This search has introduced the techniques of multi-wavelet transform and neural network for recognition 3-D object from 2-D image using patches. The proposed techniques were tested on database of different patches features and the high energy subband of discrete multi-wavelet transform DMWT (gp) of the patches. The test set has two groups, group (1) which contains images, their (gp) patches and patches features of the same images as a part of that in the data set beside other images, (gp) patches and features, and group (2) which contains the (gp) patches and patches features the same as a part of that in the database but after modification such as rotation, scaling and translation. Recognition by back propagation (BP) neural network as com

This search has introduced the techniques of multi-wavelet transform and neural network for recognition 3-D object from 2-D image using patches. The proposed techniques were tested on database of different patches features and the high energy subband of discrete multi-wavelet transform DMWT (gp) of the patches. The test set has two groups, group (1) which contains images, their (gp) patches and patches features of the same images as a part of that in the data set beside other images, (gp) patches and features, and group (2) which contains the (gp) patches and patches features the same as a part of that in the database but after modification such as rotation, scaling and translation. Recognition by back propagation (BP) neural network as

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

This study including synthesis of some new Schiff bases compounds [1‐6] from the reaction of Sulfamethoxazole drug with some aromatic aldehydes in classical Schiff base method then treatment Schiff  bases with succinic anhydride to get oxazepines rings [7-11]These derivatives were characterized by melting point, FT‐IR, ¹H NMR and mass spectra. Some of synthesized compounds were evaluated in vitro for their antibacterial activities against three kinds of pathogenic strains Staphylococcus aureus, Escherichia coli

In this work, novel copolymers of poly(adipic anhydride-co-mannitol) were synthesized by melting condensation polymerization of poly(adipic anhydride) with five percentages of mannitol sugar, 1 to 5 Wt.%. These copolymers were purified and then, characterized by FT-IR, which was proved that the cross-linking reaction was caused by nucleophilic attack of mannitol hydroxyl group to acidic anhydride groups of poly(adipic anhydride) backbone and new ester groups were formed and appeared. Also, modified organic-soluble chitosan, N-maleoyl-chitosan, were synthesized by grafting reaction of chitosan with maleic anhydride in DMF as solvent, and it was also purified and characterized by FT-IR. Biodegradation in vitro of the IPNs of poly(adipic anhyd

The deep learning algorithm has recently achieved a lot of success, especially in the field of computer vision. This research aims to describe the classification method applied to the dataset of multiple types of images (Synthetic Aperture Radar (SAR) images and non-SAR images). In such a classification, transfer learning was used followed by fine-tuning methods. Besides, pre-trained architectures were used on the known image database ImageNet. The model VGG16 was indeed used as a feature extractor and a new classifier was trained based on extracted features.The input data mainly focused on the dataset consist of five classes including the SAR images class (houses) and the non-SAR images classes (Cats, Dogs, Horses, and Humans). The Conv

Prediction of penetration rate (ROP) is important process in optimization of drilling due to its crucial role in lowering drilling operation costs. This process has complex nature due to too many interrelated factors that affected the rate of penetration, which make difficult predicting process. This paper shows a new technique of rate of penetration prediction by using artificial neural network technique. A three layers model composed of two hidden layers and output layer has built by using drilling parameters data extracted from mud logging and wire line log for Alhalfaya oil field. These drilling parameters includes mechanical (WOB, RPM), hydraulic (HIS), and travel transit time (DT). Five data set represented five formations gathered

When the guard honey bees, Apis mellifera L., form a clump at the hive entrance or on the flight board, the oriental hornet, Vespa orientails L., either creeps toward the clump or hovers over it in order to take a bee. Once the hornet creeps, only few bees facing the hornet become alert, rock their heads and antennae, open their wings, and take a posture of defense. The rest of the clump stays listless without any signal of concern. However, the clump stays dense and the defending bees do not detach themselves neither from the rest of the clump nor from each other. For this reason, it is very difficult for the hornet to grab a bee unless the latter makes a “mistake” by detaching herself from other adjacent bees. If the hornet grabs s

     Deep learning (DL) plays a significant role in several tasks, especially classification and prediction. Classification tasks can be efficiently achieved via convolutional neural networks (CNN) with a huge dataset, while recurrent neural networks (RNN) can perform prediction tasks due to their ability to remember time series data. In this paper, three models have been proposed to certify the evaluation track for classification and prediction tasks associated with four datasets (two for each task). These models are CNN and RNN, which include two models (Long Short Term Memory (LSTM)) and GRU (Gated Recurrent Unit). Each model is employed to work consequently over the two mentioned tasks to draw a road map of deep learning mod

Metoprolol is a β₁ adrenergic blocker used in treatment of heart diseases. Metoprolol (100mg) tablets was formulated as a modified release oral system utilizing the concept of bilayer system, first layer contained (30mg) as immediate release and the other (70mg) in the sustained release matrix.  The immediate release layer consisted of lactose or microcrystalline cellulose as diluents with sodium starch glycolate or sodium croscarmellose as disintegrants. The result showed that the layer contains microcrystalline cellulose and 2% sodium starch glycolate gave disintegration time similar to that of conventional metoprolol tartrate tablet. This result was subjected in the subsequent preparation of the bilayer tablet. The