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You can try it at: http://geneura.ugr.es/~amorag/kohonants/KAnts.zip This is the presentation of KohonAnts (KANTS), an hybrid Ant Colony and Self-organizing Map algorithm for clustering and pattern classification. Esta es la presentación de KohonAnts (KANTS), un algoritmo que combina conceptos de los algortimos de hormigas y los mapas autoorganizativos y que se puede utilizar para agrupamiento (clustering) o clasificación de patrones.
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You can try it at: http://geneura.ugr.es/~amorag/kohonants/KAnts.zip This is the presentation of KohonAnts (KANTS), an hybrid Ant Colony and Self-organizing Map algorithm for clustering and pattern classification. Esta es la presentación de KohonAnts (KANTS), un algoritmo que combina conceptos de los algortimos de hormigas y los mapas autoorganizativos y que se puede utilizar para agrupamiento (clustering) o clasificación de patrones.
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Steganography is the ability of hiding the very occurrence of communiqué by embedding secret messages into innocent looking cover up documents, such as digital images. Recognition of steganography, evaluation of message length, and its extraction belong to the field of steganalysis, which is actually a route for perceiving Stegnography. Bacterial foraging optimization (BFO) is a optimization technique projected by K.M. Passino in 2002, and is one of the latest techniques under Swarm Intelligence. To covenant with multifarious exploration problems of the real world, scientists have been drawing inspiration from environment and natural creatures for years. Bacterial foraging optimization is a burgeoning nature inspired procedure to find the finest elucidation of the problem. In this paper an algorithm for perceiving Steganography has been introduced using the BFO Technique. The test to the RGB model based imagery through the proposed algorithm will help out to detect if something is steganographed in the image or not.
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Swarm intelligence is the discipline that deals with natural and artificial systems composed of many individuals that coordinate using decentralized control and self-organization. In particular, the discipline focuses on the collective behaviors that result from the local interactions of the individuals with each other and with their environment. Examples of systems studied by swarm intelligence are colonies of ants and termites, schools of fish, flocks of birds, herds of land animals. Some human artifacts also fall into the domain of swarm intelligence, notably some multi-robot systems, and also certain computer programs that are written to tackle optimization and data analysis problems. Taxonomy of Swarm Intelligence Swarm intelligence has a marked multidisciplinary character since systems with the above mentioned characteristics can be observed in a variety of domains. Research in swarm intelligence can be classified according to different criteria. Natural vs. Artificial: It is customary to divide swarm intelligence research into two areas according to the nature of the systems under analysis. We speak therefore of natural swarm intelligence research, where biological systems are studied; and of artificial swarm intelligence, where human artifacts are studied. Scientific vs. Engineering: An alternative and somehow more informative classification of swarm intelligence research can be given based on the goals that are pursued: we can identify a scientific and an engineering stream. The goal of the scientific stream is to model swarm intelligence systems and to single out and understand the mechanisms that allow a system as a whole to behave in a coordinated way as a result of local individual-individual and individual-environment interactions. On the other hand, the goal of the engineering stream is to exploit the understanding developed by the scientific stream in order to design systems that are able to solve problems of practical relevance. The two dichotomies natural/artificial and scientific/engineering are orthogonal: although the typical scientific investigation concerns natural systems and the typical engineering application concerns the development of an artificial system, a number of swarm intelligence.Natural/Scientific: Foraging Behavior of Ants In a now classic experiment done in 1990, Deneubourg and his group showed that, when given the choice between two paths of different length joining the nest to a food source, a colony of ants has a high probability to collectively choose the shorter one. Deneubourg has shown that this behavior can be explained via a simple probabilistic model in which each ant decides where to go by taking random decisions based on the intensity of pheromone perceived on the ground, the pheromone being deposited by the ants while moving from the nest to the food source and back. Artificial/Scientific: Clustering by a Swarm of Robots Several ant species cluster corpses to form cemeteries.
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Steganography is the ability of hiding the very occurrence of communiqué by embedding secret messages into innocent looking cover up documents, such as digital images. Recognition of steganography, evaluation of message length, and its extraction belong to the field of steganalysis, which is actually a route for perceiving Stegnography. Bacterial foraging optimization (BFO) is a optimization technique projected by K.M. Passino in 2002, and is one of the latest techniques under Swarm Intelligence. To covenant with multifarious exploration problems of the real world, scientists have been drawing inspiration from environment and natural creatures for years. Bacterial foraging optimization is a burgeoning nature inspired procedure to find the finest elucidation of the problem. In this paper an algorithm for perceiving Steganography has been introduced using the BFO Technique. The test to the RGB model based imagery through the proposed algorithm will help out to detect if something is steganographed in the image or not.
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Swarm intelligence is the discipline that deals with natural and artificial systems composed of many individuals that coordinate using decentralized control and self-organization. In particular, the discipline focuses on the collective behaviors that result from the local interactions of the individuals with each other and with their environment. Examples of systems studied by swarm intelligence are colonies of ants and termites, schools of fish, flocks of birds, herds of land animals. Some human artifacts also fall into the domain of swarm intelligence, notably some multi-robot systems, and also certain computer programs that are written to tackle optimization and data analysis problems. Taxonomy of Swarm Intelligence Swarm intelligence has a marked multidisciplinary character since systems with the above mentioned characteristics can be observed in a variety of domains. Research in swarm intelligence can be classified according to different criteria. Natural vs. Artificial: It is customary to divide swarm intelligence research into two areas according to the nature of the systems under analysis. We speak therefore of natural swarm intelligence research, where biological systems are studied; and of artificial swarm intelligence, where human artifacts are studied. Scientific vs. Engineering: An alternative and somehow more informative classification of swarm intelligence research can be given based on the goals that are pursued: we can identify a scientific and an engineering stream. The goal of the scientific stream is to model swarm intelligence systems and to single out and understand the mechanisms that allow a system as a whole to behave in a coordinated way as a result of local individual-individual and individual-environment interactions. On the other hand, the goal of the engineering stream is to exploit the understanding developed by the scientific stream in order to design systems that are able to solve problems of practical relevance. The two dichotomies natural/artificial and scientific/engineering are orthogonal: although the typical scientific investigation concerns natural systems and the typical engineering application concerns the development of an artificial system, a number of swarm intelligence.Natural/Scientific: Foraging Behavior of Ants In a now classic experiment done in 1990, Deneubourg and his group showed that, when given the choice between two paths of different length joining the nest to a food source, a colony of ants has a high probability to collectively choose the shorter one. Deneubourg has shown that this behavior can be explained via a simple probabilistic model in which each ant decides where to go by taking random decisions based on the intensity of pheromone perceived on the ground, the pheromone being deposited by the ants while moving from the nest to the food source and back. Artificial/Scientific: Clustering by a Swarm of Robots Several ant species cluster corpses to form cemeteries.
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