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Optimizing a Sensor to Detect Ammonium Nitrate Based IEDS in Vehicles Using Artificial Neural Networks
Bourdillon Omijeh,
Akani Okemeka Machiavelli
Issue:
Volume 5, Issue 1, June 2019
Pages:
1-6
Received:
12 April 2019
Accepted:
21 May 2019
Published:
10 June 2019
Abstract: Ammonium nitrate based explosives are a choice weapon for many terrorist groups due to its ease in manufacturing and high velocity of detonation. These explosives undergo thermal decomposition to release ammonia gas in traces of about 5- 25 Parts per Million (PPM) below the olfactory threshold. Ammonia is a reducing gas. MQ137 sensors are low cost commercially available metal oxide semiconductor ammonia gas sensors with a problem of selectivity (reacting with other reducing gasses like carbon monoxide etc) and sensitivity. We present the optimization of MQ137 metal oxide semiconductor electrochemical sensor using MATLAB, to improve its selectivity and sensitivity for accurately recognizing the characteristics of ammonia gas within specified PPM range as a sign of ammonium nitrate based explosives in vehicles. In this study, MQ137 sensor was connected with an ARDUINO microcontroller to a digital computer (2.40 GHz processor) and pre-heated for 12 hours before being exposed to ammonia gas in a controlled environment at room temperature to extract features (sensitivity constant and concentration in PPM) of ammonia gas with MQ137 sensor. 150 data samples of each feature were extracted and trained in a multilayer pattern recognition neural network with one hidden layer and 50 data samples containing features of other reducing gasses from the data sheet were used for testing. Test performance of multilayer artificial neural network has an accuracy of 100% with no misclassifications.
Abstract: Ammonium nitrate based explosives are a choice weapon for many terrorist groups due to its ease in manufacturing and high velocity of detonation. These explosives undergo thermal decomposition to release ammonia gas in traces of about 5- 25 Parts per Million (PPM) below the olfactory threshold. Ammonia is a reducing gas. MQ137 sensors are low cost ...
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An Overview of Neural Network
Mohaiminul Islam,
Guorong Chen,
Shangzhu Jin
Issue:
Volume 5, Issue 1, June 2019
Pages:
7-11
Received:
8 May 2019
Accepted:
17 June 2019
Published:
29 June 2019
Abstract: Neural networks represent a brain metaphor for information processing. These models are biologically inspired rather than an exact replica of how the brain actually functions. Neural networks have been shown to be very promising systems in many forecasting applications and business classification applications due to their ability to learn from the data. This article aims to provide a brief overview of artificial neural network. The artificial neural network learns by updating the network architecture and connection weights so that the network can efficiently perform a task. It can learn either from available training patterns or automatically learn from examples or input-output relations. Neural network-based models continue to achieve impressive results on longstanding machine learning problems, but establishing their capacity to reason about abstract concepts has proven difficult. Building on previous efforts to solve this important feature of general-purpose learning systems, our latest paper sets out an approach for measuring abstract reasoning in learning machines, and reveals some important insights about the nature of generalization itself. Artificial neural networks can learn by example like the way humans do. An artificial neural net is configured for a specific application like pattern recognition through a learning process. Learning in biological systems consists of adjustments to the synaptic connections that exist between neurons. This is true of artificial neural networks as well. Artificial neural networks can be applied to an increasing number of real-world problems of considerable complexity. They are used for solving problems that are too complex for conventional technologies or those types of problems that do not have an algorithmic solution.
Abstract: Neural networks represent a brain metaphor for information processing. These models are biologically inspired rather than an exact replica of how the brain actually functions. Neural networks have been shown to be very promising systems in many forecasting applications and business classification applications due to their ability to learn from the ...
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Robust Minimal Spanning Tree Using Intuitionistic Fuzzy C-means Clustering Algorithm for Breast Cancer Detection
Nithya,
Bhuvaneswari,
Senthil
Issue:
Volume 5, Issue 1, June 2019
Pages:
12-22
Received:
10 May 2019
Accepted:
10 June 2019
Published:
29 June 2019
Abstract: Breast cancer is the most common cause of death in women and the second leading cause of cancer deaths worldwide. Primary prevention in the early stages of the disease becomes complex as the causes remain almost unknown. However, some typical signatures of this disease, such as lumps and microcalcifications appearing on mammograms, can be used to improve early diagnostic techniques, which is critical for womens quality of life. X-ray mammography is the main test used for screening and early diagnosis, and its analysis and processing are the keys to improving breast cancer prognosis. In this paper, we have presented a novel approach to identify the presence of breast cancer lumps in mammograms. The proposed algorithm for selecting initial cluster centers on the basis of minimal spanning tree (MST) is presented. MST initialization method for the intuitionistic fuzzy c-means clustering algorithm for clear to identify of abnormalities for mammography images and Breast cancer patients symptoms used to predictive probability calculated by Pearson Chi-Square (χ2) test at 0.05 significance level indicate a highly significant correlation between mammography performance and clinical symptoms of breast cancer. Our findings suggest that mammography is highly efficient and promising technique.
Abstract: Breast cancer is the most common cause of death in women and the second leading cause of cancer deaths worldwide. Primary prevention in the early stages of the disease becomes complex as the causes remain almost unknown. However, some typical signatures of this disease, such as lumps and microcalcifications appearing on mammograms, can be used to im...
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Content-Based Image Retrieval Based on Texture and Color Combinations Using Tamura Texture Features and Gabor Texture Methods
Issue:
Volume 5, Issue 1, June 2019
Pages:
23-27
Received:
22 April 2019
Accepted:
24 June 2019
Published:
4 July 2019
Abstract: As the development of data storage technology from various sources of information then increasingly cause problems in the search and processing, of course with the existence of this problem will be feared can cause big losses. Various existing search techniques have not been able to provide clear results between query testing and training. To overcome this problem required a Content-Based Image Retrieval (CBIR) approach which is a technique for content-based image search. In this study using texture and color information from image training to present the results both in query testing and database training using Tamura texture method features Gabor texture features. Before displaying the query results first the image testing in extracts using Tamura texture features and Gabor texture features to get the feature values used for image testing and then matching it with the value of features available in the training database. The application used in this research is an application from LIRE and database image that used is database from image. orig. the results obtained in this research is, the application of texture Tamura method features and Gabor texture features based on the features and colors can provide significant results between image testing and image training.
Abstract: As the development of data storage technology from various sources of information then increasingly cause problems in the search and processing, of course with the existence of this problem will be feared can cause big losses. Various existing search techniques have not been able to provide clear results between query testing and training. To overc...
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Random Walk-Based Semantic Annotation for On-demand Printing Products
Mingxi Zhang,
Guanying Su
Issue:
Volume 5, Issue 1, June 2019
Pages:
28-35
Received:
29 April 2019
Accepted:
24 June 2019
Published:
4 July 2019
Abstract: Nowadays, the scale of real network is increasing day by day, while also brings sparse problems. It is usually necessary to maintain a large number of product information. To organize this product information, a feasible way is to add semantic tags to the information. In this article, we aim to solve the problem of semantic annotation of on-demand printing products. Based on good properties of random walk in global networks, we deal with the sparsity problem by applying it, and then propose an efficient ProRWR algorithm. Firstly, it processes the text description dataset of printed products based on TF-IDF algorithm, and builds “product-term” bipartite network. Secondly, ProRWR builds square matrix using the TF-IDF weight matrix, rewrite the equation of random walk, and use the normalized square matrix as the input of rewrite ProRWR algorithm. By random walks, terms with the highest convergence probability in each product document are selected as the most relevant feature terms of the product. A large number of experiments have been done on Amazon dataset. The results show that the precision and recall of our algorithm are 73.5% and 60%, respectively, indicating that ProRWR has discovered the potential semantic association and implemented the semantic annotation of on-demand printed products.
Abstract: Nowadays, the scale of real network is increasing day by day, while also brings sparse problems. It is usually necessary to maintain a large number of product information. To organize this product information, a feasible way is to add semantic tags to the information. In this article, we aim to solve the problem of semantic annotation of on-demand ...
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Segmentation and Measurement of Medical Image Quality Using K-means Clustering Algorithm
Ahmed Mohamed Ali Karrar,
Jun Sun
Issue:
Volume 5, Issue 1, June 2019
Pages:
36-44
Received:
16 April 2019
Accepted:
27 June 2019
Published:
16 July 2019
Abstract: In this paper we have segmented an image by using a k-clustering algorithm, using the Gaussian Mixture Model cluster to generate the initial centroid. Many types of research have been done in the area of image segmentation using clustering especially medical images, these techniques help medical scientists in the diagnosis of diseases thereby to cure this diseases K-means clustering algorithm one of these techniques, it is an unsupervised algorithm and it is used to segment the interest area from the background. We used also partial contrast stretching to improve the quality of the original image. And the final segmented result is comparing with the k-means clustering algorithm and we can conclude that the proposed clustering algorithm has better segmentation. Finally, MSE and PSNR are checked and discovered that they have small and large value respective, which are the condition for good image segmentation quality. And comparison for MSE and PSNR are done for the proposed method and classical K-means algorithm and it is found that the proposed method has better performance result.
Abstract: In this paper we have segmented an image by using a k-clustering algorithm, using the Gaussian Mixture Model cluster to generate the initial centroid. Many types of research have been done in the area of image segmentation using clustering especially medical images, these techniques help medical scientists in the diagnosis of diseases thereby to cu...
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