Dynamic malware detection is based on understanding the behavioral patterns captured in execution logs. In this study, we propose an approach that uses BERT, a transformerbased architecture, to classify malware based on time-ordered system API calls and sequences of registry keys. Our methodology begins with extensive data preprocessing, including filtering redundant and irrelevant information from the Avast-CTU CAPE dataset, followed by neural network-driven feature selection to identify the most critical APIs and registry keys for classification. The experimental evaluation shows significant improvements in classification accuracy, achieving a weighted average F1-score of 0.97 and 0.98 for Malware family and type classification, respectively. Additionally, the model maintains robust performance at early stages of the temporal chain of malware API calls and register Keys access with reduced input completeness, achieving high accuracy metrics at $\mathbf{5 0 \%}$ completeness of the temporal chain.

Analysis of publicly available human and drug trafficking crime data faces the challenge of finding a comprehensive dataset that includes a sufficiently large number of crime incidents. Our proposed methodology attempts to address this challenge by using entity resolution techniques to merge multiple state-wide crime datasets and a county-wide incident report dataset to get a clearer picture of a category of criminal activity in a geographical area. This methodology combines incident reports, crime reports, and court records to close any gaps that may be present in a single data source. We apply this methodology to create a dataset that includes drug and human trafficking related crimes and incidents from three distinct sources (from Louisville Open Data Crime Reports, Federal Bureau of Investigation Kentucky Crime Incidents, and the Kentucky Online Offender Lookup website) to provide researchers data to study the link between drug and human trafficking related crimes. In a case study performed with the new merged dataset, an XGBoost classifier was able to label a 7-day sliding time window, within any given county, as containing a human trafficking related incident or not with a Matthews correlation coefficient of 0.86.

A primary focus of the healthcare industry is to improve patient experience and quality of service. Practitioners and health workers are generating large volumes of text that are captured in Electronic Medical Records, clinical reports, and publications. Additionally, patients post millions of comments on social media related to healthcare, on diverse topics such as hospital services, disease symptoms, and drugs effects. Unifying various data sources can guide physicians and healthcare workers to avoid unnecessary, irrelevant information and expedite access to helpful information. The main challenge to creating Biomedical Natural Language Understanding is the lack of standard datasets and the extensive computational resources needed to develop different models. This paper proposes a model trained on low-tier GPU computers, producing comparable results to larger models like BioBERT. We propose BINER, a Biomedical Named Entity Recognition architecture using limited data and computational resources.

Hypertension is one of the severest and most common diseases nowadays. It is considered one of the leading contributors to death worldwide. Specialists tend to diagnose hypertension taking into consideration both systolic and diastolic blood pressure (BP) measurements. However, some clinical hypothesis states that under 50 years of age, diastolic may be slightly more predictive of adverse events, while above that age, systolic may be more predictive. The question is should we give more value to systolic BP or diastolic BP when diagnosing diseases such as hypertension? Three different experiments were conducted in this study using magnetic resonance angiography (MRA) data to investigate this question. In each of these experiments, the following methodology was followed: 1) preprocess MRA data to remove noise, bias, or inhomogeneities, 2) segment the cerebral vasculature for each subject using a CNN-based approach, 3) extract vascular features that represent cerebral alterations that precede and accompany the development of hypertension, and 4) finally build feature vectors and classify data into either normotensives or hypertensives based on the cerebral alterations and the blood pressure measurements. The first experiment was conducted on original data set of 342 subjects. While the second and third experiments enlarged the original data set by generating more synthetic samples to make original data set large enough and balanced. Experimental results showed that systolic blood pressure might be more predictive than diastolic blood pressure in diagnosing hypertension with a classification accuracy of 89.3%.

A precise computerized lung nodule diagnosis framework is very important for helping radiologists to diagnose lung nodules at an early stage. In this manuscript, a novel system for pulmonary nodule diagnosis, utilizing features extracted from single computed tomography (CT) scans, is proposed. This system combines robust descriptors for both texture and contour features to give a prediction of the nodule's growth rate, which is the standard clinical information for pulmonary nodules diagnosis. Spherical Sector Isosurfaces Histogram of Oriented Gradient is developed to describe the nodule's texture, taking spatial information into account. A Multi-views Peripheral Sum Curvature Scale Space is used to demonstrate the nodule's contour complexity. Finally, the two modeled features are augmented together utilizing a deep neural network to diagnose the nodules malignancy. For the validation purpose, the proposed system utilized 727 nodules from the Lung Image Database Consortium. The proposed system classification accuracy was 94.50%.

Early diagnosis of pulmonary nodules is critical for lung cancer clinical management. In this paper, a novel framework for pulmonary nodule diagnosis, using descriptors extracted from single computed tomography (CT) scan, is introduced. This framework combines appearance and shape descriptors to give an indication of the nodule prior growth rate, which is the key point for diagnosis of lung nodules. Resolved Ambiguity Local Binary Pattern and 7thOrder Markov Gibbs Random Field are developed to describe the nodule appearance without neglecting spatial information. Spherical harmonics expansion and some primitive geometric features are utilized to describe how the nodule shape is complicated. Ultimately, all descriptors are combined using denoising autoencoder to classify the nodule, whether malignant or benign. Training, testing, and parameter tuning of all framework modules are done using a set of 727 nodules extracted from the Lung Image Database Consortium (LIDC) dataset. The proposed system diagnosis accuracy, sensitivity, and specificity were 94.95%, 94.62%, 95.20% respectively, all of which show that our system has promise to reach the accepted clinical accuracy threshold.

The large-scale parallel computation that became available on the new generation of graphics processing units (GPUs) and on cloud-based services can be exploited for use in healthcare data analysis. Furthermore, computation workstations suited for deep learning are usually equipped with multiple GPUs allowing for workload distribution among multiple GPUs for larger datasets while exploiting parallelism in each GPU. In this paper, we utilize distributed and parallel computation techniques to efficiently analyze healthcare data using deep learning techniques. We demonstrate the scalability and computational benefits of this approach with a case study of longitudinal assessment of approximately 150 000 type 2 diabetic patients. Type 2 diabetes mellitus (T2DM) is the fourth case of mortality worldwide with rising prevalence. T2DM leads to adverse events such as acute myocardial infarction, major amputations, and avoidable hospitalizations. This paper aims to establish a relation between laboratory and medical assessment variables with the occurrence of the aforementioned adverse events and its prediction using machine learning techniques. We use a raw database provided by Basque Health Service, Spain, to conduct this study. This database contains 150 156 patients diagnosed with T2DM, from whom 321 laboratory and medical assessment variables recorded over four years are available. Predictions of adverse events on T2DM patients using both classical machine learning and deep learning techniques were performed and evaluated using accuracy, precision, recall and F1-score as metrics. The best performance for the prediction of acute myocardial infarction is obtained by linear discriminant analysis (LDA) and support vector machines (SVM) both balanced and weight models with an accuracy of 97%; hospital admission for avoidable causes best performance is obtained by LDA balanced and SVMs balanced both with an accuracy of 92%. For the prediction of the incidence of at least one adverse event, the model with the best performance is the recurrent neural network trained with a balanced dataset with an accuracy of 94.6%. The ability to perform and compare these experiments was possible through the use of a workstation with multi-GPUs. This setup allows for scalability to larger datasets. Such models are also cloud ready and can be deployed on similar architectures hosted on AWS for even larger datasets.

Pressure ulcer (PU) is a type of chronic wounds (CWs), which is remaining unhealed for a period longer than six weeks. PU results from applying pressure and friction on the skin of the patient for a long time. It has a complex structure as it has different kinds of tissues. Reliable assessment of PU is essential to the success of the treatment and care decision. In this paper, we propose a tissue classification system for PU based on 3D convolutional neural network (CNN). The main idea of the proposed system is to provide a 3D CNN network with five different models of the colored PU RGB images to accurately segment slough, granulation, and necrotic eschar tissues. Each model of the PU RGB image is provided to the CNN as an independent pathway. The first and second models are the original RGB PU images and its equivalent HSV images. The third model is the smoothed image by convolving the original image with a preselected Gaussian kernel. The last two models are the first-order models of the current and prior visual appearance. The models approximate empirical marginal probability distributions of voxel-wise signals with linear combinations of discrete Gaussians (LCDG). The proposed system was trained and tested on 193 color PU images. The proposed tissue segmentation system is evaluated by using three various metrics, which are the area under the ROC curve (AUC), the Dice similarity coefficient (DSC), and the percentage area distance (PAD). The system achieved an average AUC equals to 95%, DSC equals to 92%, and PAD equals to 10%, which are a promising result.

A novel framework for the classification of lung nodules using computed tomography (CT) scans is proposed in this paper. To get an accurate diagnosis of the detected lung nodules, the proposed framework integrates the following two groups of features: (i) appearance features that is modeled using higher-order Markov Gibbs random field (MGRF)-model that has the ability to describe the spatial inhomogeneities inside the lung nodule; and (ii) geometric features that describe the shape geometry of the lung nodules. The novelty of this paper is to accurately model the appearance of the detected lung nodules using a new developed 7th-order MGRF 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 (AE) classifier is fed by the above two 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 (LIDC). 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 92.20%.

This paper introduces a 3D segmentation approach using an adaptive patient-specific retinal atlas and an appearance model for 3D Optical Coherence Tomography (OCT) data. In order to reconstruct the 3D patient-specific retinal atlas, we started by segmenting the macula central area where the fovea is clearly identified in the data to be segmented. The segmentation of this selected foveal area inside the retina is accomplished by using joint Markov Gibbs Random Field (MGRF) integrating shape, intensity, and spatial information of 12 retinal layers. A 2D shape prior was built using a series of co-registered training OCT images that were collected from 200 different subjects. The shape prior was then adapted to the first order appearance and second order spatial interaction MGRF model of the data to be segmented. Once the middle of the macula “foveal area” had been segmented, its segmented layers' labels and their appearances were used to segment the adjacent slices. The previous step was propagated until the complete 3D OCT patient-data was segmented. The proposed approach was tested on 30 different subjects, with either normal or pathological OCT scans, and then compared with a delineated ground truth and the results were then verified by retina specialists. Performance was measured using the Dice Similarity Coefficient (DSC), agreement coefficient (AC), and average deviation (AD) metrics. The accuracy achieved by the segmentation approach clearly demonstrates the promise of the proposed segmentation approach and shows improvement over a state-of-the-art 3D OCT segmentation approach currently in use.

The ultimate goal of this paper is to develop a novel personalized comprehensive computer aided diagnostic (CAD) system for precise diagnosis of autism spectrum disorder (ASD) based on the 3D shape analysis of the cerebral cortex (Cx), To achieve the main goal of the proposed system, we used structural MRI modality (sMRI) to be able to extract the shape features of the brain cortex. After segmenting the brain cortex from sMRI, we used a spherical harmonics analysis to measure the surface complexity, in addition to studying surface curvatures. Finally, a multi-stage deep network based on several autoencoders and softmax classifiers is constructed to provide the final global diagnosis. The presented CAD system was tested on several datasets, achieving an average accuracy of 92.15%. In addition to its global diagnostic accuracy, the local diagnostic accuracies of the most significant areas also demonstrated the ability of the proposed system to construct very promising local maps of ASD-related brain abnormalities, which can be considered an important step towards personalized medicine for autistic individuals.

Pressure ulcer (PU) is a clinical pathology of localized deterioration to the underlying tissues as well as to the skin, which is generated by friction and pressure. A trustworthy diagnosis of PU, which is supported by accurate assessment, is critical to have effective therapy and save the patient's life. In this paper, we propose an automatic classification framework to segment and classify various tissues to help in diagnosis and treatment of PU. The proposed framework consists of two main stages, which are region of interest (ROI) extraction and tissue segmentation stages. The main idea is to extract various models and features from PU RGB images and supply them to multi-path 3D convolution neural network (CNN) to segment slough, necrotic eschar, and granulation tissues to help in assessing the status of PU. ROI is extracted by supplying three different color models to the CNN, which are RGB, HSV, and YCbCr. Then, the PU tissues are classified by providing four various models to the 3D CNN. These models are the original RGB image, the smoothed image with a pre-selected Gaussian kernel, and the 1st-order models of prior and current visual appearance. The framework was trained and tested on 100 color RGB PU images. The classification accuracy was evaluated using the area under the curve (AUC), the percentage area distance (PAD), and Dice similarity coefficient (DSC). The obtained preliminary results have AUC of 96%, PAD of 10%, and DSC of 93%. These experimental results are promising and can lead to an accurate assessment of the PU status.

This paper addresses the issue of early diagnosis of prostate cancer from diffusion-weighted magnetic resonance imaging (DWI) using a convolutional neural network (CNN) based computer-aided diagnosis (CAD) system. The proposed CNN-based CAD system first segments the prostate using a geometric deformable model. The evolution of this model is guided by a stochastic speed function that exploits first- and second-order appearance models besides shape prior. The fusion of these guiding criteria is accomplished using a nonnegative matrix factorization (NMF) model. Then, the apparent diffusion coefficients (ADCs) within the segmented prostate are calculated at each b-value. They are used as imaging markers for the blood diffusion of the scanned prostate. For the purpose of classification/diagnosis, a three dimensional CNN has been trained to extract the most discriminatory features of these ADC maps for distinguishing malignant from benign prostate tumors. The performance of the proposed CNN-based CAD system is evaluated using DWI datasets acquired from 45 patients (20 benign and 25 malignant) at seven different b-values. The acquisition of these DWI datasets is performed using two different scanners with different magnetic field strengths (1.5 Tesla and 3 Tesla). The conducted experiments on in-vivo data confirm that the use of ADCs makes the proposed system nonsensitive to the magnetic field strength.

This paper proposes a novel framework for the classification of lung nodules using computed tomography (CT) scans. The proposed framework is based on the integrating the following features to get accurate diagnosis of detected lung nodules: (i) Spherical Harmonics-based shape features that have the ability to describe the shape complexity of the lung nodules; (ii) Higher-Order Markov Gibbs Random Field (MGRF)-based appearance model that has the ability to describe the spatial inhomogeneities in the lung nodule; and (iii) volumetric features that describe the size of lung nodules. To accurately model the surface/shape of the detect lung nodules, we used spherical harmonics expansion due to its ability to approximate the surfaces of complicated shapes. We will use the reconstruction error curve as a new metric to describe the shape complexity of the detected lung nodules. Moreover, we developed a new higher 7th-order MGRF model that has the ability to model the existing the spatial inhomogeneities for both small and large detected lung nodules. Finally, a deep autoencoder (AE) classifier is fed by the above three features 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 (LIDC). We used a total of 116 nodules that were collected from 60 patients. By achieving a classification accuracy of 96.00%, the proposed system demonstrates promise to be a valuable tool for the detection of lung cancer.

A novel framework for the detection of radiation-induced lung injury (RILI) from 4D computed tomography (CT) has been proposed. Our framework performs 4D-CT lung fields segmentation, deformable image registration (DIR), extraction of textural and functional features, and classification of lung voxels using deep 3D convolutional neural networks (CNN). The 4D-CT images segmentation extracts the lung fields inside the exhale phase using our multi-scale Gaussian adaptive shape prior technique followed by label propagation to other 4D-CT phases using a newly developed adaptive shape model. Then, the 4D-CT DIR locally aligns consecutive phases of the respiratory cycle using the 3D Laplace equation for finding voxel correspondences between the iso-surfaces for the fixed and moving lungs and generalized Gaussian Markov random field (GGMRF) as an anatomical consistency constraint. In addition to common lung functionality features, such as ventilation and elasticity, specific regional textural features are estimated by modeling the segmented images as samples of a novel 7th-order contrast-offset-invariant Markov-Gibbs random field (MGRF). Finally, a deep 3D CNN is applied to distinguish between the injured and normal lung tissues. 4D-CT datasets collected from 13 patients, who undergone the radiation therapy (RT), have been used in the evaluation of the proposed framework. The experimental results show the promise of our framework.

This paper proposes a novel framework for the identification of the radiation-induced lung injury (RILI) after radiation therapy (RT) using 4D computed tomography (CT) scans. The proposed methodology consists of four components: (i) elastic image registration; (ii) segmentation of the lung fields; (iii) extraction of functional and texture features; and (iv) classification of the lung tissues. The registration step locally aligns the consecutive phases of the respiratory cycle using an elastic image registration approach based on descent minimization of the sum of squared difference similarity metric. Secondly, lung fields are segmented using a hybrid framework that integrates an adaptive shape prior model, a first-order intensity model, and a second order homogeneity descriptor of the lung tissues. Next, regional features that describe both the texture features using the novel 7th-order Markov-Gibbs random field (MGRF) model in addition to the lung functionality features (e.g., ventilation and elasticity) are estimated from a segmented lungs. Finally, a random forest classifier (RF) is applied to distinguish between injured and normal lung tissues. To evaluate the proposed framework, we used data sets that have been collected from 13 patients who had underwent RT treatment. Experimental results demonstrate the promise of the proposed framework for the identification of the injured lung region, and thus hold the promise as a valuable tool for early detection of RILI.

A novel framework for assessing wall thickening from 4D cine cardiac magnetic resonance imaging (CMRI) is proposed. The proposed approach is primarily based on using geometrical features to track the left ventricle (LV) wall during the cardiac cycle. The 4D tracking approach consists of the following two main steps: (i) Initially, the surface points on the LV wall are tracked by solving a 3D Laplace equation between two successive LV surfaces; and (ii) Secondly, the locations of the tracked LV surface points are iteratively adjusted through an energy minimization cost function using a generalized Gauss-Markov random field (GGMRF) image model in order to remove inconsistencies and preserve the anatomy of the heart wall during the tracking process. Then the myocardial wall thickening is estimated by co-allocation of the corresponding points, or matches between the endocardium and epicardium surfaces of the LV wall using the solution of the 3D Laplace equation. Experimental results on in vivo data confirm the accuracy and robustness of our method. Moreover, the comparison results demonstrate that our approach outperforms 2D wall thickening estimation approaches.

We propose a framework for speech emotion analysis that maps sequential acoustic features into descriptors that integrate the temporal ordering of the original features and the discrimination between emotions. Using the topology preserving property of Self Organizing Maps and the continuous nature of speech, the proposed framework maps a speech utterance into a temporally ordered trajectory that reflects the emotion activity over time in the underlying utterance. Using a standard emotional database, we show that the proposed framework is efficient for analysis, visualization and classification of emotions.

A novel non-invasive approach for the early diagnosis of prostate cancer from diffusion-weighted MRI is proposed. The proposed diagnostic approach consists of three main steps. The first step is to isolate the prostate from the surrounding anatomical structures based on a Maximum a Posteriori (MAP) estimate of a new log-likelihood function that accounts for the shape priori, the spatial interaction, and the current appearance of prostate tissues and its background (surrounding anatomical structures). In the second step, a nonrigid registration algorithm is employed to account for any local deformation between the segmented prostates at different b-values that could occur during the scanning process due to patient breathing and local motion. In the final step, a kn-Nearest Neighbor-based classifier is used to classify the prostate into benign or malignant based on four appearance features extracted from registered images. Moreover, in this paper we introduce a new approach to generate color maps that illustrate the propagation of diffusion in prostate tissues based on the analysis of the 3D spatial interaction of the change of the gray level values of prostate voxel using a Generalized Gauss-Markov Random Field (GGMRF) image model. Finally, the tumor boundaries are determined using a level set deformable model controlled by the diffusion information and the spatial interactions between the prostate voxels. Experimental results on 28 clinical diffusion-weighted MRI data sets yield promising results.

Prostate segmentation is an essential step in developing any non-invasive Computer-Assisted Diagnostic (CAD) system for the early diagnosis of prostate cancer using Magnetic Resonance Images (MRI). In this paper, a novel framework for 3D segmentation of the prostate region from Diffusion-Weighted Magnetic Resonance Imaging (DW-MRI) is proposed. The framework is based on a Maximum A Posteriori (MAP) estimate of a new log-likelihood function that accounts for Markov-Gibbs shape and appearance models of the object-of-interest and its background. The framework was evaluated on in vivo prostate DW-MRI with available manual expert segmentation. The performance evaluation of the proposed segmentation approach, based on voxel-based and distance-based metrics between manually drawn and automatically segmented contours, confirmed the robustness and accuracy of the proposed segmentation approach.

In this paper, we introduce a novel hierarchical approach for multiphase image segmentation. The approach presents a unified framework that unifies two basic segmentation approaches; level set methods and graph cut algorithms. In the work of El-Zehiry et al. (2007), we have presented a bimodal image segmentation approach that have the advantages of the level set methods such as robustness to noise, blurred edges and topology changes and the advantages of graph cuts vis-a-vis global optimization and speed. Our main objective in this paper is to extend our previous approach to segment n classes. The results section will show that our algorithm outperforms the multiphase image segmentation approach introduced in the work of Vese and Chan (2002).

The paper presents a graph cut based active contour without edges segmentation model to track pedestrian in thermal images. The deformable model is based on the Mumford- Shah piecewise constant energy formulation. However, the model presented here relaxes the global homogeneity assumption of the Mumford- Shah functional. A discrete energy formulation is presented and the optimization is performed using graph cuts. The major advantages of our approach are; 1) The optimization using graph cuts makes the segmentation process much faster than solving it using level sets. 2) Relaxing the global homogeneity assumption makes the model more practical.

This paper presents a novel graph cut based segmentation approach with shape priors. The model incorporates statistical shape prior information with the active contour without edges model . Our model also relaxes the homogeneity constraint that assumes that the image is modeled by a piecewise constant approximation. The major contribution of this paper is to present a graph cut optimization for the energy function. Hence, the resultant approach is a fully automatic shape based segmentation approach that is insensitive to initialization and does not require any user interaction. Due to the polynomial time complexity of graph cut optimization approaches, our segmentation technique is much faster than the state of the art deformable models segmentation approaches.

Presents the introductory welcome message from the conference proceedings.

Data centers (DC) suffer from an increasing number of threats; these are exaggerated by the incredible growth of critical data volumes in addition to vulnerabilities in technologies used for distributed architectures. This paper demonstrates the need to define a new metric for ldquoresilience levelrdquo to complement other system metrics such as performance, availability, robustness and fault tolerance. In addition, ldquodata centerrdquo resiliencepsilas evaluation framework and its advantages are presented.

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.

In this paper, we address the problem of transforming relational features into an Euclidian space so that standard classification methods that assume that data is in a vector form could be used. Our approach has three main steps. First, a relational matrix that represents the pair-wise dissimilarities between all objects is constructed. Second, a fuzzy relational clustering algorithm is used to partition the data into groups of similar objects. Third, the relational data features are mapped to a unit hyper-cube space where each object is represented by its membership vectors in all clusters. The proposed method is validated by comparing the performance of several classifiers with different feature sets on the original and the transformed spaces. We show that the transformed space conserves the discriminative information of the original features. We also show that, using the transformed space, a richer set of standard classifiers could be used.

The biological concept of bilateral symmetry as a marker of developmental stability and good health is well established. Although most individuals deviate slightly from perfect symmetry, humans are essentially considered bilaterally symmetrical. Studies have shown that if an individual is exposed to genetic mutations or environmental stresses, the homeostatic mechanisms that maintain symmetry of paired structures (such as breasts) tend to break down. Consequently, increased fluctuating asymmetry of paired structures could be an indicator of poor health. This preliminary study tested if bilateral morphological breast asymmetry in screening mammograms correlates with the presence of breast cancer. Following the biological definition of breast asymmetry in terms of volume, we applied automated computer algorithms for screening mammograms that segment the breast region and then measure each segmented breast's volume. These parameters were measured separately for each breast in each mammographic view (CC and MLO). Then, the normalized absolute differences of these parameters were investigated as measurements of fluctuating asymmetry (FA). Based on 268 cancer cases and 82 normal cases from the DDSM database, we observed that cancer patients demonstrate statistically significantly higher fluctuating asymmetry in their screening mammograms than patients with normal screening studies. Using an artificial neural network to combine FA measurements from both views along with the patient's age and breast parenchymal density resulted in an ROC area of 0.80plusmn0.03. These results suggest that bilateral breast volume asymmetry estimated in screening mammograms should be studied as a risk factor for breast cancer.

We propose a technique for the automated detection of malignant masses in screening mammography. The technique is based on the presence of concentric layers surrounding a focal area with suspicious morphological characteristics and low relative incidence in the breast region. Mammographic locations with high concentration of concentric layers with progressively lower average intensity are considered suspicious deviations from normal parenchyma. The multiple concentric layers (MCLs) technique was trained and tested using the craniocaudal views of 270 mammographic cases with biopsy proven malignant masses from the digital database of screening mammography. One-half of the available cases were used for optimizing the parameters of the detection algorithm. The remaining cases were used for testing. During testing, malignant masses were detected with 92%, 88%, and 81% sensitivity at 5.4, 2.4, and 0.6 false positive marks per image. Testing on 82 normal screening mammograms showed a false positive rate of 5.0, 1.7, and 0.2 marks per image at the previously reported operating points. Furthermore, additional evaluation on 135 benign cases produced a significantly lower detection rate for benign masses (61.6%, 58.3%, and 43.7% at 5.1, 2.8, and 1.2 false positives per image, respectively). Overall, MCL is a promising computer-assisted detection strategy for screening mammograms to identify malignant masses while maintaining the detection rate of benign masses considerably lower.

In the real world, we have to frequently deal with searching for and tracking an optimal solution in a dynamic environment. This demands that the algorithm not only find the optimal solution but also track the trajectory of the solution in a dynamic environment. Particle swarm optimization (PSO) is a population-based stochastic optimization technique, which can find an optimal, or near optimal, solution to a numerical and qualitative problem. However, the traditional PSO algorithm lacks the ability to track the optimal solution in a dynamic environment. In this paper, we present a modified PSO algorithm that can be used for tracking a non-stationary optimal solution in a dynamically changing environment.

In the following paper the process of knowledge generation from the Visiting Nurse Association database for elderly care is explored. This inquiry is concerned with predicting falls. Predicting which patients are likely to fall can assist the clinician in the identification of high-risk patients and suggest the need for early falls prevention programs. The entire data mining process is described beginning with data gathering followed by cleaning, aggregation, and integration. Issues faced while conducting the research are discussed Results of decision trees and decision rules, and artificial neural networks used to predict falls are presented

We provide a biasing expansion swarm approach (BESA) for using multiple simple mobile agents, with limited sensing and communication capabilities, to collaboratively search and locate an indeterminate number of emission sources in an unknown large-scale area. The key concept in this approach is swarm behavior. By applying the three properties of the swarm behavior: separation, cohesion and alignment, our approach can ensure the agent group attains dynamically stable ad-hoc connectivity and fast target convergence. Using a grid map to represent the unknown environment, an ad-hoc network for wireless communication and our biasing expansion algorithm for path planning, each agent simultaneously considers all concentration values collected by other swarm members and determines the positive gradient direction of the whole coverage area of the swarm. This will make the swarm immune to the random sensor errors, local aerosol accumulations and other local interference effects during their search. We present a simulated environment that has multiple emission sources and complex aerosol accumulation and distribution. Based on the simulation, our approach can achieve better performance than the gradient descent approach, which currently appears to be the most popular algorithm for emission source localization.

A major problem raised by a region-based image retrieval system is the proper description of regions for efficient and semantically meaningful retrieval. We present CoIRS a novel cluster oriented image retrieval system. A distinguishing aspect of CoIRS is its integration of a robust unsupervised learning for the detection of regions. The segmentation is based on local color and texture features that allows cluster- or region-based search. In addition, a privileged component is the constructing of cluster signatures (CS) that include, color, texture, and shape features of the clusters centroids. The system constructs region signatures (RS) as well which includes region based features such as the invariant moments, area, and eccentricity. Also, another distinctive feature of the system is Feature ranking. Three features were used for ranking the signatures, color, texture, or shape. CoIRS framework proved to provide successful retrieval results supported by precision estimation. The system is evaluated using a database of 2000 images composed of different categories of images.

The pilot pollution interference (PPI) problem occurs when the mobile station conducts handoff process and searches for strong pilot signals from the neighboring cells. The improper handoff search window setting plays a key role in the creation of the PPI problem. Consequently, the existence of the PPI problem affects the progress and termination of the ongoing calls, impacts the origination of fresh calls, delays the handoff process and decreases the system capacity. In this paper, a base station assisted search (BSAS) approach is proposed to minimize the impact of the PPI problem. The proposed BSAS approach is integrated with a modified MAHAR-A approach to alleviate the influence of the PPI as well as improve the system's QoS measured by the call blocking probability and the handoff dropping probability. Simulation experiments clearly indicate the effectiveness of the proposed approach.

We describe a swarm-based fuzzy logic control (FLC) mobile sensor network approach for collaboratively locating the hazardous contaminants in an unknown large-scale area. The mobile sensor network is composed of a collection of distributed nodes (robots), each of which has limited sensing, intelligence and communication capabilities. An ad-hoc wireless network is established among all nodes, and each node considers other nodes as extended sensors. By gathering other nodes' locations and measurement data, each node's FLC can independently determine its next optimal deployment location. Simultaneously, by applying the three properties of the swarm behavior: separation, cohesion and alignment, the approach can ensure the sensor network attains wide regional coverage and dynamically stable connectivity. The simulation presented in this paper shows the swarm-based FLC mobile sensor network can achieve better performance and have higher fault tolerance in the event of partial node failures and sensor measurement errors.

Biomedical image registration, or geometric alignment of two-dimensional and/or three-dimensional (3D) image data, is becoming increasingly important in diagnosis, treatment planning, functional studies, computer-guided therapies, and in biomedical research. Registration based on intensity values usually requires optimization of some similarity metric between the images. Local optimization techniques frequently fail because functions of these metrics with respect to transformation parameters are generally nonconvex and irregular and, therefore, global methods are often required. In this paper, a new evolutionary approach, particle swarm optimization, is adapted for single-slice 3D-to-3D biomedical image registration. A new hybrid particle swarm technique is proposed that incorporates initial user guidance. Multimodal registrations with initial orientations far from the ground truth were performed on three volumes from different modalities. Results of optimizing the normalized mutual information similarity metric were compared with various evolutionary strategies. The hybrid particle swarm technique produced more accurate registrations than the evolutionary strategies in many cases, with comparable convergence. These results demonstrate that particle swarm approaches, along with evolutionary techniques and local methods, are useful in image registration, and emphasize the need for hybrid approaches for difficult registration problems.

Mobile stations, in CDMA systems, have the capability to communicate with multiple base stations whenever it is located in the fringe areas covered by overlapping base station. However, it is not desirable for an overlay area to contain pilot signals from a large number of base stations. As this would create pilot pollution interference and overload the mobile station's RAKE receiver. Therefore, the mobile station's RAKE receiver attempts to separate paths from three or more dominant pilot signals in the time domain to utilize path diversity. In each diversity (path/branch/finger), a large interference component exists due to the finger's own signal and other's signals. We demonstrated the power of using novel multi-carrier chip pilot pollution interference. Simulation studies have been carried out to verify the appropriateness of the proposed approach and we have obtained very promising results.

Abstract In CDMA systems, existence of large number of equally strong pilot signals is not desirable. As a consequence, this would create pilot pollution interference (PPI). Additionally, the limited number of fingers in the RAKE receiver adds a different dimension to the problem. The PPI has the influence of overloading the mobile station's RAKE receiver with excessive amount of signaling on the dedicated control channels. Therefore, the pilot signal's orthogonality erodes dramatically, and the system suffers from dropping of ongoing calls, blocking of originating fresh calls and decrease in the system capacity. In this paper, we demonstrate the power of using a novel multi‐carrier chip shaping to alleviate the impact of the PPI. Consequently, the mobile's RAKE receiver will have the capability to perform frequency diversity rather than multipath diversity. This will make efficient use of the received signal energy in the frequency domain and boost the performance of the system. Simulation studies have been carried out to verify the appropriateness of the proposed approach and we have obtained very promising results. Copyright © 2003 John Wiley & Sons, Ltd.

Hybrid mobile communication is a mixed mode network version of wired and wireless components. Hybrid networks occur in many situations such as a military warfare theatre and extended range commercial cellular networks. This paper proposes an integrated configuration management approach for hybrid communication networks. The issues addressed and resolved by the proposed approach are: (i) an automated configuration management system using a clustering technique, self organizing map (SOM) implemented by neural networks; (ii) a novel network self-healing survivability approach to handle the network failure scenario; (iii) network performance integration in the design and reconfiguration of hybrid networks.

We present a variable pricing policy for the dynamic allocation of bandwidth capacity. Simulation studies using different scenarios illustrate the different features of a dynamic and scalable service delivery policy that provides an improved level of bandwidth utilization. The recommended policy generates an increased revenue stream without sacrificing quality of service (QoS) demanded by the users. The proposed policy is based on a scalable pricing model that extends previous work done with a concentration on video on demand (VOD) applications. The proposed dynamic resource planning approach encourages the users to request a wider range of bandwidth as a scalable part of the bandwidth resource. Users, in our model, decide what on a desired service start time (t/sub s/) and end time (t/sub e/), and on the low and the high level of bandwidth for an acceptable level of quality. Our results show that, by selecting an appropriate bandwidth allocation policy and a corresponding pricing mechanism, it is possible to maintain specified levels of QoS and achieve increased revenue, reduce the number of blocked users, and improve resource utilization.

Presents a neural-based approach to classifying and estimating the statistical parameters of speckle noise found in biomedical ultrasound images. Speckle noise, a very complex phenomenon, has been modeled in a variety of different ways: and there is currently no clear consensus as to its precise statistical characteristics. In this study, different neural network architectures are used to classify ultrasound images contaminated with three types of noise, based upon three one-parameter statistical distributions. At the same time: the parameter is estimated. It is expected that accurate characterization of ultrasound speckle noise will benefit existing post-processing methods, and may lead to new refinements in these techniques.

A congestion control strategy has been developed in this paper to improve the performance of the network under heavy load. It takes two actions; one is a preventive measure and the other is a reactive measure. The preventive measure tracks the link and buffer space utilization. This measure restricts the call's entry into the network beyond the preset level of link utilization. Whenever the buffer gets filled beyond a threshold point, data cells coming in are dropped based on their priority. The reactive measure monitors the buffer space and its utilization. Whenever the buffers get filled to peak value, control cells are generated and are sent to the host computers responsible for buffer overflow. The control cells have a field set to the rate that the host computer should use. The new rate set in the control cells is based on the current rate of the call. After reducing the rate, the host will not reduce it further for a period of time called the recovery period that is also based on the current call rate. After the recovery period is over, the host computer will again transmit at the previous call rate.

Presented in this paper is a new methodology for detection of neural-network gaps (NNG's) based on error analysis and the visualization that is applicable for n-dimensional I/O domain. The generalization problem in artificial neural networks (ANN) training is analyzed and the concept of NNG's is introduced. The NNG's are highly undesirable in ANN generalization and methods for detecting, analyzing, and eliminating them are necessary. Previous methods for NNG detection, based on two-dimensional (2-D) and three-dimensional (3-D) visualization, were not applicable for ANN's with more than three inputs. Experiments demonstrate advantages of this new methodology, which allows better understanding of the NNG phenomena using a quantitative approach.

This paper discusses a performance and reliability optimization approach for distributed systems under a given budget constraint using the genetic algorithm (GA). The overall effectiveness of a distributed system is measured in terms of the average network throughput. This measure computes performance and reliability from network connectivity point of view. In order to carry out this optimization a distributed genetic algorithm (DGA) based scheme is developed. To demonstrate the effectiveness of the proposed approach, the results obtained from the distributed genetic algorithm approach is compared with single machine genetic algorithm and optimal solutions computed using exhaustive searches. Moreover, a brief discussion on the speed up over single machine implementation is also included.

The Time Warp distributed simulation algorithm uses checkpointing to save process states after certain event executions for later recovery at the time of a rollback. Two main techniques have been used for checkpointing: periodic state saving and incremental state saving. The former technique introduces large overheads in reconstructing a desired state by coasting forward from an earlier checkpointed state if the computational granularity is large. The latter technique also has large overheads in applications with large rollback distances. A hybrid checkpointing technique is proposed which uses both periodic and incremental state saving simultaneously in such a way that it reduces checkpointing time overheads. A detailed analytical model is developed for the hybrid technique, and comparisons are made using similar analytical models with periodic and incremental state saving techniques. Results show that when the system parameters are chosen to represent large and complex simulated systems, the hybrid approach has less checkpointing time overhead than the other two techniques.

The aggressive adaptive-risk (AAR) approach is an efficient clustered and adaptive approach for parallel simulation which provides optimistic logical processes the ability to adjust their degree of risk at run time, based on observed behavior. The AAR approach is implemented on a network of workstations. Performance results using large synthetic loads are reported and compared to those obtained for the time warp optimistic technique.

An efficient adaptive approach for parallel and distributed simulation (PADS) is formalized and implemented. The aggressive adaptive-risk (AAR) approach aims at reducing cascading rollbacks in large and complex simulations by clustering optimistic logical processes on each processor, and providing these processes the ability to adjust their degree of risk, at run time, to a good operating point based on observed behavior. The AAR approach is used to develop the Clustered Adaptive Distributed Simulator (CADS), which is implemented on a network of workstations. Details of the CADS implementation are described. Performance results for large synthetic loads are reported and compared to those obtained for the Time Warp optimistic technique.

A review of the state of the art in parallel and distributed simulation (PADS) is given. Sample performance results of currently used PADS techniques are presented using a network of workstations. The results demonstrate the capabilities and limitations of these techniques, and verify the logic of the current directions in PADS research.

A dynamic socioeconomic model of a small oil-producing country has been set up, based on a two-sector economy (oil sector and non-oil sector) and including population dynamics, internal consumption, and external investment. With the aid of a computer program, the model can be used to optimize the rates of oil production and investment in oil production. The criterion used for optimization is the present value of future income (including the worth of remaining oil reserves in the ground). Comparative results for a 50-year period are obtained, showing that optimum oil production peaks in the first 10-year period and drops gradually. The optimality of this production pattern is almost unaffected by different planning parameters, while the optimal schedule for investment in the oil sector is sensitive to other planning parameter choices.