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Ultrasound-based AI for COVID-19 detection: a comprehensive review of public and private lung ultrasound datasets and studies

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Multimedia Tools and Applications 2025年 1-55页

作者： Morshed, Abrar Al Shihab, Abdulla Jahin, Md Abrar Al Nahian, Md Jaber Hossain Sarker, Md Murad Ibne Wadud, Md Sharjis Uddin, Mohammad Istiaq Imtiaz Siraji, Muntequa Anjum, Nafisa Shristy, Sumiya Rajjab Rahman, Tanvin Khatun, Mahmuda Rubel Dewan, Md Hossain, Mosaddeq Sultana, Razia Chakma, Ripel Emon, Sonet Barua Islam, Towhidul Hussain, Mohammad Arafat Department of Computer Science and Engineering Ahsanullah University of Science and Technology Dhaka1208 Bangladesh Department of Electrical and Telecommunication Engineering Daffodil International University Dhaka1216 Bangladesh Khulna9203 Bangladesh Okinawa904-0412 Japan Department of Information and Communication Technology Bangladesh University of Professionals Dhaka1216 Bangladesh Department of Information and Communication Technology Comilla University Cumilla3506 Bangladesh Department of Biomedical Physics and Technology University of Dhaka Dhaka1000 Bangladesh Department of Computer Science and Engineering International Islamic University Chittagong Chittagong4318 Bangladesh Department of Electrical and Electronic Engineering Islamic University of Technology Gazipur1704 Bangladesh Department of Electronics and Telecommunication Engineering Chittagong University of Engineering and Technology Chittagong4349 Bangladesh Department of Computer Science and Engineering Notre Dame University Bangladesh Dhaka1000 Bangladesh Department of Electrical and Electronic Engineering Khulna University of Engineering & Technology Khulna9203 Bangladesh Department of Computer Science and Engineering Institute of Science and Technology Dhaka1209 Bangladesh Department of Electronics and Communication Engineering Institute of Science and Technology Dhaka1209 Bangladesh Department of Mathematics Jahangirnagar University Dhaka1342 Bangladesh Department of Electrical and Electronic Engineering Daffodil International University Dhaka1216 Bangladesh Department of Electrical and Electronic Engineering Dhaka University of Engineering and Technology Gazipur1707 Bangladesh Department of Computer Science and Telecommunication Engineering Noakhali Science and Technology University Noakhali3814 Bangladesh Department of Computer Science and Engineering Northern University Bangladesh Dhaka1230 Bangladesh Boston Children’s Hospital Harvard Medical School BostonMA02

The COVID-19 pandemic has affected millions of people globally, with respiratory organs being strongly affected in individuals with comorbidities. Medical imaging-based diagnosis and prognosis have become increasingly popular in clinical settings for detecting COVID-19 lung infections. Among various medical imaging modalities, ultrasound stands out as a low-cost, mobile, and radiation-safe imaging technology. In this comprehensive review, we focus on AI-driven studies utilizing lung ultrasound (LUS) for COVID-19 detection and analysis. We provide a detailed overview of both publicly available and private LUS datasets and categorize the AI studies according to the dataset they used. Additionally, we systematically analyzed and tabulated the studies across various dimensions, including data preprocessing methods, AI models, cross-validation techniques, and evaluation metrics. In total, we reviewed 60 articles, 41 of which utilized public datasets, while the remaining employed private data. Our findings suggest that ultrasound-based AI studies for COVID-19 detection have great potential for clinical use, especially for children and pregnant women. Our review also provides a useful summary for future researchers and clinicians who may be interested in the field. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2025.

关键词： COVID-19

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Investigation of a New Stator Permanent Magnet with the effects of Flux-Reversal and Flux-Switching 16

Investigation of a New Stator Permanent Magnet with the effe...

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16th Annual Power Electronics, Drive Systems, and Technologies Conference, PEDSTC 2025

作者： Sarshar, Mohammad Reza Kondelaji, Mohammad Amin Jalali Mirsalim, Mojtaba Montazeri, Seyed Hamid Amirkabir University of Technology Tehran Polytechnic Department of Electrical Engineering Tehran Iran Department of Electrical Engineering and Computer Science Cambridge United States

ISBN: (纸本)9798331533946

This article studies a new stator-permanent magnet (PM) motor with flux-switching (FS) and flux-reversal (FR) effects synergies. The proposed structure benefits from the splitting stator pole PMs and consequent-pole FRPM, which reduces the PM utilization compared to the conventional ones. Also, the proposed structure is optimized by a random search multiobjective algorithm to attain the structure with the highest output torque and minimum torque ripple. Additionally, the proposed motor is compared to a conventional FSPM to investigate the advantages of the main motor. Using finite element analysis (FEA) studies, the flux density distribution results show that the FR effect lowers the saturation risk compared to FSPM. Also, the back-EMF generation of the proposed motor has improved by 135% compared to its benchmark. Correspondingly, fullload results show that the proposed topology has enhanced the average torque by 75% and deteriorated the ripple by about 55% compared to FSPM. Therefore, the proposed structure offers a profitable for high torque density applications. © 2025 IEEE.

关键词： Permanent magnets

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Continuous Query-based Data Trading

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Performance Evaluation Review 2024年第1期52卷 73-74页

作者： Cheng, Jin Ding, Ningning Lui, John C.S. Huang, Jianwei School of Science and Engineering Shenzhen Institute of Artificial Intelligence and Robotics for Society The Chinese University of Hong Kong Shenzhen Shenzhen China Department of Electrical and Computer Engineering Northwestern University Evanston United States Department of Computer Science and Engineering The Chinese University of Hong Kong Hong Kong Hong Kong

In the era of big data, traditional data trading methods designed for one-time queries on static databases fail to meet the demands of continuous query-based trading on streaming data, often resulting in repeated and inaccurate charges due to neglecting computation sharing in continuous query execution. To address this, we introduce CQTrade, the first trading framework tailored for continuous queries, which incorporates computation sharing across different time windows and queries, enhancing integration with existing trading systems. Our contributions include a theoretical analysis of computation-sharing techniques, the development of a general optimization problem to maximize seller revenue adaptable to various techniques, and the proposal of a branch-and-price algorithm to handle the problem's complexity. Our evaluation shows that the proposed framework improves the success rate of data trading by 12.8% and boosts the seller's revenue by an average of 28.7%, compared to the one-time query-based data trading methods used in the current data market. © 2024 Owner/Author.

关键词： Integer programming

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Coprocessor Actor Critic: A Model-Based Reinforcement Learning Approach For Adaptive Brain Stimulation 41

Coprocessor Actor Critic: A Model-Based Reinforcement Learni...

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41st International Conference on Machine Learning, ICML 2024

作者： Pan, Michelle Schrum, Mariah Myers, Vivek Bıyık, Erdem Dragan, Anca Department of Electrical Engineering and Computer Sciences UC Berkeley United States Department of Computer Science University of Southern California United States

Adaptive brain stimulation can treat neurological conditions such as Parkinson's disease and post-stroke motor deficits by influencing abnormal neural activity. Because of patient heterogeneity, each patient requires a unique stimulation policy to achieve optimal neural responses. Model-free reinforcement learning (MFRL) holds promise in learning effective policies for a variety of similar control tasks, but is limited in domains like brain stimulation by a need for numerous costly environment interactions. In this work we introduce Coprocessor Actor Critic, a novel, model-based reinforcement learning (MBRL) approach for learning neural coprocessor policies for brain stimulation. Our key insight is that coprocessor policy learning is a combination of learning how to act optimally in the world and learning how to induce optimal actions in the world through stimulation of an injured brain. We show that our approach overcomes the limitations of traditional MFRL methods in terms of sample efficiency and task success and outperforms baseline MBRL approaches in a neurologically realistic model of an injured brain. Copyright 2024 by the author(s)

关键词： Reinforcement learning

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Addressing Imbalance in Health Datasets: A New Method NR-Clustering SMOTE and Distance Metric Modification

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computers, Materials & Continua 2025年第2期82卷 2931-2949页

作者： Hairani Hairani Triyanna Widiyaningtyas Didik Dwi Prasetya Afrig Aminuddin Department of Electrical Engineering and Informatics Faculty of EngineeringUniversitas Negeri MalangMalang65145Indonesia Department of Computer Science Universitas BumigoraMataram83127Indonesia Department of Computer Graphic and Multimedia Faculty of ComputingCollege of Computing and Applied SciencesUniversiti Malaysia Pahang Al-Sultan AbdullahPekan26600Malaysia

An imbalanced dataset often challenges machine learning, particularly classification methods. Underrepresented minority classes can result in biased and inaccurate models. The Synthetic Minority Over-Sampling Technique (SMOTE) was developed to address the problem of imbalanced data. Over time, several weaknesses of the SMOTE method have been identified in generating synthetic minority class data, such as overlapping, noise, and small disjuncts. However, these studies generally focus on only one of SMOTE’s weaknesses: noise or overlapping. Therefore, this study addresses both issues simultaneously by tackling noise and overlapping in SMOTE-generated data. This study proposes a combined approach of filtering, clustering, and distance modification to reduce noise and overlapping produced by SMOTE. Filtering removes minority class data (noise) located in majority class regions, with the k-nn method applied for filtering. The use of Noise Reduction (NR), which removes data that is considered noise before applying SMOTE, has a positive impact in overcoming data imbalance. Clustering establishes decision boundaries by partitioning data into clusters, allowing SMOTE with modified distance metrics to generate minority class data within each cluster. This SMOTE clustering and distance modification approach aims to minimize overlap in synthetic minority data that could introduce noise. The proposed method is called “NR-Clustering SMOTE,” which has several stages in balancing data: (1) filtering by removing minority classes close to majority classes (data noise) using the k-nn method;(2) clustering data using K-means aims to establish decision boundaries by partitioning data into several clusters;(3) applying SMOTE oversampling with Manhattan distance within each cluster. Test results indicate that the proposed NR-Clustering SMOTE method achieves the best performance across all evaluation metrics for classification methods such as Random Forest, SVM, and Naїve Bayes, compared t

关键词： SMOTE modification Clustering-SMOTE manhattan distance

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Energy Measurement by Smart Metering using Blockchain Technology 10

Energy Measurement by Smart Metering using Blockchain Techno...

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10th International Conference on electrical Energy Systems, ICEES 2024

作者： Aishwarya, R. Chandrakala, D. Jeyanthi Yegammai, S. Varrshini, V. Kanaga Suba Raja, S. Vishnu Prasad, V. Easwari Engineering College Electrical & Electronics Engineering Chennai India SRM Institute of Science and Technology Computer Science & Engineering Tiruchirappalli India

ISBN: (数字)9798350353778

ISBN: (纸本)9798350353778

The development of demand-side management with controlled loads has received a lot of attention as a result of the smart grid's ongoing growth and the energy market's volatility. The large number of household loads in the residential sector has generated a great deal of interest in managing the energy consumption of individual residences. This paper suggests simulating the monitoring of several SHEMS (smart home energy management system) characteristics. We have concentrated on the functionalities of the suggested design that gather data and analyse it, and we'll apply them to common home loads like electric water heaters and charging stations for electric cars. Overall, we discovered that the modelling of the loads results in considerable financial savings for consumers when a SHEMS is put into place. Here, we'll use blockchain technology to transfer data securely. Every piece of energy data has a timestamp and a hash value. Since blockchain technology operates through code and encrypts all data, it is becoming more and more difficult for hackers to have a field day. Additionally, since all records are linked to one another via time stamps and hash codes, any change would have a significant impact on the entire database. © 2024 IEEE.

关键词： Energy utilization

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Quick Hosting Capacity Evaluation Based on Distributed Dispatching for Smart Distribution Network Planning with Distributed Generation

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Journal of Modern Power Systems and Clean Energy 2024年第1期12卷 128-140页

作者： Bing Sun Ruipeng Jing Leijiao Ge Yuan Zeng Shimeng Dong Luyang Hou the School of Electrical and Information Engineering Tianjin UniversityTianjin 300072China State Grid Corporation of China BeijingChina the School of Computer Science(National Pilot Software Engineering School) Beijing University of Posts and TelecommunicationsBeijingChina

The smart distribution network(SDN)is integrat ing increasing distributed generation(DG)and energy storage(ES).Hosting capacity evaluation is important for SDN plan ning with *** and ES are usually invested by users or a third party,and they may form friendly microgrids(MGs)and operate *** centralized dispatching meth od no longer suits for hosting capacity evaluation of SDN.A quick hosting capacity evaluation method based on distributed optimal dispatching is ***,a multi-objective DG hosting capacity evaluation model is established,and the host ing capacity for DG is determined by the optimal DG planning *** steady-state security region method is applied to speed up the solving process of the DG hosting capacity evalua tion ***,the optimal dispatching models are estab lished for MG and SDN respectively to realize the operating *** the distributed dispatching strategy,the dual-side optimal operation of SDN-MGs can be realized by several iterations of power exchange ***,an SDN with four MGs is conducted considering multiple flexible *** shows that the DG hosting capacity of SDN oversteps the sum of the maximum active power demand and the rated branch ***,the annual DG electricity oversteps the maximum active power demand value.

关键词： Smart distribution network(SDN) microgrid hosting capacity multi-objective optimization distributed opti mal dispatching flexible resource

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Deep Learning Based Cross-Location Building Load Prediction Using Domain Adaptation 13

Deep Learning Based Cross-Location Building Load Prediction ...

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13th IEEE International Conference and Expositions on Electric and Power engineering, EPEi 2024

作者： Hasan, A.S.M. Jahid Islam, Md Shazid Rahman, Md Saydur Sajol, Md Saiful Islam Yusuf, Jubair North South University Department of Electrical and Computer Engineering Dhaka Bangladesh University of California Riverside Department of Electrical and Computer Engineering RiversideCA United States Louisiana State University Department of Computer Science and Engineering Baton RougeLA United States

ISBN: (纸本)9798350356199

Buildings consume a significant portion of a nation's total energy, accounting for about 70 percent of electricity use in the US. Predicting building energy demand accurately is crucial for utilities to ensure efficient operation. Deep learning-based approaches have demonstrated great potential for this task. However, building loads are heavily influenced by local weather conditions. Conventional deep learning models are typically location-specific and demand extensive local data for accurate predictions. A location independent model can offer great advantage, which can be attained through application of domain adaptation. This method involves training a deep learning model initially on a source domain dataset and then use this pre-trained model to perform prediction on target domain dataset, where the probability distributions may differ but are comparable. This study applies domain adaptation techniques to develop a deep learning model that predicts building energy demand independent of location. Our proposed model uses a pre-trained deep learning architecture trained on data from one location to predict building load in another location, requiring only minimal data from the target domain. The findings demonstrate that the domain-adapted model achieves improvements in both accuracy and computational speed compared to non-adapted models. © 2024 IEEE.

关键词： Prediction models

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Exploring the Usage of Mobile Phones in the Assessment Process: A Case Study 47

Exploring the Usage of Mobile Phones in the Assessment Proce...

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47th ICT and Electronics Convention, MIPRO 2024

作者： Kovačević, Željko Bubnic, Bostjan Kosar, Tomaž Zagreb University Of Applied Sciences Department Of Computer Science And Information Technology Zagreb Croatia University Of Maribor Faculty Of Electrical Engineering And Computer Science Maribor Slovenia

ISBN: (纸本)9798350382495

Technology is changing how students learn in the 21st century significantly. Integrating mobile devices in teaching, learning, and assessment processes has emerged as an important strategy for improving teaching methodologies and students' learning. However, adopting mobile devices presents several challenges, including understanding the students' performance, impact on time, and efficiency in the assessment process. This study explores this area with an empirical study of using mobile devices and compares the results to those using traditional desktop computers. In the educational environment, we investigated students' effective use while interacting with mobile and desktop versions of the Second Introductory programming summative assessment. Multinational/multi-institutional research involved a comparative analysis between two student groups undertaking identical tasks on mobile phones and desktop computers. Our results suggest that the students' results are not affected significantly by using the mobile version of the assessment application. These results highlight the usability of mobile devices as a flexible and effective tool for various educational applications, strengthening their role in the modern learning environment. © 2024 IEEE.

关键词： Object oriented programming

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In Search for Model-Driven eXplainable Artificial Intelligence 1

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10th IFIP International Workshop on Artificial Intelligence for Knowledge Management, AI4KMES 2023

作者： Ligęza, Antoni Sepiolo, Dominik Faculty of Electrical Engineering Automatics Computer Science and Biomedical Engineering Department of Applied Computer Science AGH University of Krakow al. Mickiewicza 30 Krakow30-059 Poland

ISBN: (数字)9783031610691

ISBN: (纸本)9783031610684

This paper reports on ongoing and innovative research in the area of eXplainable Artificial Intelligence (XAI). A classical XAI task is considered as finding an explanation of the model generated via Machine Learning by identifying the most influential variables for local decision-making. Such an approach suffers from severe limitations. The proposed approach moves the explanatory process to a new, knowledge-level dimension. It is oriented towards Model Discovery, i.e. the internal structure and functions of the components. The concept of Model-Driven XAI is put forward and explained with examples. An experiment on Function Discovery via Grammatical Evolution is reported in brief. © IFIP International Federation for Information Processing 2024.

关键词： Decision making

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