Advanced Research in Applied Artificial Intelligence: 25th by Qing He, Ye Duan, Danyang Zhang (auth.), He Jiang, Wei Ding,

By Qing He, Ye Duan, Danyang Zhang (auth.), He Jiang, Wei Ding, Moonis Ali, Xindong Wu (eds.)

This quantity constitutes the completely refereed convention complaints of the twenty fifth foreign convention on business Engineering and different functions of utilized Intelligend structures, IEA/AIE 2012, held in Dalian, China, in June 2012. the whole of eighty two papers chosen for the lawsuits have been conscientiously reviewed and chosen from various submissions. The papers are geared up in topical sections on laptop studying tools; cyber-physical method for clever transportation functions; AI functions; evolutionary algorithms, combinatorial optimization; modeling and aid of cognitive and affective human approaches; average language processing and its functions; social community and its functions; mission-critical purposes and case stories of clever structures; AI equipment; sentiment research for asian languages; facets on cognitive computing and clever interplay; spatio-temporal datamining, established studying and their functions; determination making and data established platforms; development reputation; agent established structures; choice making innovations and leading edge wisdom administration; desktop studying applications.

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Additional info for Advanced Research in Applied Artificial Intelligence: 25th International Conference on Industrial Engineering and Other Applications of Applied Intelligent Systems, IEA/AIE 2012, Dalian, China, June 9-12, 2012. Proceedings

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We conducted the experiments with 8,000 documents. Fig. 90. Fig. 953. 0 is the highest for harmful documents. 1 is the highest. 36 T. Yoshimura, Y. Fujii, and T. Ito safe documents harmful documents positive negative 5864 2136 532 7468 Fig. 4. Co-occurrence filtering with Gray Robinson safe documents harmful documnets positive negative 6548 1452 468 7532 Fig. 5. Co-occurrence filtering with Gray Robinson Grouping Co-occurrence Filtering Based on Bayesian Filtering 37 Fig. 5 is a result of altering Fig.

3 27 Fig. 5. Synthetic dataset with clusters found by N C(6, 6); black object represents the outliers Results In this section we present a sample of results, extensive tables for all datasets for various values of m and k are available in the Appendix of the technical report [6]. In the first experiment, with planted outliers in the synthetic data set, we observed that RBDA, ODMR, ODMRD, and RADA were able to detect all six outliers. Algorithm DBCOD detected only three of them, namely, {A, B, D}.

Recently, many people are using communication tools on the Web, but some send harmful information to others. Most operators manually deal with harmful information, which is expensive. In this paper, we implement two-word co-occurrence filtering by applying the Bayesian filtering method as a spam filter. We propose grouping co-occurrence filtering based on Bayesian filtering and experimentally verify our approach. Grouping co-occurence filtering detect harmful or safe documents at low cost. Our result suggests that grouping co-occurrence filtering is more stable and has a higher accuracy than co-occurrence filtering baesd on Bayesian filtering.

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