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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

Record Acceleration of the Two-Dimensional Ising Model Using a High-Performance Wafer-Scale Engine

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SSRN

SSRN 2024年

作者： Shi, Wei Van Essendelft, Dirk Saidi, Wissam Abdo Almolyki, Hayl Jordan, Terry Wang, Mei-Yu The National Energy Technology Laboratory 3610 Collins Ferry Rd MorgantownWV26505 United States Department of Computer Science and Engineering Tandon School of Engineering New York University 6 MetroTech Center BrooklynNY11201 United States The National Energy Technology Laboratory 626 Cochrans Mill Rd PittsburghPA15236 United States HPC AI and Big Data Group Pittsburgh Supercomputing Center Carnegie Mellon University 300 S. Craig St. PittsburghPA15213 United States The National Energy Technology Laboratory 626 Cochrans Mill Rd PittsburghPA15236 United States

The versatility and wide-ranging applicability of the Ising model, originallyintroduced to study phase transitions in magnetic materials, have made it a cornerstonein statistical physics and a valuable tool for evaluating the performanceof emerging computer hardware. Here, we present a novel implementation of thetwo-dimensional Ising model on Cerebras Wafer-Scale Engine (WSE) – a revolutionaryprocessor that is opening new frontiers in computing. In our deploymentof the checkerboard algorithm, we optimized the Ising model to take advantage of the unique WSE architecture. Specifically, we employed a compressed bit representationstoring 16 spins on each int16 word, and efficiently distributed the spinsover the processing units enabling seamless weak scaling and limiting communicationsto only immediate neighboring units. Our implementation can handleup to 754 simulations in parallel, achieving an aggregate of over 61.8 trillion flipattempts per second for Ising models with up to 200 million spins. This representsa gain of up to 148 times over previously reported single-devices with a highlyoptimized implementation on NVIDIA V100 and up to 88 times in productivitycompared to NVIDIA H100. Our findings highlight the significant potential ofthe WSE in scientific computing, particularly in the field of materials modeling. © 2024, The Authors. All rights reserved.

关键词： Ising model

MoCA: Incorporating multi-stage domain pretraining and cross-guided multimodal attention for Textbook Question Answering

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arXiv 2021年

作者： Xu, Fangzhi Lin, Qika Liu, Jun Zhang, Lingling Zhao, Tianzhe Chai, Qi Pan, Yudai Department of Computer Science and Technology Xi'an Jiaotong University China National Engineering Lab for Big Data Analytics Xi'an Jiaotong University China Shaanxi Province Key Laboratory of Satellite and Terrestrial Network Tech. R&D Xi'an Jiaotong University China

Textbook Question Answering (TQA) is a complex multimodal task to infer answers given large context descriptions and abundant diagrams. Compared with Visual Question Answering(VQA), TQA contains a large number of uncommon terminologies and various diagram inputs. It brings new challenges to the representation capability of language model for domain-specific spans. And it also pushes the multimodal fusion to a more complex level. To tackle the above issues, we propose a novel model named MoCA, which incorporates multi-stage domain pretraining and multimodal cross attention for the TQA task. Firstly, we introduce a multi-stage domain pretraining module to conduct unsupervised post-pretraining with the span mask strategy and supervised pre-finetune. Especially for domain post-pretraining, we propose a heuristic generation algorithm to employ the terminology corpus. Secondly, to fully consider the rich inputs of context and diagrams, we propose cross-guided multimodal attention to update the features of text, question diagram and instructional diagram based on a progressive strategy. Further, a dual gating mechanism is adopted to improve the model ensemble. The experimental results show the superiority of our model, which outperforms the state-of-the-art methods by 2.21% and 2.43% for validation and test split respectively. Copyright © 2021, The Authors. All rights reserved.

关键词： Textbooks

Artificial Intelligence without Restriction Surpassing Human Intelligence with Probability One: Theoretical Insight into Secrets of the Brain with ai Twins of the Brain

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arXiv 2024年

作者： Huang, Guang-Bin Westover, M. Brandon Tan, Eng-King Wang, Haibo Cui, Dongshun Ma, Wei-Ying Wang, Tiantong He, Qi Wei, Haikun Wang, Ning Tian, Qiyuan Lam, Kwok-Yan Yao, Xin Wong, Tien Yin School of Automation Southeast University Nanjing China Key Laboratory of Measurement and Control of Complex Systems of Engineering Ministry of Education Nanjing China Beth Israel Deaconess Medical Center Harvard Medical School Boston United States Department of Neurology National Neuroscience Institute Singapore Research Centre of Big Data and Artificial Intelligence for Medicine First Affiliated Hospital of Sun Yat-Sen University Guangzhou China Duke-NUS Medical School National University of Singapore Singapore Mind PointEye Singapore Institute for AI Industry Research Tsinghua University Beijing China College of Computing and Data Science Nanyang Technological University Singapore School of Biomedical Engineering Tsinghua University Beijing China Singapore AI Safety Institute Nanyang Technological University Singapore School of Data Science Lingnan University Hong Kong School of Computer Science University of Birmingham United Kingdom Singapore Eye Research Institute Singapore National Eye Centre Singapore Tsinghua Medicine Tsinghua University Beijing China

Artificial Intelligence (ai) has apparently become one of the most important techniques discovered by humans in history while the human brain is widely recognized as one of the most complex systems in the universe. One fundamental critical question which would affect human sustainability remains open: Will artificial intelligence (ai) evolve to surpass human intelligence in the future? This paper shows that in theory new ai twins with fresh cellular level of ai techniques for neuroscience could approximate the brain and its functioning systems (e.g. perception and cognition functions) with any expected small error and ai without restrictions could surpass human intelligence with probability one in the end. This paper indirectly proves the validity of the conjecture made by Frank Rosenblatt 70 years ago about the potential capabilities of ai, especially in the realm of artificial neural networks. This paper also gives the answer to the two widely discussed fundamental questions: 1) whether ai could have potentials of discovering new principles in nature;2) whether error backpropagation (BP) algorithm commonly and efficiently used in tuning parameters in ai applications is also adopted in the brain. Intelligence is just one of fortuitous but sophisticated creations of the nature which has not been fully discovered. Like mathematics and physics, with no restrictions artificial intelligence would lead to a new subject with its self-contained systems and principles. We anticipate that this paper opens new doors for 1) ai twins and other ai techniques to be used in cellular level of efficient neuroscience dynamic analysis, functioning analysis of the brain and brain illness solutions;2) new worldwide collaborative scheme for interdisciplinary teams concurrently working on and modelling different types of neurons and synapses and different level of functioning subsystems of the brain with ai techniques;3) development of low energy of ai techniques with the aid of fundament

关键词： Neurology

Lecture Notes in Electrical Engineering

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Preface

Lecture Notes in Electrical engineering 2023年 1027 LNEE卷 xi-xii页

作者： Dubey, Ashwani Kumar Chong, Peter Han Joo Sugumaran, Vijayan Department of Electronics and Communication Engineering Amity School of Engineering and Technology Amity University Uttar Pradesh Noida India School of Engineering Computer and Mathematical Sciences Auckland University of Technology Auckland New Zealand Department of Decision and Information Sciences Center for Data Science and Big Data Analytics Oakland University RochesterMI United States

Addressing information gaps in household waste sorting using a mobile application

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Addressing information gaps in household waste sorting using...

ASME 2019 International Design engineering Technical Conferences and computers and Information in engineering Conference, IDETC-CIE 2019

作者： Pagels, Kelvin Østergaard Rasmussen, Mikkel Bayard Ramanujan, Devarajan Electrical and Computer Engineering Section Department of Engineering Aarhus University Aarhus C8000 Denmark Mechanical Engineering Section Department of Engineering Center for Digitalization Big Data and Data Analytics Aarhus University Aarhus C8000 Denmark

ISBN: (纸本)9780791859223

The Danish government has outlined a target of recycling 50% of total household waste by the year 2022. Improving household waste sorting is an important consideration towards achieving this goal. This paper focuses on understanding existing waste sorting practices among Danish residents and exploring whether a mobile application can help address any existing information gaps. We conducted a preliminary survey (N = 180) that assessed preference for sorting strategies and the types of waste sorted. Following this a more detailed survey was conducted (N = 357) that assessed residents’ motivation to sort household waste, knowledge of local sorting requirements, information gaps that prevent effective sorting, and need for specific features in a mobile application. Results show over one-third of respondents felt they needed additional waste sorting information. Respondents had fewer inaccuracies disposing items within a single waste stream (e.g., electronics waste) compared to items with mixed waste streams (e.g., milk carton with a plastic cap). Based on these findings we propose the design of a mobile application that can potentially improve household waste sorting. Copyright © 2019 ASME.

关键词： Surveys

A Brief Review of Explainable Artificial Intelligence in Healthcare

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arXiv 2023年

作者： Sadeghi, Zahra Alizadehsani, Roohallah Cifci, Mehmet Akif Kausar, Samina Rehman, Rizwan Mahanta, Priyakshi Bora, Pranjal Kumar Almasri, Ammar Alkhawaldeh, Rami S. Hussain, Sadiq Alatas, Bilal Shoeibi, Afshin Moosaei, Hossein Hladík, Milan Nahavandi, Saeid Pardalos, Panos M. Institute for Big Data Analytics Faculty of Computer Science Dalhousie University Canada Deakin University Geelong Australia The Institute of Computer Technology Tu Wien University Vienna1040 Austria University of Kotli Azad Jammu and Kashmir Azad Kashmir Kotli Pakistan Centre for Computer Science and Applications Dibrugarh University Assam India Department of Management Information Sys Al-Balqa Applied University Salt19117 Jordan Department of Computer Information Systems The University of Jordan Aqaba77110 Jordan Examination Branch Dibrugarh University Assam Dibrugarh India Department of Software Eng. Firat University Elazig23100 Turkey Data Science and Computational Intelligence Institute University of Granada Spain Department of Informatics Faculty of Science Jan Evangelista Purkyně University in Ústí nad Labem Czech Republic Department of Applied Mathematics School of Computer Science Faculty of Mathematics and Physics Charles University Prague Czech Republic Harvard Paulson School of Engineering and Applied Sciences Harvard University AllstonMA02134 United States Center for Applied Optimization Department of Industrial and Systems Engineering University of Florida Gainesville32611 United States

Xai refers to the techniques and methods for building ai applications which assist end users to interpret output and predictions of ai models. Black box ai applications in high-stakes decision-making situations, such as medical domain have increased the demand for transparency and explainability since wrong predictions may have severe consequences. Model explainability and interpretability are vital successful deployment of ai models in healthcare practices. ai applications' underlying reasoning needs to be transparent to clinicians in order to gain their trust. This paper presents a systematic review of Xai aspects and challenges in the healthcare domain. The primary goals of this study are to review various Xai methods, their challenges, and related machine learning models in healthcare. The methods are discussed under six categories: Features-oriented methods, global methods, concept models, surrogate models, local pixel-based methods, and human-centric methods. Most importantly, the paper explores Xai role in healthcare problems to clarify its necessity in safety-critical applications. The paper intends to establish a comprehensive understanding of Xai-related applications in the healthcare field by reviewing the related experimental results. To facilitate future research for filling research gaps, the importance of Xai models from different viewpoints and their limitations are investigated. © 2023, CC BY-NC-ND.

关键词： Decision making

Temporal Output Discrepancy for Loss Estimation-Based Active Learning

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arXiv 2022年

作者： Huang, Siyu Wang, Tianyang Xiong, Haoyi Wen, Bihan Huan, Jun Dou, Dejing The Harvard John A. Paulson School of Engineering and Applied Sciences Harvard University CambridgeMA02134 United States The Department of Computer Science and Information Technology Austin Peay State University ClarksvilleTN37044 United States The Big Data Laboratory Baidu Research Beijing100193 China The School of Electrical and Electronic Engineering Nanyang Technological University Singapore639798 Singapore The AWS AI Lab Amazon SeattleWA98109 United States

While deep learning succeeds in a wide range of tasks, it highly depends on the massive collection of annotated data which is expensive and time-consuming. To lower the cost of data annotation, active learning has been proposed to interactively query an oracle to annotate a small proportion of informative samples in an unlabeled dataset. Inspired by the fact that the samples with higher loss are usually more informative to the model than the samples with lower loss, in this article we present a novel deep active learning approach that queries the oracle for data annotation when the unlabeled sample is believed to incorporate high loss. The core of our approach is a measurement temporal output discrepancy (TOD) that estimates the sample loss by evaluating the discrepancy of outputs given by models at different optimization steps. Our theoretical investigation shows that TOD lower-bounds the accumulated sample loss thus it can be used to select informative unlabeled samples. On basis of TOD, we further develop an effective unlabeled data sampling strategy as well as an unsupervised learning criterion for active learning. Due to the simplicity of TOD, our methods are efficient, flexible, and task-agnostic. Extensive experimental results demonstrate that our approach achieves superior performances than the state-of-the-art active learning methods on image classification and semantic segmentation tasks. In addition, we show that TOD can be utilized to select the best model of potentially the highest testing accuracy from a pool of candidate models. Copyright © 2022, The Authors. All rights reserved.

关键词： Semantics

A Brief Review of Explainable Artificial Intelligence in Healthcare

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SSRN

SSRN 2023年

作者： Sadeghi, Zahra Alizadehsani, Roohallah Cifci, Mehmet Akif Kausar, Samina Rehman, Rizwan Mahanta, Priyakshi Bora, Pranjal Kumar Almasri, Ammar Alkhawaldeh, Rami S. Hussain, Sadiq Alatas, Bilal Shoeibi, Afshin Moosaei, Hossein Hladík, Milan Nahavandi, Saeid Pardalo, Panos M. Institute for Big Data Analytics Faculty of Computer Science Dalhousie University Canada Deakin University Geelong Australia The Institute of Computer Technology Tu Wien University Vienna1040 Austria University of Kotli Azad Jammu and Kashmir Azad Kashmir Kotli Pakistan Centre for Computer Science and Applications Dibrugarh University Assam India Department of Management Information Sys Al-Balqa Applied University Salt19117 Jordan Department of Computer Information Systems The University of Jordan Aqaba77110 Jordan Examination Branch Dibrugarh University Assam Dibrugarh India Department of Software Eng. Firat University Elazig23100 Turkey Data Science and Computational Intelligence Institute University of Granada Spain Department of Informatics Faculty of Science Jan Evangelista Purkyně University in Ústí nad Labem Czech Republic Department of Applied Mathematics School of Computer Science Faculty of Mathematics and Physics Charles University Prague Czech Republic Harvard Paulson School of Engineering and Applied Sciences Harvard University AllstonMA02134 United States Swinburne University of Technology HawthornVIC3122 Australia Center for Applied Optimization Department of Industrial and Systems Engineering University of Florida Gainesville32611 United States

关键词： Decision making