The Multi-Agents Architecture for Emotion Recognition

2014 ◽  
Vol 2 (1) ◽  
pp. 73-85 ◽  
Author(s):  
Mohamed Néji ◽  
Ali Wali ◽  
Adel M. Alimi
Keyword(s):  
Information Retrieval ◽  
Facial Expression ◽  
Emotion Recognition ◽  
Retrieval System ◽  
Emotional State ◽  
Physiological State ◽  
Information Retrieval System ◽  
User Needs ◽  
Degree Of Satisfaction ◽  
Multi Agents

The author's research focuses on the problem of Information Retrieval System (IRS) that integrates the human emotion recognition. This system must be able to recognize the degree of satisfaction of the user for the result found through its facial expression, its physiological state, its gestures and its voice. This paper is an algorithm for recognizing the emotional state of a user during a search session in order to issue the relevant documents that the user needs. The authors also present the architecture agent of the envisaged system and the organizational model.

Strategi Pengembangan Content Based Video Retrieval Di Perpustakaan Sekolah Tinggi Multi Media MMTC Yogyakarta

2021 ◽  
pp. 36-60
Author(s):  
Sri Wahyuni
Keyword(s):  
Information Retrieval ◽  
System Design ◽  
Swot Analysis ◽  
Retrieval System ◽  
Video Retrieval ◽  
Information Retrieval System ◽  
User Needs ◽  
Video Content ◽  
Multi Media ◽  
Content Based Video Retrieval

ABSTRACT Introduction One of the efforts to provide the best service for users is by developing innovative library services. One of them is by developing a video content-based library collection. MMTC Yogyakarta Multi Media College Library has developed a video content-based information retrieval system. It is hoped that by utilizing this video content-based STKI, users will be helped and get accelerated information in finding the material needed, especially searching for material in video files. Data Collection Method. In this paper the writer uses qualitative research with a library research approach, while the data analysis uses content analysis techniques. This method the authors use to observe and analyze an information system. Results and Discussions. In developing a Content Based Video Retrieval strategy in the MMTC Yogyakarta Multi Media High School Library, it begins with identifying user needs, creating a system design, evaluating the system design, pouring the system design into a programming language, testing the system, evaluating the system and using it. Then, the authors also provide an overview of the development of the STKI by conducting a SWOT analysis. Based on the macro analysis, the opportunity and threat variables will be formulated, while the internal analysis will formulate the strength and weakness variables. The last stage is the STKI analysis, while the stages are: complete definition, problem analysis, needs analysis, logic design and needs analysis. Conclusions. In the Content Based Video Retrieval development strategy at the MMTC Yogyakarta Multi Media College Library, there are several things that need to be considered in the development of an information retrieval system, including: User needs, development budget (budget), human resources, support from leaders and facilities (software and hardware) and IT infrastructure (internet network). The development of the STKI should begin with identifying user needs and conducting a SWOT analysis to determine the strengths and weaknesses of the system, as well as the goal so that the system can be optimally empowered by users. Keywords: Library, Information Retrieval System, Video Content

Information Retrieval (IR) and Extracting Associative Rules

2016 ◽  
pp. 713-732
Author(s):  
Asmae Dami ◽  
Mohamed Fakir ◽  
Belaid Bouikhalene
Keyword(s):  
Information Retrieval ◽  
Text Mining ◽  
Knowledge Discovery ◽  
Association Rules ◽  
Retrieval System ◽  
Information Retrieval System ◽  
User Needs ◽  
Retrieval Process ◽  
Research Themes ◽  
Document Database

This chapter is located in the intersection of two research themes, namely: Information Retrieval and Knowledge Discovery from texts (Text mining). The purpose of this paper is two-fold: first, it focuses on Information Retrieval (IR) whose purpose is to implement a set of models and systems for selecting a set of documents satisfying user needs in terms of information expressed as a query. An information retrieval system is composed mainly of two processes the representation and retrieval process. The process of representation is called indexing, which allows representation of documents and queries by descriptors, or indexes. These descriptors reflect the contents of documents. The retrieval process consists on the comparison between documents representations and query representation. The second aim of this paper is to discover the relationships between terms (keywords) descriptors of documents in a document database. The correlations (relationships) between terms are extracted by using a technique of the Text mining, mainly association rules.

Information Retrieval (IR) and Extracting Associative Rules

2014 ◽  
Vol 7 (4) ◽  
pp. 42-62
Author(s):  
Asmae Dami ◽  
Mohamed Fakir ◽  
Belaid Bouikhalene
Keyword(s):  
Information Retrieval ◽  
Text Mining ◽  
Knowledge Discovery ◽  
Association Rules ◽  
Retrieval System ◽  
Information Retrieval System ◽  
User Needs ◽  
Retrieval Process ◽  
Research Themes ◽  
Document Database

This paper is located in the intersection of two research themes, namely: Information Retrieval and Knowledge Discovery from texts (Text mining). The purpose of this paper is two-fold: first, it focuses on Information Retrieval (IR) whose purpose is to implement a set of models and systems for selecting a set of documents satisfying user needs in terms of information expressed as a query. An information retrieval system is composed mainly of two processes the representation and retrieval process. The process of representation is called indexing, which allows representation of documents and queries by descriptors, or indexes. These descriptors reflect the contents of documents. The retrieval process consists on the comparison between documents representations and query representation. The second aim of this paper is to discover the relationships between terms (keywords) descriptors of documents in a document database. The correlations (relationships) between terms are extracted by using a technique of the Text mining, mainly association rules.

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