Recurrent strokes can be devastating, often resulting in severe disability or death. However, nearly 90% of the causes of recurrent stroke are modifiable, which means recurrent strokes can be averted by controlling risk factors, which are mainly behavioral and metabolic in nature. Thus, it shows that from the previous works that recurrent stroke prediction model could help in minimizing the possibility of getting recurrent stroke. Previous works have shown promising results in predicting first-time stroke cases with machine learning approaches. However, there are limited works on recurrent stroke prediction using machine learning methods. Hence, this work is proposed to perform an empirical analysis and to investigate machine learning al

Two years field experiment was carried out at Agricultural Fields, College of Agriculture, Baghdad University, Al-Jadriya during 2014-2015 and 2015-2016 to determine the effect of salinity of irrigation water on growth and grain yield of three oat cultivars. The experiments were laid out according to randomized complete blocks design having split plot arrangements with two factors; first factor included three oat cultivars (Shifaa, Hamel and Pimula) while the second factor included three levels of salinity of irrigation water (3, 6 and 9 dS.m-1 ) in addition to the control (river water with salinity level of 1.164 dS.m-1 ) with three replicates. Results revealed a significant effect of salinity of irrigation water on all studied traits. Mea

This research delts with study seven species of seeds and wild varieties wild belonging to the genus Medicago L., these species are: M. constricta Dur., M. coronata (L.) Bartal., M. intertexta (L.) Mill., M. intertexta.var. ciliaris (L.) Hyen., M. laciniata (L.) Mill., M. lupulina L., M. minima (L.) Bartal. and M. sativa L., the research involved characteristics of shapes, dimensions, colors and the nature of the surface ornamentation of seeds and also the hilum site. the seeds forms ranged between crescent, reniform and ovate, in addition there was a clear difference in seeds dimensions in height and width, while, the color has been vary between light brown to brown and dark brown. The nature of the surface ornamentation was smooth, retic

Study showed structure of pecten oculi in the Kestrel Falco tinnunculus L.was
Pleated type and consisted of 17 folds which were thick. While in the Collared Dove
Streptopelia decaocto F. was Vaned type and consisted of 13 folds and it described
thin. The illustrated histological study of pecten oculi folds in the Kestrel and the
Collared Dove was composed of large number of capillaries, large blood vessels and
pigment cells which were few in Kestrel compare with the Collared Dove. The bridge
in the Kestrel and the Collared Dove pecten oculi was consisted of connective tissue,
many pigment cells, and contains on little capillaries and it linked the membrane to
the internal limiting membrane of the retina in the Kes

Islam was keen to get every man and woman a share of those benefits and wanted to marry and urged him. In order to unite efforts and articulates the arrow and clarifies the goal and I have a share in building a sober Islamic society, for all this and other research title is ((modern or planting and its impact on marital happiness)).

The current study was conducted for studying the impact of cold plasma on the expression level of three genes that participate in the biosynthesis of the phenylpropanoid pathway in Ocimum basilicum. These studied genes were cinnamate 4-hydroxylase (c4h), 4-coumarate CoA ligase (4cl), and eugenol O-methyl transferase (eomt). Also, the cold plasma impact was studied on the essential oil components and their relation with the gene expression level. The results demonstrated that cold plasma seeds germination of the treated groups 2 (initially for 3 minutes and 3 minutes after 7 days) ,and group 3(initially for 5 minutes and 3 minutes after 7 days)  were faster than the control group. Also, the height average of the mature plants of

With the vast usage of network services, Security became an important issue for all network types. Various techniques emerged to grant network security; among them is Network Intrusion Detection System (NIDS). Many extant NIDSs actively work against various intrusions, but there are still a number of performance issues including high false alarm rates, and numerous undetected attacks. To keep up with these attacks, some of the academic researchers turned towards machine learning (ML) techniques to create software that automatically predict intrusive and abnormal traffic, another approach is to utilize ML algorithms in enhancing Traditional NIDSs which is a more feasible solution since they are widely spread. To upgrade t

Software-defined networks (SDN) have a centralized control architecture that makes them a tempting target for cyber attackers. One of the major threats is distributed denial of service (DDoS) attacks. It aims to exhaust network resources to make its services unavailable to legitimate users. DDoS attack detection based on machine learning algorithms is considered one of the most used techniques in SDN security. In this paper, four machine learning techniques (Random Forest, K-nearest neighbors, Naive Bayes, and Logistic Regression) have been tested to detect DDoS attacks. Also, a mitigation technique has been used to eliminate the attack effect on SDN. RF and KNN were selected because of their high accuracy results. Three types of ne