Intended for getting good estimates with more accurate results, we must choose the appropriate method of estimation. Most of the equations in classical methods are linear equations and finding analytical solutions to such equations is very difficult. Some estimators are inefficient because of problems in solving these equations. In this paper, we will estimate the survival function of censored data by using one of the most important artificial intelligence algorithms that is called the genetic algorithm to get optimal estimates for parameters Weibull distribution with two parameters. This leads to optimal estimates of the survival function. The genetic algorithm is employed in the method of moment, the least squares method and the weighted

The analysis of time series considers one of the mathematical and statistical methods in explanation of the nature phenomena and its manner in a specific time period.

Because the studying of time series can get by building, analysis the models and then forecasting gives the priority for the practicing in different fields, therefore the identification and selection of the model is of great importance in spite of its difficulties.

The selection of a standard methods has the ability for estimation the errors in the estimated the parameters for the model, and there will be a balance between the suitability and the simplicity of the model.

In the analysis of d

For several applications, it is very important to have an edge detection technique matching human visual contour perception and less sensitive to noise. The edge detection algorithm describes in this paper based on the results obtained by Maximum a posteriori (MAP) and Maximum Entropy (ME) deblurring algorithms. The technique makes a trade-off between sharpening and smoothing the noisy image. One of the advantages of the described algorithm is less sensitive to noise than that given by Marr and Geuen techniques that considered to be the best edge detection algorithms in terms of matching human visual contour perception.

Autism Spectrum Disorder, also known as ASD, is a neurodevelopmental disease that impairs speech, social interaction, and behavior. Machine learning is a field of artificial intelligence that focuses on creating algorithms that can learn patterns and make ASD classification based on input data. The results of using machine learning algorithms to categorize ASD have been inconsistent. More research is needed to improve the accuracy of the classification of ASD. To address this, deep learning such as 1D CNN has been proposed as an alternative for the classification of ASD detection. The proposed techniques are evaluated on publicly available three different ASD datasets (children, Adults, and adolescents). Results strongly suggest that 1D

In this study, a platinum(II) complex ([Pt(H2L)(PPh3)] complex) containing a thiocarbohydrazone as the ligand was tested as an anti-proliferative agent against ovarian adenocarcinoma (Caov-3) and human colorectal adenocarcinoma (HT-29) through MTT assays. Apoptotic markers were tested by the AO/PI double staining assay and DNA fragmentation test. Flow cytometry was conducted to measure cell cycle distribution, while the p53 and caspase-8 pathways were tested via immunofluorescence assay. Results demonstrated that the cytotoxic effect of the Pt(II)- thiocarbohydrazone complexes against Caov-3 and HT-29 cells was highly significant, and this effect triggered the activation of the p53 and caspase-8 pathways. Besides, apoptosis stimulated by th

Natural gas and oil are one of the mainstays of the global economy. However, many issues surround the pipelines that transport these resources, including aging infrastructure, environmental impacts, and vulnerability to sabotage operations. Such issues can result in leakages in these pipelines, requiring significant effort to detect and pinpoint their locations. The objective of this project is to develop and implement a method for detecting oil spills caused by leaking oil pipelines using aerial images captured by a drone equipped with a Raspberry Pi 4. Using the message queuing telemetry transport Internet of Things (MQTT IoT) protocol, the acquired images and the global positioning system (GPS) coordinates of the images' acquisition are

This study aimed to detect of contamination of milk and local soft cheese with Staphylococcus aureus and their enterotoxins with attempt to detect the enterotoxin genes in some isolates of this bacteria. A total of 120 samples, 76 of raw milk and 44 of soft cheese were collected from different markets of Baghdad city. Enterotoxins in these samples were detected by VIDAS Set 2 system and it was found that enterotoxin A is present in a rate of 44.74% in milk samples and in a rate 54.50% in cheese samples. While other enterotoxins B, C, D, E were not found in any rate in any samples.
Through the study 60 isolates obtained from milk and cheeses were identified as Staphylococcus aureus by cultural, morphological and biochemical test by u

Background: Parvovirus B19 is a human pathogenic virus associated with a wide range of clinical conditions. During pregnancy congenital infection with parvovirus B19 can be associated with poor outcome, including miscarriage, fetal anemia and non-immune hydrops.  

Objective: The study aimed to determine the prevalenceof Parvovirus B19 DNA in pregnant women attending the Military hospital in Khartoum, demonstrating the association between the virus and poor pregnancy outcomes.

Subjects and methods: This study was a cross sectional study, testing pregnant Sudanese women whole blood samples (n= 97) for the presence of Parvovirus B1