Fuzzy granular classifier approach for spam detection

Advances in spam detection for email spam, web spam, social network spam, and review spam: ML-based and nature-inspired-based techniques

Despite the great advances in spam detection, spam remains a major problem that has affected the global economy enormously. Spam attacks are popularly perpetrated through different digital platforms with a large electronic audience, such as emails, microblogging websites (e.g. Twitter), social networks (e.g. Facebook), and review sites (e.g. Amazon). Different spam detection solutions have been proposed in the literature, however, Machine Learning (ML) based solutions are one of the most effective. Nevertheless, most ML algorithms have computational complexity problem, thus some studies introduced Nature Inspired (NI) algorithms to further improve the speed and generalization performance of ML algorithms. This study presents a survey of recent ML-based and NI-based spam detection techniques to empower the research community with information that is suitable for designing effective spam filtering systems for emails, social networks, microblogging, and review websites. The recent success and prevalence of deep learning show that it can be used to solve spam detection problems. Moreover, the availability of large-scale spam datasets makes deep learning and big data solutions (such as Mahout) very suitable for spam detection. Few studies explored deep learning algorithms and big data solutions for spam detection. Besides, most of the datasets used in the literature are either small or synthetically created. Therefore, future studies can consider exploring big data solutions, big datasets, and deep learning algorithms for building efficient spam detection techniques.

Sustainable Communication Systems: A Graph-Labeling Approach for Cellular Frequency Allocation in Densely-Populated Areas

The need for smart and sustainable communication systems has led to the development of mobile communication networks. In turn, the vast functionalities of the global system of mobile communication (GSM) have resulted in a growing number of subscribers. As the number of users increases, the need for efficient and effective planning of the “limited” frequency spectrum of the GSM is inevitable, particularly in densely-populated areas. As such, there are ongoing discussions about frequency (channel) allocation methods to resolve the challenges of channel allocation, which is a complete NP (Nondeterministic Polynomial time) problem. In this paper, we propose an algorithm for channel allocation which takes into account soft constraints (co-channel interference and adjacent channel interference). By using the Manhattan distance concept, this study shows that the formulation of the algorithm is correct and in line with results in the literature. Hence, the Manhattan distance concept may be useful in other scheduling and optimization problems. Furthermore, this unique concept makes it possible to develop a more sustainable telecommunication system with ease of connectivity among users, even when several subscribers are on a common frequency.