LIVIVO - Das Suchportal für Lebenswissenschaften

switch to English language
Zurück zur letzten Suche Suchhistorie
Erweiterte Suche

Ihre letzten Suchen

  1. AU="Patan, Rizwan"
  2. AU=Koretzky Gary A
  3. AU="Vardakas, Georgios"
  4. AU="Fogg, Ryan"
  5. AU="Viviane M. Parra"
  6. AU="Kushner, Adam"
  7. AU="Claude Pasquier"
  8. AU="Guomin Zhang"
  9. AU=van der Donk Lieve E H
  10. AU="Reynaerts, Audrey"
  11. AU="Alberts, Susan C"
  12. AU="Kosicki, Jakub Z"
  13. AU=Eifling Michael
  14. AU="Xing, Xinxin"
  15. AU="Baigun, Claudio"
  16. AU="Abu-Hamad, Ghassan"
  17. AU="Mulla, Zuber D"
  18. AU="Schröder, H"
  19. AU=Ruiz Michael Anthony
  20. AU="Kemmoku, Haruka"
  21. AU="Meseguer, M"
  22. AU="Pillaye, Jayshree"
  23. AU="Andrew Pettitt"
  24. AU="Malawski, M"
  25. AU=Marhofer P
  26. AU=Mandel H G
  27. AU="Duffy, Richard"
  28. AU=Kaseb Hatem AU=Kaseb Hatem
  29. AU=Kong Tak?kwan AU=Kong Tak?kwan
  30. AU=Nagaraja Sridevi
  31. AU="Bu, Yingzi"
  32. AU=Seddighi Hamed AU=Seddighi Hamed
  33. AU="De Keyser, Johan"
  34. AU="Zhenqiang Bi"
  35. AU=Wang Jun
  36. AU=Zhang Fuping
  37. AU="Shatilov, D N"

Suchergebnis

Treffer 1 - 6 von insgesamt 6

Suchoptionen

  1. Buch ; Online: Blockchain Education

    Patan, Rizwan / Parizi, Reza M. / Dorodchi, Mohsen / Pouriyeh, Seyedamin / Rorrer, Audrey

    Current State, Limitations, Career Scope, Challenges, and Future Directions

    2023  

    Abstract: Blockchain is a revolutionary technology, and its growth started in various industries (such as IT, education, business, banking, and many others) to capitalize on it. Currently, in higher education institutions (HEIs) adoption of blockchain education ... ...

    Abstract Blockchain is a revolutionary technology, and its growth started in various industries (such as IT, education, business, banking, and many others) to capitalize on it. Currently, in higher education institutions (HEIs) adoption of blockchain education needs to be improved in the academic programs and curriculums. In addition, HEIs must make many intense changes in the teaching and learning methods to educate learners about blockchain technology and its applications to meet the current industry workforce demand. Due to a lack of academic programs and courses, students nowadays rely on online resources and pay non-academic organizations a high fee. This paper provides a comprehensive survey of blockchain education's current state of the art by reviewing the different academic programs and industry workforce demand. In addition, blockchain application trends which include market growth and demands are discussed. Moreover, the blockchain career scope for different disciplines of students is examined.
    Schlagwörter Computer Science - Computers and Society ; Computer Science - Cryptography and Security
    Thema/Rubrik (Code) 027
    Erscheinungsdatum 2023-01-19
    Erscheinungsland us
    Dokumenttyp Buch ; Online
    Datenquelle BASE - Bielefeld Academic Search Engine (Lebenswissenschaftliche Auswahl)

    Volltext online

    Zusatzmaterialien

    Kategorien

    Fernleihe an ZB MED

    Sie können sich den gewünschten Titel als lokale Nutzerin oder lokaler Nutzer von ZB MED direkt an den Standort Köln schicken lassen.

  2. Artikel: Partial derivative Nonlinear Global Pandemic Machine Learning prediction of COVID 19.

    Kavadi, Durga Prasad / Patan, Rizwan / Ramachandran, Manikandan / Gandomi, Amir H

    Chaos, solitons, and fractals

    2020  Band 139, Seite(n) 110056

    Abstract: The recent worldwide outbreak of the novel coronavirus disease 2019 (COVID-19) opened new challenges for the research community. Machine learning (ML)-guided methods can be useful for feature prediction, involved risk, and the causes of an analogous ... ...

    Abstract The recent worldwide outbreak of the novel coronavirus disease 2019 (COVID-19) opened new challenges for the research community. Machine learning (ML)-guided methods can be useful for feature prediction, involved risk, and the causes of an analogous epidemic. Such predictions can be useful for managing and intercepting the outbreak of such diseases. The foremost advantages of applying ML methods are handling a wide variety of data and easy identification of trends and patterns of an undetermined nature.In this study, we propose a partial derivative regression and nonlinear machine learning (PDR-NML) method for global pandemic prediction of COVID-19. We used a Progressive Partial Derivative Linear Regression model to search for the best parameters in the dataset in a computationally efficient manner. Next, a Nonlinear Global Pandemic Machine Learning model was applied to the normalized features for making accurate predictions. The results show that the proposed ML method outperformed state-of-the-art methods in the Indian population and can also be a convenient tool for making predictions for other countries.
    Schlagwörter covid19
    Sprache Englisch
    Erscheinungsdatum 2020-06-25
    Erscheinungsland England
    Dokumenttyp Journal Article
    ZDB-ID 2003919-0
    ISSN 1873-2887 ; 0960-0779
    ISSN (online) 1873-2887
    ISSN 0960-0779
    DOI 10.1016/j.chaos.2020.110056
    Datenquelle MEDical Literature Analysis and Retrieval System OnLINE

    Volltext online

    Zusatzmaterialien

    Kategorien

    Über subito bestellen

    Dieser Service ist kostenpflichtig (siehe Lieferbedingungen von subito). Bestellungen, die einen Artikel nebst Supplementary Material umfassen, werden grundsätzlich wie mehrfache Bestellungen bearbeitet. Gebühren fallen in diesen Fällen für jede einzelne Bestellung an.

  3. Artikel ; Online: Adaptive Diagnosis of Lung Cancer by Deep Learning Classification Using Wilcoxon Gain and Generator.

    Obulesu, O / Kallam, Suresh / Dhiman, Gaurav / Patan, Rizwan / Kadiyala, Ramana / Raparthi, Yaswanth / Kautish, Sandeep

    Publikation ZURÜCKGEZOGEN

    Journal of healthcare engineering

    2021  Band 2021, Seite(n) 5912051

    Abstract: Cancer is a complicated worldwide health issue with an increasing death rate in recent years. With the swift blooming of the high throughput technology and several machine learning methods that have unfolded in recent years, progress in cancer disease ... ...

    Abstract Cancer is a complicated worldwide health issue with an increasing death rate in recent years. With the swift blooming of the high throughput technology and several machine learning methods that have unfolded in recent years, progress in cancer disease diagnosis has been made based on subset features, providing awareness of the efficient and precise disease diagnosis. Hence, progressive machine learning techniques that can, fortunately, differentiate lung cancer patients from healthy persons are of great concern. This paper proposes a novel Wilcoxon Signed-Rank Gain Preprocessing combined with Generative Deep Learning called Wilcoxon Signed Generative Deep Learning (WS-GDL) method for lung cancer disease diagnosis. Firstly, test significance analysis and information gain eliminate redundant and irrelevant attributes and extract many informative and significant attributes. Then, using a generator function, the Generative Deep Learning method is used to learn the deep features. Finally, a minimax game (i.e., minimizing error with maximum accuracy) is proposed to diagnose the disease. Numerical experiments on the Thoracic Surgery Data Set are used to test the WS-GDL method's disease diagnosis performance. The WS-GDL approach may create relevant and significant attributes and adaptively diagnose the disease by selecting optimal learning model parameters. Quantitative experimental results show that the WS-GDL method achieves better diagnosis performance and higher computing efficiency in computational time, computational complexity, and false-positive rate compared to state-of-the-art approaches.
    Mesh-Begriff(e) Deep Learning ; Humans ; Lung Neoplasms/diagnosis ; Machine Learning
    Sprache Englisch
    Erscheinungsdatum 2021-10-13
    Erscheinungsland England
    Dokumenttyp Journal Article ; Retracted Publication
    ZDB-ID 2545054-2
    ISSN 2040-2309 ; 2040-2295
    ISSN (online) 2040-2309
    ISSN 2040-2295
    DOI 10.1155/2021/5912051
    Datenquelle MEDical Literature Analysis and Retrieval System OnLINE

    Zusatzmaterialien

    Kategorien

    Über subito bestellen

    Dieser Service ist kostenpflichtig (siehe Lieferbedingungen von subito). Bestellungen, die einen Artikel nebst Supplementary Material umfassen, werden grundsätzlich wie mehrfache Bestellungen bearbeitet. Gebühren fallen in diesen Fällen für jede einzelne Bestellung an.

  4. Artikel ; Online: Deming least square regressed feature selection and Gaussian neuro-fuzzy multi-layered data classifier for early COVID prediction.

    Mydukuri, Rathnamma V / Kallam, Suresh / Patan, Rizwan / Al-Turjman, Fadi / Ramachandran, Manikandan

    Expert systems

    2021  Band 39, Heft 4, Seite(n) e12694

    Abstract: Coronavirus disease (COVID-19) is a harmful disease caused by the new SARS-CoV-2 virus. COVID-19 disease comprises symptoms such as cold, cough, fever, and difficulty in breathing. COVID-19 has affected many countries and their spread in the world has ... ...

    Abstract Coronavirus disease (COVID-19) is a harmful disease caused by the new SARS-CoV-2 virus. COVID-19 disease comprises symptoms such as cold, cough, fever, and difficulty in breathing. COVID-19 has affected many countries and their spread in the world has put humanity at risk. Due to the increasing number of cases and their stress on administration as well as health professionals, different prediction techniques were introduced to predict the coronavirus disease existence in patients. However, the accuracy was not improved, and time consumption was not minimized during the disease prediction. To address these problems, least square regressive Gaussian neuro-fuzzy multi-layered data classification (LSRGNFM-LDC) technique is introduced in this article. LSRGNFM-LDC technique performs efficient COVID prediction with better accuracy and lesser time consumption through feature selection and classification. The preprocessing is used to eliminate the unwanted data in input features. Preprocessing is applied to reduce the time complexity. Next, Deming Least Square Regressive Feature Selection process is carried out for selecting the most relevant features through identifying the line of best fit. After the feature selection process, Gaussian neuro-fuzzy classifier in LSRGNFM-LDC technique performs the data classification process with help of fuzzy if-then rules for performing prediction process. Finally, the fuzzy if-then rule classifies the patient data as lower risk level, medium risk level and higher risk level with higher accuracy and lesser time consumption. Experimental evaluation is performed by Novel Corona Virus 2019 Dataset using different metrics like prediction accuracy, prediction time, and error rate. The result shows that LSRGNFM-LDC technique improves the accuracy and minimizes the time consumption as well as error rate than existing works during COVID prediction.
    Sprache Englisch
    Erscheinungsdatum 2021-03-26
    Erscheinungsland England
    Dokumenttyp Journal Article
    ZDB-ID 2016958-9
    ISSN 1468-0394 ; 0266-4720
    ISSN (online) 1468-0394
    ISSN 0266-4720
    DOI 10.1111/exsy.12694
    Datenquelle MEDical Literature Analysis and Retrieval System OnLINE

    Volltext online

    Zusatzmaterialien

    Kategorien

    Über subito bestellen

    Dieser Service ist kostenpflichtig (siehe Lieferbedingungen von subito). Bestellungen, die einen Artikel nebst Supplementary Material umfassen, werden grundsätzlich wie mehrfache Bestellungen bearbeitet. Gebühren fallen in diesen Fällen für jede einzelne Bestellung an.

  5. Artikel: Partial derivative Nonlinear Global Pandemic Machine Learning prediction of COVID 19

    Kavadi, Durga Prasad / Patan, Rizwan / Ramachandran, Manikandan / Gandomi, Amir H.

    Chaos Solitons Fractals

    Abstract: The recent worldwide outbreak of the novel coronavirus disease 2019 (COVID-19) opened new challenges for the research community. Machine learning (ML)-guided methods can be useful for feature prediction, involved risk, and the causes of an analogous ... ...

    Abstract The recent worldwide outbreak of the novel coronavirus disease 2019 (COVID-19) opened new challenges for the research community. Machine learning (ML)-guided methods can be useful for feature prediction, involved risk, and the causes of an analogous epidemic. Such predictions can be useful for managing and intercepting the outbreak of such diseases. The foremost advantages of applying ML methods are handling a wide variety of data and easy identification of trends and patterns of an undetermined nature.In this study, we propose a partial derivative regression and nonlinear machine learning (PDR-NML) method for global pandemic prediction of COVID-19. We used a Progressive Partial Derivative Linear Regression model to search for the best parameters in the dataset in a computationally efficient manner. Next, a Nonlinear Global Pandemic Machine Learning model was applied to the normalized features for making accurate predictions. The results show that the proposed ML method outperformed state-of-the-art methods in the Indian population and can also be a convenient tool for making predictions for other countries.
    Schlagwörter covid19
    Verlag WHO
    Dokumenttyp Artikel
    Anmerkung WHO #Covidence: #614270
    Datenquelle COVID19

    Kategorien

    Fernleihe an ZB MED

    Sie können sich den gewünschten Titel als lokale Nutzerin oder lokaler Nutzer von ZB MED direkt an den Standort Köln schicken lassen.

  6. Artikel ; Online: Partial derivative Nonlinear Global Pandemic Machine Learning prediction of COVID 19

    Kavadi, Durga Prasad / Patan, Rizwan / Ramachandran, Manikandan / Gandomi, Amir H.

    Chaos, Solitons & Fractals

    2020  Band 139, Seite(n) 110056

    Schlagwörter General Mathematics ; covid19
    Sprache Englisch
    Verlag Elsevier BV
    Erscheinungsland us
    Dokumenttyp Artikel ; Online
    ZDB-ID 2003919-0
    ISSN 1873-2887 ; 0960-0779
    ISSN (online) 1873-2887
    ISSN 0960-0779
    DOI 10.1016/j.chaos.2020.110056
    Datenquelle BASE - Bielefeld Academic Search Engine (Lebenswissenschaftliche Auswahl)

    Volltext online

    Zusatzmaterialien

    Kategorien

    Über subito bestellen

    Dieser Service ist kostenpflichtig (siehe Lieferbedingungen von subito). Bestellungen, die einen Artikel nebst Supplementary Material umfassen, werden grundsätzlich wie mehrfache Bestellungen bearbeitet. Gebühren fallen in diesen Fällen für jede einzelne Bestellung an.

Zum Seitenanfang