752 publications from this institution
The purpose of classification in medical informatics is to predict the presence or absence of a particular disease as well as disease types from historical data. Medical data often contain irrelevant features and noise, and an appropriate subset of the significant features can improve classification accuracy. Therefore, researchers apply feature selection to identify and remove irrelevant and redundant features. The authors propose a versatile feature selection approach called Swarm Search Feature Selection (SS-FS), based on stochastic swarm intelligence. It is designed to overcome NP-hard combinatorial search problems such as the selection of an optimal feature subset from an extremely large array of features--which is not uncommon in biomedical data. SS-FS is demonstrated to be a feasible computing tool in achieving high accuracy in classification via testing with two empirical biomedical datasets. This article is part of a special issue on life sciences computing.
Based on Universal Mobile Telecommunications System (UMTS) Wideband Code Division Multiple Access (WCDMA) technology, the Mobile User Objective System (MUOS) provides a wide range of wireless telecommunications services to the war fighter over geosynchronous satellites. As MUOS is a multiple beam system, it is ofparamount importance for the User Entry (UE) to select the best beam, which in turn requires accurate transmit powerfrom the satellite. To minimize the satellite path gain uncertaintyfrom the base station to the UE, the Enhanced Gain Variation Reduction (EGVR) algorithm is proposed. Implemented on the basis of the measurements of the received signal code power (RSCP) ofthe pilot channel from the UEand the measurements ofthe received signal code power at the terrestrial base station, the EGVR process generates the transmission power estimate and integrates it into the overall gain adjust system to keep the transmission power levels as desired. Thefeatures ofthe algorithm include the weighted gain adjusts generated from the estimation error, appropriate timers to control the measurementflow, and differential measurements to eliminate the common errors between satellite beams. The preliminary study shows that the EGVR can reduce the power uncertaintyfrom 2.4 dB to 0.4 dB, in addition to reducing any differential errors encountered. With the blending ofthe telemetryfeedback into the algorithm, it can make the transmitpower uncertainty even smaller.
No abstract is provided for this article.
The firefly algorithm has become an increasingly important tool of Swarm Intelligence that has been applied in almost all areas of optimization, as well as engineering practice. Many problems from various areas have been successfully solved using the firefly algorithm and its variants. In order to use the algorithm to solve diverse problems, the original firefly algorithm needs to be modified or hybridized. This paper carries out a comprehensive review of this living and evolving discipline of Swarm Intelligence, in order to show that the firefly algorithm could be applied to every problem arising in practice. On the other hand, it encourages new researchers and algorithm developers to use this simple and yet very efficient algorithm for problem solving. It often guarantees that the obtained results will meet the expectations.
Social Algorithms• Algorithm: An algorithm is a step-by-step, computational procedure or a set of rules to be followed by a computer in calculations or computing an answer to a problem.• Ant colony optimization: Ant colony optimization (ACO) is an algorithm for solving optimization problems such as routing problems using multiple agents.ACO mimics the local interactions of social ant colonies and the use of chemical messenger -pheromone to mark paths.No centralized control is used and the system evolves according to simple local interaction rules.• Bat algorithm: Bat algorithm (BA) is an algorithm for optimization, which uses frequencytuning to mimic the basic behaviour of echolocation of microbats.BA also uses the variations of loudness and pulse emission rates and a solution vector to a problem corresponds to a position vector of a bat in the search space.Evolution of solutions follow two algorithmic equations for positions and frequencies.• Bees-inspired algorithms: Bees-inspired algorithms are a class of algorithms for optimization using the foraging characteristics of honeybees and their labour division to carry out search.Pheromone may also be used in some variants of bees-inspired algorithms.
No abstract is provided for this article.
The use of fiber-reinforced polymer (FRP) shear strengthening systems for the strength enhancement of existing reinforced concrete structures is discussed. An experimental and analytical research programme is under way to investigate the performance of bonded passive and unbonded prestressed FRP shear systems, and to quantify the effect of the load history on the strengthed behavior. Non-linear finite-element analysis are being developed to model the strengthed behavior. The results will provide insight into the optimum system parameters and contribute to the formulation of design guidance for advanced FRP strengthing strategies.
No abstract is provided for this article.
The pressure vessel design problem is a well-known design benchmark for validating bio-inspired optimization algorithms. However, its global optimality is not clear and there has been no mathematical proof put forward. In this paper, a detailed mathematical analysis of this problem is provided that proves that 6059.714335048436 is the global minimum. The Lagrange multiplier method is also used as an alternative proof and this method is extended to find the global optimum of a cantilever beam design problem.
No abstract is provided for this article.
No abstract is provided for this article.
No abstract is provided for this article.
In this paper, a clustering based color image segmentation technique is proposed and the clustering technique is optimized by the cuckoo search method. The proposed approach consists of two phase segmentation processes. In the first phase, cluster centres are optimized by using the cuckoo search algorithm and in the second phase, empty and frequent clutters are removed and merged according to pre-defined rules. This cluster centre based clustering technique is then used to find the optimum centre within a cluster, while cuckoo search is applied to find the optimum cluster centre for each segment in the image. Comparison of the proposed method is performed with the genetic algorithm (GA), dynamic control particle swarm optimization (DCPSO) algorithm and firefly algorithm based color image segmentation methods over five benchmark color images. The parameters of the proposed method are tuned through empirical testing. Results demonstrated that the proposed method can be an effective tool for image segmentation.
