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

The objective of the study is the application of the artificial neural networks, to the spatial interpolation of rainfall variables. This methodology is applied on mean rainfall variable of 102-rainguage station of Chhattisgarh, India. We have developed 3 layer perceptron feed forward back propagation artificial neural network model and achieved good results are presented through this paper.

Spatial interpolation of rainfall variables using artificial neural network

Modelling of silicon epitaxy using silicon tetrachloride as the source

Attempt to recognize pattern of meteorological parameters over the smaller scale geographical region (district) artificial neural network models have been developed 54 years data for 1951-2004 have been used, of which the first 41 years (1951-1991) of data are used for training the network and data for the period 1991-2004 are used independently for validation We have found that the mean absolute deviation (% of mean) between actual and predicted values of the each model is less than and half of the standard deviation (% of mean) in the independent period (1991-2004) The performances of these models in pattern recognition and prediction have been found to be extremely good The models are developed and their evaluations have been presented in this paper

Development of Artificial Neural Network Models for Long-Range Meteorological Parameters Pattern Recognition over the Smaller Scale Geographical Region-District

Attempts to recognize pattern of monsoon rainfall over the smaller scale geographical region (district) 8-Parameter Probabilistic ANN model have been developed. Eleven neurons in input layer to input eleven years rainfall data time series. Eleven neurons in hidden layer, one output neuron for observation of twelve year rainfall, 132 trainable weights in three layers, transfer function sigmoid f (x) = 1/1+e-deltax+ eta and network training error level ranging from 0.0005 to 0.0001 have been used in the model development. 54 years data for 1951-2004 have been used, of which the first 41 years (1951-1991) of data are used for training the network and data for the period 1991-2004 are used independently for validation. The performances of the model in pattern recognition and prediction have been found to be extremely good and better evaluated over statistical models. The model developed and their evaluations have been presented in this paper.

Development of an 8-Parameter Probabilistic Artificial Neural Network Model for Long-Range Monsoon Rainfall Pattern Recognition over the Smaller Scale Geographical Region -District

Imaging has gained importance due to the improvements of performances in computer speed, media storage and network bandwidth. Along with this advancement, the fundamental issue of image security has become a major concern and because of this reason, research in image confidentiality has gained popularity amongst security experts. In this paper, a novel quasi pseudo random based Genetic algorithm for data encryption has been proposed. The encryption by this method is such that the real time signal can be transformed into completely disordered data. The proposed algorithm improves the scrambling efficiency with less memory usage requirement.

M. K. Kowar

Papers

Spatial interpolation of rainfall variables using artificial neural network

Modelling of silicon epitaxy using silicon tetrachloride as the source

Development of Artificial Neural Network Models for Long-Range Meteorological Parameters Pattern Recognition over the Smaller Scale Geographical Region-District

Development of an 8-Parameter Probabilistic Artificial Neural Network Model for Long-Range Monsoon Rainfall Pattern Recognition over the Smaller Scale Geographical Region -District

An improved evolutionary algorithm for secured image using adaptive genetic algorithm