Department of Mathematics

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Author: search.filters.author.Dr. Adeel FarooqSubject: search.filters.subject.MATHEMATICS

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    Consensus Based Group Decision Making With Incomplete Reciprocal Fuzzy Preference Relations Using Product T-norm.
    (Library Information Services, COMSATS University Islamabad, Lahore Campus, 2019) Mustanser Hussain; FA17-RMT-035; Dr. Adeel Farooq; LHR TP 5661
    In this dissertation, a consensus-based method for group decision making (GDM) using product transitivity with incomplete fuzzy preference relations (IFPRs) is proposed. Additionally, an average aggregation operator has been used at the first level to estimate the missing preference values and construct the complete fuzzy preference relation (FPR). Then it is confirmed to be product consistent by using the transitive closure formula. Following this, weights of decision makers (DMs) are evaluated by merging consistency weights and predefined priority weights (if any). The consistency weights for the DMs are estimated through product consistency investigation of the information provided by each DM. The consensus process determines whether the selection procedure should be initiated or not. The hybrid comprises of a quitting process and feedback mechanism, and is used to enhance the consensus level amongst DMs in case of an inadequate state. The quitting process arises when some DMs decided to leave the course, and is common in GDM while dealing with a large number of alternatives. The feedback mechanism is the main novelty of the proposed technique which helps the DMs to improve their given preferences based on this consistency. At the end, a numerical example is deliberated to measure the efficiency and applicability of the proposed method after the comparison with some existing models under the same assumptions. The results show that proposed method can offer useful comprehension into the GDM process. ix
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    Locating some Subgroups of the Baby Monster Group
    (Library Information Services, COMSATS University Islamabad, Lahore Campus, 2022) Abdul Sattar; FA19-RMT-029; LHR TP 7907; Dr. Adeel Farooq
    In this thesis, we have computed the generators for some subgroup of Y-Groups. These are the subgroups of the bimonster group whose generators satisfy the coxeter relations. It can be presented by Y-diagrams whose nodes represent the generators and satisfy the coxeter relations. We have computed the coxeter generators satisfying coxeter relations for Y111, Y211, Y411, Y421 and Y441. The generators of Y-group have been determined by computing successive centralizers. We have also surveyed some maximal subgroups of the Baby Monster Group.
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    Fractal geometry
    (Library Information Services, COMSATS University Islamabad, Lahore Campus, 2025) Madiha Iftikhar; SP21-BSM-015; Dr. Adeel Farooq; LHR TP 9884
    Fractal geometry explores the complex, self-replicating structures found in mathematical, natural, and computational systems. Originating from the foundational work of Benoˆıt B. Mandelbrot, fractals represent shapes exhibiting self-similarity across scales and fractional dimensions. This study delves into key mathematical constructs like the Cantor set, Koch curve, Mandelbrot set, and Julia sets, showcasing their recursive nature and infinite com plexity. The research emphasizes the significance of fractals in modeling natural phenom ena, including coastlines, mountain ranges, and biological patterns, and highlights their transformative applications in computer graphics, medical imaging, and economic analy sis. The iterative methodologies and Hausdorff dimension analysis offer a framework to describe and simulate intricate, real-world patterns. By bridging theoretical and practical realms, fractal geometry not only enriches mathematical theory but also provides innova tive tools for scientific and artistic endeavors
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    Machine Learning: Modern Techniques and Mathematical Approach to Neural Networks
    (Library Information Services, COMSATS University Islamabad, Lahore Campus, 2025) Sardar Abdul Wahab CH (FA20-BSM-012) : Abdul Raheem (SP20-BSM-003); Dr. Adeel Farooq; LHR TP 9911
    This thesis explores the mathematical foundations of machine learning algorithms, providing a comprehensive exploration of various key techniques and models. It covers fundamental concepts and methodologies in regression, classification, support vector machines (SVM), decision trees, neural networks, and perceptrons. Each of these algorithms is analyzed in terms of their mathematical underpinnings, operational mechanisms, and practical applications. The study aims to elucidate the core principles that drive these algorithms, offering insights into their theoretical and practical aspects. By understanding the mathematics behind these models, this research contributes to a deeper appreciation and effective utilization of machine learning techniques in solving complex real-world problems.