Faculty Publications
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Item Quantum Machine Learning and Recent Advancements(Institute of Electrical and Electronics Engineers Inc., 2023) Manjunath, T.D.; Bhowmik, B.Quantum Computing is a fastly growing area with many applications, including quantum machine learning (QML). Due to the rapid increase of computational power, machine learning models based on artificial neural networks (ANN) have become highly effective. Even though classical machine learning models have been performing well, quantum computing with machine learning enhances the performance in multiple ways. This paper studies different aspects of quantum machine learning. It introduces quantum computing over classical computation, followed by the recent tools and techniques developed in the area. We look at multiple QML models like quantum kernel, quantum support vector machine (QSVM), etc. Finally, we present the literature survey to encourage researchers and academicians. © 2023 IEEE.Item Ontology for Contextual Fake News Assessment Based on Text and Images(Institute of Electrical and Electronics Engineers Inc., 2024) Chandrasekaran, K.; Kandasamy, A.; Venkatesan, M.; Prabhavathy, P.; Gokuldhev, M.; Aishwarya, C.The spread of false news on social networks is a major challenge in the digital age across various sectors, encompassing technology, politics, public health, and finance. This paper introduces an ontology-based method that combines text and image analysis to evaluate the accuracy of news stories in the context of social media. We investigate the role of social engineering tactics in crafting and dispersing fake news and advocate for a comprehensive multi-contextual perspective that covers content, source, social media, psychological, and impact aspects. Using OWL (Web Ontology Language), we present an ontology framework for assessing fake news, providing a structured approach to analyze text, visuals, audio, audience behavior, source credibility, and news propagation patterns. This framework serves as a foundation for advanced detection systems, contributing to the fight against digital misinformation. © 2024 IEEE.Item Quantum-Enhanced Deep Q Learning with Parametrized Quantum Circuit(Institute of Electrical and Electronics Engineers Inc., 2024) Manjunath, T.D.; Bhowmik, B.Quantum Computing (QC) is fastly growing to replace current computing systems for many high-performance applications. Due to the rapid increase of computational power, machine learning models based on artificial neural networks (ANN) have become highly effective. Even though classical machine learning models have been performing well, quantum computing with machine learning, i.e., Quantum Machine Learning (QML), will enhance the performance in multiple ways. Subsequently, Deep Q learning is a prominent approach to reinforcement learning used in solving complex applications for a desired performance. But this performance can be improved using a quantum computer. This paper proposes a quantum-enhanced Deep Q Learning algorithm to see its advantages over its classical counterparts. We implement our approach using parametrized quantum circuit (PQC). The evaluation of the proposed method achieves the metrics rewards and episodes up to 500 points and 1200, respectively. The performance shows that it collects roughly four times more rewards in a given time and takes significantly fewer episodes to converge compared to the classical approach. © 2024 IEEE.Item Deepfake Audio Detection Using Quantum Learning Models(Institute of Electrical and Electronics Engineers Inc., 2024) Pandey, A.; Rudra, B.Artificial intelligence makes it easy for humans to create high-quality images, speech, audio dubbing, and more. However, this technology is often misused to create fake content, such as phony speech, which is then made public to tarnish someone's image. This technology is known as deepfake, which uses deep learning, a field of artificial intelligence, to generate fake content. Advancements in deepfake technology pose the challenge of detecting fake content. Although many classical models exist to detect fake content, they often do not consider suitable audio features, and training these classical models is resource-intensive. Therefore, in this paper, we use a recently created real-time AI-generated fake speech dataset and propose a method to detect fake content using quantum learning models. This emerging technology leverages the properties of quantum mechanics to increase processing speed. We have trained the quantum learning models using the Lightning Qubit simulator. © 2024 IEEE.Item Hybrid Classical Quantum Learning Model Framework for Detection of Deepfake Audio(Science and Technology Publications, Lda, 2025) Pandey, A.; Rudra, B.Artificial intelligence (AI) has simplified individual tasks compared to earlier times. However, it also enables the creation of fake images, audio, and videos that can be misused to tarnish the reputation of a person on social media. The rapid advancement of deepfake technology presents significant challenges in detecting such fabricated content. Therefore, in this paper, we particularly focus on the deepfake audio detection. Many Classical models exist to detect deepfake audio, but they often overlook critical audio features, and training these models can be computationally resource-intensive. To address this issue, we used a real-time AI-generated fake speech dataset, which includes all the necessary features required to train models and used Quantum Machine Learning (QML) techniques, which follow principles of quantum mechanics to process the data simultaneously. We propose a hybrid Classical-Quantum Learning Model that takes advantage of Classical and Quantum Machine Learning. The hybrid model is trained on a real-time AI-generated fake speech dataset, and we compare the performance with existing Classical and Quantum models in this area. Our results show that the hybrid Classical-Quantum model gives an accuracy of 98.81% than the Quantum Support vector Machine (QSVM) and Quantum Neural Network (QNN). © 2025 by SCITEPRESS – Science and Technology Publications, Lda.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.