Department of Statistics

Permanent URI for this communityhttps://repository.cuilahore.edu.pk/handle/123456789/25

Browse

End date: 2025Subject: search.filters.subject.FA20

Search Results

Now showing 1 - 10 of 21
  • No Thumbnail Available
    Item
    INTERNSHIP AT AUR-LAB
    (Library Information Services COMSATS University Islamabad Lahore Campus, 2024-04-02) FAZEEL UMAR; FA20-BST-002; Dr. Mian Muhammad Farooq
    AUR 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 Iqbal
  • No Thumbnail Available
    Item
    INTERNSHIP REPORT AUR LAB
    (Library Information Services COMSATS University Islamabad Lahore Campus, 2024-04-02) Nouman Ali; Dr. Mian Muhammad Farooq
    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.
  • No Thumbnail Available
    Item
    CRESCENT BAHUMAN LIMITED
    (Library Information Services COMSATS University Islamabad Lahore Campus, 2024-04-02) Maydah Azhar; FA20-BST-008; Dr. Riffat Jabeen
    Bahaman Limited. The purpose of this report is to demonstrate the practical application of my academic knowledge and skills in a real-world business setting. With more than 60 years of experience in industries as diverse as textiles, sugar, banking, insurance, food, agriculture and steel, Crescent Group has built a solid reputation in Pakistan's business community. Crescent Bahaman was initially formed as a joint venture between Crescent Group and Greenwood Mills. It has been wholly owned by the Crescent Group since 2001. In FY21, the Pakistan's commodities expanded by 14% insufferable from a low-base impact, coming about because of the pandemic prompted lockdown in 2020. By and large, the figures of commodities stay inside the scope of USD 22-25b, which is in accordance with the pattern of past ten years. The portion of materials in complete products has generally been 54-59%; coming in at 57% during FY21. The low-base impact was articulated in material area also, with the post-lockdown increase of around 13% in the products. Pakistan's material commodities have CAGR of 4.4% during long term period from FY19 to FY21. Inside the materials, high worth added fragment held the most extreme offer, which has been moving vertically or most recent three years, expanding from 73.9% in FY19 to 80.7% in FY21. Knitwear, readymade pieces of clothing, and bed wear comprise the significant part of this portion.
  • No Thumbnail Available
    Item
    INTERNSHIP ON AUR-LAB
    (Library Information Services COMSATS University Islamabad Lahore Campus, 2024-04-02) Muhammad Samran; FA20-BST-003; Dr. Muhammad Noor-ul-Amin
    AUR-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.
  • No Thumbnail Available
    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 9953
    This 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.
  • No Thumbnail Available
    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 9948
    This 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.
  • No Thumbnail Available
    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 9955
    This 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.
  • No Thumbnail Available
    Item
    Generalized Transmuted Exponential Distribution
    (Library Information Services, COMSATS University Islamabad, Lahore Campus., 2020-04-01) BILAL MAJEED; CIIT/FA19-BST-008/LHR; Mr. Salman Abbas; LHR TP 9942
    In this study, we use the Transmuted Family of distributions to develop a novel generalized exponential model. Transmuted Exponential distribution is the name of the suggested distribution. For the proposed distribution, moments, generating functions, reliability functions, among other mathematical aspects, are explored. The maximum likelihood approach is considered to estimate the distribution parameters. The use of the under study distribution in real-world scenarios is then explained. The proposed family can be used for the generalization of the classical models.
  • No Thumbnail Available
    Item
    Development Of Calibration Log Estimator to Estimate Population Mean Using Randomized Response Technique
    (Library Information Services, COMSATS University Islamabad, Lahore Campus., 2020-04-01) Rehmina; CIIT/FA19-BST-011/LHR; Dr. Riffat Jabeen; LHR TP 9944
    Calibration method is a useful method in daily life meanwhile it allows us to gather a single sample without repeating the process. In the presence of sensitive information, we calculate the variation of population with the help of calibration method. We proposed calibration log ratio estimator by using the method of (Jabeen, et al. 2022) in the presence of sensitive data.
  • No Thumbnail Available
    Item
    Generalized Transmuted Weibull Distribution
    (Library Information Services, COMSATS University Islamabad, Lahore Campus., 2020-04-01) MOHSIN QUDDUS; CIIT/FA19-BST-012/LHR; Salman Abbas; LHR TP 9946
    In this research, we introduce a new generalized Weibull distribution using a new Transmuted family. An account of statistical characteristics of the generalized model is discussed in detail. Estimation of the model parameter is followed by method of maximum likelihood. A real-life application is presented to illustrate the applicability of the derived model. This new family of distribution can be utilized by researchers for developing generalized distributions in the future.