In order to contribute in facing the current challenging problems related to traffic congestion, we propose an intelligent traffic light control system called TIR-Light. Our system is based on the combination of recommendation systems and deep learning algorithms to maintain a fluid flow, prevent traffic jams and ensure a good quality of service for road users. TIR-Light predicts the hourly flow of each intersection and identifies the optimal timing planes by minimizing the waiting time and queue length. In addition, system efficiency and performance as well as its intelligent algorithms are evaluated through a simulation of the road network.
{"title":"Traffic signals control system based on intelligent recommendation","authors":"Sahar Smaali, Chafia Bouanaka, Samah Smaali, Khaoula Kitouni","doi":"10.1109/ISIA55826.2022.9993579","DOIUrl":"https://doi.org/10.1109/ISIA55826.2022.9993579","url":null,"abstract":"In order to contribute in facing the current challenging problems related to traffic congestion, we propose an intelligent traffic light control system called TIR-Light. Our system is based on the combination of recommendation systems and deep learning algorithms to maintain a fluid flow, prevent traffic jams and ensure a good quality of service for road users. TIR-Light predicts the hourly flow of each intersection and identifies the optimal timing planes by minimizing the waiting time and queue length. In addition, system efficiency and performance as well as its intelligent algorithms are evaluated through a simulation of the road network.","PeriodicalId":169898,"journal":{"name":"2022 5th International Symposium on Informatics and its Applications (ISIA)","volume":"112 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124265646","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 : 2022-11-29DOI: 10.1109/ISIA55826.2022.9993505
A. Kermi, Hadj Cheikh Djennelbaroud, M. T. Khadir
This paper presents an automated COVID-19 lung lesions segmentation method based on a deep three-dimensional convolutional neural network model which automatically detects and extracts multifocal, bilateral and peripheral lung lesions from chest 3D-CT scans. The proposed CNN model is based on a modified 11-layer U-net architecture and employs a loss function that combines Dice coefficient and Cross-Entropy. It has been tested and evaluated on Covid-19-20_v2 training dataset containing a total of 199 3D-CT scans of different subjects with COVID-19 lesions representing different sizes, shapes and locations in CT images. The obtained results have proven to be satisfactory and objective, as well as similar and close to ground truth data provided by medical experts. On these challenging CT data, the proposed CNN obtained average scores of 0.7639, 0.8129 and 0.9986 corresponding to Dice Similarity Coefficient, Sensitivity and Specificity metrics respectively.
{"title":"A Deep Learning-based 3D CNN for Automated COVID-19 Lung Lesions Segmentation from 3D Chest CT Scans","authors":"A. Kermi, Hadj Cheikh Djennelbaroud, M. T. Khadir","doi":"10.1109/ISIA55826.2022.9993505","DOIUrl":"https://doi.org/10.1109/ISIA55826.2022.9993505","url":null,"abstract":"This paper presents an automated COVID-19 lung lesions segmentation method based on a deep three-dimensional convolutional neural network model which automatically detects and extracts multifocal, bilateral and peripheral lung lesions from chest 3D-CT scans. The proposed CNN model is based on a modified 11-layer U-net architecture and employs a loss function that combines Dice coefficient and Cross-Entropy. It has been tested and evaluated on Covid-19-20_v2 training dataset containing a total of 199 3D-CT scans of different subjects with COVID-19 lesions representing different sizes, shapes and locations in CT images. The obtained results have proven to be satisfactory and objective, as well as similar and close to ground truth data provided by medical experts. On these challenging CT data, the proposed CNN obtained average scores of 0.7639, 0.8129 and 0.9986 corresponding to Dice Similarity Coefficient, Sensitivity and Specificity metrics respectively.","PeriodicalId":169898,"journal":{"name":"2022 5th International Symposium on Informatics and its Applications (ISIA)","volume":"73 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121716785","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 : 2022-11-29DOI: 10.1109/ISIA55826.2022.9993572
A. Nehar, Slimane Bellaouar, Djamila Mahfoud, Fatima Zohra Daoudi
Query-document vocabulary mismatch, the lack of query expressiveness for user needs and the phenomenon of short queries are the main issues associated with information retrieval systems. Query Expansion (QE) is one of the well-known alternative for overcoming these problems. It mainly involves finding synonyms or related words for the query terms. There are several approaches in the query expansion field such as statistical and semantic approaches; they focus on expanding the individual query terms rather than the entire query during the expansion process. An other category of approaches deals with the whole query by using a neural approach based on Pseudo Relevance feedback (PRF) documents. In this work, we carried out an ablation study to measure the impact of the classical and semantic (word embedding, order, context) based query expansion on the retrieval performance. The experiments conducted on the Arabic EveTAR dataset reveal that our hybrid proposed approach combining classical (PRF) and transformer (AraBERT) is competitive with the state-of-the-art methods. In fact, the obtained result in terms of the Mean Average Precision (MAP) is up to 0.72.
{"title":"A Hybrid Semantic Statistical Query Expansion for Arabic Information Retrieval Systems","authors":"A. Nehar, Slimane Bellaouar, Djamila Mahfoud, Fatima Zohra Daoudi","doi":"10.1109/ISIA55826.2022.9993572","DOIUrl":"https://doi.org/10.1109/ISIA55826.2022.9993572","url":null,"abstract":"Query-document vocabulary mismatch, the lack of query expressiveness for user needs and the phenomenon of short queries are the main issues associated with information retrieval systems. Query Expansion (QE) is one of the well-known alternative for overcoming these problems. It mainly involves finding synonyms or related words for the query terms. There are several approaches in the query expansion field such as statistical and semantic approaches; they focus on expanding the individual query terms rather than the entire query during the expansion process. An other category of approaches deals with the whole query by using a neural approach based on Pseudo Relevance feedback (PRF) documents. In this work, we carried out an ablation study to measure the impact of the classical and semantic (word embedding, order, context) based query expansion on the retrieval performance. The experiments conducted on the Arabic EveTAR dataset reveal that our hybrid proposed approach combining classical (PRF) and transformer (AraBERT) is competitive with the state-of-the-art methods. In fact, the obtained result in terms of the Mean Average Precision (MAP) is up to 0.72.","PeriodicalId":169898,"journal":{"name":"2022 5th International Symposium on Informatics and its Applications (ISIA)","volume":"41 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117001862","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 : 2022-11-29DOI: 10.1109/ISIA55826.2022.9993605
Rached Yagoubi, A. Moussaoui, Ali Dabba, M. Yagoubi
The knowledge of the protein structural class is one of the most important sources of information in many biological fields, such as function analysis, protein structure, drug design, and protein folding. However, the protein structural class prediction is still a challenge when dealing with low similarity sequences. Therefore, the accuracy of the top-performing prediction methods remains unsatisfying, especially for proteins from the + ß class. This paper proposes a novel approach for Protein Structural Class Prediction using a Convolutional Neural Network (PSCP-CNN). Our approach consists of two stages. The first is the preprocessing stage which allows the preparation of the data. The second stage is a CNN classifier that automatically extracts the needed features for the classification. To evaluate the performance of our approach, we performed the jackknife test on four low similarity benchmark datasets: 25PDB, 640, 1189, and FC699. The experimental results show that PSCP-CNN achieved high prediction accuracy, where the overall accuracy on datasets 25PDB, 640, 1189, and FC699 is 93.8%, 94.5%, 94.0%, and 98.0%, respectively. Furthermore, comparing the results obtained with existing methods shows that PSCP-CNN outperforms state-of-the-art techniques and confirms that using a convolutional neural network allows a better prediction of protein structural classes.
{"title":"PSCP-CNN: Protein Structural Class Prediction using a Convolutional Neural Network","authors":"Rached Yagoubi, A. Moussaoui, Ali Dabba, M. Yagoubi","doi":"10.1109/ISIA55826.2022.9993605","DOIUrl":"https://doi.org/10.1109/ISIA55826.2022.9993605","url":null,"abstract":"The knowledge of the protein structural class is one of the most important sources of information in many biological fields, such as function analysis, protein structure, drug design, and protein folding. However, the protein structural class prediction is still a challenge when dealing with low similarity sequences. Therefore, the accuracy of the top-performing prediction methods remains unsatisfying, especially for proteins from the + ß class. This paper proposes a novel approach for Protein Structural Class Prediction using a Convolutional Neural Network (PSCP-CNN). Our approach consists of two stages. The first is the preprocessing stage which allows the preparation of the data. The second stage is a CNN classifier that automatically extracts the needed features for the classification. To evaluate the performance of our approach, we performed the jackknife test on four low similarity benchmark datasets: 25PDB, 640, 1189, and FC699. The experimental results show that PSCP-CNN achieved high prediction accuracy, where the overall accuracy on datasets 25PDB, 640, 1189, and FC699 is 93.8%, 94.5%, 94.0%, and 98.0%, respectively. Furthermore, comparing the results obtained with existing methods shows that PSCP-CNN outperforms state-of-the-art techniques and confirms that using a convolutional neural network allows a better prediction of protein structural classes.","PeriodicalId":169898,"journal":{"name":"2022 5th International Symposium on Informatics and its Applications (ISIA)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123168745","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 : 2022-11-29DOI: 10.1109/ISIA55826.2022.9993583
Salma Louanas, Hichem Debbi
Answering questions over images is a challenging task, it requires reasoning over both images and text. In this paper, we introduce Residual Attention Network(RAN), a new visual question answering model, and compare it with baseline models such as stacked attention model and CNN-LSTM model. We find that our model performs better than these baseline models. In addition to our model, we also evaluate several holistic models and compare them with neural module networks frameworks, and the results show that neural modules networks perform better in questions reasoning. All the experiments have been done on the CLEVER dataset, which is a recent VQA dataset for evaluating multiple-step reasoning VQA models.
{"title":"Residual Attention Network: A new baseline model for visual question answering","authors":"Salma Louanas, Hichem Debbi","doi":"10.1109/ISIA55826.2022.9993583","DOIUrl":"https://doi.org/10.1109/ISIA55826.2022.9993583","url":null,"abstract":"Answering questions over images is a challenging task, it requires reasoning over both images and text. In this paper, we introduce Residual Attention Network(RAN), a new visual question answering model, and compare it with baseline models such as stacked attention model and CNN-LSTM model. We find that our model performs better than these baseline models. In addition to our model, we also evaluate several holistic models and compare them with neural module networks frameworks, and the results show that neural modules networks perform better in questions reasoning. All the experiments have been done on the CLEVER dataset, which is a recent VQA dataset for evaluating multiple-step reasoning VQA models.","PeriodicalId":169898,"journal":{"name":"2022 5th International Symposium on Informatics and its Applications (ISIA)","volume":"19 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125011174","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 : 2022-11-29DOI: 10.1109/ISIA55826.2022.9993482
Khadidja Bouchelouche, A. R. Ghomari, Leila Zemmouchi-Ghomari
The release of Open Government Data (OGD) in recent years has maintained a very rapid pace to enable the OGD initiative to reach its full potential, such as enhancing transparency, citizen collaboration, and participation and boosting economic innovation value. Moreover, by publishing OGD, citizens can participate in governance processes, like policy-making and decision-making. Using Linked Open Data (LOD) technology allows us to understand and correctly use the released data by humans and machines. However, expert evidence shows that releasing data without quality control can threaten the reuse of datasets and negatively affect the benefits of the OGD initiative. Data accessibility is classified among the essential categories in Linked Open Data (LOD) quality models to enable efficient access to the released datasets. Most existing evaluations of data accessibility for the OGD portals focus on defining dimensions and measures, but there is no closed formulation to apply them. Some works propose marks to assess the data that meet the defined measures, and there is no broad scale of marks to standardize the application of these measures. This leads to difficulties in comparing and benchmarking evaluations. This paper aims to propose a percentage scale of marks for metrics to assess the accessibility of data in the OGD portals. Finally, we experiment with the proposed scale of marks on the American OGD portal since America launched the OGD initiative, and its portal is considered an example of OGD initiatives.
{"title":"Enhanced analysis of Open Government Data: Proposed metrics for improving data quality assessment","authors":"Khadidja Bouchelouche, A. R. Ghomari, Leila Zemmouchi-Ghomari","doi":"10.1109/ISIA55826.2022.9993482","DOIUrl":"https://doi.org/10.1109/ISIA55826.2022.9993482","url":null,"abstract":"The release of Open Government Data (OGD) in recent years has maintained a very rapid pace to enable the OGD initiative to reach its full potential, such as enhancing transparency, citizen collaboration, and participation and boosting economic innovation value. Moreover, by publishing OGD, citizens can participate in governance processes, like policy-making and decision-making. Using Linked Open Data (LOD) technology allows us to understand and correctly use the released data by humans and machines. However, expert evidence shows that releasing data without quality control can threaten the reuse of datasets and negatively affect the benefits of the OGD initiative. Data accessibility is classified among the essential categories in Linked Open Data (LOD) quality models to enable efficient access to the released datasets. Most existing evaluations of data accessibility for the OGD portals focus on defining dimensions and measures, but there is no closed formulation to apply them. Some works propose marks to assess the data that meet the defined measures, and there is no broad scale of marks to standardize the application of these measures. This leads to difficulties in comparing and benchmarking evaluations. This paper aims to propose a percentage scale of marks for metrics to assess the accessibility of data in the OGD portals. Finally, we experiment with the proposed scale of marks on the American OGD portal since America launched the OGD initiative, and its portal is considered an example of OGD initiatives.","PeriodicalId":169898,"journal":{"name":"2022 5th International Symposium on Informatics and its Applications (ISIA)","volume":"10 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132696140","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 : 2022-11-29DOI: 10.1109/ISIA55826.2022.9993580
Amel Dembri, M. Redjimi
The design and development of graphical tools for new domain-specific languages is still a challenge for designers; the Model-Driven Architecture (MDA) makes a qualitative difference in the creation of Domain Specific Language (DSL). We aim in this paper to analyze and evaluate the performance of some language workbenches that makes the development of domain-specific language simpler and more specialised. To evaluate these tools, a formal specification of a Petri net called Agent Petri Net is selected. We analyze criteria related to abstraction level, facilities to tailor DSL to specific domains, simplicity of development and the productivity guarantee with these tools. Practical experience highlights the real capabilities of each tool and considers as an evaluation support to select the adequate solution to design DSL that responds to user requirements.
{"title":"Towards a Simplified Evaluation of Graphical DSL Workbenches","authors":"Amel Dembri, M. Redjimi","doi":"10.1109/ISIA55826.2022.9993580","DOIUrl":"https://doi.org/10.1109/ISIA55826.2022.9993580","url":null,"abstract":"The design and development of graphical tools for new domain-specific languages is still a challenge for designers; the Model-Driven Architecture (MDA) makes a qualitative difference in the creation of Domain Specific Language (DSL). We aim in this paper to analyze and evaluate the performance of some language workbenches that makes the development of domain-specific language simpler and more specialised. To evaluate these tools, a formal specification of a Petri net called Agent Petri Net is selected. We analyze criteria related to abstraction level, facilities to tailor DSL to specific domains, simplicity of development and the productivity guarantee with these tools. Practical experience highlights the real capabilities of each tool and considers as an evaluation support to select the adequate solution to design DSL that responds to user requirements.","PeriodicalId":169898,"journal":{"name":"2022 5th International Symposium on Informatics and its Applications (ISIA)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129328875","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 : 2022-11-29DOI: 10.1109/ISIA55826.2022.9993530
Ouennoughi Nedjmedine, Mehenni Tahar
Road accidents become a worldwide health issue. With the enormous number of death and injuries, this problem pushes governments to create solutions to reduce those statistics. One of the solving ways is using machine learning algorithms, and with the data collected from road accidents, we can increase traffic safety. In this research, we use a decision tree model to analyze road accidents that happened in Algeria. Then, we do a comparison with some similar works using accuracy as a performance evaluation metric. This work can help government and traffic safety entities to improve road safety and minimize the number of accidents, also, it can help other researchers to develop other models in the analysis of traffic accidents in Algeria and other countries.
{"title":"Analysis of road accident factors using Decision Tree Algorithm: a case of study Algeria","authors":"Ouennoughi Nedjmedine, Mehenni Tahar","doi":"10.1109/ISIA55826.2022.9993530","DOIUrl":"https://doi.org/10.1109/ISIA55826.2022.9993530","url":null,"abstract":"Road accidents become a worldwide health issue. With the enormous number of death and injuries, this problem pushes governments to create solutions to reduce those statistics. One of the solving ways is using machine learning algorithms, and with the data collected from road accidents, we can increase traffic safety. In this research, we use a decision tree model to analyze road accidents that happened in Algeria. Then, we do a comparison with some similar works using accuracy as a performance evaluation metric. This work can help government and traffic safety entities to improve road safety and minimize the number of accidents, also, it can help other researchers to develop other models in the analysis of traffic accidents in Algeria and other countries.","PeriodicalId":169898,"journal":{"name":"2022 5th International Symposium on Informatics and its Applications (ISIA)","volume":"4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128353838","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 : 2022-11-29DOI: 10.1109/ISIA55826.2022.9993513
S. Ouamour, H. Sayoud
In the present work, we intend to present a thorough study developed on a digital library, called HAT corpus, for a purpose of authorship attribution. Thus, a dataset of 300 documents that are written by 100 different authors, was extracted from the web digital library and processed for a task of author style analysis. All the documents are related to the travel topic and written in Arabic. Basically, three important rules in stylometry should be respected: the minimum document size, the same topic for all documents and the same genre too. In this work, we made a particular effort to respect those conditions seriously during the corpus preparation. That is, three lexical features: Fixed-length words, Rare words and Suffixes are used and evaluated by using a centroid based Manhattan distance. The used identification approach shows interesting results with an accuracy of about 0.94.
{"title":"Computational Identification of Author Style on Electronic Libraries - Case of Lexical Features","authors":"S. Ouamour, H. Sayoud","doi":"10.1109/ISIA55826.2022.9993513","DOIUrl":"https://doi.org/10.1109/ISIA55826.2022.9993513","url":null,"abstract":"In the present work, we intend to present a thorough study developed on a digital library, called HAT corpus, for a purpose of authorship attribution. Thus, a dataset of 300 documents that are written by 100 different authors, was extracted from the web digital library and processed for a task of author style analysis. All the documents are related to the travel topic and written in Arabic. Basically, three important rules in stylometry should be respected: the minimum document size, the same topic for all documents and the same genre too. In this work, we made a particular effort to respect those conditions seriously during the corpus preparation. That is, three lexical features: Fixed-length words, Rare words and Suffixes are used and evaluated by using a centroid based Manhattan distance. The used identification approach shows interesting results with an accuracy of about 0.94.","PeriodicalId":169898,"journal":{"name":"2022 5th International Symposium on Informatics and its Applications (ISIA)","volume":"7 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115954542","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 : 2022-11-29DOI: 10.1109/ISIA55826.2022.9993590
Zakaria Sahraoui, A. Labed
Development of complex systems requires the use of design methodologies in order to respect time constraints and accurately fit in the specifications. We propose a general methodology to help fast prototyping of distributed real-time systems. It consists of taking advantage from available tools based on rigorous methods and implementing fast and not very costly distributed and real-time complex systems. Firstly, the tool Tina is used to specify and verify the system's discrete part and subsequently, the Stateflow tool is used to design it. Finally the Simulink tool is used to design the system's continuous part. The ultimate purpose of this methodology is to generate the code for the final test automatically.
{"title":"Methodology for fast prototyping of distributed real-time systems","authors":"Zakaria Sahraoui, A. Labed","doi":"10.1109/ISIA55826.2022.9993590","DOIUrl":"https://doi.org/10.1109/ISIA55826.2022.9993590","url":null,"abstract":"Development of complex systems requires the use of design methodologies in order to respect time constraints and accurately fit in the specifications. We propose a general methodology to help fast prototyping of distributed real-time systems. It consists of taking advantage from available tools based on rigorous methods and implementing fast and not very costly distributed and real-time complex systems. Firstly, the tool Tina is used to specify and verify the system's discrete part and subsequently, the Stateflow tool is used to design it. Finally the Simulink tool is used to design the system's continuous part. The ultimate purpose of this methodology is to generate the code for the final test automatically.","PeriodicalId":169898,"journal":{"name":"2022 5th International Symposium on Informatics and its Applications (ISIA)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130878833","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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