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Smart Health Monitoring Wearable with Accessibility using IoT

Smart Health Monitoring Wearable with Accessibility using Io...

引用

2024 International Conference on Intelligent Computing and Sustainable Innovations in Technology, IC-SIT 2024

作者： Thangam, S. Gurupriya, M. Aaron Xavier, S. Reddy, Akhil Rajmohan, Navneeth Amrita Vishwa Vidyapeetham Amrita School of Computing Dept. of Computer Science and Engineering Bengaluru India

ISBN: (纸本)9798350369175

In our day-to-day lives, monitoring health regularly is essential to ensure the body functions well under various conditions. This paper outlines the development of a smart health monitoring wearable device utilizing an Arduino Uno as the core processing unit. The device is designed to monitor health parameters continuously, transmitting data wirelessly for real-time analysis and feedback. Modern connectivity options enable data sharing with a centralized platform, allowing efficient health management and user interaction through mobile or web applications. This wearable aims to enhance healthcare by providing immediate health insights and alerting providers to potential health issues, embodying a proactive approach to health monitoring in everyday life. © 2024 IEEE.

关键词： Electronic health record

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An Experimental Analysis of RL based Compiler Optimization Techniques using Compiler GYM 9

An Experimental Analysis of RL based Compiler Optimization T...

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9th IEEE International Conference for Convergence in Technology, I2CT 2024

作者： Nikith, Boda Venkata Reeha, Shaik Reddy, G. Mani Prakash Belwal, Meena Amrita Vishwa Vidyapeetham Amrita School of Computing Dept. of Computer Science & Engineering Bengaluru India

ISBN: (纸本)9798350394474

Compiler optimization significantly impacts algorithm performance, whether it may be graph-based, parallel computing workloads and more. Understanding how these algorithms' performance varies under different optimization scenarios is crucial. This study explores diverse Reinforcement Learning (RL) algorithms for code optimization in Compiler Gym, including Actor-Critic, Random Walk, Tabular Q, and a Brute Force approach. Three benchmark datasets, Dijkstra, CRC32, and npbv0 from Compiler Gym libraries, and are evaluated using execution time and maximum reward metrics. This study reveals that the npbv0 dataset consistently outperforms others across all RL algorithms, demonstrating its compatibility with RL-based compiler optimization. This research highlights the role of compiler optimization in algorithm performance, emphasizing the potential of RL techniques in diverse workloads, with npbv0 excelling as a benchmark dataset. © 2024 IEEE.

关键词： Reinforcement learning

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An In-dept. Exploration of Image Captioning Training Approaches and Performance Analysis 9

An In-Depth Exploration of Image Captioning Training Approac...

引用

9th IEEE International Conference for Convergence in Technology, I2CT 2024

作者： Kushal, K. Manoj, Madhav Reddy, Kevansh Nair, Priyanka C. Amrita Vishwa Vidyapeetham Amrita School of Computing Dept. of Computer Science And Engineering Bengaluru India

ISBN: (纸本)9798350394474

In the rapidly evolving landscape of computer vision, image captioning has emerged as a challenging task. This report explores the techniques involved in image captioning using deep learning techniques, especially the encoder-decoder framework. Models such as CNN-LSTM, CNN-GRU,Xception - YOLO v4, GIT Based Model are used. Recognition is given to the significance of ample and well-annotated datasets in teaching the algorithms to understand the complex relationships between visual elements and textual descriptions. Along with traditional evaluation metrics like BLEU score, this study also employs metrics such as METEOR, ROUGE-L, and SPICE to compare performance between models. The findings highlight on the impact of deep learning in enabling computers to generate captions for diverse visual content. © 2024 IEEE.

关键词： Long short-term memory

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A Boosting-Based Machine Learning Approach for Intrusion Detection in Vehicle Address Verification Systems 5

A Boosting-Based Machine Learning Approach for Intrusion Det...

引用

5th International Conference on Electronics and Sustainable Communication Systems, ICESC 2024

作者： Naga, Siva Gayatri Venkata Ammanamanchi, Datta Sai Chadha, Angela Raj Reddy, Kethamreddy Karthikeya Shanmugam, Udhaya Kumar Amrita School of Computing Amrita Vishwa Vidyapeetham Dept. of Computer Science & Engineering Chennai India

ISBN: (纸本)9798350379945

Autonomous vehicles (AVs) are susceptible to cyberattacks due to their reliance on vehicle-to-everything (V2X) communication. This research proposes a Gradient Boosting-based intrusion detection system to safeguard AV networks. By employing advanced machine learning techniques, including Adaboost, Decision Trees, and Random Forests, the system effectively identifies various cyberattacks. Evaluated on standard datasets, the proposed IDS achieves a remarkable accuracy of 99.49%, surpassing existing models. The combination of feature selection and ensemble learning enhances the system's detection rate while maintaining computational efficiency. © 2024 IEEE.

关键词： Adversarial machine learning

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IoT-Based Health Monitoring System for Forbidden Patients 10

IoT-Based Health Monitoring System for Forbidden Patients

引用

10th International Conference on Advanced Computing and Communication Systems, ICACCS 2024

作者： Thangam, S. Niranjan, D.K. Shyam, Katragadda Megha Reddy, Malireddy Charan Kumar Sai, Kandlapalli Aravind Dept. of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Bengaluru India

ISBN: (纸本)9798350384369

The device is wearable and user-friendly;the patient's health monitoring system may be built using cutting-edge technology like wearable sensors and the IoT. patient's blood pressure, heart rate, temperature, pulse, ECG. The other physiological data may be continually monitored and recorded by the system. The healthcare system may become independent and less dependent on physical intervention by using sensors to analyze and diagnose patient activities in real time. By making the state may be continually checked. Furthermore, the system might be designed to be wireless and cloud-based, connecting several therapeutic devices for patient care and health tracking. To protect patient data, however, security and privacy concerns with implanted medical devices must be addressed. © 2024 IEEE.

关键词： Blood pressure

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Anylogic and Python-Based Gunshot Detection System Using CNN and Trilateration

Anylogic and Python-Based Gunshot Detection System Using CNN...

引用

2024 IEEE International Conference on Information Technology, Electronics and Intelligent Communication Systems, ICITEICS 2024

作者： Shendre, Sanjivani P. Priya, M.Yagnasri Sridhar, Swetha Kamble, Samrudhi S. Khanna, Manju Amrita School of Computing Amrita Vishwa Vidyapeetham Dept. of Computer Science and Engineering Bengaluru India

ISBN: (纸本)9798350382693

Gunshot detection has been applied in fields such as defense and forensic sciences. This plays a very critical role in law enforcement and public safety. Detecting a gunshot begins with automatic detection of the presence of an audio event. These rare events can be analyzed and prevented by using a system that detects the audio and location of gunshots. The success of this system relies ultimately on its accuracy. In this paper, audio data has been collected and a machine learning(ML) model using a Convolution Neural Network (CNN) has been trained that classifies the audio and detects a gunshot acoustic. The prediction of the gunshot location has been implemented using the trilateration method with the use of AI agents as sensors. The model has been trained in Python programming language. This simulation has been implemented in the Java programming language with AnyLogic software as the user interface. © 2024 IEEE.

关键词： Java programming language

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Examiti: Revolutionizing Exam Paper Creation and Quality Evaluation Using Artificial Intelligence 4

Examiti: Revolutionizing Exam Paper Creation and Quality Eva...

引用

4th International Conference on Innovations in computer science, ICONICS 2024

作者： Jawwad, Muhammad Zaryab, Syed Muhammad Chohan, Abdul Rafay Ahmed, Muhammad Rohan Khan, Muhammad Mubashir Qamar, Rohail NED University of Engineering and Technology Dept. of Computer Science & I.T. Karachi Pakistan

ISBN: (纸本)9798331504342

Teachers often spend a significant amount of time and effort creating and managing exams to meet Outcome-Based Education (OBE) standards and ensure the quality of examination papers, which can take away from their primary focus on educating students. Examiti is an innovative platform that automates the examination management process, utilizing AI-powered evaluation through the Lama 2.0 model to assess the quality, difficulty, and originality of exam papers. The platform allows educators to categorize and store questions in a dynamic question bank facilitating quick paper assembly. Its reactive editor simplifies document formatting so that teachers can concentrate on producing high-quality content. This paper outlines the development and testing of Examiti, highlighting its multi-layered testing strategy and user-centered design, which provide a reliable, scalable, and easy-to-use exam management system. Examiti possesses the capability to significantly improve the exam experience for students in educational institutions. © 2024 IEEE.

关键词： Students

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Novel Frontier in Brain Vasculature Analysis with Graphical Neural Networks 15

Novel Frontier in Brain Vasculature Analysis with Graphical ...

引用

15th International Conference on Computing Communication and Networking Technologies, ICCCNT 2024

作者： Aditya, R. Advaith, A.J. Sabarinath, G. Kumar, Aiswarya S. Dept. of Computer Science and Engineering Amrita School Of ComputingAmrita Vishwa Vidyapeetham Amritapuri India

ISBN: (纸本)9798350370249

Brain graphs provide valuable insights into neurological illnesses and disease modeling by visualizing the complex connections among different brain regions. This paper presents a novel link attribute prediction method designed specifically for brain graph analysis. In contrast to other approaches that focus only on missing link prediction, this method goes the extra length of predicting measurable vascular characteristics including vessel length, volume, radius, and several others across known connections in the brain. The approach, which has its roots in Graph Neural Networks (GNNs), develops intricate representations of data that are graph structured. We demonstrate the effectiveness of this method in calculating vascular link properties through tests on the Open Graph Benchmark Vessel Graph dataset. The task entails developing a unique deep neural network architecture that has been optimized to forecast critical vascular characteristics from known brain network connections. Considering the mouse brain vasculature, this methodology performs exceptionally well, particularly in the prediction of various vessel attributes with R2 values more than 0.9 when using positional features. Although there has been significant progress in generalization among various mouse strains, there are still difficulties in accurately predicting features based on shape. These findings highlight how graphical neural networks can shed light on previously undiscovered aspects of the complex vascular system in the brain, leading to revolutionary advancements in the field of neurobiological research. ©2024 IEEE.

关键词： Graph neural networks

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Exploration of Automatic Spam/Ham Message Classifier Using NLP 9

Exploration of Automatic Spam/Ham Message Classifier Using N...

引用

9th IEEE International Conference for Convergence in Technology, I2CT 2024

作者： Asmitha, M. Kavitha, C.R. Amrita Vishwa Vidyapeetham Amrita School of Computing Dept. of Computer Science And Engineering Bengaluru India

ISBN: (数字)9798350394474

ISBN: (纸本)9798350394474

Text classification holds great significance for analyzing textual information in one or more predetermined categories. Text classification tools allows to arrange all different kinds of texts, emails, court records, advertisements, databases, and other materials. This saves time in making informed decisions based on accurate information. A text classifier is software functionality designed to segment unresolved text into a number of preordained groups within a given text. Such software is frequently incorporated into various applications, including sentiment analysis, online content moderation, spam detection in emails, and language detection. Classifying a text is an essential but complex undertaking, as it requires a level of language comprehension akin to that of humans. Consequently, text classification has emerged as a subject of ongoing research interest. This study's primary goal is to investigate how cutting-edge natural language processing techniques are used to automatically detect spam messages. The proposed model will examine two distinct datasets to construct and categorize the disaster tweets. One dataset was obtained from the NUS SMS Corpus (NSC), while the other was specifically generated for this study using Grumbletext. When a spam email turns up, the system will inevitably identify the category using procedures like GloVe and BERT. The methodology, Naive Bayes DTM and XGBoost functionalities are used. The system's performance will be assessed through metrics such as precision, recall, f-score, FNR, FPR. © 2024 IEEE.

关键词： Sentiment analysis

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Enhancing Healthcare Image Record Security via CNN-based Tamper Detection, Watermarking, and Digital Signatures 2

Enhancing Healthcare Image Record Security via CNN-based Tam...

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

作者： Kumaran, U. Gurupriya, M. Thodathara, Harshitha Reddy Aryagopal Vijjapu, Aditya Harish, Gattamaneni Amrita Vishwa Vidyapeetham Amrita School of Computing Dept. of Computer Science and Engineering Bengaluru India

ISBN: (纸本)9798350368413

computers have been seamlessly integrated into every domain including health care where one of its applications is in the storage of health care records such as medical images. Electronic healtheare records contain a lot of sensitive information about patients, such as the diseases, diagnosis, treatment plans, drugs administered or medicines prescribed and hence this vital information must not be disclosed at any cost as it can have huge repercussions. As each day passes, more vulnerabilities and threats surface and it is important to come up with secure systems and apt security measures to counter these threats and keep the attackers at bay. This study provides a holistic approach to secure and safeguard medical image records by addressing two problems, the first is image tamper detection using medical image deepfake detection to decipher whether the image has been modified or not, and the second is to employ various cryptographic techniques such as hashing, watermarking and digital signatures to protect the images from getting tampered itself. © 2024 IEEE.

关键词： Electronic health record

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