The analysis of survival and reliability considered of topics and methods of vital statistics at the present time because of their importance in the various demographical, medical, industrial and engineering fields. This research focused generate random data for samples from the probability distribution Generalized Gamma: GG, known as: "Inverse Transformation" Method: ITM, which includes the distribution cycle integration function incomplete Gamma integration making it more difficult classical estimation so will be the need to illustration to the method of numerical approximation and then appreciation of the function of survival function. It was estimated survival function by simulation the way "Monte Carlo". The Entropy method used for the purposes of assessment and estimating and fitting, this along with the use of the classical method. It was to identify the best estimation method through the use of a of comparison criteria: Root of Mean Square Error: RMSE, and the Mean Absolute Percentage Error: MAPE. Sample sizes were selected as (n = 18, 30, 50, 81) which represents the size of data generation n = 18 five-year age groups for the phenomenon being studied and the sample size n = 81 age group represents a unilateral, and replicated the experiment (500) times. The results showed the simulation that the Maximum Likelihood method is the best in the case of small and medium-sized samples where it was applied to the data for five-year age groups suffering from disturbances and confusion of Iraq Household socio-Economic survey: IHSES II2012 while entropy method outperformed in the case of large samples where it was applied to age groups monounsaturated resulting from the use of mathematical method lead to results based on the staging equation data (Formula for Interpolation) placed Sprague (Sprague) and these transactions or what is called Sprague transactions (Sprague multipliers) are used to derive the preparation of deaths and the preparation of the population by unilateral age within the age groups a five-year given the use of the death toll and the preparation of the population in this age group and its environs from a five-year categories by using Excel program where the use of age groups monounsaturated data for accuracy not detect any age is in danger of annihilation.
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A system was used to detect injuries in plant leaves by combining machine learning and the principles of image processing. A small agricultural robot was implemented for fine spraying by identifying infected leaves using image processing technology with four different forward speeds (35, 46, 63 and 80 cm/s). The results revealed that increasing the speed of the agricultural robot led to a decrease in the mount of supplements spraying and a detection percentage of infected plants. They also revealed a decrease in the percentage of supplements spraying by 46.89, 52.94, 63.07 and 76% with different forward speeds compared to the traditional method.
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3D models delivered from digital photogrammetric techniques have massively increased and developed to meet the requirements of many applications. The reliability of these models is basically dependent on the data processing cycle and the adopted tool solution in addition to data quality. Agisoft PhotoScan is a professional image-based 3D modelling software, which seeks to create orderly, precise n 3D content from fixed images. It works with arbitrary images those qualified in both controlled and uncontrolled conditions. Following the recommendations of many users all around the globe, Agisoft PhotoScan, has become an important source to generate precise 3D data for different applications. How reliable is this data for accurate 3D mo
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: zonal are included in phraseological units, form metaphorical names for a person, give him various emotional and evaluative characteristics. This article examines the topic of zoomorphic metaphors that characterize a person in the Russian and Arabic languages in the aspect of their comparative analysis, since the comparative analysis of the metaphorical meanings of animalisms is an important method for studying cultural linguistics, since zoomorphic metaphors are a reflection of culture in a language.
Various theories have been proposed since in last century to predict the first sighting of a new crescent moon. None of them uses the concept of machine and deep learning to process, interpret and simulate patterns hidden in databases. Many of these theories use interpolation and extrapolation techniques to identify sighting regions through such data. In this study, a pattern recognizer artificial neural network was trained to distinguish between visibility regions. Essential parameters of crescent moon sighting were collected from moon sight datasets and used to build an intelligent system of pattern recognition to predict the crescent sight conditions. The proposed ANN learned the datasets with an accuracy of more than 72% in comp
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