Computer software is frequently used for medical decision support systems in different areas. Magnetic Resonance Images (MRI) are widely used images for brain classification issue. This paper presents an improved method for brain classification of MRI images. The proposed method contains three phases, which are, feature extraction, dimensionality reduction, and an improved classification technique. In the first phase, the features of MRI images are obtained by discrete wavelet transform (DWT). In the second phase, the features of MRI images have been reduced, using principal component analysis (PCA). In the last (third) stage, an improved classifier is developed. In the proposed classifier, Dragonfly algorithm is used instead of backpropagation as training algorithm for artificial neural network (ANN). Some other recent training-based Neural Networks, SVM, and KNN classifiers are used for comparison with the proposed classifier. The classifiers are utilized to classify image as normal or abnormal MRI human brain image. The results show that the proposed classifier is outperformed the other competing classifiers.
Background: Acute urinary tract infection is a common bacterial infection causing illness in infants and children. At age of seven, 8% of girls and 2% of boys will have at least one episode. Although drinking water and using home remedies are known to help to flush away bacteria and keeps them from sticking to the bladder wall, researches to test the efficacy and safety of hydrochlorothiazide's diuretic effect as adjuvant to the antibiotics in pediatric age groups are lacking, and so this research was to address this subject.
Objectives: To assess the effectiveness and the safety of hydrochlorothiazide as adjuvant therapy to the antibiotics in treating acute urinary tract infect
... Show MoreMethylotrophs bacteria are ubiquitous, and they have the ability to consume single carbon (C1) which makes them biological conversion machines. It is the first study to find facultative methylotrophic bacteria in contaminated soils in Iraq. Conventional PCR was employed to amplify MxaF that encodes methanol dehydrogenase enzyme. DNA templates were extracted from bacteria isolated from five contaminated sites in Basra. The gene specific PCR detected Methylorubrum extorquens as the most dominant species in these environments. The ability of M. extorquens to degrade aliphatic hydrocarbons compound was tested at the laboratory. Within 7 days, gas chromatographic (GC) studies of remaining utilize
... Show MoreA comparative study was done on the adsorption of methyl orange dye (MO) using non-activated and activated corn leaves with hydrochloric acid as an adsorbent material. Scanning electron microscopy (SEM) and Fourier Transform Infrared spectroscopy (FTIR) were utilized to specify the properties of adsorbent material. The effect of several variables (pH, initial dye concentration, temperature, amount of adsorbent and contact time) on the removal efficiency was studied and the results indicated that the adsorption efficiency increases with the increase in the concentration of dye, adsorbent dosage and contact time, while inversely proportional to the increase in pH and temperature for both the treated and untreated corn leav
... Show MoreIn aspect-based sentiment analysis ABSA, implicit aspects extraction is a fine-grained task aim for extracting the hidden aspect in the in-context meaning of the online reviews. Previous methods have shown that handcrafted rules interpolated in neural network architecture are a promising method for this task. In this work, we reduced the needs for the crafted rules that wastefully must be articulated for the new training domains or text data, instead proposing a new architecture relied on the multi-label neural learning. The key idea is to attain the semantic regularities of the explicit and implicit aspects using vectors of word embeddings and interpolate that as a front layer in the Bidirectional Long Short-Term Memory Bi-LSTM. First, we
... Show MoreThis work aims to see the positive association rules and negative association rules in the Apriori algorithm by using cosine correlation analysis. The default and the modified Association Rule Mining algorithm are implemented against the mushroom database to find out the difference of the results. The experimental results showed that the modified Association Rule Mining algorithm could generate negative association rules. The addition of cosine correlation analysis returns a smaller amount of association rules than the amounts of the default Association Rule Mining algorithm. From the top ten association rules, it can be seen that there are different rules between the default and the modified Apriori algorithm. The difference of the obta
... Show MoreMangrove landscaping in the Segara Anakan Lagoon (SAL) is an adaptation pattern of mangrove ecosystems to live and grow in unstable areas. This research aimed to develop a mangrove landscape to mitigate the impacts of ocean waves, currents, and inundation due to climate change. The study was conducted in SAL and Cilacap Coast (CC) using the environmental properties and climate change data. The data obtained were analyzed using mapping and trendline analyses. The results showed that mangrove landscaping in Segara Anakan had four zones with Nypa frutican, Rhizophora styllosa, Aegiceras corniculatum, Rhizophora apiculata, Avicennia marina, Sonneratia alba identified as the best adaptation of mangrove species. Climate change give a high impa
... Show MoreThe research aimed to identify "the effectiveness of educational-learning design according to the model of brain compatibility in achievement among firstmiddle grade students in mathematics", in schools affiliated with the Second Karkh Directorate of Education. To achieve the goal of research, the following zero hypothesis has been formulated: " There is no statistically significant difference at the semantic level (05.0) between the average scores of experimental group students who will study with design accreditation (educational - learning) according to the brain compatibility model and the grades of control group students who will study in the usual way in the achievement thinking test". The research community, which is represented by
... Show MoreThe logistic regression model is an important statistical model showing the relationship between the binary variable and the explanatory variables. The large number of explanations that are usually used to illustrate the response led to the emergence of the problem of linear multiplicity between the explanatory variables that make estimating the parameters of the model not accurate.
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