Department of Statistics

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Author: search.filters.author.Dr. Mian Muhammad FarooqSubject: search.filters.subject.FA20

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    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
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    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.
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    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.
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    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.
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    Drought Analysis using Transmuted Weibull Distribution
    (Library Information Services, COMSATS University Islamabad, Lahore Campus., 2020-04-01) Ali Hassan; CIIT/FA19-BST-005/LHR; Dr. Mian Muhammad Farooq; LHR TP 9940
    The study aims to predict the drought analysis using monthly rainfall and temperature data. Over the past years drought is becoming important factor that is affected by climate changes in Pakistan. In this study we analyze the impact of drought in Punjab Districts. For this purpose, firstly data collected by Pakistan Metrological Department of Punjab Districts for the years 1993 to 2022 and then further competitive strategy analysis that fits our analysis or predictions are sought and critically reviewed. Then the chosen parts from strategy analysis are applied for the graphical presentation. Firstly, Applying Standardized Precipitation Index based on precipitation data by fitting transmuted Weibull distribution from the year 1993 to 2022 of each District (Bahawalnagar, Bahawalpur, Jehlum, Jhang, Lahore, Sialkot, Sargodha, Faisalabad, and Multan) to get know about classification of drought against each observed precipitation values and understand the impact of precipitation on drought. Transmuted Weibull Distribution is fitted on precipitation data to normalize it as SPI is calculated after fitting distribution on it. Further to identify trends of drought SPI calculated at different time scales by moving average of Three months, six months, and Twelve months to find SPI values and then these values are classified according to their drought conditions as moderate, severe, extreme near to normal, slightly wet, and extreme wet. Secondly applying Standardized Anomaly Index is another index that is widely used for drought analysis based on temperature data. SAI is calculated against each District (Bahawalnagar, Bahawalpur, Jehlum, Jhang, Lahore, Sialkot, Sargodha, Faisalabad, and Multan) from 1993 to 2023. By SAI values we can analyze the impact of temperature on drought. SAI values are shown in graphical form below in Chapter Results, that shows the trend and the SAI values against each year of a particular District.
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    Transmuted Marshall Olkin Modified Burr-III Distribution
    (Library Information Services, COMSATS University Islamabad, Lahore Campus., 2020-04-01) Ali Zain; CIIT/FA19-BST-004/LHR; Dr. Mian Muhammad Farooq; LHR TP 9939
    Transmutation of classical probability distributions play a significant role in real life modeling. In this work, we develop a new Transmuted Marshall Olkin Modified Burr-III distribution. The proposed model is named Transmuted Marshall Olkin Modified Burr-III distribution (TMOMBIIID). Several statistical characteristics of TMOMBIIID such as survival function and hazard rate, are discussed. The Maximum likelihood method is followed for the estimation of model parameters.
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    Spatial and Temporal Analysis of Drought
    (Library Information Services, COMSATS University Islamabad, Lahore Campus., 2020-04-01) Muhammad Furqan Ahmad; CIIT/FA19-BST-002/LHR; Dr. Mian Muhammad Farooq; LHR TP 9938
    In 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.