare the metric dimension of these graphs with other well-studied graph families. The results of this study contribute to a deeper understanding of the interplay between graph structure and metric dimension, with potential applications in network design, sensor placement and graph labeling.Item Network Analysis Using Graph Indices(Library Information Services, COMSATS University Islamabad, Lahore Campus, 2024) Shamsa Liaqat (FA20-BSM-010) : Mahnoor (FA20-BSM-043); Dr. Hani Shaker; LHR TP 9917In the digital age, social networking platforms like Twitter, LinkedIn, and Facebook have significantly influenced how individuals interact and form communities. These platforms, along with transportation networks, play crucial roles in shaping social dynamics and fa cilitating physical mobility. This thesis employs graph theory to analyze these networks, focusing on the complexities of their interactions using Graph Indices, a mathematical framework that enhances the precision of network analysis. Graph theory provides a robust foundation for understanding the structure and flow of information within networks. This study specifically utilizes concepts such as betweenness centrality and the beta index to an alyze network properties. Betweenness centrality identifies key influencers by measuring how often a node lies on paths between other nodes, while the beta index assesses network complexity by calculating the ratio of edges to vertices. The research encompasses a de tailed examination of subgraphs, which reveal clusters or communities within the network, providing insights into user behavior and network dynamics. This analysis is applied to various social networks and transportation systems, demonstrating how graph indices can be used to optimize and enhance network structures. By integrating Graph Indices into graph theory, this study offers a more versatile framework for capturing social interactions’ complexities. The findings underscore the importance of mathematical tools in developing strategies for managing and leveraging social networks, leading to improved efficiency and robustness of network systems. This research not only advances theoretical understand ing but also provides practical solutions for network analysis, paving the way for future applications in the field.