Titlebook: Science, Technology and Innovation Ecosystem: An Indian and Global Perspective; Kashmir Singh,Nirmala Chongtham,Sukhdeep Kaur Book 2024 Th

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Technology Push, Demand Pull, Networks, and Public Policy: What Drives and Facilitates Innovation?and emphasises the more realistic dynamic models of innovation facilitated by external knowledge networks and public policies. The final part of this chapter suggests the need for considering systemic interactions between the instruments and provides some discussion on India’s evolving innovation ecosystem.

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Book 2024uire a deep understanding of the STI ecosystem, which includes the interactions between actors, their technologies, and their business models. This book, "Science, Technology, and Innovation Ecosystems: A National and Global Perspective," focuses on the STI ecosystem of India in comparison to other

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Science, Technology and Innovation Ecosystem: An Indian and Global Perspective

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Science, Technology and Innovation Ecosystem: An Indian and Global Perspective978-981-97-2815-2

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the igneous and metamorphic stable continent. Extensive parts of the interior of Africa are covered by Mesozoic to Cenozoic sandstone-dominated basins. Soil rejuvenating volcanic rocks extruded from lower parts of the earth crust and mantle mainly during Mesozoic and Cenozoic times in linear zones.

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Radhika Trikha,Kashmir Singh Data were analyzed using Microsoft Excel. Result shows that compared with the base level, structural change in fertilizer procurement arrangement (reducing farm gate price by 15%) led to 6% additional farm income (US $125 million) under low elasticity; 22% (US $472 million) under medium elasticity;

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Naveen Thakur,Saurabh Sharma,Kuldeep Kumar,Sanjay Thakurady has a georeferenced tree cadastre of around 15,000 solitary trees in the city area, which is also used to generate helpful information. All mentioned data is then joined and visualised in an interactive web application allowing arborists and foresters to generate individual and flexible evaluati

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Monika Kajal,Radhika Trikha,Kashmir Singh various pre-trained deep learning models in addition to augmenting the data to achieve the best performance. We observed that data augmentation increases the F1 score. After fine-tuning the QARiB model and augmenting the data we achieved the best F1 score of 0.49.