Pub Date : 2022-05-28DOI: 10.1109/SETIT54465.2022.9875530
Thair A. Kadhim, Nadia Smaoui Zghal, Walid Hariri, Dalenda Ben Aissa
Face Recognition (FR) has been widely used in the tracking and identification of individuals. However, because face images vary depending on expressions, ages, individual locations, and lighting conditions, the facial photographs of the same sample may appear to be distinct, making face recognition more difficult. Deep learning (DL) is now a suitable solution for face recognition and computer vision. In this study, features and traits were extracted from images of a large data set (called FERET) consisting of 14,126 images that were divided into 80% for training data and 20% for testing data using a Convolutional Neural Network (CNN). The CNN is first pre-trained using supplementary data for the purpose of obtaining updated weights, and then trained with the target dataset in order to uncover more hidden facial characteristics. Three different deep learning models are implemented: AlexNet, Resnet18, and DenseNet-161. The performance of these models is compared experimentally in terms of their classification accuracy. The obtained results showed that the DenseNet-161 has the highest accuracy of 98.6%, while the accuracies of the Resnet18 and AlexNet are 96.3% and 93.3%, respectively.
{"title":"Face Recognition in Multiple Variations Using Deep Learning and Convolutional Neural Networks","authors":"Thair A. Kadhim, Nadia Smaoui Zghal, Walid Hariri, Dalenda Ben Aissa","doi":"10.1109/SETIT54465.2022.9875530","DOIUrl":"https://doi.org/10.1109/SETIT54465.2022.9875530","url":null,"abstract":"Face Recognition (FR) has been widely used in the tracking and identification of individuals. However, because face images vary depending on expressions, ages, individual locations, and lighting conditions, the facial photographs of the same sample may appear to be distinct, making face recognition more difficult. Deep learning (DL) is now a suitable solution for face recognition and computer vision. In this study, features and traits were extracted from images of a large data set (called FERET) consisting of 14,126 images that were divided into 80% for training data and 20% for testing data using a Convolutional Neural Network (CNN). The CNN is first pre-trained using supplementary data for the purpose of obtaining updated weights, and then trained with the target dataset in order to uncover more hidden facial characteristics. Three different deep learning models are implemented: AlexNet, Resnet18, and DenseNet-161. The performance of these models is compared experimentally in terms of their classification accuracy. The obtained results showed that the DenseNet-161 has the highest accuracy of 98.6%, while the accuracies of the Resnet18 and AlexNet are 96.3% and 93.3%, respectively.","PeriodicalId":126155,"journal":{"name":"2022 IEEE 9th International Conference on Sciences of Electronics, Technologies of Information and Telecommunications (SETIT)","volume":"52 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-05-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134205704","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-05-28DOI: 10.1109/SETIT54465.2022.9875452
M. Losavio, P. Pastukov, Svetlana Polyakova
Multimedia objects may be used in adjudicative fora as direct or circumstantial evidence along the evidentiary spectrum. There are risks under US and Russian law to justice in the analysis of multimedia objects. This are issues for everyone in an electronic world.
{"title":"Multimedia Objects and Forensic Determinations of Criminal Responsibility","authors":"M. Losavio, P. Pastukov, Svetlana Polyakova","doi":"10.1109/SETIT54465.2022.9875452","DOIUrl":"https://doi.org/10.1109/SETIT54465.2022.9875452","url":null,"abstract":"Multimedia objects may be used in adjudicative fora as direct or circumstantial evidence along the evidentiary spectrum. There are risks under US and Russian law to justice in the analysis of multimedia objects. This are issues for everyone in an electronic world.","PeriodicalId":126155,"journal":{"name":"2022 IEEE 9th International Conference on Sciences of Electronics, Technologies of Information and Telecommunications (SETIT)","volume":"49 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-05-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133407624","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-05-28DOI: 10.1109/SETIT54465.2022.9875732
Abdallah Abuzaid, Ayman Atia
An evaluation of the deep learning neural network in artificial intelligence (AI) technologies is proposed to provide a rapid recognition and immediate proper classification of the different beef retail cuts (Liver, Roast Beef, Beef Chuck, Beef Round, Strip-Lion, Round Fillet, Beef Flank) to classify them accordingly. The problem is that many of the modern consumers face difficulties in recognizing the different retail beef cuts. Thus, a solution was created through collecting a dataset for retail cuts and creating an algorithm to classify them. A dataset, which is available for public, of 7 different beef retail cuts was proposed. This dataset includes colored images from our own image library, a total of 1638 images for validation testing and training are used for this project. The deep learning neural network algorithm-based model was able to identify specific beef retail cuts. 5 models were used in this paper to reach the highest accuracy for the classification of our dataset (MobileNet, ResNet50, InceptionV3, EfficientNetB0 and our customized model). EffecientNetB0 pretrained model is one of the best and easiest pretrained models in Keras CNN. The employment of this model, after training and data augmentation techniques, was able to achieve the highest accuracy by a 99.81%. Based on our trained model and the huge results, deep learning technology evidently showed a promising effort for beef cuts recognition in the meat science industry.
{"title":"Exploring and Classifying Beef Retail Cuts Using Transfer Learning","authors":"Abdallah Abuzaid, Ayman Atia","doi":"10.1109/SETIT54465.2022.9875732","DOIUrl":"https://doi.org/10.1109/SETIT54465.2022.9875732","url":null,"abstract":"An evaluation of the deep learning neural network in artificial intelligence (AI) technologies is proposed to provide a rapid recognition and immediate proper classification of the different beef retail cuts (Liver, Roast Beef, Beef Chuck, Beef Round, Strip-Lion, Round Fillet, Beef Flank) to classify them accordingly. The problem is that many of the modern consumers face difficulties in recognizing the different retail beef cuts. Thus, a solution was created through collecting a dataset for retail cuts and creating an algorithm to classify them. A dataset, which is available for public, of 7 different beef retail cuts was proposed. This dataset includes colored images from our own image library, a total of 1638 images for validation testing and training are used for this project. The deep learning neural network algorithm-based model was able to identify specific beef retail cuts. 5 models were used in this paper to reach the highest accuracy for the classification of our dataset (MobileNet, ResNet50, InceptionV3, EfficientNetB0 and our customized model). EffecientNetB0 pretrained model is one of the best and easiest pretrained models in Keras CNN. The employment of this model, after training and data augmentation techniques, was able to achieve the highest accuracy by a 99.81%. Based on our trained model and the huge results, deep learning technology evidently showed a promising effort for beef cuts recognition in the meat science industry.","PeriodicalId":126155,"journal":{"name":"2022 IEEE 9th International Conference on Sciences of Electronics, Technologies of Information and Telecommunications (SETIT)","volume":"134 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-05-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121321454","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-05-28DOI: 10.1109/SETIT54465.2022.9875713
Nesrine Khelifi, Soufien Jaffali, Fatma Mallouli, Aya Hellal, H. Youssef
The Internet of Things (IoT) is primarily based on constrained devices in memory which are connected by lossy links. These networks are commonly known as Low-power and Lossy Networks (LLNs). Few years ago, the IPv6 Routing Protocol for Low-power and Lossy Networks (RPL) was proposed by IETF as the routing standard designed for LLNs in which both nodes and their interconnects are constrained. Many Applications in IoT need a support of routing for mobile nodes. However, RPL have a slow response to topology changes. In fact, it is necessary to make a modification for RPL to support the mobility of IoT devices in the network. In this context, many improvements have been made in order to make RPL suitable for mobility in multiple use cases. We aim to provide an insight into relevant recent works around RPL protocol under mobility. We highlighted the factors and parameters affected, the scenario and the gain of each studied solution. We draw also some lessons learned and gave useful guidelines for future developments.
{"title":"A survey on Mobility Under RPL Routing Protocol","authors":"Nesrine Khelifi, Soufien Jaffali, Fatma Mallouli, Aya Hellal, H. Youssef","doi":"10.1109/SETIT54465.2022.9875713","DOIUrl":"https://doi.org/10.1109/SETIT54465.2022.9875713","url":null,"abstract":"The Internet of Things (IoT) is primarily based on constrained devices in memory which are connected by lossy links. These networks are commonly known as Low-power and Lossy Networks (LLNs). Few years ago, the IPv6 Routing Protocol for Low-power and Lossy Networks (RPL) was proposed by IETF as the routing standard designed for LLNs in which both nodes and their interconnects are constrained. Many Applications in IoT need a support of routing for mobile nodes. However, RPL have a slow response to topology changes. In fact, it is necessary to make a modification for RPL to support the mobility of IoT devices in the network. In this context, many improvements have been made in order to make RPL suitable for mobility in multiple use cases. We aim to provide an insight into relevant recent works around RPL protocol under mobility. We highlighted the factors and parameters affected, the scenario and the gain of each studied solution. We draw also some lessons learned and gave useful guidelines for future developments.","PeriodicalId":126155,"journal":{"name":"2022 IEEE 9th International Conference on Sciences of Electronics, Technologies of Information and Telecommunications (SETIT)","volume":"108 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-05-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122409013","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-05-28DOI: 10.1109/SETIT54465.2022.9875868
Amine Kherraki, Muaz Maqbool, Rajae El Ouazzani
Recently, Intelligent Transportation Systems (ITS) has obtained a large interest in scientific research, due to the intense increase in the number of vehicles in the traffic scene. In fact, ITS is able to solve many problems using computer vision, such as traffic signs recognition. Lately, Convolutional Neural Network (CNN) approaches have been applied in traffic signs classification due to the robust feature extraction with size and rotational invariance. However, the majority of the work realized in this task focuses on accuracy rather than the number of required parameters, which makes applications of traffic signs classification inappropriate for real-time uses. To solve this issue, we propose a lighter and efficient CNN model called Lightweight Traffic Signs Network (LTSNet), which requires fewer parameters while having good accuracy. The experiments are performed on the public benchmark dataset of traffic signs GTSRB to prove the effectiveness of our proposed network in terms of accuracy and parameter requirements.
{"title":"Lightweight and Efficient Convolutional Neural Network for Traffic Signs Classification","authors":"Amine Kherraki, Muaz Maqbool, Rajae El Ouazzani","doi":"10.1109/SETIT54465.2022.9875868","DOIUrl":"https://doi.org/10.1109/SETIT54465.2022.9875868","url":null,"abstract":"Recently, Intelligent Transportation Systems (ITS) has obtained a large interest in scientific research, due to the intense increase in the number of vehicles in the traffic scene. In fact, ITS is able to solve many problems using computer vision, such as traffic signs recognition. Lately, Convolutional Neural Network (CNN) approaches have been applied in traffic signs classification due to the robust feature extraction with size and rotational invariance. However, the majority of the work realized in this task focuses on accuracy rather than the number of required parameters, which makes applications of traffic signs classification inappropriate for real-time uses. To solve this issue, we propose a lighter and efficient CNN model called Lightweight Traffic Signs Network (LTSNet), which requires fewer parameters while having good accuracy. The experiments are performed on the public benchmark dataset of traffic signs GTSRB to prove the effectiveness of our proposed network in terms of accuracy and parameter requirements.","PeriodicalId":126155,"journal":{"name":"2022 IEEE 9th International Conference on Sciences of Electronics, Technologies of Information and Telecommunications (SETIT)","volume":"77 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-05-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126037154","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-05-28DOI: 10.1109/SETIT54465.2022.9875673
S. Bennour, D. Mezghani, A. Mami
In this paper, we conduct a comparative study between two numerical analysis methods of RF circuit, Phase Jump-Fast Modal Transform Wave Concept Iterative Process "PJ-FMT-WCIP" and 2D Discrete Wavelet Transform-WCIP "2D DWT-WCIP". Thus, we propose to use the two techniques for the analysis of the same planar structure representing a microwave filter. The first part of this paper is reserved to a brief overview of both methods. The second part is reserved to the use of the methods "PJ-FMT WCIP" and "2D DWT-WCIP" to the analysis of the same structure in order to compare the performances of the two-optimization methods in term of convergence, optimization ratio, average error and computation time. We will end up presenting a summary study in order to conclude on the advantages and limitations of each method.
{"title":"Numerical analysis of a Microwave Filter using PJ-FMT-WCIP and 2D DWT-WCIP","authors":"S. Bennour, D. Mezghani, A. Mami","doi":"10.1109/SETIT54465.2022.9875673","DOIUrl":"https://doi.org/10.1109/SETIT54465.2022.9875673","url":null,"abstract":"In this paper, we conduct a comparative study between two numerical analysis methods of RF circuit, Phase Jump-Fast Modal Transform Wave Concept Iterative Process \"PJ-FMT-WCIP\" and 2D Discrete Wavelet Transform-WCIP \"2D DWT-WCIP\". Thus, we propose to use the two techniques for the analysis of the same planar structure representing a microwave filter. The first part of this paper is reserved to a brief overview of both methods. The second part is reserved to the use of the methods \"PJ-FMT WCIP\" and \"2D DWT-WCIP\" to the analysis of the same structure in order to compare the performances of the two-optimization methods in term of convergence, optimization ratio, average error and computation time. We will end up presenting a summary study in order to conclude on the advantages and limitations of each method.","PeriodicalId":126155,"journal":{"name":"2022 IEEE 9th International Conference on Sciences of Electronics, Technologies of Information and Telecommunications (SETIT)","volume":"07 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-05-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129783516","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-05-28DOI: 10.1109/SETIT54465.2022.9875681
Rafik Amari, Abdelkarim Mars, M. Zrigui
Despite advances in speech recognition technology, Arabic speech recognition remains largely unsolved due to its many difficulties and challenges. The performance of the best existing recognizers is much lower than those developed in English. Deep Neural Networks (DNNs) have shown excellent performance in acoustic modeling for speech recognition. In this work, a new discontinuous Arabic speech recognition model is proposed. It associates a deep convolutional neural network (CNN) architecture with a long-term bi-directional memory (BLSTM). The optimal network structure and training strategy for the model are examined. The Arabic Speech Corpus of Isolated Words (ASDS) and the Spoken Arabic Digits (SAD) database were used for all experiments. The results demonstrate the strength and benefits of the CNN-BLSTM method, which provides the best detection accuracy.
{"title":"Arabic speech recognition based on a CNN-BLSTM combination","authors":"Rafik Amari, Abdelkarim Mars, M. Zrigui","doi":"10.1109/SETIT54465.2022.9875681","DOIUrl":"https://doi.org/10.1109/SETIT54465.2022.9875681","url":null,"abstract":"Despite advances in speech recognition technology, Arabic speech recognition remains largely unsolved due to its many difficulties and challenges. The performance of the best existing recognizers is much lower than those developed in English. Deep Neural Networks (DNNs) have shown excellent performance in acoustic modeling for speech recognition. In this work, a new discontinuous Arabic speech recognition model is proposed. It associates a deep convolutional neural network (CNN) architecture with a long-term bi-directional memory (BLSTM). The optimal network structure and training strategy for the model are examined. The Arabic Speech Corpus of Isolated Words (ASDS) and the Spoken Arabic Digits (SAD) database were used for all experiments. The results demonstrate the strength and benefits of the CNN-BLSTM method, which provides the best detection accuracy.","PeriodicalId":126155,"journal":{"name":"2022 IEEE 9th International Conference on Sciences of Electronics, Technologies of Information and Telecommunications (SETIT)","volume":"30 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-05-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125741869","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-05-28DOI: 10.1109/SETIT54465.2022.9875449
Raghda Alilouch, F. Slaoui-Hasnaoui
The detection of faults on transmission lines is an essential and important part of power system monitoring and control. Providing high-quality electric power requires an efficient, reliable, and intelligent protection, a system that can handle transmission line outages that result from a variety of random reasons. This system will allow a fast detection and gives an accurate fault location, thus isolating the faulted section and avoiding catastrophic damage to material and human assets.In this paper, the use of artificial neural network algorithm ANN is proposed, which can be implemented in a numerical relay, this approach has been noticed by many researchers in the field of power system protection. ANN is trained using the measurements of the three-phase currents and voltages. The feedforward neural network was used together with the backpropagation algorithm to detect, classify, and localize the fault. To validate the choice of the neural network, a detailed analysis was performed with a different number of hidden layers. Simulation results show that the present artificial neural network-based method performs satisfactorily in detecting, classifying, and locating faults on transmission lines. To test the proposed method, different fault scenarios were simulated
{"title":"Intelligent Relay Based on Artificial Neural Networks ANN for Transmission Line","authors":"Raghda Alilouch, F. Slaoui-Hasnaoui","doi":"10.1109/SETIT54465.2022.9875449","DOIUrl":"https://doi.org/10.1109/SETIT54465.2022.9875449","url":null,"abstract":"The detection of faults on transmission lines is an essential and important part of power system monitoring and control. Providing high-quality electric power requires an efficient, reliable, and intelligent protection, a system that can handle transmission line outages that result from a variety of random reasons. This system will allow a fast detection and gives an accurate fault location, thus isolating the faulted section and avoiding catastrophic damage to material and human assets.In this paper, the use of artificial neural network algorithm ANN is proposed, which can be implemented in a numerical relay, this approach has been noticed by many researchers in the field of power system protection. ANN is trained using the measurements of the three-phase currents and voltages. The feedforward neural network was used together with the backpropagation algorithm to detect, classify, and localize the fault. To validate the choice of the neural network, a detailed analysis was performed with a different number of hidden layers. Simulation results show that the present artificial neural network-based method performs satisfactorily in detecting, classifying, and locating faults on transmission lines. To test the proposed method, different fault scenarios were simulated","PeriodicalId":126155,"journal":{"name":"2022 IEEE 9th International Conference on Sciences of Electronics, Technologies of Information and Telecommunications (SETIT)","volume":"34 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-05-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129366578","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-05-28DOI: 10.1109/SETIT54465.2022.9875614
Mohamed Wajdi Ouertani, G. Manita, O. Korbaa
Finding the most suitable sites for charging stations (CSs) presents the main challenge to expand the usage of electric vehicle (EV). For this reason, we propose a new model to solve the problem of CSs placement by taking into consideration several parameters. In this work, the travel cost, maintenance, and installation charges of several types of stations are the main variables for calculating the objective function. In addition, we take into account two important constraints: budget limitation and charging station capacity. This problem is described as an NP-hard problem, hence the need to use an optimization method based on meta-heuristics that have proven their effectiveness before.For this purpose, we propose an Improved Antlion Algorithm (IALO) combined with a search heuristic. To assess this approach, we compare it with the most commonly used and recent optimization algorithms, in particular the Genetic Algorithm (GA), Particle Swarm Optimization (PSO), Grey Wolf Optimizer (GWO), Whale Optimization Algorithm (WOA) and Atom Search Optimization (ASO). Experimental results show that improved antlion algorithm provide better solutions than algorithms mentioned above.
{"title":"Improved Antlion Algorithm for Electric Vehicle Charging Station Placement","authors":"Mohamed Wajdi Ouertani, G. Manita, O. Korbaa","doi":"10.1109/SETIT54465.2022.9875614","DOIUrl":"https://doi.org/10.1109/SETIT54465.2022.9875614","url":null,"abstract":"Finding the most suitable sites for charging stations (CSs) presents the main challenge to expand the usage of electric vehicle (EV). For this reason, we propose a new model to solve the problem of CSs placement by taking into consideration several parameters. In this work, the travel cost, maintenance, and installation charges of several types of stations are the main variables for calculating the objective function. In addition, we take into account two important constraints: budget limitation and charging station capacity. This problem is described as an NP-hard problem, hence the need to use an optimization method based on meta-heuristics that have proven their effectiveness before.For this purpose, we propose an Improved Antlion Algorithm (IALO) combined with a search heuristic. To assess this approach, we compare it with the most commonly used and recent optimization algorithms, in particular the Genetic Algorithm (GA), Particle Swarm Optimization (PSO), Grey Wolf Optimizer (GWO), Whale Optimization Algorithm (WOA) and Atom Search Optimization (ASO). Experimental results show that improved antlion algorithm provide better solutions than algorithms mentioned above.","PeriodicalId":126155,"journal":{"name":"2022 IEEE 9th International Conference on Sciences of Electronics, Technologies of Information and Telecommunications (SETIT)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-05-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130271486","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-05-28DOI: 10.1109/SETIT54465.2022.9875481
Amal Hafsa, J. Malek, Mohsen Machhout
Cryptographic functions involve protecting personal information transmitted over the network against destructive attacks. The objective of this paper is to reinforce the safety of the transmission and the storage of medical images by using a hybrid framework for medical image encryption and authentication. The proposed model is based on symmetric and asymmetric approaches. To evaluate the strength of the proposed approach; our cryptosystem is tested by various tools. The experimental findings prove that our cryptographic system provides high robustness that can resist the most known cryptanalysis attacks.
{"title":"An Improved Security Approach for Medical Images and Patients’ Information Transmission","authors":"Amal Hafsa, J. Malek, Mohsen Machhout","doi":"10.1109/SETIT54465.2022.9875481","DOIUrl":"https://doi.org/10.1109/SETIT54465.2022.9875481","url":null,"abstract":"Cryptographic functions involve protecting personal information transmitted over the network against destructive attacks. The objective of this paper is to reinforce the safety of the transmission and the storage of medical images by using a hybrid framework for medical image encryption and authentication. The proposed model is based on symmetric and asymmetric approaches. To evaluate the strength of the proposed approach; our cryptosystem is tested by various tools. The experimental findings prove that our cryptographic system provides high robustness that can resist the most known cryptanalysis attacks.","PeriodicalId":126155,"journal":{"name":"2022 IEEE 9th International Conference on Sciences of Electronics, Technologies of Information and Telecommunications (SETIT)","volume":"19 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-05-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134340977","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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