Shailendra Pratap Singh – författare

This laboratory reference compiles the essential protocols used in cyanobacterial research. Cyanobacteria is a model system for biofuel production and sequestration of carbon dioxide to tackle global climate change due to increasing levels of greenhouse gases. Topics range from identification and growth parameters to biomolecule estimation and isolation. It also covers genome analysis and bioengineering of cyanobacteria for novel products.

Key Features:

This collection is useful to students and researchers in life sciences. It is also meant for industry experts who are involved in the production of biofuels, biofertilizers, other value-added products, and carbon sequestration using cyanobacteria.

This laboratory reference compiles the essential protocols used in cyanobacterial research. Cyanobacteria is a model system for biofuel production and sequestration of carbon dioxide to tackle global climate change due to increasing levels of greenhouse gases. Topics range from identification and growth parameters to biomolecule estimation and isolation. It also covers genome analysis and bioengineering of cyanobacteria for novel products.

Key Features:

This collection is useful to students and researchers in life sciences. It is also meant for industry experts who are involved in the production of biofuels, biofertilizers, other value-added products, and carbon sequestration using cyanobacteria.

The book aims to develop methodologies and throw light on the advances in empirical research of various machine learning systems through data mining and parallel programming using GPU approaches. It reviews concepts of existing machine learning and deep learning techniques and how these can be implemented in GPU computing with CUDA architecture. The book also discusses modern machine learning techniques for effective big data management in accordance with worldwide standards in the field.

Covering diverse areas, this publication is meant for academicians, data scientists, industrial professionals, researchers and students interested in uncovering the latest innovations in the field.

The book aims to develop methodologies and throw light on the advances in empirical research of various machine learning systems through data mining and parallel programming using GPU approaches. It reviews concepts of existing machine learning and deep learning techniques and how these can be implemented in GPU computing with CUDA architecture. The book also discusses modern machine learning techniques for effective big data management in accordance with worldwide standards in the field.

Covering diverse areas, this publication is meant for academicians, data scientists, industrial professionals, researchers and students interested in uncovering the latest innovations in the field.