Recent years have seen an explosion in graph data from a variety of scientific, social and technological fields. From these fields, emotion recognition is an interesting research area because it finds many applications in real life such as in effective social robotics to increase the interactivity of the robot with human, driver safety during driving, pain monitoring during surgery etc. A novel facial emotion recognition based on graph mining has been proposed in this paper to make a paradigm shift in the way of representing the face region, where the face region is represented as a graph of nodes and edges and the gSpan frequent sub-graphs mining algorithm is used to find the frequent sub-structures in the graph database of each emotion. T

    Petrophysical properties including volume of shale, porosity and water saturation are significance parameters for petroleum companies in evaluating the reservoirs and determining the hydrocarbon zones. These can be achieved through conventional petrophysical calculations from the well logs data such as gamma ray, sonic, neutron, density and deep resistivity. The well logging operations of the targeted limestone Mishrif reservoirs in Ns-X Well, Nasiriya Oilfield, south of Iraq could not be done due to some problems related to the well condition. The gamma ray log was the only recorded log through the cased borehole. Therefore, evaluating the reservoirs and estimating the perforation zones has not performed and the drilled well was

     Agriculture improvement is a national economic issue that extremely depends on productivity. The explanation of disease detection in plants plays a significant role in the agriculture field. Accurate prediction of the plant disease can help treat the leaf as early as possible, which controls the economic loss. This paper aims to use the Image processing techniques with Convolutional Neural Network (CNN). It is one of the deep learning techniques to classify and detect plant leaf diseases. A publicly available Plant village dataset was used, which consists of 15 classes, including 12 diseases classes and 3 healthy classes.  The data augmentation techniques have been used. In addition to dropout and weight reg

     The growing relevance of printed and digitalized hand-written characters has necessitated the need for convalescent automatic recognition of characters in Optical Character Recognition (OCR). Among the handwritten characters, Arabic is one of those with special attention due to its distinctive nature, and the inherent challenges in its recognition systems. This distinctiveness of Arabic characters, with the difference in personal writing styles and proficiency, are complicating the effectiveness of its online handwritten recognition systems. This research, based on limitations and scope of previous related studies, studied the recognition of Arabic isolated characters through the identification o

Franck-Condon factors (FCF), which are proportional to peak intensities, were calculated as the square of the overlap between the initial and final state wavefunctions in order to calculate of molecule Be9Cl35 their overlap using numerical Integration for the vibrational band (′v =0 - 4) to (v″=0 –4) of the electronic transition A 2Π - Χ 2 Σ and by using an appropriate Potential for each of them .for vibrational ground and first excited states.this new vibrational level must be instantaneously compatible with the nuclear positions and momenta of the vibrational level of the molecule in the originating electronic state. In the semiclassical picture of vibrations (oscillations) of a simple harmonic oscillator, the necessary condit

خلال الربع الأخير من القرن العشرين ، شهد الاقتصاد العالمي تحولا في مختلف المجالات التجارية والتكنولوجية والمالية التي غيرت هيكلها وأنتجت وضعا جديدا يتمثل بشكل رئيس في زيادة حركة رأس المال الأجنبي والتوسع السريع للإنتاج الدولي والتجارة بالإضافة إلى التطور التكنولوجي الهائل ونقل التكنولوجيا ، مما أدى إلى هوس الدول بالمنافسة على المستوى العالمي والسعي لدخول الأسواق الدولية وتحسين قدرتها التنافسية. وت

Abstract Iraq has an important place in the regional and international balance, and the competition among countries has increased in rapprochement with Iraq, not only for its geography or its privileged location, but because it has many other components that make regional and international countries want to strengthen their relations with it (Regional and international powers). In addition to that, Iraq has become increasingly important for these international and regional powers after its victory over the terrorist organization (ISIS), which proved an important fact that Iraq represents the most important pillars in the international balance.

This research was conducted to investigate the water quality of the lesser Zab
river, to evaluate the water suitability for agricultural usings and report the effect of
human activities on this water. Samples were collected along the river stream
starting from Dukan area till the convergence with Tigris River. All the tests were
conducted at the labs of Environmental sciences and Technology College /
University of Mosul and Erbil water project/Erbil city.
The results of using Piper diagram showed that the water is alkine, due to the effect of the origional rock's components of that area on the water quality. While the using of Stiff diagram elucidate that the water samples contain ions belong to the area of the lesser Z

     Foreign Object Debris (FOD) is defined as one of the major problems in the airline maintenance industry, reducing the levels of safety. A foreign object which may result in causing serious damage to an airplane, including engine problems and personal safety risks. Therefore, it is critical to detect FOD in place to guarantee the safety of airplanes flying. FOD detection systems in the past lacked an effective method for automatic material recognition as well as high speed and accuracy in detecting materials. This paper proposes the FOD model using a variety of feature extraction approaches like Gray-level Co-occurrence Matrix (GLCM) and Linear Discriminant Analysis (LDA) to extract features and Deep Learning (DL) for classifi

—Medical images have recently played a significant role in the diagnosis and detection of various diseases. Medical imaging can provide a means of direct visualization to observe through the human body and notice the small anatomical change and biological processes associated by different biological and physical parameters. To achieve a more accurate and reliable diagnosis, nowadays, varieties of computer aided detection (CAD) and computer-aided diagnosis (CADx) approaches have been established to help interpretation of the medical images. The CAD has become among the many major research subjects in diagnostic radiology and medical imaging. In this work we study the improvement in accuracy of detection of CAD system when comb