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2023 Conference on Lasers and Electro-Optics, CLEO 2023

作者： Senanian, Alen Wright, Logan G. Wade, Peter F. Doyle, Hannah K. McMahon, Peter L. Department of Physics Cornell University IthacaNY14853 United States School of Applied and Engineering Physics Cornell University IthacaNY14853 United States NTT Physics and Informatics Laboratories NTT Research Inc. SunnyvaleCA94085 United States School of Electrical and Computer Engineering Cornell University IthacaNY14853 United States Kavli Institute Cornell for Nanoscale Science Cornell University IthacaNY14853 United States

We present a programmable analog simulator with up to 105 lattice sites capable of simulating 2D and 3D lattices, as well as lattices with non-planar connectivity. We also demonstrate the injection of arbitrary lattic...

ISBN: (纸本)9781957171258

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First steps towards modeling the interaction of cardiovascular agents and smooth muscle activation in arterial walls

First steps towards modeling the interaction of cardiovascul...

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92nd Annual Meeting of the International Association of applied Mathematics and Mechanics, GAMM 2022

作者： Nurani Ramesh, S. Uhlmann, K. Saßmannshausen, L. Rheinbach, O. Klawonn, A. Heinlein, A. Balzani, D. Civil and Environmental Engineering Ruhr University Bochum Universitätsstraße 150 Bochum44801 Germany University of Cologne Department of Mathematics and Computer Science Cologne Germany Technische Universität Bergakademie Freiberg Department of Mathematics and Informatics Freiberg Germany Delft Institute of Applied Mathematics Delft University of Technology Mekelweg 4 Delft2628 CD Netherlands

Numerical simulation of the response of healthy and pathological arteries to cardiovascular agents can provide valuable information to the physician in the treatment of diseases such as hypertension, atherosclerosis, and the Marfan syndrome. Here, we provide a first step towards a computational framework to model the effects of antihypertensive agents on the mechanical response of arterial walls. A material model is developed by extending an existing formulation for wall tissue to incorporate the effects of calcium-ion channel blockers. The resulting coupled deformation-diffusion problem is then solved using the finite element method. Simulation results with drug activity show that, indeed, an increased lumen diameter due to reduced contraction is obtained. Additionally, a decrease in the rate of arterial contraction is observed, which is also consistent with expected behavior. Finally, we compare results for an implicit or explicit treatment of the the deformation-diffusion coupling, and we observe that both coupling schemes yield comparable results for a wide range of time step sizes. © 2023 The Authors. Proceedings in applied Mathematics & Mechanics published by Wiley-VCH GmbH.

关键词： Diseases

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Multi-Parameter Analysis of Biosignals in Long-Term Control Medical Devices Management

Multi-Parameter Analysis of Biosignals in Long-Term Control ...

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Human-computer Interaction, Optimization and Robotic Applications (HORA), International Congress on

作者： Serhii Holdobin Natalia Brynza Olha Tiutiunyk Olena Serhiienko Ihor Sosnov Pavlo Samus Industrial and Biomedical Electronics Department Kharkiv polytechnic institute National Technical University Kharkiv Ukraine Department of Informatics and Computer Technology Simon Kuznets Kharkiv National University of Economics Kharkiv Ukraine Department of Applied Mathematics Kharkiv National University of Radio Electronics Kharkiv Ukraine Department of Informatics and Computer Engineering Simon Kuznets Kharkiv National University of Economics Kharkiv Ukraine Business Trade and Logistics Department Kharkiv Polytechnic Institute National Technical University Kharkiv Ukraine

ISBN: (数字)9798331510886

ISBN: (纸本)9798331510893

The development of data processing technologies, microelectronics and sensor systems allows for high-precision multiparametric analysis of biosignals in real time. The paper considers the problem of automating medical processes to reduce the influence of the human factor and increase the accuracy of diagnostics. An improved method of multiparametric analysis of biosignals is proposed for long-term monitoring of the state of the cardiovascular system using modern sensor devices, data processing algorithms and artificial intelligence technologies. The research is aimed at improving the methods of collecting, transmitting and analyzing biosignals, which contributes to the creation of personalized medical devices and effective prediction of cardiovascular pathologies. The issues of classification of devices and biosignals, as well as their mathematical modeling to increase the accuracy of diagnostics, are considered.

关键词： Cardiovascular system Wireless sensor networks Accuracy Medical devices Heuristic algorithms Robot sensing systems Prediction algorithms Real-time systems Classification algorithms Monitoring

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Comparison of Google Analytics with Similar Web for Statistical Analysis of Website Traffic 1st

Comparison of Google Analytics with Similar Web for Statisti...

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1st International Conference on Advanced Network Technologies and Computational Intelligence, ICANTCI 2024

作者： Garg, Bindu Kasar, Manisha Kotecha, Ketan Rahmani, Mohammad Khalid Imam University College of Engineering Pune India Symbiosis Center for Applied Artificial Intelligence Symbiosis Institute of Technology Symbiosis International University Pune India Department of Computer Science College of Computing and Informatics Saudi Electronic University Riyadh Saudi Arabia

ISBN: (纸本)9783031860713

This study addresses the burgeoning demand for website data collection and analysis in business operations, emphasizing the pivotal role of web analytics in providing crucial insights into customer behaviour. Despite the prevalence of various website traffic analysing tools, there exists a notable lack of comprehensive testing on the precision and reliability of these services. The research compares four key analytics measures from Google Analytics (GA) with SimilarWeb (SW) using average monthly statistics over a one-year period for 50+ websites across different nations and business verticals. Employing various statistical tests, including paired t-tests, Shapiro-Wilk tests, and correlation analysis, the study reveals statistically significant variations in key metrics between GA and SW. Notably, SimilarWeb tends to provide lower estimates for the number of visits and unique visitors compared to Google Analytics. However, the findings underscore the potential complementarity of using both tools in cases where direct access to a site’s analytics is not feasible, particularly for competitive intelligence and benchmarking purposes. The study’s extensive statistical analysis covers unique visitors, total visits, bounce rates, and average session duration, offering valuable insights into the nuanced variations and potential sources of error. There are statistically significant differences between the two web traffic tools in terms of unique visitors, total visits, average session time & bounce rates. In comparison to Google Analytics, SimilarWeb figures were on average, 40% less for unique visitors, 50% greater for average session time, 25% greater for bounce rate, and 20% lesser for total visits. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Error statistics

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Improving Communication Systems Through Federated Learning: An Optimization of Decision-Making

Improving Communication Systems Through Federated Learning: ...

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IEEE World Symposium on Communication engineering (WSCE)

作者： Konstantinos D. Stergiou Yutaka Ishibashi Sotirios K. Goudos Thomas Lagkas Panagiotis Sarigiannidis Konstantinos E. Psannis Department of Applied Informatics University of Macedonia Thessaloniki Greece Graduate School of Engineering Nagoya Institute of Technology Nagoya Japan Department of Physics Aristotle University of Thessaloniki Thessaloniki Greece Department of Computer Science International Hellenic University Kavala Greece Department of Informatics and Telecommunications Engineering University of Western Macedonia Kozani Greece

This paper discusses the use of federated learning as a method for optimizing decision-making in communication systems. Federated learning is a machine learning technique that enables the training of models on decentralized data, allowing for the collection and analysis of data from multiple sources while maintaining the privacy and security of the data. This approach is particularly useful in communication systems, as it allows for the optimization of decision-making across a wide range of devices and networks. The paper examines the advantages of federated learning, including the ability to collect a large amount of data from a diverse range of devices, the protection of sensitive data, and the ability to adapt to changing conditions in real-time. The paper also provides specific examples of how federated learning can be used in the optimization of mobile networks and content delivery. The conclusion highlights the growing importance of federated learning in improving communication systems.

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How we can use text classification in the Back-Office environment of a bank as ‘business as usual’ solution

How we can use text classification in the Back-Office enviro...

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International Symposium on applied Computational Intelligence and informatics ( SACI)

作者： Zsolt Krutilla Attila Kovari Doctoral School of Applied Informatics and Applied Mathematics Óbuda University Budapest Hungary Informatics Institution University of Dunaújváros Dunaújváros Hungary Institute of Engineering Alba Regia Technical Faculty Óbuda University Budapest Hungary Institute of Digital Technology Faculty of Informatics Eszterházy Károly Catholic University Eger Hungary Department of Natural Science Institute of Engineering University of Dunaújváros Dunaújváros Hungary GAMF Faculty of Engineering and Computer Science John von Neumann University Kecskemét Hungary

Natural Language Processing nowadays provides scientists with many research areas and opportunities, but as with most applied sciences, our goal in Natural Language Processing is to refine the underlying science and technology until it can be used reliably for business purposes. Transformer models, such as GPT or BERT, are currently showing outstanding results in the field of natural-language processing, but they require huge computational power and data to teach, but these conditions can only be met by larger research centers and large companies, and their inaccuracy makes them unsuitable for use as a ‘business as usual’ (BAU) solution. In this paper, we present a solution that is able to overcome these problems by focusing on accuracy and usability, and that can also bring a new perspective to the process of teaching deep learning models.

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An Enhanced Residual Networks Based Framework for Early Alzheimer’s Disease Classification and Diagnosis 6th

An Enhanced Residual Networks Based Framework for Early Alzh...

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6th International Conference on Communication, Devices and Networking, ICCDN 2022

作者： Awotunde, Joseph Bamidele Sur, Nath Samarendra Imoize, Agbotiname Lucky Misra, Sanjay Gaber, Tarek Department of Computer Science Faculty of Information and Communication Sciences University of Ilorin Kwara State Ilorin240003 Nigeria Department of Electronics and Communication Engineering Sikkim Manipal Institute of Technology Sikkim Manipal University East Sikkim Rangpo Majitar737136 India Department of Electrical and Electronics Engineering Faculty of Engineering University of Lagos Akoka Lagos100213 Nigeria Department of Electrical Engineering and Information Technology Institute of Digital Communication Ruhr University Bochum44801 Germany Department of Applied Data Science Institute of Energy Technology Halden Norway Faculty of Computers and Informatics Suez Canal University Ismailia41522 Egypt School of Science Engineering and Environment University of Salford ManchesterM5 4WT United Kingdom

ISBN: (纸本)9789819919826

Alzheimer's disease (AD) is a long-lasting, degenerative brain illness for which there is now no successful treatment. However, there are medications that can delay its growth and stop its tracks. Therefore, stopping and regulating the progression of AD depends greatly on its early diagnosis. computer-aided diagnosis (CAD) in health care has become more and more dependent on deep learning technologies. This has help in early diagnosis and detection of various disease in health care. New techniques for the early diagnosis of neurocognitive abnormalities are now possible thanks to advancements in medical imaging and computational capacity in an effort to stop or slow down cognitive aging. Therefore, this study proposes a residual network (ResNet) type 50 for early diagnosis of AD. Convolutional neural networks (CNNs) are first used in the study for features extraction and then make the ultimate diagnosis using the ResNet-50. To create a better architecture, the problem of AD diagnosis will be addressed by utilizing and incorporating ResNet-50, which improves the current categorization problem's accuracy to its maximum. With a 98.8% accuracy rate, the proposed model using ResNet-50 produces encouraging findings for AD prediction. The study reveals that the model can effectively circumvent CNNs for the diagnostic issue with AD. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： computer aided diagnosis

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Numerical Approach of Laser-Induced Spherical Microcavitation Bubbles: Effect of Enthalpy

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Bulletin of the Russian Academy of Sciences: Physics 2024年第Suppl 3期88卷 S287-S292页

作者： Ali F. Abu-Bakr Abu-Nab, Ahmed K. AbuShaeer, Zain Department of Mathematics and Computer Science Faculty of Science Menoufia University Shebin El-Koom32511 Egypt Phystech School of Applied Mathematics and Informatics Moscow Institute of Physics and Technology Dolgoprudny Moscow141700 Russia Center for Photonics and 2D Materials Moscow Institute of Physics and Technology Dolgoprudny141700 Russia Department of Basic Science Higher Institute of Engineering and Technology Kafrelsheikh33511 Egypt

Abstract: Over the past decades, interest has increased in studying different techniques to detect the importance of microbubbles in many industrial and medical applications such as mechanisms of hydraulic machinery cavitation erosion, histotripsy, and lithotripsy. The behavior from early irregular oscillations to late regular oscillations of spherical microcavitation bubbles produced by lasers in water was studied by means of plasma photography. Repetition of such delayed oscillations results in quantities of gas/vapour inside the spherical bubbles. Using the modified Gilmore model of cavitation microbubble dynamics, calculations were made for the laser pulse energy, microbubble radius behavior, and microbubble growth rate. Additionally, the pressure evolution inside the bubble, and pressures distribution surrounding of microbubbles in liquid are investigated under the effect of different values of initial cavitation of microbubble radius. The effect of enthalpy is considered into account. The proposed model is solved numerically using numerical techniques as a finite difference method. In addition, the radius of the microbubbles is calculated as a function of time under the influence of the initial radius of these laser-induced microcavitations. Moreover, some physical properties and numerical calculations of the presented mathematical model for cavitation induced by lasers bubbles are deduced, analyzed and investigated. © Pleiades Publishing, Ltd. 2024.

关键词： Cavitation

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Utilization of IMU-Based Gesture Recognition in the Treatment of Diabetes

Utilization of IMU-Based Gesture Recognition in the Treatmen...

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IEEE International Conference on Automation, Quality and Testing, Robotics, AQTR

作者： Marcell Szá ntó Gergő Strasser Lá szló Szá sz Lehel Dé nes-Fazakas Gyö rgy Eigner Gá bor Kerté sz Levente Ková cs Physiological Controls Research Center &#x00D3 buda University Budap&#x00E9 st Software Engineering Institute John von Neumann Faculty of Informatics &#x00D3 buda University Budapest Biomatics and Applied Artificial Intelligence Institute John von Neumann Faculty of Informatics &#x00D3 Applied Informatics and Applied Mathematics Doctoral School &#x00D3 E&#x00F6 tv&#x00F6 s L&#x00F3 r&#x00E1 nd Research Network Institute for Computer Science and Control Budapest Hungary

ISBN: (数字)9781665479332

ISBN: (纸本)9781665479349

Changes in blood sugar levels are significantly affected by dietary carbohydrate intake, which clearly affects insulin therapy. However, currently available insulin pumps do not perform continuous blood glucose monitoring, and the patient should do so. The quality of the treatment can be further improved if the phenomenon of eating can be recognized based on the characteristic movements, and then by sending an automatic signal of the event to the insulin pump, it can be taken into account that an increase in blood sugar levels is expected. In our work, we focused on this task and, using 4 different tools, collected data from more than 8 people on their hand movements during their meals, on which we performed various data manipulations. Finally, using the aggregation of the obtained data set, we tested different machine learning techniques, from which we will select the optimal algorithm that can recognize the motion characteristic of carbohydrate intake independently of the person with the smallest error. The article presents the tools used for the measurement, the operations performed on the data, as well as the tried and tested machine learning methods and their comparative analysis. Our goal is to improve the quality of life of people with diabetes by increasing the effectiveness of base bolus therapy.

关键词： Medical treatment Insulin pumps Gesture recognition Data collection Diabetes Character recognition Biosensors

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Detection of Misinformation Related to Pandemic Diseases using Machine Learning Techniques in Social Media Platforms

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EAI Endorsed Transactions on Pervasive Health and Technology 2024年 10卷

作者： Naeem, J. Gul, O.M. Parlak, I.B. Karpouzis, K. Salman, Y.B. Kadry, S.N. Department of Computer Engineering Bahcesehir University Istanbul Turkey Informatics Institute Istanbul Technical University Istanbul Turkey Department of Computer Engineering Galatasaray University Istanbul Turkey Panteion University of Social and Political Sciences Greece Department of Software Engineering Bahcesehir University Istanbul Turkey Department of Applied Data Science Noroff University College Kristiansand Norway Ajman University Ajman346 United Arab Emirates Department of Electrical and Computer Engineering Lebanese American University Byblos Lebanon MEU Research Unit Middle East University Amman11831 Jordan

INTRODUCTION: The advent of the COVID-19 pandemic has brought with it not only a global health crisis but also an infodemic characterized by the rampant spread of misinformation on social media platforms. OBJECTIVES: In response to the urgent need for effective misinformation detection, this study presents a comprehensive approach harnessing machine learning and deep learning techniques, culminating in ensemble methods, to combat the proliferation of COVID-19 misinformation on Facebook, Twitter, Instagram, and YouTube. METHODS: Drawing from a rich dataset comprising user comments on these platforms, encompassing diverse COVID-19-related discussions, our research applies Support Vector Machine (SVM), Decision tree, logistic regression, and neural networks to perform in depth analysis and classification of comments into two categories: positive and negative information. The innovation of our approach lies in the final phase, where we employ ensemble methods to consolidate the strengths of various machine learning and deep learning algorithms. This ensemble approach significantly improves the model’s overall accuracy and adaptability. RESULTS: Experimental results underscore the efficacy of our methodology, showcasing marked improvements in detection performance compared to individual models. After applying ensemble learning, we achieve an accuracy of 91% for Facebook data, 79% for Instagram data, 80% for Twitter data and 95% for YouTube data. CONCLUSION: Our system not only aids in curbing the dissemination of COVID-19 misinformation but also provides a robust framework for addressing misinformation across various contexts on social media platforms. © 2024 J. Naeem et al., licensed to EAI.

关键词： COVID-19

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