Shukla uday (9 Ergebnisse)
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Softcover. Zustand: Gut. Gebraucht - Gut Verlagsmängelex., XXI, 735 p. (Also available as online version This book constitutes the refereed proceedings of the 9th International Conference on High Performance Computing, HiPC 2002, held in Bangalore, India in December 2002. The 57 revised full contributed papers and 9 invited pape…rs presented together with various keynote abstracts were carefully reviewed and selected from 145 submissions. The papers are organized in topical sections on algorithms, architecture, systems software, networks, mobile computing and databases, applications, scientific computation, embedded systems, and biocomputing. For Researchers and professionals.
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Taschenbuch. Zustand: Neu. Druck auf Anfrage Neuware - Printed after ordering - To conclude, modi ed design and implementation of a middleware, NICAN, to dynamically supply local network information to distributed scienti c app- cations was presented. The application provides adaptive capabilities, is n- intrusive, exible, easy…to use and extensible. The distributed scienti c appli- tion can use this tool for information collection to its advantage without s- stantial code instrumentation. References [1] D.Andersen,D.Bansal,D.Curtis,S.Seshan,andH.Balakrishnan. Systems- portforbandwidthmanagementandcontentadaptationininternetapplications. In Proceedings of 4th Symposium on Operating Systems Design and Implemen- tion San Diego,CA.USENIXAssociation.:213-226,October2000. 555 [2] G.Chen.Providingdynamicnetworkinformationtodistributedapplications.May 2001. Computer Science Deparment, University of Minnesota Duluth. Master s Thesis. 559 [3] Xiaodong Fu, Weisong Shi, Anatoly Akkerman, and Vijay Karamcheti. Cans: Composable, adaptive network services infrastructure. In USENIX Symposium on Internet Technologies and Systems (USITS),March2001. 555 [4] D.KulkarniandM.Sosonkina. Usingdynamicnetworkinformationtoimprove theruntimeperformanceofadistributedsparselinearsystemsolution.Technical ReportUMSI-2002-10,2002. 559 [5] DevdattaKulkarniandMashaSosonkina. Aframeworkforintegratingnetwork information intodistributediterative solution of sparse linearsystems. In P- ceedings of the 5th International Meeting of Vector and Parallel Processing, VEC- PAR 2002. 560 [6] Z.Li,Y.Saad,and M.Sosonkina. pARMS:Aparallel version of thealgebraic recursivemultilevelsolver. TechnicalReportUMSI-2001-100,MinnesotaSup- computerInstitute,UniversityofMinnesota,Minneapolis,MN,2001. 559 [7] B.Lowekamp,N.Miller,D.Sutherland,T.Gross,P.Steenkiste,andJ.Subhlok.
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Zustand: New. Dr. Siddharth Singh Chouhan is currently working as a Senior Assistant Professor Grade 2 in the School of Computing Science and Engineering, VIT Bhopal University, Sehore, India. He has a B.Tech. (with Honours) 2010, an MTech (with Honours), 2013 .
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Hardcover. Zustand: Brand New. 436 pages. 6.14x1.00x9.21 inches. In Stock.
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Buch. Zustand: Neu. Neuware - Agriculture is going through a technology renaissance with deep learning leading the way. This volume addresses how advanced AI methods are transforming the future of farming, food systems, and environmental sustainability. It is forward-looking guide to integrating smart systems in agriculture acro…ss all scales - from soil to satellite. The volume comprises contributions from international leaders in AI, agronomy, genomics, remote sensing, and robotics. It addresses a broad array of emerging topics, such as autonomous farming systems, UAVs, and deep reinforcement learning for field operations; computer vision and hybrid models for crop, weed, and livestock detection; and advanced edge and federated learning approaches to real-time, privacy-conscious decision-making. It also discusses explainable AI for transparent agricultural models, deep learning in crop genomics and trait prediction, biodiversity monitoring, and time-series forecasting of pests and climate stress. Generative AI for generating synthetic agricultural data and simulating digital farms with AR/VR and digital twins are also covered. With a focus on climate-smart agriculture, the book addresses the role that deep learning can play in promoting resilient, adaptive, and sustainable food systems under climate change and food insecurity globally. It concludes with an examination of the ethical, regulatory, and policy issues, presenting a vision for inclusive, human-centric AI in agriculture.Key Features: - Integrates autonomous agriculture, explainable artificial intelligence, edge/federated learning, genomics of crops, and biodiversity monitoring. - Highlights climate-resilient agriculture and future-proof simulations. - Incorporates real-world applications, case studies, and multidisciplinary viewpoints. - Connects AI research, policy, and ethics with agricultural adoption.
