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arXiv 2023年

作者： Zhao, Wanying Guo, Fiona Lerman, Kristina Ahn, Yong-Yeol Center for Complex Networks and Systems Research Luddy School of Informatics Computing and Engineering Indiana University United States Department of Computer Science University of Southern California United States Information Sciences Institute University of Southern California United States Network Science Institute Indiana University BloomingtonIN United States

Narrative is a foundation of human cognition and decision making. Because narratives play a crucial role in societal discourses and spread of misinformation and because of the pervasive use of social media, the narrative dynamics on social media can have profound societal impact. Yet, systematic and computational understanding of online narratives faces critical challenge of the scale and dynamics;how can we reliably and automatically extract narratives from massive amount of texts? How do narratives emerge, spread, and die? Here, we propose a systematic narrative discovery framework that fill this gap by combining change point detection, semantic role labeling (SRL), and automatic aggregation of narrative fragments into narrative networks. We evaluate our model with synthetic and empirical data-two Twitter corpora about COVID-19 and 2017 French Election. Results demonstrate that our approach can recover major narrative shifts that correspond to the major events. © 2023, CC BY-NC-ND.

关键词： Decision making

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ASCAPE: An open AI ecosystem to support the quality of life of cancer patients

arXiv

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arXiv 2023年

作者： Lampropoulos, Konstantinos Kosmidis, Thanos Autexier, Serge Savić, Miloš Athanatos, Manos Kokkonidis, Miltiadis Koutsouri, Tzortzia Vizitiu, Anamaria Valachis, Antonios Padron, Miriam Quintero Department of Electrical & Computer Engineering University of Patras Patras Greece CareAcross Research CareAcross LTD London United Kingdom Bremen Germany Division of Informatics Faculty of Sciences University of Novi Sad Novi Sad Serbia Institute of Computer Science Foundation for Research and Technology – Hellas Heraklion Crete Greece Research & Innovation Development INTRASOFT International S.A. Luxembourg Luxembourg SPHYNX Research SPHYNX Technology Solutions AG Zug Switzerland Department of Automation and Information Technology Transilvania University of Brasov Brasov Romania Advanta Siemens SRL Brasov Romania Department of Oncology Faculty of Medicine and Health Örebro University Örebro Sweden Atos Research Innovation Atos S.A.E Madrid Spain

The latest cancer statistics indicate a decrease in cancer-related mortality. However, due to the growing and ageing population, the absolute number of people living with cancer is set to keep increasing. This paper presents ASCAPE, an open AI infrastructure that takes advantage of the recent advances in Artificial Intelligence (AI) and Machine Learning (ML) to support cancer patients’ quality of life (QoL). With ASCAPE health stakeholders (e.g. hospitals) can locally process their private medical data and then share the produced knowledge (ML models) through the open AI infrastructure. Copyright © 2023, The Authors. All rights reserved.

关键词： Intelligent systems

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Network security analysis for signal strength based packet filitering 3

Network security analysis for signal strength based packet f...

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3rd International Conference on Intelligent Sustainable Systems, ICISS 2020

作者： Sundaram, B. Barani Rajkumar, P. Ananthi, M. Sravan Kumar, D. Vijaykumar, Janga Karthika, P. Bule Hora University College of Informatics Department of Computer Science Bule hora Ethiopia KGiSL Institute of Technology Dept of Computer Science and Engineering Coimbatore Tamilnadu India Institute of Technology Dept of IT KGiSL Coimbatore Tamilnadu India Balaji Institute of Technology Science Dept of CSE Warangal India Bule Hora University College of Informatics Dept. of Information Technology Bule Hora144 Ethiopia Kalasalingam Academy of Research and Education Department of Computer Applications TamilNadu India

ISBN: (纸本)9781728170893

Signal Strength based position check is all the more encouraging when contrasted with other Sybil assault discovery methods since it is lightweight and doesn't need centralization. Hubs can locally decide their areas through got signal quality varieties and illuminate neighbors about area refreshes likewise. In position confirmation, network hubs check the situation of every hub and further guarantee that each actual area is limited by just a single character at a specific time. This paper will overview the Sybil assault recognition methods with their connected problem. At last will introduce in thoughts regarding the Sybil assault discovery through one-time restriction (just the passage into organization) with fundamentally decreases correspondence in the clouds dissimilar to different plans where per parcel area following or intermittent spread of area data is utilized to distinguish Sybil assaults. © 2020 IEEE.

关键词： Network security

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Orbital angular momentum of spin electromagnetic wave

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Results in Physics 2024年 61卷

作者： Sun, Zhiwei Du, Mingzhu Guo, Zhenkun Xu, Ruolei Zhao, Junming Feng, Yijun School of Electronic Science and Engineering Nanjing University 210023 China School of Microelectronics and Communication Engineering Chongqing University 400044 China Shanghai Radio Equipment Research Institute Shanghai Academy of Spaceflight Technology 201109 China Department of Electrical and Computer Engineering National University of Singapore 117583 Singapore

Spin angular momentum (SAM) and Orbital angular momentum (OAM) are two distinct forms of electromagnetic angular momentum (AM). Generally, circularly-polarized (CP) wave is deemed as the typical form of spin wave, which carries SAM only. In this work, the OAM characteristic of spin wave is found after stating the CP wave into the cylindrical coordinate: the spin wave contains two components along r and ϕ directions, which carry both SAM and OAM. Based on the OAM character of spin wave, an electromagnetic waveform with SAM in the center and SAM-OAM beside is demonstrated. In addition, since spin wave carries OAM naturally, a spin–orbit coupling mechanism is performed, which demands no spin Pancharatnam–Berry phase nor any phase modulation structure. All the results have been experimentally verified. Our research can be utilized in the theoretical studies of rotational electromagnetic and may be potentially applied in metamaterial, information and quantum research areas. © 2024

关键词： Angular momentum Helical beam waves Polarization-phase Spatial-phase Spin-orbit coupling

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Mid-Infrared Spectroscopy as a Real-Time Diagnostic Tool for Chronic Endometritis: A Preliminary Study

SSRN

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SSRN 2024年

作者： Bilgory, Asaf David, Liron Haimovich, Sergio Malonek, Dov Dekel, Ben Zion Shechtman, Lea Groisman, Gabriel M. Shalom-Paz, Einat IVF Unit Hillel Yaffe Medical Center Hadera Israel Rappaport Faculty of Medicine Technion Israel Institute of Technology Haifa3109601 Israel Gynecology Ambulatory Surgery Unit Hillel Yaffe Medical Center Hadera Israel Department of Electrical & Computer Engineering Ruppin Academic Center Emek Hefer Israel Department of Pathology Hillel Yaffe Medical Center Hadera Israel

research question: Can attenuated total reflection-Fourier transform infrared spectroscopy combined with machine learning techniques be used to develop a real-time diagnostic modality for chronic endometritis by analysing endometrial biopsies obtained during hysteroscopy?Design: Women undergoing hysteroscopy for infertility assessment were enrolled in this prospective study from January 2020–March 2021. Endometrial biopsies were evaluated using a spectrophotometer and subsequently via histopathology, including immunohistochemical staining for the multiple myeloma oncogene-1 (MUM-1). Spectroscopy analyses of the positive and the negative chronic endometritis groups were compared across various cut-offs of MUM-1 positive cells per 10 high-power fields (HPF). Machine learning techniques were used to build discrimination models with Matlab and Unscrambler software ***: Fifty-four women were recruited. Four of the 54 measured spectra were discarded due to high measurement noise. Fifty biopsies were finally assessed. A discriminant model was developed using the Principal Component Analysis and Linear Discriminant Analysis techniques (machine learning). The model was evaluated for accuracy using different MUM-1 cut-offs. Distinct spectral disparities (p7 MUM-1 cells/10 HPF, the model differentiated positive and negative chronic endometritis with 84% accuracy, 88.8% sensitivity and 82.9% ***: Our findings in this preliminary study support the potential availability of a bedside diagnostic tool based on a model developed using spectroscopy coupled with machine learning. This allowed us to diagnose chronic endometritis in real-time with 84% accuracy, facilitating immediate initiation of appropriate treatment. © 2024, The Authors. All rights reserved.

关键词： Fourier transform infrared spectroscopy

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Time-dependent elastic numerical model for Optical Coherence Elastography of the murine retina

Time-dependent elastic numerical model for Optical Coherence...

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Bioengineering (ENBENG), Portuguese Meeting in

作者： Carlos Correia Ana Batista Sílvia Barbeiro João Cardoso José P. Domingues Rafael Henriques Custódio Loureiro Mário J. Santos Pedro Serranho Rui Bernardes Miguel Morgado Institute for Biomedical Imaging and Translational Research (CIBIT) University of Coimbra Coimbra Portugal Center of Mathematics of the University of Coimbra (CMUC) Coimbra Portugal Department of Physics Faculty of Science and Technology University of Coimbra Coimbra Portugal Department of Electrical and Computer Engineering Faculty of Science and Technology University of Coimbra Coimbra Portugal Department of Science and Technology Mathematics Section Universidade Aberta Lisbon Portugal Faculty of Medicine Clinical Academic Center of Coimbra (CACC) University of Coimbra Portugal

We present the initial stages of development of a Finite Element Method-based time-dependent elastic numerical model which seeks to support the employment of our Optical Coherence Elastography system for assessing murine retinal elasticity. The current model is able to reconstruct displacement maps in both homogeneous and heterogeneous domains with errors up to a few hundredths relatively to a known exact displacement map, within 1 millisecond. The results demonstrate the robustness of the numerical algorithm under different elastic domains, and model parametrization with real Optical Coherence Elastography data is already in progress.

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A novel rule-based expert system for early diagnosis of bipolar and Major Depressive Disorder

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Smart Health 2025年 35卷

作者： Zolfagharnasab, Mohammad Hossein Damari, Siavash Soltani, Madjid Ng, Artie Karbalaeipour, Hengameh Haghdadi, Amin Saghayan, Masood Hamed Matinfar, Farzam Department of Statistics Mathematics and Computer Science University of Allameh Tabataba'i Tehran Iran Department of Mechanical Engineering K. N. Toosi University of Technology Tehran Iran Centre for Sustainable Business International Business University Toronto Canada Department of Electrical and Computer Engineering University of Waterloo Waterloo Canada Centre for Biotechnology and Bioengineering (CBB) University of Waterloo Waterloo Canada Department of Integrative Oncology BC Cancer Research Institute Vancouver Canada Department of Psychology Science and Research Branch Islamic Azad University Tehran Iran

A confident and timely diagnosis of mental illnesses is one of the primary challenges practitioners repeatedly encounter when they start treating new patients. However, diagnosing can quickly become problematic as the subjects expose comparative symptoms among mental illnesses. Due to influencing a broad populace among mental ailments, an adjusted differentiation between Major Depressive Disorder, Mania Bipolar Disorder, Depressive Bipolar Disorder, and ordinary individuals with mild symptoms is one of the critical subjects for community health. This study responded to the described problem by proposing a novel rule-based Expert System, which evaluates the impact of disorder symptoms on the Certainty Factor concerning each mental status. The semantic rules are developed based on the recommendation of experts, and the implementation is carried out using Prolog and C# languages. Furthermore, an easy-to-use user interface is considered to facilitate the system workflow. The consistency of the developed framework is established by performing rigorous tests by expert psychiatrists as well as 120 clinical samples collected from private samples. Based on the results, the current model classifies mental disorder cases with a success rate of 93.33% using only the 17 symptoms specified in the ontology model. Furthermore, a questionnaire that measures user satisfaction after the test also achieves a mean score of 3.56 out of 4, which indicates a high degree of user acceptance. As a result, it is concluded that the current framework is a reliable tool for achieving a solid diagnosis in a shorter period. © 2024

关键词： Bipolar disorder Clinical Decision Support computer science Expert system Healthcare informatics Rule-based Reasoning

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Fundus Image Translation with Scale-Aware Registration and Gradient-Guided GAN

SSRN

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SSRN 2024年

作者： Kim, JuChan Le, Duc-Tai Bum, Junghyun Son, Chang-Hwan Lee, Eun Jung Han, Jong Chul Choo, Hyunseung Department of Artificial Intelligence Systems Engineering Sungkyunkwan University Korea Republic of College of Computing and Informatics Sungkyunkwan University Korea Republic of Sungkyun AI Research Institute Sungkyunkwan University Korea Republic of Software Science and Engineering Major Kunsan National University Korea Republic of Department of Ophthalmology Samsung Medical Center Korea Republic of School of Medicine Sungkyunkwan University Korea Republic of Department of Medical Device Management and Research SAIHST Sungkyunkwan University Korea Republic of Department of Electrical and Computer Engineering Sungkyunkwan University Korea Republic of

Two major fundus imaging types, conventional fundus (CF) imaging and ultrawidefield fundus (UF) imaging, are widely used in ophthalmology. In a typical clinical setting, both imaging methods are used simultaneously to diagnose diseases depending on the case. This study introduced an enhanced method of modality transformation to boost the diagnostic support capabilities of deep-learning models in ophthalmology. The method proposed in this study to translate UF images (UFIs) to CF images (CFIs) can replace the common clinical approach of both imaging methods, thereby reducing financial and temporal burdens on patients. This study learns and transforms the relationship between these modalities using UFI–CFI image pairs obtained from the same patient on the same day to minimize distorted information and accurately convert between different modalities. Our proposed model uses scale-aware registration and gradient-guided translation methods to align the UFI–CFI pairs, The generated CFIs not only enhance the quality of images and better represent the retinal area compared to existing methods but also effectively preserve the disease-related details in UFIs, aiding in the diagnosis. In addition, we optimize the image transformation performance, resulting in an 22% improvement in image quality assessment scales compared with previous methods. Furthermore, we acquired the observation that our results are nearly impossible to discern any differences between the real CFIs and the generated CFIs in the view of ophthalmologists. © 2024, The Authors. All rights reserved.

关键词： Ophthalmology

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Automated sleep apnea detection in pregnant women using wavelet-based features

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informatics in Medicine Unlocked 2022年 32卷

作者： Sharma, Manish Bapodara, Sagar Tiwari, Jainendra Acharya, U. Rajendra Department of Electrical and Computer Science Engineering Institute of Infrastructure Research and Management (IITRAM) Technology Ahmedabad India Department of Electronics and Computer Engineering Ngee Ann Polytechnic Singapore 599489 Singapore Department of Bioinformatics and Medical Engineering Asia University Taiwan Department of Biomedical Engineering School of Science and Technology SUSS University Singapore

Sleep apnea is a severe sleep disorder that degrades the quality of sleep and causes patients to feel restless and fatigued even after a full night's sleep. Sleep-disordered breathing (SDB) research has grown its importance in recent decades, owing to patients' substantial health effects and poor quality of life. Obstructive sleep apnea (OSA), the most prevalent kind of sleep disorder, that is caused due to a partial blockage in the nasal airway, resulting in low oxygen levels in the patient's blood. The drop in oxygen level may cause high blood pressure, which can lead to major cardiac issues. Further, OSA is also a risk for pregnancy complications in first-time pregnant women (nulliparous pregnancy), as emerging research and studies suggest a strong relationship between sleep-disordered breathing patients and critical pregnancy outcomes like preeclampsia, preterm birth, gestational diabetes, and intrauterine growth retardation. Polysomnography (PSG) recordings with multiple-channel various modalities signals are often used to detect sleep problems. This study aims to detect sleep apnea in nulliparous pregnant women using a simplified PSG-based system that employed only airflow signals. We used the ‘nulliparous pregnancy outcomes study monitoring mothers-to-be (nuMOM2b)’ database, containing 3012 women subjects for sleep-disordered breathing (SDB) related sub-studies. To the best of our knowledge, this is the first computer-aided diagnostic tool developed to automatically identify sleep apnea in pregnant women. In this work on the detection of OSA, we proposed the use of a single-channel airflow (AF) signal in combination with an orthogonal wavelet filter bank. The L₁ − norm features are extracted from wavelet coefficients. The selected features are then fed into various machine learning classifiers to automatically detect apnea. The developed model obtained a highest accuracy of 83.9% with an F1 score of 0.91 using the RUSBoosted tree classifier with te

关键词： nulliparous pregnancy obstructive sleep apnea pregnancy sleep apnea sleep-disordered breathing

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Digital Twin Definition for Detecting Abnormal Perspiration Patterns in High-Stress Professions

Digital Twin Definition for Detecting Abnormal Perspiration ...

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Roedunet International Conference (RoEduNet)

作者： Cristian Vizitiu Ioan Turcin Kevin Alexander Dominey Selver Softic Adrian-Cătălin Dinculescu Vlad I. Bocăneţ Cosmin Dugan Sorin-Aurel Moraru The Space Applications and Technologies Laboratory Institute of Space Science -Subsidiary of INFLPR Magurele Romania Department of Automatics and Information Technology Faculty of Electrical Engineering and Computer Science Transilvania University of Brasov Brasov Romania Department Automation Technology CAMPUS 02 University of Applied Sciences Graz Austria Department IT & Business Informatics CAMPUS 02 University of Applied Sciences Graz Austria Department of Manufacturing Engineering Faculty of Industrial Engineering Robotics and Production Management Technical University of Cluj-Napoca Cluj-Napoca Romania Bucharest University Emergency Hospital Bucharest Romania

ISBN: (数字)9798331540388

ISBN: (纸本)9798331540395

Perspiration is a physiological response in high-stress situations, that also plays a key role in thermoregulation and stress management. Understanding perspiration patterns is used for assessing physiological responses and optimizing human performance especially in professions such as emergency medical services and military operations, as well as for individuals exposed to Isolated Confined Extreme (ICE) environments such as astronauts and space analog mission participants. This paper defines a Digital Twin (DT) model, that will be integrated with an existing physical sweat simulator module (SSM) prototype. Its goal is to simulate human perspiration under high-stress conditions by integrating environmental, physiological, and psychological data. The system architecture is developed via Model-Based Systems engineering (MBSE). The scope of the designed virtual model is to accurately reflect physical perspiration, detect abnormal perspiration patterns, and generate recommendations regarding stress countermeasures. The goal is to influence the behavioral and/or physiological state of the physical twin, thereby creating a dynamic, responsive system. Machine learning (ML) models will be used to identify patterns in the environmental, physiological, and psychological inputs and predict sweat volume, chemical composition, and regional sweat distributions across the body. ML will also be used to detect abnormal perspiration patterns based on input data. The application of generative AI for creating test data from simulation templates is anticipated to facilitate the comparative analysis of two to three distinct profiles, such as astronauts, soldiers, and paramedics or firefighters, using simulated values. The SSM, with its integrated sensors, will serve the dual purpose of validating the model and providing realistic physical simulation.

关键词： Adaptation models Generative AI Psychology Human factors Machine learning Predictive models Physiology Digital twins Modeling Biomedical monitoring

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