Adel Elmaghraby

Adaptive tracking of network behavioral signals for real time forensic analysis of service quality degradation

The current shift from the static access based service model to the dynamic application based service model introduced major challenges for effective forensics of any quality degradation of the provided service. In addition, about 55 percent of the Tier 1 and Tier 2 providers are planning to offer managed security services to guarantee an attack free IP service. In this article, we propose a novel approach of modeling the network behavior in order to select meaningful metrics to be used in tracking the network behavior changes. Based on the deftly selected metrics, we utilize an adaptive exponentially weighted moving average (EWMA) with a moving centerline control chart to monitor the changes of the network behavior. Signaling the network behavior changes in association with the service objective based network behavioral model should provide the required information for effective forensic of the service quality degradation. Our methodology is applied on both simulated and real traces of network behavioral metrics. We illustrate the effectiveness of the forensic analysis model for the selection of relevant behavioral metrics. As well, we show how the adaptive EWMA can be used for tracking the changes in the network behavior from normal to abnormal and vice versa.

Work in progress - Cybersecurity curriculum development initiated by public interests

The investigators are engaged in developing Cybersecurity curriculum and tutorial material that illuminate this essential issue from the public interest point of view. This concerns needs, priorities, and specific relevant technologies, based on sources published by the Federal government and public organizations. As a whole, the material will cover a complete 500-level (graduate, open to undergraduates) 3 credit-hours course, with a limited prerequisite in computer networks. The work mainly consists of compiling slides, to be downloaded by interested instructors from a dedicated Website developed by the investigators. The material was organized in a way that also facilitates the use of various parts of it as supplementary material to courses in business and management, computer science, electrical engineering and information systems. It is further intended to address a wide range of students populations and education institutes, including continuing education and institutes granting associate degrees

Deep Learning Role in Early Diagnosis of Prostate Cancer

The objective of this work is to develop a computer-aided diagnostic system for early diagnosis of prostate cancer. The presented system integrates both clinical biomarkers (prostate-specific antigen) and extracted features from diffusion-weighted magnetic resonance imaging collected at multiple b values. The presented system performs 3 major processing steps. First, prostate delineation using a hybrid approach that combines a level-set model with nonnegative matrix factorization. Second, estimation and normalization of diffusion parameters, which are the apparent diffusion coefficients of the delineated prostate volumes at different b values followed by refinement of those apparent diffusion coefficients using a generalized Gaussian Markov random field model. Then, construction of the cumulative distribution functions of the processed apparent diffusion coefficients at multiple b values. In parallel, a K-nearest neighbor classifier is employed to transform the prostate-specific antigen results into diagnostic probabilities. Finally, those prostate-specific antigen–based probabilities are integrated with the initial diagnostic probabilities obtained using stacked nonnegativity constraint sparse autoencoders that employ apparent diffusion coefficient–cumulative distribution functions for better diagnostic accuracy. Experiments conducted on 18 diffusion-weighted magnetic resonance imaging data sets achieved 94.4% diagnosis accuracy (sensitivity = 88.9% and specificity = 100%), which indicate the promising results of the presented computer-aided diagnostic system.

Classification of pressure ulcer tissues with 3D convolutional neural network

AbstractA 3D convolution neural network (CNN) of deep learning architecture is supplied with essential visual features to accurately classify and segment granulation, necrotic eschar, and slough tissues in pressure ulcer color images. After finding a region of interest (ROI), the features are extracted from both the original and convolved with a pre-selected Gaussian kernel 3D HSI images, combined with first-order models of current and prior visual appearance. The models approximate empirical marginal probability distributions of voxel-wise signals with linear combinations of discrete Gaussians (LCDG). The framework was trained and tested on 193 color pressure ulcer images. The classification accuracy and robustness were evaluated using the Dice similarity coefficient (DSC), the percentage area distance (PAD), and the area under the ROC curve (AUC). The obtained preliminary DSC of 92%, PAD of 13%, and AUC of 95% are promising.n Graphical AbstractThe Classification of Pressure Ulcer Tissues Based on 3D Convolutional Neural Network.

Identifying Personalized Autism Related Impairments Using Resting Functional MRI and ADOS Reports

In this study, a personalized computer aided diagnosis system for autism spectrum disorder is introduced. The proposed system uses resting state functional MRI data to build local classifiers, global classifier, and correlate the classification findings with ADOS behavioral reports. This system is composed of 3 main phases: (i) Data preprocessing to overcome the motion and timing artifacts and normalize the data to standard MNI152 space, (ii) using a small subset (40 subjects) to extract significant activation components, and (iii) utilize the extracted significant components to build a deep learning based diagnosis system for each component, combine the probabilities for global diagnosis and calculate the correlation with ADOS reports. The deep learning based classification system showed accuracies of more than 80% in the significant components, moreover, the global diagnosis accuracy is 93%. Out of the significant components, 2 components are found to be correlated with neuro-circuits involved in autism related impairments as reported in ADOS reports.

A Generalized Deep Learning-Based Diagnostic System for Early Diagnosis of Various Types of Pulmonary Nodules

A novel framework for the classification of lung nodules using computed tomography scans is proposed in this article. To get an accurate diagnosis of the detected lung nodules, the proposed framework integrates the following 2 groups of features: (1) appearance features modeled using the higher order Markov Gibbs random field model that has the ability to describe the spatial inhomogeneities inside the lung nodule and (2) geometric features that describe the shape geometry of the lung nodules. The novelty of this article is to accurately model the appearance of the detected lung nodules using a new developed seventh-order Markov Gibbs random field model that has the ability to model the existing spatial inhomogeneities for both small and large detected lung nodules, in addition to the integration with the extracted geometric features. Finally, a deep autoencoder classifier is fed by the above 2 feature groups to distinguish between the malignant and benign nodules. To evaluate the proposed framework, we used the publicly available data from the Lung Image Database Consortium. We used a total of 727 nodules that were collected from 467 patients. The proposed system demonstrates the promise to be a valuable tool for the detection of lung cancer evidenced by achieving a nodule classification accuracy of 91.20%.

DIEDA: discriminative information based on exponential discriminant analysis combined with local features representation for face and kinship verification

Face and kinship verification using facial images is a novel and challenging problem in computer vision. In this paper, we propose a new system that uses discriminative information, which is based on the exponential discriminant analysis (DIEDA) combined with multiple scale descriptors. The histograms of different patches are concatenated to form a high dimensional feature vector, which represents a specific descriptor at a given scale. The projected histograms for each zone use the cosine similarity metric to reduce the feature vector dimensionality. Lastly, zone scores corresponding to various descriptors at different scales are fused and verified by using a classifier. This paper exploits discriminative side information for face and kinship verification in the wild (image pairs are from the same person or not). To tackle this problem, we take examples of the face samples with unlabeled kin relations from the labeled face in the wild dataset as the reference set. We create an optimized function by minimizing the interclass samples (with a kin relation) and maximizing the neighboring interclass samples (without a kinship relation) with the DIEDA approach. Experimental results on three publicly available face and kinship datasets show the superior performance of the proposed system over other state-of-the-art techniques.

Automatized colon polyp segmentation via contour region analysis

The increasing use of colorectal cancer screening programs has contributed to the growing number of colonoscopies performed by health centers. Hence, in recent years there has been a tendency to develop medical diagnosis support tools in order to assist specialists. This research has designed an automatized polyp detection system that allows a reduction in the rate of missed polyps that can lead to interval cancer; one of the main risks existing in colonoscopy. A characterization has therefore been made of the shape, color and curvature of edges and their regions, enabling the segmentation of polyps present in colonoscopy images. A 90.53% polyp detection rate has been achieved using the designed system, and 76.29% and 71.57% segmentation quality for the Annotated Area Covered and Dice Coefficient indicators respectively. This system aims to offer assistance with medical diagnosis that has a positive impact on patient health.

Tissue classification and segmentation of pressure injuries using convolutional neural networks

BACKGROUND AND OBJECTIVESnThis paper presents a new approach for automatic tissue classification in pressure injuries. These wounds are localized skin damages which need frequent diagnosis and treatment. Therefore, a reliable and accurate systems for segmentation and tissue type identification are needed in order to achieve better treatment results.nnnMETHODSnOur proposed system is based on a Convolutional Neural Network (CNN) devoted to performing optimized segmentation of the different tissue types present in pressure injuries (granulation, slough, and necrotic tissues). A preprocessing step removes the flash light and creates a set of 5x5 sub-images which are used as input for the CNN network. The network output will classify every sub-image of the validation set into one of the three classes studied.nnnRESULTSnThe metrics used to evaluate our approach show an overall average classification accuracy of 92.01%, an average total weighted Dice Similarity Coefficient of 91.38%, and an average precision per class of 97.31% for granulation tissue, 96.59% for necrotic tissue, and 77.90% for slough tissue.nnnCONCLUSIONSnOur system has been proven to make recognition of complicated structures in biomedical images feasible.

Automated framework for accurate segmentation of pressure ulcer images

Ulcer tissue segmentation is of immense importance in helping medical personnel to assess wounds. This paper introduces a new computational framework employing state-of-the-art image processing techniques to segment pressure ulcer tissue structures from color images. The framework integrates a visual appearance model of an observed input image with prior color information from an available database of previously stored color RGB images. The following four processing steps are performed. First, to minimize the execution time and enhance the segmentation accuracy, a region-of-interest (ROI) of the whole ulcer area is automatically identified based on contrast changes. This step exploits synthetic frequencies of pixelwise intensities, which are calculated by using an electric field energy model to describe relations between the pixelwise intensities. Secondly, visual appearance of the observed image is modeled by a linear combination of discrete Gaussians (LCDG) model in order to estimate marginal probability distributions of three main tissue classes for the grayscale ROI image. Next, the pixel-wise probabilities of these classes for the color ROI image are calculated using the available prior information about the RGB colors on manually segmented database images. Initial labeling is obtained based on both the observed and prior probabilities of pixelwise colors. Finally, to preserve continuity, the labels are refined and normalized using the generalized Gauss-Markov random field (GGMRF) model. Experimental validation on 24 clinical images of pressure ulcers, provided by the Centre IGURCO, showed the high segmentation accuracy of 90.4%.