FARS: Fuzzy Ant based Recommender System for Web Users

Recommender systems are useful tools which provide an adaptive web environment for web users. Nowadays, having a user friendly website is a big challenge in e-commerce technology. In this paper, applying the benefits of both collaborative and content based filtering techniques is proposed by presenting a fuzzy recommender system based on collaborative behavior of ants (FARS). FARS works in two phases: modeling and recommendation. First, user’s behaviors are modeled offline and the results are used in second phase for online recommendation. Fuzzy techniques provide the possibility of capturing uncertainty among user interests and ant based algorithms provides us with optimal solutions. The performance of FARS is evaluated using log files of “Information and Communication Technology Center” of Isfahan municipality in Iran and compared with ant based recommender system (ARS). The results shown are promising and proved that integrating fuzzy Ant approach provides us with more functional and robust recommendations

Mathematical models for chemotaxis and their applications in self-organisation phenomena

Chemotaxis is a fundamental guidance mechanism of cells and organisms, responsible for attracting microbes to food, embryonic cells into developing tissues, immune cells to infection sites, animals towards potential mates, and mathematicians into biology. The Patlak-Keller-Segel (PKS) system forms part of the bedrock of mathematical biology, a go-to-choice for modellers and analysts alike. For the former it is simple yet recapitulates numerous phenomena; the latter are attracted to these rich dynamics. Here I review the adoption of PKS systems when explaining self-organisation processes. I consider their foundation, returning to the initial efforts of Patlak and Keller and Segel, and briefly describe their patterning properties. Applications of PKS systems are considered in their diverse areas, including microbiology, development, immunology, cancer, ecology and crime. In each case a historical perspective is provided on the evidence for chemotactic behaviour, followed by a review of modelling efforts; a compendium of the models is included as an Appendix. Finally, a half-serious/half-tongue-in-cheek model is developed to explain how cliques form in academia. Assumptions in which scholars alter their research line according to available problems leads to clustering of academics and the formation of "hot" research topics.Comment: 35 pages, 8 figures, Submitted to Journal of Theoretical Biolog

SACOC: A spectral-based ACO clustering algorithm

The application of ACO-based algorithms in data mining is growing over the last few years and several supervised and unsupervised learning algorithms have been developed using this bio-inspired approach. Most recent works concerning unsupervised learning have been focused on clustering, where ACO-based techniques have showed a great potential. At the same time, new clustering techniques that seek the continuity of data, specially focused on spectral-based approaches in opposition to classical centroid-based approaches, have attracted an increasing research interest–an area still under study by ACO clustering techniques. This work presents a hybrid spectral-based ACO clustering algorithm inspired by the ACO Clustering (ACOC) algorithm. The proposed approach combines ACOC with the spectral Laplacian to generate a new search space for the algorithm in order to obtain more promising solutions. The new algorithm, called SACOC, has been compared against well-known algorithms (K-means and Spectral Clustering) and with ACOC. The experiments measure the accuracy of the algorithm for both synthetic datasets and real-world datasets extracted from the UCI Machine Learning Repository

Energy Efficient Ant Colony Algorithms for Data Aggregation in Wireless Sensor Networks

In this paper, a family of ant colony algorithms called DAACA for data aggregation has been presented which contains three phases: the initialization, packet transmission and operations on pheromones. After initialization, each node estimates the remaining energy and the amount of pheromones to compute the probabilities used for dynamically selecting the next hop. After certain rounds of transmissions, the pheromones adjustment is performed periodically, which combines the advantages of both global and local pheromones adjustment for evaporating or depositing pheromones. Four different pheromones adjustment strategies are designed to achieve the global optimal network lifetime, namely Basic-DAACA, ES-DAACA, MM-DAACA and ACS-DAACA. Compared with some other data aggregation algorithms, DAACA shows higher superiority on average degree of nodes, energy efficiency, prolonging the network lifetime, computation complexity and success ratio of one hop transmission. At last we analyze the characteristic of DAACA in the aspects of robustness, fault tolerance and scalability.Comment: To appear in Journal of Computer and System Science

Stigmergy-based modeling to discover urban activity patterns from positioning data

Positioning data offer a remarkable source of information to analyze crowds urban dynamics. However, discovering urban activity patterns from the emergent behavior of crowds involves complex system modeling. An alternative approach is to adopt computational techniques belonging to the emergent paradigm, which enables self-organization of data and allows adaptive analysis. Specifically, our approach is based on stigmergy. By using stigmergy each sample position is associated with a digital pheromone deposit, which progressively evaporates and aggregates with other deposits according to their spatiotemporal proximity. Based on this principle, we exploit positioning data to identify high density areas (hotspots) and characterize their activity over time. This characterization allows the comparison of dynamics occurring in different days, providing a similarity measure exploitable by clustering techniques. Thus, we cluster days according to their activity behavior, discovering unexpected urban activity patterns. As a case study, we analyze taxi traces in New York City during 2015

Big data clustering using grid computing and ant-based algorithm

Big data has the power to dramatically change the way institutes and organizations use their data. Transforming the massive amounts of data into knowledge will leverage the organizations performance to the maximum.Scientific and business organizations would benefit from utilizing big data. However, there are many challenges in dealing with big data such as storage, transfer, management and manipulation of big data.Many techniques are required to explore the hidden pattern inside the big data which have limitations in terms of hardware and software implementation. This paper presents a framework for big data clustering which utilizes grid technology and ant-based algorithm

Modeling Spatial Organization with Swarm Intelligence Processes

International audienceUrban Dynamics modeling needs to implement spatial organization emergence in order to describe the development of services evolution and their usage within spatial centers. In this paper, we propose an extension of the nest building algorithm with multi-center, multi-criteria and adaptive processes. We combine a decentralized approach based on emergent clustering mixed with spatial constraints or attractions. Typically, this model is suitable to analyse and simulate urban dynamics like the evolution of cultural equipment in urban area