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arXiv 2024年

作者： Moazemi, Sobhan Adams, Tim Ng, Hwei Geok Kühnel, Lisa Schneider, Julian Näher, Anatol-Fiete Fluck, Juliane Fröhlich, Holger Department of Bioinformatics Fraunhofer Institute for Algorithms and Scientific Computing SCAI Germany ZB MED – Information Centre for Life Sciences Germany Faculty of Technology Bielefeld University Germany Digital Global Public Health Hasso Plattner Institute for Digital Engineering University of Potsdam Germany Institute of Medical Informatics Charité–Universitätsmedizin Germany Method Development Research Infrastructure and Information Technology Robert Koch Institute Germany The Agricultural Faculty University of Bonn Germany Institute for Geodesy and Geoinformation University of Bonn Germany University of Bonn Germany

Individual health data is crucial for scientific advancements, particularly in developing Artificial Intelligence (AI);however, sharing real patient information is often restricted due to privacy concerns. A promising solution to this challenge is synthetic data generation. This technique creates entirely new datasets that mimic the statistical properties of real data, while preserving confidential patient information. In this paper, we present the workflow and different services developed in the context of Germany’s National Data Infrastructure project NFDI4Health. First, two state-of-the-art AI tools (namely, VAMBN and MultiNODEs) for generating synthetic health data are outlined. Further, we introduce SYNDAT (a public web-based tool) which allows users to visualize and assess the quality and risk of synthetic data provided by desired generative models. Additionally, the utility of the proposed methods and the web-based tool is showcased using data from Alzheimer’s Disease Neuroimaging Initiative (ADNI) and the Center for Cancer Registry Data of the Robert Koch Institute (RKI). © 2024, CC BY-SA.

关键词： Electronic health record

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Retinal Width Estimation of High-Resolution Fundus Images For Diabetic Retinopathy Detection

Retinal Width Estimation of High-Resolution Fundus Images Fo...

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IEEE EMBS Conference on Biomedical engineering and Sciences (IECBES)

作者： Aziah Ali W Mimi Diyana W Zaki Aini Hussain Wan Haslina Wan Abdul Halim Faculty of Computing and Informatics Multimedia University Cyberjaya Selangor Malaysia Faculty of Engineering and Built Environment Smart Engineering Systems Research Group Universiti Kebangsaan Malaysia Selangor Malaysia University Kebangsaan Malaysia Medical Centre (UKMMC) Kuala Lumpur Malaysia

ISBN: (数字)9781728142456

ISBN: (纸本)9781728142463

Studies have shown some correlations between retinal vessel morphologies and multiple systemic diseases. While this could pave the way to timely diagnosis of such diseases by examining vessels from fundus images, practical application of measuring and quantifying changes in vessel width over time remains a challenge. In this study, we propose a semi-automated estimation method to efficiently summarize vessel width characteristics from fundus images. The method consists of retinal vessel segmentation, optic disc (OD) localization and vessel width parameters estimation. The proposed method is validated using a public database of high-resolution fundus images called HRF, where the significance of obtained vessel width parameters in differentiating the three image groups in the database are analysed. Results indicate that the obtained parameter using the method that summarises the ratio between width of veins to arteries, AVR (Artery-Vein Ratio) can be used to differentiate images from patients with Diabetic Retinopathy against healthy and glaucomatous patients.

关键词： Image segmentation Retinopathy Databases Veins Estimation Retinal vessels Diabetes

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Breast Cancer Detection and Classification on Mammogram Images Using Morphological Approach

Breast Cancer Detection and Classification on Mammogram Imag...

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International Conference on engineering Technology and their Applications (IICETA)

作者： Wan Azani Mustafa Arrif Amirul Azmi Mohd Aminudin Jamlos Hiam Alquran Wan Khairunizam Shahrina Ismail Ahmed Alkhayyat Juhara Haron Faculty of Electrical Engineering Technology Advanced Computing (COE) Universiti Malaysia Perlis (UniMAP) Arau Perlis Malaysia Faculty of Electrical Engineering Technology Universiti Malaysia Perlis (UniMAP) Arau Perlis Malaysia Faculty of Electronic Engineering Technology Engineering Centre of Excellence (ACE) University Malaysia Perlis Malaysia Biomedical Systems and Medical Informatics Engineering Yarmouk University Irbid Jordan Faculty of Science and Technology Universiti Sains Islam Malaysia (USIM) Bandar Baru Nilai Negeri Sembilan Malaysia Faculty of Engineering The Islamic University Najaf Iraq School of Medical Sciences Universiti Sains Malaysia Kota Bharu Kelantan Malaysia

ISBN: (数字)9781665472159

ISBN: (纸本)9781665472166

Breast cancer is one of the most common cancers affecting women worldwide. Mammography is the most well-known and effective method to detect early signs of breast cancer. The purpose of this paper is to detect breast cancer on the mammogram image to classify the disease through morphological techniques. Using conventional methods makes radiology difficult to detect cancer found in the patient’s breast. This proposal can be divided into several elements, which are input database, image preprocessing, image segmentation, morphological analysis, and object recognition. First, image preprocessing will be done using the Weiner and Median filters. Second, the thresholding method for image segmentation will be performed, and lastly, morphology will remove imperfections introduced during the image segmentation process. Finally, the image is classified into two classes: normal and cancerous images. A median filter and 0.95 thresholding achieve an accuracy of 93.71%, a sensitivity of 94.36%, and a specificity of 82.53% for the cancerous images.

关键词： Image segmentation Sensitivity Morphology Radiology Breast cancer Mammography Proposals

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Detection of Polycystic Ovary Syndrome (PCOS) Using Machine Learning Algorithms

Detection of Polycystic Ovary Syndrome (PCOS) Using Machine ...

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International Conference on engineering Technology and their Applications (IICETA)

作者： Dana Hdaib Noor Almajali Hiam Alquran Wan Azani Mustafa Waleed Al-Azzawi Ahmed Alkhayyat Department of Biomedical Engineering Jordan University Of Science And Technology Irbid Jordan Biomedical Systems and Medical Informatics Engineering Yarmouk University Irbid Jordan Faculty of Electrical Engineering Technology Univerisiti Malaysia Perlis (UniMAP) Pauh Putra Campus Advanced Computing Centre of Excellence Universiti Malaysia Perlis (UniMAP) Pauh Putra Campus Arau Perlis Malaysia Medical technical college Al-Farahidi University Baghdad Iraq Faculty of Engineering The Islamic University Najaf Iraq

ISBN: (数字)9781665472159

ISBN: (纸本)9781665472166

One of the most common diseases in women of reproductive age is Polycystic Ovary Syndrome (PCOS). PCOS diagnosis can be tricky, because not everyone with PCOS has polycystic ovaries (PCO), nor does everyone with ovarian cysts have PCOS, hence the pelvic ultrasound as a stand-alone diagnosis is not sufficient. The full diagnostic plan is mainly a combination of a pelvic ultrasound besides blood tests of specific parameters that indicate the presence of PCOS. Since PCOS is a hard-to-diagnose widespread hormonal disorder, blood tests, symptoms, and other parameters with the help of a computer can form a new and easy method to diagnose it. Therefore, we had successfully built a high performing diagnostic model using MATLAB. The data was obtained from the website Kaggle, and the dataset is called Polycystic Ovary Syndrome. In this paper various machine algorithms were employed by utilizing seven classifiers. Results demonstrated that Linear Discriminant classifier exhibits the best performance in terms of accuracy, while in terms of sensitivity, the KNN classifier had the best result. Also, a comparison with four other research papers that exploited the same PCOS dataset was done in terms of implementation platforms, evaluation methods, classifiers, classes, accuracy, and precision of each classifier. Our research excelled among all in terms of accuracy and varied in precedence with precision. MATLAB had shown substantial results and a great model fitting embedded approaches, scoring a high accuracy and precision outcome compared to other studies. Other improvements on the overall PCOS prediction can involve employing preprocessed ultrasound images with the features presented in the dataset.

关键词： Solid modeling Ultrasonic imaging Sensitivity Fitting Predictive models Feature extraction Prediction algorithms

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A Toolbox of Feminist Wonder: Theories and methods that can make a difference 26

A Toolbox of Feminist Wonder: Theories and methods that can ...

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26th ACM Conference on Computer-Supported Cooperative Work and Social computing, CSCW 2023

作者： Hansson, Karin Bardzell, Shaowen Bhandari, Aparajita Boulicault, Marion Doyle, Dylan Thomas Erete, Sheena Cerratto Pargman, Teresa Lazem, Shaimaa Muller, Michael Normark, Maria Petterson, Adrian Rosén, Anton Poikolainen Taylor, Alex S. Thomas, Jakita O. Watson, Julia Södertörn University The School of Natural Sciences Technology and Environmental Studies Sweden Georgia Institute of Technology The School of Interactive Computing United States Cornell University Department of Communication United States University of Edinburgh Department of Philosophy United States University of Colorado Boulder Department of Information Science United States University of Maryland College of Information United States Stockholm University The Department of Computer and Systems Sciences Sweden City for Scientific Research and Technological Applications The Informatics Research Institute Egypt Ibm Research Human Centered Ai United States Uppsala University Department of Informatics and Media Sweden University of Toronto Faculty of Information Canada Aalto University The School of Arts Design and Architecture Finland City University of London The Centre for Human Computer Interaction Design United Kingdom Auburn University Department of Computer Science and Software Engineering United States University of Toronto Department of Computer Science Canada

ISBN: (纸本)9798400701290

This one-day hybrid workshop builds on previous feminist CSCW workshops to explore feminist theoretical and methodological approaches that have provided us with useful tools to see things differently and make space for change. Since its inception over a decade ago, feminist HCI has progressed from the margins to mainstream HCI, with numerous references in the literature. Feminist HCI has also evolved to incorporate other critical HCI practices such as Queer HCI, participatory design, and speculative design. While feminist approaches have grown in popularity and become mainstream, it is getting more difficult to distinguish the feminist emancipatory core from other attempts of developing and improving society in various ways. In this workshop, we therefore want to revisit our feminist roots, where theory is a liberatory and creative practice, motivated by affect, curiosity, and wonder. From this standpoint, we consider which of our feminist tools can make a significant difference today, in a highly datafied world. The goal of this workshop is to;1) create an inventory of feminist theories and concepts that have had an impact on our work as designers, educators, researchers, and activists;2) develop a feminist toolbox for the CSCW community to strengthen our feminist literacy. © 2023 Owner/Author.

关键词： Computation theory

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Machine Learning and Computer Vision Techniques in Continuous Beehive Monitoring Applications: A Survey

arXiv

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arXiv 2022年

作者： Bilik, Simon Zemcik, Tomas Kratochvila, Lukas Ricanek, Dominik Richter, Miloslav Zambanini, Sebastian Horak, Karel Department of Control and Instrumentation Faculty of Electrical Engineering and Communication Brno University of Technology Technická 3058/10 Brno61600 Czech Republic Computer Vision and Pattern Recognition Laboratory Department of Computational Engineering Lappeenranta-Lahti University of Technology LUT Yliopistonkatu 34 Lappeenranta53850 Finland Computer Vision Lab Institute of Visual Computing & Human-Centered Technology Faculty of Informatics TU Wien Favoritenstr. 9/193-1 ViennaA-1040 Austria

Wide use and availability of machine learning and computer vision techniques allows development of relatively complex monitoring systems in many domains. Besides the traditional industrial domain, new applications appears also in biology and agriculture, where they may be used to detect infections, parasites and weeds, but also for automated monitoring and early warning systems. This goes in concordance with the introduction of the easily accessible hardware and development kits such as the Arduino, or RaspberryPi families. In this paper, we survey 50 papers focusing on the methods of automated beehive monitoring using computer vision techniques. Particularly on the pollen and Varroa mite detection together with the bee traffic monitoring. Such systems could also be used for monitoring of honeybee colonies and for the inspection of their health state, which could potentially identify dangerous states before the situation is critical, or to better plan periodic bee colony inspections and therefore save significant costs. Further on, we also include analysis of the research trends in this application field and we outline the possible directions of new development. Our paper is also aimed at veterinary and apidology professionals and experts, who may not be familiar with machine learning to introduce them to its capabilities, hence each family of techniques is prefaced by a brief theoretical introduction and motivation related to its base method. We hope that this paper will inspire other scientists to use machine learning techniques for other applications in beehive monitoring. © 2022, CC BY.

关键词： Computer vision

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Evaluation of KNN-NMF Algorithm for Recommendation Systems in E-Commerce

Evaluation of KNN-NMF Algorithm for Recommendation Systems i...

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IEEE Conference on Systems, Process & Control (ICSPC)

作者： Hew Sok Yen Samuel-Soma M. Ajibade Muhammed Basheer Jasser Bayan Issa Yoke Leng Yong Faizah Mohammed Bashir Emmanuel Falude Yakubu Aminu Dodo Department of Computing and Information Systems School of Engineering and Technology Sunway University Bandar Sunway Selangor Darul Ehsan Malaysia Research Centre for Nanomaterials and Energy Technology (RCNMET) School of Engineering and Technology Sunway University Petaling Jaya Selangor Malaysia Faculty of Informatics Engineering University of Aleppo Syria School of Information and Communication Technology HELP University Persiaran Cakerawala Subang Bestari Seksyen Shah Alam Malaysia Department of Decoration and Interior Design Engineering College of Engineering University of Hail Hail Kingdom of Saudi Arabia Malaysia-Japan International Institute of Technology (MJIIT) Universiti Teknologi Malaysia Jalan Sultan Yahya Petra Kuala Lumpur Malaysia Architectural Engineering Department College of Engineering Najran University Najran Kingdom of Saudi Arabia

ISBN: (数字)9798350391398

ISBN: (纸本)9798350391404

Due to growth in e-commerce, recommender systems have gained importance in providing recommendations to the user to help him or her make a choice. These systems not only assist the users in searching through the extensive product list but also increase businesses’ productivity by recommending products. The aim of this project is to suggest a hybrid recommender system which combines the aspects of collaborative and content-based filtering so as to enhance the efficiency of the recommendation system in e-commerce. In addition, it is to evaluate the performance of this model with the individual collaborative and content-based models. The study involved creating the CF and CBF models with the various algorithms like the Matrix Factorization, KNN, SVD, and the TF-IDF vectorization. The best models from CF and CBF were then combined with a Hybrid Filtering (HF) model by using weighted and switching combination to enhance the model’s effectiveness. The performance of the CF, CBF and the HF models was compared, in order to check if the proposed hybrid approach is better than the existing models. The results of the experiment show that the hybrid switching model provided better results than the CF and CBF models on their own. The hybrid filtering model had also managed to reduce the RMSE to $\mathbf{1. 0 6 5}$ and improved precision to 83.2%. This demonstrates the advantage of the proposed model over the traditional CF and CBF approaches. Our study validates that the hybrid filtering recommender system, especially the switching combination strategy, outperformed the traditional standalone models by integrating the benefits of both collaborative and content-based filtering. This provides a good starting point for future work and there are many directions in which the research can be expanded.

关键词： Productivity Machine learning algorithms Filtering Collaboration Switches Hafnium Nearest neighbor methods Electronic commerce User preference Recommender systems

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Tripartite measurement uncertainty in Schwarzschild space-time

arXiv

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arXiv 2024年

作者： Dolatkhah, Hazhir Czerwinski, Artur Ali, Asad Al-Kuwari, Saif Haddadi, Saeed RCQI Institute of Physics Slovak Academy of Sciences Dúbravská cesta 9 Bratislava84511 Slovakia Institute of Physics Faculty of Physics Astronomy and Informatics Nicolaus Copernicus University in Torun ul. Grudziadzka 5 Torun87–100 Poland Qatar Centre for Quantum Computing College of Science and Engineering Hamad Bin Khalifa University Doha Qatar Faculty of Physics Semnan University P.O. Box 35195-363 Semnan Iran Saeed’s Quantum Information Group P.O. Box 19395-0560 Tehran Iran

The effect of Hawking radiation on tripartite measurement uncertainty in a Schwarzschild black hole background is analyzed in this study. Two scenarios are examined. In the first, quantum memory particles approach a Schwarzschild black hole and are positioned near the event horizon, while the particle being measured remains in the asymptotically flat region. In the second scenario, the measured particle moves toward the black hole, and the quantum memories stay in the asymptotically flat region. This study considers two initial quantum states, namely GHZ and W states. Our findings reveal that in both cases, measurement uncertainty increases steadily with rising Hawking temperature. When comparing the GHZ and W states, the GHZ state initially exhibits lower measurement uncertainty at low Hawking temperatures than the W state, indicating greater resilience to Hawking radiation. Additionally, when the quantum memories remain in the asymptotically flat region while the measured particle falls toward the black hole, the uncertainties for GHZ and W states do not align at high temperatures. The GHZ state consistently demonstrates lower measurement uncertainty, showcasing its superior robustness against Hawking radiation. © 2024, CC BY.

关键词： Radiation effects

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Assistive Technical Communication of Montenegrin eServices: a Case Study

Assistive Technical Communication of Montenegrin eServices: ...

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International Scientific-Professional Conference on Information Technology (IT)

作者： Aniko Simon Luka Laković Pavle Kovačević Peter A. Kara Ivana Ognjanović Ramo Šendelj Christoph Reich Milovan Roganović John Mantas Laszlo Bokor Sigma Technology 1093 Budapest Hungary Faculty for Information Systems and Technologies University of Donja Gorica 81000 Podgorica Montenegro Budapest University of Technology and Economics 1111 Budapest Hungary Department of Networked Systems and Services Faculty of Electrical Engineering and Informatics Budapest University of Technology and Economics 1111 Budapest Hungary Department of Computer Science School of Computer Science and Mathematics Faculty of Science Engineering and Computing Wireless Multimedia and Networking Research Group Kingston University London KT1 2EE UK Humanistic Studies University of Donja Gorica 81000 Podgorica Montenegro Institute of Data Science Cloud Computing and IT Security Furtwangen University 78120 Furtwangen Germany Clinic for Neurology Clinical Centre of Montenegro 81000 Podgorica Montenegro Faculty of Nursing School of Health Sciences National and Kapodistrian University of Athens 11527 Goudi Greece

With the advancement of information technology in the recent decades, digital and online services have become essential components of everyday life in Europe. Montenegro is no different in this regard. However, as digital literacy in the country is still in a developing phase in comparison with most countries of the EU, a higher portion of its citizens may rely on assistive technical communication. In this paper, we provide a case study on the assistive technical communication of Montenegrin eServices. The work addresses the sectors of online banking, healthcare services, higher education, national administrative services, and utilities. Our findings highlight the diversity in terms of implemented assistive technical communication, and distinguish usage contexts on the level of adequacy. Yet as the functionalities of Montenegrin eServices are expected to gradually expand in the upcoming years, assistive technical communication must aim to handle the higher extents of service complexity.

关键词： Online banking Education Europe Medical services Complexity theory Information technology

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Evaluation of the Montenegrin Academic Digital Innovation Hub

Evaluation of the Montenegrin Academic Digital Innovation Hu...

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International Scientific-Professional Conference on Information Technology (IT)

作者： Luka Laković Peter A. Kara Ivana Ognjanović Laszlo Bokor Ramo Šendelj Christoph Reich Milovan Roganović Emmanouil Zoulias Jevto Eraković Tanja Radusinović Nada Rakočević Aniko Simon Faculty for Information Systems and Technologies University of Donja Gorica 81000 Podgorica Montenegro Department of Networked Systems and Services Faculty of Electrical Engineering and Informatics Budapest University of Technology and Economics 1111 Budapest Hungary Department of Computer Science School of Computer Science and Mathematics Wireless Multimedia and Networking Research Group Faculty of Science Engineering and Computing Kingston University London KT1 2EE UK Humanistic Studies University of Donja Gorica 81000 Podgorica Montenegro Institute of Data Science Cloud Computing and IT Security Furtwangen University 78120 Furtwangen Germany Clinic for Neurology Clinical Centre of Montenegro 81000 Podgorica Montenegro Faculty of Nursing School of Health Sciences National and Kapodistrian University of Athens 11527 Goudi Greece Chamber of Economy of Montenegro 81000 Podgorica Montenegro Sigma Technology 1093 Budapest Hungary

In the era of global digitalization, digital innovation hubs have become essential, as they function as transforming forces. In this paper, we provide an analysis of the Montenegrin Academic Digital Innovation Hub by applying multi-criteria qualitative evaluation over several criteria: process flow of fully digitized operations of the Hub (with aspects of the innovation life-cycle, ethics, and security), resource management, strategic goals, and feasibility. The paper also proposes a model of comprehensive resource management via the perspective of smart specialization priorities of Montenegro.

关键词： Technological innovation Ethics Resource management Security Information technology

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