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.
In this paper we deal with the problem of ciphering and useful from group isomorphism for construct public key cipher system, Where construction 1-EL- Gamal Algorithm. 2- key- exchange Algorithm
Emotion recognition has important applications in human-computer interaction. Various sources such as facial expressions and speech have been considered for interpreting human emotions. The aim of this paper is to develop an emotion recognition system from facial expressions and speech using a hybrid of machine-learning algorithms in order to enhance the overall performance of human computer communication. For facial emotion recognition, a deep convolutional neural network is used for feature extraction and classification, whereas for speech emotion recognition, the zero-crossing rate, mean, standard deviation and mel frequency cepstral coefficient features are extracted. The extracted features are then fed to a random forest classifier. In
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Nowadays, it is convenient for us to use a search engine to get our needed information. But sometimes it will misunderstand the information because of the different media reports. The Recommender System (RS) is popular to use for every business since it can provide information for users that will attract more revenues for companies. But also, sometimes the system will recommend unneeded information for users. Because of this, this paper provided an architecture of a recommender system that could base on user-oriented preference. This system is called UOP-RS. To make the UOP-RS significantly, this paper focused on movie theatre information and collect the movie database from the IMDb website that provides informatio
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Background: War represents a major human crisis; it destroys communities and results in ingrained consequences for public health and well-being
Objective: We set this study to shed light on the public health status in Iraq after the successive wars, sanctions, sectarian conflicts, and terrorism, in light of certain health indicators.
Design: The primary source of data for this analysis comes from the Iraqi Ministry of Health, and The World Health Organization disease surveillance.
Results: Most of the morbidity indicators are high, even those that are relatively declining recently, are still higher than those repor
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Dust is a frequent contributor to health risks and changes in the climate, one of the most dangerous issues facing people today. Desertification, drought, agricultural practices, and sand and dust storms from neighboring regions bring on this issue. Deep learning (DL) long short-term memory (LSTM) based regression was a proposed solution to increase the forecasting accuracy of dust and monitoring. The proposed system has two parts to detect and monitor the dust; at the first step, the LSTM and dense layers are used to build a system using to detect the dust, while at the second step, the proposed Wireless Sensor Networks (WSN) and Internet of Things (IoT) model is used as a forecasting and monitoring model. The experiment DL system
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In the last few years, following the relative stability of the political, economic, and security environments, Iraq has embarked on a transformation towards an ambitious program of automation across various sectors. However, this automation program faces numerous challenges, including significant investments in technology and training, addressing social impacts, and combating widespread illiteracy