Background:-The Modified Alvarado Scoring
System (MASS) has been reported to be a cheap
and quick diagnostic tool in patients with acute
appendicitis. However, differences in diagnostic
accuracy have been observed if the scores were
applied to various populations and clinical settings.
Objectives:- The purpose of this study was to
evaluate the diagnostic value of Modified Alvarado
Scoring System in patients with acute appendicitis
in our setting.
Methods:-one hundre twenty eight patients, were
included in this study, admitted to Al-Kindy
teaching hospital from June 2009 to June 2010.
Patients’ age ranged from 8 to 56 years (21±10)
they were divided into three groups; paediatrics,
child bearing age females & adult males,. MASS
was calculated for each patient included as the
diagnosis & treatment were done on the bases of
surgeon's clinical decision,confirmation was done
by histopathological examination. Finally statistics
done included negative appendectomy rate,
sensitivity, specificity, positive predictive
value,negative predictive value & accuracy.
Results: - Our negative appendectomy rate was
19.5% (22.22% for paediatrics 40.9% for females
4.2% for males). MASS showed sensitivity of
61%(92.8% for paediatrics 38% for females & 58%
for males), specificity 80% (75% for paediatrics
88% for females & 50% for males), positive
predictive value 92%(92.8% for paediatrics 83%
for females 50% for males), negative predictive
value 33% (75%for paediatrics 50% for females
5% for males) & accuracy 65% (88.9% for
paediatrics 59% for females 58% for males).
Conclusion:- MASS was of limited help to junior
doctors in our setting,clinical assessment &
experience are still the gold standard for acute
appendicitis.
Some of the main challenges in developing an effective network-based intrusion detection system (IDS) include analyzing large network traffic volumes and realizing the decision boundaries between normal and abnormal behaviors. Deploying feature selection together with efficient classifiers in the detection system can overcome these problems. Feature selection finds the most relevant features, thus reduces the dimensionality and complexity to analyze the network traffic. Moreover, using the most relevant features to build the predictive model, reduces the complexity of the developed model, thus reducing the building classifier model time and consequently improves the detection performance. In this study, two different sets of select
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This work was conducted to study the coefficient of performance for solar absorption refrigeration by using direct solar energy using aqueous ammonia 0.45 mass fraction (ammonia – water).The experiments were carried out in solar absorption system .The system consisted of solar collector generator (0.25 m × 0.25 m × 0.04m) and condenser cooled by a water bath followed by liquid receiver and evaporator. The results showed that the maximum generator temperature was (92° - 97°) during June 2009, and the minimum evaporator temperature was (5°C - 10°C) for aqua ammonia system.. It was, also, found that the coefficient of performance, cooling ratio and amount of cooling obtainable increased with increasing maximum generator temperature
... Show MoreThe subject of an valuation of quality of construction projects is one of the topics which it becomes necessary of the absence of the quantity standards in measuring the control works and the quality valuation standards in constructional projects. In the time being it depends on the experience of the workers which leads to an apparent differences in the valuation.
The idea of this research came to put the standards to evaluate the quality of the projects in a special system depending on quantity scale nor quality specifying in order to prepare an expert system “ Crystal “ to apply this special system to able the engineers to valuate the quality of their projects easily and in more accurate ways.
Software Defined Network (SDN) is a new technology that separate the control plane from the data plane. SDN provides a choice in automation and programmability faster than traditional network. It supports the Quality of Service (QoS) for video surveillance application. One of most significant issues in video surveillance is how to find the best path for routing the packets between the source (IP cameras) and destination (monitoring center). The video surveillance system requires fast transmission and reliable delivery and high QoS. To improve the QoS and to achieve the optimal path, the SDN architecture is used in this paper. In addition, different routing algorithms are used with different steps. First, we eva
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Passes the Arab order moment political precision of disruption and discord and
differences of countless between its components and its parts because of the suffering he
endured and the suffering now from internal disturbances as a result of lack of cohesion
relations intra-and cultural, historical and lack of interaction between these components so
that became the focus of the policies and interests is an unprecedented degree , reduced with
the joint Arab action to the minimum, and began to focus on the interests of special
regulations.
This is with regard suffering internally, but externally the regime Arab Regional is absent the
biggest influence on international decisions because of the courtesies which were ca
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An Intelligent Internet of Things network based on an Artificial Intelligent System, can substantially control and reduce the congestion effects in the network. In this paper, an artificial intelligent system is proposed for eliminating the congestion effects in traffic load in an Intelligent Internet of Things network based on a deep learning Convolutional Recurrent Neural Network with a modified Element-wise Attention Gate. The invisible layer of the modified Element-wise Attention Gate structure has self-feedback to increase its long short-term memory. The artificial intelligent system is implemented for next step ahead traffic estimation and clustering the network. In the proposed architecture, each sensing node is adaptive and able to
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Electromyogram (EMG)-based Pattern Recognition (PR) systems for upper-limb prosthesis control provide promising ways to enable an intuitive control of the prostheses with multiple degrees of freedom and fast reaction times. However, the lack of robustness of the PR systems may limit their usability. In this paper, a novel adaptive time windowing framework is proposed to enhance the performance of the PR systems by focusing on their windowing and classification steps. The proposed framework estimates the output probabilities of each class and outputs a movement only if a decision with a probability above a certain threshold is achieved. Otherwise (i.e., all probability values are below the threshold), the window size of the EMG signa
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