Ration power plants, to generate power, have become common worldwide. One such one is the steam power plant. In such plants, various moving parts of heavy machines generate a lot of noise. Operators are subjected to high levels of noise. High noise level exposure leads to psychological as well physiological problems; different kinds of ill effects. It results in deteriorated work efficiency, although the exact nature of work performance is still unknown. To predict work efficiency deterioration, neuro-fuzzy tools are being used in research. It has been established that a neuro-fuzzy computing system helps in identification and analysis of fuzzy models. The last decade has seen substantial growth in development of various neuro-fuzzy systems

Five bread wheat cultivars was selected namely, IPA99, Tamouz 2, Rasheed, Abu-ghraib And Iraq, were selected for susceptibility to green bug, Schizaphis graminum R.(Aphididae: Homoptera) infestation. Experiments done in under laboratory temperature 20±2C˚, humidity 65±5 % at 12h light. Results have indicated that IPA99 was highly preferred by in S. graminum and no significant of differences with Rasheed and Abu-ghraib, while Iraq and Tamouz2 came second. Although all wheat cultivars were infested by S. graminum except Iraq cultivar which showed some tolerance when exposed to three densities of 5, 10, ap plant during the time of test was 4 weeks. the development time of nymphs were 8, 8, 9, 9.3and 8.3 days for cultivars IPA99, Tamoz2,

The process of selection assure the objective of receiving for chosen ones to high levels more than other ways , and the problem of this research came by these inquires (what is the variables of limits we must considered when first preliminaries selections for mini basket ? and what is the proper test that suits this category ? and is there any standards references it can be depend on it ?) also the aims of this research that knowing the limits variables to basketball mini and their tests as a indicators for preliminaries for mini basketball category in ages (9-12) years and specifies standards (modified standards degrees in following method) to tests results to some limits variables for research sample. Also the researchers depends on (16)

     In this work, the characterization and application of thin films composite incorporated titanium dioxide (TiO₂) (0.8)% volume ratio for (Rutile) nanostructure with poly [2- methoxy-5-(2’-ethylhexoxy-p-phenylene vinylene] (MEH-PPV) were deposited by a spin-coating technique. The optical properties for deposited (MEH-PPV/TiO₂) nanocomposite thin films have two peaks which are the Q-band in the visible region and B-band in ultraviolet. This study shows that the absorption spectrum of organic polymer mixing with TiO₂ increased with increasing the volume ratios TiO₂. The I-V characteristic of nanocomposite thin films shows that the current at dark and light condition

This paper proposes a new structure of the hybrid neural controller based on the identification model for nonlinear systems. The goal of this work is to employ the structure of the Modified Elman Neural Network (MENN) model into the NARMA-L2 structure instead of Multi-Layer Perceptron (MLP) model in order to construct a new hybrid neural structure that can be used as an identifier model and a nonlinear controller for the SISO linear or nonlinear systems. Weight parameters of the hybrid neural structure with its serial-parallel configuration are adapted by using the Back propagation learning algorithm. The ability of the proposed hybrid neural structure for nonlinear system has achieved a fast learning with minimum number

Monaural source separation is a challenging issue due to the fact that there is only a single channel available; however, there is an unlimited range of possible solutions. In this paper, a monaural source separation model based hybrid deep learning model, which consists of convolution neural network (CNN), dense neural network (DNN) and recurrent neural network (RNN), will be presented. A trial and error method will be used to optimize the number of layers in the proposed model. Moreover, the effects of the learning rate, optimization algorithms, and the number of epochs on the separation performance will be explored. Our model was evaluated using the MIR-1K dataset for singing voice separation. Moreover, the proposed approach achi

Machine learning models have recently provided great promise in diagnosis of several ophthalmic disorders, including keratoconus (KCN). Keratoconus, a noninflammatory ectatic corneal disorder characterized by progressive cornea thinning, is challenging to detect as signs may be subtle. Several machine learning models have been proposed to detect KCN, however most of the models are supervised and thus require large well-annotated data. This paper proposes a new unsupervised model to detect KCN, based on adapted flower pollination algorithm (FPA) and the k-means algorithm. We will evaluate the proposed models using corneal data collected from 5430 eyes at different stages of KCN severity (1520 healthy, 331 KCN1, 1319 KCN2, 1699 KCN3 a