Conference Papers
Permanent URI for this collectionhttps://idr.nitk.ac.in/handle/123456789/28506
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Item Recent Advancements and Challenges in FinTech(Institute of Electrical and Electronics Engineers Inc., 2023) Girish, K.K.; Bhowmik, B.The rapid advancement of technology in recent years has brought about numerous changes in various industries, and the financial sector is no exception. The rise of financial technology (FinTech) has disrupted traditional banking and financial services by offering more convenient, accessible, and personalized services to customers. Contrarily, financial services have become more efficient, cost-effective, and secure with FinTech, enabling people to manage their finances with just a few clicks, even on their smartphones. FinTech has also created new opportunities for financial inclusion, making it possible for people who were previously unbanked or underbanked to access financial services. Despite its many benefits, the rise of FinTech has also brought about several challenges. This paper gives an overview of FinTech, its progress, and its importance. Following this, significant challenges of FinTech are highlighted to ensure its long-term success and continued growth. The recent literature shows the way how it is transforming our perceptions. © 2023 IEEE.Item A Comprehensive Survey on Breast Cancer Diagnostics: From Artificial Intelligence to Quantum Machine Learning(Institute of Electrical and Electronics Engineers Inc., 2025) Reddy, M.R.V.S.R.S.; Kumar, S.; Bhowmik, B.Breast cancer remains a leading cause of mortality among women worldwide, where early detection significantly improves survival rates. Traditional diagnostic methods like mammography, biopsy, and ultrasonography face challenges like diagnostic errors and low sensitivity. Recent advancements in Artificial Intelligence (AI), including deep learning for image analysis and natural language processing for patient data interpretation, have shown promise in enhancing diagnostic capabilities. The integration of these AI techniques with Quantum Machine Learning (QML) leverages quantum parallelism to process high-dimensional medical data and extract intricate imaging patterns more efficiently. This paper provides a comprehensive overview of cancer, its subtypes, symptoms, and the limitations of conventional diagnostics while highlighting the transformative potential of QML in improving diagnostic accuracy and efficiency for breast cancer detection and prognosis. © 2025 IEEE.Item Improving CNN-Based Breast Cancer Detection Integrating Quantum Layers(Institute of Electrical and Electronics Engineers Inc., 2025) Reddy, M.R.V.S.R.S.; Bhowmik, B.Breast cancer continues to be a significant burden on global healthcare systems, as early and accurate diagnosis is crucial for improving patient outcomes. Conventional methods used for diagnosis include mammography and biopsy; although they do supply critical information, they often have poor accuracy and are operator-dependent. Artificial Intelligence(AI), particularly Convolutional Neural Networks, presents a promising tool for analyzing medical images; however, conventional CNNs face significant challenges in generalizing from one dataset to another. This paper presents a hybrid Quantum Convolutional Neural Networks(QCNN) framework by integrating the classical feature extraction models VGG16, VGG19, and InceptionV3 with a Quantum Convolutional Layer (QCL). It uses the principles of quantum, such as superposition and entanglement, which process high-dimensional data for capturing non-linear patterns. Therefore, it improves the model's accuracy, sensitivity, and specificity. This hybrid framework presents a scalable and robust solution for the early detection of breast cancer, thereby advancing automated diagnostic systems to enhance reliability and adaptability. © 2025 IEEE.