{"title":"A fuzzy expert system in evaluation for E-learning","authors":"Khalid Salmi, H. Magrez, A. Ziyyat","doi":"10.1109/CIST.2014.7016623","DOIUrl":"https://doi.org/10.1109/CIST.2014.7016623","url":null,"abstract":"With the flourish of online education and distance learning, it's become very important to make the evaluation in e-learning scientifically and objectively. This paper presents an intelligent fuzzy evaluation system based on our innovative evaluation method where management rules made by the experts are used to help, optimize and decide. On this basis, we develop an evaluation method and algorithm based on the fuzzy logic concepts and new information technologies. Thus, intelligent applications are possible if one wish to automate the management and decision while making an alarm system based on relevant indicators. Finally, we come up with an “expert system” that can provide some solutions to the problems of measurement theory. In addition, we present a concrete example to compare this method with the traditional one then draw conclusions on the effectiveness of this method.","PeriodicalId":106483,"journal":{"name":"2014 Third IEEE International Colloquium in Information Science and Technology (CIST)","volume":"91 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134479346","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}
{"title":"A new method to construct a statistical model for Arabic language","authors":"Ali Sadiqui, Ahmed Zinedine","doi":"10.1109/CIST.2014.7016635","DOIUrl":"https://doi.org/10.1109/CIST.2014.7016635","url":null,"abstract":"Language models are one of the key components in modern systems of automatic language processing. In this study we present a new approach for the realization of a statistical model of Arabic language for non-vocalized texts. This approach allows to overcome the morphological complexity of the Arabic language and to address the limitations of existing morphological analyzers. Indeed the classic approach adopted by most of the morphological analyzers, bring the word out of its context and therefore generate several options for segmentation. Our solution proposes using trellises at a time to keep the possibilities of segmentation generated by the morphological analyzer and then create the model language. In order to realize this solution, we have used these tools: AraMorph and Lattice-Tool from the box SRILM and AT & WSF. The language was estimated from a corpus composed of 100 K words and has been tested on a corpus of 7 K words. The results and analysis are presented in this document.","PeriodicalId":106483,"journal":{"name":"2014 Third IEEE International Colloquium in Information Science and Technology (CIST)","volume":"5 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130891593","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}
Abdelhamid Zouhair, E. En-Naimi, B. Amami, H. Boukachour, P. Person, C. Bertelle
{"title":"The impact of the implementation of our system IDCBR-MAS","authors":"Abdelhamid Zouhair, E. En-Naimi, B. Amami, H. Boukachour, P. Person, C. Bertelle","doi":"10.1109/CIST.2014.7016622","DOIUrl":"https://doi.org/10.1109/CIST.2014.7016622","url":null,"abstract":"In this paper we present our approach in the field of Intelligent Tutoring System (ITS), in fact the risk of dropping out for learners have emerged as crucial issues to be solved. So it is necessary to ensure an individualized and continuous learner's follow-up during learning process. Several research effort has been spent on the development of ITS. However the available literature does not generally concentrate on the individual realtime continuous follow up of learners. Our contribution in this field is to design and implement a computer system able to initiate learning and provide an individualized monitoring of learners. This approach involves 1) the use of Dynamic Case Based Reasoning to retrieve the past experiences that are similar to the learners' traces (traces in progress), and 2) the use of Multi-Agents System. Our Work focuses on the use of the learner traces. When interacting with the platform, every learner leaves his/her traces in the machine. The traces are stored in database, this operation enriches collective past experiences. Via monitoring, comparing and analyzing these traces, the system keeps a constant intelligent watch on the platform, and therefore it detects the difficulties hindering progress, and it avoids possible dropping out. The system can support any learning subject.","PeriodicalId":106483,"journal":{"name":"2014 Third IEEE International Colloquium in Information Science and Technology (CIST)","volume":"8 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125372238","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}
{"title":"Arabic medical terms compilation from Wikipedia","authors":"J. Vivaldi, H. Rodríguez","doi":"10.1109/CIST.2014.7016627","DOIUrl":"https://doi.org/10.1109/CIST.2014.7016627","url":null,"abstract":"Domain terms are a useful mean for tuning both resources and NLP processors to domain specific tasks. This paper proposes an improved method for obtaining terms from potentially any domain using the Wikipedia graph structure as a knowledge source.","PeriodicalId":106483,"journal":{"name":"2014 Third IEEE International Colloquium in Information Science and Technology (CIST)","volume":"21 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122430030","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}
{"title":"Enhanced mobile positioning technique for UMTS users in both outdoor and Indoor environments","authors":"Ilham El Mourabit, A. Badri, A. Sahel, A. Baghdad","doi":"10.1109/CIST.2014.7016642","DOIUrl":"https://doi.org/10.1109/CIST.2014.7016642","url":null,"abstract":"Mobile positioning by cellular networks has received growing attention and several researches are carried out to enhance positioning algorithms and techniques that will be able to give better performance at multiple environments (outdoors/indoors). In this paper, we are interested in UTDOA (uplink time difference of arrival) approach to determine the location of a Mobile Station within an acceptable accuracy and respect of emergency cases in both areas (Indoor/Outdoor). The enhancement of this method is performed with adaptive filtering, with MATLAB software, using two different algorithm to show the advantages of the chosen one, and its efficiency in emergency calls even with legacy phones.","PeriodicalId":106483,"journal":{"name":"2014 Third IEEE International Colloquium in Information Science and Technology (CIST)","volume":"7 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127800716","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}
{"title":"Towards a flexible open-source software library for multi-layered scholarly textual studies: An Arabic case study dealing with semi-automatic language processing","authors":"A. D. Grosso, Ouafae Nahli","doi":"10.1109/CIST.2014.7016633","DOIUrl":"https://doi.org/10.1109/CIST.2014.7016633","url":null,"abstract":"This paper presents both the general model and a case study of the Computational and Collaborative Philology Library (CoPhiLib), an ongoing initiative underway at the Institute for Computational Linguistics (ILC) of the National Research Council (CNR), Pisa, Italy. The library, designed and organized as a reusable, abstract and open-source software component, aims at solving the needs of multi-lingual and cross-lingual analysis by exposing common Application Programming Interfaces (APIs). The core modules, coded by the Java programming language, constitute the groundwork of a Web platform designed to deal with textual scholarly needs. The Web application, implemented according to the Java Enterprise specifications, focuses on multi-layered analysis for the study of literary documents and related multimedia sources. This ambitious challenge seeks to obtain the management of textual resources, on the one hand by abstracting from current language, on the other hand by decoupling from the specific requirements of single projects. This goal is achieved thanks to methodologies declared by the “agile process”, and by putting into effect suitable use case modeling, design patterns, and component-based architectures. The reusability and flexibility of the system have been tested on an Arabic case study: the system allows users to choose the morphological engine (such as AraMorph or Al-Khalil), along with linguistic granularity (i.e. with or without declension). Finally, the application enables the construction of annotated resources for further statistical engines (training set).","PeriodicalId":106483,"journal":{"name":"2014 Third IEEE International Colloquium in Information Science and Technology (CIST)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117139849","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}
{"title":"Enhancing Arabic WordNet with the use of Princeton WordNet and a bilingual dictionary","authors":"R. Gratta, Ouafae Nahli","doi":"10.1109/CIST.2014.7016632","DOIUrl":"https://doi.org/10.1109/CIST.2014.7016632","url":null,"abstract":"This paper describes an heuristic-based approach to enhance existing WordNets with freely available bilingual resources. The approach has been applied to the Arabic WordNet using the AraMorph bilingual dictionary as bilingual resource, but its guidelines are quite general to be effectively applied to other languages. The English words extracted from the bilingual resource are checked against Princeton WordNet in order to quantify their coverage and to select only those words which share the same set of synsets. This strongly reduces the number of Arabic words of the pairs. These latter are then checked against the Arabic WordNet to make new words emerge and - possibly - add new synonyms.","PeriodicalId":106483,"journal":{"name":"2014 Third IEEE International Colloquium in Information Science and Technology (CIST)","volume":"84 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115771453","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}
{"title":"Off-line recognition handwriting Arabic words using combination of multiple classifiers","authors":"Ahlam Maqqor, A. Halli, K. Satori, H. Tairi","doi":"10.1109/CIST.2014.7016629","DOIUrl":"https://doi.org/10.1109/CIST.2014.7016629","url":null,"abstract":"We present in this paper a system of Arabic handwriting recognition based on combining methods of decision fusion approach. The proposed approach introduces a methodology using the HMM-Toolkit (HTK) for a rapid implementation of our designed recognition system. After the image preprocessing, the text is segmented into lines, the obtained images are then used for features extraction with Sliding window technique. These features are extracted on binary images of characters and are modeled separately using Hidden Markov Models classifiers. The combination of the multiple HMMs classifiers was applied by using the different methods of decision fusion approach. The proposed system is evaluated using the IFN/ENIT database. Experimental results for Arabic handwritten recognition demonstrate that the Weighted Majority Voting (WMV) combination method have given better recognition rate 76.54% in top1, with Gaussian distribution.","PeriodicalId":106483,"journal":{"name":"2014 Third IEEE International Colloquium in Information Science and Technology (CIST)","volume":"107 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123231431","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}
{"title":"HCHIRSIMEX: An extended method for domain ontology learning based on conditional mutual information","authors":"O. Idrissi, B. Frikh, B. Ouhbi","doi":"10.1109/CIST.2014.7016600","DOIUrl":"https://doi.org/10.1109/CIST.2014.7016600","url":null,"abstract":"This paper presents HCHIRSIMEX, an extended version of our previous algorithm HCHIRSIM for building domain ontology from web corpus. The new version introduces a novel measure based on the Conditional Mutual Information (CMI) statistic method to define the taxonomic relations and the similarity between selected concepts. By using this method, the ontology extracted by HCHIRSIMEX is more concise and contains a richer concept knowledge base compared with the previous version HCHIRSIM. To evaluate our new algorithm effectiveness, we apply the two algorithms and Sanchez et al. algorithm in Finance domain ontology constructed from the web. Then, we compare the obtained concepts with those on the “Financial glossary” provided by Yahoo.com.","PeriodicalId":106483,"journal":{"name":"2014 Third IEEE International Colloquium in Information Science and Technology (CIST)","volume":"30 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122775267","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}
Mohammed Rais, Abdelmonaime Lachkar, Abdelhamid Lachkar, S. A. Ouatik
{"title":"A comparative study of biomedical named entity recognition methods based machine learning approach","authors":"Mohammed Rais, Abdelmonaime Lachkar, Abdelhamid Lachkar, S. A. Ouatik","doi":"10.1109/CIST.2014.7016641","DOIUrl":"https://doi.org/10.1109/CIST.2014.7016641","url":null,"abstract":"Recognizing Biomedical Named Entities (BioNEs) such as genes, proteins, cells, drugs, diseases, etc. play a vital role in many Biomedical Text Mining applications. BioNER fall into five approaches: Dictionary-Based, Rule-Based, Machine-Learning-Based, Statistical-Based, and Hybrid-Based. Methods Based Machine Learning approach, are more effective than those of other approaches, and therefore have been widely used for learning to recognize BioNEs. In this paper, we present a comparative theoretical and experimental study between seven Machine Learning methods, by summarizing their advantages and weaknesses, and comparing their performance on two standard biomedical Corpora (GENIA and JNLPBA). The obtained results show that CRF outperforms all the other Machine-Learning methods on both corpora. That method (CRF) will be integrated in our future works.","PeriodicalId":106483,"journal":{"name":"2014 Third IEEE International Colloquium in Information Science and Technology (CIST)","volume":"58 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122492929","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}