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Classification of suspected objects and severity assessment of COVID-19 from chest X-ray images using deep transfer learning

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Research on Biomedical engineering 2023年第3期39卷 705-718页

作者： Verma, Karan Kumar, Ashok Swaraj, Aman Sagar, Aditi Department of Computer Science and Engineering National Institute of Technology Delhi Delhi India Department of Electronics and Communication Engineering Government Mahila Engineering College Rajasthan Ajmer India Department of Computer Science and Engineering Indian Institute of Technology Roorkee India

Purpose: Coronavirus disease 2019 (COVID-19) has infected about 418 million people across the globe. So, the analysis of biomedical imaging accompanied with artificial intelligence (AI) approaches has transpired a vital role in diagnosing COVID-19. Until now, numerous classification approaches have been demonstrated for the detection of COVID-19. The assessment of COVID-19 patients according to severity level is not so far investigated. For this motivation, the classification of COVID-19 chest X-ray (CXR) images according to severity of the infection is presented in this work. Methods: Primarily, the 1527 CXR images are pre-processed to reshape images into unique size, denoised, and enhanced images through median filter and histogram equalization (HE) techniques, respectively. Afterward, reshaped, denoised, and enhanced CXR images are augmented using synthetic minority oversampling technique (SMOTE) to achieve the balanced dataset of 1752 CXR images. After augmentation, a pre-trained VGG16 and residual network 50 (Resnet50) deep transfer learning models with random forest (RF) and support vector machine (SVM) classifiers are utilized for feature extraction and classification of 1752 CXR images into diverse class labels such as normal, severe COVID-19, and non-severe COVID-19. Results: Our proposed ResNet50 model with SVM classifier provides the highest accuracy of about 95% for severity assessment and classification of COVID-19 CXR images as compared to other permutations. For the ResNet50 model with SVM classifier model, the average value of precision, recall, and F1-score are 91%, 94%, and 92%, respectively. Conclusion: The multi-class classification deep transfer learning models are presented to determine the severity assessment and classification of COVID-19 by using CXR images. Out of these proposed models, the ResNet50 model with SVM classifier will be highly favorable for doctors to classify patients according to their severity assessment and detection of COV

关键词： COVID-19

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Characterizing the Preceding Discharges of the Positive Cloud-to-Ground Lightning First Strokes in Winter Thunderstorms in Japan 37

Characterizing the Preceding Discharges of the Positive Clou...

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37th International Conference on Lightning Protection, ICLP 2024

作者： Yang, Qingliu Wang, Daohong Wu, Ting Yang, Junchen Takagi, Nobuyuki Morimoto, Takeshi Department of Electrical Electronic and Computer Engineering Gifu University Gifu Japan Department of Electrical Electronic and Communication Engineering Kindai University Osaka Japan

ISBN: (纸本)9783800764174

We have successfully located in 3 dimensions the first return stroke preceding discharges of 78 positive cloud-to-ground (+CG) lightning flashes in Hokuriku winter thunderstorms in Japan using a discone-antenna lightning mapping array (DALMA). We found that among these flashes, 49 (63%) can be characterized with an upward preliminary breakdown (PB) process, while 29 (37%) with a downward preliminary process. Compared with those summer positive CG lightning flashes, the proportion of downward develop PB increased significantly. A statistical study shows that the development directions of PB will not affect the peak currents of the following return strokes. Also, the relationship between the peak current of +CG and the development speed and development distance of PB is not obvious. However, the peak current is inversely proportional to the time when the return stroke occurs. The earlier the return stroke occurs after the PB, the greater the probability of a strong positive return stroke. © 2024 IEEE, © VDE VERLAG GMBH ∙ Berlin ∙ Offenbach.

关键词： Lightning

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Performance Analysis of Low Power Hybrid Full Adder Using Pass Transistor Logic 2

Performance Analysis of Low Power Hybrid Full Adder Using Pa...

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2nd IEEE International Conference on Interdisciplinary Approaches in Technology and Management for Social Innovation, IATMSI 2024

作者： Sharma, Priyank Mishra, Vishwas Akashe, Shyam Meerut Institute of Engineering and Technology Electronic and Communication Department Meerut India Swami Vivekanand Subharti University Electrical and Electronic Engineering Department Meerut India Itm University Electronic and Communication Engineering Department Gwalior India

ISBN: (纸本)9798350360523

Adders play an most vital role in the logical operations of VLSI circuits, with the fast increasing of the electronics technologies, speed and size of the system improved which is indirectly depends on the performance speed of the adder. In this proposed paper hybrid full adder is implemented by XNOR and AND logics with pass transistor logic (PTL) same as CMOS with less no of transistors that optimize the area, power, delay and increases the reliability of the circuit. Here the hybrid adder is implemented with 16 transistors and the performances of the parameters are more optimized, when we compare to the previous designs. It is performed in the various operating frequencies and temperature to calculate the power which is reduced up to 25% and delay, noise are optimized to 40% in this circuit. We calculated the parameters like power, delay, leakage current, and leakage voltage in the different temperatures and removed the knot in the simulation of the hybrid full adder. These total values are calculated with the DC voltage of 0.7 V and at 27°c temperature, with the decrease in the supply voltage or operating voltage there is the decrease in the delay and the noise. These total simulation works are done in cadence virtuoso tool in 45 nm technology. © 2024 IEEE.

关键词： Electric power utilization

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Correction‐enabled reversible data hiding with pixel repetition for high embedding rate and quality preservation

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IET Cyber-Systems and Robotics 2024年第3期6卷 49-61页

作者： Mohammad Ali Kawser Hussain Nyeem Md Abdul Wahed School of Transportation Science and Engineering Beihang UniversityBeijingChina Department of Electrical Electronic and Communication Engineering(EECE)Military Institute of Science and Technology(MIST)DhakaBangladesh

A novel correction‐enabled Pixel Repetition(PR)‐based Reversible Data Hiding(RDH)framework,featuring a new embedding scheme is *** proposed RDH scheme uses contextually redundant block pixels,generated via PR,in a two‐phase adaptive embedding process,enhancing both image quality and data embedding ***,each 2×2 block encodes 4 bits of data using new mapping conditions that facilitate seed pixel reconstruction from remaining block pixels and provide additional embedding ***,an innovative post‐embedding error correction technique,based on 2^(k)‐bit error‐correction,minimises post‐embedding distortion,further improving image *** error correction approach augments data embedding robustness,vital for applications like medical imaging,telemedicine,and digital watermarking that requires high embedding capacity with minimum possible *** proposed scheme surpasses existing state‐of‐the‐art methods in embedding ratedistortion performance,validated through subjective and objective ***,statistical analysis,including histogram and fragility testing,confirms the scheme's potential for image authentication across diverse multimedia *** correctionenabled RDH with PR offers enhanced embedding capacity and image quality preservation,making it particularly advantageous for applications requiring robust data hiding while maintaining visual fidelity.

关键词： data assimilation vision

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Super-Wideband Fractal Antenna for Future Generations of Wireless communication

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Progress In Electromagnetics Research C 2023年 136卷 137-149页

作者： Fouad, Mohamed S. Farahat, Asmaa E. Hussein, Khalid Fawzy Ahmed Shaalan, Abdel A. Ahmed, Mai F. High Institute of Electronic Engineering Belbis Egypt Cairo11843 Egypt Department of Electronics and Communication Engineering Faculty of Engineering Zagazig University Zagazig Egypt

A super-wideband (SWB) antenna of enhanced performance is proposed to cover the frequency band from 3 to 30 GHz. The proposed antenna can be regarded as a two-arm antenna of fractal structure. Each of the antenna arms can be viewed as composed of multiple merged wideband fractal elements. Each fractal element is a wide-flare metallic sector-shaped radiator with circular (arc-shaped) edges to enhance the bandwidth over which the antenna impedance is matched to 50 Ω-feeder. A novel SWB balun is proposed for feeding the two-arm antenna of its balanced structure through the conventional coaxial feeder of its unbalanced structure. For experimental assessment of its performance, the proposed antenna is fabricated and measured by a vector network analyzer (VNA). The experimental results come in agreement with the results obtained by the CST ® simulator. It is shown that the proposed antenna has a ratio bandwidth (RBW) of 10: 1, percentage bandwidth (%BW) of 164%, and bandwidth-dimension ratio (BDR) of 1952. The efficiency of radiation of the proposed antenna is shown to be greater than 98% over most of the operational frequency band. © 2023, Electromagnetics Academy. All rights reserved.

关键词： Bandwidth

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Detecting and Preventing of Attacks in Cloud Computing Using Hybrid Algorithm

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Intelligent Automation & Soft Computing 2023年第1期35卷 79-95页

作者： R.S.Aashmi T.Jaya Department of Computer Science and Engineering CSI Institute of TechnologyThovalaiIndia Department of Electronic Communications and Engineering CSI Institute of TechnologyThovalaiIndia

Cloud computing is the technology that is currently used to provide users with infrastructure,platform,and software services *** this system,Platform as a Service(PaaS)offers a medium headed for a web development platform that uniformly distributes the requests and *** using Denial of service(DoS)and Distributed Denial of Service(DDoS)attacks abruptly interrupt these *** though several existing methods like signature-based,statistical anomaly-based,and stateful protocol analysis are available,they are not sufficient enough to get rid of Denial of service(DoS)and Distributed Denial of Service(DDoS)attacks and hence there is a great need for a definite *** this issue,we propose an improved hybrid algorithm which is a combination of Multivariate correlation analysis,Spearman coefficient,and mitigation *** can easily differentiate common traffic and attack *** only that,it greatly helps the network to distribute the resources only for authenticated *** effects of comparing with the normalized information have shown an extra encouraging detection accuracy of 99%for the numerous DoS attack as well as DDoS attacks.

关键词： Hybrid algorithm(HA) distributed denial of service(DDoS) denial of service(DoS) platform as a service(PaaS) infrastructure as a service(IaaS) software as a service(SaaS)

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Achieving Secured Medical Network (SMN) through Stateless Mechanism and SkeyM in Medical-Internet of Things (M-IoT)

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Journal of engineering and Applied Science 2024年第1期71卷 128页

作者： Nithya, S. Palanisamy, SatheeshKumar Nivethitha, T. Department of Computer Science and Engineering Kumaraguru College of Technology Tamil Nadu Coimbatore641049 India Department of Electronics and Communication Engineering BMS Institute of Technology and Management Karnataka Bengaluru560064 India Department of Electronics and Communication Engineering Sri Eshwar College of Engineering Tamil Nadu Coimbatore641202 India

Medical Internet of Things (M-IoT) synchronizes medical devices in a network to provide smart healthcare monitoring to doctors and to provide an interactive model for patients. This embedded networked system gained lots of importance in the last few years. Now almost every hospital adopts M-IoT. Though it had a lot of challenges in the initial stages of implementation such as feasibility, accuracy, and autonomy, now it has bridged the flaws with the help of several researchers in this field. But still, M-IoT lags behind in security and privacy aspects due to which attackers can intrude on the network and exploit patients’ health data. By examining the various intensive security threats imposed on M-IoT a unique way of handling and transferring data over the network is proposed in this paper. From our research findings, this would resolve the data security issues in M-IoT and commence the next advancements. © The Author(s) 2024.

关键词： Internet of things

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THE EFFICIENCY AND SECURITY OF MULTI-UNMANNED AERIAL VEHICLE communication NETWORKS: FEDERATED LEARNING BASED ON LIGHTWEIGHT BLOCKCHAIN

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International Journal of Electrical engineering 2024年第1期31卷 23-31页

作者： Chung, Hao-Tse Wang, Li-Chun Cheng, Shao-Hung Institute of Electrical and Computer Engineering National Yang Ming Chiao Tung University Taiwan Department of Electrical and Electronic Engineering Chung Cheng Institute of Technology NDU Taiwan

This study develops an innovative federated learning framework specifically designed for multi-unmanned aerial vehicles (UAVs), utilizing blockchain technology to achieve lightweight, secure, and efficient data processing. In this architecture, UAVs directly participate in federated learning without the need for a central server. Instead, a series of base stations execute blockchain nodes to process gradient information and maintain the system. We eliminate single points of failure by transferring gradient aggregation from the central server to blockchain nodes, thereby enhancing the system’s stability and reliability. Furthermore, the use of a private Inter Planetary File System (IPFS) reduces storage and communication costs. A Byzantine Fault Tolerance (BFT) consensus mechanism is adopted to improve system efficiency and security. Experiments confirm that our proposed architecture effectively prevents model poisoning attacks, even in scenarios where malicious blockchain nodes are in the majority, thus ensuring learning performance. Finally, a node reputation selection mechanism based on the Multi-Krum algorithm is introduced to increase the likelihood of nodes submitting high-quality gradients, thereby enhancing overall system efficiency. © 2024, Chinese institute of Electrical engineering. All rights reserved.

关键词： Aircraft communication

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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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Customized Graph-Cut Segmentation with Superpixels for Snowfall Detection on Solar Panels 4

Customized Graph-Cut Segmentation with Superpixels for Snowf...

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4th International Conference on Sentiment Analysis and Deep Learning, ICSADL 2025

作者： Pavithra, V. Danisha, B.R. Divyadharshini, V. Koresh, H. James Deva Kpr Institute of Engineering and Technology Department of Computer Science and Engineering Coimbatore India Kpr Institute of Engineering and Technology Department of Electronics and Communication Engineering Coimbatore India

ISBN: (纸本)9798331523923

Snow accumulation on solar panels reduces their performance in energy generation regions. Hence certain image processing methods were developed for segmenting snow from the normal areas of solar panels. Graph Cut and Flood Fill are the popular semi-automatic method used for this application where it relies on detecting an efficient region for marking the segmentation process. Similarly, the manual intervention fails to provide consistent results in such methods. Therefore, a customized graph cut methodology is proposed in this work by combining super pixel-based feature extraction and k-means clustering. At first, the input images are preprocessed by dividing the pixels to form super pixels as a group to reduce the computational complexity without losing the meaningful information. Features like intensity, edge density, texture, and color are extracted from these super pixel groups for segmenting the snow-covered regions on a solar panel. Further, the extracted features are normalized by a k-means clustering algorithm for identifying the highest intensity regions to observe the snow-covered regions. A graph cut-based segmentation technique is applied at the end to ensure the precise boundary delineation. The work is also incorporated with several post-processing techniques for improving the segmentation mask quality. This proposed method is evaluated against Graph cut and flood fill and satisfied with an average dice similarity coefficient of 93.09% and 95.11%. © 2025 IEEE.

关键词： Image segmentation

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