Harmony search based algorithm for the optimum design of grillage systems to LRFD-AISC

Harmony search based optimum design method is presented for the grillage systems. This numerical technique imitates the musical performance process that takes place when a musician searches for a better state of harmony. Jazz improvisation seeks to find musically pleasing harmony similar to the optimum design process which seeks to find the optimum solution. The design algorithm considers the serviceability and ultimate strength constraints which are implemented from Load and Resistance Factor Design—American Institute of Steel Construction (LRFD-AISC). It selects the appropriate W-sections for the transverse and longitudinal beams of the grillage system out of 272 discrete W-section designations given in LRFD-AISC. This selection is carried out such that the design limitations described in LRFD-AISC are satisfied and the weight of the system is the minimum. Many design examples are considered to demonstrate the efficiency of the algorithm presented.     

An artificial neural network model for the effects of chicken manure on ground water

In the areas where broiler industry is located, poultry manure from chicken farms could be a major source of ground water pollution, and this may have extensive effects particularly when the farms use nearby ground water as their fresh water supply. Therefore the prediction the extent of this pollution, either from rigorous mathematical diffusion modeling or from the perspective of experimental data evaluation bears importance. In this work, we have investigated modeling of the effects of chicken manure on ground water by artificial neural networks. An ANN model was developed to predict the total coliform in the ground water well in poultry farms. The back-propagation algorithm was employed for training and testing the network, and the Levenberg-Marquardt algorithm was utilized for optimization. The MATLAB 7.0 environment with Neural Network Toolbox was used for coding. Given the associated input parameters such as the number of chickens, type of manure pool management and depth of well, the model estimates the possible amount of total coliform in the wells to a satisfactory degree. Therefore it is expected to be of help in future for estimating the ground water pollution resulting from chicken farms.     

Wavelet ridges for musical instrument classification

The time-varying frequency structure of musical signals have been analyzed using wavelets by either extracting the instantaneous frequency of signals or building features from the energies of sub-band coefficients. We propose to benefit from a combination of these two approaches and use the time-frequency domain energy localization curves, called as wavelet ridges, in order to build features for classification of musical instrument sounds. We evaluated the representative capability of our feature in different musical instrument classification problems using support vector machine classifiers. The comparison with the features based on parameterizing the wavelet sub-band energies confirmed the effectiveness of the proposed feature.     

Fractional wavelet transform for the quantitative spectral resolution of the composite signals of the active compounds in a two-component mixture

Fractional calculus is a powerful tool that has been applied successfully for the analysis of the complex systems. One interesting example of a complex mixture is given by the multicomponent pharmaceutical samples having constant matrix content. The main aim of this study is to develop a new approach based on the combined use of the fractional wavelet transform (FWT) and the continuous wavelet transform (CWT) in order to quantify atorvastatin (ATO) and amlodipine (AML) in their mixtures without requiring a chemical pretreatment. In the first step, the absorption spectra of the compounds and their samples were processed by the FWT method. In the next step, the CWT approach was applied to the fractional wavelet spectra obtained in the above step. The aim of the application of FWT is data reduction corresponding to the spectra of compounds and their commercial samples. In the following step, the CWT was used for the quantitative resolution of the composite signals of the analyzed compounds. After method validation, the proposed signal processing methods based on the combined use of the FWT and the CWT were successfully applied to the resolution of the composite spectra for the quantitation of atorvastatin (ATO) and amlodipine (AML) in tablets.     

Faster RRT-based Nonholonomic Path Planning in 2D Building Environments Using Skeleton-constrained Path Biasing

This paper presents a faster RRT-based path planning approach for regular 2-dimensional (2D) building environments. To minimize the planning time, we adopt the idea of biasing the RRT tree-growth in more focused ways. We propose to calculate the skeleton of the 2D environment first, then connect a geometrical path on the skeleton, and grow the RRT tree via the seeds generated locally along this path. We conduct batched simulations to find the universal parameters in manipulating the seeds generation. We show that the proposed skeleton-biased locally-seeded RRT (skilled-RRT) is faster than the other baseline planners (RRT, RRT*, A*-RRT, Theta*-RRT, and MARRT) through experimental tests using different vehicles in different 2D building environments. Given mild assumptions of the 2D environments, we prove that the proposed approach is probabilistically complete. We also present an application of the skilled-RRT for unmanned ground vehicle. Compared to the other baseline algorithms (Theta*-RRT and MARRT), we show the applicability and fast planning of the skilled-RRT in real environment.     

A novel permission-based Android malware detection system using feature selection based on linear regression

Neural Computing and Applications - With the developments in mobile and wireless technology, mobile devices have become an important part of our lives. While Android is the leading operating system...     

A Comparison of Various Artificial Intelligence Methods in the Prediction of Bank Failures

The strong relationship between bank failure and economic growth attaches far more importance to the predictability of bank failures. Consequently, numerous statistical prediction models exist in the literature focusing on this particular subject. Besides, artificial intelligence techniques began to attain an increasing level of importance in the literature due to their predictive success. This study distinguishes itself from the similar ones in the sense that it presents a comparison of three different artificial intelligence methods, namely support vector machines (SVMs), radial basis function neural network (RBF-NN) and multilayer perceptrons (MLPs); in addition to subjecting the explanatory variables to principal component analysis (PCA). The extent of this study encompasses 37 privately owned commercial banks (17 failed, 20 non-failed) that were operating in Turkey for the period of 1997–2001. The main conclusions drawn from the study can be summarized as follows: (i) PCA does not appear to be an effective method with respect to the improvement of predictive power; (ii) SVMs and RBF demonstrated similar levels of predictive power; albeit SVMs was found to be the best model in terms of total predictive power; (iii) MLPs method stood out among the SVMs and RBF methods in a negative sense and exhibits the lowest predictive power.     

Design of highly effective multilayer feedforward neural network by using genetic algorithm

This paper presents a highly effective and precise neural network method for choosing the activation functions (AFs) and tuning the learning parameters (LPs) of a multilayer feedforward neural network by using a genetic algorithm (GA). The performance of the neural network mainly depends on the learning algorithms and the network structure. The backpropagation learning algorithm is used for tuning the network connection weights, and the LPs are obtained by the GA to provide both fast and reliable learning. Also, the AFs of each neuron in the network are automatically chosen by a GA. The present study consists of 10 different functions to accomplish a better convergence of the desired input–output mapping. Test studies are performed to solve a set of two-dimensional regression problems for the proposed genetic-based neural network (GNN) and conventional neural network having sigmoid AFs and constant learning parameters. The proposed GNN has also been tested by applying it to three real problems in the fields of environment, medicine, and economics. Obtained results prove that the proposed GNN is more effective and reliable when compared with the classical neural network structure.     

Chemical changes of three native Turkish hazelnut varieties (Corylus avellana L.) during fruit development

Three native hazelnut varieties from Turkey, namely, Tombul, Palaz, and Badem, were examined for their proximate composition, minerals, and fatty acid profiles, as well as polyphenol oxidase (PPO), peroxidase (POD), and lipase activities during fruit development stages (early stage: ES, middle stage: MS, and harvest stage: HS). Proximate composition varied considerably (dry weight basis) from ES to MS. Fat was the predominant component at all stages and showed increasing trends. Six essential minerals (calcium, iron, magnesium, phosphorus, potassium, and zinc) were analysed (dry weight basis). Consuming recommended daily amount of 42.5 g hazelnut at HS supplies 23.3–25.0% of phosphorus, 11.6–18.1% of magnesium, 7.0–18.9% of iron, 4.9–8.9% of zinc, 5.1–5.7% of calcium, and 5.1–5.3% of potassium for recommended dietary allowances or adequate intake for adults. Significant (P < 0.05) decreasing trends were found in all mineral contents from early development to maturity, with some exceptions. Sixteen fatty acids were identified, among which 18:1ω9 was by far the most predominant one, followed by 18:2ω6, 16:0, and 18:0. As expected, total monounsaturated fatty acids constituted the main group of fatty acids ranging from 75.51 to 81.07% in Tombul, from 78.21 to 82.71% in Palaz, and from 73.69 to 81.65% in Badem through the maturation stages. In contrast, total polyunsaturated fatty acids showed decreasing trends from ES to HS. No significant changes (P > 0.05) were observed in total saturated fatty acids at different maturation stages. Tombul variety had the lowest PPO activity compared to those of Palaz and Badem. Badem showed highest POD activity compared to Tombul and Palaz at three stages of maturation and significant decreases (P < 0.05) in all hazelnut samples were observed in POD activity from ES to HS. No lipase activity was detected in any hazelnut samples at ES and MS, except in Badem at MS. In contrast, lipase activity was detected in all hazelnut samples at HS. These results suggest that some proximate compositions, minerals, and fatty acids gave good indications during fruit development stages, whereas enzymatic activities of PPO, POD, and lipase behaved differently among varieties and fruit development stages.     

PROANTHOCYANIDIN-FREE BARLEY—MALTING AND BREWING

Colloidal haze in beer is due to the precipitation of proteins by proanthocyanidins. Carlsberg Research Center reported in 1977 that the use of barley mutants which block the biosynthesis of catechins and proanthocyanidins in the grain prevents the formation of haze in beer. The results from ten years of malting and brewing research with proanthocyanidin-free barley and malt are reviewed.