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

A Novel Image Encryption Algorithm Using Multi Chaotic Schemes as Elliptic Curve, Quasi Group and also with Genetic Algorithm

Fuzzy systems offer numerous methods that prove helpful in solving application problems, where the governing parameters bear fuzziness in nature. In particular, many variants of fuzzy decision making models have been successfully explored as vocational guidance systems to assist decision makers in recognition and guidance processes, specially in the Medical diagnostic realm. In the present communication, an attempt is made to unreveal the fact that even the precisely chosen text fragments from a closed world domain incorporate the fuzzy boundaries among their term-to-term associations for machine-assisted acquisition and subsequent evaluation of Natural Language Semantics of that domain. This inspires further, to provide a decision-making tool that assists the Academicians in assessing the online submitted subjective answers at the candidatespsila end.

Intelligent Fuzzy Decision Making for Subjective Answer Evaluation Using Utility Functions

The present communication aims to report the TAC forum about the system-incorporated towards Entity-Linking task. The noun-phrases relevant to the search term were used to correlate the entityrelevant happenings mentioned in the source documents to that of the entity-relevant information in the knowledge-base. Care was taken in identifying the abbreviation and full form variants of search names if present in the same set of queries or in the provided Knowledge base as Wikipedia structured nodes. The subsequent knowledge-base node hits led to the ranking of their respective content for retrieving entity-linking node-id responses. Three different runs were submitted the Task Challenge following the different ways to search for the most meaningfully relevant information about the entities mentioned in Entity-Linking Target list from the Knowledge-base of the track. The team’s spirits feels elevated at the thought of using the free text from the Wikipedia pages associated with the knowledge base nodes, while building the evaluation model as it adds to the robustness and reliability of the system even at the time of inaccessibility of the Web.

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Bhilai Institute of Technology Durg at TAC 2010: Knowledge Base Population Task Challenge.

A method of generating a practically unlimited number of quasigroups of a (theoretically) arbitrary order using the computer algebra system Maple 7 is presented. This problem is crucial to cryptography and its solution permits to implement practical quasigroup-based endomorphic cryptosystems. The order of a quasigroup usually equals the number of characters of the alphabet used for recording both the plaintext and the cipher text. Moreover, it can be used for varied information viz . text, image, etc. Many of the on going algorithms uses NLFSR to generate pseudo random sequence and thus the suggested method can be integrated in any of the existing pseudo random sequence to further enhance their complexity. The implementation of PRSG using quasi group processing is highly scalable and fairly unpredictable. It has passed all publicly available random sequence generator tests. That is exactly what this paper provides: fast and easy ways of generating quasigroups of order up to 256 and a little more.

Generation of quasigroup for cryptographic application

Single-sensor digital color cameras use a process called color demosaicking to produce full color images from the data captured by a color filter array (CFA). The quality of demosaicked images is degraded due to the sensor noise introduced during the image acquisition process. The traditional solution for combating CFA sensor noise is demosaicking first, followed by a separate noise reduction i.e. denoising processing. This strategy will generate many noise-caused color artifacts in the demosaicking process, which are hard to remove in the denoising process. This paper presents a novel and modified principle component analysis (PCA) based denoising algorithm, which works directly on the CFA data. By exploiting the spatial and spectral correlations existed in the CFA image, the proposed method can effectively suppress noise while preserving color edges and details. Experiments using both simulated and real CFA images indicate that the proposed scheme outperforms many existing approaches viz. the conventional PCA based noise reduction method, including those sophisticated demosaicking and denoising schemes,in terms of both objective measurement and visual evaluation.

M. K. Kowar

Papers

A Novel Image Encryption Algorithm Using Multi Chaotic Schemes as Elliptic Curve, Quasi Group and also with Genetic Algorithm

Intelligent Fuzzy Decision Making for Subjective Answer Evaluation Using Utility Functions

Bhilai Institute of Technology Durg at TAC 2010: Knowledge Base Population Task Challenge.

Generation of quasigroup for cryptographic application

A Novel PCA based Noise Reduction for Color Images