Dr. El-Sayed El-Alfy Associate Professor, CCSE, KFUPM, KSA
Dr. El-Sayed El-Alfy Associate Professor, CCSE, KFUPM, KSA
DBLP Scholar Scopus ResearchGate
Title: AdaBoost-based artificial neural network learning
Author(s): Baig, MM (Baig, Mirza M.); Awais, MM (Awais, Mian M.); El-Alfy, ESM (El-Alfy, El-Sayed M.)
Source: NEUROCOMPUTING Volume: 248 Special Issue: SI Pages: 120-126 DOI: 10.1016/j.neucom.2017.02.077 Published: JUL 26 2017
Abstract: A boosting-based method of learning a feed-forward artificial neural network (ANN) with a single layer of hidden neurons and a single output neuron is presented. Initially, an algorithm called Boostron is described that learns a single-layer perceptron using AdaBoost and decision stumps. It is then extended to learn weights of a neural network with a single hidden layer of linear neurons. Finally, a novel method is introduced to incorporate non-linear activation functions in artificial neural network learning. The proposed method uses series representation to approximate non-linearity of activation functions, learns the coefficients of nonlinear terms by AdaBoost. It adapts the network parameters by a layer-wise iterative traversal of neurons and an appropriate reduction of the problem. A detailed performances comparison of various neural network models learned the proposed methods and those learned using the least mean squared learning (LMS) and the resilient back-propagation (RPROP) is provided in this paper. Several favorable results are reported for 17 synthetic and real-world datasets with different degrees of difficulties for both binary and multi-class problems. (C) 2017 Published by Elsevier B.V.
Accession Number: WOS:000401398000014
Title: Special issue on soft computing and intelligent systems: Tools, techniques and applications
Author(s): Thampi, SM (Thampi, Sabu M.); El-Alfy, ESM (El-Alfy, El-Sayed M.)
Source: JOURNAL OF INTELLIGENT & FUZZY SYSTEMS Volume: 32 Issue: 4 Pages: 2791-2795 DOI: 10.3233/JIFS-169221 Published: 2017
Accession Number: WOS:000399823000001
Title: Evaluation of sequential adaptive testing with real-data simulation: A case study
Author(s): El-Alfy, EM (El-Alfy, El-Sayed M.)
Source: JOURNAL OF INTELLIGENT & FUZZY SYSTEMS Volume: 32 Issue: 4 Pages: 2977-2986 DOI: 10.3233/JIFS-169241 Published: 2017
Abstract: Computer-based testing systems take advantage of the interaction between computers and individuals to sequentially customize the presented test items to the test-taker's ability estimate. Administering such sequential adaptive tests has many benefits including personalized tests, accurate measurement, item security, and substantial cost reduction. However, the design of such intelligent tests is a complex process and it is important to explore the impact of various parameters and options on the performance before switching from traditional tests in a particular environment. Although Monte Carlo simulation is a typical tool for achieving this purpose, it depends on generating pseudo-random samples, which may fail to effectively represent the environment under study and thus incorrect inferences can be drawn. This paper presents a comprehensive case study to evaluate and compare the performance of a number of sequential adaptive testing procedures but using post-hoc simulation, where items of a real conventional test are re-administered adaptively. The comparisons are based on the number of administered items, standard error of measurement, item exposure rates, and correlation between adaptive and non-adaptive estimates. It is found that the results varies based on the settings. However, Bayesian estimation with adaptive item selection can lead to greater savings in terms of the number of test items without jeopardizing the estimated ability. It also has the lowest average exposure rate for each item.
Accession Number: WOS:000399823000021
Title: A multiclass cascade of artificial neural network for network intrusion detection
Author(s): Baig, MM (Baig, Mirza M.); Awais, MM (Awais, Mian M.); El-Alfy, ESM (El-Alfy, El-Sayed M.)
Source: JOURNAL OF INTELLIGENT & FUZZY SYSTEMS Volume: 32 Issue: 4 Pages: 2875-2883 DOI: 10.3233/JIFS-169230 Published: 2017
Abstract: This paper presents a cascade of ensemble-based artificial neural network for multi-class intrusion detection (CANID) in computer network traffic. The proposed system learns a number of neural-networks connected as a cascade with each network trained using a small sample of training examples. The proposed cascade structure uses the trained neural network as a filter to partition the training data and hence a relatively small sample of training examples are used along with a boosting-based learning algorithm to learn an optimal set of neural network parameters for each successive partition. The performance of the proposed approach is evaluated and compared on the standard KDD CUP 1999 dataset as well as a very recent dataset, UNSW-NB15, composed of contemporary synthesized attack activities. Experimental results show that our proposed approach can efficiently detect various types of cyber attacks in computer networks.
Accession Number: WOS:000399823000010
Title: A multiclass cascade of artificial neural network for network intrusion detection
Author(s): Baig, MM (Baig, Mirza M.); Awais, MM (Awais, Mian M.); El-Alfy, ESM (El-Alfy, El-Sayed M.)
Source: JOURNAL OF INTELLIGENT & FUZZY SYSTEMS Volume: 32 Issue: 4 Pages: 2875-2883 DOI: 10.3233/JIFS-169230 Published: 2017
Abstract: This paper presents a cascade of ensemble-based artificial neural network for multi-class intrusion detection (CANID) in computer network traffic. The proposed system learns a number of neural-networks connected as a cascade with each network trained using a small sample of training examples. The proposed cascade structure uses the trained neural network as a filter to partition the training data and hence a relatively small sample of training examples are used along with a boosting-based learning algorithm to learn an optimal set of neural network parameters for each successive partition. The performance of the proposed approach is evaluated and compared on the standard KDD CUP 1999 dataset as well as a very recent dataset, UNSW-NB15, composed of contemporary synthesized attack activities. Experimental results show that our proposed approach can efficiently detect various types of cyber attacks in computer networks.
Accession Number: WOS:000399823000010
Title: Robust content authentication of gray and color images using lbp-dct markov-based features
Author(s): El-Alfy, ESM (El-Alfy, El-Sayed M.); Qureshi, MA (Qureshi, Muhammad A.)
Source: MULTIMEDIA TOOLS AND APPLICATIONS Volume: 76 Issue: 12 Pages: 14535-14556 DOI: 10.1007/s11042-016-3855-7 Published: JUN 2017
Abstract: This paper presents a robust method for passive content authentication of gray and color images. The idea is to capture local and global artifacts resulting from the image manipulation through combining intra-block Markov features in both LBP and DCT domains. An optimized support-vector machine with radial-basis kernel is then trained to classify images as being tampered or authentic. We intensively investigate the authentication capabilities of the proposed method for separate color channels and for various combinations of them. The proposed method, without and withfeature-level fusion, is evaluated on three benchmark datasets with a variety of forgery and post-processing operations. The results show that fusing Markov features from LBP and DCT modalities leads to consistent improvement in terms of detection accuracy as compared to the state-of-the-art passive methods. Furthermore, using information from all YCbCr channels help enhancing the detection rate to more than 99.7 % on CASIA TIDE v2.0 image collection.
Accession Number: WOS:000402732800037
Title: Spam filtering framework for multimodal mobile communication based on dendritic cell algorithm
Author(s): El-Alfy, EM (El-Alfy, El-Sayed M.); AlHasan, AA (AlHasan, Ali A.)
Source: FUTURE GENERATION COMPUTER SYSTEMS-THE INTERNATIONAL JOURNAL OF ESCIENCE Volume: 64 Pages: 98-107 DOI: 10.1016/j.future.2016.02.018 Published: NOV 2016
Abstract: With the continual growth of mobile devices, they become a universal portable platform for effective business and personal communication. They enable a plethora of textual communication modes including electronic mails, instant messaging, and short messaging services. A downside of such great technology is the alarming rate of spam messages that are not only annoying to end-users but raises security concerns as well. This paper presents an intelligent framework for filtering multimodal textual communication including emails and short messages. We explore a novel methodology for information fusion inspired by the human immune system and hybrid approaches of machines learning. We study a number of methods to extract and select more relevant features to reduce the complexity of the proposed model to suite mobile applications while preserving good performance. The proposed framework is intensively evaluated on a number of benchmark datasets with remarkable results achieved. (C) 2016 Elsevier B.V. All rights reserved.
Accession Number: WOS:000381843200009
Title: Towards scalable rough set based attribute subset selection for intrusion detection using parallel genetic algorithm in MapReduce
Author(s): El-Alfy, EM (El-Alfy, El-Sayed M.); Alshammari, MA (Alshammari, Mashaan A.)
Source: SIMULATION MODELLING PRACTICE AND THEORY Volume: 64 Pages: 18-29 DOI: 10.1016/j.simpat.2016.01.010 Published: MAY 2016
Abstract: Attribute subset selection based on rough sets is a crucial preprocessing step in data mining and pattern recognition to reduce the modeling complexity. To cope with the new era of big data, new approaches need to be explored to address this problem effectively. In this paper, we review recent work related to attribute subset selection in decision-theoretic rough set models. We also introduce a scalable implementation of a parallel genetic algorithm in Hadoop MapReduce to approximate the minimum reduct which has the same discernibility power as the original attribute set in the decision table. Then, we focus on intrusion detection in computer networks and apply the proposed approach on four datasets with varying characteristics. The results show that the proposed model can be a powerful tool to boost the performance of identifying attributes in the minimum reduct in large-scale decision systems. (C) 2016 Elsevier B.V. All rights reserved.
Accession Number: WOS:000377028000003
Title: A Novel Approach for Face Recognition Using Fused GMDH-Based Networks
Author(s): El-Alfy, EM (El-Alfy, El-Sayed M.); Baig, Z. A.; Abdel-Aal, R. E.,
Source: International Arab Journal of Information Technology
Abstract:
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Title: Selectivity estimation of extended XML query tree patterns based on prime number labeling and synopsis modeling
Author(s): Mohammed, S (Mohammed, Salahadin); Barradah, AF (Barradah, Ahmad F.); El-Alfy, EM (El-Alfy, El-Sayed M.)
Source: SIMULATION MODELLING PRACTICE AND THEORY Volume: 64 Pages: 30-42 DOI: 10.1016/j.simpat.2016.01.008 Published: MAY 2016
Abstract: With the new era of big data and the proliferation of XML documents for representing and exchanging data over the web, selectivity estimation of XML query patterns has become a crucial component of database optimizers. It helps the optimizer choose the best possible plan for query evaluation. Existing selectivity estimators for XML queries can only support basic Query Tree Patterns (QTPs) with logical AND operator. In this paper, we propose a novel approach, called XQuest, for selectivity estimation that supports extended QTPs that may contain logical operators or wildcards. This approach is based on a modified implementation of prime number labeling to construct a structural summary model of the XML data. Subsequently, a simulator of an XML query evaluator runs on the resulting model from the previous stage and aggregates the estimate for each target QTP. We conducted several experiments to study the performance of the proposed approach on three XML benchmark datasets; in terms of synopsis generation time, storage requirements, and estimation accuracy. The results show that the proposed approach can have more accurate estimates with low memory and time requirements. For example, when compared to a Sampling algorithm with the same allocated memory budget, the error rate of the proposed approach never reached 5% whereas it reached 98.5% for the Sampling algorithm. (C) 2016 Elsevier B.V. All rights reserved.
Accession Number: WOS:000377028000004
Title: XHQE: A hybrid system for scalable selectivity estimation of XML queries
Author(s): El-Alfy, ESM (El-Alfy, E. -S. M.); Mohammed, S (Mohammed, S.); Barradah, AF (Barradah, A. F.)
Source: INFORMATION SYSTEMS FRONTIERS Volume: 18 Issue: 6 Pages: 1233-1249 DOI: 10.1007/s10796-015-9561-6 Published: DEC 2016
Abstract: With the increasing popularity of XML applications in enterprise and big data systems, the use of efficient query optimizers is becoming very essential. The performance of an XML query optimizer depends heavily on the query selectivity estimators it uses to find the best possible query execution plan. In this work, we propose a novel selectivity estimator which is a hybrid of structural synopsis and statistics, called XHQE. The structural synopsis enhances the accuracy of estimation and the structural statistics makes it scalable to the allocated memory space. The structural synopsis is generated by labeling the nodes of the source XML dataset using a fingerprint function and merging subtrees with similar fingerprints (i.e. having similar structures). The generated structural synopsis and structural statistics are then used to estimate the selectivity of given queries. We studied the performance of the proposed approach using different types of queries and four benchmark datasets with different structural characteristics. We compared XHQE with existing algorithms such as Sampling, TreeSketch and one histogram-based algorithm. The experimental results showed that the XHQE is significantly better than other algorithms in terms of estimation accuracy and scalability for semi-uniform datasets. For non-uniform datasets, the proposed algorithm has comparable estimation accuracy to TreeSketch as the allocated memory size is highly reduced, yet the estimation data generation time of the proposed approach is much lower (e.g., TreeSketch took more than 50 times longer than that of the proposed approach for XMark dataset). Comparing to the histogram-based algorithm, our approach supports regular twig quires in addition to having higher accuracy when both run under similar memory constraints.
Accession Number: WOS:000389266900014
Title: Improved selectivity estimator for XML queries based on structural synopsis
Author(s): Mohammed, S (Mohammed, Salahadin); El-Alfy, ESM (El-Alfy, El-Sayed M.); Barradah, AF (Barradah, Ahmad F.)
Source: WORLD WIDE WEB-INTERNET AND WEB INFORMATION SYSTEMS Volume: 18 Issue: 4 Pages: 1123-1144 DOI: 10.1007/s11280-014-0311-3 Published: JUL 2015
Abstract: With the increasing popularity of XML database applications, the use of efficient XML query optimizers is becoming very essential. The performance of an XML query optimizer depends heavily on the query selectivity estimators it uses to find the best possible query execution plan. In this work, we propose and evaluate a novel selectivity estimator, based on a structural synopsis, called SynopTech. The main idea of SynopTech is the generation of a summary tree by labeling the nodes of the source XML data tree using a fingerprint function and merging subtrees with similar structures. The generated summary tree is then used by SynopTech to estimate the selectivity of given queries. We experimented the proposed approach with four benchmark datasets of different structural characteristics and using different types of queries. Comparing with the Sampling algorithm, one of the state-of-the-art algorithms for selectivity estimations, SynopTech achieved lower selectivity estimation error rates, yet with very low memory budget. For example, for linear and existential queries, SynopTech had perfect estimations whereas the Sampling algorithm had an error rate of up to 70 %. For regular twig queries, SynopTech had a maximum error rate of 4.12 % whereas the Sampling algorithm had more than 55 %.
Accession Number: WOS:000356936800015
Title: Combining spatial and DCT based Markov features for enhanced blind detection of image splicing
Author(s): El-Alfy, ESM (El-Alfy, E-Sayed M.); Qureshi, MA (Qureshi, Muhammad Ali)
Source: PATTERN ANALYSIS AND APPLICATIONS Volume: 18 Issue: 3 Pages: 713-723 DOI: 10.1007/s10044-014-0396-4 Published: AUG 2015
Abstract: Nowadays, it is extremely simple to manipulate the content of digital images without leaving perceptual clues due to the availability of powerful image editing tools. Image tampering can easily devastate the credibility of images as a medium for personal authentication and a record of events. With the daily upload of millions of pictures to the Internet and the move towards paperless workplaces and e-government services, it becomes essential to develop automatic tampering detection techniques with reliable results. This paper proposes an enhanced technique for blind detection of image splicing. It extracts and combines Markov features in spatial and Discrete Cosine Transform domains to detect the artifacts introduced by the tampering operation. To reduce the computational complexity due to high dimensionality, Principal Component Analysis is used to select the most relevant features. Then, an optimized support vector machine with radial-basis function kernel is built to classify the image as being tampered or authentic. The proposed technique is evaluated on a publicly available image splicing dataset using cross validation. The results showed that the proposed technique outperforms the state-of-the-art splicing detection methods.
Accession Number: WOS:000358217600017
Title: Detecting Cyber-Attacks on Wireless Mobile Networks Using Multicriterion Fuzzy Classifier with Genetic Attribute Selection
Author(s): El-Alfy, ESM (El-Alfy, El-Sayed M.); Al-Obeidat, FN (Al-Obeidat, Feras N.)
Source: MOBILE INFORMATION SYSTEMS Article Number: UNSP 585432 DOI: 10.1155/2015/585432 Published: 2015
Abstract: With the proliferation of wireless and mobile network infrastructures and capabilities, a wide range of exploitable vulnerabilities emerges due to the use of multivendor and multidomain cross-network services for signaling and transport of Internet-and wireless-based data. Consequently, the rates and types of cyber-attacks have grown considerably and current security countermeasures for protecting information and communication may be no longer sufficient. In this paper, we investigate a novel methodology based on multicriterion decision making and fuzzy classification that can provide a viable second-line of defense for mitigating cyber-attacks. The proposed approach has the advantage of dealing with various types and sizes of attributes related to network traffic such as basic packet headers, content, and time. To increase the effectiveness and construct optimal models, we augmented the proposed approach with a genetic attribute selection strategy. This allows efficient and simpler models which can be replicated at various network components to cooperatively detect and report malicious behaviors. Using three datasets covering a variety of network attacks, the performance enhancements due to the proposed approach are manifested in terms of detection errors and model construction times.
Accession Number: WOS:000363186100001
Title: Boosting paraphrase detection through textual similarity metrics with abductive networks
Author(s): El-Alfy, ESM (El-Alfy, El-Sayed M.); Abdel-Aal, RE (Abdel-Aal, Radwan E.); Al-Khatib, WG (Al-Khatib, Wasfi G.); Alvi, F (Alvi, Faisal)
Source: APPLIED SOFT COMPUTING Volume: 26 Pages: 444-453 DOI: 10.1016/j.asoc.2014.10.021 Published: JAN 2015
Abstract: A number of metrics have been proposed in the literature to measure text re-use between pairs of sentences or short passages. These individual metrics fail to reliably detect paraphrasing or semantic equivalence between sentences, due to the subjectivity and complexity of the task, even for human beings. This paper analyzes a set of five simple but weak lexical metrics for measuring textual similarity and presents a novel paraphrase detector with improved accuracy based on abductive machine learning. The objective here is 2-fold. First, the performance of each individual metric is boosted through the abductive learning paradigm. Second, we investigate the use of decision-level and feature-level information fusion via abductive networks to obtain a more reliable composite metric for additional performance enhancement. Several experiments were conducted using two benchmark corpora and the optimal abductive models were compared with other approaches. Results demonstrate that applying abductive learning has significantly improved the results of individual metrics and further improvement was achieved throughfusion. Moreover, building simple models of polynomial functional elements that identify and integrate the smallest subset of relevant metrics yielded better results than those obtained from the support vector machine classifiers utilizing the same datasets and considered metrics. The results were also comparable to the best result reported in the literature even with larger number of more powerful features and/or using more computationally intensive techniques. (C) 2014 Elsevier B.V. All rights reserved.
Accession Number: WOS:000345517500037
Title: Introduction to the special issue on Cognitive Radio Networks
Author(s): Thampi, SM (Thampi, Sabu M.); El-Alfy, ESM (El-Alfy, El-Sayed M.)
Source: COMPUTERS & ELECTRICAL ENGINEERING Volume: 42 Special Issue: SI Pages: 115-116 DOI: 10.1016/j.compeleceng.2015.03.009 Published: FEB 2015
Accession Number: WOS:000353847600011
Title: Abductive Learning Ensembles for Hand Shape Identification
Author(s): El-Alfy, EM (El-Alfy, El-Sayed M.); Abdel-Aal, RE (Abdel-Aal, Radwan E.)
Source: COGNITIVE COMPUTATION Volume: 6 Issue: 3 Special Issue: SI Pages: 321-330 DOI: 10.1007/s12559-013-9241-0 Published: SEP 2014
Abstract: A novel method is presented for hand shape identification based on abductive machine learning. We developed several models and investigated their performance on raw hand shape data for 20 and 40 participants in the form of three different categories of geometric measurements: twelve finger features, two palm features, and three whole hand features. Performance was compared when using each category of features separately and when combining them together. Moreover, we describe two novel and more effective approaches using an ensemble of three abductive networks combined at either the score level or the decision level. The effect of doubling the number of participants from 20 to 40 was studied as well. The ensemble approach achieved overall identification accuracies of 100 and 98.3333 % for the 20-participant and 40-participant datasets, respectively. This compares favorably with other learning approaches tried on the same datasets, including decision trees, support vector machines, and rule-based classifiers.
Accession Number: WOS:000341593600005
Title: Impact of Stock Market Indices and Other Regional Exogenous Factors on Predictive Modeling of Border Traffic with Neural Network Models
Author(s): El-Alfy, ESM (El-Alfy, El-Sayed M.); Ratrout, NT (Ratrout, Nedal T.); Gazder, U (Gazder, Uneb)
Source: ARABIAN JOURNAL FOR SCIENCE AND ENGINEERING Volume: 40 Issue: 2 Pages: 303-312 DOI: 10.1007/s13369-014-1438-3 Published: FEB 2015
Abstract: This paper analyzes the impact of stock market indices, as indicators of political and economic stability, and other regional exogenous factors on the performance of predictive modeling of border traffic using neural network models. To prove the concept, the Saudi-Bahrain corridor through King Fahd causeway is selected as our area of study. These two countries have strong cultural ties and a wide variety of variables affects the incoming and outgoing traffic flows. Various models of artificial neural networks are constructed for different prediction horizons and look-back periods using a dataset prepared for the period from 2003 till 2013. In our study, stock market indices are proposed, for the first time, to be used in border traffic forecasting. These indices are added as a surrogate measure of the political and economic conditions of the countries which are under study. Their effects on models with varying ranges of time-series inputs and different prediction horizons are studied in detail. It is found that including stock market indices and other most relevant local factors has generally improved the prediction performance of the neural network models in all cases. Additional reduction in the prediction error is achieved by the proposed ensemble model trained with different time lags. Yet, the degree of improvement depends on the look-ahead horizon for prediction.
Accession Number: WOS:000348236300004
Title: A multicriterion fuzzy classification method with greedy attribute selection for anomaly-based intrusion detection
Author(s): El-Alfy, ESM (El-Alfy, El-Sayed M.); Al-Obeidat, FN (Al-Obeidat, Feras N.)
Edited by: Shakshuki EM
Source: 9TH INTERNATIONAL CONFERENCE ON FUTURE NETWORKS AND COMMUNICATIONS (FNC'14) / THE 11TH INTERNATIONAL CONFERENCE ON MOBILE SYSTEMS AND PERVASIVE COMPUTING (MOBISPC'14) / AFFILIATED WORKSHOPS Book Series: Procedia Computer Science Volume: 34 Pages: 55-62 DOI: 10.1016/j.procs.2014.07.037 Published: 2014
Abstract: Intrusion is widely recognized as a chronic and recurring problem of computer systems' security with the continual changes and increasing volume of hacking techniques. This paper explores a new countermeasure approach for anomaly-based intrusion detection using a multicriterion fuzzy classification method combined with a greedy attribute selection. The proposed approach has the advantage of dealing with various types of attributes including network traffic basic TCP/IP packet headers, as well as contentbased, time-based and host-based attributes. At the same time, to reduce the dimensionality and increase the computational efficiency, the greedy attribute selection algorithm enables it to choose an optimal subset of attributes that is most relevant for detecting intrusive events. The simplicity of the constructed model allows it to be replicated at various network components in emerging open system infrastructures such as sensor networks, wireless ad hoc networks, cloud computing, and smart grids. The proposed approach is evaluated and compared on a commonly-used intrusion detection benchmark dataset. The results show more than 99.9% overall accuracy with high detection rates for various types of intrusions can be achieved with about 26% only of the available attributes. (C) 2014 Elsevier B.V.
Accession Number: WOS:000349979900006
Title: Effects of using average annual daily traffic (AADT) with exogenous factors to predict daily traffic
Author(s): Ratrout, NT (Ratrout, Nedal T.); Gazder, U (Gazder, Uneb); El-Alfy, EM (El-Alfy, El-Sayed M.)
Edited by: Shakshuki E; Yasar A
Source: 5TH INTERNATIONAL CONFERENCE ON AMBIENT SYSTEMS, NETWORKS AND TECHNOLOGIES (ANT-2014), THE 4TH INTERNATIONAL CONFERENCE ON SUSTAINABLE ENERGY INFORMATION TECHNOLOGY (SEIT-2014) Book Series: Procedia Computer Science Volume: 32 Pages: 325-330 DOI: 10.1016/j.procs.2014.05.431 Published: 2014
Abstract: Traffic demand can be highly correlated to the exogenous factors that exist outside the road system under study. Factors like day of week, presence of vacation or salary disbursement or economic parameters are readily available and may affect traffic flow between two countries. Collection of traffic data on a consistent basis is a cumbersome process in terms of time and resources. Considering these two factors in mind, this paper investigated the feasibility of using exogenous factors with Average Annual Daily Traffic (AADT). It was found that inclusion of AADT for traffic prediction is beneficial and further analysis will be done in the future with detailed traffic data. (C) 2014 Published by Elsevier B.V.
Accession Number: WOS:000361562600039
Title: A Pareto-based hybrid multiobjective evolutionary approach for constrained multipath traffic engineering optimization in MPLS/GMPLS networks
Author(s): El-Alfy, ESM (El-Alfy, El-Sayed M.); Mujahid, SN (Mujahid, Syed N.); Selim, SZ (Selim, Shokri Z.)
Source: JOURNAL OF NETWORK AND COMPUTER APPLICATIONS Volume: 36 Issue: 4 Pages: 1196-1207 DOI: 10.1016/j.jnca.2013.02.008 Published: JUL 2013
Abstract: This paper proposes a hybrid evolutionary algorithm for solving the constrained multipath traffic engineering problem in MPLS (Multi-Protocol Label Switching) network and its extended architecture GMPLS (Generalized MPLS). Multipath traffic engineering is gaining more importance in contemporary networks. It aims to satisfy the requirements of emerging network applications while optimizing the network performance and the utilization of the available resources within the network. A formulation of this problem as a multiobjective constrained mixed-integer program, which is known to be NP-hard, is first extended. Then, we develop a hybrid heuristic algorithm based on combining linear programming with a devised Pareto-based genetic algorithm for approximating the optimal Pareto curve. A numerical example is adopted from the literature to evaluate and compare the performance of six variations of the proposed heuristic. We study the statistical significance of the results using Kruskal-Wallis nonparametric test. We also compare the results of the heuristic approach with the lexicographic weighted Chebyshev method using a variety of performance metrics. (c) 2013 Elsevier Ltd. All rights reserved.
Accession Number: WOS:000320750600010
Title: Editorial, ACM International Conference Proceeding Series: Preface
Title: Comparing a class of dynamic model-based reinforcement learning schemes for handoff prioritization in mobile communication networks
Author(s): El-Alfy, EM (El-Alfy, El-Sayed M.); Yao, YD (Yao, Yu-Dong)
Source: EXPERT SYSTEMS WITH APPLICATIONS Volume: 38 Issue: 7 Pages: 8730-8737 DOI: 10.1016/j.eswa.2011.01.082 Published: JUL 2011
Abstract: This paper presents and compares three model-based reinforcement learning schemes for admission policy with handoff prioritization in mobile communication networks. The goal is to reduce the handoff failures while making efficient use of the wireless network resources. A performance measure is formed as a weighted linear function of the blocking probability of new connection requests and the handoff failure probability. Then, the problem is formulated as a semi-Markov decision process with an average cost criterion and a simulation-based learning algorithm is developed to approximate the optimal control policy. The proposed schemes are driven by a dynamic model estimated simultaneously while learning the control policy using samples generated from direct interactions with the network. Extensive simulations are provided to assess and compare their effectiveness of the algorithm under a variety of traffic conditions with some well-known policies. (C) 2011 Elsevier Ltd. All rights reserved.
Accession Number: WOS:000289047700092
Title: Using machine learning techniques for the classification of the Makki and Madani Suras of the Holy
Title: Constructing optimal educational tests using GMDH-based item ranking and selection
Author(s): Abdel-Aal, RE (Abdel-Aal, Radwan E.); El-Alfy, ESM (El-Alfy, Ei-Sayed M.)
Source: NEUROCOMPUTING Volume: 72 Issue: 4-6 Special Issue: SI Pages: 1184-1197 DOI: 10.1016/j.neucom.2008.02.004 Published: JAN 2009
Abstract: Item ranking and selection plays a key role in constructing concise and informative educational tests. Traditional techniques based on the item response theory (IRT) have been used to automate this task, but they require model parameters to be determined a priori for each item and their application becomes more tedious with larger item banks. Machine-learning techniques can be used to build data-based models that relate the test result as output to the examinees' responses to various test items as inputs. With this approach, test item selection can benefit from the vast amount of literature on feature selection in many areas of machine learning and artificial intelligence that are characterized by high data dimensionality. This paper describes a novel technique for item ranking and selection using abductive network pass/fail classifiers based on the group method of data handling (GMDH). Experiments were carried out on a dataset consisting of the response of 2000 examinees to 45 test items together with the examinee's true ability level. The approach utilizes the ability of GMDH-based learning algorithms to automatically select optimum input features from a set of available inputs. Rankings obtained by iteratively applying this procedure are similar to those based on the average item information function (IIF) at the pass-fail ability threshold, IIF (theta = 0), and the average information gain (IG). An optimum item subset derived from the GMDH-based ranking contains only one third of the test items and performs pass/fail classification with 91.2% accuracy on a 500-case evaluation subset, compared to 86.8% for a randomly selected item subset of the same size and 92% for a subset of the 15 items having the largest values for IIF (theta = 0). Item rankings obtained with the proposed approach compare favorably with those obtained using neural network modeling and popular filter type feature selection methods, and the proposed approach is much faster than wrapper methods employing genetic search. (C) 2008 Elsevier B.V. All rights reserved.
Accession Number: WOS:000263372000054
Title: Learning Methods for Spam Filtering
Title: Construction and analysis of educational tests using abductive machine learning
Author(s): El-Alfy, ESM (El-Alfy, El-Sayed M.); Abdel-Aal, RE (Abdel-Aal, Radwan E.)
Source: COMPUTERS & EDUCATION Volume: 51 Issue: 1 Pages: 1-16 DOI: 10.1016/j.compedu.2007.03.003 Published: AUG 2008
Abstract: Recent advances in educational technologies and the wide-spread use of computers in schools have fueled innovations in test construction and analysis. As the measurement accuracy of a test depends on the quality of the items it includes, item selection procedures play a central role in this process. Mathematical programming and the item response theory (IRT) are often used in automating this task. However, when the item bank is very large, the number of item combinations increases exponentially and item selection becomes more tedious. To alleviate the computational complexity, researchers have previously applied heuristic search and machine learning approaches, including neural networks, to solve similar problems. This paper proposes a novel approach that uses abductive network modeling to automatically identify the most-informative subset of test items that can be used to effectively assess the examinees without seriously degrading accuracy. Abductive machine learning automatically selects only effective model inputs and builds an optimal network model of polynomial functional nodes that minimizes a predicted squared error criterion. Using a training dataset of 1500 cases (examinees) and 45 test items, the proposed approach automatically selected only 12 items which classified an evaluation population of 500 cases with 91%) accuracy. Performance is examined for various levels of model complexity and compared with that of statistical IRT-based techniques. Results indicate that the proposed approach significantly reduces the number of test items required while maintaining acceptable test quality. (C) 2007 Elsevier Ltd. All rights reserved.
Accession Number: WOS:000257103500001
Title: Applications of genetic algorithms to optimal multilevel design of MPLS-based networks
Author(s): El-Alfy, ESM (El-Alfy, El-Sayed M.)
Source: COMPUTER COMMUNICATIONS Volume: 30 Issue: 9 Pages: 2010-2020 DOI: 10.1016/j.comcom.2007.03.005 Published: JUN 30 2007
Abstract: This paper proposes a design methodology based on the application of genetic algorithms (GA) to find a minimal-cost topological structure of MPLS-based networks. MPLS technology is currently deployed in designing the backbone infrastructure of service provider networks whereas other parts of the network are still operated using the traditional IP protocol. This makes the overall topological structure of MPLS-based networks naturally breaks into two prime sub-problems: access network design and backbone network design. The ultimate goal is to identify the locations of label-edge routers and label-switching routers, and to determine the interconnection links and their capacities to accommodate expected traffic demands. The locations of label edge routers depend on the demands of a given set of terminal networks which in turn affect the design of the backbone network. This problem is a highly constrained NP-hard optimization problem for which exact solution approaches do not scale well. We first present a multilevel design model that divides the optimal topology design into a set of linear programs. Then, we propose GA-based meta-heuristics for solving them. We also discuss the impact of encoding methods and genetic operators and parameters on the performance. Numerical results for the considered cases show that the proposed methodology is effective and gives optimal or close to optimal solutions as compared with the exact branch and bound method. (C) 2007 Elsevier B.V. All rights reserved.
Accession Number: WOS:000248162300007
Title: A learning approach for prioritized handoff channel allocation in mobile multimedia networks
Author(s): El-Alfy, ES (El-Alfy, El-Sayed); Yao, YD (Yao, Yu-Dong); Heffes, H (Heffes, Harry)
Source: IEEE TRANSACTIONS ON WIRELESS COMMUNICATIONS Volume: 5 Issue: 7 Pages: 1651-1660 DOI: 10.1109/TWC.2006.01023 Published: JUL 2006
Abstract: An efficient channel allocation policy that prioritizes handoffs is an indispensable ingredient in future cellular networks in order to support multimedia traffic while ensuring quality of service requirements (QoS). In this paper we study the application of a reinforcement-learning algorithm to develop an alternative channel allocation scheme in mobile cellular networks that supports multiple heterogeneous traffic classes. The proposed scheme prioritizes handoff call requests over new calls and provides differentiated services for different traffic classes with diverse characteristics and quality of service requirements. Furthermore, it is asymptotically optimal, computationally inexpensive, model-free, and can adapt to changing traffic conditions. Simulations are provided to compare the effectiveness of the proposed algorithm with other known resource-sharing policies such as complete sharing and reservation policies.
Accession Number: WOS:000239132300015
Title: The impact of random early detection on the performance of different queueing disciplines
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Book Translation Project: Network Security Essentials, Stallings; 2007
الخوارزميات الجينية، السيد الألفي، محمد عبيدو، حنفي عمر، مكتبة العبيكان قيد النشر 2011. Book Translation Project: Genetic Algorithms
Book Translation Project: Fuzzy Logic,
D. Liu, S. Xie, Y. Li, D. Zhao, E-S. M. El-Alfy (Eds) Neural Information Processing (Part I), LNCS, Volume 10634, Springer, 2017
D. Liu, S. Xie, Y. Li, D. Zhao, E-S. M. El-Alfy (Eds) Neural Information Processing (Part II), LNCS, Volume 10635, Springer, 2017
D. Liu, S. Xie, Y. Li, D. Zhao, E-S. M. El-Alfy (Eds) Neural Information Processing (Part III), LNCS, Volume 10636, Springer, 2017
D. Liu, S. Xie, Y. Li, D. Zhao, E-S. M. El-Alfy (Eds) Neural Information Processing (Part IV), LNCS, Volume 10637, Springer, 2017
D. Liu, S. Xie, Y. Li, D. Zhao, E-S. M. El-Alfy (Eds) Neural Information Processing (Part V), LNCS, Volume 10638, Springer, 2017
D. Liu, S. Xie, Y. Li, D. Zhao, E-S. M. El-Alfy (Eds) Neural Information Processing (Part VI), LNCS, Volume 10639, Springer, 2017
Juan M. C. Rodriguez, Sushmita Mitra, Sabu M. Thampi, El-Sayed El-Alfy (Eds). Intelligent Systems Technologies and Applications, Springer;2016.
El-Alfy, E.-S.M., Thampi, S.M., Takagi, H., Piramuthu, S., Hanne, Th. (Eds.) Advances in Intelligent Informatics. Springer 2015.
W. S. Awad, E.-S. M. El-Alfy,
Y. Al-Bastaki (Eds.) "Improving
Information Security Practices through Computational Intelligence. IGI
Global, 2016.
E-S. M. El-Alfy, "Routing and Traffic Management," in S. Misra, S. C. Misra, and I. Woungang (Eds.). Selected Topics in Communication Networks and Distributed Systems, World Scientific, 2009. ISBN: 978-981-283-943-5. (Chapter 13)
E.-S. M. El-Alfy, "Learning Methods for Spam Filtering," in H. Peters and M. Vogel (Eds.). Machine Learning Research Progress, Nova Science Publishers, 2008. ISBN: 978-1-60456-646-8. Also published in Computer Systems, Support and Technology, pp. 175-217.
E-S. M. El-Alfy, "A review of network security," IEEE Distributed Systems Online, vol. 8, no. 7, July 2007. (Book Review) <pdf file>
E.-S. M. El-Alfy, �A general look at building applications for mobile devices,� IEEE Distributed Systems Online, vol. 6, no. 9, September 2005. (Book Review) <pdf file>
E-S. M. El-Alfy, �Software agents,� ACM Intelligence, 2000. (Book Review)pdf file Publications in Refereed Conference Proceedings
El-Alfy, E.-S.M., Binsaadoon, A.G. (2017) Silhouette-Based Gender Recognition in Smart Environments Using Fuzzy Local Binary Patterns and Support Vector Machines. Procedia Computer Science, 109, pp. 164-171. DOI: 10.1016/j.procs.2017.05.313
Al-Azani, S., El-Alfy, E.-S.M. (2017) Using Word Embedding and Ensemble Learning for Highly Imbalanced Data Sentiment Analysis in Short Arabic Text. Procedia Computer Science, 109, pp. 359-366. DOI: 10.1016/j.procs.2017.05.365
Binsaadoon, A.G., El-Alfy, E.-S.M. (2017)
Kernel-Based Fuzzy Local Binary Pattern for Gait Recognition
Proceedings - UKSim-AMSS 2016: 10th European Modelling Symposium on
Computer Modelling and Simulation, art. no. 7920225, pp. 35-40.
DOI: 10.1109/EMS.2016.016
Alshammari, M.A., El-Alfy, E.-S.M. MapReduce implementation for minimum reduct using parallel genetic algorithm. Proc. 6th International Conference on Information and Communication Systems, ICICS 2015, art. no. 7103194, pp. 13-18.
Mohammed, S., El-Alfy, E.-S.M., Barradah, A.F. Online prime labeling and generation of synopsis for XML query estimation. Proc. 6th International Conference on Information and Communication Systems, ICICS 2015, art. no. 7103195, pp. 19-24.
Baig, M.M., Awais, M.M., El-Alfy, E.-S.M. BOOSTRON: Boosting based perceptron learning. Lecture Notes in Computer Science, 8834, 2014, pp. 199-206.
El-Alfy, E.-S.M. ANFIS-based model for improved paraphrase rating prediction. Lecture Notes in Computer Science, 8834, 2014, pp. 397-404.
El-Alfy, E.-S.M., Al-Hasan, A.A. A novel bio-inspired predictive model for spam filtering based on dendritic cell algorithm. Proc. IEEE Symposium on Computational Intelligence in Cyber Security, CICS, 2014.
El-Alfy, E.-S.M., Ghaleb, A.A. Biobjective NSGA-II for optimal spread spectrum watermarking of color frames: Evaluation study. Proc. IEEE Symposium on Computational Intelligence in Cyber Security, CICS, 2014.
El-Alfy, E.-S.M., Riaz, M.R. Image quality assessment using ANFIS approach. Proc. 6th International Conference on Agents and Artificial Intelligence, ICAART 2014, pp. 169-177.
Al-Obeidat, F.N., El-Alfy, E.-S.M. Network intrusion detection using multi-criteria PROAFTN classification. Proc. 5th International Conference on Information Science and Applications, ICISA 2014.
El-Alfy, E.-S.M. Statistical analysis of ml-based paraphrase detectors with lexical similarity metrics. Proc. 5th International Conference on Information Science and Applications, ICISA 2014.
Baig, M., El-Alfy, E.-S.M., Awais, M.M. Intrusion detection using a cascade of boosted classifiers (CBC). Proc. IEEE International Joint Conference on Neural Networks, IJCNN 2014, pp. 1386-1392.
El-Alfy, E.-S.M. Enhanced hand shape identification using random forests. Lecture Notes in Computer Science, 8227 LNCS (PART 2), 2013, pp. 441-447.
El-Alfy, E.-S.M. Detecting pixel-value differencing steganography using Levenberg-Marquardt neural network. Proc. IEEE Symposium on Computational Intelligence and Data Mining, CIDM 2013, pp. 160-165.
El-Alfy, E.-S.M., Al-Sadi, A.A. High-Capacity Image Steganography Based on Overlapped Pixel Differences and Modulus Function. Communications in Computer and Information Science, 294 PART 2, 2012, pp. 243-252.
El-Alfy, E.-S.M., BinMakhashen, G.M. Improved Personal Identification Using Face and Hand Geometry Fusion and Support Vector Machines. Communications in Computer and Information Science, 294 PART 2, 2012, pp. 253-261.
Alvi, F., El-Alfy, E.-S.M., Al-Khatib, W.G., Abdel-Aal, R.E. Analysis and extraction of sentence-level paraphrase sub-corpus in CS education. Proc. ACM Special Interest Group for Information Technology Education Conference, SIGITE'12, pp. 49-54.
El-Alfy, E.-S.M., Abdel-Aal, R.E., Baig, Z.A. Abductive neural network modeling for hand recognition using geometric features. Lecture Notes in Computer Science, 7666 LNCS (PART 4), 2012, pp. 593-602.
Binmakhashen, G.M., El-Alfy, E.-S.M. Fusion of multiple texture representations for palmprint recognition using neural networks. Lecture Notes in Computer Science, 7667 LNCS (PART 5), 2012, pp. 410-417.
El-Alfy, E.-S.M., Bin Makhashen, G.M. Evaluation of support vector machine with universal kernel for hand-geometry based identification. Proc. IEEE International Conference on Innovations in Information Technology, IIT 2012, pp. 117-122.
El-Alfy, E.-S.M. Automatic identification based on hand geometry and probabilistic neural networks. Proc. IEEE 5th International Conference on New Technologies, Mobility and Security, NTMS 2012.
El-Alfy, E.-S.M., Al-Sadi, A.A. Improved pixel value differencing steganography using logistic chaotic maps. Proc. IEEE International Conference on Innovations in Information Technology, IIT 2012, pp. 129-133.
El-Alfy, E.-S.M. Classification of deformable geometric shapes using radial-basis function networks and ring-wedge energy features. Proc. 4th International Conference on Agents and Artificial Intelligence, ICAART 2012, pp. 355-362.
El-Alfy, E.-S. M. and Al-Sadi, A. �A More Effective Steganographic Approach for Color Images by Combining Simple Methods,� in Proceeding of the 7th International Computing Conference in Arabic, ICCA 2011, Riyadh, Saudi Arabia, May 2011. (in Arabic)
Abido,
M. A. and El-Alfy, E.-S. M.,
�Design of Damping Controllers in Electrical Power Systems Using
Continuous Genetic Algorithms,� in Proceeding of the 7th International
Computing Conference in Arabic, ICCA 2011, Riyadh, Saudi Arabia, May
2011. (in Arabic)
El-Alfy, E.-S. M. and Al-Sadi, A. �A Comparative Study of PVD-Based Schemes for Data Hiding in Digital Images,� in Proceedings of the 9th ACS/IEEE International Conference on Computer Systems and Applications, (AICCSA 2011), Sharm El-Sheikh, Egypt, June 27-30, 2011.
El-Alfy, E.-S. M. �A Reinforcement Learning Approach for Sequential Mastery Testing,� in Proceedings of IEEE Symposium Series in Computational Intelligence (SSCI 2011), Adaptive Dynamic Programming and Reinforcement Learning, Paris, France, April 11-15, 2011.
El-Alfy, E.-S. M. and Al-Utaibi, K. �A Color Image Encryption
Scheme Based on Chaotic Maps and Genetic Operators for Confusion and
Diffusion,� in Proceedings of the 7th International Conference on
Network Services (ICNS 2011), IARIA, Venice/Mestre, Italy, May 22-27,
2011.
Al-Sadi, A. A. and El-Alfy, E.-S. M., �An Adaptive Steganographic Method for Color Images Based on LSB Substitution and Pixel Value Differencing,� in Proceedings of the International Conference on Advances in Computing and Communications (AC 2011), Kochi Kerala, India, Jul 22-24, 2011
El-Alfy, E.-S. M. �
El-Alfy, E.-S. M., �A Hierarchical Group Method of Data Handling Polynomial Neural Network for Handwritten Numeral Recognition,� Proceedings of the IEEE/INNS International Joint Conference on Neural Networks (IJCNN 2010), Barcelona, Spain, July 18-23, 2010.
Al-Utaibi, K. and El-Alfy, E.-S. M., �A Bio-Inspired Image Encryption Algorithm Based on Chaotic Maps,� Proceedings of the IEEE Congress on Evolutionary Computation (CEC 2010), Barcelona, Spain, July 18-23, 2010.
El-Sayed M. El-Alfy, "Discovering classification rules for email spam filtering with an ant colony optimization algorithm," IEEE Congress on Evolutionary Computation (CEC�09), Trondheim, Norway, May 18-21, 2009.
El-Sayed M. El-Alfy, and Said A. Muhammad, �E-Restaurant: A collaborative distributed system integrating restaurant operation automation and meal recommendation,� in Proceedings of the Fourth eServices Symposium in the Eastern Province of Saudi Arabia: eServices Integration, Al-Khobar, Saudi Arabia, March 9-11, 2009.
El-Sayed M. El-Alfy, and Anas M.N. Orwani, "The need for a unified framework for evaluating web cache replacement strategies," in Proceedings of the 5th IEEE-GCC Conference, Kuwait, March 17-19, 2009.
El-Sayed M. El-Alfy, "Abductive learning approach for geometric shape recognition,� in Proceedings of the International Conference on Intelligent Systems and Exhibition, Bahrain, Dec. 1-3, 2008.
El-Sayed M. El-Alfy and Radwan E. Abdel-Aal, �Spam filtering with abductive networks,� in Proceedings of the IEEE International Joint Conference on Neural Networks (IJCNN�08), Hong Kong, June 1-6, 2008. <pdf file>
El-Sayed M. El-Alfy and Fares S. Al-Qunaieer, �A fuzzy similarity approach for automated spam filtering,� in Proceedings of IEEE International Conference on Computer Systems and Applications (AICCSA�08), Qatar, April 2008. <pdf file>
El-Sayed M. El-Alfy, Shokri Z. Selim and Syed N. Mujahid, �Solving the minimum-cost constrained multipath routing with load balancing in MPLS networks using an evolutionary method,� in Proceedings of the IEEE Congress on Evolutionary Computation (CEC 2007), Singapore, September 2007.
El-Sayed M. El-Alfy and Syed S. Jafri, �A recurrent neural network for sequential estimation of examinees' knowledge state,� in Proceedings of the First International Conference on Interactive Computer aided Blended Learning (ICBL 2007), IEEE Student Track, Brazil, May 2007.
El-Sayed M. El-Alfy and Shokri Z. Selim, �On optimal firewall rule ordering,� in Proceedings of IEEE International Conference on Computer Systems and Applications (AICCSA�07), Amman, Jordan, May 13�16, 2007. <pdf file>
El-Sayed M. El-Alfy, �A heuristic approach for firewall policy optimization,� in Proceedings of IEEE International Conference on Advanced Communication Technology (ICACT�07), Phoenix Park, Republic of Korea, February 12�14, 2007. <pdf file>
El-Sayed M. El-Alfy and Syed S. Jafri, �A neural network approach for estimating examinees� proficiency levels in computerized adaptive testing,� in Proceedings of IASTED International Conference on Web-based Education (WBE�07), Chamonix, France, March 14�16, 2007. <pdf file>
Nada M. El-Shenawy, Mohamed N. El-Drini, Mahmoud M. Fahmy and El-Sayed M. El-Alfy, �Performance comparison of CBT and PIM multicast routing protocols over MPLS networks,� in Proceedings of IEEE International Conference on Computer Engineering & Systems (ICCES'06), Cairo, Egypt, November 2006. <pdf file>
El-Sayed M. El-Alfy, �MPLS network topology design using genetic algorithms,� in Proceedings of IEEE International Conference on Computer Systems and Applications (AICCSA'06), Dubai/Sharjah, UAE, pp. 1059 � 1065, March 8-11, 2006. <pdf file>
Hafiz M. Asif and El-Sayed M. El-Alfy, �Performance evaluation of queuing disciplines for multi-class traffic using OPNET simulator,� in Proceedings of the 7th WSEAS International Conference on Mathematical Methods and Computational Techniques in Electrical Engineering, Sofia, Bulgaria, October 2005.
El-Sayed M. El-Alfy, Yu-Dong Yao, and Harry Heffes, �Autonomous call admission control with prioritized handoff in cellular networks�, in Proceedings of IEEE International Conference on Communications (ICC�01), Finland, June 2001.
El-Sayed M. El-Alfy, Yu-Dong Yao, and Harry Heffes, �A learning approach for call admission control with prioritized handoff in mobile multimedia networks,� in Proceedings of IEEE Vehicular Technology Conference (VTC�01-Spring), Greece, May 2001.
El-Sayed M. El-Alfy, Yu-Dong Yao, and Harry Heffes, �A model-based learning scheme for wireless channel allocation with prioritized handoff,� in Proceedings of IEEE Global Telecommunication Conference (Globecom�06), San Antonio, TX, November 2001.
El-Sayed M. El-Alfy, Yu-Dong Yao, and Harry Heffes, �Adaptive resource allocation with prioritized handoff in cellular mobile networks under QoS provisioning,� in Proceedings of IEEE Vehicular Technology Conference (VTC�01-Fall), Atlantic City, NJ, October 2001.
El-Sayed M. Abdel-Kareem (El-Alfy), Yehia M. Enab, and El-Sayed H. El-Konyalay, �Obstacle avoidance and motion control for mobile robots,� SPIE, Mobile Robots IX, vol. 2352, pp. 167-177, 1995.
El-Sayed M. Abdel-Kareem (El-Alfy), Yehia M. Enab, and El-Sayed H. El-Konyalay, �Obstacle avoidance navigator for mobile robots,� In Proceedings of the International Engineering Conference (IEC'95), Mansoura, Egypt, 1995. [held every two years, latest is IEC 2008]
El-Sayed M. El-Alfy, TR1, 2010.
El-Sayed M. El-Alfy, TR2, 2010.
El-Sayed M. El-Alfy, Traffic Routing and Management in Communication Networks, Technical Report, KFUPM-CCSE-2008-001/ICS, December 2007.
El-Sayed M. El-Alfy, Computerized Adaptive Testing Methodologies, Technical Report, KFUPM-CCSE-2006-001/ICS, September 2006. (Also submitted to DSR-KFUPM)
El-Sayed M. El-Alfy, Applications of Genetic Algorithms to MPLS-Based Network Design, Technical Report, KFUPM-CCSE-2005-005/ICS, September 2005. (Also submitted to DAD-KFUPM)
El-Sayed M. El-Alfy et al., Real Time Data Acquisition System. Technical Report, FRCU, El-Mansoura, Egypt, 1992.
El-Sayed M. El-Alfy, Intelligent System for Channel Allocation with Prioritized Handoff in Mobile Cellular Multimedia Networks. PhD Dissertation, July 2001.
El-Sayed M. El-Alfy, Wheeled Mobile Obstacle Avoidance. M. Sc. Thesis, July 1994.
El-Sayed M. El-Alfy et al., Computer-Based Control System for Inspection of Raw Materials. B.Sc. Senior Project, 1991 (a basis for a paper published by the advisor in Port Said Magazine, Egypt, 1991).
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