Heart diagnosis is not always possible at every medical center, especially in the rural areas where less support and care, due to lack of advanced heart diagnosis equipment. Also, physician intuition and experience are not always sufficient to achieve high quality medical procedures results. Therefore, medical errors and undesirable results are reasons for a need for unconventional computer-based diagnosis systems, which in turns reduce medical fatal errors, increasing the patient safety and save lives. The proposed solution, which is based on an Artificial Neural Networks (ANNs), provides a decision support system to identify three main heart diseases: mitral stenosis, aortic stenosis and ventricular septal defect. Furthermore, the system deals with an encouraging opportunity to develop an operational screening and testing device for heart disease diagnosis and can deliver great assistance for clinicians to make advanced heart diagnosis. Using real medical data, series of experiments have been conducted to examine the performance and accuracy of the proposed solution. Compared results revealed that the system performance and accuracy are acceptable, with a heart diseases classification accuracy of 92%.
Text classification is an important topic. The number of electronic documents available on line is massive. Text classification aims to classify documents into a set of predefined categories. Number of researches conducted on English dataset is great in comparison with number of researches done using Arabic dataset. This research could be considered as reference for most researchers who deal with Arabic dataset. This research used the most well-known algorithms used in text classification with Arabic dataset. Besides that, dataset used in this research is large enough in comparison with most dataset for Arabic language used in other researches. In addition, this research used different selections and weighting methods for documents. I expect that all researchers who would write researches using Arabic dataset will find this work helpful. Algorithms used in this research are naïve Bayesian, support vector machines, artificial neural networks, k- nearest neighbors, C4.5 decision tree and rocchio classifier.
Ransomware is a malicious program that can affect any person or organization. Ransomware is a complicated malicious attack that aims at lock or encrypt user files. Up to this date, there is no individual method, tool, which guarantee to protect against ransomware. Most tools available can detect some types of ransomware but it fails to detect other types of ransomware. In this research author talks about several methods, tools, procedures which can be taken to reduce the possibility of ransomware occurrences. Up to this moment, the main methods used by attacker to infect your machine are malicious emails and malicious links. After analyzing several reports written by some anti-viruses’ company such as Kaspersky ,McAfee, and several researches which talks about ransomware, author conclude two points: first point, educating users, following up a strict security policy, procedures and backup strategies are the best methods which can be taken to minimize the possibility of ransomware. second point, future methods to detect ransomware mainly will be based on artificial intelligence.
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