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Author Archives: Kajal Tripathi

AI Based Smart Energy Meter for Data Analytics

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AI Based Smart Energy Meter for Data Analytics/strong>
Authors:-Assistant Professor Mrs.B. Christyjuliet, Dinesh Kumar.B, Divya.G, Kaviraj.S, Monisha.R

Abstract- The proliferation of smart meter technology offers vast opportunities for harnessing real-time data to optimize energy consumption, predict demand, and support sustainable energy grids. This paper explores the integration of artificial intelligence (AI) techniques, such as machine learning and deep learning, into smart meter data analytics, enhancing the accuracy of predictions and anomaly detection. With the rise of big data from millions of connected devices, AI-based analytics are vital to efficient energy management. We present a comparative analysis of various AI models used for smart meter data analytics and propose improvements for their real-time applications.

DOI: 10.61137/ijsret.vol.10.issue6.404

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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

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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
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

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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

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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

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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

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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

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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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Power Consumption Analytics Using Cloud Platforms

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Power Consumption Analytics Using Cloud Platforms/strong>
Authors:-Muthuraja M, Krishnan T, Prakash Dass R, Deepak kumaran RMG, Bharath G

Abstract- The increasing demand for electricity and environmental concerns have created a critical need for advanced energy management solutions. This study presents an IoT and cloud-based analytics system that provides real-time insights into power consumption, enabling efficient energy utilization. Leveraging ThingSpeak as the cloud platform, the system integrates smart meters to monitor voltage, power factor, and energy trends. Key contributions include real-time anomaly detection, dynamic visualization, and customizable alert systems. The proposed methodology enhances user engagement and supports scalability for diverse energy applications.

DOI: 10.61137/ijsret.vol.10.issue6.399

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Predictive Maintenance with AI for Smart Homes

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Predictive Maintenance with AI for Smart Homes/strong>
Authors:-Revathi Renjini, Associate Professor S R Raja

Abstract- As homes are increasingly adopt smart technologies, their reliability as well as longevity have become paramount to avoid unnecessary downtime and ensure continuous, efficient operations. By incorporating Artificial Intelligence (AI) and Internet of Things (IoT) technologies this research enhances predictive maintenance and thereby contributing sustainability goals. Sensors are utilized to monitor real-time data like temperature, pressure, and vibrations from connected devices and systems. Using the machine learning models – linear regression and decision trees, this research demonstrates how AI can extract actionable insights from sensor data. This research showcases the potential to create more reliable, sustainable, and efficient predictive maintenance solutions that are not only low-cost and accessible but can be adapted for both small-scale and large industrial applications. These advancements will further enhance the predictive capabilities of the system and support long-term environmental sustainability by continuously optimizing resource consumption and reducing waste generation.

DOI: 10.61137/ijsret.vol.10.issue6.398

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Energy Storage Systems

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Energy Storage Systems/strong>
Authors:-Ahmed R. Alharbi

Abstract- This review paper provides an in-depth analysis of diverse energy storage systems, emphasizing their significance, operating principles, and practical applications in tackling contemporary energy issues. As the global shift towards sustainable energy gains momentum, effective Energy Storage Systems (ESS) play a pivotal role in maintaining the balance between supply and demand, especially in the integration of renewable energy sources. The paper explores an extensive array of energy storage solutions, such as Thermal Energy Storage (TES), Chemical Energy Storage (CES), Electrochemical Energy Storage (EcES), Electrical Energy Storage (EES), Hybrid Energy Storage Systems (HES), and Mechanical Energy Storage (MES). By conducting a comparative assessment, it highlights the strengths and weaknesses of each approach and provides insights into emerging trends and challenges within the sector. Furthermore, the study focuses on optimizing Gravity Energy Storage (GES) systems using the Taguchi method to improve energy efficiency and system reliability, showcasing the potential of GES as a viable and adaptable solution for sustainable energy storage.

DOI: 10.61137/ijsret.vol.10.issue6.397

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