In recent years the highestdegree of communication happens through e-mails which are often affected by passive or active attacks. Effective spam filtering measures are the timely requirement to handle such attacks. Many efficient spam filters are available now-a-days with different degrees of performance and usually the accuracy level varies between 60-80% on an average. But most of the filtering techniques are unable to handle frequent changing scenario of spam mails adopted by the spammers over the time. Therefore improved spam control algorithms or enhancing the efficiency of various existing data mining algorithms to its fullest extent are the utmost requirement.In this paper three types of decision tree classifying techniques which are basically data mining classifiers namely Naïve Bayes Tree classifier (NBT), C 4.5 (or J48) decision tree classifier and Logistic Model Tree classifier (LMT) are studied and analyzed for spam mail filtration. The test results depict that LMT is giving the most efficient result in terms of performance with almost 90% accuracy level to detect spam mails and non-spam (HAM) mails.

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With the rapid progress of technologies in the arena of remote sensing and satellite imagery, Synthetic Aperture Rader (SAR) images have become an important source of data for research concerning changed detection. Out of the numerous techniques and approaches available for change detection in a particular location, most of them are initially targeted toward producing the difference image. In this article, a change detection approach is suggested that produces the result without finding a difference image. We are motivated toward the design of such an approach to reduce the effect of the difference image. In this method, we generate the training and testing sample for CNN classification directly from the original SAR image without any pre‐processing operations. This reduces the effect of the difference image on the final classification result. Since traditional fuzzy c means (FCM) are highly susceptible to noise and do not give desired results, we use spatial fuzzy c means (sFCM) with an intuitionistic approach. The intuitionistic approach refers to the degree of hesitation resulting from a lack of information. The approach is less hampered by noise and yields better outcomes. The basic idea of this method is to find false levels using spatial intuitionistic fuzzy c means clustering. Thereafter, the CNN is trained using the samples that are selected from the original samples using false labels. Finally, the classification results are generated through the trained CNN. Investigational results using the proposed approach show promising results when applied over two SAR image datasets compared to several existing methodologies.

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Minimization of multiple output functions of a digital logic circuit is a classic research problem. Minimalcircuit is obtained by using multiple Karnaugh Maps (K-map), one for each function. In this paper wepropose a novel technique that uses a single Karnaugh Map for minimizing multiple outputs of a singlecircuit. The algorithm basically accumulates multiple K-Maps into a single K-Map. Finding minimalnumbers of minterms are easier using our proposed clustering technique. Experimental results show thatminimization of digital circuits where more than one output functions are involved, our extended K-Mapapproach is more efficient as compare to multiple K-Map approach