Augmented Session Similarity Based Framework for Measuring Web User Concern from Web Server Logs

In this paper, an augmented sessions similarity based framework is proposed to measure web user concern from web server logs. This proposed framework will consider the best usage similarity between two web sessions based on accessed page relevance and URL based syntactic structure of website within the session. The proposed framework is implemented using K-medoids clustering algorithms with independent and combined similarity measures. The clusters qualities are evaluated by measuring average intra-cluster and inter-cluster distances. The experimental results show that combined augmented session dissimilarity metric outperformed the independent augmented session dissimilarity measures in terms of cluster validity measures

A Performance Review of Intra and Inter-Group MANET Routing Protocols under Varying Speed of Nodes

Mobile Ad-hoc Networks (MANETs) are a cluster of self-organizing and self-governing wireless nodes without any backbone infrastructure and centralized administration. The various nodes in MANET move randomly, and this node mobility may pose challenges on the performance of routing protocols.  In this paper, an Intra and intergroup performance review of various MANET routing protocols are performed under varying speed of nodes. The routing protocols included in this study are reactive, proactive, and hybrid protocols. This performance review is done using the NS2 simulator and random waypoint model. The routing protocols performance is assessed through standard performance measure metrics including packet delivery ratio, throughput, routing overhead and end to end delivery with varying speed of nodes. The simulations result shows that there is no significant impact of varying speed of nodes on standard performance evaluation metrics

Distinct Multiple Learner-Based Ensemble SMOTEBagging (ML-ESB) Method for Classification of Binary Class Imbalance Problems

Traditional classification algorithms often fail in learning from highly imbalanced datasets because the training involves most of the samples from majority class compared to the other existing minority class. In this paper, a Multiple Learners-based Ensemble SMOTEBagging (ML-ESB) technique is proposed. The ML-ESB algorithm is a modified SMOTEBagging technique in which the ensemble of multiple instances of the single learner is replaced by multiple distinct classifiers. The proposed ML-ESB is designed for handling only the binary class imbalance problem. In ML-ESB the ensembles of multiple distinct classifiers include Naïve Bays, Support Vector Machine, Logistic Regression and Decision Tree (C4.5) is used. The performance of ML-ESB is evaluated based on six binary imbalanced benchmark datasets using evaluation measures such as specificity, sensitivity, and area under receiver operating curve. The obtained results are compared with those of SMOTEBagging, SMOTEBoost, and cost-sensitive MCS algorithms with different imbalance ratios (IR). The ML-ESB algorithm outperformed other existing methods on four datasets with high dimensions and class IR, whereas it showed moderate performance on the remaining two low dimensions and small IR value datasets