Detection of COVID-19 Using Convolutional Neural Networks DOI

Arshita Srivastava,

Shubham Mishra

Опубликована: Сен. 8, 2023

A highly contagious illness caused by the COVID-19 pandemic is proven to havoc with people's health and well-being all over globe. Chest radiography one of most crucial databases for applying detection techniques. The respiratory system contaminated COVID-19, which also affects alveoli replicates itself. Conventional approaches such as RT - PCR tests, rapid antigen serological etc. are generally used COVID came out be costly time-consuming. There have been several suggested artificial intelligence (AI)-based models in individuals' using lung ultrasound images, voice patterns, chest sounds, proposed model shows how disease cases could identified features variations images. deep convolutional neural network (CNN) ResNet50 modified configuration has identification from image dataset. depicts comparison technique Resnet 101 model. dataset containing Covid infected, normal, pneumonia-infected Additionally, can identify covid-19 patients' current conditions real-time identifying coronavirus diseases CT scan pictures. database ability monitor detected patients keep their order improve training model's accuracy. provides approximately 96.73% accuracy explicit competency ResNet other existing models.

Язык: Английский

Detection of Covid-19 Using AI Application DOI Creative Commons
K. Ravikumar,

Mohammed Ishaque,

Bhawani Sankar Panigrahi

и другие.

EAI Endorsed Transactions on Pervasive Health and Technology, Год журнала: 2023, Номер 9

Опубликована: Июнь 28, 2023

INTRODUCTION: In December of 2019, the infection which caused pandemic started in Hubei territory Wuhan, China. They were identified as SARS-CoV-2, a highly infectious, easily transmissible virus that has an increasing number deaths worldwide. Covid can be perceived with testing strategy known RT-PCR. As now, this technique is broadly utilized for identifying infection. OBJECTIVES: The imaging modalities are various degrees seriousness from asymptomatic to basic cases. Side effects individual contaminated COVID-19 incorporate gentle hack, fever, chest torment, weakness, and so forth An extremefundamental ailment requires consideration. Imaging assumed larger part during flare-up, CT being better option than invert transcriptase-polymerase chain response testing. METHODS: With artificial intelligence robotics, variety devices solutions have been introduced improve contactless service forhumans. presentation AI technology may distinct advantage treatment patients. Information could solve tracking system without any human interaction. RESULTS: methods permit radiologists doctors distinguish inner structures see their shape, size, thickness, surface,which help early discovery CONCLUSION: This detailed information data decide whether there's clinical issue, provide extent accurate area matter, uncover other significant details will assist doctor deciding best treatment.

Язык: Английский

Процитировано

86
Applications of Artificial Intelligence in the Economy, Including Applications in Stock Trading, Market Analysis, and Risk Management DOI Creative Commons
Amir Masoud Rahmani, Bahareh Rezazadeh, Majid Haghparast

и другие.

IEEE Access, Год журнала: 2023, Номер 11, С. 80769 - 80793

Опубликована: Янв. 1, 2023

In an increasingly automated world, Artificial Intelligence (AI) promises to revolutionize how people work, consume, and develop their societies. Science technology advancement has led humans seek solutions problems; however, AI-based is not novel a wide range of economic applications. This paper examines AI applications in economics, including stock trading, market analysis, risk assessment. A comprehensive taxonomy proposed investigate various scopes the categories. Furthermore, we will discuss this area's most significant techniques evaluation criteria. As final step, identify challenges, open issues, future work suggestions.

Язык: Английский

Процитировано

28
Piranha Foraging Optimization Algorithm with Deep Learning Enabled Fault Detection in Blockchain-Assisted Sustainable IoT Environment DOI Open Access
Haitham Assiri

Sustainability, Год журнала: 2025, Номер 17(4), С. 1362 - 1362

Опубликована: Фев. 7, 2025

As the acceptance of Internet Things (IoT) systems quickens, guaranteeing their sustainability and reliability poses an important challenge. Faults in IoT can result resource inefficiency, high energy consumption, reduced security, operational downtime, obstructing goals. Thus, blockchain (BC) technology, known for its decentralized distributed characteristics, offer significant solutions networks. BC technology provides several benefits, such as traceability, immutability, confidentiality, tamper proofing, data integrity, privacy, without utilizing a third party. Recently, consensus algorithms, including ripple, proof stake (PoS), work (PoW), practical Byzantine fault tolerance (PBFT), have been developed to enhance efficiency. Combining detection algorithms more reliable secure environment. this study presents sustainable BC-Driven Edge Verification with Consensus Approach-enabled Optimal Deep Learning (BCEVCA-ODL) approach recognition environments. The proposed BCEVCA-ODL technique incorporates merits BC, IoT, DL techniques networks’ trustworthiness, efficacy. devices substantial level decision-making capacity achieve on accomplishment intrablock transactions. A stacked sparse autoencoder (SSAE) model is employed detect faults Lastly, Piranha Foraging Optimization Algorithm (PFOA) used optimum hyperparameter tuning SSAE approach, which assists enhancing rate. wide range simulations was accomplished highlight efficacy technique. achieved superior FDA value 100% at probability 0.00, outperforming other evaluated methods. highlights significance embedding into systems, underlining how advanced provide environmental benefits. experimental outcomes pave way greener technologies that support global initiatives.

Язык: Английский

Процитировано

1
Detection of SARS-CoV-2 Virus Using Lightweight Convolutional Neural Networks DOI
Ankit Kumar, Brijesh Kumar Chaurasia

Wireless Personal Communications, Год журнала: 2024, Номер 135(2), С. 941 - 965

Опубликована: Март 1, 2024

Язык: Английский

Процитировано

7
Research on university laboratory management and maintenance framework based on computer aided technology DOI Open Access
Jiaqing Yao,

Zheng Yuan

Applied Mathematics and Nonlinear Sciences, Год журнала: 2025, Номер 10(1)

Опубликована: Янв. 1, 2025

Abstract This With the development of information technology, university laboratories play an increasingly important role in teaching and research. However, traditional laboratory management methods have many shortcomings terms resource scheduling, system flexibility, automation, making it difficult to adapt constantly changing demands complex experimental environments. Traditional often rely on manual management, resulting low utilization efficiency potential waste or scheduling imbalance under high concurrency conditions. Moreover, models lack real-time monitoring flexible capabilities, failing meet requirements efficient modern management. To address these issues, this paper proposes a computer method based virtualization technology. By designing multi-layer platform architecture, including layer, desktop service foundation complete is formed, enhancing automation levels. also introduces Column Generation-based Shared Resource Constrained Project Scheduling Algorithm (CGS) achieve allocation optimized scheduling. Experimental results show that proposed outperforms utilization, task completion time, providing effective solution for

Язык: Английский

Процитировано

0
Netizens' concerns during COVID-19: a topic evolution analysis of Chinese social media platforms DOI
Zhaohua Deng, Rongyang Ma, Manli Wu

и другие.

Kybernetes, Год журнала: 2023, Номер 54(2), С. 1109 - 1127

Опубликована: Ноя. 19, 2023

Purpose This study analyzes the evolution of topics related to COVID-19 on Chinese social media platforms with aim identifying changes in netizens' concerns during different stages pandemic. Design/methodology/approach In total, 793,947 posts were collected from Zhihu, a Question and Answer website, Dingxiangyuan, online healthcare community, 31 December, 2019, 4 August, 2021. Topics extracted prodromal outbreak stages, abatement–resurgence cycle. Findings Netizens' varied stages. During netizens showed greater concern about news, impact prevention control COVID-19. first round abatement resurgence stage, remained concerned news pandemic, however, less attention was paid later popularity grew concerning COVID-19, while engaged more discussions international events raising spirits fight global Practical implications contributes practice by providing way for government policy makers retrospect pandemic thereby make good preparation take proper measures communicate citizens address their demands similar situations future. Originality/value literature applying an adapted version Fink's (1986) crisis life cycle create five-stage model understand repeated Mainland China.

Язык: Английский

Процитировано

1
Deep learning for multisource medical information processing DOI
Mavis Gezimati, Ghanshyam Singh

Elsevier eBooks, Год журнала: 2024, Номер unknown, С. 45 - 76

Опубликована: Янв. 1, 2024

Язык: Английский

Процитировано

0
Boosting medical diagnostics with a novel gradient-based sample selection method DOI
Samet Aymaz

Computers in Biology and Medicine, Год журнала: 2024, Номер 182, С. 109165 - 109165

Опубликована: Сен. 24, 2024

Язык: Английский

Процитировано

0
Multi-Classification Model for Distinguishing Covid-19 from Different Lung Diseases based on Deep Learning Algorithms DOI Open Access

Mohammed Al-Salamony

Deleted Journal, Год журнала: 2023, Номер 0(0), С. 0 - 0

Опубликована: Июнь 22, 2023

The Corona-Virus is a worldwide pandemic classified as one of the scariest viruses, according to World Health Organization (WHO).That because its effect on person's lungs, which causes high deaths.Among vital effectiveness indicators for identifying some diseases, including coronavirus, are computerized tomography (CT) scans and chest X-rays.Data heterogeneity between X-ray CT biomarkers makes learning capability models more challenging.Furthermore, they utilize multistage diagnosing COVID-19 from lung diseases.Hence, proposed solution behind this research leverage form deep architecture applying many classification resolve these problems using fusion two images that can identify COVID-19, pneumonia, cancer in single procedure.Firstly, patches extracted multimodal by every patch convolutional neural network (CNN) address issues.Then, available features combined further AlexNet classifier, CNN Deep Feature Concatenation (DFC) mechanism.All learned straightforward CNN.Finally, experimental results demonstrated + DFC exceeded comparable work already done with 98.47 % accuracy.Lithium-ion batteries Li1.3Nb0.3Mn0

Язык: Английский

Процитировано

0
Detection of COVID-19 Using Convolutional Neural Networks DOI

Arshita Srivastava,

Shubham Mishra

Опубликована: Сен. 8, 2023

A highly contagious illness caused by the COVID-19 pandemic is proven to havoc with people's health and well-being all over globe. Chest radiography one of most crucial databases for applying detection techniques. The respiratory system contaminated COVID-19, which also affects alveoli replicates itself. Conventional approaches such as RT - PCR tests, rapid antigen serological etc. are generally used COVID came out be costly time-consuming. There have been several suggested artificial intelligence (AI)-based models in individuals' using lung ultrasound images, voice patterns, chest sounds, proposed model shows how disease cases could identified features variations images. deep convolutional neural network (CNN) ResNet50 modified configuration has identification from image dataset. depicts comparison technique Resnet 101 model. dataset containing Covid infected, normal, pneumonia-infected Additionally, can identify covid-19 patients' current conditions real-time identifying coronavirus diseases CT scan pictures. database ability monitor detected patients keep their order improve training model's accuracy. provides approximately 96.73% accuracy explicit competency ResNet other existing models.

Язык: Английский

Процитировано

0