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2023 IEEE International Ultrasonics Symposium, IUS 2023

作者： Dou, Yimeng Mu, Fangzhou Li, Yin Varghese, Tomy -Madison Department of Electrical and Computer Engineering MadisonWI United States University of Wisconsin School of Medicine and Public Health Department of Medical Physics MadisonWI United States UW-Madison Department of Computer Science MadisonWI United States University of Wisconsin School of Medicine and Public Health Department of Biostatistics & Medical Informatics MadisonWI United States

ISBN: (纸本)9798350346459

Three-dimensional ultrasound (3D US) imaging finds broad applications in clinical practice. Compared to using 'wobbler' (motor embedded) or 'matrix array' transducers which suffers from a restricted field of view, freehand US offers more flexibility in mapping the 3D volume along the scanning path. Nevertheless, current methods for reconstructing 3D US volumes using freehand scanning are challenged by significant elevational shifts along the scanning trajectory. Previous research explored the integration of motion sensors to resolve transducer drift, yet frequent motion artifacts often compromise the benefits brought by these sensors. Recent work turned to deep neural networks (DNNs) for estimating the relative pose of imaging planes between frames in the scanning trajectory, allowing for sensorless freehand US. However, these data-driven models fall short in accuracy as they lack physical constraints, hence limiting their use in clinical practice. Inspired by the physical properties of US, we design a physics inspired DNN architecture that can better leverage the contextual cues between elevational frames for sensorless freehand 3D US reconstruction, and demonstrate substantially improving reconstruction accuracy. © 2023 IEEE.

关键词： Deep neural networks

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Hardware Software Codesign Using an Open-Source Authentication Algorithm 14

Hardware Software Codesign Using an Open-Source Authenticati...

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14th Annual Undergraduate Research Conference on "ICT for Resilient and Sustainable Infrastructure", URC 2022

作者： Albader, Sadoon L. Rehman, Haseeb Ur Zaher, Ashraf A. Public Authority for Applied Education & Training Computer and Information Center Adailiya Kuwait University of Ottawa Faculty of Engineering Ottawa Canada German International University Department of Electrical Engineering Cairo Egypt

ISBN: (纸本)9798350346800

This paper explores the use of hardware/software codesign to implement algorithms for open-source message authentication code while investigating the required tools, the problems to be overcome, and satisfying the industry standards for synthesizing the hardware/software codesign. Technical issues related to using the DE10-Nano were carefully analysed, which include the very poor documentation, and the needed specific Linux kernel patches that only work with certain versions of the Linux kernel. This research builds the fundamental blocks for using the DE10-Nano for hardware/software codesign. The results are expected to pave the way to focus on promoting the current trends of hardware/software codesigns and to encourage the hardware manufacturers to offer more help for programmers and engineers. © 2022 IEEE.

关键词： Field programmable gate arrays (FPGA)

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Smart Happy Device with IoT : For those who suffer anxiety, stress, and depression 12

Smart Happy Device with IoT : For those who suffer anxiety, ...

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12th IEEE Region 10 Humanitarian Technology Conference, R10-HTC 2024

作者： Yakub, Fitri Azizan, Azizul Dhamanti, Inge Khudzari, Ahmad Zahran Md. Govindan, Niranjana Muljono, Muljono UTM Malaysia-Japan International Institute of Technology Electronic System Engineering Department Kuala Lumpur Malaysia Universiti Teknologi Malaysia Cyberphysical Research Group Embedded AI & ML Science Lab Faculty of Artificial Intelligence Kuala Lumpur Malaysia Universitas Airlangga Faculty of Public Health Department of Health Policy and Administration Surabaya Indonesia UTM Faculty of Engineering Department of Biosciences & Medical Engineering Johor Bahru Malaysia SRMIST Faculty of Engineering & Technology Department of Computing Technologies Chennai India Universitas Dian Nuswantoro Faculty of Computer Science Semarang Indonesia

ISBN: (纸本)9798350340204

Due to the rising cases of Covid-19 in the world especially in Malaysia, the impact had been hit hard for many people. Due to this all people must be isolated in their home. It has affected the well-being and working life of millions of people across the world. Due to this disease, the mental health among people has increased significantly for the past 2 years. According to MoH, out of 983 Malaysian students, about 20.4%, 6.6%, and 2.8% are experienced minimal to moderate, marked to severe, and most extreme levels of anxiety, respectively. The main stressors are the remote online teaching, and uncertainty about the future regarding study and career which can affect mental health. Thus, the main objective of this project is to develop a product that may provide different medication methods to its users by helping people who suffer anxiety, stress, and depression. We used aromatherapy, positive quotes, and music therapy in order to release stress. The user can also check his or her pulse rate by only touching the fingerprint through the sensor. The microcontrollers used in this project are Arduino Uno and Arduino Mega to build the project circuit. The experimental results can improve body health, mind and spirit which enhances both physical and emotional health. © 2024 IEEE.

关键词： COVID-19

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A bias evaluation checklist for predictive models and its pilot application for 30-day hospital readmission models (May, 10.1093/jamia/ocac065, 2022)

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JOURNAL OF THE AMERICAN MEDICAL INFORMATICS ASSOCIATION 2022年第9期29卷 1656-1656页

作者： Wang, H. Echo Landers, Matthew Adams, Roy Subbaswamy, Adarsh Kharrazi, Hadi Gaskin, Darrell J. Saria, Suchi Department of Health Policy and Management Johns Hopkins Bloomberg School of Public Health Baltimore Maryland USA Department of Computer Science University of Virginia Charlottesville Virginia USA Department of Psychiatry and Behavioral Sciences Johns Hopkins School of Medicine Baltimore Maryland USA Department of Computer Science and Statistics Whiting School of Engineering Johns Hopkins University Baltimore Maryland USA

Objective Health care providers increasingly rely upon predictive algorithms when making important treatment decisions, however, evidence indicates that these tools can lead to inequitable outcomes across racial and socio-economic groups. In this study, we introduce a bias evaluation checklist that allows model developers and health care providers a means to systematically appraise a model’s potential to introduce bias. Materials and Methods Our methods include developing a bias evaluation checklist, a scoping literature review to identify 30-day hospital readmission prediction models, and assessing the selected models using the checklist. Results We selected 4 models for evaluation: LACE, HOSPITAL, Johns Hopkins ACG, and HATRIX. Our assessment identified critical ways in which these algorithms can perpetuate health care inequalities. We found that LACE and HOSPITAL have the greatest potential for introducing bias, Johns Hopkins ACG has the most areas of uncertainty, and HATRIX has the fewest causes for concern. Discussion Our approach gives model developers and health care providers a practical and systematic method for evaluating bias in predictive models. Traditional bias identification methods do not elucidate sources of bias and are thus insufficient for mitigation efforts. With our checklist, bias can be addressed and eliminated before a model is fully developed or deployed. Conclusion The potential for algorithms to perpetuate biased outcomes is not isolated to readmission prediction models; rather, we believe our results have implications for predictive models across health care. We offer a systematic method for evaluating potential bias with sufficient flexibility to be utilized across models and applications.

关键词： predictive model hospital readmission bias health care disparity clinical decision-making

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Investigating Ultra-Short-Term Heart Rate Variability as an Indicator of Craving in Recently Treated Patients with Opioid Use Disorder 46

Investigating Ultra-Short-Term Heart Rate Variability as an ...

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46th Annual International Conference of the IEEE engineering in Medicine and Biology Society, EMBC 2024

作者： Abbaraju, Vikram Bashar, Syed Khairul Nawar, Afra Rahman, Farhan N. Choi, Jaiyoun Lambert, Tamara P. Gazi, Asim H. Harrison, Anna B. Robinson, Madeline R. Mesfin, Hewitt Gray, Trinity A. Mermin-Bunnell, Kellen Jacquement, Nora Tomic, Nikolina Welsh, Justine W. Patel, Santosh Vaccarino, Viola Shah, Amit J. Douglas Bremner, J. Inan, Omer T. Georgia Institute of Technology School of Electrical and Computer Engineering AtlantaGA United States Emory University School of Medicine Department of Psychiatry and Behavioral Sciences AtlantaGA United States Georgia Institute of Technology The Wallace H. Coulter Department of Biomedical Engineering AtlantaGA United States Emory University Department of Epidemiology Rollins School of Public Health AtlantaGA United States

ISBN: (纸本)9798350371499

Induced craving frequently leads to relapse in patients with a history of opioid use disorder (OUD). Quantifying the physiological manifestations of craving can enable caregivers of patients with OUD to continuously monitor craving and thus help inform treatment plans. Heart rate variability (HRV) is a promising candidate to capture the trends in such manifestations due to the ease of measuring changes in heartbeat intervals using the electrocardiogram. However, the relationship between craving and HRV measured over periods shorter than 5 minutes, i.e., ultra-short-term HRV, has not yet been investigated in detail. In this work, we present the first analysis on the relationship between 12 HRV features computed over ~2-minute periods and subjective craving reported on the visual analog scale (VAS-craving). Patients with OUD stable on medication (N = 12) went through an approximately 2-hour protocol involving audio/visual opioid cues to induce craving, alongside active transcutaneous vagus nerve stimulation or sham stimulation. Regardless of stimulation type, changes in VAS-craving scores over the course of the protocol were negatively correlated with changes in two ultra-short-term HRV features: the number of adjacent normal heartbeat intervals differing by more than 50 milliseconds (r = -0.63, p = 0.03) and the total power across all frequency bands (r = -0.69, p = 0.03).Clinical relevance - This preliminary study suggests that certain ultra-short-term HRV features can potentially serve as indicators of craving in patients with a history of OUD. Using such metrics computed over small data intervals can enable efficient clinical decision making and may help prevent opioid relapse. © 2024 IEEE.

关键词： Heart

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Compressed VGGNet for Automatic COVID-19 Disease Detection from CT Scan Images 8th

Compressed VGGNet for Automatic COVID-19 Disease Detection f...

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8th IFIP TC 12 International Conference on computer, Communication and Signal Processing, ICCCSP 2024

作者： Vinothini, R. Niranjana, G. Dhamanti, Inge Lailiyah, Syifa’ul Yakub, Fitri Department of Computer Science and Engineering SRM Institute of Science and Technology Kattankulathur 603203 India Department of Computing Technologies School of Computing SRM Institute of Science and Technology Kattankulathur 603203 India Department of Health Policy and Administration Faculty of Public Health Universitas Airlangga Surabaya Indonesia Faculty of Health Medicine and Life Sciences Universitas Airlangga East Java Indonesia Electronic System Engineering Department Malaysia-Japan International Institute of Technology Kuala Lumpur Malaysia

ISBN: (纸本)9783031736162

Millions of people across the globe were affected by this rapidly spreading disease by the year 2020. Contamination of the respiratory system results from contracting COVID-19. Nonetheless, establishing a conclusive diagnosis of COVID-19 may prove difficult due to the subtle differences it shares with conventional pneumonia and the complexities involved in identifying areas of infection. A growing number of approaches based on deep learning (DL) are being proposed for the automated detection of COVID-19 from CT scan images. By employing the data pretreatment methodology, unprocessed image data becomes prepared for further analysis. The proposed framework utilised transfer learning to construct VGGNet, which is capable of discerning the Covid-19 disease. The model was subsequently assessed in comparison to the most sophisticated models, VGG16 and VGG19, using the SARS-COV2 CT scan dataset. This dataset contains 1230 CT scans of non-infected images and 1252 CT scans of Covid-19-infected images. The model has achieved 99% accuracy, 98% precision, 97% recall, and 98% F1-score, among other performance metrics. © IFIP International Federation for Information Processing 2025.

关键词： COVID-19

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Nexus Cognizant Pricing of Workplace Electric Vehicle Charging

Nexus Cognizant Pricing of Workplace Electric Vehicle Chargi...

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American Control Conference (ACC)

作者： Minghao Mou Sean Qian Junjie Qin Elmore Family School of Elec-trical and Computer Engineering Purdue University Department of Civil and Environment Engineering Heinz College of Information Systems and Public Policy Carnegie Mellon University

ISBN: (数字)9798350382655

ISBN: (纸本)9798350382662

This paper studies the problem of designing work-place electric vehicle (EV) charging tariffs (i.e., fee schedules) while considering their impact to the transportation-electricity nexus. In particular, we consider the morning commute problem where a collection of commuters who drive EV to work must go through a common traffic bottleneck. Individual commuters determine when to leave for work and whether to charge their EV at work by optimizing a payoff function accounting for their generalized travel cost and payoff from charging. As an arrival time dependent charging tariff can directly impact the commuter decisions at the user equilibrium, we tackle the problem of designing tariffs that optimize (a) only the transportation component of the social cost, (b) only the electricity component of the social cost, and (c) the total social cost for the coupled transportation and electricity system. Tariffs incentivizing user equilibria that achieve the same performance as centralized social cost minimization are derived for the first two settings. For the last setting, we establish a tight condition under which it is possible to decentralize social optimal decisions via tariffs, and design optimal and suboptimal tariffs when the condition holds and fails, respectively.

关键词： Schedules Costs Electricity Employment Tariffs Transportation Pricing

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Electrocoagulation-based AZO DYE (P4R) Removal Rate Prediction Model using Deep Learning

Electrocoagulation-based AZO DYE (P4R) Removal Rate Predicti...

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2023 International Symposium on Green Technologies and Applications, ISGTA 2023

作者： Akoulih, Meryem Tigani, Smail Byoud, Fouzia Rharib, Meryem El Saadane, Rachid Pierre, Samuel Chehri, Abdellah Ghachtouli, Sanae El IME Lab Faculty of Science Hassan 2 University Casablanca20250 Morocco Research and Development Unit Accsellium LLC Fez Morocco Electrical Engineering Department SIRC-LaGeS Hassania School of Public Labors Casablanca20250 Morocco Department of Computer and Software Engineering Polytechnique Montréal MontréalQC Canada Department of Mathematics and Computer Science Royal Military College of Canada KingstonON11 K7K 7B4 Canada

This article presents a study on the effectiveness of electrocoagulation (EC) for the removal of azo dyes from wastewater. The analysis was performed using a combination of statistical methods, including density estimation, correlation analysis, and deep learning for electrocoagulation performance prediction. The results showed that electrocoagulation was able to effectively remove azo dyes from the wastewater, considering the energy consumption and the mass of flocs being important factors in the process. Deep Learning (DL) is used to build our predictive model using the datasets collected during the experimentation stage. Overall, the findings suggest that electrocoagulation is a promising technique for the treatment of wastewater containing azo dyes, and that the use of statistical and machine learning methods can aid in the optimization of the process. © 2024 The Authors. Published by ELSEVIER B.V.

关键词： Azo dyes

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Detecting Reentrancy Vulnerability in Smart Contracts using Graph Convolution Networks

Detecting Reentrancy Vulnerability in Smart Contracts using ...

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Consumer Communications and Networking Conference, CCNC IEEE

作者： Hozefa Lakadawala Komla Dzigbede Yu Chen Dept. of Electrical & Computer Engineering Binghamton University Binghamton NY Department of Public Administration Binghamton University Binghamton NY

Because of many advanced features, Decentralized Finance (DeFi) has become a hot topic in the past decade. As an application of blockchain technology, DeFi allows people to trade cryptocurrencies and other financial products more efficiently, securely, and privately. Specifically, using smart contracts further improves transaction rate and the quality of user experience (QoE) by defining the business logic via code. However, if smart contracts are not audited before compiling on an immutable blockchain, it may result in losses of millions. Therefore, there is a compelling need for efficient and effective measures to ensure the robustness and genuineness of smart contracts. In this paper, we propose a Homogeneous Graph Machine Learning Algorithm for Reentrancy attacks DEtection using Graph Convolution Networks (HARDEN). Reentrancy attacks are one of the most infamous vulnerabilities in smart contracts. The experimental results are encouraging and validate the feasibility of applying a Deep Learning (DL) approach to detect vulnerabilities in smart contracts. We hope this preliminary study will inspire more interest and discussions in the raising area.

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Machine Learning and Deep Learning Approach for Predicting Neonatal Mortality in Indonesia

Machine Learning and Deep Learning Approach for Predicting N...

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International Conference on Applied System Innovation (ICASI)

作者： Nur Hamid Fidya Rumiati Rizal Dwi Prayogo Hidetaka Nambo Division of Electrical Engineering and Computer Science Kanazawa University Japan Department of Epidemiology Faculty of Public Health Universitas Indonesia Indonesia

ISBN: (数字)9798350394924

ISBN: (纸本)9798350394931

This research explores machine learning approaches to determine the most significant features related to neonatal mortality in Indonesia. We create prediction tasks with deep learning models including MLP, LSTM, and CNN. We found that low birth weight and early breastfeeding becomes the most significant features related to neonatal mortality in Indonesia. For prediction task, implementing feature importance task as feature selection can improve prediction performance and reduce algorithm complexity. LSTM and CNN achieved the best prediction model with 90.91% accuracy.

关键词： Deep learning Pediatrics Technological innovation Predictive models Feature extraction Prediction algorithms Complexity theory

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