Browsing by Author "Muhammad Bilal"

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Calibration estimators for estimating population mean using different distance measures
(Library Information Services, COMSATS University Islamabad, Lahore Campus., 2025-04-01) Muhammad Bilal; CIIT/FA20-BST-007/LHR; N/A; LHR TP 9952
Abstract: In order to increase the accuracy of population parameter estimations based on auxiliary data, calibration is frequently utilized. To estimate the population variance, we suggested three estimators: a calibration variance estimator, a calibration exponential ratio estimator, and a calibration coefficient estimator. The suggested assessment was contrasted with those of Bhal and Tuteja (1991), Koyuncu and Kahilar (2012), and Isaki (1981). Compared to current estimators, the calibration variance estimators that are proposed perform better.
Connection Numbers on Carbon Nanocone Line Graph and Various Chemical Lin
(Library Information Services COMSATS University Lahore Campus, 2023-03-13) Muhammad Bilal; Dr.Imran Zulfiqar Cheem; SP22-RMT-024; LHR TP 8730
In this study, we delve into the mathematical exploration of the Connection Number in carbon nanocone line graphs and diverse chemical line graphs within the realm of Graph Theory. The theoretical framework is developed based on the principles of connectivity and network structures. These structures are represented as graphs, where atoms con stitute nodes and chemical bonds form edges. The novel metric, Connection Number, is introduced to quantify connectivity within these unique molecular configurations. Em ploying graph invariants and specialized connectivity indices, the study characterizes and compares the structural features of carbon nanocones and various chemical line graphs. Advanced graph algorithms are utilized to unveil the intricate relationship be tween Connection Number and structural stability, offering insights into the impact of connectivity on molecular properties and reactivity. The analysis extends to identifying recurring structural motifs and patterns, contributing to a systematic classification of connectivity profiles. Beyond theoretical considerations, the study explores practical applications in nanotechnology, materials science, and chemical engineering. This con cise investigation harnesses the power of graph theory to deepen our understanding of molecular connectivity in carbon nanocones and diverse chemical line graphs, paving the way for innovative advancements in nanoscience and materials engineering
Exam Management Portal
(Library Information Services, COMSATS University Islamabad, Lahore Campus., 2020-11-20) Muhammad Bilal; FA16-BSE-210; Mahwish Waqas; LHR TP 6229
The current management of the examination is done manually, and so it is hectic process. Having a standard software that enables the maintenance of the same is the need of hour. The project aims at creating a system that helps maintain student’s database of their exam coupons. For this, student exam coupon and teacher’s invigilation duty schedule is generated on the basis of students sitting plan. The system have an administrative module protected by username and password. The module is focusing on maintenance like master data entry operation, and ordinary users of the system can not be permitted to enter the administrative module. And administrative is the intended user of the system. There is also a date sheet generation module, which creates the clash free date sheet of every student, along with the time, slot and room NO. Apart from that, student’s exam coupon is also generated following their respective datasheets. There is a result compilation feature in which the result of every student that is under graduated is managed and the record of graduated students result also be maintained and saved.
Investigation and Fabrication of Triboelectric base Nanogenerators for Energy Harvesting Applications
(Library Information Services, COMSATS University Islamabad, Lahore Campus, 2025) Muhammad Bilal; CIIT/FA23-RPH-026/LHR; Dr. Amna Mir; LHR TP 9855
The increased need for renewable and sustainable energy sources has led to the idea of the Triboelectric Nanogenerators (TENGs), which are considered one of the promising sources of energy that can be converted into electricity at low frequencies. This article reports the design synthesis and fabrication of a compact, high-performing TENG using a composite high-surface-area polyacrylonitrile (PAN) nanofiber with zeolitic imidazolate framework-8 (ZIF-8), a metal-organic framework (MOF) that exhibits structural discretion. The production of ZIF-8 was based on a solvothermal approach, utilizing zinc nitrate and 2-methylimidazole in methanol. The output of PAN nanofibers was achieved through the electrospinning process. A vertical contact- separation mode TENG was developed using the PAN@ZIF-8 composite as a tribo- positive layer and polyethylene terephthalate (PET & Teflon) as the tribo-negative layer. XRD, SEM, FTIR, Raman spectroscopy, UV-Vis, and IV were conducted to characterize the materials. The crystallinity of ZIF-8 was established through the XRD, whereas Raman and FTIR spectra ratified the Zn-N bonding and the integrity of the functional groups. UV-Visible analysis gave a broad optical bandgap of 5.04 eV. The TENG-fabricated device produced a periodically alternating current in response to mechanical stimulation, making it suitable for energy harvesting. The outcomes suggest the potential use of MOF-polymer composites in enhancing the triboelectric capabilities of the material and its applications in wearable electronic systems, self- powered sensors, and IoT devices.
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
On Cluster Analysis of Some Portfolio Optimization
(Library Information Services, COMSATS University Islamabad, Lahore Campus, 2023) Muhammad Bilal; CIIT/SP22-RMT-009/LHR; Dr. Sarfraz Ahmad; LHR TP 8716
The classic mean-variance portfolio optimization approach is criticized in large part for its propensity to overstate estimate error. An estimated inaccuracy of a few percent can skew the entire package. The Black-Litterman technique (Bayesian method) and the resampling methodaretwocommonapproachestosolvingthisproblem. Amorerecentapproachtothe issue’s solution is the clustering method. By clustering, we initially combine the stocks that have a strong correlation and handle the group as a single stock. Following the grouping of the stocks, we will have a few stock clusters. For these clusters, we do the standard mean-variance portfolio optimization. By using the clustering approach, the influence of estimating error may be minimized and the portfolio’s stability can be increased. In this project, we’ll examine how it functions and run experiments to see if clustering techniques enhance the portfolio’s performance and stabilities