Early detection of brain tumors is critical for enhancing treatment options and extending patient survival. Magnetic resonance imaging (MRI) scanning gives more detailed information, such as greater contrast and clarity than any other scanning method. Manually dividing brain tumors from many MRI images collected in clinical practice for cancer diagnosis is a tough and time-consuming task. Tumors and MRI scans of the brain can be discovered using algorithms and machine learning technologies, making the process easier for doctors because MRI images can appear healthy when the person may have a tumor or be malignant. Recently, deep learning techniques based on deep convolutional neural networks have been used to analyze med

The electrical properties of polycrystalline cadmium telluride thin films of different thickness (200,300,400)nm deposited by thermal evaporation onto glass substrates at room temperature and treated at different annealing temperature (373, 423, 473) K are reported. Conductivity measurements have been showed that the conductivity increases from 5.69X10-5 to 0.0011, 0.0001 (?.cm)-1 when the film thickness and annealing temperature increase respectively. This increasing in ?d.c due to increasing the carrier concentration which result from the excess free Te in these films.

Thin films of GexS1-x were fabricated by thermal evaporating under vacuum of 10-5Toor on glass substrate. The effect of increasing of germanium content (x) in sulfide films on the electrical properties like d.c conductivity (σDC), concentration of charge carriers (nH) and the activation energy (Ea) and Hall effect were investigated. The measurements show that (Ea) increases with the increasing of germanium content from 0.1to0.2 while it get to reduces with further addition, while charge carrier density (nH) is found to decrease and increase respectively with germanium content. The results were explained in terms of creating and eliminating of states in the band gap

Thin films of ZnSxSe1-x with different sulfide content(x)
(0, 0.02, 0.04, 0.06, 0.8, and 0.1), thickness (t) (0.3, 0.5, and 0.7 μm) and annealing temperature (Ta) (R.T 373 and 423K) were fabricated by thermal evaporating under vacuum of 10-5 Toor on glass substrate. The results show that the increasing of sulfide content (x)and annealing temperature lead to decrease the d.c conductivity σDC of and concentration of charge carriers (nH) but increases the activation energy (Ea1,Ea2), while the increasing of t increases σDC and nH but decrease (Ea1,Ea2). The results were explained in different terms

The information required for construction quantities surveying is not only generated by various participants in different construction phases but also stored in different forms including graphics, text, tables, or various combinations of the three. To report a bill of quantities (BOQ), the project manager has to continuously excerpt information from various resources and record it on papers. Without adequate staff and time, this repetitive and tedious process is difficult for the project manager to handle properly and thus reduces the effectiveness and the accuracy of the quantities surveying process which creates problems during the design, tender, and construction supervision of construction projects for designers and contractors pract

Electrical properties were studied for Pectin/PVA graphene composites films and the effect of aqueous interaction on their properties. The conductivity and the dielectric constant of this composite are important because Polysaccharide like pectin is increasingly being used in biomedical applications and as nanoparticles coating materials. The Dielectric and conductivity of composite films were compared in dry and wet condition the differences in the results were attributed to the water molecules and the hydrogen bond which connect the three composite compounds (Pectin, PVA and Graphene) together. These connections were allowed the hydrogen and hydroxyl group’s migrations in the composite super molecules. On the other hand, graphene was pr

Using the Neural network as a type of associative memory will be introduced in this paper through the problem of mobile position estimation where mobile estimate its location depending on the signal strength reach to it from several around base stations where the neural network can be implemented inside the mobile. Traditional methods of time of arrival (TOA) and received signal strength (RSS) are used and compared with two analytical methods, optimal positioning method and average positioning method. The data that are used for training are ideal since they can be obtained based on geometry of CDMA cell topology. The test of the two methods TOA and RSS take many cases through a nonlinear path that MS can move through that region. The result

Offline handwritten signature is a type of behavioral biometric-based on an image. Its problem is the accuracy of the verification because once an individual signs, he/she seldom signs the same signature. This is referred to as intra-user variability. This research aims to improve the recognition accuracy of the offline signature. The proposed method is presented by using both signature length normalization and histogram orientation gradient (HOG) for the reason of accuracy improving. In terms of verification, a deep-learning technique using a convolution neural network (CNN) is exploited for building the reference model for a future prediction. Experiments are conducted by utilizing 4,000 genuine as well as 2,000 skilled forged signatu