It is the regression analysis is the foundation stone of knowledge of statistics , which mostly depends on the ordinary least square method , but as is well known that the way the above mentioned her several conditions to operate accurately and the results can be unreliable , add to that the lack of certain conditions make it impossible to complete the work and analysis method and among those conditions are the multi-co linearity problem , and we are in the process of detected that problem between the independent variables using farrar –glauber test , in addition to the requirement linearity data and the lack of the condition last has been resorting to the

In this paper, thirty six samples of canned vegetables were collected randomly from
different markets in Baghdad city from October 2013 till March 2014. The study
includes identifying the concentration of some heavy metals (lead, nickel, zinc and iron)
by flameless atomic absorption spectrophotometery. It was found that the higher
concentrations of heavy metals in canned vegetables, was lead 1.179 ppm in olive,
nickel 0.9078 ppm in olive, while zinc 10.143 ppm green peas and iron 90.601ppm in
white asparagus; but the lower concentrations represents with lead 0.0021 ppm in green
asparagus, nickel 0.0202 ppm in mushroom, while zinc 0.528 ppm in white asparagus
and iron 4.061 ppm in green peas. Canned food has been r

    There are many events that took place in Al Mosul province between 2013 and 2018. These events led to many changes in the area under study. These changes involved a decrease in agricultural crops and water due to the population leaving the area. Therefore, it is imperative that planners, decision-makers, and development officials intervene in order to restore the region's activity in terms of environment and agriculture. The aim of this research is to use remote sensing (RS) technique and geographic information system (GIS) to detect the change that occurred in the mentioned period. This was achieved through the use of the ArcGIS software package for the purpose of assessing the state of lands of agricultural crops and

Doppler broadening technique is suggested to monitor the development of tumours. It depends on the sensitivity of positronium (Ps) annihilation parameters to the sub- microstructural changes in biological tissues. This technique uses high resolution HpGe detector to measure the lineshape parameters (S and W) in normal mice's mammary tissues and adenocarcinoma mammary tissues as a function of tumour growth. The results demonstrate that the central parameter (S) decreases and the wing parameter (W) increases as the tumour grow. It is found that the S parameter changes considerably with the distribution of voids which are affected by the tumour development. Therefore the present technique can successfully be employed to monitor the developm

Detection of early clinical keratoconus (KCN) is a challenging task, even for expert clinicians. In this study, we propose a deep learning (DL) model to address this challenge. We first used Xception and InceptionResNetV2 DL architectures to extract features from three different corneal maps collected from 1371 eyes examined in an eye clinic in Egypt. We then fused features using Xception and InceptionResNetV2 to detect subclinical forms of KCN more accurately and robustly. We obtained an area under the receiver operating characteristic curves (AUC) of 0.99 and an accuracy range of 97–100% to distinguish normal eyes from eyes with subclinical and established KCN. We further validated the model based on an independent dataset with

Lymphoma is a cancer arising from B or T lymphocytes that are central immune system ‎components. It is one of the three most common cancers encountered in the canine; ‎lymphoma affects middle-aged to older dogs and usually stems from lymphatic tissues, ‎such as lymph nodes, lymphoid tissue, or spleen. Despite the advance in the management of ‎canine lymphoma, a better understanding of the subtype and tumor aggressiveness is still ‎crucial for improved clinical diagnosis to differentiate malignancy from hyperplastic ‎conditions and to improve decision-making around treating and what treatment type to use. ‎This study aimed to evaluate a potential novel biomarker related to iron metabolism,

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

Extracting moving object from video sequence is one of the most important steps
in the video-based analysis. Background subtraction is the most commonly used
moving object detection methods in video, in which the extracted object will be
feed to a higher-level process ( i.e. object localization, object tracking ).
The main requirement of background subtraction method is to construct a
stationary background model and then to compare every new coming frame with it
in order to detect the moving object.
Relied on the supposition that the background occurs with the higher appearance
frequency, a proposed background reconstruction algorithm has been presented
based on pixel intensity classification ( PIC ) approach.

The main work of this paper is devoted to a new technique of constructing approximated solutions for linear delay differential equations using the basis functions power series functions with the aid of Weighted residual methods (collocations method, Galerkin’s method and least square method).