The increasing use of social media and the idea of extracting meaningful expressions from renewable and usable data which is one of the basic principles of data mining has increased the popularity of Sentiment Analysis which is an important working area recently and has expanded its usage areas. Compiled messages shared from social media can be meaningfully labeled with sentiment analysis technique. Sentiment analysis objectively indicates whether the expression in a text is positive, neutral, or negative. Detecting Arabic tweets will help for politicians in estimating universal incident-based popular reports and people’s comments. In this paper, classification was conducted on sentiments twitted in the Arabic language. The fact that Arabic has twisted language features enabled it to have a morphologically rich structure. In this paper we have used the Long Short Term Memory (LSTM), a widely used type of the Recurrent Neural Networks (RNNs), to analyze Arabic twitter user comments. Compared to conventional pattern recognition techniques, LSTM has more effective results in terms of having less parameter calculation, shorter working time and higher accuracy.

Abstract-Liver is a needful body organ that forms an important barrier between the gastrointestinal blood, which contains large amounts of toxins, and antigens. Liver diseases contain hepatitis B and hepatitis C virus infections, alcoholic liver disease, nonalcoholic fatty liver disease and associated cirrhosis, liver failure and hepatocellular carcinoma are primary causes of death. The main purpose of this study is to investigate which attributes are important for effective diagnosis of liver disorders by performing the machine learning approach based on the combination of Stability Selection and Random Forest methods. In order to generate more accuracy, dataset was balanced by utilizing the Random Under-Sampling method. Important ones in all attributes were detected by utilizing the Stability Selection method which was performed on sub-datasets, which were obtained with 5 fold cross-validation technique. By sending these datasets to the Random Forest algorithm, the performance of the proposed approach was evaluated within the frame of accuracy and sensitive metrics. The experimental results clearly show that the Random Under-Sampling method can potentially improve the performance of the combination of Stability Selection and Random Forest methods in machine learning. And, the combination of these methods provides new perspectives for the diagnosis of this disease and other medical diseases.

Learning objects are one of the innovations in domain of teaching technology. Shared and reusable learning objects are based on the understanding that content should be designed in a modular way rather than designed as a single integrated software. Learning objects are separate and independent objects, allowing each individual to create lessons in accordance with his or her own learning style or method, enabling the realization of customized learning. Since learning objects have descriptive information (metadata), they can be easily searched, so that they can access the learning content on time. The metadata definitions are made in the XML file. For this reason, learning objects can be shared with the use of XML, while the reuse of learning objects cannot be provided because XML is insufficient in the semantic definition of learning objects. Semantic Web (Web 3.0) technologies can be used to produce workable and interpretable web pages. Ontologies are being developed with the use of these technologies. Thanks to the ontology, intelligent learning environments can be developed to access the learning objects that are distributed on the web about each learning acquisition. In this study, according to the curriculum of Computer Engineering, ComputerEngineringCurriculum learning object ontology was defined. The defined learning object ontology provides better sharing and reusability of learning objects. Protégé ontology development editor was used for this purpose. This paper shows that learning environments developed using semantic web technologies and ontologies can offer intelligent solutions for individualized instruction and rapid access to accurate instructional content on the web.

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Usually, a common data model is used for creating and getting a résumé in web applications. Though different web applications provide the same quality résumé information, they encounter difficulties in analyzing and processing data in their different sources. Linked data technology allows overcoming these problems by integrating the data coming from different sources, linking large, voluminous, and distributed data sets with semantic sources in web of data, and forming an open linked data cloud. This study mainly aims to combine, publish, and explore the semantic information in the academic résumés of scientists/researchers working in universities and/or research establishments by use of linked data. The study deals with the use and exploration of academic résumé information through linked data approach. It was intended to conduct effective SPARQL queries on linked data network via FOAF-Academic, DBLP and Résumé/Curriculum Vitae (CV) ontologies so that different data sources would be integrated.