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  1. Book ; Online ; E-Book: Epidemic analytics for decision supports in COVID19 crisis

    Marques, Joao Alexandre Lobo / Fong, Simon James

    2022  

    Author's details edited by Joao Alexandre Lobo Marques, Simon James Fong
    Keywords Decision making/Data processing
    Subject code 614.40285
    Language English
    Size 1 online resource (161 pages)
    Publisher Springer
    Publishing place Cham, Switzerland
    Document type Book ; Online ; E-Book
    Remark Zugriff für angemeldete ZB MED-Nutzerinnen und -Nutzer
    ISBN 3-030-95281-9 ; 3-030-95280-0 ; 978-3-030-95281-5 ; 978-3-030-95280-8
    Database ZB MED Catalogue: Medicine, Health, Nutrition, Environment, Agriculture

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  2. Book: Artificial Intelligence for Coronavirus Outbreak

    Fong, Simon James / Chaki, Jyotismita / Dey, Nilanjan

    (SpringerBriefs in Applied Sciences and Technology)

    2021  

    Abstract: This book examines how the wonders of AI have contributed to the battle against COVID-19. Just as history repeats itself, so do epidemics and pandemics. In the face of the novel coronavirus disease, COVID-19, the book explores whether, in this digital ... ...

    Author's details Simon Fong graduated from La Trobe University, Australia, with a 1st Class Honors B.E. Computer Systems degree and a Ph.D. Computer Science degree in 1993 and 1998, respectively. Simon is now working as an Associate Professor at the Computer and Information Science Department of the University of Macau. He is a co-founder of the Data Analytics and Collaborative Computing Research Group in the Faculty of Science and Technology. Prior to his academic career, Simon took up various managerial and technical posts, such as Systems Engineer, IT Consultant, and E-commerce Director in Australia and Asia. Dr. Fong has published over 380 international conference and peer-reviewed journal papers, mostly in the areas of data mining, data stream mining, big data analytics, meta-heuristics optimization algorithms, and their applications. He serves on the editorial boards of the Journal of Network and Computer Applications of Elsevier, IEEE IT Professional Magazine, and various special issues of SC
    Series title SpringerBriefs in Applied Sciences and Technology
    Abstract This book examines how the wonders of AI have contributed to the battle against COVID-19. Just as history repeats itself, so do epidemics and pandemics. In the face of the novel coronavirus disease, COVID-19, the book explores whether, in this digital era where artificial intelligence is successfully applied in all areas of industry, we are doing any better than our ancestors did in dealing with pandemics. One of the most contagious diseases ever known, COVID-19 is spreading like wildfire aro...
    Keywords FINSA ; FIND ; MHMA060 ; datamining ; DeepLearning ; DataAnalytics ; DataScience ; PublicHealth ; COVID-2019 ; ArtificialIntelligence ; DiseaseSurveillance ; EpidemicMonitoringandControl ; Coronavirus ; Artificial Intelligence ; Disease Surveillance ; Epidemic Monitoring and Control ; Public Health ; Data Mining ; Deep Learning ; Data Analytics ; Data Science
    Language English
    Size 88 p.
    Edition 1
    Publisher Springer Nature Singapore
    Document type Book
    Note PDA Manuell_9
    Format 155 x 235 x 6
    ISBN 9789811559358 ; 981155935X
    Database PDA

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  3. Article ; Online: A review on multimodal machine learning in medical diagnostics.

    Yan, Keyue / Li, Tengyue / Marques, João Alexandre Lobo / Gao, Juntao / Fong, Simon James

    Mathematical biosciences and engineering : MBE

    2023  Volume 20, Issue 5, Page(s) 8708–8726

    Abstract: Nowadays, the increasing number of medical diagnostic data and clinical data provide more complementary references for doctors to make diagnosis to patients. For example, with medical data, such as electrocardiography (ECG), machine learning algorithms ... ...

    Abstract Nowadays, the increasing number of medical diagnostic data and clinical data provide more complementary references for doctors to make diagnosis to patients. For example, with medical data, such as electrocardiography (ECG), machine learning algorithms can be used to identify and diagnose heart disease to reduce the workload of doctors. However, ECG data is always exposed to various kinds of noise and interference in reality, and medical diagnostics only based on one-dimensional ECG data is not trustable enough. By extracting new features from other types of medical data, we can implement enhanced recognition methods, called multimodal learning. Multimodal learning helps models to process data from a range of different sources, eliminate the requirement for training each single learning modality, and improve the robustness of models with the diversity of data. Growing number of articles in recent years have been devoted to investigating how to extract data from different sources and build accurate multimodal machine learning models, or deep learning models for medical diagnostics. This paper reviews and summarizes several recent papers that dealing with multimodal machine learning in disease detection, and identify topics for future research.
    MeSH term(s) Machine Learning ; Humans ; Datasets as Topic ; Diagnostic Imaging
    Language English
    Publishing date 2023-05-09
    Publishing country United States
    Document type Journal Article ; Research Support, Non-U.S. Gov't ; Review
    ZDB-ID 2265126-3
    ISSN 1551-0018 ; 1551-0018
    ISSN (online) 1551-0018
    ISSN 1551-0018
    DOI 10.3934/mbe.2023382
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  4. Article ; Online: Exploring ChatGPT's Potential for Consultation, Recommendations and Report Diagnosis

    Jiaming Zhou / Tengyue Li / Simon James Fong / Nilanjan Dey / Rubén González-Crespo

    International Journal of Interactive Multimedia and Artificial Intelligence, Vol 8, Iss 2, Pp 7-

    Gastric Cancer and Gastroscopy Reports’ Case

    2023  Volume 13

    Abstract: Artificial intelligence (AI) has shown its effectiveness in helping clinical users meet evolving challenges. Recently, ChatGPT, a newly launched AI chatbot with exceptional text comprehension capabilities, has triggered a global wave of AI popularization ...

    Abstract Artificial intelligence (AI) has shown its effectiveness in helping clinical users meet evolving challenges. Recently, ChatGPT, a newly launched AI chatbot with exceptional text comprehension capabilities, has triggered a global wave of AI popularization and application in seeking answers through human‒machine dialogues. Gastric cancer, as a globally prevalent disease, has a five-year survival rate of up to 90% when detected early and treated promptly. This research aims to explore ChatGPT's potential in disseminating gastric cancer knowledge, providing consultation recommendations, and interpreting endoscopy reports. Through experimentation, the GPT-4 model of ChatGPT achieved an appropriateness of 91.3% and a consistency of 95.7% in a gastric cancer knowledge test. Furthermore, GPT-4 has demonstrated considerable potential in consultation recommendations and endoscopy report analysis.
    Keywords artificial intelligence ; chatgpt ; e-assessment ; gastric cancer ; medicine ; Technology ; T
    Subject code 401
    Language English
    Publishing date 2023-06-01T00:00:00Z
    Publisher Universidad Internacional de La Rioja (UNIR)
    Document type Article ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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  5. Article ; Online: An Introduction to COVID-19

    Fong, Simon James / Dey, Nilanjan / Chaki, Jyotismita

    Artificial Intelligence for Coronavirus Outbreak ; SpringerBriefs in Applied Sciences and Technology

    2020  , Page(s) 1–22

    Keywords covid19
    Publisher Springer Singapore
    Publishing country us
    Document type Article ; Online
    ISSN 2191-530X
    DOI 10.1007/978-981-15-5936-5_1
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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  6. Article ; Online: Modelling dynamics of coronavirus disease 2019 spread for pandemic forecasting based on Simulink.

    Liu, Xian-Xian / Hu, Shimin / Fong, Simon James / Crespo, Rubén González / Herrera-Viedma, Enrique

    Physical biology

    2021  Volume 18, Issue 4

    Abstract: In this paper, we demonstrate the application of MATLAB to develop a pandemic prediction system based on Simulink. The susceptible-exposed-asymptomatic but infectious-symptomatic and infectious (severe infected population + mild infected population)- ... ...

    Abstract In this paper, we demonstrate the application of MATLAB to develop a pandemic prediction system based on Simulink. The susceptible-exposed-asymptomatic but infectious-symptomatic and infectious (severe infected population + mild infected population)-recovered-deceased (SEAI(
    MeSH term(s) COVID-19/epidemiology ; COVID-19/transmission ; Computer Simulation ; Deep Learning ; Fuzzy Logic ; Humans ; India/epidemiology ; Models, Biological ; Neural Networks, Computer ; Nonlinear Dynamics ; Pandemics ; SARS-CoV-2/physiology ; United States/epidemiology
    Language English
    Publishing date 2021-05-28
    Publishing country England
    Document type Journal Article ; Research Support, Non-U.S. Gov't
    ZDB-ID 2133216-2
    ISSN 1478-3975 ; 1478-3967
    ISSN (online) 1478-3975
    ISSN 1478-3967
    DOI 10.1088/1478-3975/abf990
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  7. Article ; Online: Conclusion

    Fong, Simon James / Dey, Nilanjan / Chaki, Jyotismita

    Artificial Intelligence for Coronavirus Outbreak

    Abstract: This book shows a buffet of artificial intelligence applications from drone to deep learning and from data analysis to the prediction of next pandemic disease along with its drug discovery. Today the entire globe is under the threat of COVID-19 affecting ...

    Abstract This book shows a buffet of artificial intelligence applications from drone to deep learning and from data analysis to the prediction of next pandemic disease along with its drug discovery. Today the entire globe is under the threat of COVID-19 affecting around 200 countries. The death toll reported in these highly affected countries has become catastrophic.
    Keywords covid19
    Publisher PMC
    Document type Article ; Online
    DOI 10.1007/978-981-15-5936-5_4
    Database COVID19

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  8. Article ; Online: A new SEAIRD pandemic prediction model with clinical and epidemiological data analysis on COVID-19 outbreak.

    Liu, Xian-Xian / Fong, Simon James / Dey, Nilanjan / Crespo, Rubén González / Herrera-Viedma, Enrique

    Applied intelligence (Dordrecht, Netherlands)

    2021  Volume 51, Issue 7, Page(s) 4162–4198

    Abstract: Measuring the spread of disease during a pandemic is critically important for accurately and promptly applying various lockdown strategies, so to prevent the collapse of the medical system. The latest pandemic of COVID-19 that hits the world death tolls ... ...

    Abstract Measuring the spread of disease during a pandemic is critically important for accurately and promptly applying various lockdown strategies, so to prevent the collapse of the medical system. The latest pandemic of COVID-19 that hits the world death tolls and economy loss very hard, is more complex and contagious than its precedent diseases. The complexity comes mostly from the emergence of asymptomatic patients and relapse of the recovered patients which were not commonly seen during SARS outbreaks. These new characteristics pertaining to COVID-19 were only discovered lately, adding a level of uncertainty to the traditional SEIR models. The contribution of this paper is that for the COVID-19 epidemic, which is infectious in both the incubation period and the onset period, we use neural networks to learn from the actual data of the epidemic to obtain optimal parameters, thereby establishing a nonlinear, self-adaptive dynamic coefficient infectious disease prediction model. On the basis of prediction, we considered control measures and simulated the effects of different control measures and different strengths of the control measures. The epidemic control is predicted as a continuous change process, and the epidemic development and control are integrated to simulate and forecast. Decision-making departments make optimal choices. The improved model is applied to simulate the COVID-19 epidemic in the United States, and by comparing the prediction results with the traditional SEIR model, SEAIRD model and adaptive SEAIRD model, it is found that the adaptive SEAIRD model's prediction results of the U.S. COVID-19 epidemic data are in good agreement with the actual epidemic curve. For example, from the prediction effect of these 3 different models on accumulative confirmed cases, in terms of goodness of fit, adaptive SEAIRD model (0.99997) ≈ SEAIRD model (0.98548) > Classical SEIR model (0.66837); in terms of error value: adaptive SEAIRD model (198.6563) < < SEAIRD model(4739.8577) < < Classical SEIR model (22,652.796); The objective of this contribution is mainly on extending the current spread prediction model. It incorporates extra compartments accounting for the new features of COVID-19, and fine-tunes the new model with neural network, in a bid of achieving a higher level of prediction accuracy. Based on the SEIR model of disease transmission, an adaptive model called SEAIRD with internal source and isolation intervention is proposed. It simulates the effects of the changing behaviour of the SARS-CoV-2 in U.S. Neural network is applied to achieve a better fit in SEAIRD. Unlike the SEIR model, the adaptive SEAIRD model embraces multi-group dynamics which lead to different evolutionary trends during the epidemic. Through the risk assessment indicators of the adaptive SEAIRD model, it is convenient to measure the severity of the epidemic situation for consideration of different preventive measures. Future scenarios are projected from the trends of various indicators by running the adaptive SEAIRD model.
    Language English
    Publishing date 2021-01-01
    Publishing country Netherlands
    Document type Journal Article
    ZDB-ID 1479519-X
    ISSN 1573-7497 ; 0924-669X
    ISSN (online) 1573-7497
    ISSN 0924-669X
    DOI 10.1007/s10489-020-01938-3
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  9. Article: AI-Enabled Technologies that Fight the Coronavirus Outbreak

    Fong, Simon James / Dey, Nilanjan / Chaki, Jyotismita

    Artificial Intelligence for Coronavirus Outbreak

    Abstract: Development of innovative designs, new applications, new technologies and heavier investment in AI are continued to be seen every day However, with the sudden impact of COVID19, so severe and urgent around the world, adoption of AI is propelled to an ... ...

    Abstract Development of innovative designs, new applications, new technologies and heavier investment in AI are continued to be seen every day However, with the sudden impact of COVID19, so severe and urgent around the world, adoption of AI is propelled to an unprecedent level, because it helps to fight the virus pandemic by enabling one or more of the following possibilities: (1) autonomous everything, (2) pervasive knowledge, (3) assistive technology and (4) rational decision support
    Keywords covid19
    Publisher WHO
    Document type Article
    Note WHO #Covidence: #825061
    Database COVID19

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  10. Article: An Introduction to COVID-19

    Fong, Simon James / Dey, Nilanjan / Chaki, Jyotismita

    Artificial Intelligence for Coronavirus Outbreak

    Abstract: A novel coronavirus (CoV) named ‘2019-nCoV’ or ‘2019 novel coronavirus’ or ‘COVID-19’ by the World Health Organization (WHO) is in charge of the current outbreak of pneumonia that began at the beginning of December 2019 near in Wuhan City, Hubei Province, ...

    Abstract A novel coronavirus (CoV) named ‘2019-nCoV’ or ‘2019 novel coronavirus’ or ‘COVID-19’ by the World Health Organization (WHO) is in charge of the current outbreak of pneumonia that began at the beginning of December 2019 near in Wuhan City, Hubei Province, China [1–4] COVID-19 is a pathogenic virus From the phylogenetic analysis carried out with obtainable full genome sequences, bats occur to be the COVID-19 virus reservoir, but the intermediate host(s) has not been detected till now
    Keywords covid19
    Publisher WHO
    Document type Article
    Note WHO #Covidence: #825060
    Database COVID19

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