A cascading scheme for speeding up multiple classifier systems

Pattern Analysis and Applications - The accuracy of multi-class classification problems is improving at a good pace. However, improving the accuracy often leads to slowing down the processing...     

Concept learning games

In this paper, we intend to have a game theoretic study on the concept learning problem in a multi-agent system. Concept learning is a very essential and well-studied domain of machine learning when it is studied under the characteristics of a multi-agent system. The most important reasons are the partiality of the environment perception for any agent and also the communication holdbacks, resulting into a deep need for a collaborative protocol in favor of multi-agent transactions. Here we wish to investigate multi-agent concept learning with the help of its components, thoroughly with a game theoretic taste, esp. on the pre-learning processes. Based on two standard notations, we address the non-unanimity of concepts, classification of objects, voting and communicating protocol, and also the learning itself. In such a game of concept learning, we consider a group of agents, communicating and consulting to upgrade their ontologies based on their conceptualizations of the environment. For this purpose, we investigate the problem in two separate and standard distinctions of game theory study, cooperation and competition. Several solution concepts and innovative ideas from the multi-agent realm are used to produce an approach that contains the reasoning process of the agents in this system. Some experimentations come at the end to show the functionality of our approach. These experimentations come distinctly for both cooperative and competitive views.     

A semantic social network-based expert recommender system

This research work presents a framework to build a hybrid expert recommendation system that integrates the characteristics of content-based recommendation algorithms into a social network-based collaborative filtering system. The proposed method aims at improving the accuracy of recommendation prediction by considering the social aspect of experts’ behaviors. For this purpose, content-based profiles of experts are first constructed by crawling online resources. A semantic kernel is built by using the background knowledge derived from Wikipedia repository. The semantic kernel is employed to enrich the experts’ profiles. Experts’ social communities are detected by applying the social network analysis and using factors such as experience, background, knowledge level, and personal preferences. By this way, hidden social relationships can be discovered among individuals. Identifying communities is used for determining a particular member’s value according to the general pattern behavior of the community that the individual belongs to. Representative members of a community are then identified using the eigenvector centrality measure. Finally, a recommendation is made to relate an information item, for which a user is seeking an expert, to the representatives of the most relevant community. Such a semantic social network-based expert recommendation system can provide benefits to both experts and users if one looks at the recommendation from two perspectives. From the user’s perspective, she/he is provided with a group of experts who can help the user with her/his information needs. From the expert’s perspective she/he has been assigned to work on relevant information items that fall under her/his expertise and interests.     

Large-scale parallel lattice Boltzmann–cellular automaton model of two-dimensional dendritic growth

An extremely scalable lattice Boltzmann (LB)–cellular automaton (CA) model for simulations of two-dimensional (2D) dendritic solidification under forced convection is presented. The model incorporates effects of phase change, solute diffusion, melt convection, and heat transport. The LB model represents the diffusion, convection, and heat transfer phenomena. The dendrite growth is driven by a difference between actual and equilibrium liquid composition at the solid–liquid interface. The CA technique is deployed to track the new interface cells. The computer program was parallelized using the Message Passing Interface (MPI) technique. Parallel scaling of the algorithm was studied and major scalability bottlenecks were identified. Efficiency loss attributable to the high memory bandwidth requirement of the algorithm was observed when using multiple cores per processor. Parallel writing of the output variables of interest was implemented in the binary Hierarchical Data Format 5 (HDF5) to improve the output performance, and to simplify visualization. Calculations were carried out in single precision arithmetic without significant loss in accuracy, resulting in 50% reduction of memory and computational time requirements. The presented solidification model shows a very good scalability up to centimeter size domains, including more than ten million of dendrites.     

An Immune-based Genetic Algorithm with Reduced Search Space Coding for Multiprocessor Task Scheduling Problem

Multiprocessor task scheduling is an important problem in parallel applications and distributed systems. In this way, solving the multiprocessor task scheduling problem (MTSP) by heuristic, meta-heuristic, and hybrid algorithms have been proposed in literature. Although the problem has been addressed by many researchers, challenges to improve the convergence speed and the reliability of methods for solving the problem are still continued especially in the case that the communication cost is added to the problem frame work. In this paper, an Immune-based Genetic algorithm (IGA), a meta-heuristic approach, with a new coding scheme is proposed to solve MTSP. It is shown that the proposed coding reduces the search space of MTSP in many practical problems, which effectively influences the convergence speed of the optimization process. In addition to the reduced search space offered by the proposed coding that eventuate in exploring better solutions at a shorter time frame, it guarantees the validity of solutions by using any crossover and mutation operators. Furthermore, to overcome the regeneration phenomena in the proposed GA (generating similar chromosomes) which leads to premature convergence, an affinity based approach inspired from Artificial Immune system is employed which results in better exploration in the searching process. Experimental results showed that the proposed IGA surpasses related works in terms of found makespan (20% improvement in average) while it needs less iterations to find the solutions (90% improvement in average) when it is applied to standard test benches.     

Application of several non-linear prediction tools for estimating uniaxial compressive strength of granitic rocks and comparison of their performances

Uniaxial compressive strength (UCS) of rock is crucial for any type of projects constructed in/on rock mass. The test that is conducted to measure the UCS of rock is expensive, time consuming and having sample restriction. For this reason, the UCS of rock may be estimated using simple rock tests such as point load index (I _s(50)), Schmidt hammer (R _n) and p-wave velocity (V _p) tests. To estimate the UCS of granitic rock as a function of relevant rock properties like R _n, p-wave and I _s(50), the rock cores were collected from the face of the Pahang–Selangor fresh water tunnel in Malaysia. Afterwards, 124 samples are prepared and tested in accordance with relevant standards and the dataset is obtained. Further an established dataset is used for estimating the UCS of rock via three-nonlinear prediction tools, namely non-linear multiple regression (NLMR), artificial neural network (ANN) and adaptive neuro-fuzzy inference system (ANFIS). After conducting the mentioned models, considering several performance indices including coefficient of determination (R ²), variance account for and root mean squared error and also using simple ranking procedure, the models were examined and the best prediction model was selected. It is concluded that the R ² equal to 0.951 for testing dataset suggests the superiority of the ANFIS model, while these values are 0.651 and 0.886 for NLMR and ANN techniques, respectively. The results pointed out that the ANFIS model can be used for predicting UCS of rocks with higher capacity in comparison with others. However, the developed model may be useful at a preliminary stage of design; it should be used with caution and only for the specified rock types.     

Nonlinear ceramics for tunable microwave devices

The research on materials and systems for tunable microwave devices has gained attraction within the last years. The radio frequency characterization and the component design of tunable microwave components based on dielectric ceramics especially barium-strontium-titanate (BST) are presented in this second part, whereas the basic material properties are discussed in detail in the first part. After a short introduction to the processing technology used for the fabrication of tunable components based on a BST thick film, the relations between microwave properties and material properties as well as the microstructure are presented in detail. The design process for tunable microwave components based on BST thick films is described. Especially the considerations related to micro- and macrostructure and their connection are highlighted. The paper closes with two different application examples: a reconfigurable array antenna for satellite communication and varactors for high power applications.