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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Item Analysis of Plasma Modified KdV Waves using Discrete Periodic Inverse Scattering Transformation(Library Information Services, COMSATS University Islamabad, Lahore Campus, 2016) Muhammad Noaman Akbar; SP15-RMT-005; LHR TP 6989; Dr. Ayesha SohailDiscrete Periodic Inverse Scattering Transformation (DPIST) are presented in this paper for solution of the well-known Korteweg-de Vries equations that has been hypothesized hypothetically such that non-linear dispersive waves spreading on interface of length only a joined of products fluid profundity fulfill a development condition with more extreme non-linearity than ordinary Korteweg-de Vries (KdV) equations for shallow water hypothesis. In this paper non-linear Fourier investigation is processed on time and space arrangement of surface relocation create at two areas downstream of a wavering film. Comes about because of a DPIST in view of the KdV equation, that decays a flag into soliton, sinusoidal and cnoidal parts, demonstrate while the amplitude of direct modes are preserved as unsettling influence spreads among two sensors, amplitudes of nonlinear modes increment. This recommends non-linearity of such surface waves is without a doubt more grounded than that anticipated by the KdV equations.Item Algorithm Development for the Bayesian Optimization of Complex Data Sets(Library Information Services, COMSATS University Islamabad, Lahore Campus, 2020) Muhammad Javed Zahoor; FA18-RMT-057; LHR TP 6461; Dr. Ayesha SohailOptimization is a subfield of applied mathematics and is used in the fields of nano-medicine, applied sciences and computational biology extensively. The recent trends of optimizations are focused on the algorithm development of nonlinear programs for complex data sets. During this research, an algorithm is developed for the application of “Bayesian Optimization”, for the selection of the most accurate hyper-parameter, using the Python framework as well as the MatlabTM 2020 packages. The developed model is then implemented on the experimental data sets, extracted from the online repositories. To ensure the optimalilty of the forecasting, the algorithms are exploited in detailed manner during this research.Item Fractional Order Model of Tumor-Induced Angiogenesis(Library Information Services, COMSATS University Islamabad, Lahore Campus, 2016) Sufian Asgher; SP15-RMT-001; LHR TP 6936; Dr. Ayesha SohailMethods of blood vessel formation is termed angiogenesis. Two distinguishing features of cancer, uncontrollable cell growth and metastasis, cannot be sustained in the absence of neovascularisation. Angiogenesis enhances the arrangement of solid tumors and their metastasis. During this research, present progress in understanding the procedure of angiogenesis in dynamic tumor development is talked about. The development of strong tumors, including neck and head squamous cell carcinomas, relies on upon the foundation of a blood supply inside the tumor (neovascularization or angiogenesis). For this procedure to occur, tumors create development components which stimulate the expansion, movement, and separation of endothelial cells. Nonlinear pde model of tumour induced angiogenesis is discussed during this research. The semi-analytic solution of this model is presented.Item Explainable Machine Learning for Medical Imaging(Library Information Services, COMSATS University Islamabad, Lahore Campus, 2020) Muhammad Shahid; FA18-RMT-043; LHR TP 6023; Dr. Ayesha SohailIn the recent literature, artificial intelligence tools have been used very suc cessfully to investigate the history of medical images, where incomplete data is available. In this thesis, data obtained from a clinical study is analyzed using medical imaging algorithms. We have used a Bayesian machine learning clas sifier i.e. Naïve Bayes to obtain the results. We have considered the medical images from 20 patients having gastric cancer. Futhermore, we have compared the accuracy of Naïve Bayes classifier to the other medical imaging techniques such as CNN.