IEEE 802.11 i Security and Vulnerabilities

Despite using a variety of comprehensive preventive security measures, the Robust Secure Networks (RSNs) remain vulnerable to a number of attacks. Failure of preventive measures to address all RSN vulnerabilities dictates the need for enhancing the performance of Wireless Intrusion Detection Systems (WIDSs) to detect all attacks on RSNs with less false positive and false negative rates

Personality, top management support, continuance intention to use electronic health record system among nurses in Jordan

Electronic health record system (EHRs) is preferred as standard documentation to track patient information and office visits. It is acclaimed as technological breakthrough capable to improve the healthcare industry’s service delivery and system quality. Accordingly, Jordanian government initiated EHRs implementation in all public hospitals. However, only eleven out of 35 public hospitals have fully implemented EHRs and their usage remains low. Moreover, empirical research associated to the particular concern of EHRs is insufficient and the effort to appraise it is low considering its extensive ongoing implementation. Besides, comprehending and explaining nurses’ continuous intention (CI) to use EHRs are crucial to gauge EHRs usage in Jordan. Considering the problem, this study highlighted on continuous intention (CI) of nurses to use EHRs model by incorporating the following theories; the Unified Theory of Acceptance and Use of Technology (UTAUT), Expectation-Confirmation Theory (ECT) and Five Factor Model (FFM). The model is insinuated to investigate whether UTAUT factors namely effort expectancy, performance expectancy, social influence, facilitating conditions, FFM domains (conscientiousness, extraversion, neuroticism, openness to experience, and agreeableness) and Top Management Support (TMS) predict nurses’ CI to use EHRs. Total responses are 497 nurses. Partial Least Squares technique used for analysis. Results revealed significant positive relationship between UTAUT factors and CI. However, there is no significant evidence of relationship between TMS and CI. The study also disclosed significant mediating influence of performance expectancy on two separate hypotheses concerning two predictors namely agreeableness and openness to experience on CI. Additionally, the study revealed significant moderation impact of conscientiousness on the relationship between both performance expectancy and social influence with CI. The study has illustrated important attention to substantive differences between acceptance and continuance to use behaviors

Childhood Lead Exposure in the Palestinian Authority, Israel, and Jordan: Results from the Middle Eastern Regional Cooperation Project, 1996–2000

In the Middle East, the major sources of lead exposure have been leaded gasoline, lead-contaminated flour from traditional stone mills, focal exposures from small battery plants and smelters, and kohl (blue color) in cosmetics. In 1998–2000, we measured blood lead (PbB) levels in children 2–6 years of age in Israel, Jordan, and the Palestinian Authority (n = 1478), using a fingerstick method. Mean (peak; percentage > 10 μg/dL) PbB levels in Israel (n = 317), the West Bank (n = 344), Jordan (n = 382), and Gaza (n = 435) were 3.2 μg/dL (18.2; 2.2%), 4.2 μg/dL (25.7; 5.2%), 3.2 μg/dL (39.3; < 1%), and 8.6 μg/dL (> 80.0; 17.2%), respectively. High levels in Gaza were all among children living near a battery factory. The findings, taken together with data on time trends in lead emissions and in PbB in children in previous years, indicate the benefits from phasing out of leaded gasoline but state the case for further reductions and investigation of hot spots. The project demonstrated the benefits of regional cooperation in planning and carrying out a jointly designed project

Scanned Documents Forgery Detection Based on Source Scanner Identification

With the increasing number of digital image editing tools, it becomes an easy task to modify any digital image by any user with any level of experience in image editing. One important type of digital images is the scanned documents as they can be used as legal evidence. Therefore, some legal issues may arise when a tampered scanned document cannot be distinguished from an authentic one. In this work we are proposing a novel technique to detect scanned documents tampering, this proposed technique is based on the used scanner identification using features intrinsic to a data-generating sensor

IEEE 802.11i Security and Vulnerabilities Studying and Enhancing WIDS Performance

Despite using a variety of comprehensive preventive security measures, the Robust Secure Networks (RSNs) remain vulnerable to a number of attacks. Failure of preventive measures to address all RSN vulnerabilities dictates the need for enhancing the performance of Wireless Intrusion Detection Systems (WIDSs) to detect all attacks on RSNs with less false positive and false negative rates. This research performs an analytical study for wireless intrusion detection techniques (WIDTs) for detecting attacks on IEEE 802.11i RSNs. The research also verifies the effectiveness of two WIDTs in detecting MAC spoofing Denial of Service (DoS) attacks. These WIDTs are Received Signal Strength Detection Technique (RSSDT) and Round Trip Time Detection Technique (RTTDT) which can run in passive mode, do not require protocol or hardware modifications, and they are computationally inexpensive. We do our verification by applying three new different DoS attacks: TKIP DoS attack, Channel Switch DoS attack, and Quite DoS attack; and study the performance of these WIDTs. Moreover, we study the correlation of the generated alarms from these WIDTs for greater reliability and robustness. Finally, we propose an algorithm to enhance the performance of the correlation of these WIDTs by optimizing the value of the detection threshold; the proposed algorithm lowers the false positive rate

Distributed Data Mining On Grid Environment

Data mining tasks considered a very complex business problem. In this research, we study the enhancement in the speedup of executing data mining tasks on a grid environment. Experiments were performed by running two main data mining algorithms Classification and Clustering algorithms, and one of the data sampling methods for classification task which is Cross Validation. These tasks were executed on large dataset. Gird environment was prepared by installing GridGain framework on the experimental machines which were connected by a LAN. Experimental results show significant enhancement in the speedup when executing data mining tasks on a grid of computing nodes

Enhancement of passive mac spoofing detection techniques

Security Networks (RSNs) vulnerabilities enforces many researchers to revise robust and reliable Wireless Intrusion Detection Techniques (WIDTs). In this paper we propose an algorithm to enhance the performance of the correlation of two WIDTs in detecting MAC spoofing Denial of Service (DoS) attacks. The two techniques are the Received Signal Strength Detection Technique (RSSDT) and Round Trip Time Detection Technique (RTTDT). Two sets of experiments were done to evaluate the proposed algorithm. Absence of any false negatives and low number of false positives in all experiments demonstrated the effectiveness of these techniques

تنقيب البيانات الموزع على شبكة

Data mining tasks considered a very complex business problem. In this research, we study the enhancement in the speedup of executing data mining tasks on a grid environment. Experiments were performed by running two main data mining algorithms Classification and Clustering algorithms, and one of the data sampling methods for classification task which is Cross Validation. These tasks were executed on large dataset. Gird environment was prepared by installing GridGain framework on the experimental machines which were connected by a LAN. Experimental results show significant enhancement in the speedup when executing data mining tasks on a grid of computing nodes.لا يوج

A Deep Learning-Based Diagnosis System for COVID-19 Detection and Pneumonia Screening Using CT Imaging

Background: Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) is a global threat impacting the lives of millions of people worldwide. Automated detection of lung infections from Computed Tomography scans represents an excellent alternative; however, segmenting infected regions from CT slices encounters many challenges. Objective: Developing a diagnosis system based on deep learning techniques to detect and quantify COVID-19 infection and pneumonia screening using CT imaging. Method: Contrast Limited Adaptive Histogram Equalization pre-processing method was used to remove the noise and intensity in homogeneity. Black slices were also removed to crop only the region of interest containing the lungs. A U-net architecture, based on CNN encoder and CNN decoder approaches, is then introduced for a fast and precise image segmentation to obtain the lung and infection segmentation models. For better estimation of skill on unseen data, a fourfold cross-validation as a resampling procedure has been used. A three-layered CNN architecture, with additional fully connected layers followed by a Softmax layer, was used for classification. Lung and infection volumes have been reconstructed to allow volume ratio computing and obtain infection rate. Results: Starting with the 20 CT scan cases, data has been divided into 70% for the training dataset and 30% for the validation dataset. Experimental results demonstrated that the proposed system achieves a dice score of 0.98 and 0.91 for the lung and infection segmentation tasks, respectively, and an accuracy of 0.98 for the classification task. Conclusions: The proposed workflow aimed at obtaining good performances for the different system’s components, and at the same time, dealing with reduced datasets used for training