Hoda Mohamed

ABSTRACT This paper proposes a methodology to prepare corpora in Arabic language from online social network (OSN) and review site for Sentiment Analysis (SA) task. The paper also proposes a methodology for generating a stopword list from the prepared corpora. The aim of the paper is to investigate the effect of removing stopwords on the SA task. The problem is that the stopwords lists generated before were on Modern Standard Arabic (MSA) which is not the common language used in OSN. We have generated a stopword list of Egyptian dialect and a corpus-based list to be used with the OSN corpora. We compare the efficiency of text classification when using the generated lists along with previously generated lists of MSA and combining the Egyptian dialect list with the MSA list. The text classification was performed using Na\"ive Bayes and Decision Tree classifiers and two feature selection approaches, unigrams and bigram. The experiments show that the general lists containing the Egyptian dialects words give better performance than using lists of MSA stopwords only.

ABSTRACT Data mining in databases is an important issue in the development of data and knowledge-base systems. It facilitates querying database knowledge and semantic query optimization. The aim of the work is to use data mining tools for intelligent query answering in database systems, which include generalization, data summarization and rule discovery. We used a model for a knowledge-rich database, which consists not only of the components from a deductive database but also the components relevant to knowledge discovery tools. The discovered rule set constitutes a graph whose edges are the rules. This condition dependency graph provides a map of possible query reformulation operations so that semantic query optimization could be performed. Semantic knowledge is needed to optimize queries. Semantic knowledge may be in the form of generalized rules or association rules. Both will be applied to semantic query optimization system.

Page 1. Collective Sequential Pattern Mining in Distributed Evolving Data Streams Amany F. Soliman1,a+ , Gamal A. Ebrahim1,b and Hoda K. Mohammed1,c 1 Computer and Systems Engineering Department, Faculty of Engineering, Ain Shams University, Cairo, Egypt ...

ABSTRACT Cloud computing is offering utility oriented IT services to users worldwide. Based on a pay per use model, it provides a variety of computing resources, enterprise applications while enabling their hosting from consumer, scientific and business domains through a three layered architecture and different cloud types. The proliferation of cloud computing has resulted in the establishment of large-scale data centers around the world containing thousands of computing nodes which consume huge amounts of energy, contributing to high operational costs and carbon footprints to the environment. Energy consumption is not only determined by hardware efficiency, but it also depends on the resource management system deployed on the infrastructure and the efficiency of applications running in the system. The challenge is addressed in finding cloud computing solutions that not only save energy for the environment but also reduce operational costs. Our Fuzzy based contribution improves power efficiency with around 40 % than other policies.

ABSTRACT Intrusion detection systems (IDSs) have become a widely used measure for security systems. The main problem for those systems results is the irrelevant alerts on those results. We will propose a data mining based method for classification to distinguish serious alerts and irrelevant one with a performance of 99.9% which is better in comparison with the other recent data mining methods that have reached the performance of 97%. A ranked alerts list also created according to alerts importance to minimize human interventions.

The paper presents the design of a model for forecasting long-term electricity load. The model uses data mining techniques. The paper defines the load forecast and the summary of the most important factors affecting the load forecast in Egyptian electricity network. The steps needed for the knowledge discovery process is implemented to the time series data. Preprocessing the data in