Pub Date : 2019-12-01DOI: 10.1109/ICTAACS48474.2019.8988123
Sihem Klai Soukehal, Karima Chibane, M. Khadir
The genome sequence indexing is a primary step in order to facilitate other further treatments such as patterns search or assembly with a reference genome etc. And the suffix tree is one of the most used data structures for indexing the genome sequence. However, the memory required for running the suffix tree construction algorithms may exceed the amount of available main memory. Despite the efforts made by the researchers, the construction of suffix tree remains very expensive with the use of data centres to ensure optimal parallelization of treatments and reduce the execution time without forgetting the risks of breakdown and the problems that it breeds. The parallelization performed by Hadoop and MapReduce gives solutions to storage and data processing capacity limitations as well as fault tolerance, all that at reasonable costs. The emergence of Hadoop, a framework related to big data and the paradigm MapReduce that allows to model parallel and distributed processing, is investigating many domains of science in order to effectively parallel their treatments. PWOTD (Partition and Write Only Top Down) algorithm, is chosen here as it has proven itself in textual algorithms for genome sequencing. In this paper, an approach to model the parallel construction of the suffix tree using the MapReduce paradigm is designed for implementation in Hadoop with a java API.
基因组序列索引是促进其他进一步治疗如模式搜索或与参考基因组组装等的首要步骤。后缀树是基因组序列索引中最常用的数据结构之一。但是，运行后缀树构造算法所需的内存可能会超过可用的主内存。尽管研究人员做出了努力，但后缀树的构建仍然非常昂贵，需要使用数据中心来确保处理的最佳并行化，减少执行时间，同时还要考虑崩溃的风险和由此产生的问题。Hadoop和MapReduce执行的并行化解决了存储和数据处理容量限制以及容错问题，所有这些都是在合理的成本下完成的。Hadoop的出现，一个与大数据相关的框架，以及允许并行和分布式处理建模的范式MapReduce，正在研究许多科学领域，以便有效地并行它们的处理。这里选择PWOTD (Partition and Write Only Top Down)算法，因为它已经在基因组测序的文本算法中证明了自己。本文设计了一种使用MapReduce范式对后缀树的并行构建建模的方法，并通过java API在Hadoop中实现。
{"title":"Suffix Tree Construction based Mapreduce","authors":"Sihem Klai Soukehal, Karima Chibane, M. Khadir","doi":"10.1109/ICTAACS48474.2019.8988123","DOIUrl":"https://doi.org/10.1109/ICTAACS48474.2019.8988123","url":null,"abstract":"The genome sequence indexing is a primary step in order to facilitate other further treatments such as patterns search or assembly with a reference genome etc. And the suffix tree is one of the most used data structures for indexing the genome sequence. However, the memory required for running the suffix tree construction algorithms may exceed the amount of available main memory. Despite the efforts made by the researchers, the construction of suffix tree remains very expensive with the use of data centres to ensure optimal parallelization of treatments and reduce the execution time without forgetting the risks of breakdown and the problems that it breeds. The parallelization performed by Hadoop and MapReduce gives solutions to storage and data processing capacity limitations as well as fault tolerance, all that at reasonable costs. The emergence of Hadoop, a framework related to big data and the paradigm MapReduce that allows to model parallel and distributed processing, is investigating many domains of science in order to effectively parallel their treatments. PWOTD (Partition and Write Only Top Down) algorithm, is chosen here as it has proven itself in textual algorithms for genome sequencing. In this paper, an approach to model the parallel construction of the suffix tree using the MapReduce paradigm is designed for implementation in Hadoop with a java API.","PeriodicalId":406766,"journal":{"name":"2019 International Conference on Theoretical and Applicative Aspects of Computer Science (ICTAACS)","volume":"2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114233763","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 : 2019-12-01DOI: 10.1109/ICTAACS48474.2019.8988137
Sofiane Maza, Djaafar Zouache
The large dimension of datasets influences on the classification performances and computational time. For that, feature selection is among process that avoid of those problems by selecting the relevance and eliminate the redundancy features.In this paper, we propose a new algorithm for feature selection FAFS (Firefly Algorithm for Feature Selection) which is based on the firefly algorithm. FAFS uses two objectives, which are Accuracy Rate and Reduction Rate. We propose a new formula to calculate the distance r and attractive A in Firefly algorithm.The experimental results show the capability of the proposed algorithm with three classifiers (KNN, NB, and LDA) and their outperformance against PSO-FS (Particle Swarm Optimization for feature selection).
数据集的大维度影响分类性能和计算时间。特征选择是通过选择相关特征和消除冗余特征来避免这些问题的过程之一。本文在萤火虫算法的基础上，提出了一种新的特征选择算法FAFS (Firefly algorithm for feature selection)。FAFS使用两个目标，即正确率和还原率。我们提出了一个新的计算萤火虫算法中距离r和吸引a的公式。实验结果表明，该算法具有KNN、NB和LDA三种分类器，并且优于PSO-FS (Particle Swarm Optimization for feature selection)。
{"title":"Binary Firefly Algorithm for Feature Selection in Classification","authors":"Sofiane Maza, Djaafar Zouache","doi":"10.1109/ICTAACS48474.2019.8988137","DOIUrl":"https://doi.org/10.1109/ICTAACS48474.2019.8988137","url":null,"abstract":"The large dimension of datasets influences on the classification performances and computational time. For that, feature selection is among process that avoid of those problems by selecting the relevance and eliminate the redundancy features.In this paper, we propose a new algorithm for feature selection FAFS (Firefly Algorithm for Feature Selection) which is based on the firefly algorithm. FAFS uses two objectives, which are Accuracy Rate and Reduction Rate. We propose a new formula to calculate the distance r and attractive A in Firefly algorithm.The experimental results show the capability of the proposed algorithm with three classifiers (KNN, NB, and LDA) and their outperformance against PSO-FS (Particle Swarm Optimization for feature selection).","PeriodicalId":406766,"journal":{"name":"2019 International Conference on Theoretical and Applicative Aspects of Computer Science (ICTAACS)","volume":"38 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121390976","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 : 2019-12-01DOI: 10.1109/ICTAACS48474.2019.8988120
Rahima Boukerma, Salah Bougueroua, Bachir Boucheham
In Content Based-Image Retrieval (CBIR), low-level visual characteristics like color, texture and shape are used to search for relevant images. However, the result images returned to the user are generally not satisfactory to his expectations. This is due to the gap between the low-level features of the image and the semantic (high-level) concepts given by the user to the same image. To overcome this challenge, we propose in this paper a mechanism that improves CBIR performance and consequently reduce the semantic gap. In that regard, our work involves the optimization of CBIR using a specific mechanism for weighting the extracted textural characteristics of the image. The extraction of the latter is carried out by some local patterns methods. Then, the generation of the weights associated with the local patterns, is realized using the Differential Evolution algorithm. To evaluate our approach, we tested it on Wang’s database (Corel-1K). In addition, we adopted the precision as performance evaluation measure and we used Manhattan and Euclidean distances for comparing the local patterns histograms. The results of the carried-out experiments show that the obtained precisions by the weighted local patterns methods are better than those of the conventional methods.
{"title":"A Local Patterns Weighting Approach for Optimizing Content-Based Image Retrieval Using a Differential Evolution Algorithm","authors":"Rahima Boukerma, Salah Bougueroua, Bachir Boucheham","doi":"10.1109/ICTAACS48474.2019.8988120","DOIUrl":"https://doi.org/10.1109/ICTAACS48474.2019.8988120","url":null,"abstract":"In Content Based-Image Retrieval (CBIR), low-level visual characteristics like color, texture and shape are used to search for relevant images. However, the result images returned to the user are generally not satisfactory to his expectations. This is due to the gap between the low-level features of the image and the semantic (high-level) concepts given by the user to the same image. To overcome this challenge, we propose in this paper a mechanism that improves CBIR performance and consequently reduce the semantic gap. In that regard, our work involves the optimization of CBIR using a specific mechanism for weighting the extracted textural characteristics of the image. The extraction of the latter is carried out by some local patterns methods. Then, the generation of the weights associated with the local patterns, is realized using the Differential Evolution algorithm. To evaluate our approach, we tested it on Wang’s database (Corel-1K). In addition, we adopted the precision as performance evaluation measure and we used Manhattan and Euclidean distances for comparing the local patterns histograms. The results of the carried-out experiments show that the obtained precisions by the weighted local patterns methods are better than those of the conventional methods.","PeriodicalId":406766,"journal":{"name":"2019 International Conference on Theoretical and Applicative Aspects of Computer Science (ICTAACS)","volume":"41 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129039511","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 : 2019-12-01DOI: 10.1109/ICTAACS48474.2019.8988125
Hamida Bouaziz, Ali Lemouari
The Cubic Cell Formation Problem (CCFP) in cellular manufacturing systems consists in decomposing a production system into a set of manufacturing cells, and assigning workers to cells besides parts and machines. The objective is to obtain manageable cells by minimising the inter-cell moves of parts and workers and the heterogeneity in cells. In this paper, we provide a solution methodology based on a modified simulated annealing heuristic with a proposed neighbourhood search procedure. The proposed methodology allows to build multiple configurations by giving to the designer the ability to control some parameters. Experimental results show that the proposed algorithm gives a promising performance for the most problem instances found in the literature.
{"title":"Solving the Cubic Cell Formation Problem Using Simulated Annealing Algorithm to Develop Multiple Configurations","authors":"Hamida Bouaziz, Ali Lemouari","doi":"10.1109/ICTAACS48474.2019.8988125","DOIUrl":"https://doi.org/10.1109/ICTAACS48474.2019.8988125","url":null,"abstract":"The Cubic Cell Formation Problem (CCFP) in cellular manufacturing systems consists in decomposing a production system into a set of manufacturing cells, and assigning workers to cells besides parts and machines. The objective is to obtain manageable cells by minimising the inter-cell moves of parts and workers and the heterogeneity in cells. In this paper, we provide a solution methodology based on a modified simulated annealing heuristic with a proposed neighbourhood search procedure. The proposed methodology allows to build multiple configurations by giving to the designer the ability to control some parameters. Experimental results show that the proposed algorithm gives a promising performance for the most problem instances found in the literature.","PeriodicalId":406766,"journal":{"name":"2019 International Conference on Theoretical and Applicative Aspects of Computer Science (ICTAACS)","volume":"40 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121963746","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 : 2019-12-01DOI: 10.1109/ICTAACS48474.2019.8988130
Ahlem Drif, S. Giordano
Social networks, as user-generated content platforms, enable users to express their own opinions timely. To detect and predict user opinions, several works have been proposed. This paper aims to solve it as a time-series prediction problem: understanding the most important messages spreading, and predicting the evolution of the information spreading in future. We proposed a solution based on the Long Short-Term Memory (LSTM) model to capture such evolution. LSTM model was applied to features extracted from the corpus of tweets with the aim of capturing the impact of the salient tweets on information spreading process. By detecting the behaviour of the past tweets, we predict tweet diffusion in the next time period. The results with a real data-set of tweets show the strength of the proposed solution to predict the subtopic diffusion with high accuracy.
{"title":"Tracking diffusion pattern based on Salient Tweets","authors":"Ahlem Drif, S. Giordano","doi":"10.1109/ICTAACS48474.2019.8988130","DOIUrl":"https://doi.org/10.1109/ICTAACS48474.2019.8988130","url":null,"abstract":"Social networks, as user-generated content platforms, enable users to express their own opinions timely. To detect and predict user opinions, several works have been proposed. This paper aims to solve it as a time-series prediction problem: understanding the most important messages spreading, and predicting the evolution of the information spreading in future. We proposed a solution based on the Long Short-Term Memory (LSTM) model to capture such evolution. LSTM model was applied to features extracted from the corpus of tweets with the aim of capturing the impact of the salient tweets on information spreading process. By detecting the behaviour of the past tweets, we predict tweet diffusion in the next time period. The results with a real data-set of tweets show the strength of the proposed solution to predict the subtopic diffusion with high accuracy.","PeriodicalId":406766,"journal":{"name":"2019 International Conference on Theoretical and Applicative Aspects of Computer Science (ICTAACS)","volume":"16 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126495286","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 : 2019-12-01DOI: 10.1109/ICTAACS48474.2019.8988131
Amira Ichrak Tei, Z. Doukha, Youcef Zafoune
Data dissemination in Vehicular Ad-Hoc Networks (VANETs) is used as a tool for safety applications. In the real world, urban environment can be faced to some inherent VANET characteristics such as highly dynamic topology, diverse network densities, the changing speed of the vehicles, the noise, the obstacles which considerably affect the communication quality and make data dissemination fail in spreading urgent information over the network. To this effect, this work proposes a Multi-Criteria based Relay Election Protocol for Data Dissemination in urban VANETs (MCRE-DDP) where the most relevant relay node is elected based on several parameters like Signal to Noise Ratio (SNR), vehicle speed, distance between sender and receiver to determine the nodes quality and its ability to successfully relay the dissemination message because relevant relays prevent the communication system from multiple sending and guaranty the suitable data relaying as far as the boundaries of the area of interest. Simulation results show that MCRE-DDP performs data dissemination more efficiently than its peers in terms of Dissemination Speed, link load and redundancy ratio.
{"title":"Multi-criteria-based relay election for Data Dissemination in urban VANET","authors":"Amira Ichrak Tei, Z. Doukha, Youcef Zafoune","doi":"10.1109/ICTAACS48474.2019.8988131","DOIUrl":"https://doi.org/10.1109/ICTAACS48474.2019.8988131","url":null,"abstract":"Data dissemination in Vehicular Ad-Hoc Networks (VANETs) is used as a tool for safety applications. In the real world, urban environment can be faced to some inherent VANET characteristics such as highly dynamic topology, diverse network densities, the changing speed of the vehicles, the noise, the obstacles which considerably affect the communication quality and make data dissemination fail in spreading urgent information over the network. To this effect, this work proposes a Multi-Criteria based Relay Election Protocol for Data Dissemination in urban VANETs (MCRE-DDP) where the most relevant relay node is elected based on several parameters like Signal to Noise Ratio (SNR), vehicle speed, distance between sender and receiver to determine the nodes quality and its ability to successfully relay the dissemination message because relevant relays prevent the communication system from multiple sending and guaranty the suitable data relaying as far as the boundaries of the area of interest. Simulation results show that MCRE-DDP performs data dissemination more efficiently than its peers in terms of Dissemination Speed, link load and redundancy ratio.","PeriodicalId":406766,"journal":{"name":"2019 International Conference on Theoretical and Applicative Aspects of Computer Science (ICTAACS)","volume":"67 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114675253","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 the modern digital area, Internet of Things (IoT) is increasingly gathering attention for the implementation of applications in several aspects of everyday activities, intending to make our cities smarter and more comfortable. Therefore, the implementation of these IoT applications raises several challenges to overcome. One of these challenges is the efficient use of resources at each stage of the application, such as acquisition, storage, processing, and networking. In smart cities, many IoT monitoring systems continuously generate large amounts of data. These data volumes, before they can be processed and responded, must first be transmitted through the city’s networks (Wifi, Bluetooth, LTE). To deal with this considerable amount of continually transmitted data and to reduce the load on networks, we propose an approach based on the efficient use of data compression in IoT systems. This approach uses a data compression smart strategy to reduce the transmitted data during the acquisition process and thus minimize the use of network resources while providing the user with relevant information in real-time using a prioritization mechanism. In order to show the efficiency of our proposal, we conducted experiments on a case study of an air quality monitoring system.
{"title":"Towards a Smart Data Transmission Strategy for IoT Monitoring Systems: Application to Air Quality Monitoring","authors":"Razika Lounas, Dhai Eddine Salhi, Hocine Mokrani, Rachid Djerbi, M. Bennai","doi":"10.1109/ICTAACS48474.2019.8988119","DOIUrl":"https://doi.org/10.1109/ICTAACS48474.2019.8988119","url":null,"abstract":"In the modern digital area, Internet of Things (IoT) is increasingly gathering attention for the implementation of applications in several aspects of everyday activities, intending to make our cities smarter and more comfortable. Therefore, the implementation of these IoT applications raises several challenges to overcome. One of these challenges is the efficient use of resources at each stage of the application, such as acquisition, storage, processing, and networking. In smart cities, many IoT monitoring systems continuously generate large amounts of data. These data volumes, before they can be processed and responded, must first be transmitted through the city’s networks (Wifi, Bluetooth, LTE). To deal with this considerable amount of continually transmitted data and to reduce the load on networks, we propose an approach based on the efficient use of data compression in IoT systems. This approach uses a data compression smart strategy to reduce the transmitted data during the acquisition process and thus minimize the use of network resources while providing the user with relevant information in real-time using a prioritization mechanism. In order to show the efficiency of our proposal, we conducted experiments on a case study of an air quality monitoring system.","PeriodicalId":406766,"journal":{"name":"2019 International Conference on Theoretical and Applicative Aspects of Computer Science (ICTAACS)","volume":"4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115819098","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 : 2019-12-01DOI: 10.1109/ICTAACS48474.2019.8988116
M. N. Boufenara, M. Boufaida, M. Berkane
In a data-driven world, semi-supervised learning methods are motivated by the availability of large unlabeled datasets than a small amount of labeled data. However, incorporating unlabeled data into learning does not guarantee an improvement in classification performance. In this paper, we present an approach based on a deep learning system to predict missing classes by integrating a model of semi-supervised learning which is the self-training. In order to evaluate its performance, we used a set of diabetes data and four performance measures: Precision, Recall, F-Measure and Area Under the ROC Curve (AUC).
在数据驱动的世界中，半监督学习方法的动机是大量未标记数据集的可用性，而不是少量标记数据。然而，将未标记数据纳入学习并不能保证分类性能的提高。在本文中，我们提出了一种基于深度学习系统的方法，通过集成半监督学习模型(即自训练)来预测缺课。为了评估其性能，我们使用了一组糖尿病数据和四个性能指标:Precision, Recall, F-Measure和Area Under the ROC Curve (AUC)。
{"title":"A Machine learning technique dedicated for biological data","authors":"M. N. Boufenara, M. Boufaida, M. Berkane","doi":"10.1109/ICTAACS48474.2019.8988116","DOIUrl":"https://doi.org/10.1109/ICTAACS48474.2019.8988116","url":null,"abstract":"In a data-driven world, semi-supervised learning methods are motivated by the availability of large unlabeled datasets than a small amount of labeled data. However, incorporating unlabeled data into learning does not guarantee an improvement in classification performance. In this paper, we present an approach based on a deep learning system to predict missing classes by integrating a model of semi-supervised learning which is the self-training. In order to evaluate its performance, we used a set of diabetes data and four performance measures: Precision, Recall, F-Measure and Area Under the ROC Curve (AUC).","PeriodicalId":406766,"journal":{"name":"2019 International Conference on Theoretical and Applicative Aspects of Computer Science (ICTAACS)","volume":"210 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115972586","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 : 2019-12-01DOI: 10.1109/ICTAACS48474.2019.8988118
K. Abainia, Hamza Rebbani
As known in the literature, light stemmers produce more under-stemming errors, while root stemmers produce more over-stemming errors. In this investigation, we deal with the Arabic light stemming problem, where we propose an improvement to ARLSTem algorithm (i.e. ARLSTem v1.1). In particular, we introduce new rules to correct some under-stemming errors produced by ARLSTem. In addition, we compare the new version of ARLSTem with five existing stemming algorithms using ARASTEM corpus. The latter has been corrected, where we have found some errors in seven samples. The experimental results showed that ARLSTem v1.1 outperforms the other existing algorithms in terms of under-stemming and over-stemming errors. Moreover, it presents interesting performances in the Arabic text categorization task.
{"title":"Comparing the Effectiveness of the Improved ARLSTem Algorithm with Existing Arabic Light Stemmers","authors":"K. Abainia, Hamza Rebbani","doi":"10.1109/ICTAACS48474.2019.8988118","DOIUrl":"https://doi.org/10.1109/ICTAACS48474.2019.8988118","url":null,"abstract":"As known in the literature, light stemmers produce more under-stemming errors, while root stemmers produce more over-stemming errors. In this investigation, we deal with the Arabic light stemming problem, where we propose an improvement to ARLSTem algorithm (i.e. ARLSTem v1.1). In particular, we introduce new rules to correct some under-stemming errors produced by ARLSTem. In addition, we compare the new version of ARLSTem with five existing stemming algorithms using ARASTEM corpus. The latter has been corrected, where we have found some errors in seven samples. The experimental results showed that ARLSTem v1.1 outperforms the other existing algorithms in terms of under-stemming and over-stemming errors. Moreover, it presents interesting performances in the Arabic text categorization task.","PeriodicalId":406766,"journal":{"name":"2019 International Conference on Theoretical and Applicative Aspects of Computer Science (ICTAACS)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129957738","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 : 2019-12-01DOI: 10.1109/ICTAACS48474.2019.8988135
Adel Saadi, R. Maamri, Z. Sahnoun
The Belief-Desire-Intention (BDI) model is amongst the most popular approaches to design agents with flexible behaviors. It is based essentially on the processing of beliefs and goals. On the other hand, it is well accepted that the motive is another relevant concept for the agent’s reasoning and its flexibility. As the BDI model does not include this important concept, several BDI agent’ s extensions, with a new and a special component expressing the motive, were proposed in the literature. In this paper, we show that it is not necessary to add this new component to express a motive, as a motive can be expressed by a particular type of goal. This allows including and taking advantage from the reasoning about motives without additional components.
{"title":"A Natural inclusion of Motives inside BDI agents","authors":"Adel Saadi, R. Maamri, Z. Sahnoun","doi":"10.1109/ICTAACS48474.2019.8988135","DOIUrl":"https://doi.org/10.1109/ICTAACS48474.2019.8988135","url":null,"abstract":"The Belief-Desire-Intention (BDI) model is amongst the most popular approaches to design agents with flexible behaviors. It is based essentially on the processing of beliefs and goals. On the other hand, it is well accepted that the motive is another relevant concept for the agent’s reasoning and its flexibility. As the BDI model does not include this important concept, several BDI agent’ s extensions, with a new and a special component expressing the motive, were proposed in the literature. In this paper, we show that it is not necessary to add this new component to express a motive, as a motive can be expressed by a particular type of goal. This allows including and taking advantage from the reasoning about motives without additional components.","PeriodicalId":406766,"journal":{"name":"2019 International Conference on Theoretical and Applicative Aspects of Computer Science (ICTAACS)","volume":"59 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130832182","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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