In this paper we investigate the effectiveness of ensemble-based learners for web robot session identification from web server logs. We also perform multi fold robot session labeling to improve the performance of learner. We conduct a comparative study for various ensemble methods (Bagging, Boosting, and Voting) with simple classifiers in perspective of classification. We also evaluate the effectiveness of these classifiers (both ensemble and simple) on five different data sets of varying session length. Presently the results of web server log analyzers are not very much reliable because the input log files are highly inflated by sessions of automated web traverse software's, known as web robots. Presence of web robots access traffic entries in web server log repositories imposes a great challenge to extract any actionable and usable knowledge about browsing behavior of actual visitors. So web robots sessions need accurate and fast detection from web server log repositories to extract knowledge about genuine visitors and to produce correct results of log analyzers.

Advanced nanocarriers have shown various advantages like reduced side effect, low dosing frequency, high skin permeation, and ease of application over conventional transdermal delivery systems of NSAIDs in various preclinical studies. However, clinical exploration of advanced nanocarrier systems for transdermal delivery of NSAIDs is still a challenge.

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In this paper, a software bug classification algorithm, CLUBAS (Classification of Software Bugs Using Bug Attribute Similarity) is presented. CLUBAS is a hybrid algorithm, and is designed by using text clustering, frequent term calculations and taxonomic terms mapping techniques. The algorithm CLUBAS is an example of classification using clustering technique. The proposed algorithm works in three major steps, in the first step text clusters are created using software bug textual attributes data and followed by the second step in which cluster labels are generated using label induction for each cluster, and in the third step, the cluster labels are mapped against the bug taxonomic terms to identify the appropriate categories of the bug clusters. The cluster labels are generated using frequent and meaningful terms present in the bug attributes, for the bugs belonging to the bug clusters. The designed algorithm is evaluated using the performance parameters F-measures and accuracy. These parameters are compared with the standard classification techniques like Naïve Bayes, Naïve Bayes Multinomial, J48, Support Vector Machine and Weka's classification using clustering algorithms. A GUI (Graphical User Interface) based tool is also developed in java for the implementation of CLUBAS algorithm.

With growing agony of not finding a dark matter (DM) particle in direct search experiments so far (for example in XENON1T), frameworks where the freeze-out of DM is driven by number changing processes within the dark sector itself and do not contribute to direct search, like Strongly Interacting Massive Particle (SIMP) are gaining more attention.In this analysis, we ideate a simple scalar DM framework stabilised by Z 3 symmetry to serve with a SIMP-like DM (χ) with additional light scalar mediation (φ) to enhance DM self interaction. We identify that a large parameter space for such DM is available from correct relic density and self interaction constraints coming from Bullet or Abell cluster data. We derive an approximate analytic solution for freeze-out of the SIMP like DM in Boltzmann equation describing 3 DM → 2 DM number changing process within the dark sector. We also provide a comparative analysis of the SIMP like solution with the Weakly Interacting Massive Particle (WIMP) realisation of the same model framework here.

A software bug repository not only contains the data about software bugs, but also contains the information about the contribution of developers, quality engineers (testers), managers and other team members. It contains the information about the efforts of team members involved in resolving the software bugs. This information can be analyzed to identify some useful knowledge patterns. One such pattern is identifying the developers, who can help in resolving the newly reported software bugs. In this paper a new algorithm is proposed to discover experts for resolving the newly assigned software bugs. The purpose of proposed algorithm is two fold. First is to identify the appropriate developers for newly reported bugs. And second is to find the expertise for newly reported bugs that can help other developers to fix these bugs if required. All the important information in software bug reports is of textual data types like bug summary, description etc. The algorithm is designed using the analysis of this textual information. Frequent terms are generated from this textual information and then term similarity is used to identify appropriate experts (developers) for the newly reported software bug.

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