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JournalISSN: 1665-9899

Research on computing science 

About: Research on computing science is an academic journal. The journal publishes majorly in the area(s): Decision support system & Ontology (information science). It has an ISSN identifier of 1665-9899. Over the lifetime, 1716 publications have been published receiving 3860 citations.


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Book ChapterDOI
TL;DR: It is concluded that multi-agent systems do have an increasingly important role to play in health care domains, because they significantly enhance the ability to model, design and build complex, distributed health care software systems.
Abstract: In this paper we introduce the main issues related to the deployment of agent-based systems in health care. First, we comment on the characteristics of health care problems and we argue that multi-agent systems are a good choice to tackle problems with these features. This belief is supported with a number of exemplar applications of agent-based systems in medical domains. We also discuss several lines of research that have to be covered before multi-agent systems can be successfully deployed in real health care settings. We conclude that multi-agent systems do have an increasingly important role to play in health care domains, because they significantly enhance our ability to model, design and build complex, distributed health care software systems

116 citations

Journal Article
Wu Qingfeng1, Lin Kunhui1, Zhou Chang-le1, M Li, 吴清锋 
TL;DR: Experimental results prove the effectiveness and superiority of the approach for recognizing plant leaf using artificial neural network, and the prototype system has been implemented.
Abstract: Plant recognition is an important and challenging task. Leaf recognition plays an important role in plant recognition and its key issue lies in whether selected features are stable and have good ability to discriminate different kinds of leaves. From the view of plant leaf morphology (such as shape, dent, margin, vein and so on), domain–related visual features of plant leaf are analyzed and extracted first. On such a basis, an approach for recognizing plant leaf using artificial neural network is brought forward. The prototype system has been implemented. Experiment results prove the effectiveness and superiority of this method.

71 citations

Journal ArticleDOI
TL;DR: A new lexicon-based sentiment analysis approach to address the specific aspects of the vernacular Algerian Arabic fully utilized in social networks is proposed.
Abstract: Nowadays, sentiment analysis research is widely applied in a variety of applications such as marketing and politics. Several studies on the Arabic sentiment analysis have been carried out in recent years. These studies mainly focus on Modern Standard Arabic among which few studies have investigated the case of Arab dialects, in this case, Egyptian, Jordanian, and Khaliji. In this paper, we propose a new lexicon-based sentiment analysis approach to address the specific aspects of the vernacular Algerian Arabic fully utilized in social networks. A manually annotated dataset and three Algerian Arabic lexicons have been created to explore the different phases of our approach.

64 citations

Journal ArticleDOI
Caroline Brun1, Caroline Hagège1
TL;DR: Natural Language Processing techniques are proposed to apply in order to address this rather new task of extracting automatically such kind of suggestions for improvement from user's comments.
Abstract: In the context of the development of a feature-based opi- nion mining system for English, we observed that there is some very interesting information provided by customers but not yet covered by \standard" opinion mining techniques: opinion mining aims at detecting whether comments are positive or negative, but it appears that customers are very often suggesting improvements about what they are reviewing, which is quite dierent from expressing an opinion. This papers proposes to apply Natural Language Processing techniques in order to address this rather new task of extracting automatically such kind of suggestions for improvement from user's comments.

58 citations

Journal Article
TL;DR: It has been shown that the proposed ME based POS tagger outperforms the HMM based tagger and the lexi- con, named entity recognizer and dierent word suxes are eective in handling the unknown word problems and improve the accuracy of the POS taggers signicantly.
Abstract: Part of Speech (POS) tagging can be described as a task of doing automatic annotation of syntactic categories for each word in a text document. This paper presents a POS tagger for Bengali using the statistical Maximum Entropy (ME) model. The system makes use of the dierent contextual information of the words along with the variety of features that are helpful in predicting the various POS classes. The POS tagger has been trained with a training corpus of 72, 341 word forms and it uses a tagset 1 of 26 dierent POS tags, dened for the Indian languages. A part of this corpus has been selected as the development set in order to nd out the best set of features for POS tagging in Ben- gali. The POS tagger has demonstrated an accuracy of 88.2% for a test set of 20K word forms. It has been experimentally veried that the lexi- con, named entity recognizer and dierent word suxes are eective in handling the unknown word problems and improve the accuracy of the POS tagger signicantly. Performance of this system has been compared with a Hidden Markov Model (HMM) based POS tagger and it has been shown that the proposed ME based POS tagger outperforms the HMM based tagger.

52 citations

Performance
Metrics
No. of papers from the Journal in previous years
YearPapers
202253
202042
2019200
2018144
2017178
2016262