Browsing by Author "Reddy, M.R.V.S.R.S."

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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. Â© 2025 IEEE.
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. Â© 2025 IEEE.
Performance Analysis and Predictive Modeling of MPI Collective Algorithms in Multi-Core Clusters: A Comparative Study
(Institute of Electrical and Electronics Engineers Inc., 2025) Reddy, M.R.V.S.R.S.; Raju, S.R.; Girish, K.K.; Bhowmik, B.
Efficient communication is the foundation of parallel computing systems, enabling seamless coordination across multiple processors for optimal performance. At the core of this communication lies the Message Passing Interface, a crucial framework designed to facilitate data exchange between processors through collective operations. However, these MPI operations often face challenges, including fluctuating process counts, varying message sizes, and increased communication overhead. These issues can significantly impact execution times and scalability, leading to potential bottlenecks in large-scale systems. To address these concerns, this paper provides an in-depth evaluation of key MPI collective algorithms - Flat Tree, Chain, and Binary Tree - by examining their performance under varying configurations. By analyzing execution times and communication overhead, the study reveals the trade-offs inherent in each algorithm, offering insights into strategies for reducing communication costs. Through this analysis, we aim to provide valuable guidance to improve the efficiency and scalability of parallel computing, particularly in high-performance systems where communication efficiency is critical. Â© 2025 IEEE.
Performance Analysis of Hybrid MPI and OpenMP on Smith-Waterman Algorithm
(Institute of Electrical and Electronics Engineers Inc., 2025) Ninama, K.; Patel, J.; Girish, K.K.; Reddy, M.R.V.S.R.S.; Bhowmik, B.
In the rapidly advancing field of bioinformatics, sequence alignment is a pivotal task for elucidating genetic statistics and evolutionary relationships. As the volume and complexity of biological data continue to grow, it becomes imperative to employ effective computational techniques to manage this expansion. The Smith-Waterman algorithm is a key tool for sequence alignment; however, its performance can be constrained by the substantial size of contemporary datasets. To overcome this limitation, this paper explores a hybrid parallelization strategy that integrates message passing interface (MPI) with open multi-processing (OpenMP). This approach aims to significantly enhance the algorithm's efficiency by leveraging the strengths of both parallelization models. By optimizing the scalability and execution speed of the Smith-Waterman algorithm on advanced high-performance computing (HPC) systems, the hybrid technique not only improves performance but also enables more rapid and accurate biological data analysis. Â© 2025 IEEE.