A Survey of Evaluation Techniques for Android Anti-Malware using Transformation Attacks

Android an open-source operating system mainly used for mobile phones have become increasingly popular. Studies suggest that mobile malware threats have recently become a real concern and the impact of malware is getting worse. 2014 saw an astounding 75 percent increase in the Android mobile malware. It is therefore imperative to evaluate the resistance and robustness of anti-malware products for android against various malware. To evaluate existing anti-malware, a systematic framework called DroidChameleon is developed with several common transformation techniques. This survey examines the effectiveness and robustness of popular antimalware tools and compare them against one another aiding in the decision making process involved with developing a secure system

Survey Paper on Multi Keyword Similarity Search over Encrypted Cloud Data

The tremendous amount of data outsourced every day by individuals or each enterprises . It is impossible to manage or to store this complex data at individual level, as the chances of crash the system is more, and the system becomes the single point of failure.When we feel the need of storing the data in such a way that it can be accessed uninterruptedly, then there the cloud comes into picture to store the data with better flexibility and cost saving. As the data might be confidential or sensitive. Considering the privacy of the data over the cloud, for that searchable encryption can be used. At the time of retrieval of data, consider the multi-keyword search over outsourced cloud text data only as it can handle the exact keywork matching. Multi-keyword similarity search overcomes the problem of not finding any related documents on searching. while encrypting the data before storing it to the cloud will help to preserve the privacy of the files. Searchable encryption also enables searching without revealing any additional information. Using multi-keyword similarity search cloud returns the files containing more number of matches with user input keywords and similar keyworks. Finding the similarities between input keyword or similar keyword is done by edit distance metric algorithm. Final design to achieve the user privacy, and to speedup the search task. At cloud side Bloom Filter’s bit pattern is used to speedup and it is efficient in terms of the search time at the cloud side. This paper presents a review on various existing Similarity searching techniques

<b>Management of root knot nematode Meloidogyne incognita on tomato with botanicals</b>

158-161A field experiment was conducted for the management of Meloidogyne incognita infecting tomato with five botanicals, viz. leaves of Calotropis gigantea (Linn.) R. Br. ex Ait. , Tagetes erecta Linn. and  Azadirachta indica A. Juss. ; seeds of Citrullus lanatus ( Thunb.) Matsumura & Nakai and Areca catechu Linn. Results showed statistically significant increase in both seed germination as well as seedling establishment in all the treatments when compared with control. Seed treatment with dry powder of C. gigantea leaves gave the highest germination (98.0%) and high percentage of established seedlings (99.6%). Root dip treatment with leaf extract of C. gigantea resulted in significant reduction of the soil nematode population at 45 days after transplanting and at harvest (87.3% and 90.0%, respectively) and lowest gall index (1.7) with increase in fruit yield, 23.9%

Multi-label Convolution Neural Network for Personalized News Recommendation based on Social Media Mining

785-797Prediction of user’s multi label interests and recommending the users interest based popular news articles through mining the social media are difficult task in Hybrid News Recommendation System (HYPNRS). To overcome this issue, this study proposes a deep learning approach - Multi-label Convolution Neural Network for predicting users' diversified interest in 15 labels using the binary relevance method. Based on labels of user’s interest, the most popular news articles are determined and their labels were clustered by mining social media feeds Facebook and Twitter along with current trends. The reliability of retrieved popular news articles also verified for recommendation. Eventually, the latest news articles catered from news feeds integrated along popular news articles and current trends together provide a recommendation list with respect to user interest. Experimental results show the proposed method diversified users interest labels prediction performance improved 5.87%, 12.09%, and 18.49% with the following state of art Support Vector Machine (SVM), Decision Tree and Naïve Bayes. The recommendation performance concerning users’ interest achieved 90%, 93.3%, 90% with social media feeds Facebook, Twitter and News Feeds accordingly