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 advancements in Information and Communication Technology (ICT), within the previous decades, has significantly changed people’s transmit or store their information over the Internet or networks. So, one of the main challenges is to keep these information safe against attacks. Many researchers and institutions realized the importance and benefits of cryptography in achieving the efficiency and effectiveness of various aspects of secure communication.This work adopts a novel technique for secure data cryptosystem based on chaos theory. The proposed algorithm generate 2-Dimensional key matrix having the same dimensions of the original image that includes random numbers obtained from the 1-Dimensional logistic chaotic map for given con

In this work, a simple and very sensitive cloud point extraction (CPE) process was developed for the determination of trace amount of metoclopramide hydrochloride (MTH) in pharmaceutical dosage forms. The method is based on the extraction of the azo-dye results from the coupling reaction of diazotized MTH with p-coumaric acid (p-CA) using nonionic surfactant (Triton X114). The extracted azo-dye in the surfactant rich phase was dissolved in ethanol and detected spectrophotometrically at λmax 480 nm. The reaction was studied using both batch and CPE methods (with and without extraction) and a simple comparison between the two methods was performed. The conditions that may be affected by the extraction process and the sensitivity of m

This paper  deals  to how to estimate points non measured spatial data when the number of its terms (sample spatial) a few, that are not preferred for the estimation process, because we also know that whenever if the data is large, the estimation results of the points non measured to be better and thus the variance estimate less, so the idea of this paper is how to take advantage of the data other secondary (auxiliary), which have a strong correlation with the primary data (basic) to be estimated single points of non-measured, as well as measuring the variance estimate, has been the use of technique Co-kriging in this field to build predictions spatial estimation process, and then we applied this idea to real data in th

The density-based spatial clustering for applications with noise (DBSCAN) is one of the most popular applications of clustering in data mining, and it is used to identify useful patterns and interesting distributions in the underlying data. Aggregation methods for classifying nonlinear aggregated data. In particular, DNA methylations, gene expression. That show the differentially skewed by distance sites and grouped nonlinearly by cancer daisies and the change Situations for gene excretion on it. Under these conditions, DBSCAN is expected to have a desirable clustering feature i that can be used to show the results of the changes. This research reviews the DBSCAN and compares its performance with other algorithms, such as the tradit

The current research deals with the subject of spatial transformations and visual construction in contemporary theater. How the visual system works to create spatial diversity in the contemporary Iraqi theater performance and how visual construction contributes to a spatial development process capable of building a tourism system that creates an architectural architecture that leaves the topography of the scriptural architecture. And the production of various indications and patterns in the scene of theatrical presentation in order to produce the new foundational meaning by creating a new structure that leads to diversity and diversity in the visual system and the beginning of visual constructions and their applications. Knowledge and te

The speech recognition system has been widely used by many researchers using different
methods to fulfill a fast and accurate system. Speech signal recognition is a typical
classification problem, which generally includes two main parts: feature extraction and
classification. In this paper, a new approach to achieve speech recognition task is proposed by
using transformation techniques for feature extraction methods; namely, slantlet transform
(SLT), discrete wavelet transforms (DWT) type Daubechies Db1 and Db4. Furthermore, a
modified artificial neural network (ANN) with dynamic time warping (DTW) algorithm is
developed to train a speech recognition system to be used for classification and recognition
purposes. T

This study deals with the grammatical processing ability of English Subordinate Clauses which can account for variance in word recognition and production skills. The study aims at: A) Assessing whether the students can recognize correct usage and comprehension of different types of Subordinate Clauses English grammar sentences by using appropriate word. B) Showing if the students can produce the correct type of Subordinate Clauses that should be used in English grammar sentences. To achieve these aims, a test has been conducted and distributed on 50 students at third stage at the College of Education (Ibn-Rushd) for the academic year 2012-2013. A test is exposed to a jury of experts for the purpose of ascertaining their validity. The spli