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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Healthcare Informatics to Analyze Patient Health Records, for Enabling Better Clinical Decision-Makl features from exudates digitally. The five-stage classification of the severity of the disease comprises of three stages of low risk and two stages of diabetic retinopathy. By implementing an automated system for identification of this disease we have a chance to accurately detect an affected pati

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S. Ramsamy-Iranah,S. Rosunee,N. Kistamahications across different disciplines, their contribution to the real world, and a study of the architectures and methods used by each application. This chapter also introduces the differences between machine learning and deep learning. Finally, this chapter concludes with future aspects and conclusions.

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R. Skjerve,G. A. Giannoumis,S. Naseemnd applications of DL from a real-world perspective, which covers a variety of areas such as Speech Recognition, Text Classification, Document Summarization, Fraud Detection, Visual Recognition, Personalization’s, and so on.

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Understanding Deep Learning: Case Study Based Approach,ications across different disciplines, their contribution to the real world, and a study of the architectures and methods used by each application. This chapter also introduces the differences between machine learning and deep learning. Finally, this chapter concludes with future aspects and conclusions.

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Deep Learning and its Applications: A Real-World Perspective,nd applications of DL from a real-world perspective, which covers a variety of areas such as Speech Recognition, Text Classification, Document Summarization, Fraud Detection, Visual Recognition, Personalization’s, and so on.

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Malaria Parasite Enumeration and Classification Using Convolutional Neural Networking,NN and DCNN have been designed and tested to perform the grouping of diseased erythrocytes into their corresponding stages of growth. The traditional neural network ANN approach gave an accuracy of 93% and this was again overwhelmed by using customized Deep Convolutional Network by achieving an accuracy of 95%.

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