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Real time Heart Attack Detection Using Emerging Technologies 25

Real time Heart Attack Detection Using Emerging Technologies

引用

25th International Multi Topic Conference, INMIC 2023

作者： Butt, Rahma Ijaz, Tahreem Iftikhar, Nida Sharahili, Fahad Mohammed A Altouri, Mohammed Nawaf Ashraf, Usman Women University Government College Dept. of Computer Science Sialkot Pakistan Saud Islamic University Applied College Imam mohammad Bn Saudi Arabia University of Prince Muqrin Computer Science and Cybersecurity Saudi Arabia Women University Computer Science Government College Sialkot Pakistan

ISBN: (纸本)9798350317701

Heart attack, also called Myocardial infarction, is when the blood supply to the heart muscle is significantly decreased or entirely blocked. The early diagnosis of heart attacks is essential for protecting against adverse cardiac events and delivering prompt medical assistance. The real-time heart attack detection system presented in this study solves the shortcomings of previous approaches. The proposed study gathers data from patients using IoT technologies, such as temperature, heart rate, and pulse oximeter sensors. Then, for safe storage and later analysis, these sensor values are sent to a cloud-based database. The key innovation lies in the integration of cloud-based data storage with mobile application. The final result of the study is in the form of mobile application which is an interface between doctors and patients and provides real-time temperature, heart-rate, and oxygen saturation monitoring. The application compares the gathered data to predetermined thresholds and, if abnormal readings are found, sends immediate alerts to both the doctor and the patient's mobile devices. The results show that it has the potential to improve patient outcomes, lower healthcare expenses, and offer insightful data for medical research. © 2023 IEEE.

关键词： Heart

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A Preliminary Result of Food Object Detection using Swin Transformer 22

A Preliminary Result of Food Object Detection using Swin Tra...

引用

8th International Conference on computer Technology Applications, ICCTA 2022

作者： Jung, Daeil Shim, Simon Choo, Charles Hwang, Doosung Nah, Yunmook Oh, Sejong Dept. of Computer Science Dankook University Korea Republic of Dept. of Computer Engineering San Jose State University United States Dept. of Electrical Engineering San Jose State University United States Dept. of Software Science Dankook University Korea Republic of Dept. of Applied Computer Engineering Dankook University Korea Republic of

ISBN: (纸本)9781450396226

An inappropriate diet is one of the main causes of poor health. However, it is difficult to sustain a quantitative diet assessment in the general living environment. Food object detection is a key method for solving this problem;still, it is difficult to find studies that apply recent object detection techniques. In addition, the currently used high-performance food object detection models have a special architecture that combines two deep learning models-food localization and food classification-in series to achieve high accuracy. The disadvantage of this architecture is that it is difficult to predict the scalability of a model. In this study, we built an end-to-end food object detection model using the Swin Transformer, which is one of the latest backbone models. The experiment was conducted to compare the performance of the UECFOOD dataset with other food object detection studies. For the UECFOOD-100 dataset, a mAP(mean Average Precision) of 0.522 was obtained;also, a mAP of 0.52 was obtained for the UECFOOD-256 dataset. The findings show that the proposed model that uses only end-to-end object detection produces better performance than previous studies using a combination of food localization and food classification. © 2022 ACM.

关键词： Object detection

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Artificial Intelligence for Green Agriculture and Sustainable Resource Management: A Comprehensive Overview

Artificial Intelligence for Green Agriculture and Sustainabl...

引用

Computational Intelligence Approaches and Applications (ICCIAA), International Conference on

作者： Eman Alduweib Waseem Alromema Essa Hezzam Dept. of Computer Science University of Petra Amman Jordan Dept. of Computer Science and Information Applied College Taibah University Medina Saudi Arabia Dept. of Information Systems College of Computer Science and Engineering Taibah University Medina Saudi Arabia

ISBN: (数字)9798331523657

ISBN: (纸本)9798331523664

Artificial Intelligence (AI) is a revolutionary technology disrupting the process of finding solutions for various sustainability challenges. Specifically, AI is having a significant influence on many sectors, especially agriculture, energy, waste management, and biodiversity conservation. This paper offers a comprehensive overview of AI's applications in sustainable resource management, with a special focus on green agriculture as a case study, in which the application of AI greatly enhances the efficiency of resource use, crop yield, and environmental conservation efforts. This overview describes the potential application of AI to address performance concerns (ethical value, energy use, and data quality deficiency), and proposes the future research directions such as the explainable AI, circular economies, and the integration of AI with the Internet of Things (IoT) to guide researchers and policymakers in exploiting AI to enhance sustainability in key domains.

关键词： Waste management Ethics Explainable AI Data integrity Transforms Resource management Internet of Things Biodiversity Artificial intelligence Sustainable development

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Creating UML Class Diagrams with General-Purpose LLMs

Creating UML Class Diagrams with General-Purpose LLMs

引用

IEEE International Workshop on Visualizing Software for Understanding and Analysis (VISSOFT)

作者： Mina Shehata Blaire Lepore Hailey Cummings Esteban Parra Dept. of Math Computer Science and Data Science Belmont University Nashville TN USA

ISBN: (数字)9798331528485

ISBN: (纸本)9798331528492

General-purpose large language models (LLMs) have become a versatile tool in software development and maintenance. These models offer support in tasks such as under-standing, writing, and summarizing code. While these LLMs can generate code quickly their use in software modeling is relatively under-explored. This research aims to study ChatGPT's ability to generate class diagrams from source code. To do this, we engineered a prompt that takes in source code to create a UML class diagram for that system. We used this prompt to create class diagrams for 40 systems and assessed the diagrams on their correctness and structure. Our results show that ChatGPT creates class diagrams that accurately capture 90% of the classes and their attributes and 66 % of the associations. While ChatGPT performed nearly flawlessly for smaller projects, the diagrams for larger projects had more issues. We conclude that ChatGPT is best utilized as a complementary tool rather than the sole resource for software modeling and maintenance.

关键词： Visualization Codes Source coding Scalability Unified modeling language Writing Chatbots Software systems Maintenance Software development management

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BLOCK-SECURE: AI-Based Blockchain Enabled Secure Framework for IoMT Applications

BLOCK-SECURE: AI-Based Blockchain Enabled Secure Framework f...

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2024 IEEE International Conference on E-Health Networking, Application and Services, HealthCom 2024

作者： Panchal, Barkha Bhatia, Jitendra Kumhar, Malaram Tanwar, Sudeep Dutta, Ashit Kumar Rodrigues, Joel J.P.C. Department of Computer Science and Engineering Institute of Technology Nirma University Ahmedabad India Dept. of Comp. Science and Information Systems College of Applied Sciences AlMaarefa University Saudi Arabia Amazonas State University AM Manaus Brazil

ISBN: (纸本)9798350350548

The Internet of Medical Things (IoMT) revolutionizes healthcare by integrating medical devices and systems with the internet. However, the vast amounts of sensitive medical data in IoMT networks pose significant security and privacy concerns. Traditional security measures often fall short in identifying the malicious data attacks within the IoMT ecosystems. This paper introduces an AI-based non-malicious data classification scheme based on blockchain. We applied and evaluated the machine learning (ML) classifiers, such as support vector machine (SVM), random forest (RF), and K-Nearest neighbor (KNN). We evaluated the proposed framework based on various performance metrics that includes accuracy, precision, recall, and F1 score. The accuracy using SVM obtained 75.3%, RF is 75.9%, and KNN is 84.3%. The results shows that KNN performs better than other models by the factor of 9%. © 2024 IEEE.

关键词： Adversarial machine learning

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Assessment of Super-Resolution Ultrasound Imaging Using the Erythrocytes Through Comparison with Micro-CT

Assessment of Super-Resolution Ultrasound Imaging Using the ...

引用

Medical Imaging 2025: Ultrasonic Imaging and Tomography

作者： Hansen, Lauge Naur Ráth, Andre Amin-Naji, Mostafa McDermott, Amy Sørensen, Charlotte Mehlin Kjer, Hans-Martin Gundlach, Carsten Dahl, Anders Bjorholm Jensen, Jørgen Arendt Center for Fast Ultrasound Dept. of Health Technology Technical University of Denmark Kgs. Lyngby Denmark Dept. of Applied Mathematics and Computer Science Technical University of Denmark Kgs. Lyngby Denmark Dept. of Physics Technical University of Denmark Kgs. Lyngby Denmark Dept. of Biomedical Sciences University of Copenhagen Copenhagen Denmark

ISBN: (纸本)9781510686021

This work presents a dual-sample validation study of Super-Resolution Ultrasound imaging using the Erythrocytes (SURE) and state-of-the-art laboratory micro-CT data of rat kidneys. The study hypothesizes that SURE images reliably depict cortical radial blood vessels in rat kidneys, assessed by comparing the depicted blood vessels and vessel diameter estimates from registered micro-CT data. Previous qualitative comparisons between SURE images and micro-CT were either made at an insufficient micro-CT voxel size or in a small region of a rat kidney cortex scanned at a higher resolution. Here, micro-CT data of entire rat kidneys scanned at a voxel size of 5 µm is compared to images from the SURE pipeline with peak detection done after blood flow separation. The separation is achieved using Hankel singular value decomposition before peak detection (SURE-Hankel). The analysis shows a consistent depiction of cortical radial vessels in the entire rat kidney using SURE-Hankel and that these blood vessels have estimated diameters around 50 µm to 100 µm. However, discrepancies in the registered images highlight the need for more accurate CT-to-ultrasound image registration techniques. © 2025 SPIE.

关键词： Echocardiography

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The Field of Requirements Engineering Education 35

The Field of Requirements Engineering Education

引用

35th IEEE International Conference on Software Engineering Education and Training, CSEE and T 2023

作者： Daun, Marian Grubb, Alicia M. Stenkova, Viktoria Tenbergen, Bastian Technical University of Applied Sciences Würzburg-Schweinfurt Center for Robotics Schweinfurt Germany Smith College Dept. of Computer Science NorthamptonMA United States University of Duisburg-Essen Paluno Essen Germany State University of New York Dept. of Computer Science OswegoNY United States

ISBN: (纸本)9798350322026

Requirements engineering (RE) is an essential part of the software development process. Good RE, among others, is the basis for high quality software, considerably reduces the risk for software projects to fail entirely or with budget-overspending and is crucial for coordinating systems and software engineering. Thus, RE education is a vital part of software engineering curricula. However, a central concept of what RE education comprise and how to best teach RE is lacking. Therefore, we conducted a systematic literature review of the field and provide a systematic map describing the state of the RE education field. Results for key trends in RE instruction of the past decade include involvement of real or realistic stakeholders, teaching predominantly elicitation as an RE activity, and increasing student factors such as motivation or communication skills. © 2023 IEEE.

关键词： Requirements engineering

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Knowledge-based Reactive Planning and Re-planning – A Case-Study Approach

Knowledge-based Reactive Planning and Re-planning – A Case-...

引用

Artificial Intelligence (CAI), IEEE Conference on

作者： Ramzi Djemai Vassil Vassilev Karim Ouazzane Maitreyee Dey Dept. of Computer Science & Applied Computing London Metropolitan University London UK

ISBN: (数字)9798350354096

ISBN: (纸本)9798350354102

When a disaster strikes, man-made or natural, evacuation plans are put under immediate constraints, including topological, temporal, and spontaneously occurring events such as fire, smoke and obstacles introducing bottlenecks and impeding ingress and egress. Planning for uncertainties arising from indoor evacuations can be complex as there’s a fine balance to strike between a too-detailed plan and one that’s too vague. Such constraints apply to office and residential buildings, airports, mining sites, stadiums, ships, etc. Although some indoor spatial models have been developed, many are complex, and their applicability is non-universal. This paper proposes an innovative approach that harnesses the power of the Semantic Web Rule Language (SWRL) based on Web Ontology Language (OWL) to enhance existing evacuation planning methods through data-rich modelling. The OWL ontology serves as a formal representation of real-world concepts, their relationships, and properties. To demonstrate its application, the ontology is implemented in a case study involving London Metropolitan University’s Tower Building, and its design is elucidated in this paper.

关键词： Uncertainty Disasters Atmospheric modeling OWL Buildings Poles and towers Knowledge based systems

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Enhanced Outsourced and Secure Inference for Tall Sparse Decision Trees

Enhanced Outsourced and Secure Inference for Tall Sparse Dec...

引用

2024 IEEE International Performance, Computing, and Communications Conference, IPCCC 2024

作者： Quijano, Andrew Halkidis, Spyros T. Gallagher, Kevin Akkaya, Kemal Samaras, Nikolaos New York University Dept. of Computer Science and Engineering New YorkNY United States University of Macedonia Dept. of Applied Informatics Thessaloniki Greece Nova Lincs Nova School of Science and Technology Lisbon Portugal Florida International University Advanced Wireless and Security Lab MiamiFL United States

ISBN: (纸本)9798350367942

A decision tree is an easy-to-understand tool that has been widely used for classification tasks. On the one hand, due to privacy concerns, there has been an urgent need to create privacy-preserving classifiers that conceal the user's input from the classifier. On the other hand, with the rise of cloud computing, data owners are keen to reduce risk by outsourcing their model, but want security guarantees that third parties cannot steal their decision tree model. To address these issues, Joye and Salehi introduced a theoretical protocol that efficiently evaluates decision trees while maintaining privacy by leveraging their comparison protocol that is resistant to timing attacks. However, their approach was not only inefficient but also prone to side-channel attacks. Therefore, in this paper, we propose a new decision tree inference protocol in which the model is shared and evaluated among multiple entities. We partition our decision tree model by each level to be stored in a new entity we refer to as a "level-site."Utilizing this approach, we were able to gain improved average run time for classifier evaluation for a non-complete tree, while also having strong mitigations against side-channel attacks. © 2024 IEEE.

关键词： Differential privacy

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Enhancing Security: A Haar Cascade-based Face Recognition System for PGs and Hostels

Enhancing Security: A Haar Cascade-based Face Recognition Sy...

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International Conference for Convergence of Technology (I2CT)

作者： Pallavi R Kumar Janani. K Sri Vaishnavi. N Sai Pradeep Rao. K D. J. Yashwanth Reddy Dept. of Computer Science MS Ramaiah University of Applied Sciences Bangalore India

ISBN: (数字)9798350394474

ISBN: (纸本)9798350394481

This work introduces an innovative security system prototype tailored explicitly for paying guest accommodations or hostels, blending Internet of Things (IoT), artificial intelligence (AI), machine learning algorithms, and web crawling technologies. The core emphasis revolves around facial recognition, precisely distinguishing between known and unknown individuals to manage entry effectively. The system, integrating camera technology, captures visitor images and employs advanced face recognition algorithms for precise face classification. In instances where faces remain unrecognized, the system leverages web crawling to retrieve potential intruder details. Immediate notifications, featuring captured images, are swiftly dispatched to users through email and smartphone alerts, enabling prompt responses. Operated within a wireless infrastructure governed by a Raspberry Pi, this system prioritizes cost-effectiveness and user-friendliness. Rigorously tested across diverse environments encompassing homes, paying guest accommodations, and office spaces, this research establishes a remarkable balance between cutting-edge technology and pragmatic security applications. This solution offers an affordable and efficient security option tailored explicitly for the unique needs of contemporary hostels and paying guest accommodations, ensuring heightened security without exorbitant expenses.

关键词： Wireless communication Machine learning algorithms Face recognition Prototypes Security Internet of Things Communication system security

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