Unveiling AI Insights: Navigating COVID-19 with Machine Learning and Deep Learning DOI
Sonali Agrawal, Dilip Kumar Sharma

Published: Dec. 22, 2023

The COVID-19 pandemic has brought unprecedented challenges to global healthcare systems, prompting the exploration of innovative technologies mitigate its impact. This research paper provides a comprehensive review latest developments in applying deep learning (DL) and machine (ML) techniques addressing various aspects COVID-19. covers topics, including diagnostic tools, drug discovery, epidemiological modeling, patient management. Researchers leverage AI, especially DL ML, develop efficient algorithms using CT X-ray images for rapid accurate diagnosis, with overall accuracies ranging from 86.1% 99.7% [1].

Language: Английский

Automated detection and forecasting of COVID-19 using deep learning techniques: A review DOI
Afshin Shoeibi, Marjane Khodatars, Mahboobeh Jafari

et al.

Neurocomputing, Journal Year: 2024, Volume and Issue: 577, P. 127317 - 127317

Published: Jan. 26, 2024

Language: Английский

Citations

55
Machine learning and deep learning-based approach in smart healthcare: Recent advances, applications, challenges and opportunities DOI Creative Commons
Anichur Rahman, Tanoy Debnath,

Dipanjali Kundu

et al.

AIMS Public Health, Journal Year: 2024, Volume and Issue: 11(1), P. 58 - 109

Published: Jan. 1, 2024

<abstract> <p>In recent years, machine learning (ML) and deep (DL) have been the leading approaches to solving various challenges, such as disease predictions, drug discovery, medical image analysis, etc., in intelligent healthcare applications. Further, given current progress fields of ML DL, there exists promising potential for both provide support realm healthcare. This study offered an exhaustive survey on DL system, concentrating vital state art features, integration benefits, applications, prospects future guidelines. To conduct research, we found most prominent journal conference databases using distinct keywords discover scholarly consequences. First, furnished along with cutting-edge ML-DL-based analysis smart a compendious manner. Next, integrated advancement services including ML-healthcare, DL-healthcare, ML-DL-healthcare. We then DL-based applications industry. Eventually, emphasized research disputes recommendations further studies based our observations.</p> </abstract>

Language: Английский

Citations

39
Artificial Intelligence in Virtual Telemedicine Triage: A Respiratory Infection Diagnosis Tool with Electronic Measuring Device DOI Creative Commons
Naythan Villafuerte, Santiago Manzano, Paulina Ayala

et al.

Future Internet, Journal Year: 2023, Volume and Issue: 15(7), P. 227 - 227

Published: June 25, 2023

Due to the similarities in symptomatology between COVID-19 and other respiratory infections, diagnosis of these diseases can be complicated. To address this issue, a web application was developed that employs chatbot artificial intelligence detect COVID-19, common cold, allergic rhinitis. The also integrates an electronic device connects app measures vital signs such as heart rate, blood oxygen saturation, body temperature using two ESP8266 microcontrollers. measured data are displayed on OLED screen sent Google Cloud server MQTT protocol. AI algorithm accurately determines disease patient is suffering from, achieving accuracy rate 0.91% after entered. includes user interface allows patients view their medical history consultations with assistant. HTML, CSS, JavaScript, MySQL, Bootstrap 5 tools, resulting responsive, dynamic, robust secure for both server. Overall, provides efficient reliable way diagnose infections power intelligence.

Language: Английский

Citations

11
Machine learning algorithms in sepsis DOI
Luisa Agnello, Matteo Vidali, Andrea Padoan

et al.

Clinica Chimica Acta, Journal Year: 2023, Volume and Issue: 553, P. 117738 - 117738

Published: Dec. 28, 2023

Language: Английский

Citations

10
A hybridized LSTM-ANN-RSA based deep learning models for prediction of COVID-19 cases in Eastern European countries DOI
B. Murali Manohar, Raja Das,

M. Lakshmi

et al.

Expert Systems with Applications, Journal Year: 2024, Volume and Issue: 256, P. 124977 - 124977

Published: Aug. 6, 2024

Language: Английский

Citations

3
A Survey on COVID-19 Data Analysis Using AI, IoT, and Social Media DOI Creative Commons
Muhammad Junaid Butt, Ahmad Kamran Malik, Muhammad Usman Qamar

et al.

Sensors, Journal Year: 2023, Volume and Issue: 23(12), P. 5543 - 5543

Published: June 13, 2023

Coronaviruses are a well-established and deadly group of viruses that cause illness in both humans animals. The novel type this virus group, named COVID-19, was firstly reported December 2019, and, with the passage time, coronavirus has spread to almost all parts world. Coronavirus been millions deaths around Furthermore, many countries struggling COVID-19 have experimented various kinds vaccines eliminate its variants. This survey deals data analysis impact on human social life. Data information related can greatly help scientists governments controlling symptoms coronavirus. In survey, we cover areas discussion analysis, such as how artificial intelligence, along machine learning, deep IoT, worked together fight against COVID-19. We also discuss intelligence IoT techniques used forecast, detect, diagnose patients Moreover, describes fake news, doctored results, conspiracy theories were over media sites, Twitter, by applying network sentimental techniques. A comprehensive comparative existing conducted. end, Discussion section presents different techniques, provides future directions for research, suggests general guidelines handling coronavirus, well changing work life conditions.

Language: Английский

Citations

9
A Performance Study of CNN Architectures for the Autonomous Detection of COVID-19 Symptoms Using Cough and Breathing DOI Creative Commons

Meysam Effati,

Goldie Nejat

Computers, Journal Year: 2023, Volume and Issue: 12(2), P. 44 - 44

Published: Feb. 17, 2023

Deep learning (DL) methods have the potential to be used for detecting COVID-19 symptoms. However, rationale which DL method use and symptoms detect has not yet been explored. In this paper, we present first performance study compares various convolutional neural network (CNN) architectures autonomous preliminary detection of cough and/or breathing We compare analyze residual networks (ResNets), visual geometry Groups (VGGs), Alex (AlexNet), densely connected (DenseNet), squeeze (SqueezeNet), identification ResNet (CIdeR) investigate their classification performance. uniquely train validate both unimodal multimodal CNN using EPFL Cambridge datasets. Performance comparison across all modes datasets showed that VGG19 DenseNet-201 achieved highest DensNet-201 had high F1 scores (0.94 0.92) on dataset, compared next score (0.79), with comparable larger dataset. They also consistently accuracy, recall, precision. For detection, (0.91) other structures (≤0.90), having accuracy recall. Our investigation provides foundation needed select appropriate deep utilize non-contact early detection.

Language: Английский

Citations

7
Deep Learning-Based Classification of Chest Diseases Using X-rays, CT Scans, and Cough Sound Images DOI Creative Commons
Hassaan Malik, Tayyaba Anees, Ahmad Sami Al-Shamayleh

et al.

Diagnostics, Journal Year: 2023, Volume and Issue: 13(17), P. 2772 - 2772

Published: Aug. 26, 2023

Chest disease refers to a variety of lung disorders, including cancer (LC), COVID-19, pneumonia (PNEU), tuberculosis (TB), and numerous other respiratory disorders. The symptoms (i.e., fever, cough, sore throat, etc.) these chest diseases are similar, which might mislead radiologists health experts when classifying diseases. X-rays (CXR), cough sounds, computed tomography (CT) scans utilized by researchers doctors identify such as LC, PNEU, TB. objective the work is nine different types diseases, edema (EDE), pneumothorax (PNEUTH), normal, atelectasis (ATE), consolidation (COL). Therefore, we designed novel deep learning (DL)-based detection network (DCDD_Net) that uses CXR, CT scans, sound images for identification scalogram method used convert sounds into an image. Before training proposed DCDD_Net model, borderline (BL) SMOTE applied balance model trained evaluated on 20 publicly available benchmark datasets scan, images. classification performance compared with four baseline models, i.e., InceptionResNet-V2, EfficientNet-B0, DenseNet-201, Xception, well state-of-the-art (SOTA) classifiers. achieved accuracy 96.67%, precision 96.82%, recall 95.76%, F1-score 95.61%, area under curve (AUC) 99.43%. results reveal outperformed models in terms many evaluation metrics. Thus, can provide significant assistance medical experts. Additionally, was also shown be resilient statistical evaluations using McNemar ANOVA tests.

Language: Английский

Citations

7
Towards Improved XAI-Based Epidemiological Research into the Next Potential Pandemic DOI Creative Commons
Hamed Khalili, Maria A. Wimmer

Life, Journal Year: 2024, Volume and Issue: 14(7), P. 783 - 783

Published: June 21, 2024

By applying AI techniques to a variety of pandemic-relevant data, artificial intelligence (AI) has substantially supported the control spread SARS-CoV-2 virus. Along with this, epidemiological machine learning studies have been frequently published. While these models can be perceived as precise and policy-relevant guide governments towards optimal containment policies, their black box nature hamper building trust relying confidently on prescriptions proposed. This paper focuses interpretable AI-based in context recent pandemic. We systematically review existing studies, which jointly incorporate AI, epidemiology, explainable approaches (XAI). First, we propose conceptual framework by synthesizing main methodological features pipelines SARS-CoV-2. Upon proposed analyzing selected reflect current research gaps toolboxes how fill generate enhanced policy support next potential

Language: Английский

Citations

2
Comparison of Genetic Algorithm (GA) and Particle Swarm Optimization (PSO) for Estimating the Susceptible-Exposed-Infected-Recovered (SEIR) Model Parameter Values DOI Creative Commons
Aminatus Sa’adah,

Ayomi Sasmito,

Asysta Amalia Pasaribu

et al.

Journal of Information Systems Engineering and Business Intelligence, Journal Year: 2024, Volume and Issue: 10(2), P. 290 - 301

Published: June 28, 2024

Background: The most commonly used mathematical model for analyzing disease spread is the Susceptible-Exposed-Infected-Recovered (SEIR) model. Moreover, dynamics of SEIR depend on several factors, such as parameter values. Objective: This study aimed to compare two optimization methods, namely genetic algorithm (GA) and particle swarm (PSO), in estimating values, infection, transition, recovery, death rates. Methods: GA PSO algorithms were compared estimate values fitness value was calculated from error between actual data cumulative positive COVID-19 cases numerical solution Furthermore, using fourth-order Runge-Kutta (RK-4), while obtained dataset province Jakarta, Indonesia. Two datasets then success each algorithm, namely, Dataset 1, representing initial interval COVID-19, 2, an where there a high increase cases. Results: Four parameters estimated, infection rate, transition recovery due disease. In smallest method, 8.9%, occurred when , 7.5%. 31.21%, 3.46%. Conclusion: Based estimation results Datasets 1 had better fitting than GA. showed more robust provided could adapt trends epidemic. Keywords: Genetic Particle optimization, model, Parameter estimation.

Language: Английский

Citations

2