Aboul Ella Hassanien (Abo)

Multidimensionality is a distinctive aspect of real world social networks. Multidimensional social networks appeared as a result of that most social media sites such as Facebook, Twitter, and YouTube enable people to interact with each other through different social activities, reflecting different kinds of relationships between them. Recently, studying community structures hidden in multidimensional social networks has attracted a lot of attention. When dealing with these networks, the concept of community detection problem changes to be the discovery of the shared group structure across all network dimensions, such that members in the same group are tightly connected with each other, but are loosely connected with others outside the group. Studies in community detection topic have traditionally focused on networks that represent one type of interactions or one type of relationships between network entities. In this paper, we propose Discrete Group Search Optimizer (DGSO-MDNet) to solve the community detection problem in Multidimensional social networks, without any prior knowledge about the number of communities. The method aims to find community structure that maximizes multi-slice modularity, as an objective function. The proposed DGSO-MDNet algorithm adopts the locus-based adjacency representation and several discrete operators. Experiments on synthetic and real life networks show the capability of the proposed algorithm to successfully detect the structure hidden within these networks compared with other high performance algorithms in the literature.

Massive and complex data are generated every day in many fields. Complex data refer to data sets that are so large that conventional database management and data analysis tools are insufficient to deal with them. Managing and analysis of medical big data involve many different issues regarding their structure, storage and analysis. In this paper, linguistic hedges neuro-fuzzy classifier with selected features (LHNFCSF) is presented for dimensionality reduction, feature selection and classification. Four real-world data sets are provided to demonstrate the performance of the proposed neuro-fuzzy classifier. The new classifier is compared with the other classifiers for different classification problems. The results indicated that applying LHNFCSF not only reduces the dimensions of the problem, but also improves classification performance by discarding redundant, noise-corrupted, or unimportant features. The results strongly suggest that the proposed method not only help reducing the dimensionality of large data sets but also can speed up the computation time of a learning algorithm and simplify the classification tasks.

Segmentation is one of the most important tasks in image processing. It classifies the pixels into two or more groups depending on their intensity levels and a threshold value. The classical methods exhaustively search the best thresholds for a spec image. This process requires a high computational effort, to avoid this situation has been incremented the use of evolutionary algorithms. The Electro-magnetism-Like algorithm (EMO) is an evolutionary method which mimics the attraction-repulsion mechanism among charges to evolve the members of a population. Different to other algorithms, EMO exhibits interesting search capabilities whereas maintains a low computational overhead. This chapter introduces a multilevel thresholding (MT) algorithm based on the EMO and the Otsu's method as objective function. The combination of those techniques generates a multilevel segmentation algorithm which can effectively identify the threshold values of a digital image reducing the number of iterations.

In recent years, the development of distributed systems, in particular the Internet, has been influenced heavily by three paradigms: peer-to-peer, autonomous agents, and service orientation. Developing Advanced Web Services through P2P Computing and Autonomous Agents: Trends and Innovations establishes an understanding of autonomous peer-to-peer Web Service models and developments as well as extends growing literature on emerging technologies. This scholarly publication is an important reference for researchers and academics working in the fields of peer-to-peer computing, Web and grid services, and agent technologies.