International Journal of Neural Networks and Advanced Applications

ISSN: 2313-0563
Volume 5, 2018

Notice: As of 2014 and for the forthcoming years, the publication frequency/periodicity of NAUN Journals is adapted to the 'continuously updated' model. What this means is that instead of being separated into issues, new papers will be added on a continuous basis, allowing a more regular flow and shorter publication times. The papers will appear in reverse order, therefore the most recent one will be on top.

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Volume 5, 2018

Title of the Paper: Hyperspectral Image Content Identification Using Kernel Based Neural Network


Authors: Puttaswamy M. R., Balamurugan P.

Pages: 13-19

Abstract: Large dimension data of Hyperspectral Image (HSI) leads to high computation cost, more execution time and increase the memory demand; therefore, difficulty arises during the classification of HSI. Unsupervised-BSA (band selection algorithm) using linear projection (LP) dependent band metric similarity has considered for informatics band selection of HSI. However, space complexity and time constrain is very big challenge for many feature algorithm. In this paper, we will use monogenetic binary feature (MBF) to get the local and monogenic feature from each pixel. The extracted feature from the combined monogenetic parameter will be used for effective classification process. In post processing, the classification of extracted feature from the MBF is perform by the Neural Network (NN) and to provide an enhanced generalization ability we introduce a kernel based NN, and considering the unknown feature mapping. To validate the performance of proposed hyperspectral classification algorithm, we will compared with several state-of-the-art.

Title of the Paper: Data Mining Methods for Traffic Accident Severity Prediction


Authors: Qasem A. Al-Radaideh, Esraa J. Daoud

Pages: 1-12

Abstract: The growth of the population volume and the number of vehicles on the road cause congestion (jam) in cities that is one of the main transportation issues. Congestion can lead to negative effects such as increasing accident risks due to the expansion in transportation systems. The smart city concept provides opportunities to handle urban problems, and also to improve the citizens’ living environment. In recent years, road traffic accidents (RTAs) have become one of the largest national health issues in the world. Many factors (driver, environment, car, etc.) are related to traffic accidents, some of those factors are more important in determining the accident severity than others. The analytical data mining solutions can significantly be employed to determine and predict such influential factors among human, vehicle and environmental factors and thus to explain RTAs severity. In this research, three classification techniques were applied: Decision trees (Random Forest, Random Tree, J48/C4.5, and CART), ANN (back-propagation), and SVM (polynomial kernel) to detect the influential environmental features of RTAs that can be used to build the prediction model. These techniques were tested using a real dataset obtained from the Department for Transport of the United Kingdom. The experimental results showed that the highest accuracy value was 80.6% using Random Forest followed by 61.4% using ANN then by 54.8% using SVM. A decision system has been build using the model generated by the Random Forest technique that will help decision makers to enhance the decision making process by predicting the severity of the accident.