scholarly journals Image Georeferencing using Artificial Neural Network Compared with Classical Methods

2021 ◽  
pp. 5024-5034
Author(s):  
Zahra Ezz El Din
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Divided Difference ◽  
Google Earth ◽  
Image Resolution ◽  
Reference Points ◽  
The Subject ◽  
Artificial Neural ◽  
Artificial Neural Network Ann ◽  
Matlab Programming

Georeferencing process is one of the most important prerequisites for various geomatics applications; for example, photogrammetry, laser scan analysis, remotely sensing, spatial and descriptive data collection, and others. Georeferencing mostly involves the transformation of coordinates obtained from images that are inhomogeneous due to accuracy differences. The georeferencing depends on image resolution and accuracy level of measurements of reference points ground coordinates.  Accordingly, this study discusses the subject of coordinate’s transformation from the image to the global coordinates system (WGS84) to find a suitable method that provides more accurate results. In this study, the Artificial Neural Network (ANN) method was applied, in addition to several numerical methods, namely the Affine divided difference, Newton’s divided difference, and polynomial transformation. The four methods were modelled and coded using Matlab programming language based on an image captured from Google Earth. The image was used to determine reference points within the study area (University of Baghdad campus).  The findings of this study showed that the ANN enhanced the results by about 50% in terms of accuracy and 90% in terms of homogeneity, compared with the other methods.

Pengecaman Insan Berasaskan Kaedah Profil Sentroid dan Pengelas Rangkaian Neural Buatan

2012 ◽  
Author(s):  
Nooritawati Md Tahir ◽  
Aini Hussain ◽  
Salina Abdul Samad ◽  
Hafizah Husain
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Recognition Task ◽  
Classification Rate ◽  
Human Recognition ◽  
Gait Features ◽  
Static Images ◽  
The Subject ◽  
Artificial Neural ◽  
Artificial Neural Network Ann

Dalam kajian ini, teknik profil sentroid yang berdasarkan pendekatan berasaskan model digunakan bagi tugas pengecaman insan. Kaedah ini dilaksanakan secara mengekstrak ciri–ciri unik perwakilan isyarat gaya lenggang insan serta bukan insan secara automatik dan pasif berasaskan imej pegun. Untuk menilai kekuatan algoritma sarian teknik profil sentroid yang dihasilkan, Rangkaian Neural Buatan (RNB) digunakan sebagai pengelas. Keputusan yang diperolehi membuktikan ciri sarian profil sentroid sesuai digunakan sebagai perwakilan vektor ciri bagi pengelasan insan dengan kadar pengelasan RNB yang dicapai melebihi 98%. Kata kunci: Pengecaman insan; rangkaian neural tiruan; profil sentroid In this study, centroidal profile which is a model based approach is employed for human recognition task. This is done by extracting unique representation of gait features of the subject automatically and passively from static images of human or non human. To evaluate the effectiveness of the generated centroidal profile, Artificial Neural Network (RNB) is used as classifier. Results attained proven that the centroidal profile is appropriate as feature extraction to be used as feature vectors for human shape classification based on classification rate of RNB achieved specifically above 98%. Key words: Human recognition; artificial neural network (ANN); centroidal profile

scholarly journals P318 Artificial intelligence of the neural network-assistant for differential diagnosis of IBD

2020 ◽  
Vol 14 (Supplement_1) ◽  
pp. S313-S313
Author(s):  
M Skalinskaya ◽  
I Bakulin ◽  
E Skazyvaeva ◽  
I Rasmagina ◽  
G Mashevskii ◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Differential Diagnosis ◽  
Control Sample ◽  
Image Resolution ◽  
The Neural Network ◽  
Output Neurons ◽  
Artificial Neural ◽  
Artificial Neural Network Ann ◽  
Endoscopic Picture

Abstract Background Due to the lack of a ‘gold standard’ in the diagnosis of IBD the differential diagnosis between ulcerative colitis and Crohn′s disease can be very difficult. Verification of diagnosis of IBD takes a long time in majority of cases. Methods We have created an artificial neural network (ANN) of the multilayer perceptron type using the Neural Network Toolbox application from the MATLAB application package. Three types of images were used to train the ANN: the norm of the endoscopic picture of the colon, the endoscopic pictures of UC and CD. The first stage is the training of an artificial neural network to distinguish the presence or absence of pathology (29 images of the ‘normal colon’, 14 images of the CD, and 15 - UC). The second stage was to train the ANN to recognise the various forms of IBD. The network was trained on an array of 124 images (62 images of each class of pathologies). Each image was previously converted to the grayscale mode and then into a matrix of pixels. A vector with the number of elements equal to the size of the image was fed to the input of the perceptron. Results To solve the task of identifying the pathology a perceptron was built with 32,2784 input neurons, 10 hidden neurons and 2 output neurons which represent the conclusion that the image belongs to one of the two classes: norm or pathology. To solve the problem of differentiating CD and UC a perceptron was created with 364500 input neurons (this value was determined by the image resolution) and 2 output neurons representing the conclusion that the image belongs to one of the two classes: UC or CD. The best result in differentiation of pathology was shown by the ANN of MP 364500 type: 364500-20-2: 2, which total accuracy of recognition was 96,8%. The average accuracy of the developed model was 92.6%. However, in the control sample, the accuracy was 84.2%. This fact indicates that the model should be taught on more images. In addition to the ‘accuracy’ criterion, the ‘completeness’ parameter was used to evaluate the system. ‘Completeness’ for recognition of the image of the norm was the highest and equal to 1, for UC the criterion of ‘completeness’ was 0.89. The lowest ‘completeness’ was obtained when recognising the image of the CD (0.67). Conclusion ANN type MP 364500: 364500-20-2: 2 has shown the best results in the set targets. Efficiency in pathology recognition was 96.8%. The efficiency of the created ANN in solving the problem of recognition of different forms of IBD (UC/CD) can be described by the following parameters: specificity (Sp) −78.2%, sensitivity (Se) - 93.1%, accuracy (Ac) - 85,7%. The obtained ANN can be used to solve the problems of classification of endoscopic images of the intestine for the presence of IBD and for differential diagnosis.

Application of the Artificial Neural Network (ANN) Method as MPPT Photovoltaic for DC Source Storage

2019 ◽  
Vol 12 (3) ◽  
pp. 145 ◽  
Author(s):  
Epyk Sunarno ◽  
Ramadhan Bilal Assidiq ◽  
Syechu Dwitya Nugraha ◽  
Indhana Sudiharto ◽  
Ony Asrarul Qudsi ◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Artificial Neural ◽  
Artificial Neural Network Ann

Random Forest (RF) and Artificial Neural Network (ANN) Algorithms for LULC Mapping

2020 ◽  
Vol 38 (4A) ◽  
pp. 510-514
Author(s):  
Tay H. Shihab ◽  
Amjed N. Al-Hameedawi ◽  
Ammar M. Hamza
Keyword(s):  
Neural Network ◽  
Remote Sensing ◽  
Artificial Neural Network ◽  
Random Forest ◽  
Satellite Image ◽  
Landsat 8 ◽  
Optical Remote Sensing ◽  
Data Set ◽  
Artificial Neural ◽  
Artificial Neural Network Ann

In this paper to make use of complementary potential in the mapping of LULC spatial data is acquired from LandSat 8 OLI sensor images are taken in 2019.  They have been rectified, enhanced and then classified according to Random forest (RF) and artificial neural network (ANN) methods. Optical remote sensing images have been used to get information on the status of LULC classification, and extraction details. The classification of both satellite image types is used to extract features and to analyse LULC of the study area. The results of the classification showed that the artificial neural network method outperforms the random forest method. The required image processing has been made for Optical Remote Sensing Data to be used in LULC mapping, include the geometric correction, Image Enhancements, The overall accuracy when using the ANN methods 0.91 and the kappa accuracy was found 0.89 for the training data set. While the overall accuracy and the kappa accuracy of the test dataset were found 0.89 and 0.87 respectively.

Detection of Biomedical Images by Using Bio-inspired Artificial Intelligent

2020 ◽  
Vol 38 (2A) ◽  
pp. 255-264
Author(s):  
Hanan A. R. Akkar ◽  
Sameem A. Salman
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Mean Square Error ◽  
Average Rate ◽  
Mean Square ◽  
Performance Measurements ◽  
Artificial Intelligent ◽  
Average Mean Square Error ◽  
Artificial Neural ◽  
Artificial Neural Network Ann

Computer vision and image processing are extremely necessary for medical pictures analysis. During this paper, a method of Bio-inspired Artificial Intelligent (AI) optimization supported by an artificial neural network (ANN) has been widely used to detect pictures of skin carcinoma. A Moth Flame Optimization (MFO) is utilized to educate the artificial neural network (ANN). A different feature is an extract to train the classifier. The comparison has been formed with the projected sample and two Artificial Intelligent optimizations, primarily based on classifier especially with, ANN-ACO (ANN training with Ant Colony Optimization (ACO)) and ANN-PSO (training ANN with Particle Swarm Optimization (PSO)). The results were assessed using a variety of overall performance measurements to measure indicators such as Average Rate of Detection (ARD), Average Mean Square error (AMSTR) obtained from training, Average Mean Square error (AMSTE) obtained for testing the trained network, the Average Effective Processing Time (AEPT) in seconds, and the Average Effective Iteration Number (AEIN). Experimental results clearly show the superiority of the proposed (ANN-MFO) model with different features.

Low-Cost Hardware Implementation of Human Blood Identification Device Using Feed-Forward Artificial Neural Network on FPGA

2018 ◽  
Author(s):  
Rizki Eka Putri ◽  
Denny Darlis
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Low Cost ◽  
Image Resolution ◽  
Blood Type ◽  
Feed Forward ◽  
Testing Facility ◽  
System Input ◽  
Artificial Neural ◽  
Short Time

This article was under review for ICELTICS 2018 -- In the medical world there is still service dissatisfaction caused by lack of blood type testing facility. If the number of tested blood arise, a lot of problems will occur so that electronic devices are needed to determine the blood type accurately and in short time. In this research we implemented an Artificial Neural Network on Xilinx Spartan 3S1000 Field Programable Gate Array using XSA-3S Board to identify the blood type. This research uses blood sample image as system input. VHSIC Hardware Discription Language is the language to describe the algorithm. The algorithm used is feed-forward propagation of backpropagation neural network. There are 3 layers used in design, they are input, hidden1, and output. At hidden1layer has two neurons. In this study the accuracy of detection obtained are 92%, 92%, 92%, 90% and 86% for 32x32, 48x48, 64x64, 80x80, and 96x96 pixel blood image resolution, respectively.

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