Ahmed Emam

A data mining approach to developing the profiles of hotel customers.

To stay competitive, hotels need to develop a viable customer retention strategy. Since a key to the successful development of such a strategy rests with customer relationship management, hotels should identify the most profitable ways to build and maintain a loyal customer relationship. In an effort to help hotels understand their customers’ preferences and the ways to interact with the customers, we propose data mining techniques. Based on a case study of luxury hotels in South Korea, this paper demonstrates the usefulness and practicality of the proposed data mining techniques.

Developing the profiles of truck drivers for their successful recruitment and retention

Chronic driver turnover can adversely influence a trucking firms competitiveness through disrupted delivery services, equipment down time and excessive recruiting expenses. Thus, a key to the survival of the trucking firm rests with its ability to recruit and retain qualified drivers who are less likely to cause turnover. In an effort to develop the ways to recruit and retain those drivers, we propose data mining techniques. Based on an empirical study of trucking firms in the USA, this paper not only develops a viable driver recruitment and retention strategy, but it also demonstrates the usefulness of the proposed data mining techniques.

Optimal artificial neural network topology for foreign exchange forecasting

Foreign exchange market is one of the highest investments markets the average daily trade volume is 1.8 trillion USD. Foreign exchange rate forecasting has been always one of the most challenging subject and area of researches. Trader around the world is relying on the technical indicators which just following the price and has emerged a lag results. When the currency market has a random move (when the market is not trending) most of the indicators gets confused because of the fact that classical linear methods are unable to react with the non linearity in the data and hence with the market behavior. This research reports empirical results that tend to confirm the applicability of a neural network model to the prediction of the foreign exchange rates market. Artificial neural networks have proven to be efficient and profitable in forecasting financial time series in particular, feed forwarded back propagation. It is important to use an optimal ANN topology that emerged great results in short term prediction and the daily predication results showed that ANN model learns well and most likely to generalize well. Weekly predication results demonstrate good results in the low prediction while failed to have a good results on the high and the close prediction while the monthly prediction did not give a satisfactory results due to a very few data samples.

Arabic Sentiment Analysis: A Survey

Most social media commentary in the Arabic language space is made using unstructured non-grammatical slang Arabic language, presenting complex challenges for sentiment analysis and opinion extraction of online commentary and micro blogging data in this important domain. This paper provides a comprehensive analysis of the important research works in the field of Arabic sentiment analysis. An in-depth qualitative analysis of the various features of the research works is carried out and a summary of objective findings is presented. We used smoothness analysis to evaluate the percentage error in the performance scores reported in the studies from their linearly-projected values (smoothness) which is an estimate of the influence of the different approaches used by the authors on the performance scores obtained. To solve a bounding issue with the data as it was reported, we modified existing logarithmic smoothing technique and applied it to pre-process the performance scores before the analysis. Our results from the analysis have been reported and interpreted for the various performance parameters: accuracy, precision, recall and F-score.

Endoscopy video summarization based on unsupervised learning and feature discrimination

We propose a novel endoscopy video summarization approach based on unsupervised learning and feature discrimination. The proposed learning approach partitions the collection of video frames into homogeneous categories based on their visual and temporal descriptors. Also, it generates possibilistic memberships in order to represent the degree of typicality of each video frame within every category, and reduce the influence of noise frames on the learning process. The algorithm learns iteratively the optimal relevance weight for each feature subset within each cluster. Moreover, it finds the optimal number of clusters in an unsupervised and efficient way by exploiting some properties of the possibilistic membership function. The endoscopy video summary consists of the most typical frames in all clusters after discarding noise frames. We compare the performance of the proposed algorithm with state-of-the-art learning approaches. We show that the possibilistic approach is more robust. The endoscopy videos collection includes more than 90k video frames.

The artificial neural network for forecasting foreign exchange rates

A foreign exchange market is one of the highly invested markets in the world with an average daily trade volume of

Web-based performance visualization of distributed discrete event simulation

1.8 trillion. Due to extreme volatility and uncertainty associated with foreign currency fluctuations, the prediction of a foreign exchange rate has been one of the most challenging and onerous tasks for both researchers and practitioners. Traditionally, a foreign exchange forecast has been predicated on some technical indicators that simply tracked past pricing trends without considering a host of other factors (e.g., changes in government policy, trade imbalances, inflation). However, if the currency market is influenced by a random event (i.e., if the market did not follow the trend pattern), these indicators will lead to misleading forecasts. That is to say, traditional forecasting techniques such as a linear trend analysis would not work well for predicting future foreign exchange fluctuations. To overcome this shortcoming of the traditional forecasting techniques, we propose an Artificial Neural Network (ANN) that has proven to be useful for forecasting volatile financial time series such as foreign exchange rates. After applying ANN to the actual data, we discovered that the proposed ANN turned out to be very effective in predicting the daily fluctuations of foreign exchange rates. Similarly, its experiments showed favourable results for weekly forecasts, although it did not perform as well as we anticipated for monthly forecasts.

Framework for cloud intrusion detection system service

This paper reports on an effort to adapt an existing distributed simulation visualization system to become Web accessible. The system was originally developed for performance visualization and experimentation with parameters affecting PDES systems using the time Warp protocols. This paper presents a model for converting legacy PDES systems to be Web accessible, and discusses the initial results from the conversion effort on this specific application. After finishing this work, we will be able to collect a wealth of data through the Web for future data mining, and to create an intelligent agent for performance tuning of time Warp applications.

A Review of Security Risk Assessment Methods in Cloud Computing

In this Internet era, the use of cloud computing is causing a massive volume of online financial transactions, and the exchange of personal and sensitive information over the internet. Attackers use many different types of malware in searches motivated by curiosity or financial gain. In this paper, we propose an efficient framework called the Layered Intrusion Detection Framework (LIDF) that can be applied on the different layers of cloud computing in order to identify the presence of normal traffic among the monitored cloud traffic. The proposed framework uses data mining, especially an Artificial Neural Network, which makes it accurate, fast, and scalable. At the same time, the LIDF can reduce the rate of the analyzed traffic and achieve better performance by increasing the throughput without affecting its main goal.

Comprehensive Survey on Privacy Preserving Association Rule Mining: Models, Approaches, Techniques and Algorithms

The Cloud computing is a major technological trend that continues to evolve and flourish. It has potential benefits in achieving rapid and scalable resource provisioning capabilities as well as resource sharing. However, a number of security risk are emerging in association with cloud usage that need to be assessed before cloud computing is adopted. This paper presents a review of the security risk assessment methods in cloud computing. The paper aims to summarize, organize and classify the information available in the literature to identify any gaps in current research then suggest areas for further investigation. At the end, the paper suggests to have a collaborative security risk assessment method that will add great assistance to both service providers and consumers.