Pub Date : 2022-11-29DOI: 10.1109/ISIA55826.2022.9993564
Oukas Nourredine, Djouabri Abderrezak, Arab Karima, Helal Mira
This paper proposes new modeling of autonomous devices in Internet of Things (IoT) using extended Hybrid Petri nets (xHPN). This formulation uses the continuous concept of battery recharge and discharge instead quantification principle. We consider that the autonomous device is equipped with solar energy harvesting (SEH) system and deployed in diverse zones of Algeria with different photovoltaic panel orientations. To conserve energy, we adopt the famous dual sleeping mechanism. The conducted analysis proves that the proposed model is more suitable for the energy assessment of such devices.
{"title":"A Fluid Approach to Model and Assess the Energy Level of Autonomous devices in IoT with Solar Energy Harvesting Capability","authors":"Oukas Nourredine, Djouabri Abderrezak, Arab Karima, Helal Mira","doi":"10.1109/ISIA55826.2022.9993564","DOIUrl":"https://doi.org/10.1109/ISIA55826.2022.9993564","url":null,"abstract":"This paper proposes new modeling of autonomous devices in Internet of Things (IoT) using extended Hybrid Petri nets (xHPN). This formulation uses the continuous concept of battery recharge and discharge instead quantification principle. We consider that the autonomous device is equipped with solar energy harvesting (SEH) system and deployed in diverse zones of Algeria with different photovoltaic panel orientations. To conserve energy, we adopt the famous dual sleeping mechanism. The conducted analysis proves that the proposed model is more suitable for the energy assessment of such devices.","PeriodicalId":169898,"journal":{"name":"2022 5th International Symposium on Informatics and its Applications (ISIA)","volume":"276 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":"116084812","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/ISIA55826.2022.9993591
Melissa Oussaid, Farida Bouarab-Dahmani, N. Cullot
The emergence of the Internet has made available a large amount of food data in different formats. Therefore, manual relevant data extraction for food ontology population and enrichment has become a complex process. The automation of the knowledge extraction task offers significant opportunities to overcome several manual process limitations, such as complexity (time-consuming and resource-intense). In this paper, we propose a new approach that aims at the automated extraction of new ontological concepts from unstructured data to enrich a food ontology. For this purpose, an ontology and a corpus of food data have been built. This data is used to train the Word2Vec model. Then, a measure of similarity based on word embedding is done. New entities are selected as candidates according to the result of similarity scores and are used to generate new concepts. The obtained results showed the effectiveness of our proposal, with a precision score of 78%.
{"title":"Food Ontology Enrichment Using Word Embeddings and Machine Learning Technologies","authors":"Melissa Oussaid, Farida Bouarab-Dahmani, N. Cullot","doi":"10.1109/ISIA55826.2022.9993591","DOIUrl":"https://doi.org/10.1109/ISIA55826.2022.9993591","url":null,"abstract":"The emergence of the Internet has made available a large amount of food data in different formats. Therefore, manual relevant data extraction for food ontology population and enrichment has become a complex process. The automation of the knowledge extraction task offers significant opportunities to overcome several manual process limitations, such as complexity (time-consuming and resource-intense). In this paper, we propose a new approach that aims at the automated extraction of new ontological concepts from unstructured data to enrich a food ontology. For this purpose, an ontology and a corpus of food data have been built. This data is used to train the Word2Vec model. Then, a measure of similarity based on word embedding is done. New entities are selected as candidates according to the result of similarity scores and are used to generate new concepts. The obtained results showed the effectiveness of our proposal, with a precision score of 78%.","PeriodicalId":169898,"journal":{"name":"2022 5th International Symposium on Informatics and its Applications (ISIA)","volume":"147 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":"123268902","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/ISIA55826.2022.9993494
Bekhouche Abdelaali, Yamina Tlili-Guiassa
In this article, we present a new approach to word sense disambiguation for Arabic language based on the notion of local and global algorithms. We are going to use LESK defined on a distributional semantic space to compute the gloss-context overlap for disambiguation of words in the local context and the Cuckoo Optimization Algorithm to propagate local measures at the upper level. This task needs lexical resources and since Arabic lacks them, we are using English pre-trained word embeddings. Experimental results show that the proposed WSD approach significantly improves the base-line word sense disambiguation method. Furthermore, it will be easier to compare our results to other methods. In addition, we compared different pre-existing word embeddings model in our approach.
{"title":"Swarm optimization for Arabic word sense disambiguation based on English pre-trained word embeddings","authors":"Bekhouche Abdelaali, Yamina Tlili-Guiassa","doi":"10.1109/ISIA55826.2022.9993494","DOIUrl":"https://doi.org/10.1109/ISIA55826.2022.9993494","url":null,"abstract":"In this article, we present a new approach to word sense disambiguation for Arabic language based on the notion of local and global algorithms. We are going to use LESK defined on a distributional semantic space to compute the gloss-context overlap for disambiguation of words in the local context and the Cuckoo Optimization Algorithm to propagate local measures at the upper level. This task needs lexical resources and since Arabic lacks them, we are using English pre-trained word embeddings. Experimental results show that the proposed WSD approach significantly improves the base-line word sense disambiguation method. Furthermore, it will be easier to compare our results to other methods. In addition, we compared different pre-existing word embeddings model in our approach.","PeriodicalId":169898,"journal":{"name":"2022 5th International Symposium on Informatics and its Applications (ISIA)","volume":"75 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":"128718017","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/ISIA55826.2022.9993543
Khaoula Zineb Legoui, Sofiane Maza, A. Attia
One of the most critical processes is feature selection, which eliminates features that may decrease classification performance and increase computational time. In this paper, we introduce and provide a comparison study between two algorithms, which are Equilibrium Optimizer (EO) and Henry Gas Solubility Optimization (HGSO) for Feature Selection (FS). The function objective of both algorithms are based on two main objectives, such as Error Rate (ER) and feature Reduction Rates (RR). In this comparative study, three classifiers (Naive Bayes NB, k-Nearest Neighbor KNN, and Random Forest RF) have been employed. The evaluation of the work was conducted on ten datasets, including Iris, Lung Cancer, Spambase, and Musk. The two algorithms show higher performances according to the accuracy and number of features, especially HGSOFS, which in turn shows its effectiveness and provides good results in the two tasks of FS when we compare it to the PSOFS (Particle Swarm Optimization for Feature Selection) and FAFS (Fire Fly for Feature Selection).
最关键的过程之一是特征选择，它消除了可能降低分类性能和增加计算时间的特征。本文介绍了两种用于特征选择(FS)的平衡优化算法(EO)和亨利气体溶解度优化算法(HGSO)，并对其进行了比较研究。两种算法的功能目标都基于两个主要目标，即错误率(ER)和特征约简率(RR)。在这个比较研究中，使用了三种分类器(朴素贝叶斯NB, k近邻KNN和随机森林RF)。对这项工作的评估是在10个数据集上进行的，包括Iris、Lung Cancer、Spambase和Musk。与PSOFS (Particle Swarm Optimization for Feature Selection)和FAFS (Fire Fly for Feature Selection)相比，这两种算法在准确率和特征数量上都表现出更高的性能，尤其是HGSOFS，这反过来又证明了它的有效性，在FS的两个任务上都取得了很好的效果。
{"title":"Equilibrium Optimizer and Henry Gas Solubility Optimization Algorithms for Feature Selection: Comparison Study","authors":"Khaoula Zineb Legoui, Sofiane Maza, A. Attia","doi":"10.1109/ISIA55826.2022.9993543","DOIUrl":"https://doi.org/10.1109/ISIA55826.2022.9993543","url":null,"abstract":"One of the most critical processes is feature selection, which eliminates features that may decrease classification performance and increase computational time. In this paper, we introduce and provide a comparison study between two algorithms, which are Equilibrium Optimizer (EO) and Henry Gas Solubility Optimization (HGSO) for Feature Selection (FS). The function objective of both algorithms are based on two main objectives, such as Error Rate (ER) and feature Reduction Rates (RR). In this comparative study, three classifiers (Naive Bayes NB, k-Nearest Neighbor KNN, and Random Forest RF) have been employed. The evaluation of the work was conducted on ten datasets, including Iris, Lung Cancer, Spambase, and Musk. The two algorithms show higher performances according to the accuracy and number of features, especially HGSOFS, which in turn shows its effectiveness and provides good results in the two tasks of FS when we compare it to the PSOFS (Particle Swarm Optimization for Feature Selection) and FAFS (Fire Fly for Feature Selection).","PeriodicalId":169898,"journal":{"name":"2022 5th International Symposium on Informatics and its Applications (ISIA)","volume":"49 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":"126680149","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/ISIA55826.2022.9993487
Amina Khacha, Rafika Saadouni, Yasmine Harbi, Z. Aliouat
The internet of things (IoT) is expected to offer a significant impact on the industry domain leading to the concept of industrial IoT (IIoT). The IIoT comprises machine-to-machine (M2M) and communication technologies with data automation and exchange to improve product quality and decrease pro-duction costs. As a consequence, a large amount of data is collected and smartly processed to provide optimal industrial operations. This growing deployment enables adversaries to con-duct potential and destructive cyber-attacks to accomplish their malicious goals. Therefore, intelligent decision-making actions for cyber-attack detection in IIoT are sorely required. To address this challenge, we propose an intrusion detection system (IDS) using deep learning models. Specifically, the proposed system is based on the combination of convolutional neural network (CNN) and long short-term memory (LSTM) that are excellent techniques for intrusion detection and classification due to their ability in classifying main characteristics and their effectiveness in performing faster computations. We adopt the most recent dataset named Edge-IIoTset that contains a real traffic network of IoT and IIoT applications. The proposed model is evaluated in terms of accuracy, precision, false positive rate, and detection cost within binary and multi-class classifications. The obtained results show that our CNN-LSTM model provides better performance and robustness in cyber security intrusion detection for IIoT applications compared to LSTM and traditional machine learning models. Moreover, it outperforms two recent related models in terms of accuracy rate.
{"title":"Hybrid Deep Learning-based Intrusion Detection System for Industrial Internet of Things","authors":"Amina Khacha, Rafika Saadouni, Yasmine Harbi, Z. Aliouat","doi":"10.1109/ISIA55826.2022.9993487","DOIUrl":"https://doi.org/10.1109/ISIA55826.2022.9993487","url":null,"abstract":"The internet of things (IoT) is expected to offer a significant impact on the industry domain leading to the concept of industrial IoT (IIoT). The IIoT comprises machine-to-machine (M2M) and communication technologies with data automation and exchange to improve product quality and decrease pro-duction costs. As a consequence, a large amount of data is collected and smartly processed to provide optimal industrial operations. This growing deployment enables adversaries to con-duct potential and destructive cyber-attacks to accomplish their malicious goals. Therefore, intelligent decision-making actions for cyber-attack detection in IIoT are sorely required. To address this challenge, we propose an intrusion detection system (IDS) using deep learning models. Specifically, the proposed system is based on the combination of convolutional neural network (CNN) and long short-term memory (LSTM) that are excellent techniques for intrusion detection and classification due to their ability in classifying main characteristics and their effectiveness in performing faster computations. We adopt the most recent dataset named Edge-IIoTset that contains a real traffic network of IoT and IIoT applications. The proposed model is evaluated in terms of accuracy, precision, false positive rate, and detection cost within binary and multi-class classifications. The obtained results show that our CNN-LSTM model provides better performance and robustness in cyber security intrusion detection for IIoT applications compared to LSTM and traditional machine learning models. Moreover, it outperforms two recent related models in terms of accuracy rate.","PeriodicalId":169898,"journal":{"name":"2022 5th International Symposium on Informatics and its Applications (ISIA)","volume":"49 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":"130872326","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}
Question Answering (QA) systems have made remarkable progress in information retrieval techniques, especially in their ability to naturally access knowledge resources by querying and retrieving correct answers to various questions. In tutoring, these systems can help by reducing the requirement for interaction between learners and tutors and allowing learners to post their queries and receive answers for the same. Hence, we propose a disciplinary tutoring system based on a domain ontology ONTO-TDM (ontology for teaching domain modeling) and natural language processing (NLP) techniques to facilitate access to information and answer the learners' questions. Recently, deep learning algorithms have achieved impressive success in various natural language processing tasks. The basic concept of these techniques is to compute a distributed representation of words from continuous vectors, also known as word embedding. In this study, we use deep learning-based word embedding models for a disciplinary tutoring system. Our goal through this work is to find out whether word embedding could significantly improve the response generation task of the suggested system. Therefore, we have built word embeddings using the word2vec skip-gram model with different training parameters on a large corpus composed of question-answer pairs. Experimental results show that using the word2vec model has a significant impact on the accuracy of the proposed tool.
{"title":"Word Embeddings for a Disciplinary Tutoring System","authors":"Rosana Abdoune, Lydia Lazib, Farida Dahmani-Bouarab","doi":"10.1109/ISIA55826.2022.9993615","DOIUrl":"https://doi.org/10.1109/ISIA55826.2022.9993615","url":null,"abstract":"Question Answering (QA) systems have made remarkable progress in information retrieval techniques, especially in their ability to naturally access knowledge resources by querying and retrieving correct answers to various questions. In tutoring, these systems can help by reducing the requirement for interaction between learners and tutors and allowing learners to post their queries and receive answers for the same. Hence, we propose a disciplinary tutoring system based on a domain ontology ONTO-TDM (ontology for teaching domain modeling) and natural language processing (NLP) techniques to facilitate access to information and answer the learners' questions. Recently, deep learning algorithms have achieved impressive success in various natural language processing tasks. The basic concept of these techniques is to compute a distributed representation of words from continuous vectors, also known as word embedding. In this study, we use deep learning-based word embedding models for a disciplinary tutoring system. Our goal through this work is to find out whether word embedding could significantly improve the response generation task of the suggested system. Therefore, we have built word embeddings using the word2vec skip-gram model with different training parameters on a large corpus composed of question-answer pairs. Experimental results show that using the word2vec model has a significant impact on the accuracy of the proposed tool.","PeriodicalId":169898,"journal":{"name":"2022 5th International Symposium on Informatics and its Applications (ISIA)","volume":"116 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":"123486285","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/ISIA55826.2022.9993501
Adil Bouhous
In this paper, a novel approach to accurately calculate the resonant frequencies of rectangular microstrip antennas using artificial neural networks (ANN) and the method of moments (MOM) is proposed. The ANN is developed to calculate the real part and the imaginary part of the complex resonant frequency of the antenna. The ANN is designed using multilayer perceptron network (MLP). Results concerning this resonance frequency as a function of the different physical and geometrical parameters of the antenna are presented. These obtained results correspond to the trained and tested data of the ANN model. A comparison with other results calculated from Chew's algorithm clearly shows the effectiveness of the proposed approach. The objective is to reduce the computational complexities, and thus to considerably reduce the computation time.
{"title":"Prediction of resonance frequencies of rectangular patch antenna using a multilayer perceptron network","authors":"Adil Bouhous","doi":"10.1109/ISIA55826.2022.9993501","DOIUrl":"https://doi.org/10.1109/ISIA55826.2022.9993501","url":null,"abstract":"In this paper, a novel approach to accurately calculate the resonant frequencies of rectangular microstrip antennas using artificial neural networks (ANN) and the method of moments (MOM) is proposed. The ANN is developed to calculate the real part and the imaginary part of the complex resonant frequency of the antenna. The ANN is designed using multilayer perceptron network (MLP). Results concerning this resonance frequency as a function of the different physical and geometrical parameters of the antenna are presented. These obtained results correspond to the trained and tested data of the ANN model. A comparison with other results calculated from Chew's algorithm clearly shows the effectiveness of the proposed approach. The objective is to reduce the computational complexities, and thus to considerably reduce the computation time.","PeriodicalId":169898,"journal":{"name":"2022 5th International Symposium on Informatics and its Applications (ISIA)","volume":"3 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":"117102730","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/ISIA55826.2022.9993496
A. Benabdallah, A. Djebbari
The Electrocardiographic (ECG) recording is a reliable human heart vital status measurement. Automatic processing of these signals through several computational approaches such as machine learning tools has recently emerged in modern biometric systems. Evaluating ECG potential for biometrical applications has been the purpose of several research papers. In this paper, we developed a new model for individual authentication by detecting high-performance fiducial features of ECG signals. We used SVM and Naive Bayes classifiers to study the impact of high-order statistical features of QRS complexes and R-R intervals within ECG-ID and MIT-BIH Arrhythmia Databases. We integrated these features into a biometric model that we developed. The system reaches an accuracy of 96% up to 99% for the ECG-ID and MIT-BIH databases, respectively. The obtained results approve the reliability of the developed model for robust biometric recognition.
{"title":"Biometric Individual Authentication System using High Performance ECG Fiducial Features","authors":"A. Benabdallah, A. Djebbari","doi":"10.1109/ISIA55826.2022.9993496","DOIUrl":"https://doi.org/10.1109/ISIA55826.2022.9993496","url":null,"abstract":"The Electrocardiographic (ECG) recording is a reliable human heart vital status measurement. Automatic processing of these signals through several computational approaches such as machine learning tools has recently emerged in modern biometric systems. Evaluating ECG potential for biometrical applications has been the purpose of several research papers. In this paper, we developed a new model for individual authentication by detecting high-performance fiducial features of ECG signals. We used SVM and Naive Bayes classifiers to study the impact of high-order statistical features of QRS complexes and R-R intervals within ECG-ID and MIT-BIH Arrhythmia Databases. We integrated these features into a biometric model that we developed. The system reaches an accuracy of 96% up to 99% for the ECG-ID and MIT-BIH databases, respectively. The obtained results approve the reliability of the developed model for robust biometric recognition.","PeriodicalId":169898,"journal":{"name":"2022 5th International Symposium on Informatics and its Applications (ISIA)","volume":"39 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":"127081481","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/ISIA55826.2022.9993558
S. Yessad, Smail Hamadache, Sory Ibrahim Siby, Souaad Boussoufa-Lahlah, L. Bouallouche-Medjkoune
Vehicular networks are an emerging type of net-works used in several applications in Intelligent Transport Systems and Smart Cities. Recently, the set of these applications has been extended to include even more with the emergence of the connected vehicles and the Internet of Vehicles (IoV). To make these different applications work effectively, wireless communications are required between the various nodes of the network. Specifically, it is important to find an efficient way to route messages from a source node to a destination node. In this paper, we propose to adapt the well known opportunistic routing protocol PRoPHET to send a notification of traffic violation in order to help the traffic policemen in their job. So, the application installed in an OBU, RSU or smartphones broadcasts a hello message with its identification at each time interval and each vehicle receiving this last calculates the probability of encountering these nodes. This last represents the delivery predictability for the application. To evaluate our proposition, we present a case study with the application of the traffic violations notification in the Algerian city of Bejaia. We create a simulation scenario for our application in the ONE simulator and evaluate the latency, the overhead rate, the delivery probability and the number of hops using PRoPHET and Epidemic opportunistic routing protocols.
{"title":"Application-Aware Opportunistic Routing Protocol for Traffic Violations Notification in Internet of Vehicles","authors":"S. Yessad, Smail Hamadache, Sory Ibrahim Siby, Souaad Boussoufa-Lahlah, L. Bouallouche-Medjkoune","doi":"10.1109/ISIA55826.2022.9993558","DOIUrl":"https://doi.org/10.1109/ISIA55826.2022.9993558","url":null,"abstract":"Vehicular networks are an emerging type of net-works used in several applications in Intelligent Transport Systems and Smart Cities. Recently, the set of these applications has been extended to include even more with the emergence of the connected vehicles and the Internet of Vehicles (IoV). To make these different applications work effectively, wireless communications are required between the various nodes of the network. Specifically, it is important to find an efficient way to route messages from a source node to a destination node. In this paper, we propose to adapt the well known opportunistic routing protocol PRoPHET to send a notification of traffic violation in order to help the traffic policemen in their job. So, the application installed in an OBU, RSU or smartphones broadcasts a hello message with its identification at each time interval and each vehicle receiving this last calculates the probability of encountering these nodes. This last represents the delivery predictability for the application. To evaluate our proposition, we present a case study with the application of the traffic violations notification in the Algerian city of Bejaia. We create a simulation scenario for our application in the ONE simulator and evaluate the latency, the overhead rate, the delivery probability and the number of hops using PRoPHET and Epidemic opportunistic routing protocols.","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":"128975293","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/ISIA55826.2022.9993508
Rayene Amina Boukabouya, A. Moussaoui, Mohamed Berrimi
Plant health is one of the most interesting aspects in the natural cycle, it needs to be conserved to keep the life of the organisms. Several plant diseases could be observed at early stages in the leaf level, where immediate interventions should be taken to prevent the progression of the disease. The use of deep learning has dramatically increased recently, owing to its remarkable performance in multiple applications in different research areas. In this study, we focus on the detection of tomato diseases at the leaf stage using recent deep learning architectures. Several deep learning models are put in comparative experiments to achieve a stable and robust classification performance with high precision that outperforms previous SOTA results. Vision Transformers (ViT) models reported the top classification re-sults, with an accuracy of 96.7%, 98.52%, 99.1% and 99.7%. The research funding will help in the early automatic detection of diseases in the leaf plants, thus providing necessary treatments and maintaining the natural cycle.
{"title":"Vision Transformer Based Models for Plant Disease Detection and Diagnosis","authors":"Rayene Amina Boukabouya, A. Moussaoui, Mohamed Berrimi","doi":"10.1109/ISIA55826.2022.9993508","DOIUrl":"https://doi.org/10.1109/ISIA55826.2022.9993508","url":null,"abstract":"Plant health is one of the most interesting aspects in the natural cycle, it needs to be conserved to keep the life of the organisms. Several plant diseases could be observed at early stages in the leaf level, where immediate interventions should be taken to prevent the progression of the disease. The use of deep learning has dramatically increased recently, owing to its remarkable performance in multiple applications in different research areas. In this study, we focus on the detection of tomato diseases at the leaf stage using recent deep learning architectures. Several deep learning models are put in comparative experiments to achieve a stable and robust classification performance with high precision that outperforms previous SOTA results. Vision Transformers (ViT) models reported the top classification re-sults, with an accuracy of 96.7%, 98.52%, 99.1% and 99.7%. The research funding will help in the early automatic detection of diseases in the leaf plants, thus providing necessary treatments and maintaining the natural cycle.","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":"115398538","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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