scholarly journals Plant leaf identification system using convolutional neural network

2021 ◽  
Vol 10 (6) ◽  
pp. 3341-3352
Author(s):  
Amiruzzaki Taslim ◽  
Sharifah Saon ◽  
Abd Kadir Mahamad ◽  
Muladi Muladi ◽  
Wahyu Nur Hidayat
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Convolutional Neural Network ◽  
Image Classification ◽  
White Background ◽  
Identification System ◽  
Plant Identification ◽  
Software Product ◽  
And Training ◽  
Random Background

This paper proposes a leaf identification system using convolutional neural network (CNN). This proposed system can identify five types of local Malaysia leaf which were acacia, papaya, cherry, mango and rambutan. By using CNN from deep learning, the network is trained from the database that acquired from leaf images captured by mobile phone for image classification. ResNet-50 was the architecture has been used for neural networks image classification and training the network for leaf identification. The recognition of photographs leaves requested several numbers of steps, starting with image pre-processing, feature extraction, plant identification, matching and testing, and finally extracting the results achieved in MATLAB. Testing sets of the system consists of 3 types of images which were white background, and noise added and random background images. Finally, interfaces for the leaf identification system have developed as the end software product using MATLAB app designer. As a result, the accuracy achieved for each training sets on five leaf classes are recorded above 98%, thus recognition process was successfully implemented.

scholarly journals Image Classification Algorithm Based on Big Data and Multilabel Learning of Improved Convolutional Neural Network

2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Haibin Chang ◽  
Ying Cui
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Convolutional Neural Network ◽  
Image Classification ◽  
Convolutional Neural Networks ◽  
Classification Algorithm ◽  
Large Set ◽  
Data Set ◽  
Multilabel Learning ◽  
Average Maximum

More and more image materials are used in various industries these days. Therefore, how to collect useful images from a large set has become an urgent priority. Convolutional neural networks (CNN) have achieved good results in certain image classification tasks, but there are still problems such as poor classification ability, low accuracy, and slow convergence speed. This article mainly introduces the image classification algorithm (ICA) research based on the multilabel learning of the improved convolutional neural network and some improvement ideas for the research of the ICA based on the multilabel learning of the convolutional neural network. This paper proposes an ICA research method based on multilabel learning of improved convolutional neural networks, including the image classification process, convolutional network algorithm, and multilabel learning algorithm. The conclusions show that the average maximum classification accuracy of the improved CNN in this paper is 90.63%, and the performance is better, which is beneficial to improving the efficiency of image classification. The improved CNN network structure has reached the highest accuracy rate of 91.47% on the CIFAR-10 data set, which is much higher than the traditional CNN algorithm.

scholarly journals Convolutional neural network for image classification based on transfer learning technique

2021 ◽  
Author(s):  
Ghassan Mohammed Halawani
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Deep Learning ◽  
Convolutional Neural Network ◽  
Image Classification ◽  
Transfer Learning ◽  
Learning Networks ◽  
The Neural Network ◽  
Learning Technique ◽  
Common Architecture

The main purpose of this project is to modify a convolutional neural network for image classification, based on a deep-learning framework. A transfer learning technique is used by the MATLAB interface to Alex-Net to train and modify the parameters in the last two fully connected layers of Alex-Net with a new dataset to perform classifications of thousands of images. First, the general common architecture of most neural networks and their benefits are presented. The mathematical models and the role of each part in the neural network are explained in detail. Second, different neural networks are studied in terms of architecture, application, and the working method to highlight the strengths and weaknesses of each of neural network. The final part conducts a detailed study on one of the most powerful deep-learning networks in image classification – i.e. the convolutional neural network – and how it can be modified to suit different classification tasks by using transfer learning technique in MATLAB.

scholarly journals Pattern Recognition with Convolutional Neural Networks: Humpback Whale Tails

2019 ◽  
Author(s):  
Luis Fernando De Mingo Lopez ◽  
Clemencio Morales Lucas ◽  
NURIA GOMEZ BLAS ◽  
Krassimira Ivanova
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Pattern Recognition ◽  
Convolutional Neural Network ◽  
Convolutional Neural Networks ◽  
Humpback Whale ◽  
And Training

This paper presents a study and implementation of a convolutional neural network to identify and recognize humpback whale specimens from the unique patterns of their tails. Starting from a dataset composed of images of whale tails, all the phases of the process of creation and training of a neural network are detailed – from the analysis and pre-processing of images to the elaboration of predictions, using TensorFlow and Keras frameworks. Other possible alternatives are also explained when it comes to tackling this problem and the complications that have arisen during the process of developing this paper.

scholarly journals Tensorflow Based Image Classification using Advanced Convolutional Neural Network

2020 ◽  
Vol 8 (6) ◽  
pp. 994-998
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Convolutional Neural Network ◽  
Image Classification ◽  
Programming Language ◽  
Convolutional Neural Networks ◽  
Input Data ◽  
Research Study ◽  
Testing Data

In this Research study image identifications will be done by the help of Advanced CNN (Convolutional Neural Networks with Tensorflow Framework. Here we use Python as a main programming language because Tensorflow is a python library. In this study input data mainly focuses on Plants categories by the help of leaves for identifications. Selecting CNN is the best approach for the training and testing data because it produces promising and continuously improving results on automated plant identifications. Here results are divided in terms of accuracy and time. Using advanced CNN results are above 95% while on others accuracy is below 90% and taking much time than this.

scholarly journals Dog Breed Prediction using Convolutional Neural Network

2020 ◽  
Vol 9 (5) ◽  
pp. 318-322
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Deep Learning ◽  
Convolutional Neural Network ◽  
Image Classification ◽  
Convolutional Neural Networks ◽  
Graphical Representation ◽  
Outcome Analysis ◽  
Dog Breed ◽  
The One

Deep learning gives the strength on the way to train algorithms model that can handle the difficulties of info classification also prediction grounded on totally on arising information as of raw information. Convolutional Neural Networks (CNNs) gives single often used method for image classification and detection. In this exertion, we define a CNNbased approach for spotting dogs in per chance complex images and due to this fact reflect inconsideration on the identification of the one of kinds of dog breed. The experimental outcome analysis supported the standard metrics and thus the graphical representation confirms that the algorithm (CNN) gives good analysis accuracy for all the tested datasets

scholarly journals Application of convolutional neural networks in optical text recognition to junk data filtering

2021 ◽  
Vol 2127 (1) ◽  
pp. 012024
Author(s):  
T E Razumov ◽  
D V Churikov ◽  
O V Kravchenko
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Convolutional Neural Network ◽  
Image Classification ◽  
Text Recognition ◽  
Data Filtering ◽  
Service Architecture ◽  
C Language ◽  
Software Application ◽  
Fully Connected

Abstract In this paper, the problem of constructing a model for detecting and filtering unwanted spam messages is solved. A fully connected convolutional neural network (FCNN) was chosen as the model of the classifier of unwanted emails in email. It allows you to divide emails into two categories: spam and not spam. The main result of the research is a software application in the C++ language, which has a micro-service architecture and solves the problem of image classification. The app can handle more than 106 requests per minute in real-time.

scholarly journals Pattern Recognition with Convolutional Neural Networks: Humpback Whale Tails

2019 ◽  
Author(s):  
Luis Fernando de Mingo López ◽  
Clemencio Morales Lucas ◽  
Nuria Gómez Blas ◽  
Krassimira Ivanova
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Pattern Recognition ◽  
Convolutional Neural Network ◽  
Convolutional Neural Networks ◽  
Humpback Whale ◽  
And Training

This paper presents a study and implementation of a convolutional neural network to identify and recognize humpback whale specimens from the unique patterns of their tails. Starting from a dataset composed of images of whale tails, all the phases of the process of creation and training of a neural network are detailed – from the analysis and pre-processing of images to the elaboration of predictions, using TensorFlow and Keras frameworks. Other possible alternatives are also explained when it comes to tackling this problem and the complications that have arisen during the process of developing this paper.

scholarly journals Classification of surface water objects in visible spectrum images

2020 ◽  
pp. 87-95
Author(s):  
A. A. Artemyev ◽  
E. A. Kazachkov ◽  
S. N. Matyugin ◽  
V. V. Sharonov
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Surface Water ◽  
Convolutional Neural Network ◽  
Convolutional Neural Networks ◽  
Visible Spectrum ◽  
Training Dataset ◽  
And Training ◽  
Selection Of

This paper considers the problem of classifying surface water objects, e.g. ships of different classes, in visible spectrum images using convolutional neural networks. A technique for forming a database of images of surface water objects and a special training dataset for creating a classification are presented. A method for forming and training of a convolutional neural network is described. The dependence of the probability of correct recognition on the number and variants of the selection of specific classes of surface water objects is analysed. The results of recognizing different sets of classes are presented.

scholarly journals IMAGE RECOGNITION USING MACHINE LEARNING

2021 ◽  
Vol 6 (1) ◽  
Author(s):  
Abhinav N Patil
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Neural Networks ◽  
Image Processing ◽  
Image Classification ◽  
Image Recognition ◽  
Training Dataset ◽  
Test Dataset ◽  
Human Support ◽  
And Training

Image recognition is important side of image processing for machine learning without involving any human support at any step. In this paper we study how image classification is completed using imagery backend. Couple of thousands of images of every, cats and dogs are taken then distributed them into category of test dataset and training dataset for our learning model. The results are obtained using custom neural network with the architecture of Convolution Neural Networks and Keras API.

SEGMENTATION OF HEMORRHAGE AREAS ON BRAIN CT IMAGES USING CONVOLUTIONAL NEURAL NETWORKS

2017 ◽  
Author(s):  
Vladyslav Yurochkin ◽  
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Convolutional Neural Network ◽  
Convolutional Neural Networks ◽  
Ct Images ◽  
Brain Ct ◽  
And Training

The paper considers the construction of a system for visualization of hemorrhage segmentation on brain CT images by creating and training a convolutional neural network to optimize the procedure for finding pathology in CT diagnostics.

Sign in / Sign up

Export Citation Format

Share Document