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iPREDICT: AI enabled proactive pandemic prediction using biosensing wearable devices

Informatics in Medicine Unlocked 2024年 46卷

作者： Riaz, Muhammad Sajid Shaukat, Maria Saeed, Tabish Ijaz, Aneeqa Qureshi, Haneya Naeem Posokhova, Iryna Sadiq, Ismail Rizwan, Ali Imran, Ali AI4Networks Research Center School of Electrical and Computer Engineering University of Oklahoma Tulsa United States Hudson College of Public Health University of Oklahoma United States James Watt School of Engineering University of Glasgow Scotland United Kingdom

The emergence of pandemics poses a persistent threat to both global health and economic stability. While zoonotic spillovers and local outbreaks may not be fully preventable, early detection of infections in individuals before they spread to communities can make a major difference in containing an infectious disease and stopping it from becoming an epidemic and then a pandemic. In this paper, we propose a novel Artificial Intelligence (AI)-based pandemic prediction framework called iPREDICT—a concept framework designed to leverage the power of AI and crowd-sensed data for accurate and timely pandemic prediction. The core idea of iPREDICT is to leverage the deluge of data that can be harnessed from connected and wearable biosensing devices. iPREDICT system then works by monitoring anomalies in the biomarkers at the individual level and correlating them with similar anomalies observed in other members of the community. Using AI-based anomaly detection in conjunction with analysis of the spatiotemporal growth of the correlated anomalies, iPREDICT thus can potentially detect and monitor the emergence of a local outbreak in near real-time to predict a potential pandemic. However, not every outbreak has the potential to become a pandemic. We illustrate how tools like graph neural networks can be leveraged to determine optimal thresholds as a function of a large number of demographical, social, and geographical factors that determine the spatiotemporal spread of an outbreak, thus quantifying the risk of it becoming an epidemic or pandemic. We also identify essential technological and social challenges that require attention to transform iPREDICT from an idea into a globally deployable solution for future pandemic prediction and management. To provide deeper insights into iPREDICT design challenges and trigger research towards possible solutions we present a COVID-19 based case study. The results signify the impact of variation in biosensing hardware, data sampling rate, an

关键词： AI for healthcare Biosensing devices iPREDICT Pandemic prediction Pandemic prognostics Pathogen spread Wearable biosensors

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Enabling High-Precision 5G mmWave-Based Positioning for Autonomous Vehicles in Dense Urban Environments

arXiv

引用

arXiv 2023年

作者： Bader, Qamar Saleh, Sharief Elhabiby, Mohamed Noureldin, Aboelmagd The Department of Electrical and Computer Engineering Queen’s University KingstonONK7L 3N6 Canada Lab Department of Electrical and Computer Engineering Royal Military Collage of Canada KingstonONK7K 7B4 Canada Micro Engineering Tech. Inc. Canada The Public Works Department Ain Shams University Cairo11566 Egypt

5G-based mmWave wireless positioning has emerged as a promising solution for autonomous vehicle (AV) positioning in recent years. Previous studies have highlighted the benefits of fusing a line-of-sight (LoS) 5G positioning solution with an Inertial Navigation System (INS) for an improved positioning solution. However, the highly dynamic environment of urban areas, where AVs are expected to operate, poses a challenge, as non-line-of-sight (NLoS) communication can deteriorate the 5G mmWave positioning solution and lead to erroneous corrections to the INS. To address this challenge, we exploit 5G multipath and LoS signals to improve positioning performance in dense urban environments. In addition, we integrate the proposed 5G-based positioning with low-cost onboard motion sensors (OBMS). Moreover, the integration is realized using an unscented Kalman filter (UKF) as an alternative to the widely utilized EKF as a fusion engine to avoid ignoring the higher-order and non-linear terms of the dynamic system model. We also introduce techniques to evaluate the quality of each LoS and multipath measurement prior to incorporation into the filter’s correction stage. To validate the proposed methodologies, we performed two test trajectories in the dense urban environment of downtown Toronto, Canada. For each trajectory, quasi-real 5G measurements were collected using a ray-tracing tool incorporating 3D map scans of real-world buildings, allowing for realistic multipath scenarios. For the same trajectories, real OBMS data were collected from two-different low-cost IMUs. Our integrated positioning solution was capable of maintaining a level of accuracy below 30 cm for approximately 97% of the time, which is superior to the accuracy level achieved when multipath signals are not considered, which is only around 91% of the time. © 2023, CC BY.

关键词： Autonomous vehicles

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Decarbonizing Regional Multi-Modal Transportation System with Shared Electric Charging Hub

SSRN

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SSRN 2022年

作者： Ye, Zuzhao Yu, Nanpeng Wei, Ran Liu, Xiaoyue Cathy Department of Electrical and Computer Engineering University of California Riverside RiversideCA United States School of Public Policy University of California Riverside RiversideCA United States Department of Civil and Environmental Engineering University of Utah Salt Lake CityUT United States

In light of the growing concerns of global climate change, the pace of transportation electrification has greatly accelerated in recent years as an effort towards net-zero greenhouse gas (GHG) emissions. However, it remains unclear how to effectively deploy and operate public charging infrastructure to best serve an electrified transportation system within a multi-modal context while maximizing the benefits of decarbonization. To address this question, we propose a mechanism of shared charging hubs that can provide holistic energy management for both electric buses (EBs) and passenger electric vehicles (EVs).The deployment and operation of shared charging hubs is determined by a new spatio-temporal optimization model which aims to minimize GHG emission given a budget limit while avoiding the occurrence of massive spikes in peak power demand. This is achieved by coherently accommodating the charging demand of EBs and EVs, and explicitly integrating the time-varying electricity carbon intensity and vehicle-to-grid (V2G) technology. To demonstrate its effectiveness, the model is applied to the bus fleets operated by seven transit agencies and the park-and-ride facilities (for EVs) near twelve rail transit stations in Contra Costa County, California, USA. The results show that the shared charging hubs can lead to significant GHG emission reduction while mitigating the peak electricity demand. This research will help policymakers and transportation agencies make more informed decisions regarding the planning and design of charging infrastructure. © 2022, The Authors. All rights reserved.

关键词： Climate change

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Convolutional Bi-GRU-based prediction model for Climate Indicators

Convolutional Bi-GRU-based prediction model for Climate Indi...

引用

International Conference on Electronics Technology (ICET)

作者： Tsung-Ming Lo Hong-Tao Zhang Yun-Bing He Yi-Zhi Liu Wan-Ying Zhang Qiong-Dan Liang Zhen Bei Guangdong-Taiwan College of Industrial Science & Technology Dongguan University of Technology Dongguan China School of Traffic Information Engineering Guangxi Vocational and Technical College of Communications Nanning China Department of Public administration Nanfang College Guangzhou Guangzhou China School of Electrical and Computer Engineering Nanfang College-Guangzhou Guangzhou China Intelligent Manufacturing Engineering Dongguan University of Technology Dongguan China

ISBN: (数字)9798350363951

ISBN: (纸本)9798350363968

Climate prediction is critical to a wide range of fields, from agriculture to public health, but traditional methods face challenges due to the complexity and time-space characteristics of climate data. Machine learning techniques provide a promising solution by effectively extracting features from climate indicator data. In this study, we propose a new method for predicting climate indicators by combining CNN-Bi-GRU models with FNN networks. Through evaluation, our method demonstrates excellent prediction performance in terms of RMSE and MAE on both test and training sets, and robust convergence behavior is observed in model training. In addition, our method achieves high accuracy in predicting key climate metrics such as temperature and rain frequency, providing valuable information for understanding climate dynamics. Comparison experiments with LSTM-FNN, Double-LSTM, BiLSTM, and Bi-GRU models highlight the superiority of the CNNBi-GRU approach, especially in reducing temperature prediction errors. In conclusion, this study presents a promising approach to climate prediction that can enhance our understanding of climate variability and support informed decision making in various industries.

关键词： Training Measurement Accuracy Rain Predictive models Feature extraction Data models

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Empowering Healthcare with AI: Brain Tumor Detection Using MRI and Multiple Algorithms

Empowering Healthcare with AI: Brain Tumor Detection Using M...

引用

Science, engineering and Business for Sustainable Development Goals (SEB-SDG), International Conference on

作者： Micheal Olaolu Arowolo Williams Oluwatobiloba Ajayi Prisca Olawoye Oluwatomilola Babajide Happiness Eric Aigbogun Mukhtar Damola Salawu Marion Olubunmi Adebiyi Ayodele Adebiyi Department of Electrical Engineering and Computer Science University of Missouri Columbia USA Department of Computer Science Landmark University Omu-Aran Nigeria Department of Public Health Western Illinois University USA Stowers Institute for Medical Research Kansas City MO USA Department of Computer Science Kwara State University Malete Nigeria

ISBN: (数字)9798350358155

ISBN: (纸本)9798350358162

Artificial intelligence (AI) has revolutionized neuroimaging, especially in the identification of brain cancers, by giving patients greater control and providing highly accurate diagnostic imaging. utilizing MRI data, this study investigates the feasibility of AI-driven brain tumor diagnosis utilizing methods including K-nearest neighbors (KNN), Support Vector Machine (SVM), Decision Tree (DT), and Naive Bayes (NB). Careful data preprocessing allowed us to access Kaggle's brain MRI datasets labeled with cancer information, which allowed us to obtain accurate results. While Naive Bayes achieved 60% accuracy and 84% sensitivity, SVM achieved 82% sensitivity and 62% accuracy. Enhanced sensitivity and precision, in addition to 63% accuracy, were shared by Decision Tree and KNN. These findings suggest that AI could be helpful in the multi-method detection strategy that is essential for the correct diagnosis of brain tumors. This study provides clinicians with flexible diagnostic tools powered by artificial intelligence, opening a new chapter in the fight against neurological disorders and for better patient outcomes. The combination of artificial intelligence with magnetic resonance imaging (MRI) holds great promise for the better early detection and treatment planning of brain cancers. As AI keeps changing the healthcare landscape, these studies show how AI can revolutionize diagnostics and the fight against neurological illnesses.

关键词： Support vector machines Accuracy Sensitivity Magnetic resonance imaging Brain cancer Nearest neighbor methods Classification algorithms

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Radiative energy band gap of nanostructures coupled with quantum emitters around the epsilon-near-zero frequency

引用

Physical Review B 2022年第8期106卷 085422-085422页

作者： Tao Gong Iñigo Liberal Miguel Camacho Benjamin Spreng Nader Engheta Jeremy N. Munday Department of Electrical and Computer Engineering University of California 95616 Davis California USA Department of Materials Science and Engineering University of California 95616 Davis California USA Institute of Smart Cities Public University of Navarre 31006 Pamplona Spain Department of Electronics and Electromagnetism University of Seville 41012 Seville Spain Department of Electrical and Systems Engineering University of Pennsylvania Philadelphia 19104 Pennsylvania USA

Epsilon-near-zero (ENZ) materials have been demonstrated to exhibit unique electromagnetic properties. Here we propose the concept of a radiative energy band gap for an ENZ nanoparticle coupled with a quantum emitter (QE). The radiative emission of the coupled QE–nanoparticle can be significantly suppressed around the ENZ frequency and substantially enhanced otherwise, yielding an effective energy band gap for radiation. This suppression is effectively invariant with respect to particle size and is therefore an intrinsic property of the ENZ material. Our concept also heralds an alternative pathway to quench the emission from a QE, which may find potential application in quantum information storage.

关键词： Mie scattering Permittivity Photoemission Plasmonics Spontaneous emission Nanostructures

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electrically switchable Casimir forces using transparent conductive oxides

引用

Physical Review A 2022年第6期106卷 062824-062824页

作者： Tao Gong Benjamin Spreng Miguel Camacho Iñigo Liberal Nader Engheta Jeremy N. Munday Department of Electrical and Computer Engineering University of California Davis California 95616 USA Department of Materials Science Engineering University of California Davis California 95616 USA Department of Electronics and Electromagnetism University of Seville Sevilla 41012 Spain Institute of Smart Cities Public University of Navarre Pamplona 31006 Spain Department of Electrical and Systems Engineering University of Pennsylvania Philadelphia Pennsylvania 19104 USA

Casimir forces between charge-neutral bodies originate from quantum vacuum fluctuations of electromagnetic fields, which exhibit a critical dependence on material's electromagnetic properties. Over the years, in situ modulation of a material's optical properties has been enabled through various means and has been widely exploited in a plethora of applications such as electro-optical modulation, transient color generation, bio- or chemical sensing, etc. Yet Casimir force modulation has been hindered by difficulty in achieving high modulation signals due to the broadband nature of the Casimir interaction. Here we propose and investigate two configurations that allow for in situ modulation of Casimir forces through electrical gating of a metal-insulator-semiconductor junction comprised of transparent conductive oxide (TCO) materials. By switching the gate voltage on and off, a force modulation of >400 pN is predicted due to substantive charge carrier accumulation in the TCO layer, which can be easily measured using state-of-the-art force measurement techniques in an atomic force microscope. We further examine the influence of the oxide layer thickness on the force modulation, suggesting the importance of the fine control of the oxide layer deposition. Our work provides a promising pathway for modulating the Casimir effect in situ with experimentally measurable force contrast.

关键词： Casimir effect Casimir effect & related phenomena Light-matter interaction Optical materials & elements Transparent conducting oxides

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Adversarial attacks and defenses in physiological computing:a systematic review

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National Science Open 2023年第1期2卷 62-90页

作者： Dongrui Wu Jiaxin Xu Weili Fang Yi Zhang Liuqing Yang Xiaodong Xu Hanbin Luo Xiang Yu Ministry of Education Key Laboratory of Image Processing and Intelligent Control School of Artificial Intelligence and AutomationHuazhong University of Science and TechnologyWuhan 430074China Zhejiang Lab Hangzhou 311121China School of Civil and Hydraulic Engineering Huazhong University of Science and TechnologyWuhan 430074China College of Public Administration Huazhong University of Science and TechnologyWuhan 430074China Electrical Engineering and Computer Science Department University of MichiganAnn Arbor MI 48109USA School of Management and Sino-European Institute for Intellectual Property Huazhong University of Science and TechnologyWuhan 430074China

Physiological computing uses human physiological data as system inputs in real *** includes,or significantly overlaps with,brain-computer interfaces,affective computing,adaptive automation,health informatics,and physiological signal based *** computing increases the communication bandwidth from the user to the computer,but is also subject to various types of adversarial attacks,in which the attacker deliberately manipulates the training and/or test examples to hijack the machine learning algorithm output,leading to possible user confusion,frustration,injury,or even ***,the vulnerability of physiological computing systems has not been paid enough attention to,and there does not exist a comprehensive review on adversarial attacks to *** study fills this gap,by providing a systematic review on the main research areas of physiological computing,different types of adversarial attacks and their applications to physiological computing,and the corresponding defense *** hope this review will attract more research interests on the vulnerability of physiological computing systems,and more importantly,defense strategies to make them more secure.

关键词： physiological computing brain-computer interfaces health informatics biometrics machine learning adversarial attack

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Learning on Forecasting HIV Epidemic Based on Individuals’ Contact Networks

Learning on Forecasting HIV Epidemic Based on Individuals’ ...

引用

17th International Joint Conference on Biomedical engineering Systems and Technologies, BIOSTEC 2024

作者： Sun, Chaoyue Liu, Yiyang Parisi, Christina Fisk-Hoffman, Rebecca Salemi, Marco Fang, Ruogu Danforth, Brandi Prosperi, Mattia Marini, Simone Department of Electrical and Computer Engineering Herbert Wertheim College of Engineering University of Florida GainesvilleFL United States Department of Epidemiology College of Public Health and Health Professions and College of Medicine University of Florida GainesvilleFL United States Department of Pathology Immunology and Laboratory Medicine College of Medicine University of Florida GainesvilleFL United States J. Crayton Pruitt Family Department of Biomedical Engineering Herbert Wertheim College of Engineering University of Florida GainesvilleFL United States Center for Cognitive Aging and Memory McKnight Brain Institute University of Florida GainesvilleFL United States Florida Department of Health 4025 Esplanade Way TallahasseeFL United States Emerging Pathogens Institute University of Florida GainesvilleFL United States

Improving the diagnosis of HIV is a fundamental objective of the Ending the HIV Epidemic initiative, as it represents the initial step toward treatment and achieving undetectable status, thereby reducing transmission. To attain these objectives effectively, it is crucial to identify the groups most susceptible to HIV, allowing interventions to be tailored to their specific needs. In this study, we developed a predictive model designed to assess individual HIV risk within a high-risk contact network – predicting treatment or at-risk – leveraging surveillance data collected through routine HIV case interviews in Florida. Unique to our analysis, we explored the incorporation of behavioral network information with Graph Neural Networks to enhance the predictive capacity for identifying individuals within the treatment or intervention categories, when compared to models that mainly consider conventional HIV risk factors. Our deployed Graph Isomorphism Network achieved 77.3% and 73.2% balanced accuracy in inductive and trans ductive learning scenarios respectively, outperforming the traditional prediction algorithms that do not leverage the network structure. We then usedour model to further investigate the importance of demographic and behavioral factors in the HIV risk prediction process. Our findings provide valuable insights for healthcare practitioners and policymakers in their efforts to combat HIV infection. © 2024 by SCITEPRESS – Science and Technology publications, Lda.

关键词： Contrastive Learning

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Quantifying cascading power outages during climate extremes considering renewable energy integration

arXiv

引用

arXiv 2024年

作者： Xu, Luo Lin, Ning Poor, H. Vincent Xi, Dazhi Perera, A.T.D. Department of Civil and Environmental Engineering Princeton University PrincetonNJ United States Department of Electrical and Computer Engineering Princeton University PrincetonNJ United States Andlinger Center for Energy and the Environment Princeton University PrincetonNJ United States Center for Policy Research on Energy and the Environment Princeton University PrincetonNJ United States

Climate extremes, such as hurricanes, combined with large-scale integration of environment-sensitive renewables, could exacerbate the risk of widespread power outages. We introduce a coupled climate-energy model for cascading power outages, which comprehensively captures the impacts of evolving climate extremes on renewable generation, and transmission and distribution networks. The model is validated by the 2022 Puerto Rico catastrophic blackout during Hurricane Fiona - the first-ever system-wide blackout event with complete weather-induced outage records. The model presents a novel resilience pattern that was not captured by the present state-of-the-art models and reveals that early failure of certain critical components surprisingly enhances overall system resilience. Sensitivity analysis of various behind-the-meter solar integration scenarios demonstrates that lower integration levels (below 45%, including the current level) exhibit minimal impact on system resilience in this event. However, surpassing this critical level without additional flexibility resources can exacerbate the failure probability due to substantially enlarged energy imbalances. Copyright © 2024, The Authors. All rights reserved.

关键词： Outages

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