University of Jordan

About: University of Jordan is a education organization based out in Amman, Jordan. It is known for research contribution in the topics: Population & Medicine. The organization has 7796 authors who have published 13764 publications receiving 213526 citations.

Numerical Radius Inequalities for Certain 2 × 2 Operator Matrices

Abstract: We prove several numerical radius inequalities for certain 2 × 2 operator matrices. Among other inequalities, it is shown that if X, Y, Z, and W are bounded linear operators on a Hilbert space, then $$w\left( \left[\begin{array}{cc} X & Y \\ Z & W \end{array} \right] \right) \geq \max \left(w(X),w(W),\frac{w(Y+Z)}{2},\frac{w(Y-Z)}{2}\right) $$ and $$w\left( \left[\begin{array}{cc}X & Y \\ Z & W\end{array} \right] \right) \leq \max \left( w(X), w(W)\right)+\frac{w(Y+Z)+w(Y-Z)}{2}. $$ As an application of a special case of the second inequality, it is shown that $$\frac{\left\Vert X\right\Vert }{2}+\frac{\left\vert \left\Vert\operatorname{Re}{X}\right\Vert -\frac{\left\Vert X\right\Vert}{2} \right\vert }{4}+\frac{ \left\vert \left\Vert \operatorname{Im}{X} \right\Vert -\frac{\left\Vert X\right\Vert}{2} \right\vert }{4} \leq w(X), $$ which is a considerable improvement of the classical inequality \({\frac{ \left\Vert X\right\Vert }{2}\leq w(X)}\) . Here w(·) and || · || are the numerical radius and the usual operator norm, respectively.

A Deep Learning Based Artificial Neural Network Approach for Intrusion Detection

Abstract: Security of data is considered to be one of the most important concerns in today’s world. Data is vulnerable to various types of intrusion attacks that may reduce the utility of any network or systems. Constantly changing and the complicated nature of intrusion activities on computer networks cannot be dealt with IDSs that are currently operational. Identifying and preventing such attacks is one of the most challenging tasks. Deep Learning is one of the most effective machine learning techniques which is getting popular recently. This paper checks the potential capability of Deep Neural Network as a classifier for the different types of intrusion attacks. A comparative study has also been carried out with Support Vector Machine (SVM). The experimental results show that the accuracy of intrusion detection using Deep Neural Network is satisfactory.

Microbial Growth and Quorum Sensing Antagonist Activities of Herbal Plants Extracts

Abstract: Antimicrobial and antiquorum sensing (AQS) activities of fourteen ethanolic extracts of different parts of eight plants were screened against four Gram-positive, five Gram-negative bacteria and four fungi. Depending on the plant part extract used and the test microorganism, variable activities were recorded at 3 mg per disc. Among the Grampositive bacteria tested, for example, activities of Laurus nobilis bark extract ranged between a 9.5 mm inhibition zone against Bacillus subtilis up to a 25 mm one against methicillin resistant Staphylococcus aureus. Staphylococcus aureus and Aspergillus fumigatus were the most susceptible among bacteria and fungi tested towards other plant parts. Of interest is the tangible antifungal activity of a Tecoma capensis flower extract, which is reported for the first time. However, minimum inhibitory concentrations (MIC's) for both bacteria and fungi were relatively high (0.5-3.0 mg). As for antiquorum sensing activity against Chromobacterium violaceum, superior activity (>17 mm QS inhibition) was associated with Sonchus oleraceus and Laurus nobilis extracts and weak to good activity (8-17 mm) was recorded for other plants. In conclusion, results indicate the potential of these plant extracts in treating microbial infections through cell growth inhibition or quorum sensing antagonism, which is reported for the first time, thus validating their medicinal use.