Abstract Topic recognition and tracking is one of the problems in the field of Natural Language Processing (NLP). Many methods and techniques have been introduced currently for information retrieval and extracting topic from user generated reviews. Finding out topics from the group of documents is very valuable for several real-world applications.. LDA (Latent Dirichlet Allocation) and PLSA (Probabilistic Latent semantic Analysis)... Topic recognition and tracking is one of the problems in the field of Natural Language Processing (NLP). Many methods and techniques have been introduced currently for information retrieval and extracting topic from user generated reviews. Finding out topics from the group of documents is very valuable for several real-world applications.. LDA (Latent Dirichlet Allocation) and PLSA (Probabilistic Latent semantic Analysis) are used for this purpose. This research aims to extract topic from online reviews and articles using mixture model. Mixture Model is probability based model. One thousand articles from four hundred refereed journals are collected and analyzed textually through topic mining techniques. These articles are collected from six major databases namely IEEE, Science Direct, Wiley, SAGE, Cambridge and Springer. The experimental results of proposed method clearly shows that proposed model yields very promising results. Read More Read Less

Abstract Since data explosion, data mining in transactional databases are increasingly important. There are many techniques for data mining such as mining association rule, the most important and well-researched. Moreover, maximal frequent itemset mining is one of the basic but time-consuming steps in the mines of association rules. Most algorithms used in the literature find maximal frequent itemset on search space... Since data explosion, data mining in transactional databases are increasingly important. There are many techniques for data mining such as mining association rule, the most important and well-researched. Moreover, maximal frequent itemset mining is one of the basic but time-consuming steps in the mines of association rules. Most algorithms used in the literature find maximal frequent itemset on search space items that have support at least minsup and not be used again for mining. In this paper, we propose a novel algorithm called NOV-MFI for mining maximal frequent itemsets in transactional databases. Advantages of NOV-MFI algorithms are reuse and easily expanded in distributed systems. Finally, experimental results show that the proposed algorithms are better than other existing algorithms on both real and synthetic datasets. Read More Read Less

Abstract In this paper, we describe the methods and experiments that have been used in development of our system for Author Profiling and Deception Detection in Arabic shared task. There are two tasks, Author Profiling in Arabic Tweets and Deception Detection in Arabic Texts. We have submitted three runs for each task. The proposed system depends on classical machine learning approaches... In this paper, we describe the methods and experiments that have been used in development of our system for Author Profiling and Deception Detection in Arabic shared task. There are two tasks, Author Profiling in Arabic Tweets and Deception Detection in Arabic Texts. We have submitted three runs for each task. The proposed system depends on classical machine learning approaches namely Linear Classier, Support Vector Machine and Multilayer Perceptron Classier. Bag-of- Word with range of n-grams model has been used for feature extraction. Our submissions for the first task achieved the second, seventh and third ranks. For the second task, one of our submissions outperformed all other submissions developed by other teams. Read More Read Less