In this paper,we introduce a new approachto tackle the process ofextracting information aboutpeople mentioned in the Arabic text. When a person nameis mentioned in the Arabic text usually it is combined with a title,in this paperthe focus is on the properties ofthose titles. We have identifiedsix properties for each title with respect to gender, type,class, status,format, and entityexistence. We have studied each property, identified all attributes and values that belong to each one of themand classified them accordingly. Sometimes person title is attached to an entity; we have also identified some properties for these entities and weshow how they work ina harmony with person title properties. We use graphs for the implementation, nodes to represents person title, person name, entity and their properties, where edges are used topresent inherited properties from parent nodes to child nodes.
Cybersecurity solutions are traditionally static and signature-based. The traditional solutions along with the use of analytic models, machine learning and big data could be improved by automatically trigger mitigation or provide relevant awareness to control or limit consequences of threats. This kind of intelligent solutions is covered in the context of Data Science for Cybersecurity. Data Science provides a significant role in cybersecurity by utilising the power of data (and big data), high-performance computing and data mining (and machine learning) to protect users against cybercrimes. For this purpose, a successful data science project requires an effective methodology to cover all issues and provide adequate resources. In this paper, we are introducing popular data science methodologies and will compare them in accordance with cybersecurity challenges. A comparison discussion has also delivered to explain methodologies' strengths and weaknesses in case of cybersecurity projects.
Many studies uses different data mining techniques to analyze mass spectrometry data and extract useful knowledge about biomarkers. These Biomarkers allow the medical experts to determine whether an individual has a disease or not. Some of these studies have proposed models that have obtained high accuracy. However, the black-box nature and complexity of the proposed models have posed significant issues. Thus, to address this problem and build an accurate model, we use a genetic algorithm for feature selection along with a rule-based classifier, namely Genetic Rule-Based Classifier algorithm for Mass Spectra data (GRC-MS). According to the literature, rule-based classifiers provide understandable rules, but not accurate. In addition, genetic algorithms have achieved excellent results when used with different classifiers for feature selection. Experiments are conducted on real dataset and the proposed classifier GRC-MS achieves 99.7% accuracy. In addition, the generated rules are more understandable than those of other classifier models.
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