Mohamed S. Kamel

Helwan University

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Fakhri Karray

University of Waterloo

Aurélio Campilho

Universidade do Porto

Ahmed Farahat

Hitachi (Japan)

Masoud Makrehchi

University of Ontario Institute of Technology

David Zhang

University College London

Otman Basir

University of Waterloo

Ali Ghodsi

University of Waterloo

Mohammed El-Abd

American University of Kuwait

Mehrdad J. Gangeh

Ernst & Young (Israel)

Yun-Qian Miao

University of Waterloo

Cooperative Institutions

University of Waterloo

147

Cairo University

Alexandria University

University of Toronto

Ain Shams University

University of Guelph

Institute of Electrical and Electronics Engineers

Nile University

Arab Academy for Science, Technology, and Maritime Transport

Mansoura University

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Tools and approaches for topic detection from Twitter streams: survey

Knowledge and Information Systems (2017)

Rania Ibrahim Ahmed Elbagoury Mohamed S. Kamel Fakhri Karray

Kernel (algebra)

10.1007/s10115-017-1081-x

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Citations (57)

Multibiometric System Using Distance Regularized Level Set Method and Particle Swarm Optimization

Lecture notes in computer science (2012)

Kaushik Roy Mohamed S. Kamel

Iris Recognition

Feature (linguistics)

IRIS (biosensor)

10.1007/978-3-642-33564-8_71

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Citations (3)

Multiview Supervised Dictionary Learning in Speech Emotion Recognition

IEEE/ACM Transactions on Audio Speech and Language Processing (2014)

Mehrdad J. Gangeh Pouria Fewzee Ali Ghodsi Mohamed S. Kamel Fakhri Karray

Recently, a supervised dictionary learning (SDL) approach based on the Hilbert-Schmidt independence criterion (HSIC) has been proposed that learns the dictionary and the corresponding sparse coefficients in a space where the dependency between the data and the corresponding labels is maximized. In this paper, two multiview dictionary learning techniques are proposed based on this HSIC-based SDL. While one of these two techniques learns one dictionary and the corresponding coefficients in the space of fused features in all views, the other learns one dictionary in each view and subsequently fuses the sparse coefficients in the spaces of learned dictionaries. The effectiveness of the proposed multiview learning techniques in using the complementary information of single views is demonstrated in the application of speech emotion recognition (SER). The fully-continuous sub-challenge (FCSC) of the AVEC 2012 dataset is used in two different views: baseline and spectral energy distribution (SED) feature sets. Four dimensional affects, i.e., arousal, expectation, power, and valence are predicted using the proposed multiview methods as the continuous response variables. The results are compared with the single views, AVEC 2012 baseline system, and also other supervised and unsupervised multiview learning approaches in the literature. Using correlation coefficient as the performance measure in predicting the continuous dimensional affects, it is shown that the proposed approach achieves the highest performance among the rivals. The relative performance of the two proposed multiview techniques and their relationship are also discussed. Particularly, it is shown that by providing an additional constraint on the dictionary of one of these approaches, it becomes the same as the other.

Dictionary Learning

Feature (linguistics)

Feature vector

10.1109/taslp.2014.2319157

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Citations (55)

Topic Discovery from Document Using Ant-Based Clustering Combination

Lecture notes in computer science (2005)

Yan Yang Mohamed S. Kamel Fan Jin

Document Clustering

Hypergraph

Vector space model

Ant colony

10.1007/978-3-540-31849-1_11

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Citations (16)

Prostate cancer multi-feature analysis using trans-rectal ultrasound images

Physics in Medicine and Biology (2005)

Samar Mohamed M.M.A. Salama Mohamed S. Kamel Ehab F. El‐Saadany Kamilia Rizkalla

This note focuses on extracting and analysing prostate texture features from trans-rectal ultrasound (TRUS) images for tissue characterization. One of the principal contributions of this investigation is the use of the information of the images' frequency domain features and spatial domain features to attain a more accurate diagnosis. Each image is divided into regions of interest (ROIs) by the Gabor multi-resolution analysis, a crucial stage, in which segmentation is achieved according to the frequency response of the image pixels. The pixels with a similar response to the same filter are grouped to form one ROI. Next, from each ROI two different statistical feature sets are constructed; the first set includes four grey level dependence matrix (GLDM) features and the second set consists of five grey level difference vector (GLDV) features. These constructed feature sets are then ranked by the mutual information feature selection (MIFS) algorithm. Here, the features that provide the maximum mutual information of each feature and class (cancerous and non-cancerous) and the minimum mutual information of the selected features are chosen, yeilding a reduced feature subset. The two constructed feature sets, GLDM and GLDV, as well as the reduced feature subset, are examined in terms of three different classifiers: the condensed k-nearest neighbour (CNN), the decision tree (DT) and the support vector machine (SVM). The accuracy classification results range from 87.5% to 93.75%, where the performance of the SVM and that of the DT are significantly better than the performance of the CNN.

Feature (linguistics)

10.1088/0031-9155/50/15/n02

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Citations (37)

Statistical semantics for enhancing document clustering

Knowledge and Information Systems (2010)

Ahmed Farahat Mohamed S. Kamel

Document Clustering

Similarity (geometry)

Vector space model

Explicit semantic analysis

Representation

Benchmark (surveying)

Distributional semantics

10.1007/s10115-010-0367-z

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Citations (35)

A Hybrid Classifier Fusion Approach for Motor Unit Potential Classification During EMG Signal Decomposition

IEEE Transactions on Biomedical Engineering (2007)

Sarbast Rasheed Daniel W. Stashuk Mohamed S. Kamel

In this paper, we propose a hybrid classifier fusion scheme for motor unit potential classification during electromyographic (EMG) signal decomposition. The scheme uses an aggregator module consisting of two stages of classifier fusion: the first at the abstract level using class labels and the second at the measurement level using confidence values. Performance of the developed system was evaluated using one set of real signals and two sets of simulated signals and was compared with the performance of the constituent base classifiers and the performance of a one-stage classifier fusion approach. Across the EMG signal data sets used and relative to the performance of base classifiers, the hybrid approach had better average classification performance overall. For the set of simulated signals of varying intensity, the hybrid classifier fusion system had on average an improved correct classification rate (CCr) (6.1%) and reduced error rate (Er) (0.4%). For the set of simulated signals of varying amounts of shape and/or firing pattern variability, the hybrid classifier fusion system had on average an improved CCr (6.2%) and reduced Er (0.9%). For real signals, the hybrid classifier fusion system had on average an improved CCr (7.5%) and reduced Er (1.7%).

Sensor Fusion

Quadratic classifier

Word error rate

10.1109/tbme.2007.892922

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Citations (16)

A Probabilistic Model Using Information Theoretic Measures for Cluster Ensembles

Lecture notes in computer science (2004)

Hanan Ayad Otman Basir Mohamed S. Kamel

Conditional probability

Conditional probability distribution

Divergence (linguistics)

10.1007/978-3-540-25966-4_14

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Citations (15)

Cooperative Learning Algorithms for Data Fusion Using Novel $L_{1}$ Estimation

IEEE Transactions on Signal Processing (2008)

Youshen Xia Mohamed S. Kamel

Two novel L ₁ estimation methods for multisensor data fusion are developed, respectively in the case of known and unknown scaling coefficients. Two discrete-time cooperative learning (CL) algorithms are proposed to implement the two proposed methods. Compared with the high-order statistical method and the entropy estimation method, the two proposed estimation methods can minimize a convex cost function of the linearly fused information. Furthermore, the proposed estimation method can be effectively used in the blind fusion case. Compared with the minimum variance estimation method and linearly constrained least square estimation method, the two proposed estimation methods are suitable for non-Gaussian noise environments. The two proposed CL algorithms are guaranteed to converge globally to the optimal fusion solution under a fixed step length. Unlike existing CL algorithms, the proposed two CL algorithms can solve a more complex L ₁ estimation problem and are more suitable for weight learning. Illustrative examples show that the proposed CL algorithms can obtain more accurate solutions than several related algorithms.

Sensor Fusion

Entropy estimation

10.1109/tsp.2007.908966

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Citations (21)

Learning aggregation for combining classifier ensembles

Nayer Wanas Mohamed S. Kamel

Creating classifier ensembles and combining their outputs to achieve higher accuracy have been of recent interest. It was noted that when using such multiple classifier approaches the members of the ensemble should be error-independent. The ideal, in terms of ensembles of classifiers, would be a set of classifiers which do not show any coincident errors. That is, each of the classifiers generalized well, and when they did make errors on the test set, these errors were not shared with any other classifier. Various approaches for achieving this have been presented. This paper compares two approaches introduced for training multiple classifiers systems. These approaches are based on the feature based aggregation architecture and the adaptive training algorithm. An empirical evaluation using two data sets shows a reduction in the number of training cycles when applying the algorithm on the overall architecture, while maintaining the same or improved performance. The performance of these approaches is also compared to standard approaches proposed in the literature. The results substantiate the use of adaptive a-dining for both the ensemble and the aggregation architecture.

Training set

Cascading classifiers

Ensemble Learning

10.1109/iconip.2002.1198971

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Citations (1)