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International Conference on Information and Communication Technology Convergence (ICTC)

作者： Maria Mahardini Sakanti Viacheslav Siniaev Aurelia Amaris Win-Jet Luo Suhartono C. Bambang Dwi Kuncoro Graduate Institute of Precision Manufacturing National Chin-Yi University of Technology Taichung Taichung Taiwan Institute of Applied Computer Science ITMO University St. Petersburg Russian Federation Department of Informatics Engineering Faculty of Computer Science Universitas Brawijaya Malang Indonesia Ph.D. Program Prospective Technology of Electrical Engineering and Computer Science National Chin-Yi University of Technology Taichung Taichung Taiwan Department of Refrigeration Air Conditioning and Energy Engineering National Chin-Yi University of Technology Taichung Taiwan

ISBN: (数字)9798350364637

ISBN: (纸本)9798350364644

Stress, as a reaction to threatening situations, can raise heart rate and result in serious conditions that might cause significant damage or even be life-threatening. Traditional methods for evaluating stress, which rely on subjective self-reporting and clinical assessments, often suffer from biases and inconsistencies. Artificial intelligence models have been explored to predict stress levels more accurately. This paper investigates the application of Extreme Gradient Boosting in classifying psychological stress using the WESAD dataset, which includes parameters such as acceleration, electrocardiogram, electromyography, electrodermal activity, temperature, and respiration. The dataset was balanced and sampled to create a manageable subset for experimental. Extreme Gradient Boosting was chosen for its efficiency and scalability in handling complex datasets. The model was trained and validated, achieving a 95% accuracy in predicting stress levels. This study highlights the potential of integrating Extreme Gradient Boosting models into wearable devices for real-time stress monitoring. Future work involves optimizing the model to utilize fewer sensors without decreasing accuracy, ensuring it can be integrated into portable/wearable systems using tiny microcontrollers.

关键词： Accuracy Microcontrollers Psychology Human factors Predictive models Boosting Prediction algorithms Real-time systems Sensors Wearable devices

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Acoustic cavitation bubble under influence of frequency ultrasound: Numerical analysis

Acoustic cavitation bubble under influence of frequency ultr...

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Electronic engineering (ICEEM), International Conference on

作者： A. K. Abu-Nab A. F. Abu-Bakr Zain AbuShaeer Department of Mathematics and Computer Science Faculty of Science Menoufia University Shebin 32511 Egypt Phystech School of Applied Mathematics and Informatics Moscow Institute of Physics and Technology Moscow Russia Department of Mathematics and Computer Science Faculty of Science Menoufia University Shebin El-Koom Egypt Department of Basic Science Higher Institute of Engineering and Technology Kafrelsheikh Egypt

This work presents a numerical investigation of acoustic cavitation in Newtonian fluid based on frequency ultrasound. The model of ultrasonic cavitation dynamics is formulated by a modified Rayleigh-Plesset equation and an acoustic pressure equation. The exponential B-Spline collection method was employed to obtain the numerical solutions of the given cavitation bubble model, considering the effect of acoustic pressure. The behaviour of the cavitation microbubbles' radius and the internal pressure of acoustic cavitation were examined at different values of frequencies. The obtained results reveal the importance of frequencies in ultrasound, which play a vital role in changing the behaviour of cavitation bubbles.

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Analysis of the effectiveness of the optimization processes of trans active power system management with distributed multi-purpose energy resources

Analysis of the effectiveness of the optimization processes ...

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Artificial Intelligence, computer, Data Sciences and Applications (ACDSA), International Conference on

作者： Volodymyr Khilenko Halyna Bielokha Serhii Denysiuk Oleksii Stepanov Ivan Kotuliak Institute of Computer Engineering and Applied Informatics Faculty of Informatics and Information Technologies Slovak University of Technology in Bratislava Bratislava Slovakia Power Supply Department National Technical University of Ukraine "Igor Sikorsky Kyiv Polytechnic Institute" Kyiv Ukraine Department of Information Technologies National University of Life and Environmental Sciences of Ukr Article ACDSA Khilenko ID 414 09-02-2024aine Kyiv Ukraine

The article the efficiency of the Microgrid network when transitioning to a transactive power system that uses control algorithms called to optimize the distribution of power between sources of distributed generation and to minimize the time of decision-making about mode switching and at the corresponding change of the power system architecture. Tables and graphs of the conducted modeling are presented and the efficiency of this control-optimized system is calculated in comparison with the power system where uniform distribution of load power is used.

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Photonic Neuromorphic Accelerators for Event-Based Imaging Flow Cytometry

arXiv

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arXiv 2024年

作者： Tsilikas, Ioannis Tsirigotis, Aris Sarantoglou, George Deligiannidis, Stavros Bogris, Adonis Posch, Christoph Van den Branden, Gerd Mesaritakis, Charis Department of Information and Communication Systems Engineering University of the Aegean Palama 2 Samos83100 Greece Department of Physics School of Applied Mathematical and Physical Sciences Zografou Campus Athens157 80 Greece Department of Informatics and Computer Engineering University of West Attica Ag. Spyridonos Egaleo Greece Prophesee Metavision rue du faubourg Saint-Antoine 74 Paris75012 France

In this work, we present experimental results of a high-speed label-free imaging cytometry system that seamlessly merges the high-capturing rate and data sparsity of an event-based CMOS camera with lightweight photonic neuromorphic processing. This combination offers high classification accuracy and a massive reduction in the number of trainable parameters of the digital machine-learning back-end. The event-based camera is capable of capturing 1 Gevents/sec, where events correspond to pixel contrast changes, similar to the retina’s ganglion cell function. The photonic neuromorphic accelerator is based on a hardware-friendly passive optical spectrum slicing technique that is able to extract meaningful features from the generated spike-trains using a purely analogue version of the convolutional operation. The experimental scenario comprises the discrimination of artificial polymethyl methacrylate calibrated beads, having different diameters, flowing at a mean speed of 0.01m/sec. Classification accuracy, using only lightweight, FPGA-compatible digital machine-learning schemes has topped at 98.2%. On the other hand, by experimentally pre-processing the raw spike data through the proposed photonic neuromorphic spectrum slicer at a rate of 3×106 images per second, we achieved an accuracy of 98.6%. This performance was accompanied by a reduction in the number of trainable parameters at the classification back-end by a factor ranging from 8 to 22, depending on the configuration of the digital neural network. These results confirm that neuromorphic sensing and neuromorphic computing can be efficiently merged to a unified bio-inspired system, offering a holistic enhancement in emerging bio-imaging applications. Copyright © 2024, The Authors. All rights reserved.

关键词： Cameras

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Interpretable temporal graph neural network for prognostic prediction of Alzheimer's disease using longitudinal neuroimaging data

Interpretable temporal graph neural network for prognostic p...

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2021 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2021

作者： Kim, Mansu Kim, Jaesik Qu, Jeffrey Huang, Heng Long, Qi Sohn, Kyung-Ah Kim, Dokyoon Shen, Li Catholic University of Korea Department of Artificial Intelligence Bucheon Korea Republic of Ajou University Department of Computer Engineering Suwon Korea Republic of University of Pennsylvania School of Engineering and Applied Sciences Philadelphia United States University of Pittsburgh Department of Electrical and Computer Engineering Pittsburgh United States University of Pennsylvania Epidemiology Informatics Department of Biostatistics Philadelphia United States Ajou University Department of Artificial Intelligence Suwon Korea Republic of

ISBN: (纸本)9781665401265

Alzheimer's disease (AD) is a progressive neurodegenerative brain disorder characterized by memory loss and cognitive decline. Early detection and accurate prognosis of AD is an important research topic, and numerous machine learning methods have been proposed to solve this problem. However, traditional machine learning models are facing challenges in effectively integrating longitudinal neuroimaging data and biologically meaningful structure and knowledge to build accurate and interpretable prognostic predictors. To bridge this gap, we propose an interpretable graph neural network (GNN) model for AD prognostic prediction based on longitudinal neuroimaging data while embracing the valuable knowledge of structural brain connectivity. In our empirical study, we demonstrate that 1) the proposed model outperforms several competing models (i.e., DNN, SVM) in terms of prognostic prediction accuracy, and 2) our model can capture neuroanatomical contribution to the prognostic predictor and yield biologically meaningful interpretation to facilitate better mechanistic understanding of the Alzheimer's disease. Source code is available at https://***/JaesikKim/temporal-GNN. © 2021 IEEE.

关键词： Forecasting

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Theoretical and Numerical Investigation of Acoustic Cavitation Bubble Based on the Impact of Ultrasound Frequency

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Bulletin of the Russian Academy of Sciences: Physics 2023年第Suppl 3期87卷 S341-S347页

作者： Ahmed K. Abu-Nab Abu-Bakr, Ali F. Zain Abu Shaeer 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 Moscow oblast Dolgoprudny141700 Russia Department of Basic Science Higher Institute of Engineering and Technology Kafr El Sheikh33511 Egypt

Abstract: Acoustic cavitation is the expansion and contraction of existing microbubbles in liquids brought on by an ultrasonic field. The dynamics of oscillations at higher pressures and temperatures when the cavitation bubbles collapse can be used to characterize cavitation dynamical behavior. This work deals with the theoretical and numerical investigation of acoustic cavitation in viscoelastic media considering the influence of ultrasound frequency. An acoustic pressure equation combined with a modified Rayleigh–Plesset equation forms the theoretical framework for ultrasound cavitation dynamics. The numerical solutions of the given cavitation bubble model were obtained taking into account the effect of acoustic pressure, using the exponential B-Spline collection method. The dynamics of the cavitation bubbles’ radius and the internal pressure of acoustic cavitation were examined at different values of viscosity and the impact of physical parameters on the monatomic, diatomic, and adiabatic gas-filled spherical bubble’s collapse dynamics for (k = 7/5, 5/3, 1) respectively. © Pleiades Publishing, Ltd. 2023. ISSN 1062-8738, Bulletin of the Russian Academy of Sciences: Physics, 2023, Vol. 87, Suppl. 3, pp. S341–S347. Pleiades Publishing, Ltd., 2023. ISSN 1062-8738, Bulletin of the Russian Academy of Sciences: Physics, 2023. Pleiades Publishing, Ltd., 2023.

关键词： Dynamics

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On Turán-type problems and the abstract chromatic number

arXiv

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arXiv 2024年

作者： Gerbner, Dániel Karim, Hilal Hama Kucheriya, Gaurav HUN-REN Alfréd Rényi Institute of Mathematics Hungary Department of Computer Science and Information Theory Faculty of Electrical Engineering and Informatics Budapest University of Technology and Economics Műegyetem rkp. 3. BudapestH-1111 Hungary Department of Applied Mathematics Charles University Prague Czech Republic

In 2020, Coregliano and Razborov introduced a general framework to study limits of combinatorial objects, using logic and model theory. They introduced the abstract chromatic number and proved/reproved multiple Erdős-Stone-Simonovits-type theorems in different settings. In 2022, Coregliano extended this by showing that similar results hold when we count copies of Kt instead of edges. Our aim is threefold. First, we provide a purely combinatorial approach. Second, we extend their results by showing several other graph parameters and other settings where Erdős-Stone-Simonovits-type theorems follow. Third, we go beyond determining asymptotics and obtain corresponding stability, supersaturation, and sometimes even exact *** Codes 05C35 © 2024, CC BY.

关键词： Number theory

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Classification of Breast Thermal Images into Healthy/Cancer Group Using Pre-Trained Deep Learning Schemes

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Procedia computer Science 2023年 218卷 24-34页

作者： Seifedine Kadry Rubén González Crespo Enrique Herrera-Viedma Sujatha Krishnamoorthy Venkatesan Rajinikanth Faculty of Applied Computing and Technology Noroff University College Kristiansand 94612 Norway Computer Science Department School of Engineering and Technology Universidad Internacional de La Rioja 26006 Andalusian Spain Research Institute in Data Science and Computational Intelligence University of Granada Granada Spain Zhejiang Bioinformatics International Science and Technology Cooperation Center Wenzhou-Kean University Zhejiang Province China Wenzhou Municipal Key Lab of Applied Biomedical and Biopharmaceutical Informatics Wenzhou-Kean University Zhejiang Province China Department of Computer Science and Engineering Saveetha School of Engineering SIMATS Chennai 602105 Tamil Nadu India

In the women's community, Breast Cancer (BC) is a severe disease. The World Health Organization reported in 2020 that 2.26 million deaths occur due to BC. BC is curable if detected early. Since thermal imaging is non-invasive and supports disease detection, it is commonly used in clinics. Compared to other methods, it keeps BC early and accurate. The proposed work aims to evaluate the performance of the Pretrained Deep-Learning Methods (PDLM) in detecting BC using the thermal images collected from the benchmark dataset. It includes the following stages: primary image processing, deep feature mining, handcrafted feature mining, feature optimization using Firefly-Algorithm (FA), classification and validation. Visual Lab thermal images were used in the study. The investigational outcome of this study authenticates that the VGG16, along with the DT, provides better detection accuracy (95.5%) compared to other classifiers used in this study. To justify the significance of the implemented technique, the proposed work not only improved accuracy, but also improved precision, sensitivity, specificity, and F1-Scores.

关键词： Breast cancer Thermal imaging Firefly algorithm Feature optimization Classification

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SEMU5G: an Emulator for Improving Model Accuracy and Performance Evaluation of 5G Network Core Architectures based on SDN

SEMU5G: an Emulator for Improving Model Accuracy and Perform...

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International Conference on Wireless and Telematics (ICWT)

作者： Eki Ahmad Zaki Hamidi Arga Valen Lazuardi Muhammad Litfan Rahmansyah Salman Alfarizi Novel Bajri Rohmat Tulloh Ridha Muldina Negara Syaiful Ahdan Jupriyadi Electrical Engineering Department UIN Sunan Gunung Djati Bandung Bandung Indonesia School of Electrical Engineering Telkom University Bandung Indonesia Telecommunication Technology School of Applied Science Telkom University Bandung Indonesia The University Center of Excellence Intelligent Sensing-IoT Telkom University Indonesia Faculty of Engineering & Computer Science Universitas Teknokrat Indonesia Indonesia School of Electrical Engineering and Informatics Institut Teknologi Bandung Bandung Indonesia Faculty of Engineering and Computer Science Universitas Teknokrat Indonesia Bandarlampung Indonesia

ISBN: (数字)9798350377057

ISBN: (纸本)9798350377064

The rapid advancement of 5G Radio Access Network (RAN) architecture is facilitating the construction of 5G networks, marking a significant milestone in telecommunications evolution. Given the complexity of the 5G core architecture, traditional simulation methods are insufficient, necessitating novel approaches. Emulator systems are crucial for creating dynamic, controlled environments that enable exploration of real-world scenarios without physical constraints. SEMU5G, a 5G core emulator, SDN controller, and RAN simulator, utilizes Open5GS implemented in Docker Container for test system flexibility and isolation. Additionally, an SDN controller is integrated to monitor data flows in the User Plane Function (UPF) and gNB simulated by UERANSIM in Mininet-WiFi. This comprehensive integration facilitates effective and flexible real-world exploration, providing a dynamic and controlled test environment for 5G core research. Scenario testing comprises two stages: firstly, a fixed network topology is employed to compare resource usage and confirm successful SEMU5G integration. Secondly, a mobile network topology is utilized to implement a mobile device scenario and compare the Quality of Service (QoS) of SEMU5G with other available emulators. These stages ensure thorough evaluation of SEMU5G's performance and its comparative advantage over existing solutions.

关键词： Performance evaluation 5G mobile communication Network topology Emulation computer architecture Quality of service Aerodynamics

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Multimodal Clinical Benchmark for Emergency Care (MC-BEC): A Comprehensive Benchmark for Evaluating Foundation Models in Emergency Medicine

arXiv

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arXiv 2023年

作者： Chen, Emma Kansal, Aman Chen, Julie Jin, Boyang Tom Reisler, Julia Rachel Kim, David A. Rajpurkar, Pranav Department of Biomedical Informatics Harvard Medical School United States Department of Computer Science Stanford University United States Department of Emergency Medicine Stanford University School of Medicine United States Harvard John A. Paulson School of Engineering and Applied Sciences Harvard University United States

We propose the Multimodal Clinical Benchmark for Emergency Care (MC-BEC), a comprehensive benchmark for evaluating foundation models in Emergency Medicine using a dataset of 100K+ continuously monitored Emergency department visits from 2020-2022. MC-BEC focuses on clinically relevant prediction tasks at timescales from minutes to days, including predicting patient decompensation, disposition, and emergency department (ED) revisit, and includes a standardized evaluation framework with train-test splits and evaluation metrics. The multimodal dataset includes a wide range of detailed clinical data, including triage information, prior diagnoses and medications, continuously measured vital signs, electrocardiogram and photoplethysmograph waveforms, orders placed and medications administered throughout the visit, free-text reports of imaging studies, and information on ED diagnosis, disposition, and subsequent revisits. We provide performance baselines for each prediction task to enable the evaluation of multimodal, multitask models. We believe that MC-BEC will encourage researchers to develop more effective, generalizable, and accessible foundation models for multimodal clinical data. © 2023, CC BY.

关键词： Emergency rooms

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