scholarly journals Analysis of Convolutional Neural Network based Image Classification Techniques

2021 ◽  
Vol 3 (2) ◽  
pp. 100-117
Author(s):  
Milan Tripathi
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Image Classification ◽  
Rural Areas ◽  
Processing Time ◽  
Rapid Urbanization ◽  
Classification Techniques ◽  
Bar Code ◽  
Supermarket Industry

With the rapid urbanization and people moving from rural areas to urban time has become a very huge commodity. As a result of this change in people's lifestyles, there is a growing need for speed and efficiency. In the supermarket industry, item identification and billing are generally done manually, which takes a lot of time and effort. The lack of a bar code on the fruit products slows down the processing time. Before beginning the billing process, the seller may need to weigh the items in order to update the barcode, or the biller may need to input the item's name manually. This doubles the effort and also consumes a significant amount of time. As a result, several convolutional neural network-based classifiers are proposed to identify the fruits by visualizing via the camera for establishing a quick billing procedure in order to overcome this difficulty. The best model among the suggested models is capable of classifying pictures with start-of-art accuracy, which is superior than that of previously published studies.

Image Classification Using CNN With Multi-Core and Many-Core Architecture

2021 ◽  
pp. 233-266
Author(s):  
Debajit Datta ◽  
Saira Banu Jamalmohammed
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Image Classification ◽  
Medical Sciences ◽  
Classification Techniques ◽  
The Past ◽  
Distributed Framework ◽  
Multiple Cores ◽  
Many Core

Image classification is a widely discussed topic in this era. It covers a vivid range of application domains like from garbage classification applications to advanced fields of medical sciences. There have been several research works that have been done in the past and are also currently under research for coming up with better-optimized image classification techniques. However, the process of image classification turns out to be time-consuming. This work deals with the widely accepted FashionMNIST (modified national institute of standards and technology database) dataset, having a set of sixty thousand images for training a model and another popular dataset of MNIST for handwritten numbers. The work compares several convolutional neural network (CNN) models and aims in parallelizing them using a distributed framework that is provided by the python library, RAY. The parallelization has been achieved over the multiple cores of CPU and many cores of GPU. The work also shows that the overall accuracy of the system is not affected by the parallelization.

scholarly journals Real Time Multipurpose Smart Waste Classification Model for Efficient Recycling in Smart Cities Using Multilayer Convolutional Neural Network and Perceptron

Sensors ◽  
2021 ◽  
Vol 21 (14) ◽  
pp. 4916
Author(s):  
Ali Usman Gondal ◽  
Muhammad Imran Sadiq ◽  
Tariq Ali ◽  
Muhammad Irfan ◽  
Ahmad Shaf ◽  
...  
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Real Time ◽  
Multilayer Perceptron ◽  
Smart Cities ◽  
Classification Model ◽  
Rapid Urbanization ◽  
Metal Waste ◽  
Waste Classification ◽  
The City

Urbanization is a big concern for both developed and developing countries in recent years. People shift themselves and their families to urban areas for the sake of better education and a modern lifestyle. Due to rapid urbanization, cities are facing huge challenges, one of which is waste management, as the volume of waste is directly proportional to the people living in the city. The municipalities and the city administrations use the traditional wastage classification techniques which are manual, very slow, inefficient and costly. Therefore, automatic waste classification and management is essential for the cities that are being urbanized for the better recycling of waste. Better recycling of waste gives the opportunity to reduce the amount of waste sent to landfills by reducing the need to collect new raw material. In this paper, the idea of a real-time smart waste classification model is presented that uses a hybrid approach to classify waste into various classes. Two machine learning models, a multilayer perceptron and multilayer convolutional neural network (ML-CNN), are implemented. The multilayer perceptron is used to provide binary classification, i.e., metal or non-metal waste, and the CNN identifies the class of non-metal waste. A camera is placed in front of the waste conveyor belt, which takes a picture of the waste and classifies it. Upon successful classification, an automatic hand hammer is used to push the waste into the assigned labeled bucket. Experiments were carried out in a real-time environment with image segmentation. The training, testing, and validation accuracy of the purposed model was 0.99% under different training batches with different input features.

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