Swarm intelligence (SI) is briefly defined as the collective behaviour of decentralized and self-organized swarms. The well known examples for these swarms are bird flocks, fish schools and the colony of social insects such as termites, ants and bees. In 1990s, especially two approaches based on ant colony and on fish schooling/bird flocking introduced have highly attracted the interest of researchers. Although the self-organization features are required by SI are strongly and clearly seen in honey bee colonies, unfortunately the researchers have recently started to be interested in the behaviour of these swarm systems to describe new intelligent approaches, especially from the beginning of 2000s. During a decade, several algorithms have been developed depending on different intelligent behaviours of honey bee swarms. Among those, artificial bee colony (ABC) is the one which has been most widely studied on and applied to solve the real world problems, so far. Day by day the number of researchers being interested in ABC algorithm increases rapidly. This work presents a comprehensive survey of the advances with ABC and its applications. It is hoped that this survey would be very beneficial for the researchers studying on SI, particularly ABC algorithm.

A comprehensive survey: artificial bee colony (ABC) algorithm and applications

The Internet Protocol.

Two metaheuristic algorithms (WOA and MFO) are used.These algorithms are applied to multilevel thresholding image segmentation.MFO and WOA are better than compared algorithms.MFO is better than WOA for higher number of thresholds. Determining the optimal thresholding for image segmentation has got more attention in recent years since it has many applications. There are several methods used to find the optimal thresholding values such as Otsu and Kapur based methods. These methods are suitable for bi-level thresholding case and they can be easily extended to the multilevel case, however, the process of determining the optimal thresholds in the case of multilevel thresholding is time-consuming. To avoid this problem, this paper examines the ability of two nature inspired algorithms namely: Whale Optimization Algorithm (WOA) and Moth-Flame Optimization (MFO) to determine the optimal multilevel thresholding for image segmentation. The MFO algorithm is inspired from the natural behavior of moths which have a special navigation style at night since they fly using the moonlight, whereas, the WOA algorithm emulates the natural cooperative behaviors of whales. The candidate solutions in the adapted algorithms were created using the image histogram, and then they were updated based on the characteristics of each algorithm. The solutions are assessed using the Otsus fitness function during the optimization operation. The performance of the proposed algorithms has been evaluated using several of benchmark images and has been compared with five different swarm algorithms. The results have been analyzed based on the best fitness values, PSNR, and SSIM measures, as well as time complexity and the ANOVA test. The experimental results showed that the proposed methods outperformed the other swarm algorithms; in addition, the MFO showed better results than WOA, as well as provided a good balance between exploration and exploitation in all images at small and high threshold numbers.

Whale Optimization Algorithm and Moth-Flame Optimization for multilevel thresholding image segmentation

The book is meant to be useful for practitioners in applied data analysis in life sciences, engineering, physics and chemistry; it will also be valuable to PhD ...

Principal Manifolds for Data Visualization and Dimension Reduction

Certificateless cryptography involves a Key Generation Center (KGC) which issues a partial key to a user and the user also independently generates an additional public/secret key pair in such a way that the KGC who knows only the partial key but not the additional secret key is not able to do any cryptographic operation on behalf of the user; and a third party who replaces the public/secret key pair but does not know the partial key cannot do any cryptographic operation as the user either. We call this attack launched by the third party as the key replacement attack. In ACISP 2004, Yum and Lee proposed a generic construction of digital signature schemes under the framework of certificateless cryptography. In this paper, we show that their generic construction is insecure against key replacement attack. In particular, we show that the security requirements of their generic building blocks are insufficient to support some security claim stated in their paper. We then propose a modification of their scheme and show its security in a new and simplified security model. We show that our simplified definition and adversarial model not only capture all the distinct features of certificateless signature but are also more versatile when compared with all the comparable ones. We believe that the model itself is of independent interest.

Key replacement attack against a generic construction of certificateless signature

An article introduces a modified architecture of the neo-fuzzy neuron, also known as a \"multidimensional extended neo-fuzzy neuron\" (MENFN), for the face recognition problems. This architecture is marked by enhanced approximating capabilities. A characteristic property of the MENFN is also its computational plainness in comparison with neuro-fuzzy systems and neural networks. These qualities of the proposed system make it effectual for solving the image recognition problems. An introduced MENFN’s adaptive learning algorithm allows solving classification problems in a real-time fashion.

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A Multidimensional Extended Neo-Fuzzy Neuron for Facial Expression Recognition

An adaptive neural system which solves a problem of clustering data with missing values in an online mode with a permanent correction of restorable table elements and clusters’ centroids is proposed in this article. The introduced neural system is characterized by both a high speed and a simple numerical implementation. It can process information in a real-time mode.

/pdf/fuzzy-clustering-data-arrays-with-omitted-observations-1t0y4ez5zl.pdf

Fuzzy Clustering Data Arrays with Omitted Observations

Image enhancement is an important procedure of image processing and analysis This paper presents a new technique using a modified measure and blending of cuckoo search and particle swarm optimization (CS-PSO) for low contrast images to enhance image adaptively In this way, contrast enhancement is obtained by global transformation of the input intensities; it employs incomplete Beta function as the transformation function and a novel criterion for measuring image quality considering three factors which are threshold, entropy value, and gray-level probability density of the image The enhancement process is a nonlinear optimization problem with several constraints CS-PSO is utilized to maximize the objective fitness criterion in order to enhance the contrast and detail in an image by adapting the parameters of a novel extension to a local enhancement technique The performance of the proposed method has been compared with other existing techniques such as linear contrast stretching, histogram equalization, and evolutionary computing based image enhancement methods like backtracking search algorithm, differential search algorithm, genetic algorithm, and particle swarm optimization in terms of processing time and image quality Experimental results demonstrate that the proposed method is robust and adaptive and exhibits the better performance than other methods involved in the paper

/pdf/an-adaptive-image-enhancement-technique-by-combining-cuckoo-3i1mjnk6rl.pdf

An adaptive image enhancement technique by combining cuckoo search and particle swarm optimization algorithm

A neuro-fuzzy Kohonen network for data stream possibilistic clustering and its online self-learning procedure

A fuzzy clustering algorithm for multidimensional data is proposed in this article. The data is described by vectors whose components are linguistic variables defined in an ordinal scale. The obtained results confirm the efficiency of the proposed approach.

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Zhengbing Hu

Papers

A Multidimensional Extended Neo-Fuzzy Neuron for Facial Expression Recognition

Fuzzy Clustering Data Arrays with Omitted Observations

An adaptive image enhancement technique by combining cuckoo search and particle swarm optimization algorithm

A neuro-fuzzy Kohonen network for data stream possibilistic clustering and its online self-learning procedure

Fuzzy Clustering Data Given in the Ordinal Scale