In recent years, with the rapid development of the current classification system in digital content identification, automatic classification of images has become the most challenging task in the field of computer vision. As can be seen, vision is quite challenging for a system to automatically understand and analyze images, as compared to the vision of humans. Some research papers have been done to address the issue in the low-level current classification system, but the output was restricted only to basic image features. However, similarly, the approaches fail to accurately classify images. For the results expected in this field, such as computer vision, this study proposes a deep learning approach that utilizes a deep learning algorithm.

Thirty nine (12.8%) isolates of Staphylococcus aureus were isolated from 304 healthy human (Nasal swabs). It was found that percentage of males that have S. aureus is more than female's percentage. These isolates (39) were tested with different tests. Twenty seven isolates (69.23 %) were positive for Staphylococcus protein —A (SPA) ,thirty seven ( 94.8 %) were positive for tube coagulase , thirty five ( 89.7 % ) were positive with clumping factor and thirty two ( 82.05 %) had 13 — hemolytic on blood agar. It was found that 100% of the isolates (39 isolates) were positive with one, two or three tests (tube coagulase, clumping factor and SPA).

Two unsupervised classifiers for optimum multithreshold are presented; fast Otsu and k-means. The unparametric methods produce an efficient procedure to separate the regions (classes) by select optimum levels, either on the gray levels of image histogram (as Otsu classifier), or on the gray levels of image intensities(as k-mean classifier), which are represent threshold values of the classes. In order to compare between the experimental results of these classifiers, the computation time is recorded and the needed iterations for k-means classifier to converge with optimum classes centers. The variation in the recorded computation time for k-means classifier is discussed.

Los nombres propios nombran a un ser o a un objeto, distinguiéndolo de los demás seres de su misma clase, se escriben siempre con letra mayúscula a principio de palabra. Los lingüistas hacen mayor hincapié en las divergencias de referencia, entre nombres propios y nombres comunes. Así, suele decirse que el sustantivo propio no tiene como referente ningún concepto. El asunto de la traducción  de los nombres propios parecería una cuestión de gusto personal del traductor pero vemos también que en algunas épocas es más frecuente traducirlos, y en otras, por el contrario, se prefiere dejar esos nombres en su forma original, tal vez con algunas adaptaciones ortográficas. Parecería enton­ces cuestión de modas. Pero, eviden

ان ترجمة الافعال التي تصف احداث جارية وقت لحظة التكلم او في حالة استمرارية تفصح عن اختلافًا جذريا بين الإسبانية والعربية بسبب طبيعة الأفعال التي تصف هذه الأحداث، منها تعقيد بعض الصيغ، خاصة باللغة العربية، وعدم التكافؤ النحوي بين اللغتين في الكثير من البنى اللغوية. بهذا المعنى ، نجد أنه في اللغة العربية ، تتم صياغة هذه الظواهر اللغوية عن طريق أفعال تسمى الأفعال الناقصة التي تختلف طبيعتها ووظيفتها داخل الجملة

Au XIXe siècle, avec l’excès de la passion, le roman français commence à avoir de nouvelles figures. Il tend à être un reflet de la pensée et de la passion. Stendhal et Balzac révèlent ainsi le conflit de l’âme et donnent à leur héros l’impression que tout est permis.

                Ces protagonistes doivent, d’une part, subir des fluctuations psychologiques, et d’autre part, ces héros doivent être capables de faire face aux rancunes et aux conflits destructeurs. Les personnages stendhaliens et balzaciens finissent par croire que leur combat ne portera pas ses fruits, parce que toute passion déséquilibrée mènera, soit à la fol

Big data analysis has important applications in many areas such as sensor networks and connected healthcare. High volume and velocity of big data bring many challenges to data analysis. One possible solution is to summarize the data and provides a manageable data structure to hold a scalable summarization of data for efficient and effective analysis. This research extends our previous work on developing an effective technique to create, organize, access, and maintain summarization of big data and develops algorithms for Bayes classification and entropy discretization of large data sets using the multi-resolution data summarization structure. Bayes classification and data discretization play essential roles in many learning algorithms such a

A study of taxonomic quality of soil algae was conducted with some environmental variables in three sites of local gardens (Kadhimiya, Adhamiya and Dora) within the governorate of Baghdad for the period from October 2016 to March 2017. The study identified 28 species belonging to 16 species in which the predominance of blue green algae (18 species) Followed by Bacillarophyta algae (7 species) and three types of Chlorophyta. The study showed an increase in species of Oscillatoria. The results showed no significant differences between sites in temperature, pH and relative humidity, while there were clear differences between sites for salinity and nutrient The study showed a difference of irrigation water quality and use of different fertilize