Simple convolutional neural network on image classification

Guo, Tong; Dong, Jiwen; Li, Henjian; Gao, Yunxing

doi:10.1109/icbda.2017.8078730

Cited by 307 publications

(151 citation statements)

References 4 publications

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“…Explaining research chronological, including research design, research procedure (in the form of algorithms, Pseudocode or other), how to test and data acquisition [6][7][8][9]. The description of the course of research should be supported references, so the explanation can be accepted scientifically [4,10].…”

Section: Research Methods 21 Convolutional Neural Network (Cnn)mentioning

confidence: 99%

“…It has resulted in ground breaking decisions over the last decade in various fields related to pattern recognition; from image processing to voice recognition [4]. CNN's capabilities have become a known and used in various object recognition problems such as flower categorization [5], leaf recognition [6], voice analysis [7], image classification [8], fruit classification and ripeness grading recognition [9], food recognition [10], and plant disease identification [11].…”

Section: Introductionmentioning

confidence: 99%

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Flower and leaf recognition for plant identification using convolutional neural network

FatihahSahidan

Juha

Mohammad

et al. 2019

IJEECS

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This paper presents flower and leaf recognition for plant identification using Convolutional Neural Network (CNN). In this study, the performance of CNN for plant identification using images of the leaves, flowers and a combination of both are investigated. Two publicly available datasets, namely Folio leaf dataset and Flower Recognition dataset, have been used for the training and testing purposes. CNN has been proven to produce excellent results for object recognition but its performance can still be influenced by the type of images and the number of layers of the CNN architecture. Experimental results indicate that the utilization of leaf images only arrive to the highest accuracy for plant identification compared to the images of flowers only or the combination of both, that are 98%, 85% and 74%, respectively.

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Section: Research Methods 21 Convolutional Neural Network (Cnn)mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Flower and leaf recognition for plant identification using convolutional neural network

FatihahSahidan

Juha

Mohammad

et al. 2019

IJEECS

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show abstract

“…CNN is normally designed for reduced processing requirements otherwise this it is same as the simple multilayer perceptron. In the hidden layers of the CNN, multiple convolution layers, normalization layers, and pooling layers are utilized [14].…”

Section: B Convolutional Neural Networkmentioning

confidence: 99%

A Comprehensive Collaborating Filtering Approach using Extended Matrix Factorization and Autoencoder in Recommender System

Hasan¹,

Roy²,

Hasan³

et al. 2019

IJACSA

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Recommender system is an approach where users get suggestions based on their previous preferences. Nowadays, people are overwhelmed by the huge amount of information that is being present in any system. Sometimes, it is difficult for a user to find an appropriate item by searching the desired content. Recommender system assists users by providing suggestions of required information or items based on the similar features among the users. Collaborative filtering is one of the most re-known process of recommender system where the recommendation is done by similar users or similar items. Matrix factorization is an approach which can be used to decompose a matrix into two or more matrix to generate features. Again, autoencoder is a deep learning based technique which is used to find hidden features of an object. In this paper, features are calculated using extended matrix factorization and autoencoder and then a new similarity metric has been introduced that can calculate the similarity efficiently between each pair of users. Then, an improvement of the prediction method is introduced to predict the rating accurately by using the proposed similarity measure. In the experimental section, it has been shown that our proposed method outperforms in terms of mean absolute error, precision, recall, f-measures, and average reciprocal hit rank.

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“…Their experimental results showed that the time taken for classifying the images is also less which is nearly 0.77ms. Tianmei Guo [2] et.al., explained how they built a simple CNN for Image classification. They used standard datasets like MNIST and CIFAR-10 for implementing the same.…”

Section: Introductionmentioning

confidence: 99%

Visual Modeling of Data using Convolutional Neural Networks

Mohammed*,

Sowmya,

Akhila

et al. 2019

IJEAT

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Abstract: Artificial Intelligence has been showing monumental growth in filling the gap between the capabilities of humans and machines. Researchers and scientists work on many aspects to make new things happen. Computer Vision is one of them. To make the system to visualize, neural networks are used. both training and testing images. The training images are 50,000 and testing images are 10,000. The ten different classes contain airplanes, automobiles, birds, cat, ship, truck, deer, dog, frog and horse images. This paper was mainly concentrated on improving performance using normalization layers and comparing the accuracy achieved using different activation functions like ReLU and Tanh. Some of the well-known Neural Networks include CNN, Feedforward Neural Networks (FNN), and Recurrent Neural Networks (RNN) and so on. Among them, CNN is the correct choice for computer vision because they learn relevant features from an image or video similar to the human brain. In this paper, the dataset used is CIFAR-10 (Canadian Institute for Advanced Research) which contains 60,000 images in the size of 32x32. Those images are divided into 10 different classes which contains

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Simple convolutional neural network on image classification

Cited by 307 publications

References 4 publications

Flower and leaf recognition for plant identification using convolutional neural network

Flower and leaf recognition for plant identification using convolutional neural network

A Comprehensive Collaborating Filtering Approach using Extended Matrix Factorization and Autoencoder in Recommender System

Visual Modeling of Data using Convolutional Neural Networks

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