Transactions on Recent Developments in Health Sectors

Wearable Health Devices: Transforming Personal Health Management

Prof. Mehak Sharma — 2024-07-01

Wearable health devices have emerged as powerful tools for personal health management, offering real-time monitoring of vital signs and physical activities. This paper investigates the impact of wearable health technology on individual health outcomes, patient engagement, and preventive care. By evaluating various types of wearables and their applications, the study highlights the potential of these devices to enhance self-management and early disease detection.

Big Data Analytics in Healthcare: Unlocking the Potential for Predictive Medicine

Prof. James Kiran — 2024-07-01

Big data analytics is transforming healthcare by enabling predictive medicine and personalized treatment plans. This paper explores the application of big data techniques in healthcare, focusing on data collection, analysis, and utilization. Through case studies and current implementations, the study assesses how big data analytics can predict disease outbreaks, optimize treatment protocols, and improve patient outcomes.

Robotics in Surgery: Advancements and Clinical Outcomes

Prof. Nikihtha Kumar — 2024-07-01

Robotic surgery has revolutionized the field of surgery by enhancing precision, reducing invasiveness, and improving patient recovery times. This paper reviews the latest advancements in surgical robotics, assessing their impact on clinical outcomes and surgical efficiency. By examining various robotic systems and their applications, the study provides a comprehensive overview of the benefits and limitations of robotic-assisted surgery.

Explainable AI for Financial Fraud Detection: A Hybrid Deep Learning Approach

Prof. Rajeev Kishan — 2024-10-14

Financial fraud detection systems often rely on black-box AI models, making it challenging to interpret and justify their decisions. This research proposes a hybrid deep learning approach that integrates explainable AI (XAI) techniques to enhance transparency in fraud detection. By combining convolutional neural networks (CNNs) for feature extraction with attention-based recurrent networks for sequential analysis, the model achieves high accuracy while providing human-interpretable explanations. Experimental evaluations on real-world financial transaction datasets demonstrate improved fraud detection rates and regulatory compliance, making XAI a crucial tool for financial security.

Quantum Cryptography for Secure Cloud Computing: A Post-Quantum Security Framework

Prof. Payal Sharma — 2024-09-15

With the advent of quantum computing, traditional cryptographic algorithms face the risk of becoming obsolete. This paper presents a quantum cryptography-based security framework for cloud computing, leveraging quantum key distribution (QKD) and lattice-based encryption to protect sensitive data from quantum attacks. We analyze the computational complexity and security guarantees of our approach, demonstrating its resilience against Shor’s algorithm and other quantum decryption techniques. Experimental simulations highlight the feasibility of implementing post-quantum security in real-world cloud infrastructures, ensuring long-term data protection in the quantum era.