One of the most important problems of IRAQI HEALTH MINISTRY and all healthy instruments in IRAQ is Chronic Diseases because it  have a negative effects on IRAQI population, this is the aim of our study ,to specify the important Chronic diseases which make the population fell weakly, they are six diseases as the IRAQ ministry of health specified (  Diabetes, blood pressure diseases ,Brain diseases ,  Cardiology, Asthma, epilepsy) we got these data from IRAQI HEALTH MINISTRY ,bureau of planning and studies ,for the period 2009-2012,as monthly observations , represent sum of peoples have chronic diseases in Baghdad .

     Our research obj

ANN modeling is used here to predict missing monthly precipitation data in one station of the eight weather stations network in Sulaimani Governorate. Eight models were developed, one for each station as for prediction. The accuracy of prediction obtain is excellent with correlation coefficients between the predicted and the measured values of monthly precipitation ranged from (90% to 97.2%). The eight ANN models are found after many trials for each station and those with the highest correlation coefficient were selected. All the ANN models are found to have a hyperbolic tangent and identity activation functions for the hidden and output layers respectively, with learning rate of (0.4) and momentum term of (0.9), but with different data

In this paper, the using of Non-Homogenous Poisson Processes, with one of the scientific and practical means in the Operations Research had been carried out, which is the Queuing Theory, as those operations are affected by time in their conduct by one function which has a cyclic behavior, called the (Sinusoidal Function). (M_t / M / S) The model was chosen, and it is Single Queue Length with multiple service Channels, and using the estimating scales (QL_s, HOL, HOL_r) was carried out in considering the delay occurring to the customer before his entrance to the service, with the comparison of the best of them in the cases of the overload.

Through the experiments

The successful implementation of deep learning nets opens up possibilities for various applications in viticulture, including disease detection, plant health monitoring, and grapevine variety identification. With the progressive advancements in the domain of deep learning, further advancements and refinements in the models and datasets can be expected, potentially leading to even more accurate and efficient classification systems for grapevine leaves and beyond. Overall, this research provides valuable insights into the potential of deep learning for agricultural applications and paves the way for future studies in this domain. This work employs a convolutional neural network (CNN)-based architecture to perform grapevine leaf image classifi

This paper proposes a new approach, of Clustering Ultrasound images using the Hybrid Filter (CUHF) to determine the gender of the fetus in the early stages. The possible advantage of CUHF, a better result can be achieved when fuzzy c-mean FCM returns incorrect clusters. The proposed approach is conducted in two steps. Firstly, a preprocessing step to decrease the noise presented in ultrasound images by applying the filters: Local Binary Pattern (LBP), median, median and discrete wavelet (DWT),(median, DWT & LBP) and (median & Laplacian) ML. Secondly, implementing Fuzzy C-Mean (FCM) for clustering the resulted images from the first step. Amongst those filters, Median & Laplace has recorded a better accuracy. Our experimental evaluation on re