Repository COMSATS Lahore
Your gateway to the research produced across the campus. You get quick access to work created by students in every department. The space highlights new ideas, data and findings that support your study and research needs
You will find
- PhD theses from multiple departments
- MS and MPhil theses from departments and centers
- Final Year Project reports from undergraduate programs
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Recent Submissions
Finite Element Solution of Time Dependent Partial Differential Equation in Structural Dynamics
(Library Information Services, COMSATS University Islamabad, Lahore Campus, 2025) Sheer Zaman; CIIT/SP24-RMT-024/LHR; Dr. Muhammad Nauman Bashir; LHR TP 10084
This thesis investigates the Finite Element Method (FEM) solution of time dependent Par-tial Differential Equations (PDEs) in structural dynamics, focusing on the transient free vibration of a simply supported elastic cable governed by the one-dimensional wave equa-tion. The primary objective is to numerically model the dynamic behaviour of the structure under various initial conditions and rigorously validate the numerical accuracy, stability, and convergence properties of the selected time integration schemes. The study analysed two distinct vibration modes across three problem configurations:
1. First Mode Vibration (Problems 2 and 3)
The cable was initiated in its first mode shape,
f(x, 0) = sin(pi*x) and released from rest with a wave speed of c = 2m / s This configuration was analyzed using two explicit time step sizes, k = 0.0495 and k = 0.05 to investigate the critical stability condition.
2. Second Mode Vibration (Problem 1) The string was excited in its second mode shape using prescribed initial displacement and velocity conditions,
f(x, 0) = sin(2pi*x) partial f partial t (x, 0) = 2pi * sin(2pi*x) .
The wave speed for this case was c = 1m / s
Mathematical Modeling and Analysis of Pertussis Cough
(Library Information Services, COMSATS University Islamabad, Lahore Campus, 2025) Syed Shan-E- Ahmad; CIIT/SP24-RMT-017/LHR; Dr. Syed Tahir Raza Rizvi; LHR TP 10078
Mathematical modeling of dynamical systems often uses ordinary differential equations to represent how key variables evolve over time. Such models support prediction, inter-pretation, and decision-making in biology, engineering, economics, and environmental science by linking mechanisms to observable outcomes. Stability analysis is central in this setting: it determines whether equilibria persist under perturbations and clarifies long-term behavior, resilience, and the potential impact of interventions.
We apply these tools to a pertussis (whooping cough) model with five compartments: susceptible, latent-immunity, exposed, infectious, and recovered. The formulation in-corporates waning vaccine-induced immunity, reinfection, and disease-induced mortal-ity, and it is proven to be positive, bounded, and forward invariant. The basic repro-duction number Ro is derived via the next-generation matrix method; sensitivity analy-sis shows transmission, progression to infectivity, recovery, and vaccination rates most strongly influence persistence. Local and global stability of the disease-free and endem-ic equilibria are established. A nonstandard finite difference scheme preserves quali-tative dynamics and supports simulations. Numerical experiments confirm the analy-sis and illustrate how parameters shape outbreak magnitude and duration. Finally, an integral sliding mode control acting on vaccination robustly suppresses exposure and infection and can drive elimination under parameter uncertainty and disturbances.
Keywords: Mathematical modeling; pertussis; compartmental model; Vaccination; wan-ing immunity; basic reproduction number Ro; sensitivity analysis
Optical and Thermal Properties of Quantum Corrected AdS Black Hole in Kiselev Spacetime
(Library Information Services, COMSATS University Islamabad, Lahore Campus, 2025) Husnain Hafeez; CIIT/SP24-MSMATH-010/LHR; Dr. Rabia Saleem; LHR TP 10072
In this work, we investigate the optical and thermodynamic properties of a quantum-corrected AdS black hole (BH) in Kiselev spacetime. The study focuses on the effects of key param-eters, including the quantum correction parameter a, the cosmological fluid parameter λ. the electric charge Q, and the entropy correction parameter ẞ, on the Hawking temperature, entropy, specific heat, Gibbs free energy, and BH shadow. The Hawking temperature gen-erally increases with the horizon radius, while the quantum and cosmological parameters enhance the corrected entropy and specific heat, indicating regions of local thermodynamic stability. Analysis of the Gibbs free energy reveals that small black holes (BHs) exhibit global instability, which transitions to stability for larger horizons. The BH shadow and photon sphere are found to expand with increasing a and A, and the infalling matter shows characteristic Doppler-induced darkening. These results highlight how quantum correction parameter a and surrounding cosmological fluid parameter A influence both the thermal behavior and observational features of AdS black holes, providing new insights beyond classical treatments.
Application of the Bilinear Neural Network Approach to Kadomtsev-Petviashvili Model for Nonlinear Waves
(Library Information Services, COMSATS University Islamabad, Lahore Campus, 2025) Numan Ahmed; CIIT/SP24-RMT-003/LHR; Dr. Syed Tahir Raza Rizvi; LHR TP 10066
Nonlinear evolution equations (NLEEs) serve as fundamental models for wave phe-nomena in many physical systems, including fluid dynamics, plasma physics, optical pulse propagation, and atmospheric processes. They describe the spatiotemporal evolu-tion of waves in media where nonlinear interactions and dispersive effects act together. A prominent feature of such equations is the emergence of solitons, which are localized travelling waves capable of preserving their shape and speed over long distances. This remarkable property is generally attributed to a balance between nonlinear steepening and dispersive spreading. Solitons appear across a broad spectrum of physical contexts, ranging from hydrodynamics, nonlinear optics, and plasma physics to solid-state and lat-tice systems, where they often display particle-like behavior while retaining their wave nature. They have also been observed in more complex settings such as astrophysical, condensed-matter, nuclear, and other nonlinear media, highlighting their wide applica-bility and importance in both theoretical studies and practical applications of nonlinear wave dynamics.
Different types of solitons exist based on their shape and behavior. Kink solitons rep-resent a transition from one state to another, similar to steps in a staircase. Breather solitons are localized waves that expand and contract over time. Rogue waves are ex-tremely large and sudden waves that can be much higher than normal waves. Lump solitons are localized in all directions and gradually disappear with distance. These d-ifferent wave structures help researchers better understand nonlinear wave behavior in real-world systems.
This thesis presents a bilinear neural network modeling (BNNM) framework that cou-ples the Hirota bilinear approach with trainable neural parameters to obtain closed-form solutions of the (3+1)-dimensional generalized Kadomtsev-Petviashvili equation
Machine Learning Approaches in Medical Diagnosis
(Library Information Services, COMSATS University Islamabad, Lahore Campus, 2025) Muhammad Bilal; CIIT/SP24-RMT-001/LHR; Dr. Muhammad Yousaf Bhatti; LHR TP 10065
Brain tumors are among the most life-threatening neurological conditions, characterized by abnormal and uncontrolled cell growth within brain tissues. Early identification of tumor type and stage is crucial for effective intervention, yet conventional diagnostic methods often struggle to detect tumors in their early or subtle stages. This study proposes advanced deep-learning models for multi-class classification of brain tumors and machine-learning approaches for predicting chemical properties of anti-cancer drugs to support new drugs development.
A hybrid deep-learning model was developed for classifying three tumor categories glioma, Meningioma and pituitary tumors using MRI scans. the model was trained on the brain tumor MRI dataset and integrates ResNet50 and Efficient Net50 as its base ar-chitectures, combined through a novel triangular dense-layer fusion strategy to optimize multi-stage feature extraction. The proposed model achieved a test accuracy of 97%, recall of 97%, and an AUC score of 99%, demonstrating its effectiveness for early and accurate brain-tumor classification.
In second phase of this research, machine-learning algorithms and topological indices were used to analyze molecular structure of thirteen anti-cancer drugs. Topological in-dices were computed using a python program, and true physicochemical properties were retrieved from the ChemSpider database via an automated script. QSPR and SHAP anal-ysis were performed to identify indices most predictive of each physicochemical property. Machine-learning models were then trained on these features to develop generalized pre-dictive models for key durg properties, providing a foundation for computationally assisted drug discovery.
Overall, this thesis demonstrates the combined potential of deep learning for early tu-mor detection and machine-learning for anti-cancer drug analysis, ultimately contributing to improved diagnostic accuracy and future therapeutic development