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Neveen Ghali

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Gautam Buddha University

Seif Eddine NAFFOUTI

University of Tunis El Manar

Menoufiya University

Mahmoud Maqableh

The University Of Jordan

Roman MESHCHERYAKOV

Mir Shahriar Emami

Management and science university,Malaysia

Mojtaba Mousavi

Universiti Teknologi Malaysia - UTM

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Papers by Neveen Ghali

A Survey on Image Data Hiding Techniques**

International Journal of Intelligent Computing and Information Sciences

Due to the observed growth in recent years of digital image communication, computer technologies,... more Due to the observed growth in recent years of digital image communication, computer technologies, and image processing techniques image security has been an essential demand due to the different image attacks. Image security approaches are classified into cryptography and data hiding techniques, including digital watermarking and steganography. This study paper reviews existing picture data hiding techniques, their benefits and drawbacks, and future research directions. In addition to the survey, we included a brief explanation of several geometric and image processing attacks that impair picture transmission. General multimedia security ideas, primary requirements, and recent applications We addressed various approaches and their characteristics, types, requirements, and working mechanisms. We classify the techniques based on different domains. General concepts of data hiding approaches, their characteristics, recent applications used in, also recent research work for proposed techniques is discussed in the following sections, finally, a comparison between different methodologies has been presented in a table.

Exponential Particle Swarm Optimization Approach For Improving Data Clustering

In this paper we use exponential particle swarm optimization (EPSO) to cluster data. Then we comp... more In this paper we use exponential particle swarm optimization (EPSO) to cluster data. Then we compare between (EPSO) clustering algorithm which depends on exponential variation for the inertia weight and particle swarm optimization (PSO) clustering algorithm which depends on linear inertia weight. This comparison is evaluated on five data sets. The experimental results show that EPSO clustering algorithm increases the possibility to find the optimal positions as it decrease the number of failure. Also show that (EPSO) clustering algorithm has a smaller quantization error than (PSO) clustering algorithm, i.e. (EPSO) clustering algorithm more accurate than (PSO) clustering algorithm.

of 16-rounded Data Encryption Standard (DES-160 is

Abstract—Discovering the root key bits in the cryptanalysis

An Adaptive Context Modeling Approach Using Genetic Algorithm in IoTs Environments

Advances in Intelligent Systems and Computing, 2020

Internet of Things (loTs) is the future of ubiquitous and personalized intelligent service delive... more Internet of Things (loTs) is the future of ubiquitous and personalized intelligent service delivery. It depends on installing intelligent sensors to sense and control physical environment to generate enormous amount of data with various data types. Context aware computing is employed for transforming these sensor data into knowledge through three stages: collection, modeling and reasoning. In context modeling, raw data represents in according meaningful manner statically. Furthermore, with growth of IoTs live applications, static modeling is not convenient because of changing context data structure overtime. The work in this paper is dedicated to propose a new dynamic approach for context modeling based on genetic algorithm and satisfaction factor. In addition, flexibility indicator property and context based are defined to measure the performance of the proposed approach.

Enhanced Predictive Modelling for 30-Day Readmission Diabetes Patients Based on Data Normalization Analysis

International Journal of Intelligent Engineering and Systems, 2021

In the field of health care, one of the most important problems is predicting the possibility of ... more In the field of health care, one of the most important problems is predicting the possibility of hospital readmission due to its important role in caring for patients with chronic diseases such as diabetes. Such predictions affect the health care costs and the hospital's efficiency and reputation. In this paper, an intelligent-based model is developed to predict the reintroduction of the patient into the hospital. This model is based on using some Machine Learning (ML) algorithms such as Logistic Regression (LR), K-Nearest Neighbors (KNN), and Support Vector Machine (SVM). Also, it proposes the use of a Deep Learning (DL) based network such as a convolutional neural network (CNN). Both ML and DL are used as classifiers to predict hospital readmission. The main problem is the input noisy data to these classifiers. These noisy data reduce the accuracy of the readmission prediction model. Sequential pre-processing steps are proposed to get over such a problem. These pre-processing steps provide solutions to missing values, feature engineering, and normalization problems. The main contribution of this work is improving readmission prediction rate by solving the data normalization problem. Two types of data normalization (e.g. z-score and min-max normalization) are applied, results show there is a difference in accuracy, z-score normalization is better than min-max normalization when comparing ML methods and DL models, CNN is the best with an accuracy of 0.894% in case of z-score normalization. Moreover, the model performance is improved with an accuracy of 0.924% when non-normalized data is used as input to the model. The proposed Non-normalization technique successes in providing superior results compared to some previous techniques which are displayed data by using Ensemble, Normalization, and Ensemble by age group techniques.

A non-blind robust watermarking approach for 3D mesh models

This paper presents non-blind and robust watermarking approach for three-dimensional (3D) mesh mo... more This paper presents non-blind and robust watermarking approach for three-dimensional (3D) mesh models using K-means clustering technique. K-means clustering technique is used to cluster 3D mesh vertices into suitable or non-suitable watermark carrier based on feature vector. Then the watermark is inserted in the selected vertices for being carrier watermark that come out from K-means clustering technique and the other vertices are ignored. The watermark insertion is done by modifying the vertex position without causing perceptible distortion to 3D mesh models. Experimental results show that our watermarking approach is imperceptible and robust against different geometric attacks such as noise addition, cropping and mesh smoothing.

Transfer Learning Based Model for Pneumonia Detection in Chest X-ray Images

International Journal of Intelligent Engineering and Systems, 2021

Pneumonia is a lung infectious disease caused by viral bacteria. It damages one or both lungs in ... more Pneumonia is a lung infectious disease caused by viral bacteria. It damages one or both lungs in humans. Expert radiotherapists must evaluate chest X-ray to detect pneumonia. As a result, designing an automated system for detecting pneumonia would be valuable for quickly treating the disease, especially in remote areas. This paper introduces a convolutional neural network-based model for reliably detecting pneumonic lungs from chest X-rays. This model can be used by doctors to treat pneumonia in the real world. This study proposes a hybrid model of EfficientNetB0 as a transfer learning-based model and support vector machine (SVM) hinge loss. The functionality of the pre-trained EfficientNetB0 model is used as feature-extractors followed by SVM classifier for the classification of abnormal and normal chest X-Rays. The statistical findings show that using a pre-trained EfficientNetB0 model and supervised classifier algorithm to evaluate chest X-ray images, specifically to detect Pneumonia, can be very beneficial. The proposed model achieves higher classification accuracy, precision, recall, and AUC values outperforming other state of art models with an overall accuracy of 97%.

Signature Identification and Verification Systems: A Comparative Study on the Online and Offline Techniques

Future Computing and Informatics Journal, 2020

Handwritten signature identification and verification has become an active area of research in re... more Handwritten signature identification and verification has become an active area of research in recent years. Handwritten signature identification systems are used for identifying the user among all users enrolled in the system while handwritten signature verification systems are used for authenticating a user by comparing a specific signature with his signature that is stored in the system. This paper presents a review for commonly used methods for pre-processing, feature extraction and classification techniques in signature identification and verification systems, in addition to a comparison between the systems implemented in the literature for identification techniques and verification techniques in online and offline systems with taking into consideration the datasets used and results for each system

Context-Aware Reasoning Model Using Deep Learning and Fog Computing for Waste Management in Iots Environments

Recently, Internet of Things (IoTs) influences every aspect of human daily lives through intellig... more Recently, Internet of Things (IoTs) influences every aspect of human daily lives through intelligent systems as healthcare, traffic management, and smart building. These IoTs systems depend on contextualization of collecting data through context aware system to gain knowledge by using context reasoning. context reasoning is a way for deducing knowledge and providing better understanding of the collected raw data. Context reasoning is commonly carried out at the cloud due to its high processing capabilities. However, the main challenges of using cloud are high latency time and resource consumption. To meet these challenges, Fog computing is proposed as an intermediate layer between the IoTs devices and the cloud layer to comply IoTs requirements of latency time reduction and resource consumption by deploying services to the fog layer. In this paper a new context reasoning model is proposed based on three previously defined Deep Learning (DL) models which are GoogleNet, ResNet101 and ...

Facial Expressions Recognition in Thermal Images based on Deep Learning Techniques

International Journal of Image, Graphics and Signal Processing, 2019

Facial expressions are undoubtedly the best way to express human attitude which is crucial in soc... more Facial expressions are undoubtedly the best way to express human attitude which is crucial in social communications. This paper gives attention for exploring the human sentimental state in thermal images through Facial Expression Recognition (FER) by utilizing Convolutional Neural Network (CNN). Most traditional approaches largely depend on feature extraction and classification methods with a big pre-processing level but CNN as a type of deep learning methods, can automatically learn and distinguish influential features from the raw data of images through its own multiple layers. Obtained experimental results over the IRIS database show that the use of CNN architecture has a 96.7% recognition rate which is high compared with Neural Networks (NN), Autoencoder (AE) and other traditional recognition methods as Local Standard Deviation (LSD), Principle Component Analysis (PCA) and K-Nearest Neighbor (KNN).

Antlion optimization and boosting classifier for spam email detection

Future Computing and Informatics Journal, 2018

Spam emails are not necessary, though they are harmful as they include viruses and spyware, so th... more Spam emails are not necessary, though they are harmful as they include viruses and spyware, so there is an emerging need for detecting spam emails. Several methods for detecting spam emails were suggested based on the methods of machine learning, which were submitted to reduce nonrelevant emails and get results of high precision for spam email classification. In this work, a new predictive method is submitted based on antlion optimization (ALO) and boosting termed as ALO-Boosting for solving spam emails problem. ALO is a computational model imitates the preying technicality of antlions to ants in the life cycle. Where ALO was utilized to modify the actual place of the population in the separate seeking area, thus obtaining the optimum feature subset for the better classification submit based on boosting classifier. Boosting classifier is a classification algorithm that points to a group of algorithms which modifies soft learners into powerful learners. The proposed procedure is compared against support vector machine (SVM), k-nearest neighbours algorithm (KNN), and bootstrap aggregating (Bagging) on spam email datasets in a set of implementation measures. The experimental outcomes show the ability of the proposed method to successfully detect optimum features with the smallest value of selected features and a high precision of measures for spam email classification based on boosting classifier.

Classification of EEG Signals for Motor Imagery based on Mutual Information and Adaptive Neuro Fuzzy Inference System

International Journal of System Dynamics Applications, 2016

Electroencephalogram (EEG) signals based Brain Computer Interface (BCI) is employed to help disab... more Electroencephalogram (EEG) signals based Brain Computer Interface (BCI) is employed to help disabled people to interact better with the environment. EEG signals are recorded through BCI system to translate it to control commands. There are a large body of literature targeting EEG feature extraction and classification for Motor Imagery tasks. Motor imagery task have several features can be extracted to use in classification. However, using more features consume running time and using irrelevant and redundant features affect the performance of the used classifier. This paper is dedicated to extracting the best feature vector for motor imagery task. This work suggests two feature selection methods based on Mutual Information (MI) including Minimum Redundancy Maximal Relevance (MRMR) and maximal Relevance (MaxRel). Adaptive Neuro Fuzzy Inference System (ANFIS) classifier with Subtractive clustering method is utilized for EEG signals classifications. The suggested methods are applied to ...

Object-Based Image Retrieval System Based On Rough Set Theory

The goal of this paper is to create image retrieval system based on image objects. In the context... more The goal of this paper is to create image retrieval system based on image objects. In the context of Rough Set Theory we introduce an accurate Object-Based Image Retrieval (OBIR) system that can handle image-based queries, and presents an efficient algorithm to retrieve images from large databases, by defining novel image feature called Object Similarity Ratio used in the proposed system.

Social Networks: Computational Aspects and Mining

ABSTRACT

Aboul Ella Hassanien: An adaptive Watermarking Approach for Medical Imaging Using Swarm Intelligent

In this paper we present a secure patient medical images and authentication scheme which enhances... more In this paper we present a secure patient medical images and authentication scheme which enhances the security, confidentiality and integrity of medical images transmitted through the Internet. This paper proposes a watermarking by invoking particle swarm optimization (PSO) technique in adaptive quantization index modulation and singular value decomposition in conjunction with discrete wavelet transform (DWT) and discrete cosine transform (DCT). The proposed approach promotes the robustness and watermarked image quality. The experimental results show that the proposed algorithm yields a watermark which is invisible to human eyes, robust against a wide variety of common attacks and reliable enough for tracing colluders.

Object-Based Image Retrieval System Using Rough Set Approach

Intelligent Systems Reference Library, 2012

In this chapter, we present an object-based image retrieval system using the rough set theory. Th... more In this chapter, we present an object-based image retrieval system using the rough set theory. The system incorporates two major modules: Preprocessing and Object-based image retrieval. In preprocessing, an imagebased object segmentation algorithm in the context of the rough set theory is used to segment the images into meaningful semantic regions. A new object similarity measure is proposed for the image retrieval. Performance is evaluated on an image database and the effectiveness of proposed image retrieval ...

Heart Sounds Human Identification and Verification Approaches using Vector Quantization and Gaussian Mixture Models

International Journal of Systems Biology and Biomedical Technologies, 2012

In this paper the possibility of using the human heart sounds as a human print is investigated. T... more In this paper the possibility of using the human heart sounds as a human print is investigated. To evaluate the performance and the uniqueness of the proposed approach, tests using a high resolution auscultation digital stethoscope are done for nearly 80 heart sound samples. The verification approach consists of a robust feature extraction with a specified configuration in conjunction with Gaussian mixture modeling. The similarity of two samples is estimated by measuring the difference between their negative log-likelihood similarities of the features. The experimental results obtained show that the overall accuracy offered by the employed Gaussian mixture modeling reach up to 85%. The identification approach consists of a robust feature extraction with a specified configuration in conjunction with LBG-VQ. The experimental results obtained show that the overall accuracy offered by the employed LBG-VQ reach up to 88.7%.

Genetic Algorithms for Multi-Objective Community Detection in Complex Networks

Studies in Computational Intelligence, 2013

Abstract: Community detection in complex networks has attracted a lot of attention in recent year... more Abstract: Community detection in complex networks has attracted a lot of attention in recent years. Community detection can be viewed as an optimization problem, in which an objective function that captures the intuition of a community as a group of nodes with better internal connectivity than external connectivity is chosen to be optimized. Many single-objective optimization techniques have been used to solve the problem however those approaches have its drawbacks since they try optimizing one objective function and this ...

An Improved Hashing Function for Human Authentication System

International Journal of Computer Vision and Image Processing, 2013

Biometrics have the great advantage of recognition based on an intrinsic aspect of a human being ... more Biometrics have the great advantage of recognition based on an intrinsic aspect of a human being and thus requiring the person to be authenticated for physical presentation. Unfortunately, biometrics suffer from some inherent limitation such as high false rejection when the system works at a low false acceptation rate. In this paper, near set are implemented to improve the Standard Secure Hash Function SHA-1 (ISHA-1) for strict multi-modal biometric image authentication system. The proposed system is composed of five phases, starting from feature extraction and selection phase, hashing computing that uses the ISHA-1 phase, embedding watermark phase, extraction and decryption watermark phase, and finally the authentication phase. Experimental results showed that the proposed algorithm guarantees the security assurance and reduces the time of implementation.

Strict Authentication of Multimodal Biometric Images Using Near Sets

Advances in Intelligent and Soft Computing, 2011

In this paper, a strict authentication watermarking scheme based on multi-modal biometric images ... more In this paper, a strict authentication watermarking scheme based on multi-modal biometric images and near sets was designed and introduced. The proposed scheme has a number of stages including feature enrolment for extracting the human facial features. Three human facial features which are nose length, nose width and distance between eyes balls are extracted. The near sets approach is adapted to choose the best feature among the considered features. The watermark is generated from hashing the extracted facial ...

A Survey on Image Data Hiding Techniques**

International Journal of Intelligent Computing and Information Sciences

Due to the observed growth in recent years of digital image communication, computer technologies,... more Due to the observed growth in recent years of digital image communication, computer technologies, and image processing techniques image security has been an essential demand due to the different image attacks. Image security approaches are classified into cryptography and data hiding techniques, including digital watermarking and steganography. This study paper reviews existing picture data hiding techniques, their benefits and drawbacks, and future research directions. In addition to the survey, we included a brief explanation of several geometric and image processing attacks that impair picture transmission. General multimedia security ideas, primary requirements, and recent applications We addressed various approaches and their characteristics, types, requirements, and working mechanisms. We classify the techniques based on different domains. General concepts of data hiding approaches, their characteristics, recent applications used in, also recent research work for proposed techniques is discussed in the following sections, finally, a comparison between different methodologies has been presented in a table.

Exponential Particle Swarm Optimization Approach For Improving Data Clustering

In this paper we use exponential particle swarm optimization (EPSO) to cluster data. Then we comp... more In this paper we use exponential particle swarm optimization (EPSO) to cluster data. Then we compare between (EPSO) clustering algorithm which depends on exponential variation for the inertia weight and particle swarm optimization (PSO) clustering algorithm which depends on linear inertia weight. This comparison is evaluated on five data sets. The experimental results show that EPSO clustering algorithm increases the possibility to find the optimal positions as it decrease the number of failure. Also show that (EPSO) clustering algorithm has a smaller quantization error than (PSO) clustering algorithm, i.e. (EPSO) clustering algorithm more accurate than (PSO) clustering algorithm.

of 16-rounded Data Encryption Standard (DES-160 is

Abstract—Discovering the root key bits in the cryptanalysis

An Adaptive Context Modeling Approach Using Genetic Algorithm in IoTs Environments

Advances in Intelligent Systems and Computing, 2020

Internet of Things (loTs) is the future of ubiquitous and personalized intelligent service delive... more Internet of Things (loTs) is the future of ubiquitous and personalized intelligent service delivery. It depends on installing intelligent sensors to sense and control physical environment to generate enormous amount of data with various data types. Context aware computing is employed for transforming these sensor data into knowledge through three stages: collection, modeling and reasoning. In context modeling, raw data represents in according meaningful manner statically. Furthermore, with growth of IoTs live applications, static modeling is not convenient because of changing context data structure overtime. The work in this paper is dedicated to propose a new dynamic approach for context modeling based on genetic algorithm and satisfaction factor. In addition, flexibility indicator property and context based are defined to measure the performance of the proposed approach.

Enhanced Predictive Modelling for 30-Day Readmission Diabetes Patients Based on Data Normalization Analysis

International Journal of Intelligent Engineering and Systems, 2021

In the field of health care, one of the most important problems is predicting the possibility of ... more In the field of health care, one of the most important problems is predicting the possibility of hospital readmission due to its important role in caring for patients with chronic diseases such as diabetes. Such predictions affect the health care costs and the hospital's efficiency and reputation. In this paper, an intelligent-based model is developed to predict the reintroduction of the patient into the hospital. This model is based on using some Machine Learning (ML) algorithms such as Logistic Regression (LR), K-Nearest Neighbors (KNN), and Support Vector Machine (SVM). Also, it proposes the use of a Deep Learning (DL) based network such as a convolutional neural network (CNN). Both ML and DL are used as classifiers to predict hospital readmission. The main problem is the input noisy data to these classifiers. These noisy data reduce the accuracy of the readmission prediction model. Sequential pre-processing steps are proposed to get over such a problem. These pre-processing steps provide solutions to missing values, feature engineering, and normalization problems. The main contribution of this work is improving readmission prediction rate by solving the data normalization problem. Two types of data normalization (e.g. z-score and min-max normalization) are applied, results show there is a difference in accuracy, z-score normalization is better than min-max normalization when comparing ML methods and DL models, CNN is the best with an accuracy of 0.894% in case of z-score normalization. Moreover, the model performance is improved with an accuracy of 0.924% when non-normalized data is used as input to the model. The proposed Non-normalization technique successes in providing superior results compared to some previous techniques which are displayed data by using Ensemble, Normalization, and Ensemble by age group techniques.

A non-blind robust watermarking approach for 3D mesh models

This paper presents non-blind and robust watermarking approach for three-dimensional (3D) mesh mo... more This paper presents non-blind and robust watermarking approach for three-dimensional (3D) mesh models using K-means clustering technique. K-means clustering technique is used to cluster 3D mesh vertices into suitable or non-suitable watermark carrier based on feature vector. Then the watermark is inserted in the selected vertices for being carrier watermark that come out from K-means clustering technique and the other vertices are ignored. The watermark insertion is done by modifying the vertex position without causing perceptible distortion to 3D mesh models. Experimental results show that our watermarking approach is imperceptible and robust against different geometric attacks such as noise addition, cropping and mesh smoothing.

Transfer Learning Based Model for Pneumonia Detection in Chest X-ray Images

International Journal of Intelligent Engineering and Systems, 2021

Pneumonia is a lung infectious disease caused by viral bacteria. It damages one or both lungs in ... more Pneumonia is a lung infectious disease caused by viral bacteria. It damages one or both lungs in humans. Expert radiotherapists must evaluate chest X-ray to detect pneumonia. As a result, designing an automated system for detecting pneumonia would be valuable for quickly treating the disease, especially in remote areas. This paper introduces a convolutional neural network-based model for reliably detecting pneumonic lungs from chest X-rays. This model can be used by doctors to treat pneumonia in the real world. This study proposes a hybrid model of EfficientNetB0 as a transfer learning-based model and support vector machine (SVM) hinge loss. The functionality of the pre-trained EfficientNetB0 model is used as feature-extractors followed by SVM classifier for the classification of abnormal and normal chest X-Rays. The statistical findings show that using a pre-trained EfficientNetB0 model and supervised classifier algorithm to evaluate chest X-ray images, specifically to detect Pneumonia, can be very beneficial. The proposed model achieves higher classification accuracy, precision, recall, and AUC values outperforming other state of art models with an overall accuracy of 97%.

Signature Identification and Verification Systems: A Comparative Study on the Online and Offline Techniques

Future Computing and Informatics Journal, 2020

Handwritten signature identification and verification has become an active area of research in re... more Handwritten signature identification and verification has become an active area of research in recent years. Handwritten signature identification systems are used for identifying the user among all users enrolled in the system while handwritten signature verification systems are used for authenticating a user by comparing a specific signature with his signature that is stored in the system. This paper presents a review for commonly used methods for pre-processing, feature extraction and classification techniques in signature identification and verification systems, in addition to a comparison between the systems implemented in the literature for identification techniques and verification techniques in online and offline systems with taking into consideration the datasets used and results for each system

Context-Aware Reasoning Model Using Deep Learning and Fog Computing for Waste Management in Iots Environments

Recently, Internet of Things (IoTs) influences every aspect of human daily lives through intellig... more Recently, Internet of Things (IoTs) influences every aspect of human daily lives through intelligent systems as healthcare, traffic management, and smart building. These IoTs systems depend on contextualization of collecting data through context aware system to gain knowledge by using context reasoning. context reasoning is a way for deducing knowledge and providing better understanding of the collected raw data. Context reasoning is commonly carried out at the cloud due to its high processing capabilities. However, the main challenges of using cloud are high latency time and resource consumption. To meet these challenges, Fog computing is proposed as an intermediate layer between the IoTs devices and the cloud layer to comply IoTs requirements of latency time reduction and resource consumption by deploying services to the fog layer. In this paper a new context reasoning model is proposed based on three previously defined Deep Learning (DL) models which are GoogleNet, ResNet101 and ...

Facial Expressions Recognition in Thermal Images based on Deep Learning Techniques

International Journal of Image, Graphics and Signal Processing, 2019

Facial expressions are undoubtedly the best way to express human attitude which is crucial in soc... more Facial expressions are undoubtedly the best way to express human attitude which is crucial in social communications. This paper gives attention for exploring the human sentimental state in thermal images through Facial Expression Recognition (FER) by utilizing Convolutional Neural Network (CNN). Most traditional approaches largely depend on feature extraction and classification methods with a big pre-processing level but CNN as a type of deep learning methods, can automatically learn and distinguish influential features from the raw data of images through its own multiple layers. Obtained experimental results over the IRIS database show that the use of CNN architecture has a 96.7% recognition rate which is high compared with Neural Networks (NN), Autoencoder (AE) and other traditional recognition methods as Local Standard Deviation (LSD), Principle Component Analysis (PCA) and K-Nearest Neighbor (KNN).

Antlion optimization and boosting classifier for spam email detection

Future Computing and Informatics Journal, 2018

Spam emails are not necessary, though they are harmful as they include viruses and spyware, so th... more Spam emails are not necessary, though they are harmful as they include viruses and spyware, so there is an emerging need for detecting spam emails. Several methods for detecting spam emails were suggested based on the methods of machine learning, which were submitted to reduce nonrelevant emails and get results of high precision for spam email classification. In this work, a new predictive method is submitted based on antlion optimization (ALO) and boosting termed as ALO-Boosting for solving spam emails problem. ALO is a computational model imitates the preying technicality of antlions to ants in the life cycle. Where ALO was utilized to modify the actual place of the population in the separate seeking area, thus obtaining the optimum feature subset for the better classification submit based on boosting classifier. Boosting classifier is a classification algorithm that points to a group of algorithms which modifies soft learners into powerful learners. The proposed procedure is compared against support vector machine (SVM), k-nearest neighbours algorithm (KNN), and bootstrap aggregating (Bagging) on spam email datasets in a set of implementation measures. The experimental outcomes show the ability of the proposed method to successfully detect optimum features with the smallest value of selected features and a high precision of measures for spam email classification based on boosting classifier.

Classification of EEG Signals for Motor Imagery based on Mutual Information and Adaptive Neuro Fuzzy Inference System

International Journal of System Dynamics Applications, 2016

Electroencephalogram (EEG) signals based Brain Computer Interface (BCI) is employed to help disab... more Electroencephalogram (EEG) signals based Brain Computer Interface (BCI) is employed to help disabled people to interact better with the environment. EEG signals are recorded through BCI system to translate it to control commands. There are a large body of literature targeting EEG feature extraction and classification for Motor Imagery tasks. Motor imagery task have several features can be extracted to use in classification. However, using more features consume running time and using irrelevant and redundant features affect the performance of the used classifier. This paper is dedicated to extracting the best feature vector for motor imagery task. This work suggests two feature selection methods based on Mutual Information (MI) including Minimum Redundancy Maximal Relevance (MRMR) and maximal Relevance (MaxRel). Adaptive Neuro Fuzzy Inference System (ANFIS) classifier with Subtractive clustering method is utilized for EEG signals classifications. The suggested methods are applied to ...

Object-Based Image Retrieval System Based On Rough Set Theory

The goal of this paper is to create image retrieval system based on image objects. In the context... more The goal of this paper is to create image retrieval system based on image objects. In the context of Rough Set Theory we introduce an accurate Object-Based Image Retrieval (OBIR) system that can handle image-based queries, and presents an efficient algorithm to retrieve images from large databases, by defining novel image feature called Object Similarity Ratio used in the proposed system.

Social Networks: Computational Aspects and Mining

ABSTRACT

Aboul Ella Hassanien: An adaptive Watermarking Approach for Medical Imaging Using Swarm Intelligent

In this paper we present a secure patient medical images and authentication scheme which enhances... more In this paper we present a secure patient medical images and authentication scheme which enhances the security, confidentiality and integrity of medical images transmitted through the Internet. This paper proposes a watermarking by invoking particle swarm optimization (PSO) technique in adaptive quantization index modulation and singular value decomposition in conjunction with discrete wavelet transform (DWT) and discrete cosine transform (DCT). The proposed approach promotes the robustness and watermarked image quality. The experimental results show that the proposed algorithm yields a watermark which is invisible to human eyes, robust against a wide variety of common attacks and reliable enough for tracing colluders.

Object-Based Image Retrieval System Using Rough Set Approach

Intelligent Systems Reference Library, 2012

In this chapter, we present an object-based image retrieval system using the rough set theory. Th... more In this chapter, we present an object-based image retrieval system using the rough set theory. The system incorporates two major modules: Preprocessing and Object-based image retrieval. In preprocessing, an imagebased object segmentation algorithm in the context of the rough set theory is used to segment the images into meaningful semantic regions. A new object similarity measure is proposed for the image retrieval. Performance is evaluated on an image database and the effectiveness of proposed image retrieval ...

Heart Sounds Human Identification and Verification Approaches using Vector Quantization and Gaussian Mixture Models

International Journal of Systems Biology and Biomedical Technologies, 2012

In this paper the possibility of using the human heart sounds as a human print is investigated. T... more In this paper the possibility of using the human heart sounds as a human print is investigated. To evaluate the performance and the uniqueness of the proposed approach, tests using a high resolution auscultation digital stethoscope are done for nearly 80 heart sound samples. The verification approach consists of a robust feature extraction with a specified configuration in conjunction with Gaussian mixture modeling. The similarity of two samples is estimated by measuring the difference between their negative log-likelihood similarities of the features. The experimental results obtained show that the overall accuracy offered by the employed Gaussian mixture modeling reach up to 85%. The identification approach consists of a robust feature extraction with a specified configuration in conjunction with LBG-VQ. The experimental results obtained show that the overall accuracy offered by the employed LBG-VQ reach up to 88.7%.

Genetic Algorithms for Multi-Objective Community Detection in Complex Networks

Studies in Computational Intelligence, 2013

Abstract: Community detection in complex networks has attracted a lot of attention in recent year... more Abstract: Community detection in complex networks has attracted a lot of attention in recent years. Community detection can be viewed as an optimization problem, in which an objective function that captures the intuition of a community as a group of nodes with better internal connectivity than external connectivity is chosen to be optimized. Many single-objective optimization techniques have been used to solve the problem however those approaches have its drawbacks since they try optimizing one objective function and this ...

An Improved Hashing Function for Human Authentication System

International Journal of Computer Vision and Image Processing, 2013

Biometrics have the great advantage of recognition based on an intrinsic aspect of a human being ... more Biometrics have the great advantage of recognition based on an intrinsic aspect of a human being and thus requiring the person to be authenticated for physical presentation. Unfortunately, biometrics suffer from some inherent limitation such as high false rejection when the system works at a low false acceptation rate. In this paper, near set are implemented to improve the Standard Secure Hash Function SHA-1 (ISHA-1) for strict multi-modal biometric image authentication system. The proposed system is composed of five phases, starting from feature extraction and selection phase, hashing computing that uses the ISHA-1 phase, embedding watermark phase, extraction and decryption watermark phase, and finally the authentication phase. Experimental results showed that the proposed algorithm guarantees the security assurance and reduces the time of implementation.

Strict Authentication of Multimodal Biometric Images Using Near Sets

Advances in Intelligent and Soft Computing, 2011

In this paper, a strict authentication watermarking scheme based on multi-modal biometric images ... more In this paper, a strict authentication watermarking scheme based on multi-modal biometric images and near sets was designed and introduced. The proposed scheme has a number of stages including feature enrolment for extracting the human facial features. Three human facial features which are nose length, nose width and distance between eyes balls are extracted. The near sets approach is adapted to choose the best feature among the considered features. The watermark is generated from hashing the extracted facial ...