Infrastructure, especially wastewater projects, plays an important role in the life of residential communities. Due to the increasing population growth, there is also a significant increase in residential and commercial facilities. This research aims to develop two models for predicting the cost and time of wastewater projects according to independent variables affecting them. These variables have been determined through a questionnaire distributed to 20 projects under construction in Al-Kut City/ Wasit Governorate/Iraq. The researcher used artificial neural network technology to develop the models. The results showed that the coefficient of correlation R between actual and predicted values were 99.4% and 99 %, MAPE was

In data mining, classification is a form of data analysis that can be used to extract models describing important data classes. Two of the well known algorithms used in data mining classification are Backpropagation Neural Network (BNN) and Naïve Bayesian (NB). This paper investigates the performance of these two classification methods using the Car Evaluation dataset. Two models were built for both algorithms and the results were compared. Our experimental results indicated that the BNN classifier yield higher accuracy as compared to the NB classifier but it is less efficient because it is time-consuming and difficult to analyze due to its black-box implementation.

The objective of this study was tointroduce a recursive least squares (RLS) parameter estimatorenhanced by using a neural network (NN) to facilitate the computing of a bit error rate (BER) (error reduction) during channels estimation of a multiple input-multiple output orthogonal frequency division multiplexing (MIMO-OFDM) system over a Rayleigh multipath fading channel.Recursive least square is an efficient approach to neural network training:first, the neural network estimator learns to adapt to the channel variations then it estimates the channel frequency response. Simulation results show that the proposed method has better performance compared to the conventional methods least square (LS) and the original RLS and it is more robust a

Aerial Robot Arms (ARAs) enable aerial drones to interact and influence objects in various environments. Traditional ARA controllers need the availability of a high-precision model to avoid high control chattering. Furthermore, in practical applications of aerial object manipulation, the payloads that ARAs can handle vary, depending on the nature of the task. The high uncertainties due to modeling errors and an unknown payload are inversely proportional to the stability of ARAs. To address the issue of stability, a new adaptive robust controller, based on the Radial Basis Function (RBF) neural network, is proposed. A three-tier approach is also followed. Firstly, a detailed new model for the ARA is derived using the Lagrange–d’A

Background: Pilonidal sinus is a recurrence pathology that affects the gluteal area, cleft part of it.  It affects many parts but mainly the sacral area and may present with various forms; from the asymptomatic cyst to severe infection in a form of painful abscess or sinus. The treatment of pilonidal sinus ranges from observation with good hygiene to excision of the sinus left wide incision. Recurrence may follow the excisional procedure and may need more operations. Many conservative ways had been used for the management of the sinus; laser (phototherapy) is one kind that may use. this paper aims to test the efficiency of the laser (diode 980nm) in the treatment of the sinus. material:

In this research a local adsorbent was prepared from waste tires using two-step pyrolysis method. In the carbonization process, nitrogen gas flow rate was 0.2L/min at carbonization temperature of 500ºC for 1h. The char products were then preceded to the activation process at 850°C under carbon dioxide (CO2) activation flow rate of 0.6L/min for 3h. The activation method produced local adsorbent material with a surface area and total pore volume as high as 118.59m2 /g and 0.1467cm3/g, respectively. The produced . local adsorbent (activated carbon) was used for adsorption of lead from aqueous solution. The continuous fixed bed column experiments were conducted. The adsorption capacity performance of prepared activated carbons in this work

In this research, the focus was on estimating the parameters on (min- Gumbel distribution), using the maximum likelihood method and the Bayes method. The genetic algorithmmethod was employed in estimating the parameters of the maximum likelihood method as well as  the Bayes method. The comparison was made using the mean error squares (MSE), where the best  estimator  is the one who has the least mean squared error. It was noted that the best estimator was (BLG_GE).