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Daily Archives: December 27, 2024

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End-to-End Encryption, Role-Based Access Controls, and Audit Logs in Safeguarding Electronic Health Records – A closer look at the features housing EHR

End-to-End Encryption, Role-Based Access Controls, and Audit Logs in Safeguarding Electronic Health Records – A closer look at the features housing EHR
Authors:-Erumusele Francis Onotole

Abstract-The rise of Electronic Health Records (EHRs) has revolutionized the way health care is practiced globally, particularly in providing patients with effective and precise care. Nevertheless, given the types of information EHRs contain, they are vulnerable to malicious attacks and access by unauthorized persons. The paper focuses on the importance of end-to-end encryption, role-based access control, and audit logs in maintaining optimal security of EHR data. These aspects are discussed in such a way that their combined effect is presented along with the individual functionality of circumstances and how each of them contributes to security, the legal requirements, and the stakeholders.

DOI: 10.61137/ijsret.vol.10.issue6.629

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Leveraging Predictive Analytics and Cybersecurity Measures for Enhancing Risk Management and Resilience in Global Supply Chains

Leveraging Predictive Analytics and Cybersecurity Measures for Enhancing Risk Management and Resilience in Global Supply Chains
Authors:-Erumusele Francis Onotole

Abstract-In today’s interconnected global supply chains, the integration of predictive analytics and advanced cybersecurity measures has become a pivotal strategy for fortifying risk management and enhancing resilience. The COVID-19 pandemic underscored the vulnerabilities of supply chains, prompting organizations to adopt cutting-edge technologies to mitigate disruptions and ensure continuity. This paper explores the critical interrelationship between predictive analytics, cybersecurity, and supply chain resilience, highlighting their combined potential to create robust and adaptable systems. The study delves into predictive analytics for risk identification and mitigation, the role of cybersecurity in addressing digital threats, and the need for a holistic risk management approach. Empirical evidence and theoretical insights are discussed to present actionable strategies for organizations aiming to enhance their supply chain resilience in an increasingly uncertain global environment.

DOI: 10.61137/ijsret.vol.10.issue6.628

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A Comparative Analysis of Lab View and PyTorch for Machine Learning: The gap between Experimentation and Production

A Comparative Analysis of Lab View and PyTorch for Machine Learning: The gap between Experimentation and Production/strong>
Authors:-Archana Narayanan, Vishrut Jha, Joanne Anto

Abstract- This paper presents a comparative analysis of handwritten digit recognition performance between LabVIEW and PyTorch frameworks, utilizing a Convolutional Neural Network (CNN). The model is designed to classify digits from the MNIST dataset, which consists of 28×28 grayscale images of handwritten digits (0–9). The dataset includes 60,000 training images and 10,000 test images, providing a standardized benchmark for evaluating model performance. Metrics such as accuracy, training time, memory usage, and inference speed are evaluated. The results provide insight into the strengths and weaknesses of these frameworks in terms of efficiency, scalability, and usability. Results indicate that while both frameworks are effective, PyTorch offers faster training and inference, whereas LabVIEW demonstrates marginally better training accuracy.

DOI: 10.61137/ijsret.vol.10.issue6.402

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Fraud Detection in Financial Institutions: AI VS. Traditional Methods

Fraud Detection in Financial Institutions: AI VS. Traditional Methods
Authors:-Chintamani Bagwe

Abstract-This research paper provides a comparative analysis of traditional rule-based fraud detection methods and emerging AI-based approaches in financial institutions. The study examines effectiveness, adaptability, operational efficiency, regulatory compliance, and implementation considerations of both methodologies. Through detailed evaluation supported by visual representations, the paper demonstrates that while AI-based methods offer superior detection accuracy, adaptability, and reduced false positives, traditional approaches provide greater transparency and established regulatory compliance frameworks. The findings suggest that hybrid approaches combining the strengths of both methodologies represent the optimal strategy for most financial institutions. The paper concludes with an examination of future trends and recommendations for financial institutions seeking to enhance their fraud detection capabilities.

DOI: 10.61137/ijsret.vol.10.issue6.654

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Securing the Digital Age: A Look at Cryptography and Network Security

Securing the Digital Age: A Look at Cryptography and Network Security/strong>
Authors:-Professor Mugdha Dharmadhikari, Mr. Vaishnav Sabale

Abstract- The digital world thrives on the secure exchange of information across vast networks. This paper explores cryptography as a fundamental pillar of network security, ensuring data confidentiality, integrity, and authenticity. We delve into the core objectives of network security and how cryptography achieves them through encryption techniques. We explore both symmetric-key and asymmetric-key cryptography, along with their strengths and limitations. The paper further examines cryptography’s role in guaranteeing data integrity and sender authentication. We acknowledge the limitations of cryptography, including computational demands and the looming threat of quantum computers, which necessitates the development of post-quantum cryptography. Finally, the paper emphasizes the crucial role of ongoing research and development in cryptography to safeguard the ever-expanding digital landscape.

DOI: 10.61137/ijsret.vol.10.issue6.401

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Full Stack Web Application for Prediction and Diagnosis of Heart Disease

Full Stack Web Application for Prediction and Diagnosis of Heart Disease/strong>
Authors:-Assistant Professor Ms. Dornadhula Danya, Suraj A U, Moju Kumar B L, Deepak Kumar Singh D, Shubhan GC

Abstract- In the modern era, Cardio-vascular disease has high prevalence and rate of mortality which proves how critical, identification and intervention strategies are, further highlighting the importance of incorporating this in developing heart disease prediction systems. The heart prediction system research revolves around using AI-driven techniques techniques to strengthen and make heart disease risk prediction robust and effective. The paper explains methodology, dataset characteristics, experimental setup, results and the design of the models in a AI-driven techniques heart prediction system. Additionally, the practical implications of the research output are discussed regarding the use of the system in real life for alleviating heart disease predictions and strategies.

DOI: 10.61137/ijsret.vol.10.issue6.400

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