Improved handwritten digit recognition using artificial neural networks

Swain, Debabrata; Parmar, Badal; Shah, Hansal; Gandhi, Aditya

doi:10.1504/ijcsm.2023.131625

Cited by 1 publication

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“…The optimizer and loss function used in both models is Adam and sparse categorical cross-entropy respectively. ReLU (R(x)) has become one of the desired selections for a hidden layer's activation function because it is usually easier to teach and offers greater accuracy than others [17]. Additionally, it resolves the vanishing gradients, and its behavior is nearly linear as per equation 2.…”

Section: Proposed Architecturementioning

confidence: 99%

An Intelligent Fashion Object Classification Using CNN

Swain,

Pandya,

Sanghvi

et al. 2023

EAI Endorsed Trans Ind Net Intel Syst

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Every year the count of visually impaired people is increasing drastically around the world. At present time, approximately 2.2 billion people are suffering from visual impairment. One of the major areas where our model will affect public life is the area of house assistance for specially-abled persons. Because of visual improvement, these people face lots of issues. Hence for this group of people, there is a high need for an assistance system in terms of object recognition. For specially-abled people sometimes it becomes really difficult to identify clothing-related items from one another because of high similarity. For better object classification we use a model which includes computer vision and CNN. Computer vision is the area of AI that helps to identify visual objects. Here a CNN-based model is used for better classification of clothing and fashion items. Another model known as Lenet is used which has a stronger architectural structure. Lenet is a multi-layer convolution neural network that is mainly used for image classification tasks. For model building and validation MNIST fashion dataset is used.

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Section: Proposed Architecturementioning

confidence: 99%