Comparison of Packaging Technologies for Rf Mems Switch

Bansal, Deepak; Kumar, Amit; Kumar, Prem; Kaur, Maninder; Rangra, Kamaljit

doi:10.2528/pierm14073002

Cited by 7 publications

(1 citation statement)

References 17 publications

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“…Once the preprocessing is done, the dataset is divided into training and validation dataset [17,18]. 80% of the data is kept for training the model, and 20% is kept for validating the performance of the model.…”

Section: Digitization and Preprocessing Of Datasetmentioning

confidence: 99%

Recognition of Gurmukhi Handwritten City Names Using Deep Learning and Cloud Computing

Sharma

Gupta

et al. 2022

Scientific Programming

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The challenges involved in the traditional cloud computing paradigms have prompted the development of architectures for the next generation cloud computing. The new cloud computing architectures can generate and handle huge amount of data, which was not possible to handle with the help of traditional architectures. Deep learning algorithms have the ability to process this huge amount of data and, thus, can now solve the problem of the next generation computing algorithms. Therefore, these days, deep learning has become the state-of-the-art approach for solving various tasks and most importantly in the field of recognition. In this work, recognition of city names is proposed. Recognition of handwritten city names is one of the potential research application areas in the field of postal automation For recognition using a segmentation-free approach (Holistic approach). This proposed work demystifies the role of convolutional neural network (CNN), which is one of the methods of deep learning technique. Proposed CNN model is trained, validated, and analyzed using Adam and stochastic gradient descent (SGD) optimizer with a batch size of 2, 4, and 8 and learning rate (LR) of 0.001, 0.01, and 0.1. The model is trained and validated on 10 different classes of the handwritten city names written in Gurmukhi script, where each class has 400 samples. Our analysis shows that the CNN model, using an Adam optimizer, batch size of 4, and a LR of 0.001, has achieved the best average validation accuracy of 99.13.

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Section: Digitization and Preprocessing Of Datasetmentioning

confidence: 99%