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Transforming Healthcare Diagnostics With Tensorized Attention and Continual Learning on Multi-Modal Data

IEEE Transactions on Consumer Electronics 2025年

作者： Iqbal, Saeed Zhong, Xiaopin Khan, Muhammad Attique Shabaz, Mohammad Wu, Zongze AlHammadi, Dina Abdulaziz Liu, Weixiang Algamdi, Shabbab Ali Li, Yang Shenzhen University College of Mechatronics and Control Engineering Shenzhen518060 China Prince Mohammad Bin Fahd University Department of AI Alkhobar Saudi Arabia Model Institute of Engineering and Technology J and K Jammu India Princess Nourah bint Abdulrahman University College of Computer and Information Sciences Department of Information Systems Riyadh11671 Saudi Arabia Prince Sattam bin Abdulaziz University College of Computer Science and Engineering Department of Software Engineering Al Kharj Saudi Arabia Shihezi University College of Mechanical and Electrical Engineering Shihezi832003 China

Analyzing multi-modal medical data in the setting of uncertain healthcare situations continues to be a major topic in medical image analysis and healthcare big data. Traditional machine learning algorithms are severely hampered by inaccurate data fusion, a lack of adaptability to changing patient data, and challenges managing uncertainty. These difficulties are made worse by complicated medical images and diverse data sources, which results in less accurate diagnosis and worse-than-ideal healthcare choices. To tackle these urgent problems, this paper suggests two new approaches: Continual Learning using Progressive Neural Networks (PNNs) and Tensorized Attention Mechanism for Data Fusion. The Tensorized Attention Mechanism improves multi-modal data fusion by using dynamic, task-specific attention to improve feature alignment across modalities, and the PNNs framework uses continual learning, memory augmentation, and domain adaptation to ensure robust learning under data uncertainty. We test these methods on a variety of multi-modal datasets, such as MIMIC-IV, CheXpert, MOST, OAI, and Heart Murmur, which offer a comprehensive representation of medical data from clinical reports, chest X-rays, heart murmurs, and other heterogeneous data sources. Our experimental results show notable improvements in diagnostic performance, with notable results like a CFI of 0.10, a KR score of 90.4%, and an MMC score of 0.097, indicating superior generalization and robustness across domains. Healthcare AI applications could be revolutionized by the use of specialized losses, such as Conditional Variational Autoencoder (CVAE), Adversarial Contrastive Learning (ACL), Reciprocal Regularization, and domain adaptation losses, which are essential for preventing forgetting and guaranteeing learning stability across shifting data streams. © 1975-2011 IEEE.

关键词： Sensor data fusion

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Miniature Fiber-optic Modal Interferometer Based on Ultrasonic-Cutting Technique for Low-range Refractive Index Sensing 19

Miniature Fiber-optic Modal Interferometer Based on Ultrason...

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19th International Conference on Optical Communications and Networks, ICOCN 2021

作者： Wang, Qian Wu, Jixuan Song, Binbin Bai, Hua Liu, Bo Duan, Shaoxiang Tianjin Key Laboratory of Optoelectronic Detection Technology and Systems School of Electrical and Electronic Engineering Tiangong University Tianjin300387 China Key Laboratory of Computer Vision and Systems Ministry of Education School of Computer Science and Engineering Tianjin University of Technology Tianjin300384 China Key Laboratory of Optoelectronic Sensor and Sensing Network Technology College of Electronic Information and Optical Engineering Nankai University Tianjin300350 China

A fiber-optic modal interferometer is proposed and experimentally investigated for low-range refractive index sensing. This interferometer is fabricated by ultrasonic-cutting technique with a sensitivity of 12085.1 nm... 详细信息

ISBN: (纸本)9781665424462

关键词： Refractive index

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Fed-Haul: A Federated Learning Dual Band Point Multi-Point Backhaul Requirements in 5G Evolution and Beyond 23

Fed-Haul: A Federated Learning Dual Band Point Multi-Point B...

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6th International Conference on Networking, Intelligent systems and Security, NISS 2023

作者： Chehri, Abdellah Chaibi, Hasna Mettiti, Abderrahmane Saadane, Rachid Jeon, Gwanggil Dep. of Math. and Computer Science Royal Military College of Canada Kingston Canada Supmti Genius Laboratory Hassan Rabat Morocco It Rabat Center Faculty of Sciences Mohammed v University Rabat Morocco SIRC-LAGES-EHTP Hassania School of Public Works Casalanca Morocco Department of Embedded Systems Engineering Incheon National University Korea Republic of

ISBN: (纸本)9798400700194

The rapid adoption of smartphones and the explosive growth of data traffic due to these devices have been phenomenal. As the world anticipates more connected devices - the Internet of Things (IoT), vehicle-to-vehicle (V2V) communications, and wearable devices - and more value-added applications and services (ultra-high-definition video, 360° video, virtual reality, smart cars, etc.), leading industry experts are calling for the sixth generation (6G) networks. Federated learning is a common distributed machine learning framework. Through the training of the global model, the problems of large communication overhead and data privacy protection in traditional centralized machine learning are solved. Federated learning (FL) is essential in optimizing wireless communication networks' resources. On the other hand, wireless communications are crucial for FL. Therefore, the purpose of this survey paper is to bridge this gap in the literature by discussing the interdependency between FL and backhaul wireless communications. © 2023 ACM.

关键词： 5G mobile communication systems

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Development and Validation of a Prognostic Risk Score System for COVID-19 Inpatients:A Multi-Center Retrospective Study in China

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engineering 2022年第1期8卷 116-121页

作者： Ye Yuan Chuan Sun Xiuchuan Tang Cheng Cheng Laurent Mombaerts Maolin Wang Tao Hu Chenyu Sun Yuqi Guo Xiuting Li Hui Xu Tongxin Ren Yang Xiao Yaru Xiao Hongling Zhu Honghan Wu Kezhi Li Chuming Chen Yingxia Liu Zhichao Liang Zhiguo Cao Hai-Tao Zhang Ioannis Ch.Paschaldis Quanying Liu Jorge Goncalves Qiang Zhong Li Yan School of Artificial Intelligence and Automation Huazhong University of Science and TechnologyWuhan 430074China School of Mechanical Science and Engineering Huazhong University of Science and TechnologyWuhan 430074China Luxembourg Centre for Systems Biomedicine University of LuxembourgBelval L-4367Luxembourg Department of Plant Sciences University of CambridgeCambridge CB21TNUK Department of Emergency Tongji HospitalTongji Medical CollegeHuazhong University of Science and TechnologyWuhan 430030China AMITA Health Saint Joseph Hospital Chicago ChicagoIL 60657USA Department of Anesthesiology Tongji HospitalTongji Medical CollegeHuazhong University of Science and TechnologyWuhan 430030China Huazhong University of Science and Technology-Wuxi Research Institute Wuxi 214174China Division of Cardiology Department of Internal MedicineTongji HospitalTongji Medical CollegeHuazhong University of Science and TechnologyWuhan 430030China Institute of Health Informatics University College LondonLondon NW12DAUK Department of Biomedical Engineering Southern University of Science and TechnologyShenzhen 518055China Department of Infectious Diseases Shenzhen Key Laboratory of Pathogenic Microbiology and ImmunologyNational Clinical Research Center for Infectious DiseaseThe Third People’s Hospital of Shenzhen(Second Hospital Affiliated with the Southern University of Science and Technology)Shenzhen 518055China Department of Electrical and Computer Engineering&Division of Systems Engineering&Department of Biomedical Engineering Boston UniversityBostonMA 02215USA

Coronavirus disease 2019(COVID-19)has become a worldwide *** patients of COVID-19 suffer from a high mortality rate,motivating the development of convenient and practical methods that allow clinicians to promptly identify high-risk ***,we have developed a risk score using clinical data from 1479 inpatients admitted to Tongji Hospital,Wuhan,China(development cohort)and externally validated with data from two other centers:141 inpatients from Jinyintan Hospital,Wuhan,China(validation cohort 1)and 432 inpatients from The Third People’s Hospital of Shenzhen,Shenzhen,China(validation cohort 2).The risk score is based on three biomarkers that are readily available in routine blood samples and can easily be translated into a probability of *** risk score can predict the mortality of individual patients more than 12 d in advance with more than 90%accuracy across all ***,the Kaplan-Meier score shows that patients can be clearly differentiated upon admission as low,intermediate,or high risk,with an area under the curve(AUC)score of *** summary,a simple risk score has been validated to predict death in patients infected with severe acute respiratory syndrome coronavirus 2(SARS-CoV-2);it has also been validated in independent cohorts.

关键词： COVID-19 Risk score Mortality risk prediction

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Quantum Artificial Intelligence: Unlocking the Future of Technology

Quantum Artificial Intelligence: Unlocking the Future of Tec...

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Next Generation Communication & Information Processing (INCIP), International Conference on

作者： G. Ignisha Rajathi L.R. Priya A. Vegi Fernando R. Vedhapriyavadhana S. Sasindharan J. Mohanalin Department of Information Technology Manipal Academy of Higher Education Manipal Institute of Technology Bengaluru Manipal India Department of Electronics and Communication Engineeing Farncis Xavier Engineering College Tirunelveli Tamil Nadu India Department of Computer Science and Engineering (AI & ML) Dayananda Sagar University Bengaluru Karnataka India School of Computing Engineering and Physical Sciences University of the west of Scotland London UK Department of Computer Science and Business Systems Sri Krishna College of Engineering and Technology Coimbatore Tamil Nadu India Electrical and Electronics Engineering College of Engineering Perumon Kerala India

ISBN: (数字)9798331528140

ISBN: (纸本)9798331528157

Quantum Artificial Intelligence (QAI) stands for the combination of the exercise of ‘quantum computing’ and ‘artificial intelligence’ that drives breakthrough transformation across various industries. In this paper, it is discussed on how QAI is bringing change across industries including pharmaceuticals, finance, weather prediction, supply chain, security, smart cities, agriculture, telecommunication, and education. With quantum mechanics conceptions, QAI enhance computation ability to address more data, and enhance the algorithms. Quantum technologies have potential for attachment to AI systems and make the previously unachievable innovations with the help of overgrowing the classical models. As a conclusion, this paper also discusses its result on the methodological difficulties and ethical issues of QAI, the need to build strategic partnerships and the need for well-regulated legislation. The results extend our understanding of QAI to apprehend the future of technology and its application for solving some of the greatest problems that the world faces today.

关键词： Drugs Ethics Technological innovation Quantum computing Smart cities Finance Weather forecasting Security Artificial intelligence Optimization

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A computer Vision Approach to Plant Growth Monitoring in an Embedded CubeSat Module

A Computer Vision Approach to Plant Growth Monitoring in an ...

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IEEE Conference on Aerospace

作者： Alexis Lopez Olaoluwayimika Olugbenle Michael C.F. Bazzocchi Department of Mechanical and Aerospace Engineering Astronautics and Robotics Laboratory (ASTRO Lab) Clarkson University Potsdam NY USA Department of Electrical and Computer Engineering Astronautics and Robotics Laboratory (ASTRO Lab) Clarkson University Potsdam NY USA Department of Mechanical and Aerospace Engineering Astronautics and Robotics Laboratory (ASTRO Lab) Clarkson University Potsdam NY Department of Earth and Space Science and Engineering York University Toronto Canada

The investigation and development of space-based food production systems are essential to improve the reliability and availability of fresh sustenance for astronauts. With their compact size and low-cost production, CubeSats can provide a unique platform for plant-based in-space experiments. Additionally, the combination of CubeSats and computer vision can allow for monitoring the health of plants during their growth cycles. This paper investigates the electronics and data handling of a crop growth module, otherwise referred to as an environmental monitoring and control subsystem (EMCS), as well as the integration of computer vision techniques for plant growth and development. Using the Otsu thresholding and holistically-nested edge detection algorithms, image segmentation and edge detection were performed, respectively. A support vector machine (SVM) was also employed to classify foliage and provide feedback on the plant’s health. The results from the system show that the computer vision approach can accurately predict the health of the plants based on color and texture. This study builds a foundation for future plant health monitoring research in deep space environments.

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Joint Precoding and Reflection Optimization for IRS-Assisted MU-MIMO-OTFS Downlink systems

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IEEE Transactions on Vehicular Technology 2025年

作者： Zou, Tingting Wang, Fengyu Xu, Wenjun Zhang, Yimeng Pan, Miao Han, Zhu Beijing University of Posts and Telecommunications State Key Laboratory of Network and Switching Technology Beijing100876 China Beijing University of Posts and Telecommunications School of Artificial Intelligence Beijing100876 China Beijing University of Posts and Telecommunications State Key Laboratory of Networking and Switching Technology China Mobile Research Institute Beijing100876 China University of Houston Department of Electrical and Computer Engineering HoustonTX77004 United States Kyung Hee University Department of Computer Science and Engineering Seoul446-701 Korea Republic of

Orthogonal time frequency space (OTFS) modulation has been proposed as a promising technique to achieve high-reliability communications in high-mobility scenarios. In this paper, we consider a multi-user (MU) multiple-input multiple-output (MIMO)-OTFS downlink communication scenario, where an intelligent reflecting surface (IRS) is deployed to enhance the performance. Specifically, the delay- Doppler (DD) domain input-output relationship of such a system is first derived, based on which, the joint optimization of the precoding matrices and IRS reflection phase is studied to maximize the weighted sum rate (WSR) considering user fairness. Note that the corresponding optimization problem is non-convex. To tackle this challenge, we first reformulate the problem into a more traceable equivalent form by the weighted mean minimum square error (WMMSE) approach. Utilizing the characteristics of the DD domain signal relationship, we then decouple the reformulated problem and further transform it into two subproblems, which are solved by the Lagrangian function method and the project-descent-retracting (PDR) algorithm, respectively. The precoding matrices and reflection phase are jointly optimized by alternative optimization to maximize WSR. Simulation results demonstrate that, compared with benchmark schemes, the proposed WMMSE-ALT can improve the rate more than 85% in IRS-assisted MU-MIMO-OTFS systems. © 1967-2012 IEEE.

关键词： Mean square error

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Static Performance and Reliability of 4H-SiC Diodes with P+ Regions Formed by Various Profiles and Temperatures

Static Performance and Reliability of 4H-SiC Diodes with P+ ...

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2022 IEEE International Reliability Physics Symposium, IRPS 2022

作者： Mancini, Stephen A. Yup Jang, Seung Chen, Zeyu Kim, Dongyoung Lynch, Justin Liu, Yafei Raghothamachar, Balaji Kang, Minseok Agarwal, Anant Mahadik, Nadeemullah Stahlbush, Robert Dudley, Michael Sung, Woongje State University of New York Polytechnic Institute College of Nanoscale Science and Engineering AlbanyNY12203 United States Stony Brook University Department of Material Science and Chemical Engineering Stony BrookNY11794 United States The Ohio State University Department of Electrical and Computer Engineering ColumbusOH43210 United States Naval Research Laboratory WashingtonDC20375 United States

ISBN: (纸本)9781665479509

Several designs of 1.2kV-rated 4H-SiC PiN diodes and Junction Barrier Schottky (JBS) diodes have been successfully fabricated with various P+ implantation conditions resulting in different junction profiles. P+ regions were implanted at both room temperature and elevated temperature (600C) to monitor the generation of Basal Plane Dislocations (BPDs) and study their impact on device long term reliability. It was found that, when the dose in the deeper portion of the junction (implemented by high energy implantations) is well suppressed, static and long-term reliability performances of room temperature implanted devices can be maintained similar to those of high temperature implanted devices. © 2022 IEEE.

关键词： Room temperature

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Producing Lunar Steel and Oxygen using Molten Lunar Regolith Electrolysis

Producing Lunar Steel and Oxygen using Molten Lunar Regolith...

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Accelerating Space Commerce, Exploration, and New Discovery Conference, ASCEND 2023

作者： Patel, Palak B. Soto, Jose Adams, Zachary K. Koller, Scarlett Latyshev, Kir Boothe, Evan Cooper, Amber Fontanez, Jonatan Jalil, Laman Ma, Beverly Meza, Cesar Misquitta, Kristoff Na, Hyein Nguyen, Tom Pendley, Hezekiah Sangabattula, Lokesh Usey, Helena Zehner, Alice Allanore, Antoine Culpepper, Martin De Weck, Olivier Lordos, George Hoffman, Jeffrey Bergman, Dean Department of Mechanical Engineering Massachusetts Institute of Technology CambridgeMA02139 United States Department of Aeronautics and Astronautics Massachusetts Institute of Technology CambridgeMA02139 United States Department of Electrical Engineering and Computer Science Massachusetts Institute of Technology CambridgeMA02139 United States Department of Aeronautics and Astronautics Massachusetts Institute of Technology Sloan School of Management CambridgeMA02139 United States Department of Chemical Engineering Massachusetts Institute of Technology CambridgeMA02139 United States Department of Material Science and Engineering Massachusetts Institute of Technology CambridgeMA02139 United States Honeybee Robotics Exploration Systems AltadenaCA91001 United States

ISBN: (纸本)9781624107054

With the current Artemis missions planned, by 2028, NASA and its international and commercial partners may be establishing a permanent presence at the lunar South pole. The Artemis Base Camp will be the locus of international and public-private collaboration under the Artemis Accords, helping to bootstrap a lunar economy. As the lunar economy grows, traffic of people and goods will grow with it. Thus, we envision that NASA and its international and commercial partners will have increasing long-term needs for producing metal from in situ resources such as lunar regolith. These metals can be used to produce many end products on the moon including large pressure vessels, both for spacious habitats and industrial-scale storage and operations. In this paper, as a part of the NASA 2023 BIG Idea Challenge ‘Lunar Forge: Producing Metal Products on the Moon’, we design and manufacture a molten regolith electrolysis (MRE) reactor capable of producing various alloys of steel and other metal products from lunar regolith, with oxygen as a useful byproduct. We have built ARTEMIS Steelworks (Advancing Reactor Technologies for Electrolytic Manufacturing of In situ Steel), which consists of an electrolytic reactor, supporting the addition of alloying elements to produce molten steel from lunar regolith simulant. To prepare for a lunar technology demonstration, a preliminary test of the reaction on Earth was performed in a vacuum, a sonicator was tested to dislodge oxygen bubbles from the anode to increase electrode life, and an automated iron and slag collector was made to reduce the need for astronaut operation. The project’s technical objectives are to demonstrate steel-making capability while also quantifying energy efficiency, steel quality, and the expected useful life of the electrodes and vessel under different configurations relative to the state of the art. The comprehensive testing plan presented here includes functional subsystem and integrated testing in ambient conditio

关键词： Electrolysis

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Phase-field modeling and effective simulation of non-isothermal reactive transport

arXiv

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arXiv 2023年

作者： Bringedal, Carina Jaust, Alexander Department of Computer science Electrical engineering and Mathematical sciences Western Norway University of Applied Sciences Norway Institute for Modelling Hydraulic and Environmental Systems University of Stuttgart Germany Institute for Parallel and Distributed Systems University of Stuttgart Germany

We consider single-phase flow with solute transport where ions in the fluid can precipitate and form a mineral, and where the mineral can dissolve and release solute into the fluid. Such a setting includes an evolving interface between fluid and mineral. We approximate the evolving interface with a diffuse interface, which is modeled with an Allen-Cahn equation. We also include effects from temperature such that the reaction rate can depend on temperature, and allow heat conduction through fluid and mineral. As Allen-Cahn is generally not conservative due to curvature-driven motion, we include a reformulation that is conservative. This reformulation includes a non-local term which makes the use of standard Newton iterations for solving the resulting non-linear system of equations very slow. We instead apply L-scheme iterations, which can be proven to converge for any starting guess, although giving only linear convergence. The three coupled equations for diffuse interface, solute transport and heat transport are solved via an iterative coupling scheme. This allows the three equations to be solved more efficiently compared to a monolithic scheme, and only few iterations are needed for high accuracy. Through numerical experiments we highlight the usefulness and efficiency of the suggested numerical scheme and the applicability of the resulting *** Codes 35R37, 65M08, 65M22, 76D10, 76D05, 76V05, 80A32, 80M12 © 2023, CC BY.

关键词： Minerals

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