Sahar Mokhtar

A novel blackboard-based multistrategy learning system in a distributed environment

2003 46th Midwest Symposium on Circuits and Systems, 2003

... Sahar A. Mokhtarl, AshrafH. AbdelWahab1, Ayman E. Ibrahim , Samir . ... 38-53,1986 [12] lain ... more

Improving Small and Cluttered Object Detection by Incorporating Instance Level Denoising Into Single-Shot Alignment Network for Remote Sensing Imagery

IEEE Access

Object detection on satellite and aerial images has gained the attraction of the scientific commu... more Object detection on satellite and aerial images has gained the attraction of the scientific community of computer vision due to its immense value, high difficulty, and the development of large-scale datasets enough to train deep learning models. The progress is tremendous with the increase in the precision of the fast single-stage object detection models which used to sacrifice precision for speed. Aerial and satellite images are of large sizes which makes slow models infeasible for production. Single-shot Alignment Network (S2A-Net) is a fast and competitive single-stage model in terms of precision. However, there is a potential to increase its precision in detecting small and cluttered objects in a complex background. In this paper, a new hybrid approach by incorporating Instance Level Denoising (ILD) module from Small, Cluttered, and Rotated Object Detector ++ (SCRDet++) into S2A-Net is proposed. The model was trained and tested on Dota V1.0. The proposed model achieves a higher mean average precision (mAP) than S2A-Net to be 79.73% and when it was trained using the Kullback-Leibler divergence as a regression loss function, the proposed model can reach as high as 80.39% mAP. INDEX TERMS Convolutional neural network (CNN), deep learning, object detection, remote sensing.

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Towards an Ontology based integrated Framework for Semantic Web

ArXiv, 2013

This Ontologies are widely used as a means for solving the information heterogeneity problems on ... more This Ontologies are widely used as a means for solving the information heterogeneity problems on the web because of their capability to provide explicit meaning to the information. They become an efficient tool for knowledge representation in a structured manner. There is always more than one ontology for the same domain. Furthermore, there is no standard method for building ontologies, and there are many ontology building tools using different ontology languages. Because of these reasons, interoperability between the ontologies is very low. Current ontology tools mostly use functions to build, edit and inference the ontology. Methods for merging heterogeneous domain ontologies are not included in most tools. This paper presents ontology merging methodology for building a single global ontology from heterogeneous eXtensible Markup Language (XML) data sources to capture and maintain all the knowledge which XML data sources can contain

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An Intelligent Cbmir System for Detection and Localization of Lung Diseases

International Journal of Advanced Research, 2021

The diagnosis of lung diseases is a complicated and time-consuming task for radiologists. Often r... more The diagnosis of lung diseases is a complicated and time-consuming task for radiologists. Often radiologists struggle with accurately diagnosing lung diseases, They use Commonly CT imaging signs (CISs) which common appear in CT lung nodules in the diagnosis of lung diseases. Computer-aided diagnosis systems (CAD) can automatically diagnose and detect these signs by analyzing CT scans, which will reduce radiologists workload. The diagnosis and recognition efficiency and accuracy can be improved by using content-based medical image retrieval (CBMIR). This paper proposes a new intelligent CBMIR method to retrieve CISs helping in diagnosing and recognize lung diseases by using deep Convolutional Neural Network (CNN). Fine-tuned YOLOv4 (You Only Look Once) object detector model are proposed to fast detect and efficiently localize signs in real-time. The proposed CBMIR system can be applied as a useful and accurate medical instrument for diagnostics. The experimental results show an avera...

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Automatic Generation of OWL Ontology from XML Data Source

The eXtensible Markup Language (XML) can be used as data exchange format in different domains. It... more The eXtensible Markup Language (XML) can be used as data exchange format in different domains. It allows different parties to exchange data by providing common understanding of the basic concepts in the domain. XML covers the syntactic level, but lacks support for reasoning. Ontology can provide a semantic representation of domain knowledge which supports efficient reasoning and expressive power. One of the most popular ontology languages is the Web Ontology Language (OWL). It can represent domain knowledge using classes, properties, axioms and instances for the use in a distributed environment such as the World Wide Web. This paper presents a new method for automatic generation of OWL ontology from XML data sources.

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A Comparative Study of Data Mining approaches for Bag of Visual Words Based Image Classification

Journal of Computer Science

Image classification is one of the most significant and challenging tasks in computer vision. The... more Image classification is one of the most significant and challenging tasks in computer vision. The goal of this task is to build a system that is capable to reveal an image label within a collection of different image categories. This paper presents and discusses the application of various data mining techniques for image classification based on Bag of Visual Words (BoVW) feature extraction algorithm. The BoVW model is constructed using grey level features: The Speeded Up Robust Features (SURF) and Maximally Stable Extremal Regions (MSER) descriptors along with color features: Color correlograms and Improved Color Coherence Vector (ICCV). Five data mining techniques; Neural Networks (NN), Decision Trees (DT), Bayesian Network (BN), Discriminant Analysis (DA) and K Nearest Neighbor (KNN), are explored and evaluated on two large different datasets: Corel-1000 and COIL-100. The experimental results illustrate that BN and DA outperform the other data mining methods considered in this comparative study. For Corel-1000 dataset, BN and DA achieved an average accuracy and specificity of about 99.9% and an average sensitivity of about 99.5 and 99.4%, respectively. While for the COIL-100 dataset, BN and DA accomplished an average accuracy and sensitivity of about 100% and an average specificity of about 98.5 and 98.9, respectively.

Predicting the Severity of Breast Masses with Data Mining Methods

Mammography is the most effective and available tool for breast cancer screening. However, the lo... more Mammography is the most effective and available tool for breast cancer screening. However, the low positive predictive value of breast biopsy resulting from mammogram interpretation leads to approximately 70% unnecessary biopsies with benign outcomes. Data mining algorithms could be used to help physicians in their decisions to perform a breast biopsy on a suspicious lesion seen in a mammogram image or to perform a short term follow-up examination instead. In this research paper data mining classification algorithms; Decision Tree (DT), Artificial Neural Network (ANN), and Support Vector Machine (SVM) are analyzed on mammographic masses dataset. The purpose of this study is to increase the ability of physicians to determine the severity (benign or malignant) of a mammographic mass lesion from BI-RADS attributes and the patient"s age. The whole dataset is divided for training the models and test them by the ratio of 70:30% respectively and the performances of classification algorithms are compared through three statistical measures; sensitivity, specificity, and classification accuracy. Accuracy of DT, ANN and SVM are 78.12%, 80.56% and 81.25% of test samples respectively. Our analysis shows that out of these three classification models SVM predicts severity of breast cancer with least error rate and highest accuracy.

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Towards an Ontology based integrated Framework for Semantic Web

This Ontologies are widely used as a means for solving the information heterogeneity problems on ... more This Ontologies are widely used as a means for solving the information heterogeneity problems on the web because of their capability to provide explicit meaning to the information. They become an efficient tool for knowledge representation in a structured manner. There is always more than one ontology for the same domain. Furthermore, there is no standard method for building ontologies, and there are many ontology building tools using different ontology languages. Because of these reasons, interoperability between the ontologies is very low. Current ontology tools mostly use functions to build, edit and inference the ontology. Methods for merging heterogeneous domain ontologies are not included in most tools. This paper presents ontology merging methodology for building a single global ontology from heterogeneous eXtensible Markup Language (XML) data sources to capture and maintain all the knowledge which XML data sources can contain.

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Weeds Identification Using Evolutionary Artificial Intelligence Algorithm

Journal of Computer Science, 2014

In a world reached a population of six billion humans increasingly demand it for food, feed with ... more In a world reached a population of six billion humans increasingly demand it for food, feed with a water shortage and the decline of agricultural land and the deterioration of the climate needs 1.5 billion hectares of agricultural land and in case of failure to combat pests needs about 4 billion hectares. Weeds represent 34% of the whole pests while insects, diseases and the deterioration of agricultural land present the remaining percentage. Weeds Identification has been one of the most interesting classification problems for Artificial Intelligence (AI) and image processing. The most common case is to identify weeds within the field as they reduce the productivity and harm the existing crops. Success in this area results in an increased productivity, profitability and at the same time decreases the cost of operation. On the other hand, when AI algorithms combined with appropriate imagery tools may present the right solution to the weed identification problem. In this study, we introduce an evolutionary artificial neural network to minimize the time of classification training and minimize the error through the optimization of the neuron parameters by means of a genetic algorithm. The genetic algorithm, with its global search capability, finds the optimum histogram vectors used for network training and target testing through a fitness measure that reflects the result accuracy and avoids the trial-and-error process of estimating the network inputs according to the histogram data.

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Automatic generation of OWL ontology from XML data source

The eXtensible Markup Language (XML) can be used as data exchange format in different domains. It... more The eXtensible Markup Language (XML) can be used as data exchange format in different domains. It allows different parties to exchange data by providing common understanding of the basic concepts in the domain. XML covers the syntactic level, but lacks support for reasoning. Ontology can provide a semantic representation of domain knowledge which supports efficient reasoning and expressive power. One of the most popular ontology languages is the Web Ontology Language (OWL). It can represent domain knowledge using classes, properties, axioms and instances for the use in a distributed environment such as the World Wide Web. This paper presents a new method for automatic generation of OWL ontology from XML data sources.

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Multiclass Penalized Likelihood Pattern Classification Algorithm

Lecture Notes in Computer Science, 2012

ABSTRACT Penalized likelihood is a general approach whereby an objective function is defined, con... more ABSTRACT Penalized likelihood is a general approach whereby an objective function is defined, consisting of the log likelihood of the data minus some term penalizing non-smooth solutions. Subsequently, this objective function is maximized, yielding a solution that achieves some sort of trade-off between the faithfulness and the smoothness of the fit. In this paper we extend the penalized likelihood classification that we proposed in earlier work to the multi class case. The algorithms are based on using a penalty term based on the K-nearest neighbors and the likelihood of the training patterns&#39; classifications. The algorithms are simple to implement, and result in a performance competitive with leading classifiers.

Novel online tutor modeling for intelligent tutoring systems

2013 9th International Computer Engineering Conference (ICENCO), 2013

ABSTRACT

Forecast Global Carbon Dioxide Emission using Swarm Intelligence

International Journal of Computer Applications, 2013

The tremendous effects of air quality in large cities have been considered a severe environmental... more The tremendous effects of air quality in large cities have been considered a severe environmental problem all over the world. Therefore, the international community agreed to develop air quality standards to monitor and control pollution rates around industrial communities. Harmful emission into the air is a sign that could extremely affect man's health, natural life and agriculture. Forecasting models is essential for predicting air quality. CO 2 emissions have been an international concern because of fossil fuels. In this study, Particle Swarm Optimization (PSO) is used for analyzing world CO 2 emission based on the global energy consumption. A parametric PSO model is developed to forecast CO 2 emission based set of attributes. They include: global oil, natural gas, coal, and primary energy consumption. A data set collected during the years 1980 and 2010 were used in this study. Experimental results show that PSO can provide good modeling results using a limited number of measurements compared to other linear models.

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Multiclass Penalized Likelihood Pattern Classification Algorithm

ABSTRACT Penalized likelihood is a general approach whereby an objective function is defined, con... more ABSTRACT Penalized likelihood is a general approach whereby an objective function is defined, consisting of the log likelihood of the data minus some term penalizing non-smooth solutions. Subsequently, this objective function is maximized, yielding a solution that achieves some sort of trade-off between the faithfulness and the smoothness of the fit. In this paper we extend the penalized likelihood classification that we proposed in earlier work to the multi class case. The algorithms are based on using a penalty term based on the K-nearest neighbors and the likelihood of the training patterns&#39; classifications. The algorithms are simple to implement, and result in a performance competitive with leading classifiers.

Multiclass Penalized Likelihood Pattern Classification Algorithm

ABSTRACT Penalized likelihood is a general approach whereby an objective function is defined, con... more ABSTRACT Penalized likelihood is a general approach whereby an objective function is defined, consisting of the log likelihood of the data minus some term penalizing non-smooth solutions. Subsequently, this objective function is maximized, yielding a solution that achieves some sort of trade-off between the faithfulness and the smoothness of the fit. In this paper we extend the penalized likelihood classification that we proposed in earlier work to the multi class case. The algorithms are based on using a penalty term based on the K-nearest neighbors and the likelihood of the training patterns&#39; classifications. The algorithms are simple to implement, and result in a performance competitive with leading classifiers.

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