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28th Annual Conference on Medical Image Understanding and Analysis, MIUA 2024

作者： Setterdahl, Lena M. Lionheart, William R. B. Holman, Sean Skjerdal, Kyrre Ratliff, Hunter N. Ytre-Hauge, Kristian Smeland Lathouwers, Danny Meric, Ilker Department of Computer Science Electrical Engineering and Mathematical Sciences Western Norway University of Applied Sciences Bergen Norway Department of Mathematics University of Manchester Manchester United Kingdom Department of Physics and Technology University of Bergen Bergen Norway Department of Radiation Science and Technology Delft University of Technology Delft Netherlands

ISBN: (纸本)9783031669545

This study aims to investigate the capability of U-Nets in improving image reconstruction accuracy for proton range verification within the framework of the NOVO (Next generation imaging for real-time dose verification enabling adaptive proton therapy) project. NOVO aims to enhance the accuracy of proton range verification by imaging the distribution of prompt gamma-rays (PGs) and fast neutrons (FNs) produced by nuclear interactions within tissue. In this work, focus lies on FNs, leaving PGs as future work. A dataset consisting of Monte Carlo-based simple back-projection and ground truth images of FN production distributions in a homogeneous water phantom was utilized. Various U-Net models were trained to predict FN distributions, and a set of range landmark (RL) metrics were computed for evaluation. Linear regression models were established to correlate shifts in mean RL with true range shift magnitudes. Our findings demonstrate a strong linear correlation between the shifts in mean RL in U-Net predictions and the true range shift magnitudes. Multiple RL metrics, including weighted average, inflection point, edge, and peak, were explored. This study highlights the potential utility of U-Nets in enhancing image reconstruction accuracy for proton range verification. The observed correlations between RL shifts and true range shifts provide evidence of the ability of U-Nets to accurately predict images containing range information. Future research will focus on generating more realistic training data incorporating more clinically relevant phantoms, including tissue heterogeneities. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Tissue

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Design Patterns for Multilevel Modeling and Simulation 27

Design Patterns for Multilevel Modeling and Simulation

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27th IEEE/ACM International Symposium on Distributed Simulation and Real Time Applications, DS-RT 2023

作者： Serena, Luca Marzolla, Moreno D'Angelo, Gabriele Ferretti, Stefano University of Bologna Department of Computer Science and Engineering Italy University of Bologna Center for Inter-Department Industrial Research Ict Italy University of Urbino Carlo Bo Department of Pure and Applied Sciences Italy

ISBN: (纸本)9798350337846

Multilevel modeling and simulation (M&S) is becoming increasingly relevant due to the benefits that this methodology offers. Multilevel models allow users to describe a system at multiple levels of detail. From one side, this can make better use of computational resources, since the more detailed and time-consuming models can be executed only when/where required. From the other side, multilevel models can be assembled from existing components, cutting down development and verification/validation time. A downside of multilevel M&S is that the development process becomes more complex due to some recurrent issues caused by the very nature of multilevel models: how to make sub-models interoperate, how to orchestrate execution, how state variables are to be updated when changing scale, and so on. In this paper, we address some of these issues by presenting a set of design patterns that provide a systematic approach for designing and implementing multilevel models. The proposed design patterns cover multiple aspects, including how to represent different levels of detail, how to combine incompatible models, how to exchange data across models, and so on. Some of the patterns are derived from the general software engineering literature, while others are specific to the multilevel M&S application area. © 2023 IEEE.

关键词： Autonomous agents

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MEC-IoT-Healthcare: Analysis and Prospects

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computers, Materials & Continua 2023年第6期75卷 6219-6250页

作者： Hongyuan Wang Mohammed Dauwed Imran Khan Nor Samsiah Sani Hasmila Amirah Omar Hirofumi Amano Samih M.Mostafa College of Computer Science Guangdong University of Science and TechnologyDongGuan523083China Department of Medical Instrumentation Techniques Engineering Dijlah University CollegeBaghdadIraq Department of Electrical Engineering University of Engineering&TechnologyPeshawar814Pakistan Center for Artificial Intelligence Technology Faculty of Information Science and TechnologyUniversiti Kebangsaan Malaysia43600BangiSelangorMalaysia Research Institute for Information Technology Kyushu UniversityFukuoka819-0395Japan Computer Science-Mathematics Department Faculty of ScienceSouth Valley UniversityQena83523Egypt

Physical sensors,intelligent sensors,and output recommenda-tions are all examples of smart health technology that can be used to monitor patients’health and change their *** health is an Internet-of-Things(IoT)-aware network and sensing infrastructure that provides real-time,intelligent,and ubiquitous healthcare *** of the rapid development of cloud computing,as well as related technologies such as fog computing,smart health research is progressively moving in the right ***,fog computing,IoT sensors,blockchain,privacy and security,and other related technologies have been the focus of smart health research in recent *** the moment,the focus in cloud and smart health research is on how to use the cloud to solve the problem of enormous health data and enhance service performance,including cloud storage,retrieval,and calculation of health big *** article reviews state-of-the-art edge computing methods that has shifted to the collection,transmission,and calculation of health data,which includes various sensors and wearable devices used to collect health data,various wireless sensor technologies,and how to process health data and improve edge performance,among other ***,the typical smart health application cases,blockchain’s application in smart health,and related privacy and security issues were reviewed,as well as future difficulties and potential for smart health *** comparative analysis provides a reference for the the mobile edge computing in healthcare systems.

关键词： IoT mobile-edge computing cloud computing e-health

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Direct System Identification of Dynamical Networks with Partial Measurements: A Maximum Likelihood Approach

Direct System Identification of Dynamical Networks with Part...

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European Control Conference (ECC)

作者： João Victor Galvão Da Mata Anders Hansson Martin S. Andersen Department of Applied Mathematics and Computer Science Technical University of Denmark Department of Electrical Engineering Linköping University.

ISBN: (数字)9783907144107

ISBN: (纸本)9798331540920

This paper introduces a novel direct approach to system identification of dynamic networks with missing data based on maximum likelihood estimation. Dynamic networks generally present a singular probability density function, which poses a challenge in the estimation of their parameters. By leveraging knowledge about the network's interconnections, we show that it is possible to transform the problem into a more tractable form by applying linear transformations. This results in a nonsingular probability density function, enabling the application of maximum likelihood estimation techniques. Our preliminary numerical results suggest that when combined with global optimization algorithms or a suitable initialization strategy, we are able to obtain a good estimate of the dynamics of the internal systems.

关键词： Maximum likelihood estimation Sensitivity Heuristic algorithms Transfer functions Optimization methods Transforms Probability density function

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Sensor-Based Cattle Behavior Classification Using Deep Learning Approaches

Sensor-Based Cattle Behavior Classification Using Deep Learn...

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2023 International Technical Conference on Circuits/Systems, computers, and Communications, ITC-CSCC 2023

作者： Mekruksavanich, Sakorn Jantawong, Ponnipa Tancharoen, Datchakorn Jitpattanakul, Anuchit School of Information and Communication Technology University of Phayao Department of Computer Engineering Phayao Thailand Panyapiwat Institute of Management Faculty of Engineering and Technology Nonthaburi Thailand King Mongkut's University of Technology North Bangkok Intelligent and Nonlinear Dynamic Innovations Research Center Faculty of Applied Science Department of Mathematics Bangkok Thailand

ISBN: (纸本)9798350326413

The usage of precision livestock has grown due to the need for higher efficiency and productivity in response to the high demand for food. To ensure sustainable development and quality control of the inputs required by the industry, it is essential to monitor and classify the behavior of cattle. Sensor-based monitoring systems provide accurate information by capturing raw data and identifying behavior through machine learning and deep learning algorithms. This approach has allowed farmers to better understand the individual needs of their animals. This study presents a deep residual neural network for the classification of cattle behavior. The performance of the ResNeXt model was evaluated using a public real-world dataset collected from sensors attached to the neck of six different Japanese black beef cows. The experimental results showed that the presented ResNeXt model achieved the highest average accuracy of 94.96% and the highest average F1-score of 93.66%. Compared to other baseline deep learning models and the current state-of-the-art model for cattle behavior classification, the presented model outperformed them and achieved better performance. © 2023 IEEE.

关键词： Quality control

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Human Activity Recognition in Logistics Using Wearable Sensors and Deep Residual Network 38

Human Activity Recognition in Logistics Using Wearable Senso...

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38th IEEE Region 10 Conference, TENCON 2023

作者： Mekruksavanich, Sakorn Tancharoen, Datchakorn Jitpattanakul, Anuchit School of Information and Communication Technology University of Phayao Department of Computer Engineering Phayao Thailand Panyapiwat Institute of Management Faculty of Engineering and Technology Nonthaburi Thailand King Mongkut's University of Technology North Bangkok Intelligent and Nonlinear Dynamic Innovations Research Center Faculty of Applied Science Department of Mathematics Bangkok Thailand

ISBN: (纸本)9798350302196

Human action identification is a practical area of study with broad applicability in various domains, such as medical care, sport science, and manufacturing management. In logistics, it is essential to identify and examine individual actions, enabling machines to perceive and comprehend human motions for non-verbal interaction. This study specifically fo-cuses on efficiently classifying working activities in the logistics industry using wearable sensors, particularly in the context of human activity recognition. To achieve the research objective, a deep residual neural network was introduced, integrating convolutional layers, shortcut connections, and aggregated transformation for human activity recognition in logistics. The authors evaluated the effectiveness of their proposed deep learning model using the publicly accessible LARa dataset. The LARa dataset comprises a diverse range of human actions in the logistics domain, including standing, walking, cart handling, and synchronization. The details of activity were captured using wearable sensors affixed to different anatomical sites of the study participants. The experimental findings indicate that the model achieved a maximum F-measure of 85.30%. © 2023 IEEE.

关键词： Wearable sensors

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An Intelligent Reinforcement Learning–Based Method for Threat Detection in Mobile Edge Networks

An Intelligent Reinforcement Learning–Based Method for Thre...

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作者： Saeed, Muhammad Yousaf He, Jingsha Zhu, Nafei Farhan, Muhammad Dev, Soumyabrata Gadekallu, Thippa Reddy Almadhor, Ahmad College of Computer Science Beijing University of Technology Beijing China Department of Computer Science COMSATS University Islamabad Sahiwal Campus Sahiwal Pakistan School of Computer Science University College Dublin Dublin Ireland Centre of Research Impact and Outcome Chitkara University Institute of Engineering and Technology Chitkara University Punjab Rajpura India The College of Mathematics and Computer Science Zhejiang A&F University Hangzhou China Division of Research and Development Lovely Professional University Phagwara India Department of Computer Engineering and Networks College of Computer and Information Sciences Jouf University Sakaka Saudi Arabia

Traditional techniques for detecting threats in mobile edge networks are limited in their ability to adapt to evolving threats. We propose an intelligent reinforcement learning (RL)–based method for real-time threat detection in mobile edge networks. Our approach enables an agent to continuously learn and adapt its threat detection capabilities based on feedback from the environment. Through experiments, we demonstrate that our technique outperforms traditional methods in detecting threats in dynamic edge network environments. The intelligent and adaptive nature of our RL-based approach makes it well suited for securing mission-critical edge applications with stringent latency and reliability requirements. We provide an analysis of threat models in multiaccess edge computing and highlight the role of on-device learning in enabling distributed threat intelligence across heterogeneous edge nodes. Our technique has the potential, significantly enhancing threat visibility and resiliency in next-generation mobile edge networks. Future work includes optimizing sample efficiency of our approach and integrating explainable threat detection models for trustworthy human–AI collaboration. © 2024 John Wiley & Sons Ltd.

关键词： Reinforcement learning

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BRAIN-TO-TEXT DECODING WITH CONTEXT-AWARE NEURAL REPRESENTATIONS AND LARGE LANGUAGE MODELS

arXiv

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arXiv 2024年

作者： Li, Jingyuan Le, Trung Fan, Chaofei Chen, Mingfei Shlizerman, Eli Department of Electrical and Computer Engineering University of Washington United States Department of Computer Science Stanford University United States Department of Applied Mathematics Department of Electrical and Computer Engineering University of Washington United States

Decoding attempted speech from neural activity offers a promising avenue for restoring communication abilities in individuals with speech impairments. Previous studies have focused on mapping neural activity to text using phonemes as the intermediate target. While successful, decoding neural activity directly to phonemes ignores the context dependent nature of the neural activity-to-phoneme mapping in the brain, leading to suboptimal decoding performance. In this work, we propose the use of diphone - an acoustic representation that captures the transitions between two phonemes - as the context-aware modeling target. We integrate diphones into existing phoneme decoding frameworks through a novel divide-and-conquer strategy in which we model the phoneme distribution by marginalizing over the diphone distribution. Our approach effectively leverages the enhanced context-aware representation of diphones while preserving the manageable class size of phonemes, a key factor in simplifying the subsequent phoneme-to-text conversion task. We demonstrate the effectiveness of our approach on the Brain-to-Text 2024 benchmark, where it achieves state-of-the-art Phoneme Error Rate (PER) of 15.34% compared to 16.62% PER of monophone-based decoding. When coupled with finetuned Large Language Models (LLMs), our method yields a Word Error Rate (WER) of 5.77%, significantly outperforming the 8.93% WER of the leading method in the benchmark. Copyright © 2024, The Authors. All rights reserved.

关键词： Brain mapping

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Computation of Thermal Radiative Flux from a Porous Medium by Using Fractal–Fractional Operator

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International Journal of applied and Computational mathematics 2024年第6期10卷 1-14页

作者： Garalleh, Hakim AL Ramzan, Muhammad Shafique, Ahmad Abbas, Shajar Nazar, Mudassar Jan, Rashid Department of Mathematical Science College of Engineering University of Business and Technology Jeddah Saudi Arabia Centre for Advanced Studies in Pure and Applied Mathematics Bahauddin Zakariya University Multan Pakistan Department of Civil Engineering College of Engineering Institute of Energy Infrastructure (IEI) Universiti Tenaga Nasional (UNITEN) Putrajaya Campus Jalan IKRAM-UNITEN Selangor Kajang 43000 Malaysia Mathematics Research Center Near East University Mersion 10 Nicosia 99138 Turkey

Fractal–fractional derivative has numerous significance in real world problems. The main goal of this derivative is the use of memory effects to formulate the model. Due to extensive applications of fractional derivatives, in the present article fractional order model of Brinkman fluid has been analyzed. The governing equations of the present flow model are solved numerically by using Cranck-Nicolson method to obtain the solutions for mass, energy, and momentum equations. In additions the physical aspects of material and flow parameters like as Prandtl number, magnetic force, Schmidt number, Grashof thermal number, Grashof mass number, especially the fractional operators are studied by drawing the graphs of field of interest. © The Author(s), under exclusive licence to Springer Nature India Private Limited 2024.

关键词： Chemical reaction Heat flux Magnetic field Nanofluid

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Investigation of adequate closed form travelling wave solution to the space-time fractional non-linear evolution equations

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Journal of Ocean engineering and Science 2022年第3期7卷 292-303页

作者： Mohammad Asif Arefin M.Ayesha Khatun M.Hafiz Uddin Mustafa Inc Department of Mathematics Jashore University of Science and TechnologyJashore 7408Bangladesh Department of Computer Engineering Biruni UniversityIstanbulTurkey Department of Mathematics Science FacultyFirat UniversityElazigTurkey Department of Medical Research China Medical University HospitalChina Medical UniversityTaichungTaiwan

This work aims to construct exact solutions for the space-time fractional(2+1)-dimensional dispersive longwave(DLW)equation and approximate long water wave equation(ALW)utilizing the twovariable(G′/G,1/G)-expansion method and the modified Riemann-Liouville fractional *** recommended equations play a significant role to describe the travel of the shallow water *** fractional complex transform is used to convert fractional differential equations into ordinary differential *** wave solutions have been successfully achieved using the proposed approach and the symbolic computer Maple *** Maple package program was used to set up and validate all of the computations in this *** choosing particular values of the embedded parameters,we pro-duce multiple periodic solutions,periodic wave solutions,single soliton solutions,kink wave solutions,and more forms of soliton *** achieved solutions might be useful to comprehend nonlinear *** is worth noting that the implemented method for solving nonlinear fractional partial dif-ferential equations(NLFPDEs)is efficient,and simple to find further and new-fangled solutions in the arena of mathematical physics and coastal engineering.

关键词： Riemann-Liouville fractional derivative Space-time fractional(2+1)-dimensional dispersive long wave equation Approximate long water wave equation Wave transformation The two-variable(G′/G,1/G)-expansion method

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