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With the increasing popularity of online social networking platforms, the amount of social data has grown exponentially. Social data analysis is essential as spamming activities and spammers are escalating over online social networking platforms. This paper focuses on spammer detection on the Twitter social networking platform. Although existing researchers have developed numerous machine learning methods to detect spammers, these methods are inefficient for appropriately detecting spammers on Twitter due to the imbalance of spam and nonspam data distribution, the involvement of diverse features and the applicability of data mechanisms by spammers to avoid their detection. This research work proposes a novel hybrid approach of the gravitational search algorithm and the decision tree (HGSDT) for detecting Twitter spammers. The individual decision tree (DT) algorithm is not able to address the challenges as it is unstable and ineffective for the higher level of favorable data for a particular attribute. The gravitational search algorithm (GSA) constructs the DTs with improved performance as the gravitational forces act as the information-transferring agents through mass agents. Moreover, the GSA is efficient in handling the data of higher dimensional search space. In the HGSDT approach, the construction of the DT and splitting of nodes are performed with the heuristic function and Newton’s laws. The performance of the proposed HGSDT approach is determined for the Social Honeypot dataset and 1KS-10KN dataset by conducting three different experiments to analyze the impact of training data size, features and spammer ratio. The result of the first experiment shows the need of a higher proportion of training data size, the second experiment signifies the more importance of textual content-based features compared to the other feature categories and the third experiment indicates the requirement of balanced data to attain the effective performance of the proposed approach. The overall performance comparison indicates that the proposed HGSDT approach is superior to the incorporated machine learning methods of DT, support vector machine and back propagation neural network for detecting Twitter spammers.

The increasing use of digitized images has led to the need to compress such imagery to allow economical storage and fast data transfer. Despite all the advantages of JPEG compression schemes based on DCT namely simplicity, satisfactory performance, and availability of special purpose hardware for implementation; these are not without their shortcomings. The input image needs to be blocked which results in noticeable and annoying blocking artifacts particularly at low bit rates. So over the past several years, the wavelet transform has gained widespread acceptance in signal processing in general and in image compression research in particular. SPIHT codes the individual bits of the image wavelet transform coefficients following a bit-plane sequence. The evaluation of an image compression system is a difficult problem. While mean square error and peak signal to noise ratios are easily and commonly calculated, they are widely recognized to not to be completely satisfactory. The statistical parameters include high order image statistics like skewness and kurtosis which describe the shape and symmetry of the image. The aim of this paper is to provide a uniform gauge of the performance of data compression processes. In addition, it specifies how the performance of different data compression methods should be ranked so that the best compressor for a specific application can be identified. In the present work, three compression algorithms viz. the JPEG coding, the wavelet transform coding and the SPIHT coding have been discussed and compared.

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IJAIP fosters the exchange and dissemination of applications and case studies in the area of advanced intelligence paradigms among education and research professionals. The thrust of the journal is to publish papers dealing with the design, development, testing, implementation and management of advanced intelligent systems, and to provide guidelines in the development/management of these systems. IJAIP publishes archival articles and assessments of current trends, providing a medium for exchanging scientific research and technological achievements accomplished by the international community.