In the ongoing era of flourishing e-commerce, people prefer online purchasing products and services to save time. These online purchase decisions are mostly influenced by the reviews/opinions of others who already have experienced them. Malicious users use this experience sharing to promote or degrade products/ services for their iniquitous monetary...

In mobile wireless sensor network (MWSN), the lifetime of the network largely depends on energy efficient routing protocol. In the literature, cluster leader (CL) is selected based on remaining energy of mobile sensor nodes to enhance sensor network lifetime. In this study, a novel connectivity-based Low-Energy Adaptive Clustering Hierarchy-Mobile Energy Efficient and Connected (LEACH-MEEC) routing protocol was proposed, where CL is selected based on connectivity among neighboring nodes and the remaining energy of mobile sensor nodes. Consequently, it improves data delivery, network lifetime and balances the energy consumption. We studied various performance metrics including the number of alive nodes (NAN), remaining energy (RE) and packet delivery ratio (PDR). Our proposed LEACH-MEEC outperforms all other algorithms due to the connectivity metric. Moreover, the performance of mobility models was investigated through graphical and statistically tabulated results. The results show that Reference Point Group Mobility model (RPGM) is better than other mobility models.

Software Fault Prediction (SFP) research has made enormous endeavor to accurately predict fault proneness of software modules to maximize precious software test resources, reduce maintenance cost, help to deliver software products on time and satisfy customer, which ultimately contribute to produce quality software products. In this regard, Machine Learning (ML) has been successfully applied to solve classification problems for SFP. Moreover, from ML, it has been observed that Ensemble Learning Algorithms (ELA) are known to improve the performance of single learning algorithms. However, neither of ELA alone handles the challenges created by redundant and irrelevant features and class imbalance problem in software defect datasets. Therefore, the objective of this paper is to independently examine and compare prominent ELA and improves their performance combined with Feature Selection (FS) and Data Balancing (DB) techniques to identify more efficient ELA that better predict the fault proneness of software modules. Accordingly, a new framework that efficiently handles those challenges in a combined form is proposed. The experimental results confirm that the proposed framework has exhibited the robustness of combined techniques. Particularly the framework has high performance when using combined bagging ELA with DB on selected features. Therefore, as shown in this study, ensemble techniques used for SFP must be carefully examined and combined with both FS and DB in order to obtain robust performance.

Nowadays, spam has become serious issue for\ud computer security, because it becomes a main source for\ud disseminating threats, including viruses, worms and\ud phishing attacks. Currently, a large volume of received\ud emails are spam. Different approaches to combating these\ud unwanted messages, including challenge response model,\ud whitelisting, blacklisting, email signatures and different\ud machine learning methods, are in place to deal with this\ud issue. These solutions are available for end users but due\ud to dynamic nature of Web, there is no 100% secure\ud systems around the world which can handle this problem.\ud In most of the cases spam detectors use machine learning\ud techniques to filter web traffic. This work focuses on\ud systematically analyzing the strength and weakness of\ud current technologies for spam detection and taxonomy of\ud known approaches is introduced

Online purchasing became an integral part of our lives in this digital era where E-commerce websites allow people to buy as well as share their experiences about products or services in the form of reviews. Customers as well as companies use these reviews for decision making. This facility helps people to derive their buying decisions whereas malicious users use this as their tool to promote or demote products or services intentionally. This phenomenon is called review spam. Review spam detection is the classification of reviews into malign or benign. Therefore, our aim is to evaluate performance of supervised machine learning algorithms for review spam detection based on different feature sets extracted from real life dataset instead of Amazon Mechanical Turkers (AMT) tailored dataset. We study various factors including Recall, Precision, and Receiver Operating Characteristic (ROC) through experimentation. AdaBoost outperforms all others with 0.83 precision and has correctly identified all spams whereas misclassified minuscule number of normal reviews.