0000000000255427
AUTHOR
Dana Simian
A Discrete Model for a Network Having Broken Packages
Broken packages are packages with not enough information regarding the destination address and they have a negative impact on a computer network. Broken packages stay in the router too much time and overload it. This is a real problem which appears in the computer network of Sofia University. We know only the time when a broken package arrives in the router. The problem is to find the computer which send broken packages. The aim of this article is to propose a model for defining the data transmission stability and correctness in a computer network. Our theoretical model is temporal dependent and captures the transmission properties of a computer network.
A General Frame for Building Optimal Multiple SVM Kernels
The aim of this paper is to define a general frame for building optimal multiple SVM kernels. Our scheme follows 5 steps: formal representation of the multiple kernels, structural representation, choice of genetic algorithm, SVM algorithm, and model evaluation. The computation of the optimal parameter values of SVM kernels is performed using an evolutionary method based on the SVM algorithm for evaluation of the quality of chromosomes. After the multiple kernel is found by the genetic algorithm we apply cross validation method for estimating the performance of our predictive model. We implemented and compared many hybrid methods derived from this scheme. Improved co-mutation operators are u…
Wireless sensor network coverage problem using modified fireworks algorithm
Wireless sensor networks are emerging technology with increasing number of applications, and consequently an active research area. One of the problems pertinent to wireless sensor networks is the coverage problem with number of definitions, depending on the assumed conditions. In this paper we consider hard optimization area coverage problem with the goal of finding optimal sensor nodes positions that maximize probabilistic coverage of the area of interest. For such type of optimization problem swarm intelligence stochastic metaheuristics have been successfully used. In this paper we propose a modified enhanced fireworks algorithm for wireless sensor network coverage problem and compare it …
Applications of the Connection between Approximation Theory and Algebra
The aim of this paper is to illustrate a possibility of obtaining various theoretical results using the connection between multivariate interpolation and reduction process with respect to a H-basis of an ideal. Using this connection we can switch between interpolation theory and the theory of ideals. As a application of this connection, we found and proved an interesting identity, which is satisfied for all polynomials in d variables from an interpolation polynomial subspace.
On Randomness and Structure in Euclidean TSP Instances: A Study With Heuristic Methods
Prediction of the quality of the result provided by a specific solving method is an important factor when choosing how to solve a given problem. The more accurate the prediction, the more appropriate the decision on what to choose when several solving applications are available. In this article, we study the impact of the structure of a Traveling Salesman Problem instance on the quality of the solution when using two representative heuristics: the population-based Ant Colony Optimization (ACO) and the local search Lin-Kernighan (LK) algorithm. The quality of the result for a solving method is measured by the computation accuracy, which is expressed using the percent error between its soluti…
Extreme Learning Machines for Data Classification Tuning by Improved Bat Algorithm
Single hidden layer feed forward neural networks are widely used for various practical problems. However, the training process for determining synaptic weights of such neural networks can be computationally very expensive. In this paper we propose a new learning algorithm for learning the synaptic weights of the single hidden layer feedforward neural networks in order to reduce the learning time. We propose combining the upgraded bat algorithm with the extreme learning machine. The proposed approach reduces the number of evaluations needed to train a neural network and efficiently finds optimal input weights and the hidden biases. The proposed algorithm was tested on standard benchmark clas…
Computational Aspects in Spaces of Bivariate Polynomial of w-Degree n
Multivariate ideal interpolation schemes are deeply connected with H-bases. Both the definition of a H-basis and of an ideal interpolation space depend of the notion of degree used in the grading decomposition of the polynomial spaces. We studied, in the case of bivariate polynomials, a generalized degree, introduced by T. Sauer and named w-degree. This article give some theoretical results that allow us to construct algorithms for calculus of the dimension of the homogeneous spaces of bivariate polynomials of w – degree n. We implemented these algorithms in C++ language. The analysis of the results obtained, leads us to another theoretical conjecture which we proved in the end.
Quadrature Formula Based on Interpolating Polynomials: Algorithmic and Computational Aspects
The aim of this article is to obtain a quadrature formula for functions in several variables and to analyze the algorithmic and computational aspects of this formula. The known information about the integrand is {λi(f)}i=1n, where λi are linearly independent linear functionals. We find a form of the coefficients of the quadrature formula which can be easy used in numerical calculations. The main algorithm we use in order to obtain the coefficients and the remainder of the quadrature formula is based on the Gauss elimination by segments method. We obtain an expression for the exactness degree of the quadrature formula. Finally, we analyze some computational aspects of the algorithm in the pa…
Adjusted bat algorithm for tuning of support vector machine parameters
Support vector machines are powerful and often used technique of supervised learning applied to classification. Quality of the constructed classifier can be improved by appropriate selection of the learning parameters. These parameters are often tuned using grid search with relatively large step. This optimization process can be done computationally more efficiently and more precisely using stochastic search metaheuristics. In this paper we propose adjusted bat algorithm for support vector machines parameter optimization and show that compared to the grid search it leads to a better classifier. We tested our approach on standard set of benchmark data sets from UCI machine learning repositor…
Properties of Generalized Polynomial Spaces in Three Variables
Multivariate interpolation is a topic which often appears in practical modeling problems. Different type of spaces of functions are used for solving interpolation problems. When the interpolation conditions are of different kind, by example, spacial and temporal, one possibility for modeling the problem is to use a generalize degree, in which the monomials exponents are weighted with a weight vector with integer components. In order to use such a generalize polynomial space as interpolation space, it is necessary to know the dimension and a basis of it. The aim of this article is to study and prove many properties of the generalize polynomial spaces in three variables.
Computer Mediated Communication and Collaboration in a Virtual Learning Environment Based on a Multi-agent System with Wasp-Like Behavior
In this paper is presented a model for an adaptive multi-agent system for dynamic routing of the grants' activities from a learning environment, based on the adaptive wasp colonies behavior. The agents use wasp task allocation behavior, combined with a model of wasp dominance hierarchy formation. The model we introduced allows the assignment of activities in a grant, taking into account the specialization of students, their experience and the complexity of activities already taken. An adaptive method allows students to enter in the Grant system for the first time. The system is changing dynamic, because both the type of activities and the students involved in the system change. Our approach…
Energy Efficient Sink Placement in Wireless Sensor Networks by Brain Storm Optimization Algorithm
Wireless sensor networks represent one of the most promising technologies whose use has significantly increased in the past years. They are used in various applications such as health care monitoring, surveillance and monitoring in agriculture, industrial monitoring, habitat and underwater monitoring, etc. Deployment of the wireless sensor networks introduces number of hard optimization problems. Placement of the elements such as sensors, gateways, sinks and base stations, depend on different conditions and constraints such as signal propagation, distance, energy preservation, reliability. In this paper, we propose a method based on brain storm optimization algorithm for placing multiple si…
Combined Elephant Herding Optimization Algorithm with K-means for Data Clustering
Clustering is an important task in machine learning and data mining. Due to various applications that use clustering, numerous clustering methods were proposed. One well-known, simple, and widely used clustering algorithm is k-means. The main problem of this algorithm is its tendency of getting trapped into local minimum because it does not have any kind of global search. Clustering is a hard optimization problem, and swarm intelligence stochastic optimization algorithms are proved to be successful for such tasks. In this paper, we propose recent swarm intelligence elephant herding optimization algorithm for data clustering. Local search of the elephant herding optimization algorithm was im…
Automatic Identification of Watermarks and Watermarking Robustness Using Machine Learning Techniques
The goal of this article is to propose a framework for automatic identification of watermarks from modified host images. The framework can be used with any watermark embedding/extraction system and is based on models built using machine learning (ML) techniques. Any supervised ML approach can be theoretically chosen. An important part of our framework consists in building a stand-alone module, independent of the watermarking system, for generating two types of watermarks datasets. The first type of datasets, that we will name artificially datasets, is generated from the original images by adding noise with an imposed maximum level of noise. The second type contains altered watermarked image…
The λ-Error Order in Multivariate Interpolation
The aim of this article is to introduce and to study a generalization of the error order of interpolation, named λ – error order of interpolation. This generalization makes possible a deeper analysis of the error in the interpolation process. We derived the general form of the λ – error order of interpolation and then we applied it for many choices of the functional λ.
Ant Colony Models for a Virtual Educational Environment Based on a Multi-Agent System
We have designed a virtual learning environment where students interact through their computers and with the software agents in order to achieve a common educational goal. The Multi-Agent System (MAS) consisting of autonomous, cognitive and social agents communicating by messages is used to provide a group decision support system for the learning environment. Learning objects are distributed in a network and have different weights in function of their relevance to a specific educational goal. The relevance of a learning object can change in time; it is affected by students', agents' and teachers' evaluation. We have used an ant colony behavior model for the agents that play the role of a tu…
Optimization of Complex SVM Kernels Using a Hybrid Algorithm Based on Wasp Behaviour
The aim of this paper is to present a new method for optimization of SVM multiple kernels The kernel substitution can be used to define many other types of learning machines distinct from SVMs We introduced a new hybrid method which uses in the first level an evolutionary algorithm based on wasp behaviour and on the co-mutation operator LR−Mijn and in the second level a SVM algorithm which computes the quality of chromosomes The most important details of our algorithms are presented The testing and validation proves that multiple kernels obtained using our genetic approach are improving the classification accuracy up to 94.12% for the “leukemia” data set.
Adaptive Multi-agent System Based on Wasp-Like Behaviour for the Virtual Learning Game Sotirios
The aim of this paper is to propose a model for an adaptive multi-agent system based on wasp-like behaviour for dynamic allocation of puzzles and quests in the virtual learning game SOTIRIOS. This is a digital learning game integrated inside a First Person Shooter designed by the second author of this paper. The learning process is based on many puzzles hidden in the game flow. The multi-agent system is necessary to integrate a multiplayer mode into the game. The agents use wasp task allocation behaviour, combined with a model of wasp dominance hierarchy in order to create a unique multiplayer learning system, where each user has a different learning curve, based on his results. The wasp be…