Browsing by Author "Muhammad Shahid"

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    Entropy Measure of Degree Based Indices for the Boron Nanotubes
    (Library Information Services, COMSATS University Islamabad, Lahore Campus, 2021) Muhammad Shahid; CIIT/SP20-RMT-006/LHR; Dr. Kashif Ali; LHR TP 7624
    The physicochemical properties of molecules are correlated with their chemical struc- ture using degree-based topological indices. The graph entropy zone has captured the imagination of scientists because of its possible application in chemistry. Boron nan- otubular structures are high-interest materials with new electronic characteristics due to the presence of multi center bonds. Mechanical and thermal stability are two chal- lenges that these materials have in nanodevice applications. Therefor they necessitate theoretical research into the other qualities. In this thesis, study the graph entropy mea- sure by using topological invariants of different versions of atom-bond connectivity in- dex Zagreb indices M1(G), M2(G) (including redefined forms ReZG1(G), ReZG2(G) and ReZG3(G) and the forgotten topological index F(G)) which gives a better predic- tion of the chemical properties of different chemical compounds. We computed these indices for boron nanotube because of its significance in nanotechnology. To ensure the correctness of the output, a computational analysis and graphical comparison of generated indices is provided.
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    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 Sohail
    In 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.