Feature Selection for Classification with Artificial Bee Colony Programming

Feature selection and classification are the most applied machine learning processes. In the feature selection, it is aimed to find useful properties containing class information by eliminating noisy and unnecessary features in the data sets and facilitating the classifiers. Classification is used to distribute data among the various classes defined on the resulting feature set. In this chapter, artificial bee colony programming (ABCP) is proposed and applied to feature selection for classification problems on four different data sets. The best models are obtained by using the sensitivity fitness function defined according to the total number of classes in the data sets and are compared with the models obtained by genetic programming (GP). The results of the experiments show that the proposed technique is accurate and efficient when compared with GP in terms of critical features selection and classification accuracy on well-known benchmark problems

Broadband, Stable, and Non-Iterative Dielectric Constant Measurement of Low-Loss Dielectric Slabs Using a Frequency-Domain Free-Space Method

A broadband, stable, and non-iterative free-space method is proposed for dielectric constant ε′r determination of low-loss dielectric slabs from reflection-only measurements through simple calibration standards (reflect and air). It is applicable for dispersive samples and does not require thickness information. Simulations of non-disperive and dispersive samples are performed to validate our method. Dielectric constant measurements of polyethylene and Polyoxymethylene samples (9–11 GHz) are carried out to examine the accuracy of our method. IEE

The Advocate - July 19, 1962

Original title (1951-1987)--The Advocate: official publication of the Archdiocese of Newark (N.J.)

Probabilistic Dynamic Deployment of Wireless Sensor Networks by Artificial Bee Colony Algorithm

As the usage and development of wireless sensor networks are increasing, the problems related to these networks are being realized. Dynamic deployment is one of the main topics that directly affect the performance of the wireless sensor networks. In this paper, the artificial bee colony algorithm is applied to the dynamic deployment of stationary and mobile sensor networks to achieve better performance by trying to increase the coverage area of the network. A probabilistic detection model is considered to obtain more realistic results while computing the effectively covered area. Performance of the algorithm is compared with that of the particle swarm optimization algorithm, which is also a swarm based optimization technique and formerly used in wireless sensor network deployment. Results show artificial bee colony algorithm can be preferable in the dynamic deployment of wireless sensor networks

Effects of trapidil after crush injury in peripheral nerve.

In this study, we evaluated the effects of trapidil on crush injury by monitoring nitric oxide, malondialdehyde and transforming growth factor-Beta2 levels and by transmission electron microscopy in the rat sciatic nerve. The sciatic nerve was compressed for 20 sec by using a jewelers forceps. Trapidil treatment groups were administrated a single dose of trapidil (8 mg/kg) intraperitoneally just after the injury. The crush and crush + trapidil treatment groups were evaluated on the 2nd, 7th, 15th, 30th and 45th days of the post-crush period. On the 7th and 15th days, damage in thin and thick myelinated axons, endoneural edema and mitochondrial swelling were less severe in the trapidil group histopathologically. These findings supported the idea that trapidil prevented cell damage and edema at the injury site. Day/group interaction with regard to serum nitric oxide, malondialdehyde and transforming growth factor-Beta2 levels did not show significant changes.</p

Concentrations of Connective Tissue Growth Factor in Patients with Nonalcoholic Fatty Liver Disease: Association with Liver Fibrosis

Aim: In this study, we aimed to investigate the relationship between the histological fibrosis stage of nonalcoholic fatty liver disease (NAFLD) and serum connective tissue growth factor (CTGF) to determine the usefulness of this relationship in clinical practice. Methods: Serum samples were collected from 51 patients with biopsy-proven NAFLD and 28 healthy controls, and serum levels of CTGF were assayed by ELISA. Results: Levels of CTGF were significantly higher in patients with NAFLD compared with controls (P = 0.001). The serum CTGF levels were significantly increased, that correlated with histological fibrosis stage, in patients with NAFLD [in patients with no fibrosis (stage 0) 308.2 ± 142.9, with mild fibrosis (stage 1–2) 519.9±375.2 and with advanced fibrosis (stage 3–4) 1353.2 ± 610 ng/l, P < 0.001]. Also serum level of CTGF was found as an independent predictor of histological fibrosis stage in patients with NAFLD (β = 0.662, t = 5.6, P < 0.001). The area under the ROC curve was estimated 0.931 to separate patients with severe fibrosis from patients with other fibrotic stages. Conclusion: Serum levels of CTGF may be a clinical utility for distinguishing NAFLD patients with and without advanced fibrosis

Probabilistic Dynamic Deployment of Wireless Sensor Networks by Artificial Bee Colony Algorithm

As the usage and development of wireless sensor networks are increasing, the problems related to these networks are being realized. Dynamic deployment is one of the main topics that directly affect the performance of the wireless sensor networks. In this paper, the artificial bee colony algorithm is applied to the dynamic deployment of stationary and mobile sensor networks to achieve better performance by trying to increase the coverage area of the network. A probabilistic detection model is considered to obtain more realistic results while computing the effectively covered area. Performance of the algorithm is compared with that of the particle swarm optimization algorithm, which is also a swarm based optimization technique and formerly used in wireless sensor network deployment. Results show artificial bee colony algorithm can be preferable in the dynamic deployment of wireless sensor networks