International Journal of Computer and Applications

In this paper we give an overview of the Knowledge Base Population (KBP) track at the 2010 Text Analysis Conference. The main goal of KBP is to promote research in discovering facts about entities and augmenting a knowledge base (KB) with these facts. This is done through two tasks, Entity Linking -- linking names in context to entities in the KB -- and Slot Filling -- adding information about an entity to the KB. A large source collection of newswire and web documents is provided from which systems are to discover information. Attributes ("slots") derived from Wikipedia infoboxes are used to create the reference KB. In this paper we provide an overview of the techniques which can serve as a basis for a good KBP system, lay out the remaining challenges by comparison with traditional Information Extraction (IE) and Question Answering (QA) tasks, and provide some suggestions to address these challenges.

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Knowledge Base Population: Successful Approaches and Challenges

Fruit classification is quite difficult because of the various categories and similar shapes and features of fruit. In this work, we proposed two novel machine-learning based classification methods. The developed system consists of wavelet entropy (WE), principal component analysis (PCA), feedforward neural network (FNN) trained by fitness-scaled chaotic artificial bee colony (FSCABC) and biogeography-based optimization (BBO), respectively. The K-fold stratified cross validation (SCV) was utilized for statistical analysis. The classification performance for 1653 fruit images from 18 categories showed that the proposed “WE + PCA + FSCABC-FNN” and “WE + PCA + BBO-FNN” methods achieve the same accuracy of 89.5%, higher than state-of-the-art approaches: “(CH + MP + US) + PCA + GA-FNN ” of 84.8%, “(CH + MP + US) + PCA + PSO-FNN” of 87.9%, “(CH + MP + US) + PCA + ABC-FNN” of 85.4%, “(CH + MP + US) + PCA + kSVM” of 88.2%, and “(CH + MP + US) + PCA + FSCABC-FNN” of 89.1%. Besides, our methods used only 12 features, less than the number of features used by other methods. Therefore, the proposed methods are effective for fruit classification.

Fruit Classification by Wavelet-Entropy and Feedforward Neural Network Trained by Fitness-Scaled Chaotic ABC and Biogeography-Based Optimization

Accurate fruit classification is difficult to accomplish because of the similarities among the various categories. In this paper, we proposed a novel fruit-classification system, with the goal of recognizing fruits in a more efficient way. Our methodology included the following steps. First, a four-step pre-processing was employed. Second, the features colour, shape, and texture were extracted. Third, we utilized principal component analysis to remove excessive features. Fourth, a novel fruit-classification system based on biogeography-based optimization BBO and feedforward neural network FNN was proposed, with the short name of BBO-FNN. The experiment employed over 1653 chromatic fruit images 18 categories by fivefold stratified cross-validation. The results showed that the proposed BBO-FNN yielded an overall accuracy of 89.11%, which was higher than the five state-of-the-art methods: genetic algorithm-FNN, artificial bee colony-FNN, particle swarm optimization-FNN, kernel support vector machine, and ant colony optimization-FNN. Also, the BBO-FNN achieved the same accuracy as fitness-scaling chaotic artificial bee colony-FNN, but it performed much faster than the latter. The proposed BBO-FNN was effective in fruit-classification in terms of classification accuracy and computation time. This indicated that it can be applied in credible use.

Fruit classification by biogeography-based optimization and feedforward neural network

Rainfall Monthly Prediction Based on Artificial Neural Network: A Case Study in Tenggarong Station, East Kalimantan - Indonesia

To recognize application of Artificial Neural Networks (ANNs) in weather forecasting, especially in rainfall forecasting a comprehensive literature review from 1923 to 2012 is done and presented in this paper. And it is found that architectures of ANN such as BPN, RBFN is best established to be forecast chaotic behavior and have efficient enough to forecast monsoon rainfall as well as other weather parameter prediction phenomenon over the smaller geographical region.

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Application of Artificial Neural Networks in Weather Forecasting: A Comprehensive Literature Review

Background: The presence of least dense dry air and highly dense cortical bone in the path of radiation and the position of source, near or far from the surface of patient, affects the exact dose delivery like in breast brachytherapy. Aim: This study aims to find out the dose difference in the presence of inhomogenieties like cortical bone and dry air as well as to find out difference of dose due to position of source in water phantom of high dose rate (HDR) 192 Ir nucletron microselectron v2 (mHDRv2) brachytherapy source using Monte Carlo (MC) simulation EGSnrc code, so that the results could be used in Treatment Planning System (TPS) for more precise brachytherapy treatment. Settings and Design: The settings and design are done using different software of the computer. Methods and Materials: For this study, the said source, water phantom of volume 30 x 30 x 30 cm 3 , inhomogeneities each of volume 1 x 2 x 2 cm 3 with their position, water of water phantom and position of source are modeled using three-dimensional MC EGSnrc code. Statistical Analysis Used: Mean and probability are used for results and discussion. Results : The % relative dose difference is calculated here as 5.5 to 6.5% higher and 4.5 to 5% lower in the presence of air and cortical bone respectively at transverse axis of the source, which may be due to difference of linear attenuation coefficients of the inhomogeneities. However, when the source was positioned at 1 cm distance from the surface of water phantom, the near points between 1 to 2 cm and 3 to 8 cm. from the source, at its transverse axis, were 2 to 3.5% and 4 to 16% underdose to the dose when the source was positioned at mid-point of water phantom. This may be due to lack of back scatter material when the source was positioned very near to the surface of said water phantom and overlap of the additional cause of missing scatter component with the primary dose for near points from the source. These results were found in good agreement with literature data. Conclusion: The results can be used in TPS.

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Effect of inhomogeneities and source position on dose distribution of nucletron high dose rate Ir-192 brachytherapy source by Monte Carlo simulation.

The utilization of back-propagation neural network in identification of internal dynamics of chaotic motion is found appropriate. However, during its training through Rumelhart algorithm, it is found that, a high learning rate ( ) leads to rapid learning but the weights may oscillate, while a lower value of ` ' leads to slower learning process in weight updating formula   Momentum factor ( ) is to accelerate the convergence of error during the training in the equation   and   while transfer function sigmoid  . It is the most complicated and experimental task to identify optimum value of ` ' and ` ' during the training. To identify optimum value of   ` ' and  ` ' , firstly the network is trained with 103 epochs under different values of  ` ' in the close interval   and   At   the convergence of initial weights and minimization of error (i.e., mean square error) process is found appropriate. Afterwards to find optimum value of  , the network was trained again with   = 0.3 (fixed) and with different values of   in the close interval   for 103 epochs. It was observed that the convergence of initial weights and minimization of error was appropriate with  = 0.3 and   = 0.9. On this optimum value of   and   the network was trained successfully from local minima of error = 1.67029292416874E-03 at 103 epochs to global minima of error = 4.99180426869658E-04 at 15   105 epochs. At the global minima, the network has exhibited excellent performance in identification of internal dynamics of chaotic motion and in prediction of future values by past recorded data series. These essentials are presented through this research paper.

https://journalskuwait.org/kjs/index.php/KJS/article/download/394/44

Impact of learning rate and momentum factor in the performance of back-propagation neural network to identify internal dynamics of chaotic motion

Attempts to predict long-range monsoon rainfall over the subdivision EPMB, Three layer perception feed forward back propagation deterministic and probabilistic artificial neural network models have been developed. 61 years data for 1945–2006 have been used, of which the first 51 years (1945–1995) of data are used for training the network and data for the period 1996–2006 are used independently for validation. We have found that the mean absolute deviation (% of mean) of the model is less than and half of the standard deviation (% of mean) in the independent period (1996-2006) of the subdivision in deterministic forecast. Correlation between actual and particular model predicted values are more than 0.8 for the districts and 0.7 for the whole subdivision in deterministic forecast. However performance of the model in probabilistic forecast is better evaluated over deterministic forecast. The models developed and their evaluations have been presented in this paper.

Long-Range Monsoon Rainfall Pattern Recognition and Prediction for the Subdivision 'EPMB' Chhattisgarh Using Deterministic and Probabilistic Neural Network

M. K. Kowar

Papers

Application of Artificial Neural Networks in Weather Forecasting: A Comprehensive Literature Review

Effect of inhomogeneities and source position on dose distribution of nucletron high dose rate Ir-192 brachytherapy source by Monte Carlo simulation.

Impact of learning rate and momentum factor in the performance of back-propagation neural network to identify internal dynamics of chaotic motion

Long-Range Monsoon Rainfall Pattern Recognition and Prediction for the Subdivision 'EPMB' Chhattisgarh Using Deterministic and Probabilistic Neural Network

Comparison of Wavelet Transforms For Denoising And Analysis Of PCG Signal