Titlebook: Deep Learning and Edge Computing Solutions for High Performance Computing; A. Suresh,Sara Paiva Book 2021 Springer Nature Switzerland AG 2

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Heterogenous Applications of Deep Learning Techniques in Diverse Domains: A Review,tract advanced and inherent features from the big data sets using systematic learning methods. The real-world problem-solving using DL techniques demands large parallel computing infrastructure facilities for achieving high efficiency. Recent developments in deep learning techniques have demonstrate

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Malaria Parasite Enumeration and Classification Using Convolutional Neural Networking,ia in emaciated plasma films is a very tiresome, biased, and time-consuming task. This chapter delivers an unambiguous understanding of the quantification and classification of erythrocytes in tainted wafer-thin blood films diseased with plasmodium parasites. The approach mainly includes microscopic

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NoC Modeling and Topology Exploration disease in Kolkata city. By the borough level analysis, it has been found that the outbreaks of dengue diseases are mostly concentrated in the central and southern part of Kolkata. This study represents useful information about the dengue-affected areas of Kolkata city. It is also helpful to the he

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https://doi.org/10.1007/978-1-4614-4274-5nts. Furthermore, the chapter explains the concept of edge computing and how it differs from cloud computing. In addition, typical layered architecture of the edge computing ecosystem is presented, and the various types of components present in each layer are outlined. The characteristics of edge co

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John Clarkson,Patrick Langdon,Peter Robinsonings the applications in close proximity to the data (i.e., at the network edge) so as to reduce the communication cost, lower the bandwidth and latency, and also, adds a layer of security to the data. A paradigm that complements cloud computing and benefits each other is Edge Computing.

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D. E. Hughes,G. Cockerham,J. S. Paulting the temporal rationality of the data. Primary essence of the process is to diminish the cost function value. In order to acquire the minimal value the raw time series training data are levelheaded by the sensors. The next level is to scrutinize the classification accuracy of the Deep Stack Neur