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Importance of Wearable Health Monitoring Systems Using IoMT;Requirements, Advantages, Disadvantages and Challenges

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Importance of Wearable Health Monitoring Systems Using IoMT;...

International Symposium on Computational Intelligence and Informatics

作者： Fahime Khozeimeh Mohamad Roshanzamir Afshin Shoeibi Mohammad Tayarani Darbandy Roohallah Alizadehsani Hamid Alinejad-Rokny Davood Ahmadian Abbas Khosravi Saeid Nahavandi Institute for Intelligent Systems Research and Innovation (IISRI) Deakin University Victoria Australia Department of Computer Engineering Fasa University Fasa Iran Internship in Health Data Analytics Program AI-enabled Processes (AIP) Research Centre Macquarie University Sydney Australia School of Architecture Islamic Azad University Taft Taft Iran BioMedical Machine Learning Lab The Graduate School of Biomedical Engineering UNSW Sydney Sydney NSW Australia Faculty of Mathematics Statistics and Computer Sciences University of Tabriz Tabriz Iran

ISBN: (纸本)9798350398830

Providing better quality of service is one of the most important goals in medical/healthcare systems. The services provided should have features such as low latency, appropriate geographic distribution, and real-time interaction. Internet of Medical Things (IoMT) technology is one of the solutions to achieve high quality of services with these features. The wearable health monitoring systems which have become popular in the last decade are one of the foundations of achievement to these features. In this paper, the importance of wearable devices under IoMT technology is investigated. There are several reasons for using wearable devices under IoMT technology, such as reducing healthcare costs and long waiting times. Therefore, anyone working on wearable devices needs to have a general understanding of how they are designed and function. In fact, the integration of new devices to monitor and alert patients about their physical health is a promising option for improving health care systems around the world. Overcrowding in medical centers will be prevented by upgrading the facilities for the virtual presence of patients. It can also ensure real-time monitoring and reduce operating costs. In addition, it can accelerate treatment of patients and reduce the cost of investment and increase its coverage.

关键词： Costs Medical devices Wearable computers Quality of service Organizations Real-time systems Security

Predictive inference with the jackknife+

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arXiv 2019年

作者： Barber, Rina Foygel Candés, Emmanuel J. Ramdas, Aaditya Tibshirani, Ryan J. Department of Statistics University of Chicago Departments of Statistics and Mathematics Stanford University Department of Statistics and Data Science Carnegie Mellon University Machine Learning Department Carnegie Mellon University

This paper introduces the jackknife+, which is a novel method for constructing predictive confidence intervals. Whereas the jackknife outputs an interval centered at the predicted response of a test point, with the width of the interval determined by the quantiles of leave-one-out residuals, the jack-knife+ also uses the leave-one-out predictions at the test point to account for the variability in the fitted regression function. Assuming exchangeable training samples, we prove that this crucial modification permits rigorous coverage guarantees regardless of the distribution of the data points, for any algorithm that treats the training points symmetrically. Such guarantees are not possible for the original jackknife and we demonstrate examples where the coverage rate may actually vanish. Our theoretical and empirical analysis reveals that the jackknife and the jackknife+ intervals achieve nearly exact coverage and have similar lengths whenever the fitting algorithm obeys some form of stability. Further, we extend the jackknife+ to K-fold cross validation and similarly establish rigorous coverage properties. Our methods are related to cross-conformal prediction proposed by Vovk [2015] and we discuss connections. Copyright © 2019, The Authors. All rights reserved.

关键词： machine learning

Efficient sampling from the Bingham distribution

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arXiv 2020年

作者： Ge, Rong Lee, Holden Lu, Jianfeng Risteski, Andrej Duke University Computer Science Department United States Johns Hopkins University Applied Mathematics and Statistics Department United States Duke University Mathematics Department United States Carnegie Mellon University Machine Learning Department United States

We give a algorithm for exact sampling from the Bingham distribution p(x) ∝ exp(x⊺Ax) on the sphere Sd-1 with expected runtime of poly(d, λmax(A) - λmin(A)). The algorithm is based on rejection sampling, where the proposal distribution is a polynomial approximation of the pdf, and can be sampled from by explicitly evaluating integrals of polynomials over the sphere. Our algorithm gives exact samples, assuming exact computation of an inverse function of a polynomial. This is in contrast with Markov Chain Monte Carlo algorithms, which are not known to enjoy rapid mixing on this problem, and only give approximate samples. As a direct application, we use this to sample from the posterior distribution of a rank-1 matrix inference problem in polynomial time. Copyright © 2020, The Authors. All rights reserved.

关键词： Inverse problems

Optimising knee injury detection with spatial attention and validating localisation ability

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arXiv 2021年

作者： Belton, Niamh Welaratne, Ivan Dahlan, Adil Hearne, Ronan T. Hagos, Misgina Tsighe Lawlor, Aonghus Curran, Kathleen M. Science Foundation Ireland Centre for Research Training in Machine Learning School of Medicine University College Dublin Department of Radiology Mater Misericordiae University Hospital Dublin Ireland School of Electronic Engineering University College Dublin School of Computer Science University College Dublin Insight Centre for Data Analytics University College Dublin Dublin Ireland

This work employs a pre-trained, multi-view Convolutional Neural Network (CNN) with a spatial attention block to optimise knee injury detection. An open-source Magnetic Resonance Imaging (MRI) data set with image-level labels was leveraged for this analysis. As MRI data is acquired from three planes, we compare our technique using data from a single-plane and multiple planes (multi-plane). For multi-plane, we investigate various methods of fusing the planes in the network. This analysis resulted in the novel 'MPFuseNet' network and state-of-the-art Area Under the Curve (AUC) scores for detecting Anterior Cruciate Ligament (ACL) tears and Abnormal MRIs, achieving AUC scores of 0.977 and 0.957 respectively. We then developed an objective metric, Penalised Localisation Accuracy (PLA), to validate the model's localisation ability. This metric compares binary masks generated from Grad-Cam output and the radiologist's annotations on a sample of MRIs. We also extracted explainability features in a model-agnostic approach that were then verified as clinically relevant by the radiologist. Copyright © 2021, The Authors. All rights reserved.

关键词： Magnetic resonance imaging

Species-Specific Responses of Canopy Greenness to the Extreme Droughts of 2018 and 2022 for Four Abundant Tree Species in Germany

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SSRN

SSRN 2024年

作者： Wang, Yixuan Rammig, Anja Blickensdörfer, Lukas Wang, Yuanyuan Zhu, Xiao Xiang Buras, Allan Professorship for Land Surface-Atmosphere Interactions Technical University of Munich Hans-Carl-v.-Carlowitz-Platz 2 Freising85354 Germany Thünen Institute of Farm Economics Bundesallee 63 Braunschweig38116 Germany Thünen Institute of Forest Ecosystems Alfred-Moeller-Straße 1 Eberswalde16225 Germany Earth Observation Lab Geography Department Humboldt-Universität zu Berlin Unter den Linden 6 Berlin10099 Germany Chair of Data Science in Earth Observation Technical University of Munich Arcisstraße 21 Munich80333 Germany Munich Center for Machine Learning Arcisstraße 21 Munich80333 Germany

The years 2018 and 2022 witnessed extreme drought periods in Germany, which significantly affected forests. These repeated droughts were a natural experiment that provided valuable insights into how different tree species respond to drought. Understanding trees' drought response is crucial for predicting their adaptation to climate change, thereby informing effective forest regeneration strategies. In this study, we used remotely sensed peak-season canopy greenness as a proxy for tree vitality to approximate the drought response of four dominant tree species in Germany (beech, oak, pine, and spruce). We focused on two questions: (1) How were the four tree species affected by these two droughts? (2) Which environmental parameters primarily determined canopy greenness? To address these questions, we combined a recently published tree species classification map with remotely sensed canopy greenness and environmental variables related to plant available water capacity (PAWC), atmospheric vapor pressure deficit (VPD), and climatic water balance (SPEI). Our results indicated the more isohydric species (pine, spruce) featured a greater decline in canopy greenness under these droughts than the more anisohydric species (beech, oak) despite similar soil moisture conditions. The additive models suggested that the canopy greenness of the more isohydric species and more anisohydric species were primarily driven by PAWC and VPD, respectively. Our statistical analysis indicated that oak was the only species with significantly higher canopy greenness in 2022 compared to 2018. Yet, all species are likely still susceptible to accumulated drought effects related to biotic pathogens in the coming years. Our study provides critical insights into the diverse responses of different tree species to changing environments over a large gradient in Central Europe and sheds light on the complex interactions between soil moisture, climate variables, and canopy greenness. These findings contribut

关键词： Remote sensing

Model-Based Reinforcement learning with Value-Targeted Regression

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arXiv 2020年

作者： Ayoub, Alex Jia, Zeyu Szepesvári, Csaba Wang, Mengdi Yang, Lin F. Department of Computing Science University of Alberta School of Mathematical Science Peking University Department of Electrical Engineering Princeton University Center for Statistics and Machine Learning Princeton University Department of Electrical and Computer Engineering University of California Los Angeles United States DeepMind

This paper studies model-based reinforcement learning (RL) for regret minimization. We focus on finite-horizon episodic RL where the transition model P belongs to a known family of models P, a special case of which is when models in P take the form of linear mixtures: (Formula presented). We propose a model based RL algorithm that is based on optimism principle: In each episode, the set of models that are ‘consistent’ with the data collected is constructed. The criterion of consistency is based on the total squared error of that the model incurs on the task of predicting values as determined by the last value estimate along the transitions. The next value function is then chosen by solving the optimistic planning problem with the constructed set of models. We derive a bound on the regret, which, in the special case of linear mixtures, the regret bound takes the form (Formula presented), where H, T and d are the horizon, total number of steps and dimension of θ, respectively. In particular, this regret bound is independent of the total number of states or actions, and is close to a lower bound (Formula presented). For a general model family P, the regret bound is derived using the notion of the so-called Eluder dimension proposed by Russo & Van Roy (2014). Copyright © 2020, The Authors. All rights reserved.

关键词： Reinforcement learning

Modeling the dielectric constant of silicon-based nanocomposites using machine learning

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Modeling the dielectric constant of silicon-based nanocompos...

2020 International Conference on Actual Problems of Electron Devices Engineering, APEDE 2020

作者： Korchagin, Sergey Alekseevich Klinaev, Yuri Vasilievich Terin, Denis Vladimirovich Romanchuk, Sergey Petrovich Financial University Government of the Russian Federation Department of Data Analysis and Machine Learning Moscow Russia Yuri Gagarin Saratov State Technical University Department of Natural and Mathematical Sciences Engels Russia Technol. and Qual. Mgmt. Saratov National Research State University Named after N. G. Chernyshevsky Department of Materials Science Saratov Russia Yuri Gagarin Saratov State Technical University Department of Information Security of Automated Systems Saratov Russia

ISBN: (数字)9781728143095

ISBN: (纸本)9781728143088

In this work, we solve the problem of predicting the dielectric constant of silicon-based nanocomposites using machine learning methods. Mathematical models and programs have been developed to predict the electrophysical properties of new materials, as well as to select the optimal composition. The results obtained will be useful for faster and cheaper creation of new functional composites. © 2020 IEEE.

关键词： Nanocomposites

Path length bounds for gradient descent and flow

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arXiv 2019年

作者： Gupta, Chirag Balakrishnan, Sivaraman Ramdas, Aaditya Machine Learning Department Carnegie Mellon University United States Department of Statistics and Data Science Carnegie Mellon University United States

We derive bounds on the path length ζof gradient descent (GD) and gradient flow (GF) curves for various classes of smooth convex and nonconvex functions. Among other results, we prove that: (a) if the iterates are linearly convergent with factor(1â'c), then ζ is at mostO(1/c);(b) under the Polyak-Kurdyka-Lojasiewicz (PKL) condition,ζis at most O(κâ'â'√), whereκis the condition number, and at least Ω(dâ'â'√∧κ1/4);(c) for quadratics,ζis Θ(min{dâ'â'√,logκâ'â'â'â'√})and in some cases can be independent ofκ;(d) assuming just convexity,ζcan be at most 24dlogd;(e) for separable quasiconvex functions,ζis Θ(dâ'â'√). Thus, we advance current understanding of the properties of GD and GF curves beyond rates of convergence. We expect our techniques to facilitate future studies for other algorithms. Copyright © 2019, The Authors. All rights reserved.

关键词： Gradient methods

Trend filtering - II. Denoising astronomical signals with varying degrees of smoothness

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arXiv 2020年

作者： Politsch, Collin A. Cisewski-Kehe, Jessi Croft, Rupert A.C. Wasserman, Larry Department of Statistics & Data Science Carnegie Mellon University PittsburghPA15213 United States Machine Learning Department Carnegie Mellon University PittsburghPA15213 United States McWilliams Center for Cosmology Carnegie Mellon University PittsburghPA15213 United States Department of Statistics and Data Science Yale University New HavenCT06520 United States Department of Physics Carnegie Mellon University PittsburghPA15213 United States School of Physics University of Melbourne VIC3010 Australia

Trend filtering-first introduced into the astronomical literature in Paper I of this series-is a state-of-the-art statistical tool for denoising one-dimensional signals that possess varying degrees of smoothness. In this work, we demonstrate the broad utility of trend filtering to observational astronomy by discussing how it can contribute to a variety of spectroscopic and time-domain studies. The observations we discuss are (1) the Lyman-α forest of quasar spectra;(2) more general spectroscopy of quasars, galaxies, and stars;(3) stellar light curves with planetary transits;(4) eclipsing binary light curves;and (5) supernova light curves. We study the Lyman-α forest in the greatest detail-using trend filtering to map the large-scale structure of the intergalactic medium along quasar-observer lines of sight. The remaining studies share broad themes of: (1) estimating observable parameters of light curves and spectra;and (2) constructing observational spectral/light-curve templates. We also briefly discuss the utility of trend filtering as a tool for one-dimensional data reduction and compression. Copyright © 2020, The Authors. All rights reserved.

关键词： Galaxies