http://202.88.229.59:8080/xmlui/handle/123456789/5722 2026-04-14T19:43:11Z http://202.88.229.59:8080/xmlui/handle/123456789/5724 CREDIT CARD FRAUD ANALYSIS AND DETECTION METHOD Dr.Meenu Suresh Electronic payment (e-payment) has transformed financial transactions, offering speed and convenience. However, it has also brought about significant challenges, notably in credit card security. This paper explores the landscape of e-payment, focusing on credit card fraud detection—a crucial aspect of financial security systems. Advanced algorithms and machine learning techniques are employed to analyse transaction data for patterns indicative of fraudulent activity. Despite these advancements, credit cards face various security threats, including card skimming, phishing scams, and data breaches. Cybercriminals continuously adapt their tactics, necessitating ongoing advancements in credit card protection. This paper highlights the importance of evolving security measures to safeguard users' financial information in the ever-changing digital landscape. 2024-04-01T00:00:00Z http://202.88.229.59:8080/xmlui/handle/123456789/5723 PNEUMONIA DETECTION USING DEEP LEARNING Dr.Meenu Suresh This paper introduces an advanced deep learning approach for pneumonia detection using convolutional neural networks (CNNs). Trained on a large dataset of annotated chest X-ray images, our model leverages transfer learning and addresses class imbalance through data augmentation and weighted loss-functions. Visualization techniques, including Grad-CAM, enhance interpretability, aiding clinicians in understanding the model's focus. Evaluation on a benchmark dataset demonstrates superior sensitivity and specificity compared to traditional methods. Our findings highlight the model's robustness across diverse demographics, emphasizing its potential for early diagnosis and improved patient outcomes. The study underscores the transformative impact of deep learning on pneumonia diagnosis, providing a valuable tool for efficient and accurate healthcare practices. 2024-04-01T00:00:00Z