Browsing by Author "Muhammad Amin"

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    BITS
    (Library Information Services, COMSATS University Islamabad, Lahore Campus, 2021) Muhammad Amin; FA17-BCS-110; Rana Muhammad Ajmal; LHR TP 7492
    Engagement with your customers through their preferred platform is beneficial for any business. There are multiple good platforms that help you to keep engaging with your customers majorly through email or branded SMS.BITS objective is to improve Customer engagement with businesses to increase business growth and enhance business sales not through emails or branded SMS but with the most widely used chatting platform WhatsApp. BITS will provide a platform for running and managing WhatsApp campaigns to promote business services to their consumers within a specific timeframe through WhatsApp. BITS will provide a proper interface for interacting with every customer separately and manage them efficiently. A proper inbox with which you can chat with your customer and label them differently. BITS will also provide a system for reports and analytics and contact management and a system for employee management with which you can manage your employees effectively so your employees can manage your customers effectively. This system will fulfilled the gap between businesses and their consumers.
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    Predictive Modeling of Diabetes Classification using Artificial Neural Networks
    (Library Information Services, COMSATS University Islamabad, Lahore Campus, 2025) Muhammad Amin; FA23-RMT-017; Dr. Muhammad Rafiullah; LHR TP 9764
    Diabetes mellitus is a chronic disease that has become a major global public health challenge. Timely and accurate prediction not only aids in immediate treatment but also plays a crucial role in formulating effective strategies. In this research, we compare the prediction of diabetes using two popular neural network models Multi-Layer Perceptron (MLP) and General Regression Neural Network (GRNN). This analysis is based on the PIMA Indian Diabetes Dataset, which contains medical information of female patients, including glucose levels, BMI, insulin amount, age, etc. In this research, the dataset underwent stages of cleaning, normalization, and division into training and testing sets. Then, the mathematical details of the MLP and GRNN models were described, which included forward propagation, activation functions, and loss formulas. The MLP model used two hidden layers with ReLU and Sigmoid activation functions, while the GRNN model used Gaussian radial basis functions and Euclidean distance. After training, the performance of both models was evaluated using metrics such as accuracy, confusion matrix, and ROC-AUC, revealing that both models proved effective in predicting diabetes. However, GRNN demonstrated better overall performance due to its non-repetitive structure and smooth results, while MLP exhibited more effective adaptation thanks to fast computation and deep network architecture, making it suitable for large and complex data. This research indicates that if ANN models are designed and configured correctly, they can help in the timely and effective diagnosis of diabetes. This analysis provides guidance to healthcare professionals and data scientists in selecting appropriate models.