Earth’s climate changes rapidly due to the increases in human demands and rapid economic growth. These changes will affect the entire biosphere, mostly in negative ways. Predicting future changes will put us in a better position to minimize their catastrophic effects and to understand how humans can cope with the new changes beforehand. In this research, previous global climate data set observations from 1961-1990 have been used to predict the future climate change scenario for 2010-2039. The data were processed with Idrisi Andes software and the final Köppen-Geiger map was created with ArcGIS software. Based on Köppen climate classification, it was found that areas of Equator, Arid Steppes, and Snow will decrease by 3.9 %, 2.96%, an

     Governmental establishments are maintaining historical data for job applicants for future analysis of predication, improvement of benefits, profits, and development of organizations and institutions. In e-government, a decision can be made about job seekers after mining in their information that will lead to a beneficial insight. This paper proposes the development and implementation of an applicant's appropriate job prediction system to suit his or her skills using web content classification algorithms (Logit Boost, j48, PART, Hoeffding Tree, Naive Bayes). Furthermore, the results of the classification algorithms are compared based on data sets called "job classification data" sets. Experimental results indicate

A proposed feature extraction algorithm for handwriting Arabic words. The proposed method uses a 4 levels discrete wavelet transform (DWT) on binary image. sliding window on wavelet space and computes the stander derivation for each window. The extracted features were classified with multiple Support Vector Machine (SVM) classifiers. The proposed method simulated with a proposed data set from different writers. The experimental results of the simulation show 94.44% recognition rate.

In this paper an eco-epidemiological system has been proposed and studied analytically as well as numerically. The boundedness, existence and uniqueness of the solution are discussed. The local and global stability of all possible equilibrium point are investigated. The global dynamics is studied numerically. It is obtained that system has rich in dynamics including Hopf bifurcation.

Over the last few years the role of microorganisms in the pathogenesis of atherosclerosis has been widely discussed.  Advance in basic science have established a fundamental role for inflammation immediating all stages of cardiovascular diseases. Chlamydia pneumoniae activates immune cells to produce cytokines such us TNF-α that are important contributor to atherosclerosis. All blood samples were assayed for molecular detection of Chlamydia pneumoniae by using conventional polymerase chain reaction (PCR) relying on16SrRNAand the level of serum TNF-α measured by enzyme linked immunosorbent assay (ELISA).Seventy patients who suffering from CVD (angina, myocardial Infarction and atherosclerosis) aged between 33-86 y

Deep learning has recently received a lot of attention as a feasible solution to a variety of artificial intelligence difficulties. Convolutional neural networks (CNNs) outperform other deep learning architectures in the application of object identification and recognition when compared to other machine learning methods. Speech recognition, pattern analysis, and image identification, all benefit from deep neural networks. When performing image operations on noisy images, such as fog removal or low light enhancement, image processing methods such as filtering or image enhancement are required. The study shows the effect of using Multi-scale deep learning Context Aggregation Network CAN on Bilateral Filtering Approximation (BFA) for d

In this paper, an efficient method for compressing color image is presented. It allows progressive transmission and zooming of the image without need to extra storage. The proposed method is going to be accomplished using cubic Bezier surface (CBI) representation on wide area of images in order to prune the image component that shows large scale variation. Then, the produced cubic Bezier surface is subtracted from the image signal to get the residue component. Then, bi-orthogonal wavelet transform is applied to decompose the residue component. Both scalar quantization and quad tree coding steps are applied on the produced wavelet sub bands. Finally, adaptive shift coding is applied to handle the remaining statistical redundancy and attain e

Breast cancer is one of the most common malignant diseases among women;
Mammography is at present one of the available method for early detection of
abnormalities which is related to breast cancer. There are different lesions that are
breast cancer characteristic such as masses and calcifications which can be detected
trough this technique. This paper proposes a computer aided diagnostic system for
the extraction of features like masses and calcifications lesions in mammograms for
early detection of breast cancer. The proposed technique is based on a two-step
procedure: (a) unsupervised segmentation method includes two stages performed
using the minimum distance (MD) criterion, (b) feature extraction based on Gray

Anemia is one of the common types of blood diseases, it lead to lack of number of RBCs (Red Blood Cell) and amount hemoglobin level in the blood is lower than normal.
In this paper a new algorithm is presented to recognize Anemia in digital images based on moment variant. The algorithm is accomplished using the following phases: preprocessing, segmentation, feature extraction and classification (using Decision Tree), the extracted features that are used for classification are Moment Invariant and Geometric Feature.
The Best obtained classification rates was 84% is obtained when using Moment Invariants features and 74 % is obtained when using Geometric Feature. Results indicate that the proposed algorithm is very effective in detect