Islamic knowledge is gathered through the understanding the Al-Quran. It requires ontology which can capture the knowledge and present it in a machine readable structured. However, current ontology approaches is irrelevant and inaccuracy in producing true concepts of Al-Quran knowledge, because it used traditional methods that only define the concepts of knowledge without connecting to a related theme of knowledge. The themes of knowledge are important to provide true meaning and explanation of Al-Quran knowledge classification. The main aims of this paper are to demonstrate the development of ontology Al-Quran and method used for searching the Al-Quran knowledge using the semantic search approach. Expert review has been applied to validate the ontology model and evaluate the relevance and precision of searching results.
Breast cancer has been recently considered as one of the broadly spread diseases that causes death among women. Early disease diagnosis is a critical aim in building the treatment policies and is extremely related to safety of patient. Therefore, there is a necessity for computer aided detection (CAD) in order to provide accurate and rapid diagnosis for breast cancer. Recently, many classification models utilizing machine learning approaches have been adopted and modified to diagnose breast cancer disease. Moreover, the performance of each model depends on different compositions such as the number and type of data features and the parameters of model. In order to enhance the performance of classification model, this research proposes a model using modified K-means algorithm to create a new training dataset of breast cancer which can highly improve the performance of support vector machine model. A modified K-means algorithm is also proposed to build a high quality training dataset that contributes significantly to reduce the training time of classifiers, and improve the performance of classifier. The proposed model handles the noise and irregularity in data and produce high quality dataset which represents all the cases of disease. The two recognized datasets Wisconsin Breast Cancer (WBC) and Wisconsin Diagnostic Breast Cancer (WDBC) have been used to examine and appraise the performance of the proposed model. The experimental results show that the proposed model has a significant performance compared to other previous works and with accuracy level of 98.067%, sensitivity of 100%, specificity of 94.811%, precision of 97.011% and finally with area under the curve related to the receiver operating characteristic of 97.406%.
In general, multidimensional data (mobile application for example) contain a large number of unnecessary information. Web app users find it difficult to get the information needed quickly and effectively due to the sheer volume of data (big data produced per second). In this paper, we tend to study the data mining in web personalization using blended deep learning model. So, one of the effective solutions to this problem is web personalization. As well as, explore how this model helps to analyze and estimate the huge amounts of operations. Providing personalized recommendations to improve reliability depends on the web application using useful information in the web application. The results of this research are important for the training and testing of large data sets for a map of deep mixed learning based on the model of back-spread neural network. The HADOOP framework was used to perform a number of experiments in a different environment with a learning rate between -1 and +1. Also, using the number of techniques to evaluate the number of parameters, true positive cases are represent and fall into positive cases in this example to evaluate the proposed model.
Currently, manual system is being used for the matches booking is too old which need great time and efforts from both of the customers whom used to wait in long queues to get their tickets and the stadium staff whom also spend a lot of time trying to organize the booking process. In addition, cash payment with all of its disadvantages is accepted. The proposed mobile ticketing system will provide an easy way for users to booking their e-tickets for their favorite matches from anywhere and anytime, only it needs to a personal computer or a smartphone and connect to an internet. Therefore, without the need to wait in long queues for a long time. The user will get his e-ticket in a way that will gain a lot of time and efforts for both of the users and the staff. In this study, “the Method of Booking e-tickets for Iraqi Stadiums using the Smartphone” will be introduce. The study has been implemented using five major steps. The prototype will go through Awareness of the problem that will happen through the interview with the person who is in charge at stadium, and the problems that you are trying to solve as well. The main interface of the system will contain some of the box to enter the information about the customers, thus they can make the booking easily. Development, evaluation are the last steps to finalize the system usefulness to be implement in real life. Nowadays, the mobile becomes the most important thing that plays main role in many activities that we need it in daily life. Therefore, this study will depend on a mobile phone application, which is expect to give ease and effectiveness to the users and the administrator’s requirements in saving time, cost and efforts.
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