scholarly journals Park Assist: A Deep Convolution Neural Network based Parking Assistant system

2020 ◽  
Vol 8 (6) ◽  
pp. 14-18
Author(s):  
Sukhdeep Sharma ◽  
Aayushya ‎ ◽  
Dr. Madhumita Kathuria ◽  
Pronika Chawla
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Image Classification ◽  
Convolution Neural Network ◽  
The Other ◽  
Human Labour ◽  
Deep Convolution Neural Network ◽  
Classification Tasks ◽  
Parking Facilities

With the proliferation in number of vehicles an unnoticeable problem regarding parking of these vehicles has emerged in places like shopping complexes where current car parking facilities are incapable of managing the parking of vehicles without human labour . Even in current automated PGI’s human labour is required in some or the other way . Motivated by the affordable and remarkable performance of Convolutional Nueral Network in various image classification tasks, this paper presents a review on the automated parking systems based on the CNN technique . The classifier are trained and tested by deep learning of nueral network thus using of PHP and HTML to create the UI and knowledge of MySQL to create a database to store information about vehicles .Similarly by converting the process into three small procedures we will be able to evaluate the bill in accordance to the timestamp of the parked vehicle without the use of human efforts.

scholarly journals Deep Convolution Neural Network for Big Data Medical Image Classification

IEEE Access ◽  
2020 ◽  
Vol 8 ◽  
pp. 105659-105670 ◽  
Author(s):  
Rehan Ashraf ◽  
Muhammad Asif Habib ◽  
Muhammad Akram ◽  
Muhammad Ahsan Latif ◽  
Muhammad Sheraz Arshad Malik ◽  
...  
Keyword(s):  
Neural Network ◽  
Big Data ◽  
Image Classification ◽  
Medical Image ◽  
Convolution Neural Network ◽  
Deep Convolution Neural Network ◽  
Medical Image Classification

scholarly journals A Hybrid Methodology for Flower Images Segmentation & Recognition with extended Deep-Convolution Neural Network (CNN)

2021 ◽  
Author(s):  
Neeraj Kumar Rathore ◽  
Varshali Jaiswal ◽  
Varsha Sharma ◽  
Sunita Varma
Keyword(s):  
Neural Network ◽  
Image Segmentation ◽  
Deep Learning ◽  
Computation Time ◽  
Detection Algorithm ◽  
Convolution Neural Network ◽  
Color Model ◽  
Blue Color ◽  
Deep Convolution Neural Network ◽  
Hybrid Methodology

Abstract Deep-Convolution Neural Network (CNN) is the branch of computer science. Deep Learning CNN is a methodology that teaches computer systems to do what comes naturally to humans. It is a method that learns by example and experience. This is a heuristic-based method to solve computationally exhaustive problems that are not resolved in a polynomial computation time like NP-Hard problems. The purpose of this research is to develop a hybrid methodology for the detection and segmentation of flower images that utilize the extension of the deep CNN. The plant, leaf, and flower image detection are the most challenging issues due to a wide variety of classes, based on their amount of texture, color distinctiveness, shape distinctiveness, and different size. The proposed methodology is implemented in Matlab with deep learning Tool Box and the dataset of flower image is taken from Kaggle with five different classes like daisy, dandelion, rose, tulip, and sunflower. This methodology takes an input of different flower images from data sets, then converts it from RGB (Red, Green, Blue) color model to the L*a*b color model. L*a*b has reduced the effort of image segmentation. The flower image segmentation has been performed by the canny edge detection algorithm which provided better results. The implemented extended deep learning convolution neural network can accurately recognize varieties of flower images. The learning accuracy of the proposed hybrid method is up to 98% that is maximizing up to + 1.89% from state of the art.

scholarly journals Application Research of Deep Convolutional Neural Network in Computer Vision

2020 ◽  
Vol 2 (2) ◽  
pp. 23
Author(s):  
Lei Wang
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Computer Vision ◽  
Face Recognition ◽  
Human Brain ◽  
Image Classification ◽  
Convolutional Neural Networks ◽  
Convolution Neural Network ◽  
Data Set ◽  
Deep Convolution Neural Network

<p>As an important research achievement in the field of brain like computing, deep convolution neural network has been widely used in many fields such as computer vision, natural language processing, information retrieval, speech recognition, semantic understanding and so on. It has set off a wave of neural network research in industry and academia and promoted the development of artificial intelligence. At present, the deep convolution neural network mainly simulates the complex hierarchical cognitive laws of the human brain by increasing the number of layers of the network, using a larger training data set, and improving the network structure or training learning algorithm of the existing neural network, so as to narrow the gap with the visual system of the human brain and enable the machine to acquire the capability of "abstract concepts". Deep convolution neural network has achieved great success in many computer vision tasks such as image classification, target detection, face recognition, pedestrian recognition, etc. Firstly, this paper reviews the development history of convolutional neural networks. Then, the working principle of the deep convolution neural network is analyzed in detail. Then, this paper mainly introduces the representative achievements of convolution neural network from the following two aspects, and shows the improvement effect of various technical methods on image classification accuracy through examples. From the aspect of adding network layers, the structures of classical convolutional neural networks such as AlexNet, ZF-Net, VGG, GoogLeNet and ResNet are discussed and analyzed. From the aspect of increasing the size of data set, the difficulties of manually adding labeled samples and the effect of using data amplification technology on improving the performance of neural network are introduced. This paper focuses on the latest research progress of convolution neural network in image classification and face recognition. Finally, the problems and challenges to be solved in future brain-like intelligence research based on deep convolution neural network are proposed.</p>

scholarly journals Dual-Branch Deep Convolution Neural Network for Polarimetric SAR Image Classification

2017 ◽  
Vol 7 (5) ◽  
pp. 447 ◽  
Author(s):  
Fei Gao ◽  
Teng Huang ◽  
Jun Wang ◽  
Jinping Sun ◽  
Amir Hussain ◽  
...  
Keyword(s):  
Neural Network ◽  
Image Classification ◽  
Convolution Neural Network ◽  
Sar Image ◽  
Polarimetric Sar ◽  
Deep Convolution Neural Network

scholarly journals Using a Hybrid Neural Network Model DCNN–LSTM for Image-Based Nitrogen Nutrition Diagnosis in Muskmelon

Horticulturae ◽  
2021 ◽  
Vol 7 (11) ◽  
pp. 489
Author(s):  
Liying Chang ◽  
Daren Li ◽  
Muhammad Khalid Hameed ◽  
Yilu Yin ◽  
Danfeng Huang ◽  
...  
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Nitrogen Fertilizer ◽  
Precision Agriculture ◽  
Short Term Memory ◽  
Nitrogen Nutrition ◽  
Nitrogen Concentration ◽  
Convolution Neural Network ◽  
Deep Convolution Neural Network ◽  
Nutrition Diagnosis

In precision agriculture, the nitrogen level is significantly important for establishing phenotype, quality and yield of crops. It cannot be achieved in the future without appropriate nitrogen fertilizer application. Moreover, a convenient and real-time advance technology for nitrogen nutrition diagnosis of crops is a prerequisite for an efficient and reasonable nitrogen-fertilizer management system. With the development of research on plant phenotype and artificial intelligence technology in agriculture, deep learning has demonstrated a great potential in agriculture for recognizing nondestructive nitrogen nutrition diagnosis in plants by automation and high throughput at a low cost. To build a nitrogen nutrient-diagnosis model, muskmelons were cultivated under different nitrogen levels in a greenhouse. The digital images of canopy leaves and the environmental factors (light and temperature) during the growth period of muskmelons were tracked and analyzed. The nitrogen concentrations of the plants were measured, we successfully constructed and trained machine-learning- and deep-learning models based on the traditional backpropagation neural network (BPNN), the emerging convolution neural network (CNN), the deep convolution neural network (DCNN) and the long short-term memory (LSTM) for the nitrogen nutrition diagnosis of muskmelon. The adjusted determination coefficient (R2) and mean square error (MSE) between the predicted values and measured values of nitrogen concentration were adopted to evaluate the models’ accuracy. The values were R2 = 0.567 and MSE = 0.429 for BPNN model; R2 = 0.376 and MSE = 0.628 for CNN model; R2 = 0.686 and MSE = 0.355 for deep convolution neural network (DCNN) model; and R2 = 0.904 and MSE = 0.123 for the hybrid model DCNN–LSTM. Therefore, DCNN–LSTM shows the highest accuracy in predicting the nitrogen content of muskmelon. Our findings highlight a base for achieving a convenient, precise and intelligent diagnosis of nitrogen nutrition in muskmelon.

Deep learning audio magnetotellurics inversion using residual-based deep convolution neural network

2021 ◽  
pp. 104309
Author(s):  
Zhengguang Liu ◽  
Huang Chen ◽  
Zhengyong Ren ◽  
Jingtian Tang ◽  
Zhimin Xu ◽  
...  
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Convolution Neural Network ◽  
Deep Convolution Neural Network

scholarly journals Identification of Microcontroller Based Objects Using Image Classification in Python

2021 ◽  
Vol 9 (9) ◽  
pp. 87-92
Author(s):  
Zenith Nandy
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Image Classification ◽  
Programming Language ◽  
Multiple Testing ◽  
Activation Function ◽  
Convolution Neural Network ◽  
Output Layer ◽  
Deep Learning Neural Network ◽  
Percent Accuracy

Abstract: In this paper, I built an AI model using deep learning, which identifies whether a given image is of an Arduino, a Beaglebone Black or a Jetson Nano. The identification of the object is based on prediction. The model is trained using 300 to 350 datasets of each category and is tested multiple times using different images at different angles, background colour and size. After multiple testing, the model is found to have 95 percent accuracy. Model used is Sequential and uses Convolution Neural Network (CNN) as its architecture. The activation function of each layer is RELU and for the output layer is Softmax. The output is a prediction and hence it is of probability type. This is a type of an application based project. The entire scripting is done using Python 3 programming language. Keywords: image classification, microcontroller boards, python, AI, deep learning, neural network

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