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

In order to develop a forecasting model for monsoon rainfall over the districts of Chhattisgarh, 3LP (Three Layer Perception) FFBP (Feed Forward Back Propagation) Deterministic ANN models have been proposed In the proposed model, eleven neurons in input layer, one hidden layer with eleven neurons, a single neuron in output layer, 132 trainable weights in three layers, transfer function sigmoid 1/(1+e -deltax+eta)) with slope a = 1, threshold c = 0 have been used to evolve networks Training of the network is continued till the mean square error becomes less than a pre-assigned value ranging from 00005 to 0001 Data for the years 1945-2006 have been used, out of which the data of first 51 years, ie, (-deltax + eta) 1945-1995, are used for training the network and data for the remaining period, ie during the period 1996-2006 are used independently for validation In the present study, it has been observed that the mean absolute deviation (% of mean) values for the independent period (1996-2006) are less than and half of the standard deviation (% of mean) for all the districts The performances of these 3LP FFBP Deterministic ANN models have been found to be extremely good and better evaluated over statistical trend models also The models developed and their evaluations have been presented in this paper

Evolving and evaluation of 3LP FFBP deterministic ANN model for district level long range monsoon rainfall prediction.

Electrocardiogram (ECG), a non-stationary signal, is extensively used as one of the important diagnostic tools for the detection of the health of a heart. Comparison of overall ECG waveform pattern and shape enables doctors to diagnose possible diseases. Currently there is computer based analysis which employs certain signal processing to diagnose a patient based on ECG recording. The Electrocardiogram may contain various artefacts, noise and baseline wander when ECG is recorded which severely limits the utility of the recorded ECG and thus needs to be removed for better clinical evaluation. Signal pre-processing helps us remove contaminants from the ECG signals. The baseline wander and other wideband noise are not suppressed by hardware equipments. Software schemes are more powerful and feasible for offline ECG signal processing. Automatic detection of R peaks in a QRS complex is a fundamental requirement for automatic disease identification. Recently, numerous research and techniques have been developed for processing, detection of QRS complex, P and T waves of ECG signal. All these techniques and algorithms have their advantages and limitations. This proposed paper discusses various techniques and transformations proposed earlier in literature for processing, QRS complex and P and T wave detection of ECG signals and make comparison among them.

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A Review on ECG Signal De-noising, QRS Complex, P and T Wave Detection Techniques

The construction of Automated Digital Libraries has grabbed widespread popularity over recent years, rendering the value added services that suit to the tailored user requirements. In this context, carrying out traditional searches over text material by querying upon a given metadata or a set of domain keywords is predominantly observe, not to retrieve the relevant documents, although these end up in huge list of results containing query terms. In the present communication, the search is deviated from explicitly borrowed Metadata search to the context oriented search triggered from implicitly available Ontologies of the considered subject domain. The proposal is hereby analyzed to be most fruitful for Relevant Information Retrieval by User Communities, i.e. by those group of people who share common interests and benefit by their own accessed information. It is further emphasized that the search terms contributing to the background knowledge, can be implicitly made available from the collection of documents, which only need to be selectively defined, studying the User profiles of the search communities. The authors visualize this work as a tool to precisely rank the prescribed courseware material for Academic Digital Libraries.

Automated Document Ranking Evaluation in Digital Libraries

Modelling of epitaxial growth rate of silicon by vapour phase epitaxy

An improved model for simulating the growth of epitaxial silicon in the vapour phase by pyrolysis of silane (SiH4) in a horizontal reactor at atmospheric pressure has been developed. The model takes into account the variation of reactant concentration and the thickness of the boundary layer with distance inside the reactor. Using the above conditions, an expression for the growth rate of the epi-layer has been derived as a function of temperature, flow rate, mole fraction, position of wafer, etc. A comparison between the theoretically predicted values and experimental results shows satisfactory agreement.

M. K. Kowar

Papers

Evolving and evaluation of 3LP FFBP deterministic ANN model for district level long range monsoon rainfall prediction.

A Review on ECG Signal De-noising, QRS Complex, P and T Wave Detection Techniques

Automated Document Ranking Evaluation in Digital Libraries

Modelling of epitaxial growth rate of silicon by vapour phase epitaxy

Improved model of silicon epitaxial growth by VPE