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  1. Article ; Online: Artificial Intelligence and Machine Learning in Cardiology.

    Deo, Rahul C

    Circulation

    2024  Volume 149, Issue 16, Page(s) 1235–1237

    MeSH term(s) Humans ; Artificial Intelligence ; Machine Learning ; Cardiology ; Cardiovascular System
    Language English
    Publishing date 2024-04-15
    Publishing country United States
    Document type Journal Article
    ZDB-ID 80099-5
    ISSN 1524-4539 ; 0009-7322 ; 0069-4193 ; 0065-8499
    ISSN (online) 1524-4539
    ISSN 0009-7322 ; 0069-4193 ; 0065-8499
    DOI 10.1161/CIRCULATIONAHA.123.065469
    Database MEDical Literature Analysis and Retrieval System OnLINE

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    In stock of ZB MED Cologne/Königswinter

    Ui IV Zs.60: Show issues Location:
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  2. Book ; Online ; E-Book: Data analytics for pandemic disease outbreak

    Shinde, Gitanjali Rahul / Kalamkar, Asmita Balasaheb / Mahalle, Parikshit N. / Dey, Nilanjan

    A COVID-19 case study

    (Intelligent signal processing and data analysis series)

    2021  

    Author's details Gitanjali Rahul Shinde, Asmita Balasaheb Kalamkar, Parikshit N. Mahalle, Nilanjan Dey
    Series title Intelligent signal processing and data analysis series
    Keywords Electronic books
    Language English
    Size 1 Online-Ressource (xvi, 68 Seiten)
    Edition First edition
    Publisher CRC Press
    Publishing place Boca Raton
    Publishing country United States
    Document type Book ; Online ; E-Book
    Remark Zugriff für angemeldete ZB MED-Nutzerinnen und -Nutzer
    HBZ-ID HT020569091
    ISBN 978-1-00-020441-4 ; 9780367558468 ; 1-00-020441-3 ; 0367558467
    Database ZB MED Catalogue: Medicine, Health, Nutrition, Environment, Agriculture

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  3. Book ; Online ; E-Book: Advances in personalized nanotherapeutics

    Kaushik, Ajeet / Jayant, Rahul Dev / Nair, Madhavan

    2017  

    Author's details Ajeet Kaushik, Rahul Dev Jayant, Madhavan Nair editors
    Keywords Medicine ; Biomedical engineering ; Nanoscale science ; Nanoscience ; Nanostructures
    Subject code 610.28
    Language English
    Size 1 Online-Ressource (xiii, 240 Seiten)
    Publisher Springer
    Publishing place Cham
    Publishing country Switzerland
    Document type Book ; Online ; E-Book
    Remark Zugriff für angemeldete ZB MED-Nutzerinnen und -Nutzer
    HBZ-ID HT019604420
    ISBN 978-3-319-63633-7 ; 9783319636320 ; 3-319-63633-2 ; 3319636324
    DOI 10.1007/978-3-319-63633-7
    Database ZB MED Catalogue: Medicine, Health, Nutrition, Environment, Agriculture

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  4. Article ; Online: Cucurbituril curiosities.

    Mukhopadhyay, Rahul Dev / Kim, Kimoon

    Nature chemistry

    2023  Volume 15, Issue 3, Page(s) 438

    Language English
    Publishing date 2023-02-24
    Publishing country England
    Document type Journal Article
    ZDB-ID 2464596-5
    ISSN 1755-4349 ; 1755-4330
    ISSN (online) 1755-4349
    ISSN 1755-4330
    DOI 10.1038/s41557-023-01141-0
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  5. Book ; Online ; E-Book: Digital Mapping of Soil Landscape Parameters

    Garg, Pradeep Kumar / Garg, Rahul Dev / Shukla, Gaurav / Srivastava, Hari Shanker

    Geospatial Analyses using Machine Learning and Geomatics

    (Studies in Big Data, ; 72)

    2020  

    Abstract: This book addresses the mapping of soil-landscape parameters in the geospatial domain. It begins by discussing the fundamental concepts, and then explains how machine learning and geomatics can be applied for more efficient mapping and to improve our ... ...

    Author's details by Pradeep Kumar Garg, Rahul Dev Garg, Gaurav Shukla, Hari Shanker Srivastava
    Series title Studies in Big Data, ; 72
    Abstract This book addresses the mapping of soil-landscape parameters in the geospatial domain. It begins by discussing the fundamental concepts, and then explains how machine learning and geomatics can be applied for more efficient mapping and to improve our understanding and management of ‘soil’. The judicious utilization of a piece of land is one of the biggest and most important current challenges, especially in light of the rapid global urbanization, which requires continuous monitoring of resource consumption. The book provides a clear overview of how machine learning can be used to analyze remote sensing data to monitor the key parameters, below, at, and above the surface. It not only offers insights into the approaches, but also allows readers to learn about the challenges and issues associated with the digital mapping of these parameters and to gain a better understanding of the selection of data to represent soil-landscape relationships as well as the complex and interconnected links between soil-landscape parameters under a range of soil and climatic conditions. Lastly, the book sheds light on using the network of satellite-based Earth observations to provide solutions toward smart farming and smart land management. .
    Keywords Computational intelligence ; Big data ; Remote sensing ; Computational Intelligence ; Big Data ; Remote Sensing/Photogrammetry
    Subject code 631.470223
    Language English
    Size 1 online resource (159 pages)
    Edition 1st ed. 2020.
    Publisher Springer Singapore ; Imprint: Springer
    Publishing place Singapore
    Document type Book ; Online ; E-Book
    Remark Zugriff für angemeldete ZB MED-Nutzerinnen und -Nutzer
    ISBN 981-15-3238-9 ; 981-15-3237-0 ; 978-981-15-3238-2 ; 978-981-15-3237-5
    DOI 10.1007/978-981-15-3238-2
    Database ZB MED Catalogue: Medicine, Health, Nutrition, Environment, Agriculture

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  6. Article: Application of 'Readiness for Change' concept within implementation of evidence-based mental health interventions globally: protocol for a scoping review.

    Dev, Saloni / Shidhaye, Rahul

    Wellcome open research

    2024  Volume 7, Page(s) 293

    Abstract: Background: Concerning the growing burden of mental illnesses globally, there has been an increased investment into the implementation of evidence-based mental health interventions (EBmhIs) in routine care settings. However, the uptake and ... ...

    Abstract Background: Concerning the growing burden of mental illnesses globally, there has been an increased investment into the implementation of evidence-based mental health interventions (EBmhIs) in routine care settings. However, the uptake and implementation of these EBmhIs has faced challenges in the real-world context. Among the many barriers and facilitators of implementation of EBmhIs identified by implementation science frameworks, evidence on the role of readiness for change (RFC) remains sparse. RFC constitutes the willingness and perceived capacity of stakeholders across an organization to implement a new practice. Theoretically, RFC has been defined at organizational, group, and individual levels, however, its conceptualization and operationalization across all these levels have differed in studies on the implementation of EBmhIs. By conducting a scoping review, we aim to examine the literature on RFC within the implementation of EBmhIs.
    Methods: This scoping review will be conducted following the PRISMA-ScR guidelines. Iterative review stages will include a systematic and comprehensive search through four electronic databases (PubMed, Web of Science, Embase, and PsycINFO), selecting studies, charting data, and synthesizing the results. English-language studies meeting the inclusion criteria will be screened independently by two reviewers.
    Discussion: This review will synthesize knowledge on the conceptualization of RFC across organizational, group, and individual levels within the implementation of EBmhIs. In addition, it will identify how RFC has been measured in these studies and summarize the reported evidence on its impact on the implementation of EBmhIs.
    Conclusions: This review will assist mental health researchers, implementation scientists, and mental health care providers to gain a better understanding of the state of research on RFC within the implementation of EBmhIs.
    Registration: The final protocol was registered with the Open Science Framework on October 21, 2022 ( https://osf.io/rs5n7).
    Language English
    Publishing date 2024-04-11
    Publishing country England
    Document type Journal Article
    ISSN 2398-502X
    ISSN 2398-502X
    DOI 10.12688/wellcomeopenres.18602.2
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  7. Article ; Online: Machine Learning in Medicine: Will This Time Be Different?

    Deo, Rahul C

    Circulation

    2020  Volume 142, Issue 16, Page(s) 1521–1523

    MeSH term(s) Echocardiography ; Humans ; Machine Learning ; Medicine
    Language English
    Publishing date 2020-10-19
    Publishing country United States
    Document type Editorial ; Research Support, N.I.H., Extramural ; Research Support, Non-U.S. Gov't ; Comment
    ZDB-ID 80099-5
    ISSN 1524-4539 ; 0009-7322 ; 0069-4193 ; 0065-8499
    ISSN (online) 1524-4539
    ISSN 0009-7322 ; 0069-4193 ; 0065-8499
    DOI 10.1161/CIRCULATIONAHA.120.050583
    Database MEDical Literature Analysis and Retrieval System OnLINE

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    In stock of ZB MED Cologne/Königswinter

    Ui IV Zs.60: Show issues Location:
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  8. Article: A systematic review of EEG based automated schizophrenia classification through machine learning and deep learning.

    Rahul, Jagdeep / Sharma, Diksha / Sharma, Lakhan Dev / Nanda, Umakanta / Sarkar, Achintya Kumar

    Frontiers in human neuroscience

    2024  Volume 18, Page(s) 1347082

    Abstract: The electroencephalogram (EEG) serves as an essential tool in exploring brain activity and holds particular importance in the field of mental health research. This review paper examines the application of artificial intelligence (AI), encompassing ... ...

    Abstract The electroencephalogram (EEG) serves as an essential tool in exploring brain activity and holds particular importance in the field of mental health research. This review paper examines the application of artificial intelligence (AI), encompassing machine learning (ML) and deep learning (DL), for classifying schizophrenia (SCZ) through EEG. It includes a thorough literature review that addresses the difficulties, methodologies, and discoveries in this field. ML approaches utilize conventional models like Support Vector Machines and Decision Trees, which are interpretable and effective with smaller data sets. In contrast, DL techniques, which use neural networks such as convolutional neural networks (CNNs) and long short-term memory networks (LSTMs), are more adaptable to intricate EEG patterns but require significant data and computational power. Both ML and DL face challenges concerning data quality and ethical issues. This paper underscores the importance of integrating various techniques to enhance schizophrenia diagnosis and highlights AI's potential role in this process. It also acknowledges the necessity for collaborative and ethically informed approaches in the automated classification of SCZ using AI.
    Language English
    Publishing date 2024-02-14
    Publishing country Switzerland
    Document type Systematic Review
    ZDB-ID 2425477-0
    ISSN 1662-5161
    ISSN 1662-5161
    DOI 10.3389/fnhum.2024.1347082
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  9. Article: Machine learning assisted construction of a shallow depth dynamic ansatz for noisy quantum hardware.

    Halder, Sonaldeep / Dey, Anish / Shrikhande, Chinmay / Maitra, Rahul

    Chemical science

    2024  Volume 15, Issue 9, Page(s) 3279–3289

    Abstract: The development of various dynamic ansatz-constructing techniques has ushered in a new era, making the practical exploitation of Noisy Intermediate-Scale Quantum (NISQ) hardware for molecular simulations increasingly viable. However, such ansatz ... ...

    Abstract The development of various dynamic ansatz-constructing techniques has ushered in a new era, making the practical exploitation of Noisy Intermediate-Scale Quantum (NISQ) hardware for molecular simulations increasingly viable. However, such ansatz construction protocols incur substantial measurement costs during their execution. This work involves the development of a novel protocol that capitalizes on regenerative machine learning methodologies and many-body perturbation theoretical measures to construct a highly expressive and shallow ansatz within the variational quantum eigensolver (VQE) framework with limited measurement costs. The regenerative machine learning model used in our work is trained with the basis vectors of a low-rank expansion of the
    Language English
    Publishing date 2024-01-17
    Publishing country England
    Document type Journal Article
    ZDB-ID 2559110-1
    ISSN 2041-6539 ; 2041-6520
    ISSN (online) 2041-6539
    ISSN 2041-6520
    DOI 10.1039/d3sc05807g
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  10. Article ; Online: Assessing variations in land cover-land use and surface temperature dynamics for Dehradun, India, using multi-time and multi-sensor landsat data.

    Mishra, Kavach / Garg, Rahul Dev

    Environmental monitoring and assessment

    2023  Volume 195, Issue 3, Page(s) 373

    Abstract: Rapid urbanisation and industrialisation coupled with overpopulation have altered land cover/land use (LCLU) and surface temperature (ST) patterns in Dehradun. Monitoring these changes through satellite-based remote sensing is required to ensure the ... ...

    Abstract Rapid urbanisation and industrialisation coupled with overpopulation have altered land cover/land use (LCLU) and surface temperature (ST) patterns in Dehradun. Monitoring these changes through satellite-based remote sensing is required to ensure the sustained development of this ecologically fragile region. Here, LU and ST dynamics of the Dehradun municipal area have been estimated using Landsat-5 datasets for 1991, 1998, and 2008 and Landsat-8 dataset for 2018. LU maps have been extracted using a Gaussian Maximum Likelihood classifier with an overall accuracy of over 88% and Kappa coefficients above 0.85. Results reveal that the urban region expanded by 80.6% in the 27 years while dense vegetation and dry river bed classes have declined sharply. Sparse vegetation has risen by 3 km
    MeSH term(s) Temperature ; Cities ; Hot Temperature ; Environmental Monitoring/methods ; Urbanization ; India
    Language English
    Publishing date 2023-02-09
    Publishing country Netherlands
    Document type Journal Article
    ZDB-ID 782621-7
    ISSN 1573-2959 ; 0167-6369
    ISSN (online) 1573-2959
    ISSN 0167-6369
    DOI 10.1007/s10661-023-10945-z
    Database MEDical Literature Analysis and Retrieval System OnLINE

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