Survival analysis is widely applied in data describing for the life time of item until the occurrence of an event of interest such as death or another event of understudy . The purpose of this paper is to use the dynamic approach in the deep learning neural network method, where in this method a dynamic neural network that suits the nature of discrete survival data and time varying effect. This neural network is based on the Levenberg-Marquardt (L-M) algorithm in training, and the method is called Proposed Dynamic Artificial Neural Network (PDANN). Then a comparison was made with another method that depends entirely on the Bayes methodology is called Maximum A Posterior (MAP) method. This method was carried out using numerical algorithms re

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.

Topic management is the awareness of how speakers deal with initiating, developing, changing, and ending a topic and how they fix the relationship when a misunderstanding occurs. It is such an important unit of conversation as it includes the transition from one strategy to the other to be accomplished in a systematic and orderly manner. These strategies are impaired in dementia patients thus lead to communication breakdown. This study aims at detecting the dementia patients' topic management strategies in selected speeches and answering the questions of which of these strategies are fully or partially detected in these speeches. The researchers use a qualitative method to examine the speeches of those patients and they adopt an eclectic

In aired and semiarid areas like Iraq, saline soil may be considered one of the major concerns. In addition to environmental effects, they may produce significant geotechnical hazards that could interrupt the structure stability depending on the salt type and its concentration. So, it is crucial to identify the degree of the soil salinity with a proper tool for getting a qualified assessment and consequently offering a suitable treatment. In this paper, the electrical resistivity technique has been employed to detect the degree of soil salinity by considering a new electronic system. The system used a single-phase Direct Current (DC) to Alternating Current (AC) inverter accompanied by a transformer. Natural soils became artificially saline

The microbend sensor is designed to experience a light loss when force is applied to the sensor. The periodic microbends cause propagating light to couple into higher order modes, the existing higher order modes become unguided modes. Three models of deform cells are fabricated at (3, 5, 8) mm pitchand tested by using MMF and laser source at 850 nm. The maximum output power of (8, 5, 3)mm model is (3, 2.7, 2.55)nW respectively at applied force 5N and the minimum value is (1.9, 1.65, 1.5)nW respectively at 60N.The strain is calculated at different microbend cells ,and the best sensitivity of this sensor for cell 8mm is equal to 0.6nW/N.

In this work, satellite images classification for Al Chabaish marshes and the area surrounding district in (Dhi Qar) province for years 1990,2000 and 2015 using two software programming (MATLAB 7.11 and ERDAS imagine 2014) is presented. Proposed supervised classification method (Modified Vector Quantization) using MATLAB software and supervised classification method (Maximum likelihood Classifier) using ERDAS imagine have been used, in order to get most accurate results and compare these methods. The changes that taken place in year 2000 comparing with 1990 and in year 2015 comparing with 2000 are calculated. The results from classification indicated that water and vegetation are decreased, while barren land, alluvial soil and shallow water