TY  - GEN
N2  - The role of deep learning for the analysis and learning of massive amounts of data from all aspects of daily-life has dramatically changed over the last few years. It is increasingly helping uncover trends leading to great successes. This book includes a collection of research manuscripts presenting state-of-the-art work in the areas of deep learning, blockchain and big data. All the manuscripts included in this book have been peer-reviewed based on aspects of novelty, originality and rigour. The main topics covered in the book include machine learning and time series, blockchain technologies and applications, data security, deep learning, and Internet of Things.
DO  - 10.1007/978-3-030-84337-3
DO  - doi
AB  - The role of deep learning for the analysis and learning of massive amounts of data from all aspects of daily-life has dramatically changed over the last few years. It is increasingly helping uncover trends leading to great successes. This book includes a collection of research manuscripts presenting state-of-the-art work in the areas of deep learning, blockchain and big data. All the manuscripts included in this book have been peer-reviewed based on aspects of novelty, originality and rigour. The main topics covered in the book include machine learning and time series, blockchain technologies and applications, data security, deep learning, and Internet of Things.
T1  - The International Conference on Deep Learning, Big Data and Blockchain (Deep-BDB 2021) /
AU  - Awan, Irfan,
AU  - Benbernou, Salima,
AU  - Younas, Muhammad
AU  - Aleksy, Markus,
VL  - volume 309
CN  - Q325.5
N1  - Includes author index.
ID  - 1442121
KW  - Machine learning
KW  - Big data
KW  - Blockchains (Databases)
KW  - Apprentissage automatique
KW  - Données volumineuses
KW  - Chaînes de blocs
SN  - 9783030843373
SN  - 3030843378
TI  - The International Conference on Deep Learning, Big Data and Blockchain (Deep-BDB 2021) /
LK  - https://univsouthin.idm.oclc.org/login?url=https://link.springer.com/10.1007/978-3-030-84337-3
UR  - https://univsouthin.idm.oclc.org/login?url=https://link.springer.com/10.1007/978-3-030-84337-3
ER  -