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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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Real-Time Lie-Speech Determination Using Voice-Stress Technology

Iraqi Journal for Computer Science and Mathematics

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Iraqi Journal for computer science and Mathematics 2024年第2期5卷 81-93页

作者： Al-Dhaher, Fadi K. Mohammed, Duraid Y. Khalaf, Mohammed Al-Karawi, Khamis A. Sarfraz, Mohammad Al Maathidi, Muhammad Mazin Computer Engineering Department College of Engineering Al-Iraqia University Baghdad Iraq General Directorate of Education Anbar Ramadi31001 Iraq Computer Science Department Al-Maarif University College Ramadi31001 Iraq University of Diyala Iraq Electrical Engineering Department Aligarh Muslim University Aligarh India Computer Science Department University of Bahrain Bahrain

Lie detection has gained importance and is now extremely significant in a variety of fields. It plays an important role in several domains, including law enforcement, criminal investigations, national security, workplace ethics, and personal relationships. As advances in lie detection continue to develop, real-time approaches such as voice stress technology have emerged as a feasible alternative to traditional methods such as polygraph testing. Polygraph testing, a historical and generally established approach, may be enhanced or replaced by these revolutionary real-time techniques. Traditional lie detection procedures, such as polygraph testing, have been challenged for their lack of reliability and validity. Newer techniques, such as brain imaging and machine learning, might offer better outcomes, although they are still in their early phases and require additional testing. This project intends to explore a deception-detection module based on sophisticated speech-stress analysis techniques that might be applied in a real-time deception system. The purpose is to study stress and other articulation cues in voice patterns, to establish their precision and reliability in detecting deceit, by building upon previous knowledge and applying state-of-the-art architecture. The performance and accuracy of the system and its audio aspects will be thoroughly analyzed. The ultimate purpose is to contribute to the advancement of more accurate and reliable lie-detection systems, by addressing the limitations of old techniques and proposing practical solutions for varied applications. This paper proposes an efficient feature-selection strategy, which uses random forest (RF) to select only the significant features for training when a real-life trial dataset consisting of audio files is employed. Next, utilizing the RF as a classifier, an accuracy of 88% is reached through comprehensive evaluation, thereby confirming its reliability and precision for lie-detection in real-time scena

关键词： National security

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High-Precision Digital Twin Platform Based on Ray Tracing Simulation 15

High-Precision Digital Twin Platform Based on Ray Tracing Si...

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15th International Conference on Information and Communication Technology Convergence, ICTC 2024

作者： Noh, Yong Jun Choi, Kae Won Sungkyunkwan University Department of Computer Science and Engineering Korea Republic of Sungkyunkwan University Department of Electrical and Computer Engineering Korea Republic of

ISBN: (纸本)9798350364637

The evolution of wireless networks necessitates so-phisticated optimization strategies to address the challenges posed by heterogeneous traffic arising from various domains. Digital Twin (DT) concept has emerged as an innovative solution for efficiently regulating and managing complex and dynamic networks. DT enables safe and cost-effective network evaluation and optimization across different scenarios. We introduce a high-precision DT platform, capable of producing comprehensive channel datasets for real-world communication environments. This platform not only gathers high variance and large-scale channel data without affecting the quality of service to the real-world users but also shows promise as a novel tool for conducting and assessing various experiments. © 2024 IEEE.

关键词： digital twin ray tracing wireless networks

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An Efficient Encryption and Compression of Sensed IoT Medical Images Using Auto-Encoder

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computer Modeling in engineering & sciences 2023年第2期134卷 909-926页

作者： Passent El-kafrawy Maie Aboghazalah Abdelmoty M.Ahmed Hanaa Torkey Ayman El-Sayed School of Information Technology and Computer Science Nile UniversityGiza12677Egypt Math and Computer Science Department Faculty of ScienceMenoufia UniversityMenoufia32511Egypt Department of Computer Engineering College of Computer ScienceKing Khalid UniversityAbha61421Saudi Arabia Computer Science and Engineering Department Faculty of Electronic EngineeringMenoufia UniversityMenouf32952Egypt

Healthcare systems nowadays depend on IoT sensors for sending data over the internet as a common *** ofmedical images is very important to secure patient *** these images consumes a lot of time onedge computing;therefore,theuse of anauto-encoder for compressionbefore encodingwill solve such a *** this paper,we use an auto-encoder to compress amedical image before encryption,and an encryption output(vector)is sent out over the *** the other hand,a decoder was used to reproduce the original image back after the vector was received and *** convolutional neural networks were conducted to evaluate our proposed approach:The first one is the auto-encoder,which is utilized to compress and encrypt the images,and the other assesses the classification accuracy of the image after decryption and *** hyperparameters of the encoder were tested,followed by the classification of the image to verify that no critical information was lost,to test the encryption and encoding *** this approach,sixteen hyperparameter permutations are utilized,but this research discusses three main cases in *** first case shows that the combination of Mean Square Logarithmic Error(MSLE),ADAgrad,two layers for the auto-encoder,and ReLU had the best auto-encoder results with a Mean Absolute Error(MAE)=0.221 after 50 epochs and 75%classification with the best result for the classification *** second case shows the reflection of auto-encoder results on the classification results which is a combination ofMean Square Error(MSE),RMSprop,three layers for the auto-encoder,and ReLU,which had the best classification accuracy of 65%,the auto-encoder gives MAE=0.31 after 50 *** third case is the worst,which is the combination of the hinge,RMSprop,three layers for the auto-encoder,and ReLU,providing accuracy of 20%and MAE=0.485.

关键词： Auto-encoder cloud image encryption IoT healthcare

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LLM-based Skill Diffusion for Zero-shot Policy Adaptation 38

LLM-based Skill Diffusion for Zero-shot Policy Adaptation

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38th Conference on Neural Information Processing Systems, NeurIPS 2024

作者： Kim, Woo Kyung Lee, Youngseok Kim, Jooyoung Woo, Honguk Department of Computer Science and Engineering Sunkyunkwan University Korea Republic of Department of Electrical and Computer Engineering Sungkyunkwan University Korea Republic of

Recent advances in data-driven imitation learning and offline reinforcement learning have highlighted the use of expert data for skill acquisition and the development of hierarchical policies based on these skills. However, these approaches have not significantly advanced in adapting these skills to unseen contexts, which may involve changing environmental conditions or different user requirements. In this paper, we present a novel LLM-based policy adaptation framework LDuS which leverages an LLM to guide the generation process of a skill diffusion model upon contexts specified in language, facilitating zero-shot skill-based policy adaptation to different contexts. To implement the skill diffusion model, we adapt the loss-guided diffusion with a sequential in-painting technique, where target trajectories are conditioned by masking them with past state-action sequences, thereby enabling the robust and controlled generation of skill trajectories in test-time. To have a loss function for a given context, we employ the LLM-based code generation with iterative refinement, by which the code and controlled trajectory are validated to align with the context in a closed-loop manner. Through experiments, we demonstrate the zero-shot adaptability of LDuS to various context types including different specification levels, multi-modality, and varied temporal conditions for several robotic manipulation tasks, outperforming other language-conditioned imitation and planning methods. © 2024 Neural information processing systems foundation. All rights reserved.

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Parallelization is All System Identification Needs: End-to-end Vibration Diagnostics on a Multi-Core RISC-V Edge Device

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IEEE Internet of Things Journal 2025年第13期12卷 24230-24243页

作者： Kiamarzi, Amirhossein Moallemi, Amirhossein Zonzini, Federica Brunelli, Davide Rossi, Davide Tagliavini, Giuseppe University of Bologna Department of Electrical Electronic and Information Engineering Bologna40136 Italy University of Trento Department of Industrial Engineering Trento38123 Italy University of Bologna Department of Computer Science and Engineering Bologna40136 Italy

The early detection of structural malfunctions requires the installation of real-time monitoring systems ensuring continuous access to the damage-sensitive information;nevertheless, it can generate bottlenecks in terms of bandwidth and storage. Deploying data reduction techniques at the edge is recognized as a proficient solution to reduce the systemfs network traffic. However, the most effective solutions currently employed for the purpose are typically based on memory and power-hungry algorithms, making their embedding on resourceconstrained devices very challenging;this is the case of vibration data reduction based on System Identification (SysId) models. This paper presents PARSY-VDD, a fully optimized PArallel end-to-end software framework based on SYstem identification for Vibration-based Damage Detection, as a suitable solution to perform damage detection at the edge in a time and energyefficient manner, avoiding streaming raw data to the cloud. First, we evaluate the damage detection capabilities of PARSYVDD with two benchmarks: a bridge and a wind turbine blade, showcasing the robustness of the end-to-end approach. Then, we deploy PARSY-VDD on both commercial single-core (STM32 family) and a specific multi-core (GAP9) edge device. We introduce an architecture-agnostic algorithmic optimization for SysId, improving the execution by 90× and reducing the consumption by 85× compared with the state-of-the-art SysId implementation on GAP9. Results show that by utilizing the unique parallel computing capabilities of GAP9, the execution time is 751 μs with the high-performance multi-core solution operating at 370MHz and 0.8V, while the energy consumption is 37 μJ with the low-power solution operating at 240MHz and 0.65V. Compared with other single-core implementations based on STM32 microcontrollers, the GAP9 high-performance configuration is 76× faster, while the low-power configuration is 360× more energy efficient. © 2014 IEEE.

关键词： Structural health monitoring

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Blockchain-based rumor detection approach for COVID-19

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Journal of Ambient Intelligence and Humanized Computing 2024年第1期15卷 435-449页

作者： Rani, Poonam Jain, Vibha Shokeen, Jyoti Balyan, Arnav Department of Computer Engineering Netaji Subhas University of Technology Delhi India Department of Computer Science and Engineering University Institute of Engineering and Technology Maharshi Dayanand University Haryana Rohtak India

The ubiquity of handheld devices and easy access to the Internet help users get easy and quick updates from social media. Generally, people share information with their friends and groups without inspecting the posts’ veracity, which causes false information propagation in the network. Moreover, detecting false news and rumors in such a massive load of unstructured information is a very tedious task. Results, many literature papers explored different machine learning and deep learning approaches to detect the presence of rumors on social media networks. Although detection of misleading news and rumors is not sufficient, therefore, we have proposed a model for the detection and prevention of transmitted rumors in this paper. In this paper, we use blockchain technology to verify the credibility of information and design a framework with four layers: network layer, blockchain layer, machine layer, and device layer, to prevent the propagation of rumors in the network. We also use deep learning techniques to identify the anomalies in the network. The Bi-directional Long Short Term Memory (Bi-LSTM) model is used to prevent the introduction of new rumors by continuously monitoring incoming messages in the network. The experimental results demonstrate that the proposed Bi-LSTM model outperforms state-of-the-art machine learning methods and recent baseline work. Performance is compared over different metrics such as accuracy, precision, recall, f1-score, and specificity. Experiment results show that our Bi-LSTM model outperforms all the other approaches and achieved 99.63 % accuracy. Additionally, the probability of incorrect detection is significantly low with only 0.13% false positive. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2022.

关键词： COVID-19

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A Novel Approach for Network Vulnerability Analysis in IIoT

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computer Systems science & engineering 2023年第4期45卷 263-277页

作者： K.Sudhakar S.Senthilkumar Department of Computer Science and Engineering University College of EngineeringPattukkottaiRajamadam614701India

Industrial Internet of Things(IIoT)offers efficient communication among business partners and *** an enlargement of IoT tools connected through the internet,the ability of web traffic gets *** to the raise in the size of network traffic,discovery of attacks in IIoT and malicious traffic in the early stages is a very demanding issues.A novel technique called Maximum Posterior Dichotomous Quadratic Discriminant Jaccardized Rocchio Emphasis Boost Classification(MPDQDJREBC)is introduced for accurate attack detection wi th minimum time consumption in *** proposed MPDQDJREBC technique includes feature selection and ***,the network traffic features are collected from the *** applying the Maximum Posterior Dichotomous Quadratic Discriminant analysis to find the significant features for accurate classification and minimize the time *** the significant features selection,classification is performed using the Jaccardized Rocchio Emphasis Boost *** Rocchio Emphasis Boost Classification technique combines the weak learner result into strong *** Rocchio classification technique is considered as the weak learners to identify the normal and ***,proposed MPDQDJREBC technique gives strong classification results through lessening the quadratic *** assists for proposed MPDQDJREBC technique to get better the accuracy for attack detection with reduced time *** assessment is carried out with UNSW_NB15 Dataset using different factors such as accuracy,precision,recall,F-measure and attack detection *** observed results exhibit the MPDQDJREBC technique provides higher accuracy and lesser time consumption than the conventional techniques.

关键词： Industrial internet of things(iiot) attack detection features selection maximum posterior dichotomous quadratic discriminant analysis jaccardized rocchio emphasis boost classification

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Machine learning and data analytics driven model for income tax rebate estimation: A neoteric and benign approach 4th

Machine learning and data analytics driven model for income ...

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4th International Conference on Computational Methods in science and Technology, ICCMST 2024

作者： Sharma, Viney Kumar, Achal Singh, Archana Kumar, Manish Department of Computer Science & Engineering Sharda University Agra India Department of Computer Science & Engineering Anand Engineering College Agra India Department of Computer Science & Engineering Chandigarh Engineering College Landran India

ISBN: (纸本)9781032911571

The provision of rebate to needy/underprivileged sections of society has been in practice since long in government organizations. The efficacy of such provisions lies in the fact that whether this rebate reaches people for whom it is intended. The biggest problem in awarding relief lies in the fact that people may not disclose their actual income. This paper conducts a study which strives for proposing a model which helps in assessing the financial status of any person given certain information about that person. This paper aims at proposing a Machine learning and Data Analytics driven model that predict the financial status of a person. The striking feature of this study is that results are applicable not only to income tax rebate problem but with minor customization, the model can be extended to work in other problems also where financial status of a person matters. © 2025 the Author(s).

关键词： Decentralized finance

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Hybrid NOMA Based MIMO Offloading for Mobile Edge Computing in 6G Networks

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China Communications 2022年第10期19卷 12-20页

作者： Yunus Dursun Fang Fang Zhiguo Ding School of Electrical and Electronic Engineering University of ManchesterManchesterUK Department of Electrical and Computer Engineering and the Department of Computer Science Western UniversityLondonCanada

Non-orthogonal multiple access (NOMA), multiple-input multiple-output (MIMO) and mobile edge computing (MEC) are prominent technologies to meet high data rate demand in the sixth generation (6G) communication networks. In this paper, we aim to minimize the transmission delay in the MIMO-MEC in order to improve the spectral efficiency, energy efficiency, and data rate of MEC offloading. Dinkelbach transform and generalized singular value decomposition (GSVD) method are used to solve the delay minimization problem. Analytical results are provided to evaluate the performance of the proposed Hybrid-NOMA-MIMO-MEC system. Simulation results reveal that the H-NOMA-MIMO-MEC system can achieve better delay performance and lower energy consumption compared to OMA.

关键词： NOMA MEC MIMO Generalized sin-gular value decomposition sixth generation networks(6G) delay minimization

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