Adverse drug reactions (ADR) are important information for verifying the view of the patient on a particular drug. Regular user comments and reviews have been considered during the data collection process to extract ADR mentions, when the user reported a side effect after taking a specific medication. In the literature, most researchers focused on machine learning techniques to detect ADR. These methods train the classification model using annotated medical review data. Yet, there are still many challenging issues that face ADR extraction, especially the accuracy of detection. The main aim of this study is to propose LSA with ANN classifiers for ADR detection. The findings show the effectiveness of utilizing LSA with ANN in extracting ADR.
The research deals with the topic of (formal formation and its semantic projections in the design interior spaces of daily newspapers buildings) including the research problem summarized by the following question: (does the formal formation do a semantic role in the design of the interior spaces?). The research objective has been evident in discovering strong and weak points in the formal formations and their semantic projections in the design of the interior spaces for the daily newspapers buildings, reaching at the best design foundations, and shedding light on the cognitive and scientific importance of the research, and the objective, temporal and spatial limits in addition to identifying the terms. The theoretical framework includes
... Show MoreA new derivative of PAM, acrylamide was copolymerized with succinic anhydride, and the reaction product reacted with three dyes, anthocyanin, bromophenol, and thymol. The prepared polymers were characterized by X-ray diffraction, FT-IR and UV-visible spectroscopy, proton nuclear magnetic resonance spectrometry, and thermal analysis. FT-IR spectroscopy showed the disappearance of two bands near 3450 and 3380 cm-1 for the stretching vibrations of the primary amine which indicates for the formation of amides. The UV-photolysis of aqueous solutions of different concentrations of the polymers was studied. Polyacrylamide-g-succinic anhydride showed an increase in polymerization under light. An increase of ~ 50% was observed for a 200 mg/L
... Show MoreWe propose a system to detect human faces in color images type BMP by using two methods RGB and YCbCr to determine which is the best one to be used, also determine the effect of applying Low pass filter, Contrast and Brightness on the image. In face detection we try to find the forehead from the binary image by scanning of the image that starts in the middle of the image then precedes by finding the continuous white pixel after continuous black pixel and the maximum width of the white pixel by scanning left and right vertically(sampled w) if the new width is half the previous one the scanning stops.
In this paper we describe several different training algorithms for feed forward neural networks(FFNN). In all of these algorithms we use the gradient of the performance function, energy function, to determine how to adjust the weights such that the performance function is minimized, where the back propagation algorithm has been used to increase the speed of training. The above algorithms have a variety of different computation and thus different type of form of search direction and storage requirements, however non of the above algorithms has a global properties which suited to all problems.
The manual classification of oranges according to their ripeness or flavor takes a long time; furthermore, the classification of ripeness or sweetness by the intensity of the fruit’s color is not uniform between fruit varieties. Sweetness and color are important factors in evaluating the fruits, the fruit’s color may affect the perception of its sweetness. This article aims to study the possibility of predicting the sweetness of orange fruits based on artificial intelligence technology by studying the relationship between the RGB values of orange fruits and the sweetness of those fruits by using the Orange data mining tool. The experiment has applied machine learning algorithms to an orange fruit image dataset and performed a co
... Show MoreIn this paper, RBF-based multistage auto-encoders are used to detect IDS attacks. RBF has numerous applications in various actual life settings. The planned technique involves a two-part multistage auto-encoder and RBF. The multistage auto-encoder is applied to select top and sensitive features from input data. The selected features from the multistage auto-encoder is wired as input to the RBF and the RBF is trained to categorize the input data into two labels: attack or no attack. The experiment was realized using MATLAB2018 on a dataset comprising 175,341 case, each of which involves 42 features and is authenticated using 82,332 case. The developed approach here has been applied for the first time, to the knowledge of the authors, to dete
... Show Morethe study including isolation and identification of candida spp causing UTIs from patintes coming to al-yarmouk hospital