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Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)

作者： Alejandro Pereira Carlos Santos Marilton Aguiar Daniel Welfer Marcelo Dias Rafaela de Menezes Paulo Roberto Ferreira Fábio Rossi Marcos d’Ornellas Carlos Jesus Haygert Juliano Kazienko Fábio Comim Aurélio Hoppe Juliano Weber Leandro Tavares Leonardo Guths Rodrigo Guerra Postgraduate Program in Computing Federal University of Pelotas - UFPel Pelotas Brazil Federal Institute of Education Science and Technology Farroupilha - IFFar Alegrete Brazil Department of Applied Computing Federal University of Santa Maria - UFSM Santa Maria Brazil Department of Clinical Medicine Federal University of Santa Maria - UFSM Santa Maria Brazil Industrial Technical College Federal University of Santa Maria - UFSM Santa Maria Brazil Department of Clinical Medicine Federal University of Minas Gerais - UFMG Belo Horizonte Brazil Department of Information Systems and Computing Regional University of Blumenau - FURB Blumenau Brazil Federal Institute of Education Science and Technology Farroupilha - IFFar Santo Ângelo Brazil Postgraduate Program in Computing Federal University of Rio Grande do Sul - UFRGS Porto Alegre Brazil Center of Computational Sciences Federal University of Rio Grande - FURG Rio Grande Brazil

ISBN: (数字)9798350371499

ISBN: (纸本)9798350371505

Diabetic Retinopathy (DR) is a microvascular complication related to diabetes that affects approximately 33% of individuals with this condition and, if not detected and treated early, can lead to irreversible vision loss. Fundus lesions such as Hard and Soft Exudates, Hemorrhages, and Microaneurysms typically identify DR. The development of computational methods to segment these lesions plays a fundamental role in the early diagnosis of the disease. This paper proposes a new approach that uses an R2U-Net combined with data augmentation techniques for segmenting fundus lesions. We trained, adjusted, and evaluated the proposed work in the DDR dataset, achieving an accuracy of 99.87% and a mean Intersection over Union (mIoU) equal 59.69%. Furthermore, we assessed it in the IDRiD dataset, achieving an mIoU of 49.92%. The results obtained in the experiments highlight the potential contribution of the model in the lesion annotations for creating new DR datasets, which is essential given the scarcity of annotations in publicly available datasets.

关键词： Measurement Diabetic retinopathy Accuracy Annotations Semantic segmentation Feature extraction Data augmentation Data models Lesions Hemorrhaging

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Mobile Apps for Healthy Life Education for Young People 2

Mobile Apps for Healthy Life Education for Young People

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2nd International Conference on Artificial Intelligence and Smart Energy, ICAIS 2022

作者： Suwandi, Benedictus Evan Warnars, Diana Teresia Spits Warnars, Harco Leslie Hendric Spits Utomo, Wiranto Herry Ramadhan, Arief Bina Nusantara University School of Computer Science Computer Science Department Jakarta11480 Indonesia Bina Nusantara University School of Information Systems Information System Department Jakarta11480 Indonesia Bina Nusantara University Binus Graduate Program - Computer Science Computer Science Department Jakarta11480 Indonesia Faculty of Computing President University Department of Magister Science of Information Technology Bekasi17550 Indonesia

ISBN: (纸本)9781665400527

Health education is crucial for the community, especially the younger generation, because adolescent behavior/lifestyle will carry over into adulthood, and it is difficult to change it. This paper proposes designing and implementing a mobile application for young people for healthy lifestyle education. This mobile application implemented with the android studio is designed using use case diagrams to describe the business processes of this mobile application. In addition, activity diagrams describe the process details of each activity use case, and class diagrams describe the design of the database model, which is implemented using a MySQL database. © 2022 IEEE.

关键词： Mobile computing

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H∞ gain-scheduled dynamic output feedback control with transient performance applied to electrical microgrid

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Journal of the Franklin Institute 2024年第7期361卷

作者： Fuentes, R.M. Lacerda, M.J. Morais, C.F. Oliveira, R.C.L.F. Palma, J.M. Engineering Systems Doctoral Program Faculty of Engineering Universidad de Talca Curicó3340000 Chile Centre for Communications Technology School of Computing and Digital Media London Metropolitan University LondonN7 8DB United Kingdom Pontifical Catholic University of Campinas – PUC-Campinas Polythecnic School Postgraduate Program in Telecommunication Networks Management SP Campinas13086-900 Brazil SP Campinas13083-852 Brazil Department of Electrical Engineering Faculty of Engineering Universidad de Talca Curicó334000 Chile

This paper introduces an approach for designing gain-scheduled dynamic output feedback controllers for continuous-time Linear Parameter-Varying (LPV) systems. The aim is to improve transient response by incorporating both D-stability and H∞ criteria into the synthesis conditions. We achieve this by employing changes of variables and congruence transformations, which lead to new synthesis conditions expressed as linear matrix inequalities. Additional fine-tuned scalar parameters can be used to enhance further the controller performance in terms of less conservative H∞ guaranteed costs. To assess the effectiveness of our proposed synthesis procedure, we conduct computational experiments in the context of a microinverter-based distributed power generation system. These experiments take into account real-world operational characteristics, providing a comprehensive evaluation of our approach. The results demonstrate that the designed controller effectively ensures the desired closed-loop system behavior, even when dealing with state noise and discretization errors originating from digital implementation. © 2024 The Franklin Institute

关键词： Transient analysis

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Multi-agent System for the Area of Medical Emergencies for Health Surveillance (MAS-AMeHs) 1st

Multi-agent System for the Area of Medical Emergencies for H...

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1st International Conference on Advanced Research in Technologies, Information, Innovation and Sustainability, ARTIIS 2021

作者： Bautista, Jorge Alanis, Arnulfo Patiño, Efraín Hernandez-Leal, Fabiola Master’s Program in Information Technology Department of Systems and Computing National Technology of Mexico Campus Tijuana Calzada del Tecnológico S/N Fracionamiento Tomas Aquino Tijuana B.C.22414 Mexico Department of Systems and Computing National Technology of Mexico Campus Tijuana Calzada del Tecnológico S/N Fraccionamiento Tomas Aquino Tijuana B.C.22414 Mexico School of Medicine Autonomous University of Baja California Tijuana B.C. Mexico

ISBN: (纸本)9783030902407

In this paper, it is proposed an APK that works in conjunction with an emergency triage system that may help both patients and doctors using IoT. The classification system implemented on this work reads the data provided by the user (oxygen saturation, hearth rate) on the app, this will be processed, as the results are given in real time and classified on an emergency color system (green, yellow, orange and red) the doctor would be notified instantly, allow him to be up to date with its patient’s condition and help him get a better analysis and have a mor exact diagnose, this being beneficial for both patient and doctor. © 2021, Springer Nature Switzerland AG.

关键词： Oxygen

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Fusion of Mixture of Experts and Generative Artificial Intelligence in Mobile Edge Metaverse

arXiv

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arXiv 2024年

作者： Liu, Guangyuan Du, Hongyang Niyato, Dusit Kang, Jiawen Xiong, Zehui Jamalipour, Abbas Mao, Shiwen Kim, Dong In The College of Computing and Data Science The Energy Research Institute @ NTU Interdisciplinary Graduate Program Nanyang Technological University Singapore The College of Computing and Data Science Nanyang Technological University Singapore The School of Automation Guangdong University of Technology China The Pillar of Information Systems Technology and Design Singapore University of Technology and Design Singapore The Department of Electrical and Computer Engineering Auburn University Auburn United States The School of Electrical and Computer Engineering University of Sydney Australia The Department of Electrical and Computer Engineering Sungkyunkwan University Korea Republic of

In the digital transformation era, Metaverse offers a fusion of virtual reality (VR), augmented reality (AR), and web technologies to create immersive digital experiences. However, the evolution of the Metaverse is slowed down by the challenges of content creation, scalability, and dynamic user interaction. Our study investigates an integration of Mixture of Experts (MoE) models with Generative Artificial Intelligence (GAI) for mobile edge computing to revolutionize content creation and interaction in the Metaverse. Specifically, we harness an MoE model's ability to efficiently manage complex data and complex tasks by dynamically selecting the most relevant experts running various sub-models to enhance the capabilities of GAI. We then present a novel framework that improves video content generation quality and consistency, and demonstrate its application through case studies. Our findings underscore the efficacy of MoE and GAI integration to redefine virtual experiences by offering a scalable, efficient pathway to harvest the Metaverse's full potential. © 2024, CC BY.

关键词： Augmented reality

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Preprocessing Techniques for Enhancing Vision-Based Detection System for Tire Sidewall Extraction

Preprocessing Techniques for Enhancing Vision-Based Detectio...

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International Seminar on Research of Information Technology and Intelligent systems (ISRITI)

作者： Jia Hao Loo Veronica Lestari Jauw Chin Seong Lim Khairul-Rijal Sarmin Nunung Nurul Qomariyah Ahmad Nurul Fajar Department of Mechanical Materials and Manufacturing Engineering University of Nottingham Malaysia Malaysia Continental Tyre Technology Centre Malaysia Petaling Jaya Malaysia Computer Science Department School of Computing and Creative Arts Bina Nusantara University Jakarta Indonesia Information System Management Department BINUS Graduate Program-Master Information Systems Bina Nusantara University Jakarta Indonesia

ISBN: (数字)9798331519643

ISBN: (纸本)9798331519650

This study aims to develop a system for extracting crucial information from tire sidewalls using Optical Character Recognition (OCR). Initially, images of tire were captured manually by smartphone cameras, including Redmi 9T, iPhone 11, and Galaxy S23 Ultra. The captured images are then transferred to a computer for storage. Subsequently, these images were cropped according to the boundaries identified by Hough Circle Transform (HCT). The cropped images were then further pre-processed. During the pre-processing phase, geometrical transformation and image sharpening techniques are applied to enhance the clarity and readability of the text images. The text is then extracted using Google Vision, with the extracted text categorized by size, DOT, brand and pattern. The results indicated that the effectiveness of image pre-processing was constrained by the accuracy of circle detection, which reached a maximum rate of 87.1%. This causes parts of the text to be cut out inaccurately, leading to a suboptimal extraction accuracy of 55.65%. It is also observed that the Redmi 9T camera produced inconsistent results compared to other devices. Specifically, the iPhone 11 and Samsung Galaxy S23 Ultra demonstrated superior extraction accuracies of 69.71% and 66.37%, respectively, whereas the Redmi 9T achieved a lower extraction accuracy of 37.76%.

关键词： Accuracy Optical character recognition Text categorization Lighting Transforms US Department of Transportation Cameras Tires Internet Data mining

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HealthPrompt: A Zero-shot Learning Paradigm for Clinical Natural Language Processing

arXiv

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arXiv 2022年

作者： Sivarajkumar, Sonish Wang, Yanshan Intelligent Systems Program School of Computing and Information University of Pittsburgh PA United States Department of Biomedical Informatics University of Pittsburgh PA United States Department of Health Information Management University of Pittsburgh PA United States

Deep learning algorithms are dependent on the availability of large-scale annotated clinical text datasets. The lack of such publicly available datasets is the biggest bottleneck for the development of clinical Natural Language Processing(NLP) systems. Zero-Shot Learning(ZSL) refers to the use of deep learning models to classify instances from new classes of which no training data have been seen before. Prompt-based learning is an emerging ZSL technique where we define task-based templates for NLP tasks. We developed a novel prompt-based clinical NLP framework called HealthPrompt and applied the paradigm of prompt-based learning on clinical texts. In this technique, rather than fine-tuning a Pre-trained Language Model(PLM), the task definitions are tuned by defining a prompt template. We performed an in-depth analysis of HealthPrompt on six different PLMs in a no-data setting. Our experiments prove that prompts effectively capture the context of clinical texts and perform remarkably well without any training data. Copyright © 2022, The Authors. All rights reserved.

关键词： Natural language processing systems

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Employing texture features of chest x-ray images and machine learning in covid-19 detection and classification

Mendel

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Mendel 2021年第1期27卷 9-17页

作者： Alquran, Hiam Alsleti, Mohammad Alsharif, Roaa Qasmieh, Isam Abu Alqudah, Ali Mohammad Harun, Nor Hazlyna Binti Department of Biomedical Systems and Informatics Engineering Yarmouk University Irbid21163 Jordan The Institute of Biomedical Technology King Hussein Medical Center Royal Jordanian Medical Service Amman11855 Jordan College of applied medical Sciences Radiological Science Program King Saud University Jeddah21435 Saudi Arabia Data Science Research Lab School of Computing Universiti Utara Malaysia Sintok Kedah06010 Malaysia

The novel coronavirus (nCoV-19) was first detected in December 2019. It had spread worldwide and was declared coronavirus disease (COVID-19) pandemic by March 2020. Patients presented with a wide range of symptoms affecting multiple organ systems predominantly the lungs. Severe cases required intensive care unit (ICU) admissions while there were asymptomatic cases as well. Although early detection of the COVID-19 virus by Real-time reverse transcription-polymerase chain reaction (RT-PCR) is effective, it is not efficient;as there can be false negatives, it is time consuming and expensive. To increase the accuracy of in-vivo detection, radiological image-based methods like a simple chest X-ray (CXR) can be utilized. This reduces the false negatives as compared to solely using the RT-PCR technique. This paper employs various image processing techniques besides extracted texture features from the radiological images and feeds them to different artificial intelligence (AI) scenarios to distinguish between normal, pneumonia, and COVID-19 cases. The best scenario is then adopted to build an automated system that can segment the chest region from the acquired image, enhance the segmented region then extract the texture features, and finally, classify it into one of the three classes. The best overall accuracy achieved is 93.1% by exploiting Ensemble classifier. Utilizing radiological data to conform to a machine learning format reduces the detection time and increase the chances of survival. © 2021, Brno University of Technology. All rights reserved.

关键词： COVID-19

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Performance of the SegNet in the Segmentation of Breast Ultrasound Lesions

Performance of the SegNet in the Segmentation of Breast Ultr...

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Pan American Health Care Exchanges, PAHCE

作者： P. O. Vianna R. Farias W. C. A. Pereira Biomedical Engineering Program COPPE/Federal University of Rio de Janeiro RJ Brazil Systems and Computing Engineering Program COPPE/Federal University of Rio de Janeiro RJ Brazil

ISBN: (纸本)9781728170558

This work presents a performance analysis of a convolutional neural network (CNN), named SegNet, applied for automatic segmentation of breast ultrasound images. Defining the viability of CNN's could help with feature extraction and posterior classification of lesions, causing a decrease in the number of unnecessary biopsies. The breast ultrasound (BUS) dataset consists of 2054 images from 659 female patients, and each image was manually segmented by an experienced radiologist. The dataset is fed into the CNN without any filtering process applied to it and the test output is then compared with the radiologist's labels. We compared the results of different loss and activation functions, and the experiments reveal that the best performance was achieved using dice loss function, with dice coefficient of 81.1%.

关键词： Image segmentation Ultrasonic imaging Medical services Breast Feature extraction Generative adversarial networks Performance analysis

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Specialized reporting of article review - SRAR Method 4

Specialized reporting of article review - SRAR Method

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4th IEEE World Engineering Education Conference, EDUNINE 2020

作者： Rojas, Julio Ernesto Quispe Tuesta, Julio Enrique Quispe Universidad Nacional Pedro Ruiz Gallo Computing and Electronics Departament Lambayeque Peru Universidad Nacional Tecnologica de Lima sur Systems Engineering Program Lima Peru

ISBN: (纸本)9781728166384

The professional training of the engineering student must be innovated in order to meet the accreditation criterion and with the requirement of the university law that requires presenting an investigation to obtain the bachelor's degree and another to obtain the professional degree of engineer. In order to develop in students, the skills to search for scientific and technological articles, and the critical skills to evaluate documents and extract the main ideas from them in relation to a particular topic, the SRAR method or specialized report review method has been proposed. It has been applied during five academic semesters in the electronic engineering telephone systems course, with satisfactory results. The SRAR method has six stages:*** of the SRAR method and proposed topic, 2. Initial bibliographic search, 3. Autonomous training, 4. Selection of articles for review, 5. Study and evaluation of articles 6. Writing of specialized report for review of articles. © 2020 IEEE.

关键词： Students

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