Final Year Projects (FYPs) - Undergraduates
Permanent URI for this collectionhttps://repository.cuilahore.edu.pk/handle/123456789/64
This collection archives the complete set of theses produced by students of the COMSATS University Islamabad, Lahore Campus.
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Item INTERNSHIP AT AUR-LAB(Library Information Services COMSATS University Islamabad Lahore Campus, 2024-04-02) FAZEEL UMAR; FA20-BST-002; Dr. Mian Muhammad FarooqAUR Lab is a cooperative venture between Predominant College, as of now positioned No. 1 private for influence among private colleges in Pakistan (QS Positioning), and We-Plan, an Australian plan firm working in Pakistan. In the span of 10 months of foundation, it has accomplished the achievement of one of its startup projects being shortlisted among top two out of roughly 100+ entries from Pakistan in the Endeavor Cup Global contest. Current undertakings are managing in various business ventures, including archeological the travel industry; Metaverse; NFT Commercial center; administration and casting a ballot; Style plan; healthcare; Shrewd Agrarian; environmental the travel industry; structural plan; AR-based promoting; energy effectiveness; street wellbeing; orientation equity; also, land income. Our group of specialists incorporates specialized staff as well as business improvement staff: Dr. Muhammad Ashraf Khan; Prof. Saleem Zubair; Ms. Qurratulain Sonia Kashmiri; Mr. Muhmmad Ahmad; Mr. Jawad Ahmad; also, Mr. Javaid IqbalItem INTERNSHIP ON AUR-LAB(Library Information Services COMSATS University Islamabad Lahore Campus, 2024-04-02) Muhammad Samran; FA20-BST-003; Dr. Muhammad Noor-ul-AminAUR-LAB is a collaboration between Superior University, which is presently ranked first in Pakistan for effect among private universities (QS Ranking), and we Plan, an Australian design business based in Pakistan. Within 10 months of its inception, one of its startup initiatives was selected as one of the top two out of about 100+ proposals from Pakistan in the Venture Cup International competition. Current projects include archaeological tourism, Metaverse, NFT Marketplace, governance and voting, fashion design, healthcare, Smart Agriculture, ecological tourism, architectural design, AR-based advertising, energy efficiency, road safety, gender equality, and land revenue. Dr. Muhammad Ashraf Khan, Prof. Saleem Zubair, Ms. Qurratulain Sonia Kashmiri, Mr. Muhmmad Ahmad, Mr. Jawad Ahmad, and Mr. Javaid Iqbal are among our technical and business development professionals.Item Spatial and Temporal Analysis of Drought(Library Information Services, COMSATS University Islamabad, Lahore Campus, 2023) Muhammad Furqan Ahmad; CIIT/FA19-BST-002/LHR; Dr. Mian Muhammad Farooq; LHR TP 9937In the recent years climate change is the hot topic of research. Climate change can have severe effect on the environment. The drought is an important factor of the climate. If a drought occur in some area it will affect the agriculture, water resources and other important factors of the country. In this study, the impact of drought in different districts of Punjab, Pakistan was studied using Standardized Precipitation Index (SPI) and Standardized Anomaly Index (SAI). The SPI is applied on the rain data and the SAI was applied on the temperature data. The data was collected from Pakistan Metrological Department. At first, we fit the data on Length Biased Exponential Distribution and then we applied Standardized Precipitation Index on the rain data received from Pakistan Meteorological Department from 1993 to 2022. We also computed SPI using 3-month, 6-month, and 12-month moving average of the original data. We conducted spatial and temporal analysis using SPI and for the visual representation we also plot maps of the past five years for better understanding of the drought. Standardized Anomaly Index (SAI) is a useful tool to measure and monitor deviations from normal conditions in terms of temperature or other relevant variables associated with drought. We used temperature to better explain the drought impact in Pakistan. After applying SAI, we find out that temperate of the data is above average which means there is drought impact in different districts of Pakistan. The results show the clear drought pattern in different districts of Pakistan. We ignore the 0 and negative values of rain data before fitting the Length Biased Exponential Distribution as it is undefined for these values and then we compute the results. The graphs show that after taking moving average the drought patterns decrease in districts and also few years does not show any drought pattern.Item A study of ordered random variables for Reflected Power Function Distribution(Library Information Services, COMSATS University Islamabad, Lahore Campus, 2024) Maydah Azhar; CIIT/FA20-BST-008/LHR; Dr. Riffat Jabeen; LHR TP 9953This thesis presents a comprehensive study of ordered random variables for the Reflected Power Function Distribution (RPFD). The primary focus is on the statistical properties, parameter estimation methods, and applications of the RPFD. The RPFD is a transformation of the traditional Power Function Distribution, which is frequently used to model phenomena in various fields such as hydrology, economics, and engineering. By reflecting the distribution around its midpoint, the RPFD provides a mirrored perspective that is particularly useful for scenarios where probabilities are skewed in the opposite direction. The study begins with an introduction to the Power Function Distribution and its reflection to form the RPFD. The properties of the RPFD, including its probability density function, survival function, hazard rate function, and moments, are thoroughly explored. Various parameter estimation techniques such as Maximum Likelihood Method (MLM), Modified Maximum Likelihood Method (MMLM), and percentile-based estimators are discussed in detail. Furthermore, the thesis delves into generalized order statistics (GOS) and their specific application to the RPFD. This includes the derivation of the probability density function (PDF) and joint PDF for GOS, as well as the calculation of mean, variance, and covariance for different scenarios. The literature review highlights key studies and methodologies that have contributed to the development and understanding of order statistics and RPFD. Empirical analysis using real-life data sets demonstrates the practical utility and superior performance of the RPFD compared to other models. Overall, this research contributes to the statistical theory by providing new insights and tools for the application of the RPFD in various scientific and engineering disciplines.Item Modified Radial Basis Function Network (RBFN) by Bayesian Regression for predicting Synthetic Cancer(Library Information Services, COMSATS University Islamabad, Lahore Campus, 2024) Fazeel Umar; CIIT/FA20-BST-002/LHR; Dr. Mian Muhammad Farooq; LHR TP 9948This thesis investigates the use of Long Short-Term Memory (LSTM) networks to predict Google’s stock prices. The study focuses on stock data from January 2012 to December 2016 for training, and January 2017 for testing. LSTM, a type of Recurrent Neural Network (RNN), is ideal for time series forecasting because it can learn long-term dependencies. To prepare the data, stock prices were normalized using Min Max Scaler, which helps improve model performance. The data was then organized into sequences of 60-time steps using a sliding window approach. The LSTM model was built with four layers, each containing 50 units, and included dropout layers to reduce overfitting. Training was conducted using the Adam optimizer and mean squared error as the loss function over 100 epochs with a batch size of 32. The results showed that the LSTMmodel effectively captured the stock price patterns, highlighting its potential for accurate financial forecasting.Item RNN for Time series Forecasting Using Google Stock Prices(Library Information Services, COMSATS University Islamabad, Lahore Campus, 2024) Sharjeel Ahmed; CIIT/FA20-BST-011/LHR; Dr. Mian Muhammad Farooq; LHR TP 9955This thesis investigates the use of Long Short-Term Memory (LSTM) networks to predict Google’s stock prices. The study focuses on stock data from January 2012 to December 2016 for training, and January 2017 for testing. LSTM, a type of Recurrent Neural Network (RNN), is ideal for time series forecasting because it can learn long-term dependencies. To prepare the data, stock prices were normalized using Min Max Scaler, which helps improve model performance. The data was then organized into sequences of 60-time steps using a sliding window approach. The LSTM model was built with four layers, each containing 50 units, and included dropout layers to reduce overfitting. Training was conducted using the Adam optimizer and mean squared error as the loss function over 100 epochs with a batch size of 32. The results showed that the LSTMmodel effectively captured the stock price patterns, highlighting its potential for accurate financial forecasting.Item Classification of heart disease using MLP with SeLU activation function and Binary Cross Entropy as Loss function(Library Information Services, COMSATS University Islamabad, Lahore Campus, 2024) Nouman Ali; CIIT/FA20-BST-009/LHR; Dr. Mian Muhammad Farooq; LHR TP 9954Multilayer perceptron are the basic components of deep learning and neural net- work characterized by their architecture of feedforwading consisting of fully con- nected neurons with the non linear activation functions.This thesis provides a comprehensive overview of MLPs,by giving detailed about their structure and com- ponents,including the input layer,hidden layers, and output layer. This explains the role of weights and bias neurons in the process of learning and adjustment of the complex pattern in data. Different activation functions which are Sigmoid, ReLU, tanh, were used, illustrating their important in non-linearity and using the model to find its relationship.The training of MLPs by using the back propagation method is thoroughly studied. This includes the forward pass,where the input data is stored and processed though the network,and the backward pass, where gradients of loss function are calculated to update the network parameters. The importance of the loss estimation and parameter updates are used in minimiz- ing the error and improving the model’s performance.Additionally, we will discuss about the important steps in data preparation, which involves the handling missing values,feature scaling, and ensuring the proper data formatting. The importance of splitting the data into training and testing is highlighted, Technique for preventing the over fitting, such as regularization,dropout, and early stopping. The guidelines for using the MLP is provided, starting with the simple one and then increasing the complexity based on different tasks.This thesis focuses on the usage of exper- imentation with various architecture,hyper parameters and optimizing the model to get the optimal results. Different techniques used for monitoring the training and the adjustment of hyper parameters are discussed, along with the methods to calculate the model accuracy and loss checking and test datasets.This thesis aims to adopt practitioners with the understanding of tools to develop robust and efficiency of model on different applications such as image recognition,NLP, and speech recognition. By following the detailed guidelines and using the best tech- niques in data preparation, training and evaluation can increase the performance of model.Item INTERNSHIP REPORT ON BUREAU OF STATISTICS, GOVERNMENT OF PUNJAB, LAHORE(Library Information Services, COMSATS University Islamabad, Lahore Campus, 2024) Muhammad Shayan; FA21-BST-005; Dr. Tajamul Hussain; LHR TP 9928The Bureau of Statistics Punjab, Government of Punjab, is in charge for the gathering, processing and spreading statistical data via publications and electronic platforms. Being an associated department of the Planning and Development Department, Government of Punjab, the main responsibilities of Bureau of Statistics Punjab is collecting provincial level data, coordinating and supporting in different statistical activities across Punjab, working with the Federal Statistical system, and preparing and disseminating provincial data through institutional sources also conducting new censuses and surveys, These responsibilities are guided by the National Statistical Council. The Bureau of Statistics Punjab is headquartered in Lahore, with 9 divisional and 7 district field offices throughout the provinceItem INTERNSHIP REPORT ON BUREAU OF STATISTICS, GOVERNMENT OF PUNJAB, LAHORE(Library Information Services, COMSATS University Islamabad, Lahore Campus, 2024) Sania Khalid; CIIT/FA21-BST-014/LHR; Dr. Tajamul; LHR TP 9932The Bureau of statistics (BOS) of the Punjabi government is Official government statistics provider in the province. BOS is also charged with the responsibility of receiving collecting, assessing, and disseminating statistical data through electronic media and in specific periodical publications that it comes up with. It is an associated department of the Planning and Development Department which primarily focuses on the issues of youth’s participation in political leadership and governance. It works in conjunction with the Federal Statistical System; it formulates statistical initiatives, supervises provincial level data acquisition and calculates provincial scores using surveys and censuses. Seven Division/ nine District Field Offices of BOS along with Lahore headquarters are importance components of development planning and decision making in Punjab. BOS was originally set up in 1957 while in 1959 it was placed under the Power, Irrigation & Development Department of West Pakistan’s Planning and Development Department. Then it developed into a more serious form of a disease that does not have any well-defined treatment to this day. This development was part of a broader effort to address the shortcomings of Pakistan’s decentralized statistical system and to enhance the coordination and expansion of statistical activities across the country.