In this paper we describe several different training algorithms for feed forward neural networks(FFNN). In all of these algorithms we use the gradient of the performance function, energy function, to determine how to adjust the weights such that the performance function is minimized, where the back propagation algorithm has been used to increase the speed of training. The above algorithms have a variety of different computation and thus different type of form of search direction and storage requirements, however non of the above algorithms has a global properties which suited to all problems.

Vehicular ad hoc network (VANET) is a distinctive form of Mobile Ad hoc Network (MANET) that has attracted increasing research attention recently. The purpose of this study is to comprehensively investigate the elements constituting a VANET system and to address several challenges that have to be overcome to enable a reliable wireless communications within a vehicular environment. Furthermore, the study undertakes a survey of the taxonomy of existing VANET routing protocols, with particular emphasis on the strengths and limitations of these protocols in order to help solve VANET routing issues. Moreover, as mobile users demand constant network access regardless of their location, this study seeks to evaluate various mobility models for vehi

The adhesion strength between Polyethylene (PE) film and Aluminum surface by using the adhesive material (Cyanoacrylate) has been studied. Aluminum (Al) was used as a substrate, and polyethylene (PE) was used as a film adhered to the Al surface. Standard specimens were prepared to use in the peeling test in dry condition, other specimens were immersed in water for 12 days at room temperature. the results for the specimens in the dry condition had shown that high value in the peel force and the peel energy, the peel force was 0.38*103 N/m and the peel energy was 0.605*103 N/m, peeling the film from Al surface leaves a residual of the adhesive material on both adherend, the failure for this specimen were combination of adhesive and cohesive f

Dust is a frequent contributor to health risks and changes in the climate, one of the most dangerous issues facing people today. Desertification, drought, agricultural practices, and sand and dust storms from neighboring regions bring on this issue. Deep learning (DL) long short-term memory (LSTM) based regression was a proposed solution to increase the forecasting accuracy of dust and monitoring. The proposed system has two parts to detect and monitor the dust; at the first step, the LSTM and dense layers are used to build a system using to detect the dust, while at the second step, the proposed Wireless Sensor Networks (WSN) and Internet of Things (IoT) model is used as a forecasting and monitoring model. The experiment DL system

This paper deals with the F-compact operator defined on probabilistic Hilbert space and gives some of its main properties.

This work describes the effect of temperature on the phase transformation of titanium dioxide (TiO2) prepared using metal organic precursors as starting materials. X-ray diffraction (XRD) was used to investigate the structural properties of TiO2 gels calcined at different temperatures (300, 500, 700) ?C. the results showed that the samples have typical peaks of TiO2 polycrystalline brookite nanopowders after calcined at (300 ?C), which confirmed by (111), (121), (200), (012), (131), (220), (040), (231), (132) and (232) diffraction peaks. Also, XRD diffraction spectra showed the presence of crystallites of anatase with low proportion of rutile phase where calcined at (500 ?C), while rutile phase domains at (700 ?C). The crystallite size of