The Internet of Things (IoT) is one of the most important modern technologies that have attracted the most interesting areas of life, whether industrial, academic, or other, in recent years. The main goal is to integrate the physical world with the digital world through a seamless ecosystem, and this constitutes a new era for the Internet. This technology provides high commercial value to enterprises as it provides many opportunities in many applications such as energy, health, and other sectors. However, this technology suffers from many security problems, as it is considered the biggest challenge due to its complex environment and the limited resources of its devices. There is a lot of research to find successful security solutions in IoT, in this research, a proposed solution to secure IoT systems using Advanced Encryption Standard (AES) technology is achieved. Some sensors were linked as an example of the Internet of Things. The data is received by the card created and developed by Espressif Systems (ESP32) module, where its encrypted then sends to the internet site through an authorized person to be received from anywhere, and it is also possible to receive it via a published IP which is announced within the internal network of the ESP32 device module. The decryption part is proposed at last to find out the true values of the sensors. The proposed approach shows good secured and balanced results at the end
物联网(IoT)是最重要的现代技术之一，近年来吸引了最有趣的生活领域，无论是工业，学术还是其他领域。主要目标是通过无缝的生态系统将物理世界与数字世界融为一体，这构成了互联网的新时代。该技术为企业提供了很高的商业价值，因为它在能源、卫生和其他领域等许多应用中提供了许多机会。然而，由于其复杂的环境和有限的设备资源，该技术被认为是最大的挑战，因此存在许多安全问题。有很多研究在物联网中找到成功的安全解决方案，在本研究中，提出了使用高级加密标准(AES)技术保护物联网系统的解决方案。一些传感器被连接起来作为物联网的一个例子。由Espressif Systems (ESP32)模块创建和开发的卡片接收数据，然后通过授权人员将其加密发送到网站，以便从任何地方接收，也可以通过ESP32设备模块内部网络中公布的已发布IP接收数据。最后提出了解密部分，以找出传感器的真实值。结果表明，该方法具有良好的安全性和均衡性
{"title":"IoT security using AES encryption technology based ESP32 platform","authors":"M. Al-Mashhadani, M. Shujaa","doi":"10.34028/iajit/19/2/8","DOIUrl":"https://doi.org/10.34028/iajit/19/2/8","url":null,"abstract":"The Internet of Things (IoT) is one of the most important modern technologies that have attracted the most interesting areas of life, whether industrial, academic, or other, in recent years. The main goal is to integrate the physical world with the digital world through a seamless ecosystem, and this constitutes a new era for the Internet. This technology provides high commercial value to enterprises as it provides many opportunities in many applications such as energy, health, and other sectors. However, this technology suffers from many security problems, as it is considered the biggest challenge due to its complex environment and the limited resources of its devices. There is a lot of research to find successful security solutions in IoT, in this research, a proposed solution to secure IoT systems using Advanced Encryption Standard (AES) technology is achieved. Some sensors were linked as an example of the Internet of Things. The data is received by the card created and developed by Espressif Systems (ESP32) module, where its encrypted then sends to the internet site through an authorized person to be received from anywhere, and it is also possible to receive it via a published IP which is announced within the internal network of the ESP32 device module. The decryption part is proposed at last to find out the true values of the sensors. The proposed approach shows good secured and balanced results at the end","PeriodicalId":13624,"journal":{"name":"Int. Arab J. Inf. Technol.","volume":"70 1","pages":"214-223"},"PeriodicalIF":0.0,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"86268238","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}
Language variety identification tends to identify lexical and semantic variations in different varieties of a single language. Language variety identification helps build the linguistic profile of an author from written text which can be used for cyber forensics and marketing purposes. Investigating previous efforts for language variety identification, we hardly find any study that experiments with transfer learning approaches and/or performs a thorough comparison of different deep learning approaches on a range of benchmark datasets. So, to bridge this gap, we propose transfer learning approaches for language variety identification tasks and perform an extensive comparison of them with deep learning approaches on multiple varieties of four widely spoken languages, i.e., Arabic, English, Portuguese, and Spanish. This research has treated this task as a binary classification problem (Portuguese) and multi-class classification problem (Arabic, English, and Spanish). We applied two transfer learning Bidirectional Encoder Representations from Transformers (BERT), Universal Language Model Fine-tuning (ULMFiT), three deep learning-Convolutional Neural Networks (CNN), Bidirectional Long Short Term Memory (Bi-LSTM), Gated Recurrent Units (GRU), and an ensemble approach for identifying different varieties. A thorough comparison between the approaches suggests that the transfer learning based ULMFiT model outperforms all other approaches and produces the best accuracy results for binary and multi-class language variety identification tasks.
{"title":"Multi-Lingual Language Variety Identification using Conventional Deep Learning and Transfer Learning Approaches","authors":"Sameeah Noreen Hameed, M. Ashraf, Yanan Qiao","doi":"10.34028/iajit/19/5/1","DOIUrl":"https://doi.org/10.34028/iajit/19/5/1","url":null,"abstract":"Language variety identification tends to identify lexical and semantic variations in different varieties of a single language. Language variety identification helps build the linguistic profile of an author from written text which can be used for cyber forensics and marketing purposes. Investigating previous efforts for language variety identification, we hardly find any study that experiments with transfer learning approaches and/or performs a thorough comparison of different deep learning approaches on a range of benchmark datasets. So, to bridge this gap, we propose transfer learning approaches for language variety identification tasks and perform an extensive comparison of them with deep learning approaches on multiple varieties of four widely spoken languages, i.e., Arabic, English, Portuguese, and Spanish. This research has treated this task as a binary classification problem (Portuguese) and multi-class classification problem (Arabic, English, and Spanish). We applied two transfer learning Bidirectional Encoder Representations from Transformers (BERT), Universal Language Model Fine-tuning (ULMFiT), three deep learning-Convolutional Neural Networks (CNN), Bidirectional Long Short Term Memory (Bi-LSTM), Gated Recurrent Units (GRU), and an ensemble approach for identifying different varieties. A thorough comparison between the approaches suggests that the transfer learning based ULMFiT model outperforms all other approaches and produces the best accuracy results for binary and multi-class language variety identification tasks.","PeriodicalId":13624,"journal":{"name":"Int. Arab J. Inf. Technol.","volume":"13 1","pages":"705-712"},"PeriodicalIF":0.0,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"82771506","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 proposes an alternate approach to startups to estimate the size of software product to be built by them using the Software Product Points (SPP). Dataset from 20 software projects of a startup company in India was used to validate the proposed approach and learn lessons out of it. The estimated software product points and the project efforts were found to have a strong positive correlation, thereby indicating the suitability of the proposed approach for its utility by the managers of future software projects of startups. We also briefly outline the implications for project managers of startups and scope for future research.
{"title":"A Simplified Alternate Approach to Estimate Software Size of Startups","authors":"C. Sridharan, S. Parthasarathy","doi":"10.34028/iajit/19/4/12","DOIUrl":"https://doi.org/10.34028/iajit/19/4/12","url":null,"abstract":"This paper proposes an alternate approach to startups to estimate the size of software product to be built by them using the Software Product Points (SPP). Dataset from 20 software projects of a startup company in India was used to validate the proposed approach and learn lessons out of it. The estimated software product points and the project efforts were found to have a strong positive correlation, thereby indicating the suitability of the proposed approach for its utility by the managers of future software projects of startups. We also briefly outline the implications for project managers of startups and scope for future research.","PeriodicalId":13624,"journal":{"name":"Int. Arab J. Inf. Technol.","volume":"27 1","pages":"674-680"},"PeriodicalIF":0.0,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"83323111","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}
Yetunde Akinwumi, J. Ayeni, S. Arekete, M. Odim, A. Ogunde, B. Oguntunde
Extensible Markup Language (XML) is the standard medium for data exchange among businesses over the Internet, hence the need for effective management. However, since XML was not designed for storage and retrieval, its management has become an open research area in the database community. Existing mapping techniques for XML-to-relational database adopt either the structural mapping or the model mapping. Though numerous mapping approaches have been developed, mapping and reconstruction time had been problematic, especially when the document size is large and can hardly fit into main memory. In this research, an application codenamed XAPP, a new lightweight application that adopts a novel model mapping approach was developed using Simple API for XML (SAX) parser. XAPP accepts a document with or without Document Type Definition (DTD). It implements two algorithms: one maps XML data to a relational database and improves mapping time, and the other reconstructs an XML document from a relational database to improve reconstruction time and minimise memory usage. The performance of XAPP was analysed and compared with the Document Object Model (DOM) algorithm. XAPP proves to perform significantly better than the DOM-based algorithm in terms of mapping and reconstruction time, and memory efficiency. The correctness of XAPP was also verified.
可扩展标记语言(XML)是企业间通过Internet进行数据交换的标准媒介，因此需要有效的管理。然而，由于XML不是为存储和检索而设计的，因此它的管理已经成为数据库社区的一个开放研究领域。现有的xml到关系数据库的映射技术要么采用结构映射，要么采用模型映射。虽然已经开发了许多映射方法，但映射和重建时间一直存在问题，特别是当文档大小很大且难以装入主存储器时。在本研究中，使用简单API for XML (SAX)解析器开发了一个代号为XAPP的新型轻量级应用程序，它采用了一种新颖的模型映射方法。XAPP接受带有或不带有文档类型定义(DTD)的文档。它实现了两种算法:一种算法将XML数据映射到关系数据库，从而缩短了映射时间;另一种算法从关系数据库重构XML文档，从而缩短了重构时间，最大限度地减少了内存使用。分析了XAPP算法的性能，并与文档对象模型(DOM)算法进行了比较。事实证明，XAPP在映射和重建时间以及内存效率方面明显优于基于dom的算法。验证了XAPP的正确性。
{"title":"XAPP: An Implementation of SAX-Based Method for Mapping XML Document to and from a Relational Database","authors":"Yetunde Akinwumi, J. Ayeni, S. Arekete, M. Odim, A. Ogunde, B. Oguntunde","doi":"10.34028/iajit/19/4/2","DOIUrl":"https://doi.org/10.34028/iajit/19/4/2","url":null,"abstract":"Extensible Markup Language (XML) is the standard medium for data exchange among businesses over the Internet, hence the need for effective management. However, since XML was not designed for storage and retrieval, its management has become an open research area in the database community. Existing mapping techniques for XML-to-relational database adopt either the structural mapping or the model mapping. Though numerous mapping approaches have been developed, mapping and reconstruction time had been problematic, especially when the document size is large and can hardly fit into main memory. In this research, an application codenamed XAPP, a new lightweight application that adopts a novel model mapping approach was developed using Simple API for XML (SAX) parser. XAPP accepts a document with or without Document Type Definition (DTD). It implements two algorithms: one maps XML data to a relational database and improves mapping time, and the other reconstructs an XML document from a relational database to improve reconstruction time and minimise memory usage. The performance of XAPP was analysed and compared with the Document Object Model (DOM) algorithm. XAPP proves to perform significantly better than the DOM-based algorithm in terms of mapping and reconstruction time, and memory efficiency. The correctness of XAPP was also verified.","PeriodicalId":13624,"journal":{"name":"Int. Arab J. Inf. Technol.","volume":"39 1","pages":"582-588"},"PeriodicalIF":0.0,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"90512641","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}
The unbalanced nature of data makes it tough to achieve the desire performance goal for classification algorithms. The sub-optimal prediction system isn't a viable solution due to the high misclassification cost of minority events. Thus accurate imbalanced data classification could be a path changer for prediction in domains like medical diagnosis, judiciary, and disaster management systems. To date, most of the existing studies of imbalanced data are for the binary class dataset and supported by data sampling techniques that suffer from loss of information and over-fitting. In this paper, we present the modified naïve Bayesian algorithm for unbalanced data classification that eliminates the requirement of data level sampling. We compared our proposed model with the data sampling technique and cost-sensitive techniques. We use minority sensitive TP Rate, class-specific misclassification rate, and overall performance parameters such as accuracy, f-measure and G-mean. The result shows that our proposed algorithm shows a more optimal result for unbalanced data classification. Results shows reduction in misclassification rate and improve predictive performance for the minority class.
{"title":"MiNB: Minority Sensitive Naïve Bayesian Algorithm for Multi-Class Classification of Unbalanced Data","authors":"Pratik A. Barot, H. Jethva","doi":"10.34028/iajit/19/4/5","DOIUrl":"https://doi.org/10.34028/iajit/19/4/5","url":null,"abstract":"The unbalanced nature of data makes it tough to achieve the desire performance goal for classification algorithms. The sub-optimal prediction system isn't a viable solution due to the high misclassification cost of minority events. Thus accurate imbalanced data classification could be a path changer for prediction in domains like medical diagnosis, judiciary, and disaster management systems. To date, most of the existing studies of imbalanced data are for the binary class dataset and supported by data sampling techniques that suffer from loss of information and over-fitting. In this paper, we present the modified naïve Bayesian algorithm for unbalanced data classification that eliminates the requirement of data level sampling. We compared our proposed model with the data sampling technique and cost-sensitive techniques. We use minority sensitive TP Rate, class-specific misclassification rate, and overall performance parameters such as accuracy, f-measure and G-mean. The result shows that our proposed algorithm shows a more optimal result for unbalanced data classification. Results shows reduction in misclassification rate and improve predictive performance for the minority class.","PeriodicalId":13624,"journal":{"name":"Int. Arab J. Inf. Technol.","volume":"47 1","pages":"609-616"},"PeriodicalIF":0.0,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"74736240","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 Wireless Sensor Networks the nodes have restricted battery power and the exhaustion of battery depends on various issues. In recent developments, various clustering protocols have been proposed to diminish the energy depletion of the node and prolong the network lifespan by reducing power consumption. However, each protocol is inappropriate for heterogeneous wireless sensor networks. The efficiency of heterogeneous wireless sensor networks declines as changing the node heterogeneity. This paper reviews cluster head selection criteria of various clustering protocols for heterogeneous wireless sensor networks in terms of node heterogeneity and compares the performance of these protocols on several parameters like clustering technique, cluster head selection criteria, nodes lifetime, energy efficiency under two-level and three-level heterogeneous wireless sensor networks protocols Stable Election Protocol (SEP), Zonal-Stable Election Protocol (ZSEP), Distributed Energy-Efficient Clustering (DEEC), A Direct Transmission And Residual Energy Based Stable Election Protocol (DTRE-SEP), Developed Distributed Energy-Efficient Clustering (DDEEC), Zone-Based Heterogeneous Clustering Protocol (ZBHCP), Enhanced Distributed Energy Efficient Clustering (EDEEC), Threshold Distributed Energy Efficient Clustering (TDEEC), Enhanced Stable Election Protocol (SEP-E), and Threshold Stable Election Protocol (TSEP). The comparison has shown that the TDEEC has very effective results over other over two-level and three-level heterogeneous wireless sensor networks protocols and has extended the unstable region significantly. From simulations, it can also be proved that adding node heterogeneity can significantly increase the network life.
{"title":"Energy heterogeneity analysis of heterogeneous clustering protocols","authors":"Shahzad Hassan, M. Ahmad.","doi":"10.34028/iajit/19/1/6","DOIUrl":"https://doi.org/10.34028/iajit/19/1/6","url":null,"abstract":"In Wireless Sensor Networks the nodes have restricted battery power and the exhaustion of battery depends on various issues. In recent developments, various clustering protocols have been proposed to diminish the energy depletion of the node and prolong the network lifespan by reducing power consumption. However, each protocol is inappropriate for heterogeneous wireless sensor networks. The efficiency of heterogeneous wireless sensor networks declines as changing the node heterogeneity. This paper reviews cluster head selection criteria of various clustering protocols for heterogeneous wireless sensor networks in terms of node heterogeneity and compares the performance of these protocols on several parameters like clustering technique, cluster head selection criteria, nodes lifetime, energy efficiency under two-level and three-level heterogeneous wireless sensor networks protocols Stable Election Protocol (SEP), Zonal-Stable Election Protocol (ZSEP), Distributed Energy-Efficient Clustering (DEEC), A Direct Transmission And Residual Energy Based Stable Election Protocol (DTRE-SEP), Developed Distributed Energy-Efficient Clustering (DDEEC), Zone-Based Heterogeneous Clustering Protocol (ZBHCP), Enhanced Distributed Energy Efficient Clustering (EDEEC), Threshold Distributed Energy Efficient Clustering (TDEEC), Enhanced Stable Election Protocol (SEP-E), and Threshold Stable Election Protocol (TSEP). The comparison has shown that the TDEEC has very effective results over other over two-level and three-level heterogeneous wireless sensor networks protocols and has extended the unstable region significantly. From simulations, it can also be proved that adding node heterogeneity can significantly increase the network life.","PeriodicalId":13624,"journal":{"name":"Int. Arab J. Inf. Technol.","volume":"31 1","pages":"45-54"},"PeriodicalIF":0.0,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"73954679","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}
: The evolution of transforming from an electrical mechanical engineering discipline to a combination of software and electrical/mechanical engineering establishes software as a crucial technology. The current complex automotive system is the product of growth of embedded software. As a result, automotive industry focuses on a new trend Model based development rather than traditional method where software is handwritten in Assembly code or C language. This paper presents a review of the use of Model based Development to accelerate development process of embedded control systems and technologies. The paper also presents a review of the tools used to support Model-Based Development (MBD) from functional requirements to automated testing and Model based testing process
{"title":"Automotive embedded systems-model based approach review","authors":"A. Shaout, Shanmukha Pattela","doi":"10.34028/iajit/19/3A/5","DOIUrl":"https://doi.org/10.34028/iajit/19/3A/5","url":null,"abstract":": The evolution of transforming from an electrical mechanical engineering discipline to a combination of software and electrical/mechanical engineering establishes software as a crucial technology. The current complex automotive system is the product of growth of embedded software. As a result, automotive industry focuses on a new trend Model based development rather than traditional method where software is handwritten in Assembly code or C language. This paper presents a review of the use of Model based Development to accelerate development process of embedded control systems and technologies. The paper also presents a review of the tools used to support Model-Based Development (MBD) from functional requirements to automated testing and Model based testing process","PeriodicalId":13624,"journal":{"name":"Int. Arab J. Inf. Technol.","volume":"19 1","pages":"456-462"},"PeriodicalIF":0.0,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"82081281","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}
Isaac Kofi Nti, Owusu Narko-Boateng, Adebayo Felix Adekoya, Arjun Remadevi Somanathan
: Network intrusion is a subject of great concern to a variety of stakeholders. Decision fusion (ensemble) models that combine several base learners have been widely used to enhance detection rate of unauthorised network intrusion. However, the design of such an optimal decision fusion classifier is a challenging and open problem. The Matthews Correlation Coefficient (MCC) is an effective measure for detecting associations between variables in many fields; however, very few studies have applied it in selecting weak learners to the best of the authors’ knowledge. In this paper, we propose a decision fusion model with correlation-based MCC weak learner selection technique to augment the classification performance of the decision fusion model under a StackNet strategy. Specifically, the proposed model sought to improve the association between the prediction accuracy and diversity of base classifiers. We compare our proposed model with five other ensemble models, a deep neural model and two stand-alone state-of-the-art classifiers commonly used in network intrusion detection based on accuracy, the Area Under Curve (AUC), recall, precision, F1-score and Kappa evaluation metrics. The experimental results using benchmark dataset KDDcup99 from Kaggle shows that the proposed model has a identified unauthorised network traffic at 99.8% accuracy, Extreme Gradient Boosting (Xgboost) (97.61%), Catboost (97.49%), Light Gradient Boosting Machine (LightGBM) (98.3%), Multilayer Perceptron (MLP) (97.7%), Random Forest (RF) (97.97%), Extra Trees Classifier (ET) (95.82%), Different decision ( DT) (96.95%) and , K-Nearest Neighbor (KNN) (95.56), indicating that it is a more efficient and better intrusion detection system. models and proposed decision fusion model.
{"title":"Stacknet based decision fusion classifier for network intrusion detection","authors":"Isaac Kofi Nti, Owusu Narko-Boateng, Adebayo Felix Adekoya, Arjun Remadevi Somanathan","doi":"10.34028/iajit/19/3A/8","DOIUrl":"https://doi.org/10.34028/iajit/19/3A/8","url":null,"abstract":": Network intrusion is a subject of great concern to a variety of stakeholders. Decision fusion (ensemble) models that combine several base learners have been widely used to enhance detection rate of unauthorised network intrusion. However, the design of such an optimal decision fusion classifier is a challenging and open problem. The Matthews Correlation Coefficient (MCC) is an effective measure for detecting associations between variables in many fields; however, very few studies have applied it in selecting weak learners to the best of the authors’ knowledge. In this paper, we propose a decision fusion model with correlation-based MCC weak learner selection technique to augment the classification performance of the decision fusion model under a StackNet strategy. Specifically, the proposed model sought to improve the association between the prediction accuracy and diversity of base classifiers. We compare our proposed model with five other ensemble models, a deep neural model and two stand-alone state-of-the-art classifiers commonly used in network intrusion detection based on accuracy, the Area Under Curve (AUC), recall, precision, F1-score and Kappa evaluation metrics. The experimental results using benchmark dataset KDDcup99 from Kaggle shows that the proposed model has a identified unauthorised network traffic at 99.8% accuracy, Extreme Gradient Boosting (Xgboost) (97.61%), Catboost (97.49%), Light Gradient Boosting Machine (LightGBM) (98.3%), Multilayer Perceptron (MLP) (97.7%), Random Forest (RF) (97.97%), Extra Trees Classifier (ET) (95.82%), Different decision ( DT) (96.95%) and , K-Nearest Neighbor (KNN) (95.56), indicating that it is a more efficient and better intrusion detection system. models and proposed decision fusion model.","PeriodicalId":13624,"journal":{"name":"Int. Arab J. Inf. Technol.","volume":"514 1","pages":"478-490"},"PeriodicalIF":0.0,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"79463284","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}
Connected and Automated Vehicles (CAVs) is an inspiring technology that has an immense prospect in minimizing road upsets and accidents, improving quality of life, and progressing the effectiveness of transportation systems. Owing to the advancements in the intelligent transportation system, CAV plays a vital role that can keeping life lively. CAV also offers to use to transportation care in producing societies protected more reasonable. The challenge over CAV applications is a new-fangled to enhance safety and efficiency. Cloud autonomous vehicles rely on a whole range of machine learning and data mining techniques to process all the sensor data. Supervised, Unsupervised, and even reinforcement learning are also being used in the process of creating cloud autonomous vehicles with the aim of error-free ones. At first, specialized algorithms have not been used directly in the cloud autonomous vehicles which need to be trained with various traffic environments. The creation of a traffic model environment to test the cloud autonomous vehicles is the prime motto of this paper. The deep Convolutional Neural Network (CNN) has been proposed under the traffic model to drive in a heavy traffic condition to evaluate the algorithm. This paper aims to research an insightful school of thought in the current challenges being faced in CAVs and the solutions by applying CNN. From the simulation results of the traffic model that has traffic and highway parameters, the CNN algorithm has come up with a 71.8% of error-free prediction.
{"title":"Applying deep convolutional neural network (DCNN) algorithm in the cloud autonomous vehicles traffic model","authors":"Dhaya Ramakrishnan, K. Radhakrishnan","doi":"10.34028/iajit/19/2/5","DOIUrl":"https://doi.org/10.34028/iajit/19/2/5","url":null,"abstract":"Connected and Automated Vehicles (CAVs) is an inspiring technology that has an immense prospect in minimizing road upsets and accidents, improving quality of life, and progressing the effectiveness of transportation systems. Owing to the advancements in the intelligent transportation system, CAV plays a vital role that can keeping life lively. CAV also offers to use to transportation care in producing societies protected more reasonable. The challenge over CAV applications is a new-fangled to enhance safety and efficiency. Cloud autonomous vehicles rely on a whole range of machine learning and data mining techniques to process all the sensor data. Supervised, Unsupervised, and even reinforcement learning are also being used in the process of creating cloud autonomous vehicles with the aim of error-free ones. At first, specialized algorithms have not been used directly in the cloud autonomous vehicles which need to be trained with various traffic environments. The creation of a traffic model environment to test the cloud autonomous vehicles is the prime motto of this paper. The deep Convolutional Neural Network (CNN) has been proposed under the traffic model to drive in a heavy traffic condition to evaluate the algorithm. This paper aims to research an insightful school of thought in the current challenges being faced in CAVs and the solutions by applying CNN. From the simulation results of the traffic model that has traffic and highway parameters, the CNN algorithm has come up with a 71.8% of error-free prediction.","PeriodicalId":13624,"journal":{"name":"Int. Arab J. Inf. Technol.","volume":"11 1","pages":"186-194"},"PeriodicalIF":0.0,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"81691250","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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