The adversarial examples make the object detection model make a wrong judgment, which threatens the security of driverless cars. In this paper, by improving the Momentum Iterative Fast Gradient Sign Method (MI-FGSM), based on ensemble learning, combined with L∞ perturbation and spatial transformation, a strong transferable black-box adversarial attack algorithm for object detection model of driverless cars is proposed. Through a large number of experiments on the nuScenes driverless dataset, it is proved that the adversarial attack algorithm proposed in this paper have strong transferability, and successfully make the mainstream object detection models such as FasterRcnn, SSD, YOLOv3 make wrong judgments. Based on the adversarial attack algorithm proposed in this paper, the parametric noise injection with adversarial training is performed to generate a defense model with strong robustness. The defense model proposed in this paper significantly improves the robustness of the object detection model. It can effectively alleviate various adversarial attacks against the object detection model of driverless cars, and does not affect the accuracy of clean samples. This is of great significance for studying the application of object detection model of driverless cars in the real physical world.
{"title":"Research on a Method of Defense Adversarial Samples for Target Detection Model of Driverless Cars","authors":"Ruzhi Xu, Min Li, Xin Yang, Dexin Liu, Dawei Chen","doi":"10.34028/iajit/20/5/6","DOIUrl":"https://doi.org/10.34028/iajit/20/5/6","url":null,"abstract":"The adversarial examples make the object detection model make a wrong judgment, which threatens the security of driverless cars. In this paper, by improving the Momentum Iterative Fast Gradient Sign Method (MI-FGSM), based on ensemble learning, combined with L∞ perturbation and spatial transformation, a strong transferable black-box adversarial attack algorithm for object detection model of driverless cars is proposed. Through a large number of experiments on the nuScenes driverless dataset, it is proved that the adversarial attack algorithm proposed in this paper have strong transferability, and successfully make the mainstream object detection models such as FasterRcnn, SSD, YOLOv3 make wrong judgments. Based on the adversarial attack algorithm proposed in this paper, the parametric noise injection with adversarial training is performed to generate a defense model with strong robustness. The defense model proposed in this paper significantly improves the robustness of the object detection model. It can effectively alleviate various adversarial attacks against the object detection model of driverless cars, and does not affect the accuracy of clean samples. This is of great significance for studying the application of object detection model of driverless cars in the real physical world.","PeriodicalId":161392,"journal":{"name":"The International Arab Journal of Information Technology","volume":"44 4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130724459","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}
This paper introduces a new image encryption algorithm based on a Parallel Fuzzy Multi-Modular Chaotic Logistic Map (PFMM-CLM). Firstly, a new hybrid chaotic system is introduced by using four parallel cascade chaotic logistic maps with a dynamic parameter control to achieve a high Lyapunov exponent value and completely chaotic behavior of the bifurcation diagram. Also, the fuzzy set theory is used as a fuzzy logic selector to improve chaotic performance. The proposed algorithm has been tested as a Pseudo-Random Number Generator (PRNG). The randomness test results indicate that system has better performance and satisfied all random tests. Finally, the Arnold Cat Map with controllable iterative parameters is used to enhance the confusion concept. Due to excellent chaotic properties and good randomization test results, the proposed chaotic system is used in image encryption applications. The simulation and security analysis indicate that this proposed algorithm has a very high security performance and complexity
{"title":"A New Parallel Fuzzy Multi Modular Chaotic Logistic Map for Image Encryption","authors":"","doi":"10.34028/iajit/18/2/12","DOIUrl":"https://doi.org/10.34028/iajit/18/2/12","url":null,"abstract":"This paper introduces a new image encryption algorithm based on a Parallel Fuzzy Multi-Modular Chaotic Logistic Map (PFMM-CLM). Firstly, a new hybrid chaotic system is introduced by using four parallel cascade chaotic logistic maps with a dynamic parameter control to achieve a high Lyapunov exponent value and completely chaotic behavior of the bifurcation diagram. Also, the fuzzy set theory is used as a fuzzy logic selector to improve chaotic performance. The proposed algorithm has been tested as a Pseudo-Random Number Generator (PRNG). The randomness test results indicate that system has better performance and satisfied all random tests. Finally, the Arnold Cat Map with controllable iterative parameters is used to enhance the confusion concept. Due to excellent chaotic properties and good randomization test results, the proposed chaotic system is used in image encryption applications. The simulation and security analysis indicate that this proposed algorithm has a very high security performance and complexity","PeriodicalId":161392,"journal":{"name":"The International Arab Journal of Information Technology","volume":"13 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133284724","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 : 1900-01-01DOI: 10.26735/16585933.2018.001
نبيه طارق عبدالمجيد
{"title":"الأمن الإلكتروني ضرورة ملحة لأمن المجتمعات \"مقترح الأسرة الآمنة الخاص بتوعية المجتمع العربي الخليجي في أمن المعلومات لكل من الطلاب والوالدين\"","authors":"نبيه طارق عبدالمجيد","doi":"10.26735/16585933.2018.001","DOIUrl":"https://doi.org/10.26735/16585933.2018.001","url":null,"abstract":"","PeriodicalId":161392,"journal":{"name":"The International Arab Journal of Information Technology","volume":"96 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116596624","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}
Object detection technology is one of the key technologies for indoor service robots. However, due to the various types of objects in the indoor environment, the mutual occlusion between the objects is serious, which increases the difficulty of object detection. In view of the difficult challenges of object detection in the indoor environment, we propose an indoor three-dimensional object detection based on deep learning. Most existing 3D object detection techniques based on deep learning lack sufficient spatial and semantic information. To address this issue, the article presents an indoor 3D object detection method with enhanced spatial semantic information. This article proposes a new (Edge Convolution+) EdgeConv+, and based on it, a Shallow Spatial Information Enhancement module (SSIE) is added to Votenet. At the same time, a new attention mechanism, Convolutional Gated Non-Local+ (CGNL+), is designed to add Deep Semantic Information Enhancement module (DSIE) to Votenet. Experiments show that on the ScanNet dataset, the proposed method is 2.4% and 2.1% higher than Votenet at mAP@0.25 and mAP@0.5, respectively. Furthermore, it has strong robustness to deal with sparse point clouds
{"title":"Spatial and Semantic Information Enhancement for Indoor 3D Object Detection","authors":"Chunmei Chen, Zhiqiang Liang, Haitao Liu, Xin Liu","doi":"10.34028/iajit/20/5/15","DOIUrl":"https://doi.org/10.34028/iajit/20/5/15","url":null,"abstract":"Object detection technology is one of the key technologies for indoor service robots. However, due to the various types of objects in the indoor environment, the mutual occlusion between the objects is serious, which increases the difficulty of object detection. In view of the difficult challenges of object detection in the indoor environment, we propose an indoor three-dimensional object detection based on deep learning. Most existing 3D object detection techniques based on deep learning lack sufficient spatial and semantic information. To address this issue, the article presents an indoor 3D object detection method with enhanced spatial semantic information. This article proposes a new (Edge Convolution+) EdgeConv+, and based on it, a Shallow Spatial Information Enhancement module (SSIE) is added to Votenet. At the same time, a new attention mechanism, Convolutional Gated Non-Local+ (CGNL+), is designed to add Deep Semantic Information Enhancement module (DSIE) to Votenet. Experiments show that on the ScanNet dataset, the proposed method is 2.4% and 2.1% higher than Votenet at mAP@0.25 and mAP@0.5, respectively. Furthermore, it has strong robustness to deal with sparse point clouds","PeriodicalId":161392,"journal":{"name":"The International Arab Journal of Information Technology","volume":"18 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130079954","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}
Sensors used in image acquisition. This sensor technology is going on upgrading as per user need or as per need of an application. Multiple sensors collect the information of their respective wavelength band. But one sensor is not sufficient to acquire the complete information of one scene. To gain the overall data of one part, it becomes essential to cartel the images from multiple sources. This is achieved through merging. It is the method of merging the data from dissimilar input sources to create a more informative image compared with an image from a single input source. These are multisensor photos e.g., panchromatic and multispectral images. The first image offers spatial records whereas the lateral image offers spectral data. Through visible inspections, the panchromatic photo is clearer than a multispectral photo however the grey shade image is. Articles are greater clear however nownot recognized whereasmultispectral picture displays one of a kind shades however performing distortion. So comparing the characteristics of these two images, the resultant image is greater explanatory than these enter images. Fusion is done using different transform methods as well as the Genetic Algorithm (GA). Comparing the results obtained by these methods, the output image by the GA is clearer. The feature of the resultant image is verified through parameters such as Root Mean Square Error (RMSE), peak signal to noise ratio, Mutual Information (MI), and Spatial Frequency (SF). In the subjective analysis, some transform techniques also giving exact fused images. The hybrid approach combines the transform technique and a GA is used for image fusion. This is again compared with GA results. The same performance parameters are used. And it is observed that the Hybrid Genetic Algorithm (HGA) is superior tothe AG. Here the only RMSE parameter is considered under the fitness function of the GA so only this parameter is far better than the remaining parameters. If we consider all parameters in the fitness function of the GA then all parameters using a HGA will give better performance. This method is called a Hybrid Multiobjective Genetic Algorithm (HMOGA) [14].
{"title":"A Modified Technique of Hybrid Multiobjective Genetic Algorithm for Image Fusion","authors":"J. Kulkarni, R. Bichkar","doi":"10.34028/iajit/20/5/8","DOIUrl":"https://doi.org/10.34028/iajit/20/5/8","url":null,"abstract":"Sensors used in image acquisition. This sensor technology is going on upgrading as per user need or as per need of an application. Multiple sensors collect the information of their respective wavelength band. But one sensor is not sufficient to acquire the complete information of one scene. To gain the overall data of one part, it becomes essential to cartel the images from multiple sources. This is achieved through merging. It is the method of merging the data from dissimilar input sources to create a more informative image compared with an image from a single input source. These are multisensor photos e.g., panchromatic and multispectral images. The first image offers spatial records whereas the lateral image offers spectral data. Through visible inspections, the panchromatic photo is clearer than a multispectral photo however the grey shade image is. Articles are greater clear however nownot recognized whereasmultispectral picture displays one of a kind shades however performing distortion. So comparing the characteristics of these two images, the resultant image is greater explanatory than these enter images. Fusion is done using different transform methods as well as the Genetic Algorithm (GA). Comparing the results obtained by these methods, the output image by the GA is clearer. The feature of the resultant image is verified through parameters such as Root Mean Square Error (RMSE), peak signal to noise ratio, Mutual Information (MI), and Spatial Frequency (SF). In the subjective analysis, some transform techniques also giving exact fused images. The hybrid approach combines the transform technique and a GA is used for image fusion. This is again compared with GA results. The same performance parameters are used. And it is observed that the Hybrid Genetic Algorithm (HGA) is superior tothe AG. Here the only RMSE parameter is considered under the fitness function of the GA so only this parameter is far better than the remaining parameters. If we consider all parameters in the fitness function of the GA then all parameters using a HGA will give better performance. This method is called a Hybrid Multiobjective Genetic Algorithm (HMOGA) [14].","PeriodicalId":161392,"journal":{"name":"The International Arab Journal of Information Technology","volume":"16 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117335080","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}
Cybersecurity becomes a key concern in many applications as cybercrimes exploit system weaknesses. Cryptography helps protect sensitive data in everyday transactions and communications using passwords or tokens. However, the power of the encryption/decryption algorithms always depends on their ability to secure the data in any situation. This paper presents an effective hybrid encryption/decryption model that makes use of a biometric key along with an effective password to ensure data security. The biometric key utilised in the proposed model is generated from the fingerprint, which is a unique physical characteristic of an individual. The model initially encodes the data to be securely transmitted. The symmetric key encryption that makes use of a biometric key is applied over the encoded data. Another layer of defence is built by applying asymmetric key encryption to the encrypted data along with the details of the fingerprint. The Advanced Encryption Standard (AES) algorithm and Elgamal Encryption using Elliptical Curve Cryptography (E3C2) are used for symmetric and asymmetric encryptions. Experimental analysis is performed to analyse the model's computing speed and security and is compared with existing models and encoding/encryption techniques
随着网络犯罪利用系统弱点，网络安全在许多应用中成为一个关键问题。密码学使用密码或令牌帮助保护日常交易和通信中的敏感数据。然而，加密/解密算法的能力总是取决于它们在任何情况下保护数据的能力。本文提出了一种有效的混合加解密模型，该模型利用生物特征密钥和有效密码来保证数据的安全性。所提出的模型中使用的生物识别密钥是从指纹生成的，指纹是个体的唯一物理特征。模型首先对要安全传输的数据进行编码。利用生物识别密钥的对称密钥加密应用于编码的数据。另一层防御是通过对加密数据和指纹细节应用非对称密钥加密来构建的。对称加密和非对称加密采用AES (Advanced Encryption Standard)算法和E3C2 (Elgamal Encryption using ellipmcurve Cryptography)算法。实验分析了该模型的计算速度和安全性，并与现有模型和编码/加密技术进行了比较
{"title":"An Effective Hybrid Encryption Model using Biometric Key for Ensuring Data Security","authors":"S. Arumugam","doi":"10.34028/iajit/20/5/12","DOIUrl":"https://doi.org/10.34028/iajit/20/5/12","url":null,"abstract":"Cybersecurity becomes a key concern in many applications as cybercrimes exploit system weaknesses. Cryptography helps protect sensitive data in everyday transactions and communications using passwords or tokens. However, the power of the encryption/decryption algorithms always depends on their ability to secure the data in any situation. This paper presents an effective hybrid encryption/decryption model that makes use of a biometric key along with an effective password to ensure data security. The biometric key utilised in the proposed model is generated from the fingerprint, which is a unique physical characteristic of an individual. The model initially encodes the data to be securely transmitted. The symmetric key encryption that makes use of a biometric key is applied over the encoded data. Another layer of defence is built by applying asymmetric key encryption to the encrypted data along with the details of the fingerprint. The Advanced Encryption Standard (AES) algorithm and Elgamal Encryption using Elliptical Curve Cryptography (E3C2) are used for symmetric and asymmetric encryptions. Experimental analysis is performed to analyse the model's computing speed and security and is compared with existing models and encoding/encryption techniques","PeriodicalId":161392,"journal":{"name":"The International Arab Journal of Information Technology","volume":"65 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123461796","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}
In recent times, data transmission in electronic medium is found to be more susceptible to several attacks. The study aims to control the multi-level attacks in encryption and decryption process by using Advanced Encryption Standard (AES) algorithm based S box operations. In AES based variable key generation pattern, every round generates the new key. The generation of multiple keys strengthen the operation of AES-dynamic S box. The AES algorithm performs operation on a 128 bit plain text and utilizes identical key for decryption and encryption process. The proposed algorithm shows significant improvements in the quality of encryption and decryption. The performance of the proposed system has been analysed in accordance with delay, power consumption and number of slices. Further the efficiency of the proposed system has been compared with other existing methods such as Positive Polarity Reed Muller (PPRM), Modified Positive Polarity Reed Muller (MPPRM) Twisted Binary Decision Diagram (TBDD) and Composite Field (CF) architecture. The results exposed that the proposed system outperforms with superior performance.
{"title":"Multi-level Attack with Dynamic S-box Variable key Pattern Generation for Key Cohort Using AES","authors":"Anusha Padmavathi Rajendran, Dhanalakshmi Krishnan Sadhasivam","doi":"10.34028/iajit/20/5/7","DOIUrl":"https://doi.org/10.34028/iajit/20/5/7","url":null,"abstract":"In recent times, data transmission in electronic medium is found to be more susceptible to several attacks. The study aims to control the multi-level attacks in encryption and decryption process by using Advanced Encryption Standard (AES) algorithm based S box operations. In AES based variable key generation pattern, every round generates the new key. The generation of multiple keys strengthen the operation of AES-dynamic S box. The AES algorithm performs operation on a 128 bit plain text and utilizes identical key for decryption and encryption process. The proposed algorithm shows significant improvements in the quality of encryption and decryption. The performance of the proposed system has been analysed in accordance with delay, power consumption and number of slices. Further the efficiency of the proposed system has been compared with other existing methods such as Positive Polarity Reed Muller (PPRM), Modified Positive Polarity Reed Muller (MPPRM) Twisted Binary Decision Diagram (TBDD) and Composite Field (CF) architecture. The results exposed that the proposed system outperforms with superior performance.","PeriodicalId":161392,"journal":{"name":"The International Arab Journal of Information Technology","volume":"15 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125013765","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}
In today's world, fires in homes and commercial places are a serious problem that can harm the local environment as well as jeopardize people's property and lives. This study predicts the sensor dataset gained from an integrated sensor framework with an artificial neural network. The major goal of this research was to identify a convenient way to encode input data that balanced information loss with simplicity. This paper developed an Artificial Neural Network (ANN) model and applied it to the fire dataset collected from the Integrated Sensor System (ISS). Every neuron of the model will learn and hold weights that weigh information, which provides better accuracy. To mitigate loss functions and improve accuracy, various activation functions such as Sigmoid, Relu, and optimizer Stochastic Gradient Descent (SGD), Adam, and Adamax are used in the designed model. The results demonstrated that the prediction accuracy of the ANN model with Adam as the optimizer is better than that of the other two optimizers. The findings also show that the ANN model performs well in terms of prediction accuracy and is also better suited to the sensor fire dataset
{"title":"Performance Comparison of Multiple ANN Optimizer on IoT-enabled Sensor Fire Dataset","authors":"Sudip Suklabaidya, Indrani Das","doi":"10.34028/iajit/20/5/9","DOIUrl":"https://doi.org/10.34028/iajit/20/5/9","url":null,"abstract":"In today's world, fires in homes and commercial places are a serious problem that can harm the local environment as well as jeopardize people's property and lives. This study predicts the sensor dataset gained from an integrated sensor framework with an artificial neural network. The major goal of this research was to identify a convenient way to encode input data that balanced information loss with simplicity. This paper developed an Artificial Neural Network (ANN) model and applied it to the fire dataset collected from the Integrated Sensor System (ISS). Every neuron of the model will learn and hold weights that weigh information, which provides better accuracy. To mitigate loss functions and improve accuracy, various activation functions such as Sigmoid, Relu, and optimizer Stochastic Gradient Descent (SGD), Adam, and Adamax are used in the designed model. The results demonstrated that the prediction accuracy of the ANN model with Adam as the optimizer is better than that of the other two optimizers. The findings also show that the ANN model performs well in terms of prediction accuracy and is also better suited to the sensor fire dataset","PeriodicalId":161392,"journal":{"name":"The International Arab Journal of Information Technology","volume":"70 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127310729","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 : 1900-01-01DOI: 10.26735/16585933.2018.003
أ بوفاس الشريف, مريم عمّي
{"title":"اجراءات القبول والالتحاق ببرامج الماجستير في الجامعات المتناظرة المحلية والإقليمية بكليات علوم الحاسب : دراسة مقارنة","authors":"أ بوفاس الشريف, مريم عمّي","doi":"10.26735/16585933.2018.003","DOIUrl":"https://doi.org/10.26735/16585933.2018.003","url":null,"abstract":"","PeriodicalId":161392,"journal":{"name":"The International Arab Journal of Information Technology","volume":"52 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130283123","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 : 1900-01-01DOI: 10.26735/16585933.2018.005
أحمد عبدالله أحمد موالا العُمري, حسين يوسف أبو منصور
{"title":"التعاملات الإلكترونية ودورها في التنمية الإدارية من وجهة نظر العاملين بإمارة منطقة الباحة","authors":"أحمد عبدالله أحمد موالا العُمري, حسين يوسف أبو منصور","doi":"10.26735/16585933.2018.005","DOIUrl":"https://doi.org/10.26735/16585933.2018.005","url":null,"abstract":"","PeriodicalId":161392,"journal":{"name":"The International Arab Journal of Information Technology","volume":"13 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130140665","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}
京公网安备 11010802042870号
京ICP备2023020795号-1
扫码关注我们
求助内容：
应助结果提醒方式：