Titlebook: Smart Trends in Computing and Communications; Proceedings of Smart Tomonobu Senjyu,Chakchai So–In,Amit Joshi Conference proceedings 2023 Th

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,Distributed Deep Learning with Data Parallelism for Diabetic Retinopathy Classification,trained on a massive volume of data can outperform single accelerators being used, i.e., the performance of deep learning (DL) models can be enhanced by using distributed and parallel deep learning methods. But the DDL models need to be redesigned and evaluated for particular hardware resources and

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,Classification of Traffic Signal Images Using Deep Neural Networks,on to the drivers on time which helps them in driving the car without accidents. Deep Neural Networks (DNN) are gaining more attention in the field of image analysis since it produces a high accuracy rate. Recently many researchers are focusing on the recognition of traffic signs using DNN to get be

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Evolving Time Series Data Streams: A Review, trained on past data gets outdated in forecasting future behaviours as data might go through concept drift and evolve and a rapid rate. For the model to be adaptive, detect changes in the data stream and be fast enough to incorporate the changes required in itself before the performance degrades dr

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Semi-Supervised Medical Image Segmentation on Data from Different Distributions,nting this data is to determine which anatomical components are required for a particular inquiry. One of the key benefits of medical image segmentation is that it allows for a more accurate In order to separate brain tumors on the Brats2019 dataset, we will use Mean Teacher, a straightforward metho

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Identification of Cracks in High-Rise Buildings Using Drones and Deep Learning,f equipment too. There has been work done in the past where machine learning and deep learning models were used for identification of cracks using image classification. This paper proposes a better way of identifying these cracks using drones and deep learning models. A custom-made shallow convoluti

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