Shahram Rahimi

A parallel Fuzzy C-Mean algorithm for image segmentation

This paper proposes a parallel Fuzzy C-Mean (FCM) algorithm for image segmentation. The sequential FCM algorithm is computationally intensive and has significant memory requirements. For many applications such as medical image segmentation and geographical image analysis that deal with large size images, sequential FCM is very slow. In our parallel FCM algorithm, dividing the computations among the processors and minimizing the need for accessing secondary storage, enhance the performance and efficiency of image segmentation task as compared to the sequential algorithm.

Soft computing in intrusion detection: the state of the art

The state of the art is explored in using soft computing (SC) methods for network intrusion detection, including the examination of efforts in ten specific areas of SC as well as consecutive, ensemble, and hybrid combinations. Numerous comparisons of these methods are listed followed by a recommendation for future research. This paper can be used as a reference of strategies, and as a resource for planning future research.

An inference engine toolkit for computing with words

Computing with Words is an emerging paradigm in knowledge representation and information processing. It provides a mathematical model to represent the meaning of imprecise words and phrases in natural language and introduces advanced techniques to perform reasoning on inexact knowledge. Since its introduction, there have been many studies on computing with words but mostly from the theoretical point of view and the paradigm still lacks sufficient support from the software side. This paper is an attempt to fill this gap by presenting an enhanced inference engine toolkit for supporting computing with words. The scope of the presented toolkit, as opposed to many available fuzzy logic tools, goes beyond simple fuzzy-if-then rules and performs a chain of inferences on complex fuzzy propositions containing fuzzy arithmetics, fuzzy quantifiers, and fuzzy probabilities. The toolkit may be appealing to researchers, practitioners, and educators in knowledge based applications and soft computing as it implements a powerful declarative language which allows users to express their knowledge in a more natural and convenient way and performs a chain of reasoning on imprecise propositions.

A self-organizing map and its modeling for discovering malignant network traffic

Model-based intrusion detection and knowledge discovery are combined to cluster and classify P2P botnet traffic and other malignant network activity by using a Self-Organizing Map (SOM) self-trained on denied Internet firewall log entries. The SOM analyzed new firewall log entries in a case study to classify similar network activity, and discovered previously unknown local P2P bot traffic and other security issues.

CWJess: Implementation of an expert system shell for Computing with Words

Computing with Words (CW) is an emerging paradigm in knowledge representation and information processing. It provides a mathematical model to represent the meaning of imprecise words and phrases in natural language, and to perform reasoning on perceptual knowledge. This paper describes a preliminary extension to Jess, CWJess, which allows reasoning in the framework of Computing with Words (CW). The resulting inference shell significantly enhances the expressiveness and reasoning power of fuzzy expert systems and provides a Java API which allows users to express various types of fuzzy concepts, including: fuzzy graphs, fuzzy relations, fuzzy arithmetic expression, and fuzzy quantified propositions. CWJess is fully integrated with jess and utilizes jess Rete network to perform a chain of reasoning on fuzzy propositions.

A low-overhead non-block checkpointing algorithm for mobile computing environment

In this paper, we have proposed a new approach toward designing a low-overhead non-blocking single phase synchronous checkpointing algorithm suitable for distributed mobile computing environment. The algorithm produces a reduced number of checkpoints. To achieve this reduction in the number of the checkpoints we have used very simple data structure. Each process independently takes its decision whether to take a checkpoint or not. It makes the algorithm simple, fast, and efficient. The algorithm has been shown to be suitable for distributed mobile computing environment.

Performance evaluation of SDIAGENT, a multi-agent system for distributed fuzzy geospatial data conflation

A rapid growth of available geospatial data requires development of systems capable of autonomous data retrieval, integration and validation. Mobile agents may provide the suitable framework for developing such systems since this technology, in a natural way, can deal with the distributed heterogeneous nature of such data. In this paper, we evaluate SDIAGENT our, recently introduced, multi-agent architecture for geospatial data integration and conflation, and compare its model performance with that of client/server and single-agent approaches. Experimental results for several realistic scenarios, under varying conditions, are presented for these three system architectures. We analyze the performance alteration for various numbers of participating nodes, varying amount of database accesses, processing loads, and network loads.

A Multi-Agent Architecture for Distributed Domain-Specific Information Integration

On both the public Internet and private Intranets, there is a vast amount of data available that is owned and maintained by different organizations, distributed all around the world. These data resources are rich and recent; however, information gathering and knowledge discovery from them, in a particular knowledge domain, confronts major difficulties. The objective of this article is to introduce an autonomous methodology to provide for domain-specific information gathering and integration from multiple distributed sources.

A generic mobile agent framework for ambient intelligence

The purpose of this paper is to introduce an innovative framework for implementation of ambient intelligence (AmI) environments. Compared to the existing state-of-the-art approaches, this framework creates a more decentralized and distributed AmI environment. In addition, the proposed approach is not limited to one specific domain, unlike many others. The openness of the presented architecture allows it to support a variety of devices ranged from small-embedded sensors to complex computing facilities. Finally, given that this approach is formulated based on multi-agent standard concepts, it can be easily implemented as add-on for existing software agent platforms to achieve rapid deployment. Implications for the development of this framework and future directions are discussed.

A Multi-Agent Based Approach for Particle Swarm Optimization

We propose a new approach towards particle swarm optimization named agent-based PSO. The swarm is elevated to the status of a multi-agent system by giving the particles more autonomy, an asynchronous execution, and superior learning capabilities. The problem space is modeled as an environment which forms clusters of points that are known to be non-optimal and this transforms the environment into a more dynamic and informative resource