In this study, two types of local plants were chosen, the first is the plant golden pothos Epipremnum aureum and the second is the Iraqi Sheikh's chin plant Tribulus terrestris L, for the purpose of making a comparison between them in terms of their possession of chemical groups with antioxidant activity in order to use them as a natural alternative to using antioxidants Industrial that cause negative effects on human health, the samples were prepared using the method of water and alcohol extraction (ethanol 70%) for both plants. It revealed the presence of a number of chemical groups (tannins, carbohydrates, phenols, flavonoids, alkaloids) for both plants, the aqueous and alcoholic extracts. Coumarins are only found in the sheikh's chin plant, while steroids are only found in the pothos plant. It was found that the alcoholic extract of the Sheikh's chin plant was best than aqueous extract in terms of antioxidant activity using the DPPH method, as the concentration of 20 mg / ml achieved an effectiveness of 91.2, which is close to control (BHT) 92.3, while the opposite was recorded in the pothos plant as the aqueous extract was better than the alcoholic extract. As the concentration of 20 mg / ml recorded an activity of 89.22, while the alcoholic extract had a significant difference of 65.9, with a significant difference on the level of probability P˂0.01, and for the purpose of demonstrating the efficiency of the process of capturing free radicals of plant extracts, the effective concentration was determined (EC50) and found that the best concentration was achieved to capture 50% of The DPPH complex had an aqueous extract of pothos at 0.5 mg / mL which is very close to the EC50 value for control (vitamin C) followed by the alcoholic extract at 7.75 mg / mL. The Folin-Ciocalteu method was used to find the total content of phenols, it was found that the aqueous extract of the pothos plant had the highest content of phenols as it recorded the highest concentration of 49.33 compared to the alcohol extract 38.05. In contrast to the result of the Sheikh' chin plant, the alcoholic extract recorded the most phenol content at a concentration of 65.11 compared to the aqueous extract which recorded a concentration of 42.15.
In this paper, a handwritten digit classification system is proposed based on the Discrete Wavelet Transform and Spike Neural Network. The system consists of three stages. The first stage is for preprocessing the data and the second stage is for feature extraction, which is based on Discrete Wavelet Transform (DWT). The third stage is for classification and is based on a Spiking Neural Network (SNN). To evaluate the system, two standard databases are used: the MADBase database and the MNIST database. The proposed system achieved a high classification accuracy rate with 99.1% for the MADBase database and 99.9% for the MNIST database
Two unsupervised classifiers for optimum multithreshold are presented; fast Otsu and k-means. The unparametric methods produce an efficient procedure to separate the regions (classes) by select optimum levels, either on the gray levels of image histogram (as Otsu classifier), or on the gray levels of image intensities(as k-mean classifier), which are represent threshold values of the classes. In order to compare between the experimental results of these classifiers, the computation time is recorded and the needed iterations for k-means classifier to converge with optimum classes centers. The variation in the recorded computation time for k-means classifier is discussed.
Regression testing is a crucial phase in the software development lifecycle that makes sure that new changes/updates in the software system don’t introduce defects or don’t affect adversely the existing functionalities. However, as the software systems grow in complexity, the number of test cases in regression suite can become large which results into more testing time and resource consumption. In addition, the presence of redundant and faulty test cases may affect the efficiency of the regression testing process. Therefore, this paper presents a new Hybrid Framework to Exclude Similar & Faulty Test Cases in Regression Testing (ETCPM) that utilizes automated code analysis techniques and historical test execution data to
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