Mohammed Hamzah Abed | Budapest University of Technology and Economics (original) (raw)

Papers by Mohammed Hamzah Abed

Research paper thumbnail of Optimize Weight sharing for Aggregation Model in Federated Learning Environment of Brain Tumor classification

Journal of Al-Qadisiyah for Computer Science and Mathematics

Clinical diagnosis and therapy of brain tumors are greatly aided by proper classification of the ... more Clinical diagnosis and therapy of brain tumors are greatly aided by proper classification of the tumors. Brain tumors can be diagnosed more quickly and accurately if radiologists use deep learning to help the specialist and doctors examine the enormous volume of brain MRI Images. Large datasets are required in training process, and whole of such data must be centralized for be handled by such techniques. It is sometimes impossible to collect and distribute patient data on a centralized data server because of medical data privacy regulations. In this paper, federated learning (FL) is proposed, in which data is non-shareable because of patient privacy issues. Using the FL approach, we have proposed two methods of aggregation; first, which concerns ranking the weight percentage of each client, and Second average weights method. and to evaluate the suggested model, we have compared the performance of the ranking weights percentage method with the average weights of proposed CNN and pre...

Research paper thumbnail of Cluster-Based Information Retrieval by using (K-means)- Hierarchical Parallel Genetic Algorithms Approach

arXiv (Cornell University), Jul 31, 2020

Cluster-based information retrieval is one of the Information retrieval(IR) tools that organize, ... more Cluster-based information retrieval is one of the Information retrieval(IR) tools that organize, extract features and categorize the web documents according to their similarity. Unlike traditional approaches, cluster-based IR is fast in processing large datasets of document. To improve the quality of retrieved documents, increase the efficiency of IR and reduce irrelevant documents from user search. in this paper, we proposed a (K-means)-Hierarchical Parallel Genetic Algorithms Approach (HPGA) that combines the K-means clustering algorithm with hybrid PG of multi-deme and master/slave PG algorithms. K-means uses to cluster the population to k subpopulations then take most clusters relevant to the query to manipulate in a parallel way by the two levels of genetic parallelism, thus, irrelevant documents will not be included in subpopulations, as a way to improve the quality of results. Three common datasets (NLP, CISI, and CACM) are used to compute the recall, precision, and F-measure averages. Finally, we compared the precision values of three datasets with Genetic-IR and classic-IR. The proposed approach precision improvements with IR-GA were 45% in the CACM, 27% in the CISI, and 25% in the NLP. While, by comparing with Classic-IR, (k-means)-HPGA got 47% in CACM, 28% in CISI, and 34% in NLP.

Research paper thumbnail of Diabetic Retinopathy Diagnosis based on Convolutional Neural Network

arXiv (Cornell University), Jul 31, 2020

Diabetic Retinopathy DR is a popular disease for many people as a result of age or the diabetic, ... more Diabetic Retinopathy DR is a popular disease for many people as a result of age or the diabetic, as a result, it can cause blindness. therefore, diagnosis of this disease especially in the early time can prevent its effect for a lot of patients. To achieve this diagnosis, eye retina must be examined continuously. Therefore, computer-aided tools can be used in the field based on computer vision techniques. Different works have been performed using various machine learning techniques. Convolutional Neural Network is one of the promise methods, so it was for Diabetic Retinopathy detection in this paper. Also, the proposed work contains visual enhancement in the preprocessing phase, then the CNN model is trained to be able for recognition and classification phase, to diagnosis the healthy and unhealthy retina image. Three public dataset DiaretDB0, DiaretDB1 and DrimDB were used in practical testing. The implementation of this work based on Matlab-R2019a, deep learning toolbox and deep network designer to design the architecture of the convolutional neural network and train it. The results were evaluated to different metrics; accuracy is one of them. The best accuracy that was achieved: for DiaretDB0 is 100%, DiaretDB1 is 99.495% and DrimDB is 97.55%.

Research paper thumbnail of Finger knuckle pattern person identification system based on LDP-NPE and machine learning methods

Bulletin of Electrical Engineering and Informatics, Dec 1, 2022

Biometric-based individual distinguishing proof is a successful strategy for consequently perceiv... more Biometric-based individual distinguishing proof is a successful strategy for consequently perceiving, with high certainty, an individual's character. The utilization of finger knuckle pictures for individual ID has shown promising outcomes and produced a ton of interest in biometrics. By seeing that the surface example delivered by twisting the finger knuckle is profoundly particular, in this paper we present a new biometric validation framework utilizing finger-knuckle-print (FKP) imaging. In this paper, another methodology in view of neighborhood surface examples is proposed. Local derivative pattern (LDP) histogram is investigated for FKP description. Then based on neighborhood preserving embedding (NPE) is used for dimension reduction to the feature vector. The feature extraction method is computed and evaluated in the identification framework task. The machine learning methods (multiclass support vector machine (MSVM), random forest (RF), k-nearest neighbor (KNN)) are proposed for classification. The system is tested on the PolyU finger knuckle database. The empirical results proved that the proposed model has the best results with RF. Moreover, our proposed LDP-NPE model has been evaluated and the results show remarkable efficiency in comparison with previous work. Experimentally, the proposed model has better accuracy as reflected by 99.65%.

Research paper thumbnail of A Survey on Patients Privacy Protection with Steganography and Visual Encryption

Research paper thumbnail of Iris recognition model based on Principal Component analysis and 2 level Haar wavelet transform: Case study CUHK and UTIRIS iris databases

Journal of Education College Wasit University

A biometric recognition system provide automatic identification of human being based on some spec... more A biometric recognition system provide automatic identification of human being based on some special and unique physical or behavioral features of the individual. One of the most reliable identification system is iris recognition system. This work aim to recognize and identify iris among many of images that have been save in databases. Each one of database that used manipulating in many steps starting with enhance the details of iris and segment the iris and pupil then extract the raw features based on 2D Haar wavelet transform to capture both global and local features of iris image. After that by Appling reduction step to select only the useful and unique features that belong to each person. In this work PCA used as a reduction method. Finally the minimum distance are used to check the similarity between the database’s features training set and input image, also three similarity techniques are used between input iris image and the template that save in database. Weighted Euclidea...

Research paper thumbnail of Convolutional neural network for color images classification

Bulletin of Electrical Engineering and Informatics, Jun 1, 2022

Artificial intelligent and application of computer vision are an exciting topic in last few years... more Artificial intelligent and application of computer vision are an exciting topic in last few years, and its key for many real time applications like video summarization, image retrieval and image classifications. One of the most trend method in deep learning is a convolutional neural network, used for many applications of image processing and computer vision. In this work convolutional neural networks CNN model proposed for color image classification, the proposed model build using MATLAB tools of deep learning. In addition, the suggested model tested on three different datasets, with different size. The proposed model achieved highest result of accuracy, precision and sensitivity with the largest dataset and it was as following: accuracy is 0.9924, precision is 0.9947 and sensitivity is 0.9931, compare with other models.

Research paper thumbnail of Finger knuckle pattern person identification system based on LDP-NPE and machine learning methods

Bulletin of Electrical Engineering and Informatics

Biometric-based individual distinguishing proof is a successful strategy for consequently perceiv... more Biometric-based individual distinguishing proof is a successful strategy for consequently perceiving, with high certainty, an individual's character. The utilization of finger knuckle pictures for individual ID has shown promising outcomes and produced a ton of interest in biometrics. By seeing that the surface example delivered by twisting the finger knuckle is profoundly particular, in this paper we present a new biometric validation framework utilizing finger-knuckle-print (FKP) imaging. In this paper, another methodology in view of neighborhood surface examples is proposed. Local derivative pattern (LDP) histogram is investigated for FKP description. Then based on neighborhood preserving embedding (NPE) is used for dimension reduction to the feature vector. The feature extraction method is computed and evaluated in the identification framework task. The machine learning methods (multiclass support vector machine (MSVM), random forest (RF), k-nearest neighbor (KNN)) are propo...

Research paper thumbnail of Hilbert Convex Similarity for Highly Secure Random Distribution of Patient Privacy Steganography

IEEE Access

(VU), through the Digital Transformation of Small Medium Enterprises (SMEs) under Grant VUR20466.

Research paper thumbnail of Distributed brain tumor diagnosis using a federated learning environment

Bulletin of Electrical Engineering and Informatics

In the last few years, a very huge development has occurred in medical techniques using artificia... more In the last few years, a very huge development has occurred in medical techniques using artificial intelligence tools, especially in the diagnosis field. One of the essential things is brain tumor (BT) detection and diagnosis. This kind of disease needs an expert physician to decide on the treatment or surgical operation based on magnetic resonance imaging (MRI) images; therefore, the researchers focus on such kind of medical images analysis and understanding to help the specialist to make a decision. in this work, a new environment has been investigated based on the deep learning method and distributed federated learning (FL) algorithm. The proposed model has been evaluated based on cross-validation techniques using two different standard datasets, BT-small-2c, and BT-large-3c. The achieved classification accuracy was 0.82 and 0.96 consecutively. The proposed classification model provides an active and effective system for assessing BT classification with high reliability and accur...

Research paper thumbnail of A Comprehensive Survey on Federated Learning: Concept and Applications

This paper provides a comprehensive study of Federated Learning (FL) with an emphasis on componen... more This paper provides a comprehensive study of Federated Learning (FL) with an emphasis on components, challenges, applications and FL environment. FL can be applicable in multiple fields and domains in real-life models. in the medical system, the privacy of patients records and their medical condition is critical data, therefore collaborative learning or federated learning comes into the picture. On other hand build an intelligent system assist the medical staff without sharing the data lead into the FL concept and one of the applications that are used is a brain tumor diagnosis intelligent system based on AI methods that can efficiently work in a collaborative environment.this paper will introduce some of the applications and related work in the medical field and work under the FL concept then summarize them to introduce the main limitations of their work.

Research paper thumbnail of A Survey on Patients Privacy Protection with Stganography and Visual Encryption

In this survey, thirty models for steganography and visual encryption methods have been discussed... more In this survey, thirty models for steganography and visual encryption methods have been discussed to provide patients privacy protection.

Research paper thumbnail of Architectural Heritage Images Classification Using Deep Learning with CNN

Digital documentation of cultural heritage images has emerged as an important topic in data analy... more Digital documentation of cultural heritage images has emerged as an important topic in data analysis. Increasing the size and number of images to be processed making the task of categorizing them a challenging task and may take an inordinate amount of time. This research paper proposes a solution to the mentioned challenges by classifying the subject of the image of the study using Convolutional Neural Network. Classification of available images leads to improve the management of the images dataset and enhance the search of a specific item, which helps in the tasks of studying and analysis the proper heritage object. Deep learning for architectural heritage images classification has been employed during the course of this study. The pre-trained convolutional neural networks GoogLeNet, resnet18 and resnet50 proposed to be applied on public dataset Cultural Heritage images. Experimental results have shown promising outcomes with an accuracy of "87.91", "95.47" and "95.57" respectively.

Research paper thumbnail of The effect of topology adaptation on search performance in overlay networks

Research paper thumbnail of Palm Vein Identification based on hybrid features selection model

ArXiv, 2020

Palm vein identification (PVI) is a modern biometric security technique used for increasing secur... more Palm vein identification (PVI) is a modern biometric security technique used for increasing security and authentication systems. The key characteristics of palm vein patterns include, its uniqueness to each individual, unforgettable, non-intrusive and cannot be taken by an unauthorized person. However, the extracted features from the palm vein pattern are huge with high redundancy. In this paper, we propose a combine model of two-Dimensional Discrete Wavelet Transform, Principal Component Analysis (PCA), and Particle Swarm Optimization (PSO) (2D-DWTPP) to enhance prediction of vein palm patterns. The 2D-DWT Extracts features from palm vein images, PCA reduces the redundancy in palm vein features. The system has been trained in selecting high reverent features based on the wrapper model. The PSO feeds wrapper model by an optimal subset of features. The proposed system uses four classifiers as an objective function to determine VPI which include Support Vector Machine (SVM), K Nearest...

Research paper thumbnail of Wrist and Palm Vein pattern recognition using Gabor filter

Wrist and palm vein pattern can be considering as a promising biometric technique for identificat... more Wrist and palm vein pattern can be considering as a promising biometric technique for identification, through the study of the pattern of blood vessels that visible from the skin. This kind of recognition is very important for many reasons; vein exists inside of the human body makes it difficult to change pattern like shift the position of vein from part to another, unlike another method of techniques of recognition. In this paper work wrist and palm vein are studied for identification and verification, this work divided into three phases preprocessing, features extraction and recognition. in preprocessing phase apply resize and image “enhancement” using “CLAHE and 2-D Gaussian high pass filter”, the features of each image are extracted by using Gabor filters. LDA and PCA are used to minimize the dimension of the features set. For vein image, identification used Euclidean distance to measure the similarity. The average CRR of vein palm in proposed work is 94.49% and the average CRR ...

Research paper thumbnail of The Effect of the Topology Adaptation on Search Performance in Overlay Network

Expert Clouds and Applications, 2021

The work presented in this research paper has focused on the effect of network topology adaptatio... more The work presented in this research paper has focused on the effect of network topology adaptation on search performance in peer to peer overlay network. Guided search vs. blind search have been studied with the aim of improving the search results and decreasing the time a search message would take to reach the destination. The network has been formulated as a bi-direction graph with vertices represent network nodes and edges represent connections. The level of network subject of this study is on application layer, that means two nodes are connected if they know each other contact addresses. A good example of this kind of network is the social network where all the lower layers are hidden from the end user. Two different search algorithms have been studied under these circumstances, namely: depth first algorithm and breadth first algorithm. Furthermore, the algorithms performance is examined under random topology (scale free network topology) and under topology adaptation. A simulation scenario has been designed to investigate the fidelity of the system and study the suggested solutions. Simulation results have shown that the search algorithms are performing better under topology adaptation in terms of results quality and search time.

Research paper thumbnail of Diabetic Retinopathy Diagnosis based on Convolutional Neural Network

Journal of Physics: Conference Series, 2021

Research paper thumbnail of Convolutional Neural Network for Satellite Image Classification

Intelligent Information and Database Systems: Recent Developments, 2019

Multimedia applications and processing is an exciting topic, and it is a key of many applications... more Multimedia applications and processing is an exciting topic, and it is a key of many applications of artificial intelligent like video summarization, image retrieval or image classification. A convolutional neural networks have been successfully applied on multimedia approaches and used to create a system able to handle the classification without any human’s interactions. In this paper, we produce effective methods for satellite image classification that are based on deep learning and using the convolutional neural network for features extraction by using AlexNet, VGG19, GoogLeNet and Resnet50 pretraining models. The Resnet50 model achieves a promising result than other models on three different dataset SAT4, SAT6 and UC Merced Land. The accuracy of classification of this model for UC Merced Land dataset is 98%, for SAT4 is 95.8%, and the result for SAT6 is 94.1%.

Research paper thumbnail of Cluster-based information retrieval by using (K-means)- hierarchical parallel genetic algorithms approach

TELKOMNIKA (Telecommunication Computing Electronics and Control), 2021

Cluster-based information retrieval is one of the Information retrieval(IR) tools that organize, ... more Cluster-based information retrieval is one of the Information retrieval(IR) tools that organize, extract features and categorize the web documents according to their similarity. Unlike traditional approaches, cluster-based IR is fast in processing large datasets of document. To improve the quality of retrieved documents, increase the efficiency of IR and reduce irrelevant documents from user search. in this paper, we proposed a (K-means)-Hierarchical Parallel Genetic Algorithms Approach (HPGA) that combines the K-means clustering algorithm with hybrid PG of multi-deme and master/slave PG algorithms. K-means uses to cluster the population to k subpopulations then take most clusters relevant to the query to manipulate in a parallel way by the two levels of genetic parallelism, thus, irrelevant documents will not be included in subpopulations, as a way to improve the quality of results. Three common datasets (NLP, CISI, and CACM) are used to compute the recall, precision, and F-measure averages. Finally, we compared the precision values of three datasets with Genetic-IR and classic-IR. The proposed approach precision improvements with IR-GA were 45% in the CACM, 27% in the CISI, and 25% in the NLP. While, by comparing with Classic-IR, (k-means)-HPGA got 47% in CACM, 28% in CISI, and 34% in NLP.

Research paper thumbnail of Optimize Weight sharing for Aggregation Model in Federated Learning Environment of Brain Tumor classification

Journal of Al-Qadisiyah for Computer Science and Mathematics

Clinical diagnosis and therapy of brain tumors are greatly aided by proper classification of the ... more Clinical diagnosis and therapy of brain tumors are greatly aided by proper classification of the tumors. Brain tumors can be diagnosed more quickly and accurately if radiologists use deep learning to help the specialist and doctors examine the enormous volume of brain MRI Images. Large datasets are required in training process, and whole of such data must be centralized for be handled by such techniques. It is sometimes impossible to collect and distribute patient data on a centralized data server because of medical data privacy regulations. In this paper, federated learning (FL) is proposed, in which data is non-shareable because of patient privacy issues. Using the FL approach, we have proposed two methods of aggregation; first, which concerns ranking the weight percentage of each client, and Second average weights method. and to evaluate the suggested model, we have compared the performance of the ranking weights percentage method with the average weights of proposed CNN and pre...

Research paper thumbnail of Cluster-Based Information Retrieval by using (K-means)- Hierarchical Parallel Genetic Algorithms Approach

arXiv (Cornell University), Jul 31, 2020

Cluster-based information retrieval is one of the Information retrieval(IR) tools that organize, ... more Cluster-based information retrieval is one of the Information retrieval(IR) tools that organize, extract features and categorize the web documents according to their similarity. Unlike traditional approaches, cluster-based IR is fast in processing large datasets of document. To improve the quality of retrieved documents, increase the efficiency of IR and reduce irrelevant documents from user search. in this paper, we proposed a (K-means)-Hierarchical Parallel Genetic Algorithms Approach (HPGA) that combines the K-means clustering algorithm with hybrid PG of multi-deme and master/slave PG algorithms. K-means uses to cluster the population to k subpopulations then take most clusters relevant to the query to manipulate in a parallel way by the two levels of genetic parallelism, thus, irrelevant documents will not be included in subpopulations, as a way to improve the quality of results. Three common datasets (NLP, CISI, and CACM) are used to compute the recall, precision, and F-measure averages. Finally, we compared the precision values of three datasets with Genetic-IR and classic-IR. The proposed approach precision improvements with IR-GA were 45% in the CACM, 27% in the CISI, and 25% in the NLP. While, by comparing with Classic-IR, (k-means)-HPGA got 47% in CACM, 28% in CISI, and 34% in NLP.

Research paper thumbnail of Diabetic Retinopathy Diagnosis based on Convolutional Neural Network

arXiv (Cornell University), Jul 31, 2020

Diabetic Retinopathy DR is a popular disease for many people as a result of age or the diabetic, ... more Diabetic Retinopathy DR is a popular disease for many people as a result of age or the diabetic, as a result, it can cause blindness. therefore, diagnosis of this disease especially in the early time can prevent its effect for a lot of patients. To achieve this diagnosis, eye retina must be examined continuously. Therefore, computer-aided tools can be used in the field based on computer vision techniques. Different works have been performed using various machine learning techniques. Convolutional Neural Network is one of the promise methods, so it was for Diabetic Retinopathy detection in this paper. Also, the proposed work contains visual enhancement in the preprocessing phase, then the CNN model is trained to be able for recognition and classification phase, to diagnosis the healthy and unhealthy retina image. Three public dataset DiaretDB0, DiaretDB1 and DrimDB were used in practical testing. The implementation of this work based on Matlab-R2019a, deep learning toolbox and deep network designer to design the architecture of the convolutional neural network and train it. The results were evaluated to different metrics; accuracy is one of them. The best accuracy that was achieved: for DiaretDB0 is 100%, DiaretDB1 is 99.495% and DrimDB is 97.55%.

Research paper thumbnail of Finger knuckle pattern person identification system based on LDP-NPE and machine learning methods

Bulletin of Electrical Engineering and Informatics, Dec 1, 2022

Biometric-based individual distinguishing proof is a successful strategy for consequently perceiv... more Biometric-based individual distinguishing proof is a successful strategy for consequently perceiving, with high certainty, an individual's character. The utilization of finger knuckle pictures for individual ID has shown promising outcomes and produced a ton of interest in biometrics. By seeing that the surface example delivered by twisting the finger knuckle is profoundly particular, in this paper we present a new biometric validation framework utilizing finger-knuckle-print (FKP) imaging. In this paper, another methodology in view of neighborhood surface examples is proposed. Local derivative pattern (LDP) histogram is investigated for FKP description. Then based on neighborhood preserving embedding (NPE) is used for dimension reduction to the feature vector. The feature extraction method is computed and evaluated in the identification framework task. The machine learning methods (multiclass support vector machine (MSVM), random forest (RF), k-nearest neighbor (KNN)) are proposed for classification. The system is tested on the PolyU finger knuckle database. The empirical results proved that the proposed model has the best results with RF. Moreover, our proposed LDP-NPE model has been evaluated and the results show remarkable efficiency in comparison with previous work. Experimentally, the proposed model has better accuracy as reflected by 99.65%.

Research paper thumbnail of A Survey on Patients Privacy Protection with Steganography and Visual Encryption

Research paper thumbnail of Iris recognition model based on Principal Component analysis and 2 level Haar wavelet transform: Case study CUHK and UTIRIS iris databases

Journal of Education College Wasit University

A biometric recognition system provide automatic identification of human being based on some spec... more A biometric recognition system provide automatic identification of human being based on some special and unique physical or behavioral features of the individual. One of the most reliable identification system is iris recognition system. This work aim to recognize and identify iris among many of images that have been save in databases. Each one of database that used manipulating in many steps starting with enhance the details of iris and segment the iris and pupil then extract the raw features based on 2D Haar wavelet transform to capture both global and local features of iris image. After that by Appling reduction step to select only the useful and unique features that belong to each person. In this work PCA used as a reduction method. Finally the minimum distance are used to check the similarity between the database’s features training set and input image, also three similarity techniques are used between input iris image and the template that save in database. Weighted Euclidea...

Research paper thumbnail of Convolutional neural network for color images classification

Bulletin of Electrical Engineering and Informatics, Jun 1, 2022

Artificial intelligent and application of computer vision are an exciting topic in last few years... more Artificial intelligent and application of computer vision are an exciting topic in last few years, and its key for many real time applications like video summarization, image retrieval and image classifications. One of the most trend method in deep learning is a convolutional neural network, used for many applications of image processing and computer vision. In this work convolutional neural networks CNN model proposed for color image classification, the proposed model build using MATLAB tools of deep learning. In addition, the suggested model tested on three different datasets, with different size. The proposed model achieved highest result of accuracy, precision and sensitivity with the largest dataset and it was as following: accuracy is 0.9924, precision is 0.9947 and sensitivity is 0.9931, compare with other models.

Research paper thumbnail of Finger knuckle pattern person identification system based on LDP-NPE and machine learning methods

Bulletin of Electrical Engineering and Informatics

Biometric-based individual distinguishing proof is a successful strategy for consequently perceiv... more Biometric-based individual distinguishing proof is a successful strategy for consequently perceiving, with high certainty, an individual's character. The utilization of finger knuckle pictures for individual ID has shown promising outcomes and produced a ton of interest in biometrics. By seeing that the surface example delivered by twisting the finger knuckle is profoundly particular, in this paper we present a new biometric validation framework utilizing finger-knuckle-print (FKP) imaging. In this paper, another methodology in view of neighborhood surface examples is proposed. Local derivative pattern (LDP) histogram is investigated for FKP description. Then based on neighborhood preserving embedding (NPE) is used for dimension reduction to the feature vector. The feature extraction method is computed and evaluated in the identification framework task. The machine learning methods (multiclass support vector machine (MSVM), random forest (RF), k-nearest neighbor (KNN)) are propo...

Research paper thumbnail of Hilbert Convex Similarity for Highly Secure Random Distribution of Patient Privacy Steganography

IEEE Access

(VU), through the Digital Transformation of Small Medium Enterprises (SMEs) under Grant VUR20466.

Research paper thumbnail of Distributed brain tumor diagnosis using a federated learning environment

Bulletin of Electrical Engineering and Informatics

In the last few years, a very huge development has occurred in medical techniques using artificia... more In the last few years, a very huge development has occurred in medical techniques using artificial intelligence tools, especially in the diagnosis field. One of the essential things is brain tumor (BT) detection and diagnosis. This kind of disease needs an expert physician to decide on the treatment or surgical operation based on magnetic resonance imaging (MRI) images; therefore, the researchers focus on such kind of medical images analysis and understanding to help the specialist to make a decision. in this work, a new environment has been investigated based on the deep learning method and distributed federated learning (FL) algorithm. The proposed model has been evaluated based on cross-validation techniques using two different standard datasets, BT-small-2c, and BT-large-3c. The achieved classification accuracy was 0.82 and 0.96 consecutively. The proposed classification model provides an active and effective system for assessing BT classification with high reliability and accur...

Research paper thumbnail of A Comprehensive Survey on Federated Learning: Concept and Applications

This paper provides a comprehensive study of Federated Learning (FL) with an emphasis on componen... more This paper provides a comprehensive study of Federated Learning (FL) with an emphasis on components, challenges, applications and FL environment. FL can be applicable in multiple fields and domains in real-life models. in the medical system, the privacy of patients records and their medical condition is critical data, therefore collaborative learning or federated learning comes into the picture. On other hand build an intelligent system assist the medical staff without sharing the data lead into the FL concept and one of the applications that are used is a brain tumor diagnosis intelligent system based on AI methods that can efficiently work in a collaborative environment.this paper will introduce some of the applications and related work in the medical field and work under the FL concept then summarize them to introduce the main limitations of their work.

Research paper thumbnail of A Survey on Patients Privacy Protection with Stganography and Visual Encryption

In this survey, thirty models for steganography and visual encryption methods have been discussed... more In this survey, thirty models for steganography and visual encryption methods have been discussed to provide patients privacy protection.

Research paper thumbnail of Architectural Heritage Images Classification Using Deep Learning with CNN

Digital documentation of cultural heritage images has emerged as an important topic in data analy... more Digital documentation of cultural heritage images has emerged as an important topic in data analysis. Increasing the size and number of images to be processed making the task of categorizing them a challenging task and may take an inordinate amount of time. This research paper proposes a solution to the mentioned challenges by classifying the subject of the image of the study using Convolutional Neural Network. Classification of available images leads to improve the management of the images dataset and enhance the search of a specific item, which helps in the tasks of studying and analysis the proper heritage object. Deep learning for architectural heritage images classification has been employed during the course of this study. The pre-trained convolutional neural networks GoogLeNet, resnet18 and resnet50 proposed to be applied on public dataset Cultural Heritage images. Experimental results have shown promising outcomes with an accuracy of "87.91", "95.47" and "95.57" respectively.

Research paper thumbnail of The effect of topology adaptation on search performance in overlay networks

Research paper thumbnail of Palm Vein Identification based on hybrid features selection model

ArXiv, 2020

Palm vein identification (PVI) is a modern biometric security technique used for increasing secur... more Palm vein identification (PVI) is a modern biometric security technique used for increasing security and authentication systems. The key characteristics of palm vein patterns include, its uniqueness to each individual, unforgettable, non-intrusive and cannot be taken by an unauthorized person. However, the extracted features from the palm vein pattern are huge with high redundancy. In this paper, we propose a combine model of two-Dimensional Discrete Wavelet Transform, Principal Component Analysis (PCA), and Particle Swarm Optimization (PSO) (2D-DWTPP) to enhance prediction of vein palm patterns. The 2D-DWT Extracts features from palm vein images, PCA reduces the redundancy in palm vein features. The system has been trained in selecting high reverent features based on the wrapper model. The PSO feeds wrapper model by an optimal subset of features. The proposed system uses four classifiers as an objective function to determine VPI which include Support Vector Machine (SVM), K Nearest...

Research paper thumbnail of Wrist and Palm Vein pattern recognition using Gabor filter

Wrist and palm vein pattern can be considering as a promising biometric technique for identificat... more Wrist and palm vein pattern can be considering as a promising biometric technique for identification, through the study of the pattern of blood vessels that visible from the skin. This kind of recognition is very important for many reasons; vein exists inside of the human body makes it difficult to change pattern like shift the position of vein from part to another, unlike another method of techniques of recognition. In this paper work wrist and palm vein are studied for identification and verification, this work divided into three phases preprocessing, features extraction and recognition. in preprocessing phase apply resize and image “enhancement” using “CLAHE and 2-D Gaussian high pass filter”, the features of each image are extracted by using Gabor filters. LDA and PCA are used to minimize the dimension of the features set. For vein image, identification used Euclidean distance to measure the similarity. The average CRR of vein palm in proposed work is 94.49% and the average CRR ...

Research paper thumbnail of The Effect of the Topology Adaptation on Search Performance in Overlay Network

Expert Clouds and Applications, 2021

The work presented in this research paper has focused on the effect of network topology adaptatio... more The work presented in this research paper has focused on the effect of network topology adaptation on search performance in peer to peer overlay network. Guided search vs. blind search have been studied with the aim of improving the search results and decreasing the time a search message would take to reach the destination. The network has been formulated as a bi-direction graph with vertices represent network nodes and edges represent connections. The level of network subject of this study is on application layer, that means two nodes are connected if they know each other contact addresses. A good example of this kind of network is the social network where all the lower layers are hidden from the end user. Two different search algorithms have been studied under these circumstances, namely: depth first algorithm and breadth first algorithm. Furthermore, the algorithms performance is examined under random topology (scale free network topology) and under topology adaptation. A simulation scenario has been designed to investigate the fidelity of the system and study the suggested solutions. Simulation results have shown that the search algorithms are performing better under topology adaptation in terms of results quality and search time.

Research paper thumbnail of Diabetic Retinopathy Diagnosis based on Convolutional Neural Network

Journal of Physics: Conference Series, 2021

Research paper thumbnail of Convolutional Neural Network for Satellite Image Classification

Intelligent Information and Database Systems: Recent Developments, 2019

Multimedia applications and processing is an exciting topic, and it is a key of many applications... more Multimedia applications and processing is an exciting topic, and it is a key of many applications of artificial intelligent like video summarization, image retrieval or image classification. A convolutional neural networks have been successfully applied on multimedia approaches and used to create a system able to handle the classification without any human’s interactions. In this paper, we produce effective methods for satellite image classification that are based on deep learning and using the convolutional neural network for features extraction by using AlexNet, VGG19, GoogLeNet and Resnet50 pretraining models. The Resnet50 model achieves a promising result than other models on three different dataset SAT4, SAT6 and UC Merced Land. The accuracy of classification of this model for UC Merced Land dataset is 98%, for SAT4 is 95.8%, and the result for SAT6 is 94.1%.

Research paper thumbnail of Cluster-based information retrieval by using (K-means)- hierarchical parallel genetic algorithms approach

TELKOMNIKA (Telecommunication Computing Electronics and Control), 2021

Cluster-based information retrieval is one of the Information retrieval(IR) tools that organize, ... more Cluster-based information retrieval is one of the Information retrieval(IR) tools that organize, extract features and categorize the web documents according to their similarity. Unlike traditional approaches, cluster-based IR is fast in processing large datasets of document. To improve the quality of retrieved documents, increase the efficiency of IR and reduce irrelevant documents from user search. in this paper, we proposed a (K-means)-Hierarchical Parallel Genetic Algorithms Approach (HPGA) that combines the K-means clustering algorithm with hybrid PG of multi-deme and master/slave PG algorithms. K-means uses to cluster the population to k subpopulations then take most clusters relevant to the query to manipulate in a parallel way by the two levels of genetic parallelism, thus, irrelevant documents will not be included in subpopulations, as a way to improve the quality of results. Three common datasets (NLP, CISI, and CACM) are used to compute the recall, precision, and F-measure averages. Finally, we compared the precision values of three datasets with Genetic-IR and classic-IR. The proposed approach precision improvements with IR-GA were 45% in the CACM, 27% in the CISI, and 25% in the NLP. While, by comparing with Classic-IR, (k-means)-HPGA got 47% in CACM, 28% in CISI, and 34% in NLP.