Data mining with a simulated annealing based fuzzy classification system

In this paper, the use of simulated annealing (SA) metaheuristic for constructing a fuzzy classification system is presented. In several previous investigations, the capability of fuzzy systems to solve different kinds of problems has been demonstrated. Simulated annealing based fuzzy classification system (SAFCS), hybridizes the learning capability of SA metaheuristic with the approximate reasoning method of fuzzy systems. The objective of this paper is to illustrate the ability of SA to develop an accurate fuzzy classifier. The use of SA in classification is an attempt to effectively explore and exploit the large search space usually associated with classification problems, and find the optimum set of fuzzy if–then rules. The SAFCS would be capable to extract accurate fuzzy classification rules from input data sets, and applies them to classify new data instances in different predefined groups or classes. Experiments are performed with eight UCI data sets. The results indicate that the proposed SAFCS achieves competitive results in comparison with several well-known classification algorithms.     

Generalised gramian framework for model/controller order reduction of switched systems

In this article, a general method for model/controller order reduction of switched linear dynamical systems is presented. The proposed technique is based on the generalised gramian framework for model reduction. It is shown that different classical reduction methods can be developed into a generalised gramian framework. Balanced reduction within a specified frequency bound is developed within this framework. In order to avoid numerical instability and also to increase the numerical efficiency, generalised gramian‐based Petrov–Galerkin projection is constructed instead of the similarity transform approach for reduction. The framework is developed for switched controller reduction. To the best of our knowledge, there is no other reported result on switched controller reduction in the literature. The method preserves the stability under an arbitrary switching signal for both model and controller reduction. Furthermore, it is applicable to both continuous and discrete time systems for different classical gramian‐based reduction methods. The performance of the proposed method is illustrated by numerical examples.     

Method for tracking of environmental level sets by a unicycle-like vehicle

We consider a single Dubins-like vehicle traveling with a constant longitudinal speed in a planar region supporting an unknown field distribution. A sensor provides the distribution value at the vehicle location. We present a new sliding mode control method for tracking environmental level sets: the vehicle is steered to the set where the distribution assumes a pre-specified value and circulates along this set afterwards. The proposed control algorithm does not employ gradient estimation and is non-demanding with respect to both computation and motion. Its mathematically rigorous justification is provided. The effectiveness of the proposed guidance law is confirmed by computer simulations.     

An entropy approach for abnormal activities detection in video streams

Detection of aberration in video surveillance is an important task for public safety. This paper puts forward a simple but effective framework to detect aberrations in video streams using Entropy, which is estimated on the statistical treatments of the spatiotemporal information of a set of interest points within a region of interest by measuring their degree of randomness of both directions and displacements. Entropy is a measure of the disorder/randomness in video frame. It has been showed that degree of randomness of the directions (circular variance) changes markedly in abnormal state of affairs and does change only direction variation but does not change with displacement variation of the interest point. Degree of randomness of the displacements has been put in for to counterbalance this deficiency. Simple simulations have been exercised to see the characteristics of these crude elements of entropy. Normalized entropy measure provides the knowledge of the state of anomalousness. Experiments have been conducted on various real world video datasets. Both simulation and experimental results report that entropy measures of the frames over time is an outstanding way to characterize anomalies in videos.     

RETRACTED ARTICLE: Prediction compressive strength of Portland cement-based geopolymers by artificial neural networks

In the present study, compressive strength results of geopolymers produced by ordinary Portland cement (OPC) as aluminosilicate source have been modeled by artificial neural networks. Six main factors including NaOH concentration, water glass to NaOH weight ratio, alkali activator to cement weight ratio, oven curing temperature, oven curing time and water curing regime each at 4 levels were considered for designing. A total of 32 experiments were conducted according to the L32 array proposed by the method. The neural network models were constructed by 10 input parameters including NaOH concentration, water glass to NaOH weight ratio, alkali activator to cement weight ratio, oven curing temperature, oven curing time, water curing regime, water glass content, NaOH content, Portland cement content and test trial number. The value for the output layer was the compressive strength. According to the input parameters in feed-forward back-propagation algorithm, the constructed networks were trained, validated and tested. The results indicate that artificial neural networks model is a powerful tool for predicting the compressive strength of the geopolymers in the considered range.

     

A prototype classifier based on gravitational search algorithm

In recent years, heuristic algorithms have been successfully applied to solve clustering and classification problems. In this paper, gravitational search algorithm (GSA) which is one of the newest swarm based heuristic algorithms is used to provide a prototype classifier to face the classification of instances in multi-class data sets. The proposed method employs GSA as a global searcher to find the best positions of the representatives (prototypes). The proposed GSA-based classifier is used for data classification of some of the well-known benchmark sets. Its performance is compared with the artificial bee colony (ABC), the particle swarm optimization (PSO), and nine other classifiers from the literature. The experimental results of twelve data sets from UCI machine learning repository confirm that the GSA can successfully be applied as a classifier to classification problems.     

TMR at 4.2 K for Amorphous Magnetic-Tunnel-Junctions with Co60Fe20B20 as Free and Pinned Layers

Single barrier magnetic-tunnel-junctions (MTJs) with the layer structure of Ta(5)/Cu(30)/Ta(5)/Ni79Fe21(5)/Ir22Mn78(12)/Co60Fe20B20(4)/Al(0.8)-oxide/Co60Fe20B20(4)/Cu(30)/Ta(5) [thickness unit: nm] using the amorphous Co60Fe20B20 alloy as free and pinned layers were micro-fabricated. The experimental investigations showed that the tunnel magnetoresistance (TMR) ratio and the resistance decrease with increasing dc bias voltage from 0 to 500~mV or with increasing temperature from 4.2 K to RT. A high TMR ratio of 86.2% at 4.2 K, which corresponds to the high spin polarization of Co60Fe20B20 55%, was observed in the MTJs after annealing at 270℃ for 1 h. High TMR ratio of 53.1%, low junction resistance-area product RS of 3.56 kΩμm2, small coercivity Hc of ≤4 Oe, and relatively large bias-voltage-at-half-maximum TMR with the value V1/2 of greater than 570 mV at RT have been achieved in such Co-Fe-B MTJs.     

An adaptive call admission algorithm for cellular networks

In this paper, we first propose a new continuous action-set learning automaton and theoretically study its convergence properties and show that it converges to the optimal action. Then we give an adaptive and autonomous call admission algorithm for cellular mobile networks, which uses the proposed learning automaton to minimize the blocking probability of the new calls subject to the constraint on the dropping probability of the handoff calls. The simulation results show that the performance of the proposed algorithm is close to the performance of the limited fractional guard channel algorithm for which we need to know all the traffic parameters in advance.     

Comparison of multiwavelet, wavelet, Haralick, and shape features for microcalcification classification in mammograms

We present an evaluation and comparison of the performance of four different texture and shape feature extraction methods for classification of benign and malignant microcalcifications in mammograms. For 103 regions containing microcalcification clusters, texture and shape features were extracted using four approaches: conventional shape quantifiers; co-occurrence-based method of Haralick; wavelet transformations; and multi-wavelet transformations. For each set of features, most discriminating features and their optimal weights were found using real-valued and binary genetic algorithms (GA) utilizing a k-nearest-neighbor classifier and a malignancy criterion for generating ROC curves for measuring the performance. The best set of features generated areas under the ROC curve ranging from 0.84 to 0.89 when using real-valued GA and from 0.83 to 0.88 when using binary GA. The multi-wavelet method outperformed the other three methods, and the conventional shape features were superior to the wavelet and Haralick features.