Data Mining Techniques for Medical Applications: A Survey - wseas.us

Mathematical Methods in Science and Mechanics

Data Mining Techniques for Medical Applications: A Survey Ibrahim M. El-Hasnony, Hazem M. El Bakry, Ahmed A. Saleh Faculty of Computer Science & Information Systems, Mansoura University, Mansoura, EGYPT Abstract- Data mining has been used to uncover hidden patterns and relations to summarize the data in ways to be useful and understandable in all types of businesses to make prediction for future perspective. Medical data is consider most famous application to mine that data, so in this paper we introduce a survey on how medical data problems such as dealing with noisy, incomplete , heterogeneous and intensive data has been faced ,the advantages and disadvantages of each one , finally suggest a framework for enhancing and overcoming this problems. The theory of fuzzy sets has been recognized as a suitable tool to model several kinds of patterns that can hold in data. In this paper, we are concerned with the development of a general model to discover association rules among items in a (crisp) set of fuzzy transactions. This general model can be particularized in several ways; each particular instance corresponds to a certain kind of pattern and/or repository of data. We describe some applications of this scheme, paying special attention to the discovery of fuzzy association rules .to extract association rules from quantitative data, the dataset at hand must be partitioned into intervals, and then converted into Boolean type .fuzzy association rules are developed as a sharp knife by handling quantitative data using fuzzy set. Along with the proposed system we will use neural network approaches for clustering, classification, statistical analysis and data modeling. Keywords- Data Mining, association rules, fuzzy association rule, neural network..

collection of data objects, similar data are taking in the same cluster, dissimilar data are taking in different clusters [2].

1. Introduction Data mining is a step of analyzing in “Knowledge Discovery and Data Mining" process, or KDD, data mining involve methods for computational discovery of patterns in large data sets such as artificial intelligence, machine learning, statistics and database systems. It tasks has been categorized to descriptive and predictive methods. Classification, clustering and rule association mining are most common techniques for descriptive and predictive analysis [1-2].

Association: Association analysis is the discovery of association rules. It depends on the frequency of transactional data occur together in database, also depends on a threshold called support, and identifies the frequent item sets. Association data mining aimed to find association between attributes, generate rules from data sets [2]. The association rule mining role is to reach all rules having support≤ minsup (minimum support) threshold and confidence ≤minconf (minimum confidence) threshold [3].

2. Data mining consists of major elements Classification: Classification is the representation of data in given classes. Which called supervised classification, it uses given class labels to order the objects in the data collection [2]. Classification consider as an important task of data mining. Using this approach data must be defined as class label (target attribute). In binary classification, the target attribute has only two possible values: for example, high or low. Multiclass targets have more than two values: for example, low, medium, high, or unknown. Classification can applied into Business modeling, marketing, credit analysis, biomedical and drug response modeling.

Many papers have been devoted to develop algorithms to mine ordinary association rules. The early efficient algorithms like Apriori and AprioriTid [27], SETM [28], OCD [29], and DHP [30] were continued with more recent developments like DIC [31], CARMA [32], TBAR [33], and FP-Growth [34].

3. Fuzzy association rules Association rules can be merged with many techniques i.e. fuzzy rules.

Clustering:

Crisp rule Crisp set theory uses one of only two values: true or false. Crisp set cannot represent vague concepts [6]. Elements are assigned to the sets by giving them the values 0 or 1. Every element with 1 value is a member of the set, elements with 0 value is non-member of the set. The number of elements that belong to a set is called its cardinality [7].

Clustering is the representation of data in classes. However, unlike classification, in clustering, class labels are unknown and it is up to the clustering algorithm to discover acceptable classes. This called unsupervised classification. Clustering is a

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different results for the same input data based on system state (time) [11].

Fuzzy rule Fuzzy sets represented as an extension of the classical crisp sets [7]. Fuzzy set theory is that an element belongs to a fuzzy set with a certain degree of membership. Thus, a proposition is not either true or false, but may be partly true or partly false to any degree. This degree is usually taken as a real number in the interval [0, 1] [5-6].

5. Data mining applications Medical data mining: Over the past decade, nudged by new federal regulations, hospitals and medical offices around the country have been converting scribbled doctors’ notes to electronic records. Although the chief goal has been to improve efficiency and cut costs [13].

Fuzzy rules can be combined with Association rules to generate fuzzy association rules. There exists a new approach that use classical association rule mining by using fuzzy sets. Fuzzy association mining solves many problems find in huge quantities of data that exist often in database efficiently [7]. When dividing an attribute in the data into sets covering certain ranges of values, we are confronted with the sharp boundary problem. Elements near the boundaries of a crisp set will either be ignored.

Spatial data mining: Spatial data mining is the application of data mining methods to spatial data. The end objective of spatial data mining is to find patterns in data with respect to geography. So far, data mining and Geographic Information Systems (GIS) have existed as two separate technologies, each with its own methods, traditions, and approaches to visualization and data analysis. Particularly, most contemporary GIS have only very basic spatial analysis functionality. The immense explosion in geographically referenced data occasioned by developments in IT, digital mapping, remote sensing, and the global diffusion of GIS emphasizes the importance of developing data driven inductive approaches to geographical analysis and modeling [35-119]. Sensor data mining: Wireless sensor networks can be used for facilitating the collection of data for spatial data mining for a variety of applications such as air pollution monitoring. A characteristic of such networks is that nearby sensor nodes monitoring an environmental feature typically registers similar values. This kind of data redundancy due to the spatial correlation between sensor observations inspires the techniques for in-network data aggregation and mining. By measuring the spatial correlation between data sampled by different sensors, a wide class of specialized algorithms can be developed to develop more efficient spatial data mining algorithms [21]. Visual data mining: In the process of turning from analogical into digital, large data sets have been generated, collected, and stored discovering statistical patterns, trends and information which is hidden in data, in order to build predictive patterns. Studies suggest visual data mining is faster and much more intuitive than is traditional data mining [22]. Music data mining: Data mining techniques, and in particular co-occurrence analysis, has been used to discover relevant similarities among music corpora (radio lists, CD databases) for purposes including classifying music into genres in a more objective manner [23]. Pattern mining: "Pattern mining" is a data mining method that involves finding existing patterns in data. In this context patterns often means association rules. The original motivation for searching association rules came from the desire to analyze supermarket transaction data, that is, to examine customer behavior in terms

4. Neural Networks in Data Mining Neural networks have been successfully applied in supervised and unsupervised learning applications .There are two classes of approaches for data mining with neural networks .The first approach called rule extraction involves model extraction from trained neural networks ,The second approach is to directly learn simple easy-to-understand networks [8]. Neural Network Applications can be grouped in following categories •

Clustering: A clustering algorithm explores the similarity between patterns and places similar patterns in a cluster. Best known applications include data compression and data mining [9].

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Classification/Pattern recognition: The task of pattern recognition is to assign an input pattern (like handwritten symbol) to one of many classes. This category includes algorithmic implementations such as associative memory [10].

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Function approximation: The tasks of function approximation is to find an estimate of the unknown function f () subject to noise. Various engineering and scientific disciplines require function approximation [11].

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Prediction/Dynamical Systems: The task is to forecast some future values of a time sequenced data. Prediction has a significant impact on decision support systems. Prediction differs from Function approximation by considering time factor. Here the system is dynamic and may produce

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•

of the purchased products. For example, an association rules "beer Potato chips (80)" states that four out of five customers that bought beer also bought potato chips. In the context of pattern mining as a tool to identify terrorist activity, the National Research Council provides the following definition: "Pattern-based data mining looks for patterns (including anomalous data patterns) that might be associated with terrorist activity these patterns might be regarded as small signals in a large ocean of noise"[24][25]. Pattern Mining includes new areas such a Music Information Retrieval (MIR) where patterns seen both in the temporal and non-temporal domains are imported to classical knowledge discovery search methods. Subject-based data mining: "Subject based data mining" is a data mining method involving the search for associations between individuals in data. In the context of combating terrorism, the National Research Council provides the following definition: "Subject-based data mining uses an initiating individual or other datum that is considered, based on other information, to be of high interest, and the goal is to determine what other persons or financial transactions or movements, etc., are related to that initiating datum" [26].

• • • •

Many of the environments still lacks standards that impede the use and analysis of data on a wide range of global data, limiting this application to data sets collected for specific diagnostic, screening, prognostic, monitoring, therapy support or other patient management purposes [14].

6.2 Related work Here, we make a comparison among different studies that relate to our study. Many researchers aimed to reach more accurate and complete system in knowledge discovery .These include practical issues such as handling noisy and incomplete data (e.g. protein interactions have high false positive and false negative rates). Fuzzy association rule can be applied to solve this problem. Association rule mining is one of the fundamental tasks of data mining. The conventional association rule mining algorithms, using crisp set, are meant for handling Boolean data. Therefore, to extract association rules from quantitative data, the dataset at hand must be partitioned into intervals, and then converted into Boolean type. In the sequel, it may suffer with the problem of sharp boundary. Hence, fuzzy association rules are developed as a sharp knife to solve problem by handling quantitative data using fuzzy set. Another problem in knowledge discovery is, Modern medicine generates almost daily, huge amounts of heterogeneous data. For example, medical data may contain SPECT images, signals like ECG, clinical information like temperature, cholesterol levels, etc., as well as the physician's interpretation. Those who deal with such data understand that there is a widening gap between data collection and data comprehension. Computerized techniques are needed to help humans address this problem. This volume is devoted to the relatively young and growing field of medical data mining and knowledge discovery. As more and more medical procedures employ imaging as a preferred diagnostic tool, there is a need to develop methods for efficient mining in databases, chi-square or correlation that is used in clustering can be applied to solved this problem, Correlation clustering also relates to a different task, where correlations among attributes of feature vectors in a high-dimensional space are assumed to exist guiding the clustering process. These correlations may be different in different clusters, thus a global decorrelation cannot reduce this to traditional (uncorrelated) clustering. Correlations among subsets of attributes result in different spatial shapes of clusters. Hence, the similarity between cluster objects is defined by taking into account the local correlation

6. Data mining in medical data Modern medicine generates large amount of information stored in the medical database. It is necessary to extract useful knowledge and providing scientific decision-making for the diagnosis and treatment of disease from the database increasingly becomes necessary. Data mining in medicine can deal with this problem. It can also improve the management quality of hospital information and promote the development of telemedicine and community medicine. Because the medical information is characteristic of redundancy, multi-attribution, incompletion and closely related with time, medical data mining differs from other one. In this paper we have discussed the key techniques of medical data mining involving pretreatment of medical data, fusion of different pattern and resource, fast and robust mining algorithms and reliability of mining results. The methods and applications of medical data mining based on computation intelligence such as artificial neural network, fuzzy system, evolutionary algorithms, rough set, and association rules have been introduced [12][13].

6.1 Problems in medical data Extensive amounts of knowledge and data stored in medical database need us to develop specialized tools for accessing, data analysis, knowledge discovery and effective use of stored knowledge and data, Because of the increase of data volume results in difficulties in extracting useful information for decision support. The traditional manual data analysis has become insufficient. Important issues that result from the rapidly emerging inclusive of data and information are:

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The provision of standard in terminology, vocabularies and formats to support multi-liguity and sharing of data. Standards for the abstraction and visualization of data. Integration of heterogeneous types of data including image and signals ...etc Standards for interfaces between different resources of data. Reusability of data, knowledge and tools.

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algorithmic systems which use historical data to identify trends, clusters, and patterns. Unsupervised neural networks (clustering) (i.e. SOM, Hebbian law). Supervised learning are limited by their training, i.e. they can reliably recognize only the kind of information on which they were trained (i.e. Perceprton , Multilayer NN). Fuzzy association system is applied to solve several problems that faces data mining ,Fuzzy rules can be combined with association rules algorithm (Apriori or FP-Growth ),system receive input data then attribute will be selected for operations ,then selected data will be transform to linguistic variable by applying fuzzy membership function ,fuzzy variables transformed to crisp values that can generate rules easily, The user defined support will be inserted and frequent pattern is generated, then confidence inserted and system association rules generated ,after those steps the output of fuzzy linguistic variable that is crisp values combined with association rules that generate frequent fuzzy association rules, with the frequent fuzzy association rule the problem with noisy or incomplete data will be solved. Also ,transformed data will be the Input for Neural Network that may be supervised NN (i.e. perceptron ,Multilayer NN) that used in data classification , unsupervised NN(i.e. SOM ,Hebbian law) used for clustering of data ,using data clustering ,the problem of heterogeneous data and problem of intensive data will be solved.

7. The Proposed Framework Many researchers aimed to reach more accurate and complete system in knowledge discovery. These include practical issues such as handling noisy and incomplete data (e.g. protein interactions have high false positive and false negative rates). Fuzzy association rule can be applied to solve this problem. Association rule mining is one of the fundamental tasks of data mining. The conventional association rule mining algorithms, using crisp set, are meant for handling Boolean data. Therefore, to extract association rules from quantitative data, the dataset at hand must be partitioned into intervals, and then converted into Boolean type. In the sequel, it may suffer with the problem of sharp boundary. Hence, fuzzy association rules are developed as a sharp knife to solve problem by handling quantitative data using fuzzy set. Another problem in knowledge discovery is, Modern medicine generates almost daily, huge amounts of heterogeneous data. For example, medical data may contain SPECT images, signals like ECG, clinical information like temperature, cholesterol levels, etc., as well as the physician's interpretation. Those who deal with such data understand that there is a widening gap between data collection and data comprehension. Computerized techniques are needed to help humans address this problem. This volume is devoted to the relatively young and growing field of medical data mining and knowledge discovery. As more and more medical procedures employ imaging as a preferred diagnostic tool, there is a need to develop methods for efficient mining in databases, chi-square or correlation that is used in clustering can be applied to solved this problem, Correlation clustering also relates to a different task, where correlations among attributes of feature vectors in a high-dimensional space are assumed to exist guiding the clustering process. These correlations may be different in different clusters, thus a global decorrelation cannot reduce this to traditional (uncorrelated) clustering. Correlations among subsets of attributes result in different spatial shapes of clusters. Hence, the similarity between cluster objects is defined by taking into account the local correlation patterns. With this notion, the term has been introduced in simultaneously with the notion discussed above. Different methods for correlation clustering of this type are discussed in, the relationship to different types of clustering is discussed in, see also clustering high-dimensional data. Other problem is processing compute intensive tasks (e.g. large scale graph mining) i.e. Big data ,Big data is the term for a collection of data sets so large and complex that it becomes difficult to process using on hand database management tools or traditional data processing applications. The challenges include capture, storage, search, sharing, transfer, analysis and visualization. Neural network used in classification or clustering, a neural network is an interconnected assembly of simple processing elements, units or nodes whose functionality is loosely based on the animal neuron. The processing ability of the network is stored in the inter-unit connection strengths, or weights, obtained by a process of adaption to, or learning from a set of training patterns. Neural Networks are

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8. Conclusion Modern medicine generate large amount of information stored in medical database, these extensive amounts of knowledge and data in medicine need us to develop specialized tools for accessing data analysis , knowledge discovery and effective use of stored knowledge and data. In this paper, include three main practical issues: Handling noisy and incomplete data, Generating almost daily huge amounts of heterogeneous data, processing compute intensive tasks. We suggest here in this study data mining techniques as Fuzzy association rules and neural network techniques. Knowledge management is providing the facility to find out these rules any time when need.

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[49] Hazem M. El-Bakry, "New Fast Principal Component Analysis For Real-Time Face Detection," Machine Graphics & Vision Journal (MG&V), vol. 18, no.4, 2009, pp. 405-426. [50] Hazem M. El-bakry, and Mohamed Hamada “Fast Time Delay Neural Networks for Detecting DNA Coding Regions,” Springer, Lecture Notes on Artificial Intelligence (LNAI 5711), 2009, pp. 334-342. [51] Hazem M. El-Bakry, "A New Neural Design for Faster Pattern Detection Using Cross Correlation and Matrix Decomposition," Neural World journal, Neural World Journal, 2009, vol. 19, no. 2, pp. 131-164. [52] Hazem M. El-Bakry, and Ahmed Atwan, " Improving Quality of Business Networks for Information Systems," The International Journal of Computer, Information, and systems science, and Engineering, issue 3, vol. 3, July 2009, pp. 138-150. [53] Hazem M. El-Bakry, and Ahmed A. Mohammed, "Optimal Document Archiving and Fast Information Retrieval," The International Journal of Computer science, and Engineering, issue 2, vol. 3, July 2009, pp. 108-121. [54] Hazem M. El-Bakry, and Nikos Mastorakis, "Fast Word Detection in a Speech Using New High Speed Time Delay Neural Networks," WSEAS Transactions on Information Science and Applications, issue 7, vol. 5, July 2009, pp. 261-270. [55] Hazem M. El-Bakry, and Nikos Mastorakis, "Fast Information Retrieval from Web Pages," WSEAS Transactions on Information Science and Applications, issue 6, vol. 6, June 2009, pp. 1018-1036. [56] Hazem M. El-Bakry, and Nikos Mastorakis, "Fast Image Matching on Web Pages," WSEAS Transactions on Signal Processing, issue 4, vol. 5, June 2009, pp. 157-166. [57] Hazem M. El-Bakry, and Nikos Mastorakis, "Fast Detection of Specific Information in Voice Signal over Internet Protocol," WSEAS Transactions on Communications, issue 5, vol. 8, May 2009, pp. 483-494. [58] Hazem M. El-bakry, and Nikos Mastorakis “Fast Time Delay Neural Networks for Word Detection in a Video Conference,” Proc. of European Computing and Computational Intelligence International Conference, Tbilisi, Georgia, June 26-28, 2009, pp. 120-129. [59] Alaa M. Riad, Hazem M. El-bakry, and Nikos Mastorakis, “Fast Harmonic Current / Voltage Prediction by using High Speed Time Delay Neural Networks, " Proc. of WSEAS International Conference on Communication and Information, Athens, Greece, December 29-31, 2009, pp. 245-272. [60] Hazem M. El-Bakry, and Nikos Mastorakis “Fast Human Motion Tracking by using High Speed Neural Networks " Proc. of 9th WSEAS International Conference on SIGNAL, SPEECH AND IMAGE PROCESSING (SSIP '09), Budapest, Hungry, September 3-5, 2009, pp. 221-240. [61] Hazem M. El-Bakry, and Nikos Mastorakis “A Fast Computerized Method For Automatic Simplification of Boolean Functions,” Proc. of 9th WSEAS International Conference on SYSTEMS THEORY AND SCIENTIFIC COMPUTATION (ISTASC '09), Moscow, Russia, August 26-28, 2009, pp. 99-107. [62] Hazem M. El-Bakry, and Nikos Mastorakis “Fast Information Processing over Business Networks,” Proc. of 9th WSEAS International Conference on Applied Informatics and Communications (AIC'09), Moscow, Russia, August 26-28, 2009, pp.305-324. [63] Hazem M. El-Bakry, and Nikos Mastorakis “A Fast Searching Protocol for Fully Replicated System,” Proc. of of 13th WSEAS International Conference on Computers, Rodos, Greece, July 2225, 2009, pp. 588-600.

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[64] Hazem M. El-Bakry, and Nikos Mastorakis “An Efficient Electronic Archiving Approach for Office Automation,” Proc. of European Computing and Computational Intelligence International Conference, Tbilisi, Georgia, June 26-28, 2009, pp. 130-144. [65] Hazem M. El-Bakry, and Nikos Mastorakis “Fast Time Delay Neural Networks for Word Detection in a Video Conference,” Proc. of European Computing and Computational Intelligence International Conference, Tbilisi, Georgia, June 26-28, 2009, pp. 120-129. [66] Hazem M. El-Bakry, “Fast Record Detection in Large Databases Using New High Speed Time Delay Neural Networks,” Proc. of IEEE IJCNN’09, Atlanta, USA, June 14-19, 2009, pp. 757-763. [67] Hazem M. El-Bakry, and Mohamed Hamada “Fast Principal Component Analysis for Face Detection Using Cross-Correlation and Image Decomposition,” Proc. of IEEE IJCNN’09, Atlanta, USA, June 14-19, 2009, pp. 751-756. [68] Hazem M. El-Bakry, and Nikos Mastorakis “Fast Image Matching on Web Pages,” Proc. of Recent Advances in Applied Mathematics and Computational and Information Sciences, Houston, USA, April 30-May 2, 2009, pp. 470-479. [69] Hazem M. El-Bakry, and Nikos Mastorakis “Design of AntiGPS for Reasons of Security,” Proc. of Recent Advances in Applied Mathematics and Computational and Information Sciences, Houston, USA, April 30-May 2, 2009, pp. 480-500. [70] Hazem M. El-Bakry, and Nikos Mastorakis, "A Modified Hopfield Neural Network for Perfect Calculation of Magnetic Resonance Spectroscopy," WSEAS Transactions on Information Science and Applications, issue 12, vol. 5, December 2008, pp. 1654-1666. [71] Hazem M. El-Bakry, and Nikos Mastorakis, "A New Fast Forecasting Technique using High Speed Neural Networks," WSEAS Transactions on Signal Processing, issue 10, vol. 4, October 2008, pp. 573-595. [72] Hazem M. El-Bakry, and Nikos Mastorakis, "A New Technique for Detecting Dental Diseases by using High Speed Artificial Neural Network," WSEAS Transactions on Computers, Issue 12, vol. 7, December 2008, pp. 1977-1987. [73] Hazem M. El-Bakry, and Nikos Mastorakis, "A Real-Time Intrusion Detection Algorithm for Network Security," WSEAS Transactions on Communications, Issue 12, vol. 7, December 2008, pp. 1222-1234. [74] Hazem M. El-Bakry, and Nikos Mastorakis, " An Effective Method for Detecting Dental Diseases by using Fast Neural Networks," WSEAS Transactions on Biology and Biomedicine, issue 11, vol. 5, November 2008, pp. 293-301. [75] Hazem M. El-Bakry, and Nikos Mastorakis, "A Novel Fast Kolmogorov’s Spline Complex Network for Pattern Detection," WSEAS Transactions on Systems, Issue 11, vol. 7, November 2008, pp. 1310-1328. [76] Hazem M. El-Bakry, "New Faster Normalized Neural Networks for Sub-Matrix Detection using Cross Correlation in the Frequency Domain and Matrix Decomposition, " Applied Soft Computing journal, vol. 8, issue 2, March 2008, pp. 1131-1149. [77] Hazem M. El-Bakry and Mohamed Hamada, "A New Implementation for High Speed Neural Networks in Frequency Space," Lecture Notes in Artificial Intelligence, Springer, KES 2008, Part I, LNAI 5177, pp. 33-40. [78] Hazem M. El-Bakry, and Nikos Mastorakis “Fast Virus Detection by using High Speed Time Delay Neural Networks,” Proc. of 10th WSEAS Int. Conf. on NEURAL NETWORKS (NN'09), Prague, Czech Repulic, March 22-25, 2008, pp. 169183.

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[79] Hazem M. El-Bakry, and Nikos Mastorakis “New Efficient Neural Networks for Fast Record Detection in Databases,” Proc. of Recent Advances in Artificial Intelligence, Koweledge Engineering and Databases, Cambridge, UK, February 21-23, 2009, pp. 95-102. [80] Hazem M. El-Bakry, and Nikos Mastorakis “Fast Detection of Specific Information in Voice Signal over Internet Protocol,” Proc. of 7th WSEAS Int. Conf. on COMPUTATIONAL INTELLIGENCE, MAN-MACHINE SYSTEMS and CYBERNETICS (CIMMACS '08), Cairo, EGYPT, Dec. 29-31, 2008, pp. 125-136. [81] Hazem M. El-Bakry, and Nikos Mastorakis “Information Retrieval Based on Image Detection on Web Pages,” Proc. of 7th WSEAS Int. Conf. on COMPUTATIONAL INTELLIGENCE, MAN-MACHINE SYSTEMS and CYBERNETICS (CIMMACS '08), Cairo, EGYPT, Dec. 29-31, 2008, pp. 221-228. [82] Hazem M. El-Bakry, and Nikos Mastorakis “Fast Information Retrieval from Web Pages,” Proc. of 7th WSEAS Int. Conf. on COMPUTATIONAL INTELLIGENCE, MAN-MACHINE SYSTEMS and CYBERNETICS (CIMMACS '08), Cairo, EGYPT, Dec. 29-31, 2008, pp. 229-247. [83] Hazem M. El-Bakry and Mohamed Hamada, "New Fast Decision Tree Classifier for Identifying Protein Coding Regions," Proc. of ISICA 2008 Conf., China, Dec. 3-5, 2008, pp. 489-500. [84] Hazem M. El-Bakry, and Nikos Mastorakis, " An Effective Method for Detecting Dental Diseases by using Fast Neural Networks, " 8th WSEAS International Conference on SIGNAL, SPEECH AND IMAGE PROCESSING (SSIP '08), Santander, Cantabria, Spain, September 23-25, 2008, pp. 144-152. [85] Hazem M. El-Bakry, and Nikos Mastorakis, " A New Fast Forecasting Technique using High Speed Neural Networks, " 8th WSEAS International Conference on SIGNAL, SPEECH AND IMAGE PROCESSING (SSIP '08), Santander, Cantabria, Spain, September 23-25, 2008, pp. 116-138. [86] Hazem M. El-Bakry, and Nikos Mastorakis, " Realization of EUniversity for Distance Learning, " 8th WSEAS International Conference on DISTANCE LEARNING and WEB ENGINEERING (DIWEB '08), Santander, Cantabria, Spain, September 23-25, 2008, pp. 17-31. [87] Hazem M. El-Bakry, and Nikos Mastorakis, " A Novel Fast Kolmogorov's Spline Complex Network for Pattern Detection," 8th WSEAS International Conference on SIMULATION, MODELLING and OPTIMIZATION (SMO '08), Santander, Cantabria, Spain, September 23-25, 2008, pp. 261-279. [88] Hazem M. El-Bakry, and Nikos Mastorakis, " A New Technique for Detecting Dental Diseases by using High Speed NeuroComputers," European Computing Conf. (ECC '08), Malta, September 11-13, 2008, pp. 432-440. [89] Hazem M. El-Bakry, and Nikos Mastorakis, " A Modified Hopfield Neural Network for Perfect Calculation of Magnetic Resonance Spectroscopy," 1st WSEAS International Conference on Biomedical Electronics and Biomedical Informatics (BEBI '08), Rhodes, Greece, August 20-22, 2008, pp. 242-254. [90] Hazem M. El-Bakry, and Nikos Mastorakis, " A Real-Time Intrusion Detection Algorithm for Network Security, " 8st WSEAS International Conference on Applied Informatics and Communications (AIC '08), Rhodes, Greece, August 20-22, 2008, pp. 533-545. [91] Hazem M. El-Bakry, and Nikos Mastorakis "New Fast Normalized Neural Networks for Pattern Detection," Image and Vision Computing Journal, vol. 25, issue 11, 2007, pp. 17671784.

ISBN: 978-960-474-396-4

[92] Hazem M. El-Bakry, "New Fast Time Delay Neural Networks Using Cross Correlation Performed in the Frequency Domain," Neurocomputing Journal, vol. 69, October 2006, pp. 2360-2363. [93] Hazem M. El-Bakry and Nikos Mastorakis, "Fast Code Detection Using High Speed Time Delay Neural Networks," Lecture Notes in Computer Science, Springer, vol. 4493, Part III, May 2007, pp. 764-773. [94] Hazem M. El-Bakry, "New High Speed Normalized Neural Networks for Fast Pattern Discovery on Web Pages," International Journal of Computer Science and Network Security, vol. 6, No. 2A, February 2006, pp. 142-152. [95] Hazem M. El-Bakry, and Qiangfu Zhao, "Fast Normalized Neural Processors For Pattern Detection Based on Cross Correlation Implemented in the Frequency Domain," Journal of Research and Practice in Information Technology, Vol. 38, No.2, May 2006, pp. 151-170. [96] Hazem M. El-Bakry, "New Fast Time Delay Neural Networks Using Cross Correlation Performed in the Frequency Domain," Neurocomputing Journal, vol. 69, October 2006, pp. 2360-2363. [97] Hazem M. El-Bakry, and Nikos Mastorakis, "A Novel Model of Neural Networks for Fast Data Detection," WSEAS Transactions on Computers, Issue 8, vol. 5, November 2006, pp. 1773-1780. [98] Hazem M. El-Bakry, and Nikos Mastorakis, "A New Approach for Fast Face Detection," WSEAS Transactions on Information Science and Applications, issue 9, vol. 3, September 2006, pp. 1725-1730. [99] Hazem M. El-Bakry, and Qiangfu Zhao, “Fast Neural Implementation of PCA for Face Detection,” Proc. of IEEE World Congress on Computational Intelligence, IJCNN’06, Vancouver, BC, Canada, July 16-21, 2006, pp. 1785-1790. [100] Hazem M. El-Bakry, “A Simple Design for High Speed Normalized Neural Networks Implemented in the Frequency Domain for Pattern Detection,” Proc. of IEEE World Congress on Computational Intelligence, IJCNN’06, Vancouver, BC, Canada, July 16-21, 2006, pp. 2296-2303. [101] Hazem M. El-Bakry, “Fast Co-operative Modular Neural Processors for Human Face Detection,” Proc. of IEEE World Congress on Computational Intelligence, IJCNN’06, Vancouver, BC, Canada, July 16-21, 2006, pp. 2304-2311. [102] Hazem M. El-Bakry, “New Fast Time Delay Neural Networks Using Cross Correlation Performed in the Frequency Domain,” Proc. of IEEE World Congress on Computational Intelligence, IJCNN’06, Vancouver, BC, Canada, July 16-21, 2006, pp. 49904997. [103] Hazem M. El-Bakry, and Nikos Mastorakis, “A Novel Model of Neural Networks for Fast Data Detection,” Proc. of the 7th WSEAS International Conference on Neural Networks, Cavtat, Croatia, June 12-14, 2006, pp. 144-151. [104] Hazem M. El-Bakry, and Nikos Mastorakis, “A New Approach for Fast Face Detection,” Proc. of the 7th WSEAS International Conference on Neural Networks, Cavtat, Croatia, June 12-14, 2006, pp. 152-157. [105] Hazem M. El-Bakry, "Pattern Detection Using Fast Normalized Neural Networks," Lecture Notes in Computer Science, Springer, vol. 3696, September 2005, pp. 447-454. [106] Hazem M. El-Bakry, "Human Face Detection Using New High Speed Modular Neural Networks," Lecture Notes in Computer Science, Springer, vol. 3696, September 2005, pp. 543-550. [107] Hazem M. El-Bakry, and Qiangfu Zhao, "Fast Pattern Detection Using Normalized Neural Networks and Cross Correlation in the Frequency Domain," EURASIP Journal on Applied Signal Processing, Special Issue on Advances in Intelligent Vision Systems: Methods and Applications—Part I, vol. 2005, no. 13, 1 August 2005, pp. 2054-2060.

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[108] Hazem M. El-Bakry, and Qiangfu Zhao, "Fast Time Delay Neural Networks," International Journal of Neural Systems, vol. 15, no.6, December 2005, pp. 445-455. [109] Hazem M. El-Bakry, and Qiangfu Zhao, "Speeding-up Normalized Neural Networks For Face/Object Detection," Machine Graphics & Vision Journal (MG&V), vol. 14, No.1, 2005, pp. 29-59. [110] Hazem M. El-Bakry, and Qiangfu Zhao, "A New Technique for Fast Pattern Recognition Using Normalized Neural Networks," WSEAS Transactions on Information Science and Applications, issue 11, vol. 2, November 2005, pp. 1816-1835. [111] Hazem M. El-Bakry, and Qiangfu Zhao, "Fast Complex Valued Time Delay Neural Networks," International Journal of Computational Intelligence, vol.2, no.1, pp. 16-26, 2005. [112] Hazem M. El-Bakry, and Qiangfu Zhao, "Fast Pattern Detection Using Neural Networks Realized in Frequency Domain," Enformatika Transactions on Engineering, Computing, and Technology, February 25-27, 2005, pp. 89-92. [113] Hazem M. El-Bakry, "A New High Speed Neural Model For Character Recognition Using Cross Correlation and Matrix Decomposition," International Journal of Signal Processing, vol.2, no.3, 2005, pp. 183-202. [114] Hazem M. El-Bakry, and Qiangfu Zhao, "Face Detection Using Fast Neural Processors and Image Decomposition," International Journal of Computational Intelligence, vol.1, no.4, 2004, pp. 313-316.

[115] Hazem M. El-Bakry, and H. Stoyan, "FNNs for Code Detection in Sequential Data Using Neural Networks for Communication Applications," Proc. of the First International Conference on Cybernetics and Information Technologies, Systems and Applications: CITSA 2004, pp. 21-25. [116] Hazem M. El-Bakry, "Face detection using fast neural networks and image decomposition," Neurocomputing Journal, vol. 48, 2002, pp. 1039-1046. [117] Hazem M. El-Bakry, "Human Iris Detection Using Fast Cooperative Modular Neural Nets and Image Decomposition," Machine Graphics & Vision Journal (MG&V), vol. 11, no. 4, 2002, pp. 498-512. [118] Hazem M. El-Bakry "Fast Iris Detection for Personal Verification Using Modular Neural Networks," Lecture Notes in Computer Science, Springer, vol. 2206, October 2001, pp. 269283. [119] Hazem M. El-Bakry, "Automatic Human Face Recognition Using Modular Neural Networks," Machine Graphics & Vision Journal (MG&V), vol. 10, no. 1, 2001, pp. 47-73. [120] Fayyad, U., Piatetsky-Shapiro, G.Smyth, P. (1996). From Data Mining to Knowledge Discovery in Databases.AI Magazine, 17(3), 37-54.

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