Malaria is a curative disease, with therapeutics available for patients, such as drugs that can prevent future malaria infections in countries vulnerable to malaria. Though, there is no effective malaria vaccine until now, although it is an interesting research area in medicine. Local descriptors of blood smear image are exploited in this paper to solve parasitized malaria infection detection problem. Swarm intelligence is used to separate the red blood cells from the background of the blood slide image in adaptive manner. After that, the effective corner points are detected and localized using Harris corner detection method. Two types of local descriptors are generated from the local regions of the effective corners which are Gabor based features and color based features. The extracted features are finally fed to Deep Belief Network (DBN) for classification purpose. Different tests were performed and different combinations of feature types are attempted. The achieved results showed that when using combined vectors of local descriptors, the system gives the desired accuracy which is 100%. The achieved result demonstrates the effectiveness of using local descriptors in solving malaria infection detection problem.
Objectives: To assess the pediatric nurses' knowledge about the nosocomial infection owl), and to fud out the
relatiouships between their knowledge about the nosocomial infection and demographic data.
Methodology: A descriptive study was carried out at neonatal intensive care units OVICUs) of Baghdad
Pediatric Teaching Hospitals. It was started from the end of April to the end of October, 2008. A purposive
sample of (28) pediatric nurses were selected. The data were collected by self-administered questiormaire. The
validity of the questionnaire was detemined through a panel of experts, while its reliability was detemined
through the pilot study. The data were analyzed by descriptive and inferential statistics through th
Most recent studies have focused on using modern intelligent techniques spatially, such as those
developed in the Intruder Detection Module (IDS). Such techniques have been built based on modern
artificial intelligence-based modules. Those modules act like a human brain. Thus, they should have had the
ability to learn and recognize what they had learned. The importance of developing such systems came after
the requests of customers and establishments to preserve their properties and avoid intruders’ damage. This
would be provided by an intelligent module that ensures the correct alarm. Thus, an interior visual intruder
detection module depending on Multi-Connect Architecture Associative Memory (MCA)
Several Intrusion Detection Systems (IDS) have been proposed in the current decade. Most datasets which associate with intrusion detection dataset suffer from an imbalance class problem. This problem limits the performance of classifier for minority classes. This paper has presented a novel class imbalance processing technology for large scale multiclass dataset, referred to as BMCD. Our algorithm is based on adapting the Synthetic Minority Over-Sampling Technique (SMOTE) with multiclass dataset to improve the detection rate of minority classes while ensuring efficiency. In this work we have been combined five individual CICIDS2017 dataset to create one multiclass dataset which contains several types of attacks. To prove the eff
... Show MoreThe Present investigation includes the isolation and identification of Pseudomonas aeruginosa for different cases of hospital contamination from 1/ 6/2003 to 30/9/2004, the identification of bacteria depended on morphological , cultural and biochemical characters, 37 of isolates were diagnosed from 70 smears from wounds and burns beside 25 isolates were identified from 200 smears taken from operation theater and hospital wards including the floors , walls , sources of light and operation equipment the sensitivity of all isolates to antibiotic were done , which exhibited complete sensitivity to Ciprofloxacin , Ceftraixon, Tobromycin and Gentamysin ,while they were complete resist to Amoxcillin , Tetracyclin , Nitrofurantion , Clindamycin C
... Show MoreThis study detects the presence of cholesterol in an Iraqi plant named Suaeda baccata Forsk of the family Chenopodiacae, wildly and widely grown in Iraq. The absence of any publication concerning the sterol content of this Suaeda specie, and the industrial importance of cholesterol depending on its role as a precursor in the synthesis of some hormones, like progesterone, acquired this study its value. The investigations revealed the presence of cholesterol that was proved by TLC together with the standard compound cholesterol, and anisaldehyde spray reagent using three different solvent systems, then authenticated by HPLC, in which the reten
... Show MoreActive worms have posed a major security threat to the Internet, and many research efforts have focused on them. This paper is interested in internet worm that spreads via TCP, which accounts for the majority of internet traffic. It presents an approach that use a hybrid solution between two detection algorithms: behavior base detection and signature base detection to have the features of each of them. The aim of this study is to have a good solution of detecting worm and stealthy worm with the feature of the speed. This proposal was designed in distributed collaborative scheme based on the small-world network model to effectively improve the system performance.