Magda B. Fayek

Semantic Query Expansion for Arabic Information Retrieval

Traditional keyword based search is found to have some limitations. Such as word sense ambiguity, and the query intent ambiguity which can hurt the precision. Semantic search uses the contextual meaning of terms in addition to the semantic matching techniques in order to overcome these limitations. This paper introduces a query expansion approach using an ontology built from Wikipedia pages in addition to other thesaurus to improve search accuracy for Arabic language. Our approach outperformed the traditional keyword based approach in terms of both F-score and NDCG measures.

PSOSA: An Optimized Particle Swarm Technique for Solving the Urban Planning Problem

This paper introduces an optimized particle swarm technique (PSOSA) that uses simulated annealing for optimizing the inertia weight. To study the performance of the proposed technique, it has been applied on the urban planning problem involving a multi-objective fitness function that includes non-overlapping constraints as well as relative positioning requirements. Results show that the proposed technique performs much better as regards convergence speed as well as sustainability to increased load of growing number of blocks to be fitted in the urban planning problem

An enhanced method for human action recognition

This paper presents a fast and simple method for human action recognition. The proposed technique relies on detecting interest points using SIFT (scale invariant feature transform) from each frame of the video. A fine-tuning step is used here to limit the number of interesting points according to the amount of details. Then the popular approach Bag of Video Words is applied with a new normalization technique. This normalization technique remarkably improves the results. Finally a multi class linear Support Vector Machine (SVM) is utilized for classification. Experiments were conducted on the KTH and Weizmann datasets. The results demonstrate that our approach outperforms most existing methods, achieving accuracy of 97.89% for KTH and 96.66% for Weizmann.

Human-inspired features for natural scene classification

Scene classification has been the target of much research. Most psychological studies have agreed that humans perceive a scene first globally recognizing its category and then they localize and recognize objects. In previous work the same feature set were used in classifying both natural scenes and manmade scenes simultaneously. We suggest the use of different features for each. In this paper the proposed features for natural scenes classification are presented. The new proposed features are inspired from the way humans perceive and recognize scenes at a glance. Outdoor scenes global features such as openness, roughness, and dominant directions have been investigated and translated into a new feature set, focusing on characteristics that efficiently differentiate between natural scene sub-classes. The effectiveness of the proposed features is tested using two datasets consists of 4 natural scenes (coast, mountain, forest, and open country) and 6 natural scenes (the previous 4 scenes plus desert and waterfall scenes), the first dataset is a benchmark data set used for testing scene classification techniques. Results showed that a classification accuracy of up to 95% could be achieved using the proposed feature set.

Multi-objective Optimization of Technical Stock Market Indicators using GAs

financial researches showed that technical indicators are useful tools for stock prediction. Technical indicators are used to generate trading signals (buy/sell) signals. The main problem of an indicator usage is to determine its appropriate parameters. In this paper a new GA based technique for optimizing the parameters of a collection of technical indicators over two objective functions Sharpe ratio and annual profit is proposed. The technique handles four indicators DEMAC (Double Exponential Moving Average Crossovers), RSI (Relative Strength Index), MACD (Moving Average Convergence Divergence), and MARSI (Moving Average RSI) indicators. The technique was tested on 30 years of historical data of DJIA (Dow Jones Industrial Average) stock index. Results showed that the optimized parameters obtained by the proposed technique improved the profits obtained by the indicators with their typical parameters, the Buy and Hold strategy and the random strategy.

VEGK: Virtual ECC group key for wireless sensor networks

A Wireless Sensor Network (WSN) is a collection of smart sensor nodes cooperated together for achieving the desire of the assigned application. However, these nodes suffer from different limitations including memory available, computational and communicational limitations. Clustering these nodes is considered as one of the main solutions for prolonging the lifetime of the network. At the same time, security is another challenge for WSNs due to the critical information wirelessly transferred through the network. In this paper, we propose a security framework called Virtual ECC Group Key (VEGK) merging Elliptic Curve Cryptography (ECC) with symmetric pairwise keys along with virtual ECC group keys. In addition, we believe that merging the security with clustering will be beneficial to the energy saving in WSN. Based on the analysis of different scenarios, our proposed security framework is proved to protect the network from many attacks.

WSN in Monitoring Oil Pipelines Using ACO and GA

Abstract Wireless Sensor Networks (WSNs) are one of the most important technologies in the fields of wireless networking today. WSNs have a vast amount of applications including sensors embedded in the outer surface of pipeline or mounted along the supporting structure of bridges, robotics and health care. In this paper, we study the issues of linear sensor placement to monitor oil pipelines. We address the problem of optimal number of sensors to be deployed given initial energy of each sensor node and message buffering limitations. The objectives of the deployment process are: 1) maximizing the coverage of the pipe, 2) producing a connected network, and 3) prolonging the overall network lifetime. The paper utilizes two of the evolutionary algorithms to solve the deployment problem which are Genetic Algorithms (GA) and Ant Colony Optimization (ACO). Extensive set of experiments are performed for performance evaluation.

PSO2: Particle swarm optimization with PSO-based local search

Several attempts have been made to enhance PSO performance by combining it with a local search method. Following the same track, we present in this paper local search in PSO performed by smaller independent swarms of PSO producing PSO2. Different modifications are made to help basic PSO2 enhance performance. PSO2-RS and PSO2-SA are 2 modified versions of PSO2 that targeted to increase the swarm diversity. Increasing the local search swarms sizes as the search progresses is another modification made to basic PSO2 in order to change the algorithm behavior to be more exploitive. The final algorithm is examined against 4 functions of the CEC-2005 benchmark suite and results are reported.

Sketch recognition using particle swarm algorithms

Sketch recognition is defined as the process of identifying symbols that users draw using single or multiple strokes. Users draw strokes using a pen and the system immediately interprets their strokes as objects that can be easily manipulated. This paper uses Particle Swarm Optimization Algorithm (PSO) to divide the strokes the user draws into meaningful geometric primitives. These geometric primitives are grouped to formulate symbols which are further identified. The results show that using PSO improves segmentation results which guide the symbol recognition phase. This paper uses Support Vector Machines (SVM) classifier which further improves the final recognition accuracy.

A GA-BASED APPROACH FOR EPIPOLAR GEOMETRY ESTIMATION

In this paper, a genetic algorithm (GA)-based approach to estimate the fundamental matrix is presented. The aim of the proposed GA-based algorithm is to reduce the effect of noise and outliers in the corresponding points which affect the accuracy of the estimated fundamental matrix. Although in the proposed approach the GA is allowed to select the significant among all detected points, on the average half of the matched points have been determined to give optimum estimation of the fundamental matrix. Experiments with synthetic and real data show that the proposed approach is accurate especially in the presence of a high percentage of outliers. The proposed GA can always obtain good results in both high and low detailed images. Even for low detailed images which have a small number of matched points available to estimate the fundamental matrix, the proposed GA outperformed other methods.