Machine learning applications for COVID-19 outbreak management

Neural Computing and Applications, Год журнала: 2022, Номер 34(18), С. 15313 - 15348

Опубликована: Июнь 10, 2022

Язык: Английский

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Perception about Health Applications (Apps) in Smartphones towards Telemedicine during COVID-19: A Cross-Sectional Study

Journal of Personalized Medicine, Год журнала: 2022, Номер 12(11), С. 1920 - 1920

Опубликована: Ноя. 17, 2022

The use of health applications (apps) in smartphones increased exponentially during COVID-19. This study was conducted the with aim to understand factors that determine consumer's perception apps towards telemedicine COVID-19 and test any relation between these consumers Telemedicine India.

Язык: Английский

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Time Series Forecasting Utilizing Automated Machine Learning (AutoML): A Comparative Analysis Study on Diverse Datasets

Information, Год журнала: 2024, Номер 15(1), С. 39 - 39

Опубликована: Янв. 11, 2024

Automated Machine Learning (AutoML) tools are revolutionizing the field of machine learning by significantly reducing need for deep computer science expertise. Designed to make ML more accessible, they enable users build high-performing models without extensive technical knowledge. This study delves into these in context time series analysis, which is essential forecasting future trends from historical data. We evaluate three prominent AutoML tools—AutoGluon, Auto-Sklearn, and PyCaret—across various metrics, employing diverse datasets that include Bitcoin COVID-19 The results reveal performance each tool highly dependent on specific dataset its ability manage complexities thorough investigation not only demonstrates strengths limitations but also highlights criticality dataset-specific considerations analysis. Offering valuable insights both practitioners researchers, this emphasizes ongoing research development specialized area. It aims serve as a reference organizations dealing with guiding framework academic enhancing application

Язык: Английский

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The economics of deep and machine learning-based algorithms for COVID-19 prediction, detection, and diagnosis shaping the organizational management of hospitals

Oeconomia Copernicana, Год журнала: 2024, Номер 15(1), С. 27 - 58

Опубликована: Март 30, 2024

Research background: Deep and machine learning-based algorithms can assist in COVID-19 image-based medical diagnosis symptom tracing, optimize intensive care unit admission, use clinical data to determine patient prioritization mortality risk, being pivotal qualitative provision, reducing errors, increasing survival rates, thus diminishing the massive healthcare system burden relation severe inpatient stay duration, while operational costs throughout organizational management of hospitals. Data-driven financial scenario-based contingency planning, predictive modelling tools, risk pooling mechanisms should be deployed for additional equipment unforeseen demand expenses. Purpose article: We show that deep decision making systems likelihood treatment outcomes with regard susceptible, infected, recovered individuals, performing accurate analyses by modeling based on vital signs, surveillance data, infection-related biomarkers, furthering hospital facility optimization terms bed allocation. Methods: The review software employed article screening quality evaluation were: AMSTAR, AXIS, DistillerSR, Eppi-Reviewer, MMAT, PICO Portal, Rayyan, ROBIS, SRDR. Findings & value added: support tools forecast spread, confirmed cases, infection rates data-driven appropriate resource allocations effective therapeutic protocol development, determining suitable measures regulations using symptoms comorbidities, laboratory records across units, impacting financing infrastructure. As a result heightened personal protective equipment, pharmacy medication, outpatient treatment, supplies, revenue loss vulnerability occur, also due expenses related hiring staff critical expenditures. Hospital care, screening, capacity expansion, lead further losses affecting frontline workers patients.

Язык: Английский

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Deep learning for Covid-19 forecasting: State-of-the-art review.

Neurocomputing, Год журнала: 2022, Номер 511, С. 142 - 154

Опубликована: Сен. 8, 2022

Язык: Английский

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Predicting Student Employability Through the Internship Context Using Gradient Boosting Models

IEEE Access, Год журнала: 2022, Номер 10, С. 46472 - 46489

Опубликована: Янв. 1, 2022

Universities around the world are keen to develop study plans that will guide their graduates success in job market. The internship course is one of most significant courses provides an experiential opportunity for students apply knowledge and prepare start a professional career. However, internships do not guarantee employability, especially when graduate's performance satisfactory requirements met. Many factors contribute this issue making prediction employability important challenge researchers higher education field. In paper, our aim introduce effective method predict student based on context using Gradient Boosting classifiers. Our contributions consist harnessing power gradient boosting algorithms perform context-aware status processes. Student relies identifying predictive features impacting hiring graduates. Hence, we define two models, which features. Experiments conducted three classifiers: e X treme xmlns:xlink="http://www.w3.org/1999/xlink">G radient xmlns:xlink="http://www.w3.org/1999/xlink">B oosting (XGBoost), xmlns:xlink="http://www.w3.org/1999/xlink">C ategory (CatBoost) xmlns:xlink="http://www.w3.org/1999/xlink">L ight oosted xmlns:xlink="http://www.w3.org/1999/xlink">M achine (LGBM). results obtained showed applying LGBM classifiers over performs best compared context. Therefore, strong evidence predictable from

Язык: Английский

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A Framework of Intelligent Mental Health Monitoring in Smart Cities and Societies

IETE Journal of Research, Год журнала: 2023, Номер 70(2), С. 1328 - 1341

Опубликована: Фев. 9, 2023

AbstractIn any smart city and society, the citizens' mental health is one of utmost concerns. Nowadays, people from different sectors our community face a severe threat due to prolonged pandemic COVID-19. Depression, anxiety, suicidal behaviours, posttraumatic stress disorder are widespread terms nowadays for students, care workers, jobless people, etc. And Machine Learning (ML), image processing, expert systems, Internet Things (IoT) performing an essential function in significant acceleration automation process within healthcare industry. Therefore, this article aims address problem preventing disorders by early predicting individuals using developed web portal "Mind Turner"; integrating mentioned emerging tools way, later chronic can be avoided. We used Random Forest Classifier detect levels Question-Answer-based assessment, SVM facial emotions. Finally, both combined Interval Type-2 Fuzzy Logic predict probable person, i.e. acute depression, moderate depression not depressed.KEYWORDS: COVID-19DepressionImage processingInternet Health (IoHT)Machine learningMental illnessMental healthSmart Disclosure statementNo potential conflict interest was reported author(s).Correction StatementThis has been corrected with minor changes. These changes do impact academic content article.Additional informationNotes on contributorsArpita ChakrabortyArpita Chakraborty, BTech, MTech, PhD (Engg), assistant professor Electronics Communications Engineering Department at Bengal Institute Technology, Kolkata, India. Email: [email protected] Sekhar BanerjeeJyoti Banerjee, ME, PG Diploma IPR & TBM head CSE (AI ML) India visiting researcher (Post Doc) Nottingham Trent University, UK. Corresponding author. [email protected] BhadraRitam Bhadra pursuing bachelor's degree electronics communication engineering Kolkata. [email protected] DuttaAnik Dutta [email protected] GangulyShatabdi Ganguly [email protected] DasDeblina Das [email protected] KunduSouvik Kundu, graduate department Electrical Computer Engineering, Iowa State Ames, IA, USA. [email protected] MahmudMufti Mahmud associate cognitive computing Science He recipient top 2% cited scientists worldwide computer science (2020), NTU VC outstanding research award 2021, Marie-Curie postdoctoral fellowship. Mahmudis coordinator Informatics excellence framework unit assessment deputy group leader Cognitive Computing Brain Interactive Systems groups. [email protected] SahaGautam Saha, MBBS, MD, psychiatry (Calcutta University) senior psychiatrists founder director Clinic Neuropsychiatric Research Center, Kolkata-700124. diagnosis, treatment, prevention disorders, including addiction sexual disorders. At present, he president Indian Psychiatric Society (IPS), Vice-President SAARC Federation Psychiatry Association Geriatric Mental (IAGMH). [email protected]

Язык: Английский

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Identification of Drug-Side Effect Association Via Multi-View Semi-Supervised Sparse Model

IEEE Transactions on Artificial Intelligence, Год журнала: 2023, Номер 5(5), С. 2151 - 2162

Опубликована: Сен. 12, 2023

The association between drugs and side effects encompasses information about approved medications their documented adverse drug reactions. Traditional experimental approaches for studying this tend to be time-consuming expensive. To represent all drug-side effect associations, a bipartite network framework is employed. Consequently, numerous computational methods have been devised tackle problem, focusing on predicting new potential associations. However, significant gap lies in the neglect of Multi-View Learning (MVL) algorithm, which has ability integrate diverse sources enhance prediction accuracy. In our study, we developed novel predictor named Semi-Supervised Sparse Model (Mv3SM) address problem. Our approach aims explore distinctive characteristics various view features obtained from fully paired multi-view data mitigate influence noisy data. test performance Mv3SM other approaches, conducted experiments using three benchmark datasets. results clearly demonstrate that proposed method achieves superior predictive compared alternative approaches.

Язык: Английский

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Machine learning models for predicting hospitalization and mortality risks of COVID-19 patients

Expert Systems with Applications, Год журнала: 2023, Номер 240, С. 122670 - 122670

Опубликована: Ноя. 19, 2023

Язык: Английский

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Enhanced U-Net and PSO-Optimized ANFIS for Classifying Fish Diseases in Underwater Images

International Journal of Fuzzy Systems, Год журнала: 2024, Номер unknown

Опубликована: Июнь 3, 2024

Язык: Английский

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