A survey on COVID-19 impact in the healthcare domain: worldwide market implementation, applications, security and privacy issues, challenges and future prospects

Complex & Intelligent Systems, Год журнала: 2022, Номер 9(1), С. 1027 - 1058

Опубликована: Май 31, 2022

Extensive research has been conducted on healthcare technology and service advancements during the last decade. The Internet of Medical Things (IoMT) demonstrated ability to connect various medical apparatus, sensors, specialists ensure best treatment in a distant location. Patient safety improved, prices have decreased dramatically, services become more approachable, operational efficiency industry increased. This paper offers recent review current future applications, security, market trends, IoMT-based implementation. analyses advancement IoMT implementation addressing concerns from perspectives enabling technologies, services. potential obstacles issues system are also discussed. Finally, survey includes comprehensive overview different disciplines empower researchers who eager work make advances field obtain better understanding domain.

Язык: Английский

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TSA-CNN-AOA: Twitter sentiment analysis using CNN optimized via arithmetic optimization algorithm

Neural Computing and Applications, Год журнала: 2023, Номер 35(14), С. 10311 - 10328

Опубликована: Янв. 20, 2023

Язык: Английский

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A comprehensive survey on impact of applying various technologies on the internet of medical things

Artificial Intelligence Review, Год журнала: 2025, Номер 58(3)

Опубликована: Янв. 8, 2025

Abstract This paper explores the transformative impact of Internet Medical Things (IoMT) on healthcare. By integrating medical equipment and sensors with internet, IoMT enables real-time monitoring patient health, remote care, individualized treatment plans. significantly improves several healthcare domains, including managing chronic diseases, safety, drug adherence, resulting in better outcomes reduced expenses. Technologies like blockchain, Artificial Intelligence (AI), cloud computing further boost IoMT’s capabilities Blockchain enhances data security interoperability, AI analyzes massive volumes health to find patterns make predictions, offers scalable cost-effective processing storage. Therefore, this provides a comprehensive review (IoT) IoMT-based edge-intelligent smart healthcare, focusing publications published between 2018 2024. The addresses numerous studies IoT, IoMT, AI, edge computing, security, Deep Learning, blockchain. obstacles facing are also covered paper, interoperability issues, regulatory compliance, privacy concerns. Finally, recommendations for provided.

Язык: Английский

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The Deep Learning ResNet101 and Ensemble XGBoost Algorithm with Hyperparameters Optimization Accurately Predict the Lung Cancer

Applied Artificial Intelligence, Год журнала: 2023, Номер 37(1)

Опубликована: Июнь 3, 2023

Lung cancer is the most common and second leading cause of with lowest survival rate due to lack efficient diagnostic tools. Currently, researchers are devising artificial intelligence based tools improve capabilities. The machine learning (ML) requires hand-crafted features train algorithms. To extract relevant still a challenging task in field image processing. We first extracted texture gray level co-occurrence matrix features. fed these traditional ML algorithms such as k-nearest neighbor (KNN) support vector (SVM). SVM yielded an accuracy 83.0%, whereas KNN produced 97.0%. then optimized employed ensemble extreme boosting (XGBoost) algorithm, which improved detection performance precision, recall, 100%. also deep ResNet101 distinguish small cell from non-small lung obtained 100% evaluation measures. results revealed that proposed approach more robust than Based on results, methodology can be very helpful early treatment for better diagnosis system.

Язык: Английский

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Differentiating Chat Generative Pretrained Transformer from Humans: Detecting ChatGPT-Generated Text and Human Text Using Machine Learning

Mathematics, Год журнала: 2023, Номер 11(15), С. 3400 - 3400

Опубликована: Авг. 3, 2023

Recently, the identification of human text and ChatGPT-generated has become a hot research topic. The current study presents Tunicate Swarm Algorithm with Long Short-Term Memory Recurrent Neural Network (TSA-LSTMRNN) model to detect both as well text. purpose proposed TSA-LSTMRNN method is investigate model’s decision presence any particular pattern. In addition this, technique focuses on designing Term Frequency–Inverse Document Frequency (TF-IDF), word embedding, count vectorizers for feature extraction process. For detection classification processes, LSTMRNN used. Finally, TSA employed selecting parameters approach, which enables improved performance. simulation performance was investigated benchmark databases, outcome demonstrated advantage system over other recent methods maximum accuracy 93.17% 93.83% human- datasets, respectively.

Язык: Английский

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Sentiment Analysis in the Age of COVID-19: A Bibliometric Perspective

Information, Год журнала: 2023, Номер 14(12), С. 659 - 659

Опубликована: Дек. 13, 2023

The global impact of the COVID-19 pandemic has been profound, placing significant challenges upon healthcare systems and world economy. pervasive presence illness, uncertainty, fear markedly diminished overall life satisfaction. Consequently, sentiment analysis gained substantial traction among scholars seeking to unravel emotional attitudinal dimensions this crisis. This research endeavors provide a bibliometric perspective, shedding light on principal contributors emerging field. It seeks spotlight academic institutions associated with domain, along identifying most influential publications in terms both paper volume h-index metrics. To end, we have meticulously curated dataset comprising 646 papers sourced from ISI Web Science database, all centering theme during pandemic. Our findings underscore burgeoning interest exhibited by community particular evident an astonishing annual growth rate 153.49%. Furthermore, our elucidates key keywords collaborative networks within authorship, offering valuable insights into proliferation thematic pursuit. In addition this, encompasses n-gram investigation across keywords, abstracts, titles, keyword plus, complemented examination frequently cited works. results gleaned these offer crucial perspectives, contribute identification pertinent issues, guidance for informed decision-making.

Язык: Английский

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Machine learning based model for detecting depression during Covid-19 crisis

Scientific African, Год журнала: 2023, Номер 20, С. e01716 - e01716

Опубликована: Май 15, 2023

Covid-19 has impacted negatively on people all over the world. Some of ways that it affected include such as Health, Employment, Mental Education, Social isolation, Economic Inequality and Access to healthcare essential services. Apart from physical symptoms, caused considerable damage mental health individuals. Among all, depression is identified one common illnesses which leads early death. People suffering are at a higher risk developing other conditions, heart disease stroke, also suicide. The importance detection intervention cannot be overstated. Identifying treating can prevent illness becoming more severe development conditions. Early suicide, leading cause death among with depression. Millions have this disease. To proceed study individuals we conducted survey 21 questions based Hamilton tool advise psychiatrist. With use Python's scientific programming principles machine learning methods like Decision Tree, KNN, Naive Bayes, results were analysed. Further comparison these techniques done. Study concludes KNN given better than accuracy decision tree in terms latency detect person. At conclusion, learning-based model suggested replace conventional method detecting sadness by asking encouraging getting regular feedback them.

Язык: Английский

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COVID-19 Public Opinion: A Twitter Healthcare Data Processing Using Machine Learning Methodologies

International Journal of Environmental Research and Public Health, Год журнала: 2022, Номер 20(1), С. 432 - 432

Опубликована: Дек. 27, 2022

The COVID-19 pandemic has shattered the whole world, and due to this, millions of people have posted their sentiments toward on different social media platforms. This resulted in a huge information flow attracted many research studies aimed at extracting useful understand sentiments. paper analyses data imported from Twitter API for healthcare sector, emphasizing sub-domains, such as vaccines, post-COVID-19 health issues service providers. main objective this is analyze machine learning models classifying analyzing direction polarity by considering views majority people. inferences drawn analysis may be concerned authorities they work make appropriate policy decisions strategic decisions. Various were developed extract actual emotions, results show that support vector model outperforms with an average accuracy 82.67% compared logistic regression, random forest, multinomial naïve Bayes long short-term memory models, which present 78%, 77%, 68.67% 75% accuracy, respectively.

Язык: Английский

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Sentiment Analysis Method of Epidemic-related Microblog Based on Hesitation Theory

ACM Transactions on Asian and Low-Resource Language Information Processing, Год журнала: 2024, Номер 23(4), С. 1 - 25

Опубликована: Фев. 15, 2024

The COVID-19 pandemic in 2020 brought an unprecedented global crisis. After two years of control efforts, life gradually returned to the pre-pandemic state, but localized outbreaks continued occur. Toward end 2022, resurged China, leading another disruption people’s lives and work. Many pieces information on social media reflected views emotions toward second outbreak, which showed distinct differences compared first outbreak 2020. To explore emotional attitudes at different stages underlying reasons, this study collected microblog data from November 2022 January 2023 June 2020, encompassing Chinese reactions pandemic. Based hesitancy Fuzzy Intuition theory, we proposed a hypothesis: can be integrated into machine learning models select suitable corpora for training, not only improves accuracy also enhances model efficiency. hypothesis, designed hesitancy-integrated model. experimental results demonstrated model’s positive performance self-constructed database. By applying analyze pandemic, obtained their sentiments months. We found that most negative appeared beginning followed by fluctuations influenced events, ultimately showing overall trend. Combining word cloud techniques Latent Dirichlet Allocation (LDA) effectively helped reasons behind changes attitude.

Язык: Английский

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Social Media Text Sentiment Analysis: Exploration Of Machine Learning Methods

Опубликована: Янв. 8, 2024

With the increasing number of social media and online platforms, sentiment analysis has become an important research object in new fields. This study aims to explore improve text methods ability extract understand information data. A public data set called "Sentiment book review on Amazon Kindle" is used, which contains product analyses Kindle store category, with a total 982,619 records. Through effective preprocessing, including cleaning, tokenization, removal stop words lemmatization, we are ready for subsequent development models. uses natural language processing technology classify reviews into positive negative categories, RNN logistic regression machine learning algorithms comparison. Finally, basic web application prototype based FLASK, HTML, CSS Python was designed, integrating AI models provide results visualization real time.

Язык: Английский

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