Background and aim Ten percent of all accidents in the study area arerelated to accidents in urban squares. The purpose of this study was todetermine the effective parameters of accidents in urban squares and to presenta model of accident prediction. Method method of this research is descriptive-survey. In this study, 456accidents were studied that occurred in 26 squares of Ardabil city from 2014 to2016 and were collected the effective factors on each of those accidents. Factors were designed as a questionnaire to select the effective refining factorsand final parameters for modeling using Likert spectrum method and Delphimethod and polling of expert. Statistical sample was selected 102 persons. 40initial effective parameters of the accidents were selected. Questionnaires wereanalyzed by statistical tests, mean and differential power, which identified 16high-impact parameters for modeling. The analysis and comparison of themodeling results were done in two ways. The first method is using a linear-regression statistical model with MiniTab software 14 and the second methodusing artificial neural network model with matlab software. findings The results of statistical analysis show that among the presentedregression models, the best model of accident frequency prediction is consistedof three parameters of square traffic volume, number of passages leading to thesquare, existence of a production site and trip absorption. as well as the resultsof neural network analysis show that the above model in predicting the numberof accidents, is model with four main traffic volume input parameters, thepresence of the taxi station and bus, speed bump on the main pathways, thenumber of passages leading to the intersection.

To improve network-functioning, an arc discharge method was developed for the synthesis of nano iron oxide with “NEURALNETWORK” morphology. Iron wires with diameters of 0.01-0.05 cm were subjected to currents of 50-200 A, until explosions occured in the open air. The XRD and RAMAN spectra of the as-prepared-products indicate formation of nano-Fe2O3 (tetragonal and monoclinic) crystals. Their SEM images show fabrication of nano iron oxide with three different morphologies: spheres, chains, and a “neural-network” biological form. While the latter is unprecedented, our fabrications of nano iron oxide with both sphere and chain morphologies are reproductions of previously reported results. The sphere shaped nanoparticles show a uniform distribution with sizes in the range of 50-250 nm. The specifications of the chain nano products appear consistent with those reported.

Since 1993, Devices based on CNTs have applicationsranging from nanoelectronics to optoelectronics. Thechallenging issue in designing these devices is that thenonequilibrium Green's function (NEGF) method has tobe employed to solve the Schrödinger and Poissonequations, which is complex and time consuming. In thepresent study, a novel smart and optimal algorithm ispresented for fast and accurate modeling of CNT fieldeffecttransistors (CNTFETs) based on an artificial NEURALNETWORK. A new and efficient way is presented forincrementally constructing radial basis function (RBF)networks with optimized neuron radii to obtain theestimator network. An incremental extreme learningmachine (I-ELM) algorithm is used to train the RBFnetwork. To ensure the optimal radii for incrementalneurons, this algorithm utilizes a modified version of anoptimization algorithm known as the Nelder-Meadsimplex algorithm. Results confirm that the proposedapproach reduces the network size for faster errorconvergence while preserving the estimation accuracy.

Software project management has always faced challenges that have often had a great impact on the outcome of projects in future. For this, Managers of software projects always seek solutions against challenges. The implementation of unguaranteed approaches or mere personal experiences by managers does not necessarily suffice for solving the problems. Therefore, the management area of software projects requires tools and means helping software project managers confront with challenges. The estimation of effort required for software development is among such important challenges. In this study, a NEURALNETWORK-based architecture has been proposed that makes use of PSO algorithm to increase its accuracy in estimating software development effort. The architecture suggested here has been tested by several datasets. Furthermore, similar experiments were done on the datasets using various widely used methods in estimating software development. The results showed the accuracy of the proposed model. The results of this research have applications for researchers of software engineering and data mining.

A classification technique using Support Vector Machine (SVM) classifier for detection of rollingelement bearing fault is presented here. The SVM was fed from features that were extracted from ofvibration signals obtained from experimental setup consisting of rotating driveline that was mounted onrolling element bearings which were run in normal and with artificially faults induced conditions. Thetime-domain vibration signals were divided into 40 segments and simple features such as peaks in timedomain and spectrum along with statistical features such as standard deviation, skewness, kurtosis etc. were extracted. Effectiveness of SVM classifier was compared with the performance of Artificial NEURALNETWORK (ANN) classifier and it was found that the performance of SVM classifier is superior to that ofANN. The effect of pre-processing of the vibration signal by Discreet Wavelet Transform (DWT) prior tofeature extraction is also studied and it is shown that pre-processing of vibration signal with DWTenhances the effectiveness of both ANN and SVM classifiers. It has been demonstrated from experimentresults that performance of SVM classifier is better than ANN in detection of bearing condition and preprocessingthe vibration signal with DWT improves the performance of SVM classifier.