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Abstract. Image fusion is the process to derive the useful information from the scene captured by infrared (IR) and visible images. This derived information is used to improve the image content by enhancing the image visualization. Human identification or any living object identification in IR images is a challenging task. This paper proposes two fusion techniques namely Discrete Wavelet Transform with Neuro-Fuzzy (NF) and Entropy (EN) (DWT-NF-EN) and Integer Wavelet Transform with Neuro-Fuzzy and Entropy (IWT-NF-EN) and their results are compared and analyzed with existing fusion techniques using different quantitative measures. Subjective and objective evaluation of the results obtained is compared with other fusion techniques namely Redundancy Discrete Wavelet Transform (RDWT) and Integer Wavelet Transform and Neuro-Fuzzy (IWT-NF). The objective evaluation is done using the quantitative measures Entropy (EN), Peak Signal to Noise Ratio (PSNR) and Normalized Correlation Coefficient (NCC). From the experimental results it is observed that proposed methods provided better information (quality) using EN, PSNR and NCC measures for majority of the test images and the same is justified with the subjective results.

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The first and foremost task in any associative classification algorithm is mining of the association rules. Many studies have shown that the minimum support measure plays a key role in building an accurate classifier. Without the knowledge of the items and their frequency, user specified support measures are inappropriate, seldom may they coincide. .In this paper, we propose an approach called DASApriori i.e) Dynamic Adaptive Support Apriori to calculate the minimum support for mining class association rules and to build a simple and accurate classifier. Our experiments on 5 databases from UCI repository show that it achieves the best balance between the rule set size and classification accuracy even without the use of rule pruning techniques when compared with other associative classification approaches.

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