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Implementing OTP Verification and Zero Trust Security for Role-Based Access Control in Medical Records 3

Implementing OTP Verification and Zero Trust Security for Ro...

引用

3rd International Conference on Smart Technologies and Systems for Next Generation Computing, ICSTSN 2024

作者： Harshini, B.V. Ulagarchana, U. Mounika, Perla Mary Shamala, L. Vellore Institute of Technology School of Computer Science and Engineering Chennai India

ISBN: (纸本)9798350391565

This paper proposes a comprehensive approach for enhancing security in medical record systems by incorporating One-Time Password (OTP) verification and Zero Trust Security principles within a Role-Based Access Control (RBAC) framework. With the increasing digitization of medical records and the growing threat of cyberattacks, ensuring confidentiality, integrity, and availability of patient information is paramount. OTP verification adds a layer of authentication, mitigating the risk of unauthorized access even in the event of compromised credentials. Integrating Zero Trust Security principles ensures that access permissions are continuously evaluated and verified based on various contextual factors, such as device health, user behavior, and network location, rather than relying solely on static credentials. Furthermore, RBAC facilitates granular access control, allowing administrators to assign roles and permissions based on users' responsibilities and organizational hierarchy. By combining OTP verification, Zero Trust Security, and RBAC, healthcare organizations can establish a robust security framework that minimizes the risk of data breaches and ensures compliance with regulatory requirements such as HIPAA. © 2024 IEEE.

关键词： Electronic health record

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Prophet and NeuralProphet compared with Indian inflation data 11

Prophet and NeuralProphet compared with Indian inflation dat...

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11th IEEE International Conference on Signal Processing and Integrated Networks, SPIN 2024

作者： Subburaj, Brindha Agrawal, Aditya Vellore Institute of Technology School of Computer Science and Engineering Chennai India

ISBN: (纸本)9798350308433

The word inflation is a commonly used expression to describe the overall increase in the costs of products and services in the market over time. In this research work, we will look at inflation as a whole, as well as for various population categories and goods within each population group. We will investigate demographic groups such as urban, rural, and combined, and we will compute the real Consumer Price Index (CPI) using the Reserve Bank of India's (RBI) weighted average technique. This research used statistics from 2011 to 2022. The RBI calculates the real CPI using a weighted average technique that takes into consideration the relative significance of various commodities within a population category. This technique is used to guarantee that the CPI accurately reflects the actual expense of living for the population group under consideration. It is essential to observe, that inflation varies significantly between various population groups and commodities. Because of higher living expenses and a higher demand for goods and services, urban areas generally have higher inflation rates. Rural regions, on the other hand have lower inflation rates because of reduced living expenses and lower demand for products and services. We can obtain a clearer grasp of how inflation impacts various segments of the population by analyzing data for these distinct population *** the basis of inflation statistics, this article compares two time-series forecasting models, Prophet and NeuralProphet. Both algorithms will be tested on the same data and their accuracy in forecasting will be contrasted. The root mean squared error (RMSE) and Mean absolute error (MAE) will be used as comparative statistics to assess accuracy. The performance of Prophet and NeuralProphet is evaluated in time-series forecasting and to look into the possible benefits of using parallelism on Prophet. This study's results will cast light on the relative strengths of these techniques. © 2024 IEEE.

关键词： Mean square error

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A Comprehensive Analysis of Object Tracking in UAVs using computer Vision 3

A Comprehensive Analysis of Object Tracking in UAVs using Co...

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3rd International Conference on Automation, Computing and Renewable Systems, ICACRS 2024

作者： Ilavendhan, A. Paul, Bhenedix Pokala, Joshnavi School of Computer Science and Engineering Vellore Institute of Technology Chennai India

ISBN: (纸本)9798331532420

Object tracking has proven to be an essential in a wide range of applications and unmanned aerial vehicles (UAVs) provide advantages to this field in accurately observing and tracking moving targets in challenging environments. This paper presents an overview of various object tracking methodologies which can be utilized on UAVs. It discusses different approaches to attain an improved method of UAVs to help track moving objects accurately, even under challenging conditions such as occlusions or changes in object size and speed. This research study highlights the combinations of traditional tracking techniques infused with modern machine learning techniques to help explore advancements and opportunities of performance improvement. Key topics discussed are the use of algorithms that enable prediction of location of objects, techniques for tracking even small objects in dynamic environments and possibilities of UAVs cooperating with ground vehicles to improve tracking accuracy by sharing data. Overall, the paper discusses the importance of developing more efficient and reliable tracking systems which have applications in various fields such as surveillance, search and rescue operations. © 2024 IEEE.

关键词： Object tracking

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Vision Beyond the Haze: Advancements in Cataract Detection and Diagnosis 2

Vision Beyond the Haze: Advancements in Cataract Detection a...

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2nd International Conference on Self Sustainable Artificial Intelligence Systems, ICSSAS 2024

作者： Kumar, E. Sudheer Reddy, N U. Praneeth Reddy, Depatla Hemika Kanish, Tulasi Vellore Institute of Technology School of Computer Science and Engineering Chennai India

ISBN: (纸本)9798350368413

Eye diseases are among the most critical health issues impacting human life. One of the most frequently encountered eye problems is cataracts, due to which people suffer as they age. The cloudiness that forms on eye lenses is known as cataracts which can significantly damage vision. Diagnosing the disease in its acute stage rather than its onset should not blind the patient. This study presents an improved Cataract detection method using pre-trained convolutional neural networks (CNNs) on an Ocular Disease Intelligent Recognition (ODIR) dataset, Comparing the performance of ResNet50, ResNet101, and VGG19 architectures to find the most effective model for this project. The proposed approach uses deep learning to analyse fundus images, aiming to increase diagnostic accuracy, speed, and reliability. several preprocessing and data Augmentation techniques were utilized to prepare the data set, which provided robust training and improved model generalization. Samples were refined using pre-trained ImageNet weights and adjusted for binary classification of Cataract presence. Among the tested models, ResNet50 exhibited the best performance, achieving an overall accuracy of 99.08% It also showed good evaluation metrics, with a precision rate of 98.15% and 100% recall for cataract images, and 100% precision with 98.23% recall for normal images. The results highlight the potential of ResNet50 to provide accurate and effective cataract diagnosis, especially in areas where access to trained ophthalmologists is inaccessible. This study highlights the importance of using deep learning technologies to improve the early detection and treatment of cataracts, ultimately contributing to better patient outcomes and global eye health progress. © 2024 IEEE.

关键词： Convolutional neural networks

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Adaptive Edge Computing for Autonomous Vehicles in Varying Environment 2

Adaptive Edge Computing for Autonomous Vehicles in Varying E...

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2nd International Conference on Emerging Trends in Information technology and engineering, ic-ETITE 2024

作者： Shreyas, K. Manjula, V. School of Computer Science and Engineering Vellore Institute of Technology Chennai India

ISBN: (纸本)9798350328202

This research offers an in-depth analysis of the integration of Autonomous Vehicle Platooning, Vehicle-to-Everything (V2X) communication, Edge Analytics, and Real-time Traffic Management. Central to the study is 'Platooning,' where autonomous vehicles optimize road space, fuel efficiency, and safety. V2X communication, a silent yet critical interaction between vehicles and their surroundings, lays the foundation for advanced safety and navigation in autonomous vehicles. When combined with V2X, platooning generates vast data, making Edge Analytics essential for filtering realtime intelligence for swift decisions. The study emphasizes the importance of Real-time Traffic Management in managing urban congestion, with Edge Analytics providing dynamic route planning. By focusing on algorithms and practical applications, the research aims to merge these technologies for efficient, safe, and responsive Autonomous vehicles (AV) operations, offering insights for urban planning and traffic management, and setting the stage for a sustainable transportation future. © 2024 IEEE.

关键词： Autonomous vehicles

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A Novel Text Recognition Using Deep Learning Technique

A Novel Text Recognition Using Deep Learning Technique

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2024 International Conference on Advances in Data engineering and Intelligent Computing Systems, ADICS 2024

作者： Niranjan, J. Senthil Kumar, A.M. School Of Computer Science And Engineering Vellore Institute Of Technology Chennai India

ISBN: (纸本)9798350364828

The branch of OCR (Optical Character Recognition) face many challenges such as extracting text from images. These challenges are most prominent due to varied nature of images in terms of size, letter style, orientation, background, quality, and format. This challenging problem requires modern solutions directed towards the proper and precise text extraction from different types of images. The proper handling of these problems is critical for the necessary increase of OCR systems' level efficiency. This paper tracks down the changes in the base assumptions of OCR due to the arrival of the deep learning algorithms. Convolutional Recurrent Neural Networks (CRNN) along with attention mechanism is suggested in this paper in order to enhance text recognition performance. © 2024 IEEE.

关键词： Recurrent neural networks

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Optimising DOS Attacks Using Machine Learning Algorithms and Securing IOT Devices from Attacks 4

Optimising DOS Attacks Using Machine Learning Algorithms and...

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4th International Conference on Expert Clouds and Applications, ICOECA 2024

作者： Siwal, Rahul Sivakumar, P. School of Computer Science and Engineering Vellore Institute of Technology Chennai India

ISBN: (纸本)9798350385793

In today's hyperconnected world, identifying and defending against denial-of-service (DoS) attacks and securing Internet of Things (IoT) devices are increasingly crucial. Recognizing distributed denial-of-service (DDoS) attacks is essential for network security enhancement and IoT device security. This research explores various methods to mitigate the impact of DDoS attacks, aiming to reduce the likelihood of such attacks. We intend to employ machine learning strategies such as the Random Forest Algorithm, ADAboost, Blockchain, Gradient Boost, and Extra Tree Algorithm to prevent malicious network traffic from overloading servers and causing outages. The initiative offers significant advantages, particularly in theory and statistics. We will test our system using the NSL_KDD dataset, initially training the data and then subjecting it to a series of tests. Data will be presented using tables, graphs. © 2024 IEEE.

关键词： Random forests

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Unlocking Talent: Exploring the Potential of AI and ML Algorithms in Recruitment Process

Unlocking Talent: Exploring the Potential of AI and ML Algor...

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2024 International Conference on Electrical, Electronics and Computing Technologies, ICEECT 2024

作者： Kundu, Sankhadip Palani, Saravanan Vellore Institute of Technology School of Computer Science and Engineering Chennai India

ISBN: (纸本)9798350378092

A Recruitment process is complex and multifaceted. It requires a strategic approach to assess and select suitable candidates for the selected post while mitigating challenges along the way. This study aimed at predicting the likelihood of job candidates being hired, utilizing a diverse range of input features. Leveraging machine learning algorithms applied to a comprehensive dataset spanning over a decade and encompassing various populations, the research emphasizes the interpretability of the model's predictions. By integrating multiple machine learning algorithms, the study achieves remarkable prediction accuracy while providing valuable insights that HR professionals can interpret. Through meticulous analysis, distinct patterns and factors influencing hiring decisions are identified, offering practical implications for recruitment processes. The findings underscore the feasibility of predicting candidate hiring ability prior to employment, thereby facilitating the development of optimized recruitment strategies. This research significantly contributes to enhancing HR decision-making by furnishing a robust framework for evaluating job candidates' potential success. © 2024 IEEE.

关键词： Prediction models

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Context-Aware Summarization for PDF Documents using Large Language Models 4

Context-Aware Summarization for PDF Documents using Large La...

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4th International Conference on Expert Clouds and Applications, ICOECA 2024

作者： Ramprasad, Akshara Sivakumar, P. School of Computer Science and Engineering Vellore Institute of Technology B. Tech Computer Science with specialization in AI & ML Chennai India School of Computer Science and Engineering Vellore Institute of Technology Chennai India

ISBN: (纸本)9798350385793

In an era characterized by the overflow of textual information, the demand for effective text summarization techniques has become increasingly evident. This research study presents a novel solution to address this demand: the development of a PDF Summarizer Generative AI Platform for context-aware text and document summarization. The developed platform empowers users to generate informative summaries from lengthy textual documents. It features an interface powered by Gradio for enabling user-friendly accessibility. Equipped with essential components such as OpenAI's language models for text comprehension, Chroma vector enables document embedding and conversational retrieval chains for contextual understanding, where the platform offers a comprehensive solution. Users can effortlessly upload documents in various formats, including PDFs, and interactively engage with the system to obtain concise summaries tailored to their specific requirements. © 2024 IEEE.

关键词： Chroma Database (ChromaDB) Gradio LangChain Large Language Models (LLM) OpenAI

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An Integrated Framework for Real-Time Disaster Data Acquisition, Processing, and Resource Optimization in Crisis Management Systems 4

An Integrated Framework for Real-Time Disaster Data Acquisit...

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4th International Conference on Ubiquitous Computing and Intelligent Information Systems, ICUIS 2024

作者： Saranya, G. Atchaya, M. Harshini, R. Choudhary, Kartik School of Computer Science Engineering Vellore Institute of Technology Chennai India

ISBN: (纸本)9798331529635

The rapid onset and unpredictable nature of disasters necessitate a quick, organized, and efficient response. This paper proposes a comprehensive disaster management platform that integrates real-time disaster information, weather forecasting, and resource management to enhance disaster preparedness and response. The platform collects disaster data from geophysical monitoring stations, processes the data for consistency, and stores it in a MongoDB database. Geospatial visualization tools, such as Folium, are employed to present disaster events on interactive maps, helping decision-makers assess the situation and respond accordingly. Additionally, the platform includes features for weather forecasting and resource management, allowing users to monitor conditions and contribute essential resources in real time. This research demonstrates how leveraging modern technologies can improve disaster management systems, providing a more effective and coordinated response to natural and man-made disasters. © 2024 IEEE.

关键词： Weather forecasting

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