Issam Dagher - Academia.edu (original) (raw)

Papers by Issam Dagher

International Journal of Modelling and Simulation, 2007

ABSTRACT

Signal, Image and Video Processing, May 19, 2009

In this paper highly-compacted DCT coefficients (HDCT) are presented. This compactness is achieve... more In this paper highly-compacted DCT coefficients (HDCT) are presented. This compactness is achieved by sorting in ascending order the data first, then by applying the Discrete Cosine transform (DCT) to the ordered data. Images are highly correlated. DCT exhibits excellent energy compaction. It will be shown that HDCT has much better energy compactness than the DCT. This has the effect

International Journal of Computers and Applications, Jan 2, 2016

Abstract Improving email filtering (Ham vs. Spam emails) is a very important process. The objecti... more Abstract Improving email filtering (Ham vs. Spam emails) is a very important process. The objective of this paper is to increase the filtering accuracy and to decrease the processing time. It discusses different scenarios for Principal Component Analysis-Document Reconstruction (PCADR) classifier implemented for email filtering process The study highlights on the variation in the accuracy of a PCADR classifier with respect to the variation in feature preprocessing. Four scenarios were considered:• Scenario 1: Ham and Spam classes are represented with different features.• Scenario 2: Ham and Spam classes are represented with same features.• Scenario 3: Ham and Spam classes are represented with common terms.• Scenario 4: Ham and Spam classes are represented with common Features and Characteristic terms. Different experiments were done using a public corpus extracted from the University of California-Irvine Machine Learning Repository. Different training and test sets were used. A comparison of PCADR with Support Vector Machine and Bayes detector was done to prove its superior behavior.

International Conference on Neural Information Processing, 1994

Proceedings of SPIE, Apr 4, 2003

In this paper, a geometrical Fuzzy ART (G-Fuzzy ART) neural network architecture is presented. Wh... more In this paper, a geometrical Fuzzy ART (G-Fuzzy ART) neural network architecture is presented. While the original Fuzzy ART requires preprocessing of the input patterns (complement coding), the G-Fuzzy ART accept the input patterns without complement coding. The weights of the G-Fuzzy ART refer directly to the borders of the hyper-rectangle while the weights in the Fuzzy ART refer to the endpoints of the hyper-rectangle. The size of the hyper-rectangle is directly given by the size of the weight. The geometrical choice function (the Hamming distance of the input pattern to the hyper-rectangle) and the weight update formulas for the G-Fuzzy ART are presented. The G-Fuzzy ART retains the notion of resonance by retaining the vigilance criterion applied directly to the new size of the hyper-rectangle. It also retains the min-max fuzzy operators.

... that family. Windham presented a cluster validity for the FCM algorithm[5]. He obtained a mea... more ... that family. Windham presented a cluster validity for the FCM algorithm[5]. He obtained a measure by computing the ratio of the smallest membership to the largest one and transforming this ratio into a probability function. In ...

IEEE Transactions on Neural Networks, Jul 1, 1999

In this paper we introduce a procedure, based on the max-min clustering method, that identifies a... more In this paper we introduce a procedure, based on the max-min clustering method, that identifies a fixed order of training pattern presentation for fuzzy adaptive resonance theory mapping (ARTMAP). This procedure is referred to as the ordering algorithm, and the combination of this procedure with fuzzy ARTMAP is referred to as ordered fuzzy ARTMAP. Experimental results demonstrate that ordered fuzzy ARTMAP exhibits a generalization performance that is better than the average generalization performance of fuzzy ARTMAP, and in certain cases as good as, or better than the best fuzzy ARTMAP generalization performance. We also calculate the number of operations required by the ordering algorithm and compare it to the number of operations required by the training phase of fuzzy ARTMAP. We show that, under mild assumptions, the number of operations required by the ordering algorithm is a fraction of the number of operations required by fuzzy ARTMAP.

Soft Computing, Aug 30, 2005

In this paper, an L-p based Fuzzy ARTMAP neural network is presented. The category choice of this... more In this paper, an L-p based Fuzzy ARTMAP neural network is presented. The category choice of this network is based on the L-p norm. Geometrical properties of this architecture are presented. Comparisons between this category choice and the category choice of the Fuzzy ARTMAP are illustrated. And simulation results on the databases taken from the UCI repository are performed. It will be shown that using the L-p norm is geometrically more attractive. It will operate directly on the input patterns without the need for doing any preprocessing. It should be noted that the Fuzzy ARTMAP architecture requires two preprocessing steps: normalization and complement coding. Simulation results on different databases show the good generalization performance of the L-p Fuzzy ARTMAP compared to the performance of Fuzzy ARTMAP.

... Control, tracking and prediction systems will often use classifiers to determine input-output... more ... Control, tracking and prediction systems will often use classifiers to determine input-output relationships. ... This is the order according to which the patterns in the in the training set will be presented to the Ordered Fuzzy ARTMAP. 4 Experimental Results - Com-parisons ...

Expert Systems, Jan 21, 2019

Gender recognition has been playing a very important role in various applications such as human-c... more Gender recognition has been playing a very important role in various applications such as human-computer interaction, surveillance, and security. Nonlinear support vector machines (SVMs) were investigated for the identification of gender using the Face Recognition Technology (FERET) image face database. It was shown that SVM classifiers outperform the traditional pattern classifiers (linear, quadratic, Fisher linear discriminant, and nearest neighbour). In this context, this paper aims to improve the SVM classification accuracy in the gender classification system and propose new models for a better performance. We have evaluated different SVM learning algorithms; the SVM-radial basis function with a 5% outlier fraction outperformed other SVM classifiers. We have examined the effectiveness of different feature selection methods. AdaBoost performs better than the other feature selection methods in selecting the most discriminating features. We have proposed two classification methods that focus on training subsets of images among the training images. Method 1 combines the outcome of different classifiers based on different image subsets, whereas method 2 is based on clustering the training data and building a classifier for each cluster. Experimental results showed that both methods have increased the classification accuracy.

Proceedings of International Conference on Neural Networks (ICNN'97), Nov 22, 2002

This paper discusses a variation of the Fuzzy ART algorithm referred to as the Fuzzy ART Variant.... more This paper discusses a variation of the Fuzzy ART algorithm referred to as the Fuzzy ART Variant. The Fuzzy ART Variant is a Fuzzy ART algorithm that uses a very large choice parameter value. Based on the geometrical interpretation of the weights in Fuzzy ART, useful properties of learning associated with the Fuzzy ART Variant are presented and proven. One of these properties establishes an upper bound on the number of list presentations required by the Fuzzy ART Variant to learn an arbitrary list of input patterns. This bound is small and demonstrates the short-training time property of the Fuzzy ART Variant. Through simulation, it is shown that the Fuzzy ART Variant is as good a clustering algorithm as a Fuzzy ART algorithm that uses typical (i.e. small) values for the choice parameter.

Computing, Nov 21, 2010

Prototype classifier is based on representing every cluster by a prototype. All the input pattern... more Prototype classifier is based on representing every cluster by a prototype. All the input patterns that belong to that cluster will have the same label as the prototype. It should be noted that a prototype does not have to be only one data. A cluster could be represented by more than one data. In this paper, the M-dimensional rectangle of

Iet Image Processing, 2011

In this study a new algorithm 'adaptive bandwidth mode detection' (ABMD) algorithm has been devel... more In this study a new algorithm 'adaptive bandwidth mode detection' (ABMD) algorithm has been developed to recover the correct density function without the need to either specify the correct number of Gaussians in the model or the correct bandwidth. The ABMD is employed in modelling visual features in applications such as image segmentation and real-time visual tracking. A simple type of model for these visual features are the Gaussian mixtures, where the number of Gaussian components is variable, thus, making it a flexible method for multimodal representation. This algorithm is used at initialisation for target modelling, where the target update will be done based on the mode propagation with adaptive bandwidth tracker method. It is based on an optimisation technique where a gradient ascent method is used and the optimal solution is selected based on a log-likelihood function. The mode detection ability of ABMD algorithm is compared with both the expectation maximisation and mean-shift algorithms. Furthermore, different video sequences have been employed to show how this approach has the ability to track an object regardless of whether the target model is corrupted with unwanted data at new frames.

International Journal of Wavelets, Multiresolution and Information Processing, Nov 1, 2019

Handwriting recognition is a very active research in the machine learning community. In this pape... more Handwriting recognition is a very active research in the machine learning community. In this paper, we tackled two important applications: handwritten digit recognition and Signature verification using convolution neural network (CNN). Signature is one of the most popular personal attributes for authentication. It is basic, shabby and adequate to individuals, official associations and courts. This paper focuses on offline signature verification (SV). It is a kind of a classification problem, which classifies the signature as genuine, or forgery. We use CNN in two types of datasets: the MNIST database, and UTSIG database. In order to obtain better accuracy, we propose to preprocess the data in the wavelet domain and in the Gabor filter combining the outputs of both CNN. This combination resulted in higher recognition accuracy compared to other techniques.

Artificial Intelligence Review, May 28, 2011

... Complex fuzzy c-means algorithm Issam Dagher © Springer Science+Business Media BV 2011 ... Ha... more ... Complex fuzzy c-means algorithm Issam Dagher © Springer Science+Business Media BV 2011 ... Hard clustering algorithms (kmeans) are based on classical set theory, and require that a datum either does or does not belong to a cluster. ...

EURASIP Journal on Advances in Signal Processing, Mar 22, 2007

A human hand recognition system is introduced. First, a simple preprocessing technique which extr... more A human hand recognition system is introduced. First, a simple preprocessing technique which extracts the palm, the four fingers, and the thumb is introduced. Second, the eigenpalm, the eigenfingers, and the eigenthumb features are obtained using a fast incremental principal non-Gaussian directions analysis algorithm, called IPCA-ICA. This algorithm is based on merging sequentially the runs of two algorithms: the principal component analysis (PCA) and the independent component analysis (ICA) algorithms. It computes the principal components of a sequence of image vectors incrementally without estimating the covariance matrix (so covariance-free) and at the same time transforming these principal components to the independent directions that maximize the non-Gaussianity of the source. Third, a classification step in which each feature representation obtained in the previous phase is fed into a simple nearest neighbor classifier. The system was tested on a database of 20 people (100 hand images) and it is compared to other algorithms.

... tant differences between F&amp;amp;#x27;uzzy ARTVar and ARTEMAP, ARTEMAPQ and ARTMAP-... more ... tant differences between F&amp;amp;#x27;uzzy ARTVar and ARTEMAP, ARTEMAPQ and ARTMAP-IC is that the Performance Of the latter three algorithms depends on network param-eters (P for ARTEMAP, (2 for ARTEMAPQ, and 7 and Q for ARTMAP-IC), while Fuzzy ARTVar does not. ...

Journal of Global Optimization, Jun 13, 2007

A new quadratic kernel-free non-linear support vector machine (which is called QSVM) is introduce... more A new quadratic kernel-free non-linear support vector machine (which is called QSVM) is introduced. The SVM optimization problem can be stated as follows: Maximize the geometrical margin subject to all the training data with a functional margin greater than a constant. The functional margin is equal to W T X +b which is the equation of the hyper-plane used for linear separation. The geometrical margin is equal to 1 ||W ||. And the constant in this case is equal to one. To separate the data non-linearly, a dual optimization form and the Kernel trick must be used. In this paper, a quadratic decision function that is capable of separating non-linearly the data is used. The geometrical margin is proved to be equal to the inverse of the norm of the gradient of the decision function. The functional margin is the equation of the quadratic function. QSVM is proved to be put in a quadratic optimization setting. This setting does not require the use of a dual form or the use of the Kernel trick. Comparisons between the QSVM and the SVM using the Gaussian and the polynomial kernels on databases from the UCI repository are shown.

IEEE Transactions on Pattern Analysis and Machine Intelligence, Jun 1, 2006

In this paper, a fast incremental principal non-Gaussian directions analysis algorithm, called IP... more In this paper, a fast incremental principal non-Gaussian directions analysis algorithm, called IPCA-ICA, is introduced. This algorithm computes the principal components of a sequence of image vectors incrementally without estimating the covariance matrix (so covariance-free) and at the same time transforming these principal components to the independent directions that maximize the non-Gaussianity of the source. Two major techniques are used sequentially in a real-time fashion in order to obtain the most efficient and independent components that describe a whole set of human faces database. This procedure is done by merging the runs of two algorithms based on principal component analysis (PCA) and independent component analysis (ICA) running sequentially. This algorithm is applied to face recognition problem. Simulation results on different databases showed high average success rate of this algorithm compared to others.

International Journal of Imaging Systems and Technology, Jul 21, 2018

Discrete Cosine Transform (DCT) and Haar wavelet transform are very important transforms in image... more Discrete Cosine Transform (DCT) and Haar wavelet transform are very important transforms in image compression. While DCT works extremely well for highly correlated data, the Haar transform gives superior results for images exhibiting rapid gradient variations. The objective of this paper is to combine the advantages of these 2 transforms into one transform in order to get better peaksignal-to-noise-ratio (PSNR) and keeping good compression ratio. Following the JPEG, our first approach was to apply separately on each 8x8 block the Haar and the DCT transforms. A signaling bit along the transform which gave better PSNR are quantized and transmitted. This approach gave better PSNR than each one separately. Our second approach was to construct a new hybrid transform which is a combination of these 2 transforms. We mixed both DCT and Haar transforms into one transform. We derived the new transform coding formulas. We applied our hybrid transform on each block obtained. Results show that our approach outperforms the existing DCT and Haar methods, keeping good quality of the image even for high compression ratios, giving a higher PSNR than DCT for the same compression ratio, and permitting better edge recovery than the Haar transform.