Enhancing query interpretation by combining textual and visual analyses

Fakhfakh, Rim; Ksibi, Amel; Ammar, Anis Ben; Amar, Chokri Ben

doi:10.1109/icadlt.2013.6568454

Cited by 5 publications

(2 citation statements)

References 15 publications

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“…Previous researchers have shed light on the importance of context awareness [22], [23]. However, 'context' meaning differs across studies, and there is no real consensus on the definition of the concept.…”

Section: Introductionmentioning

confidence: 99%

Predicting Student Employability Through the Internship Context Using Gradient Boosting Models

et al. 2022

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Universities around the world are keen to develop study plans that will guide their graduates to success in the job market. The internship course is one of the most significant courses that provides an experiential opportunity for students to apply knowledge and to prepare to start a professional career. However, internships do not guarantee employability, especially when a graduate's internship performance is not satisfactory and the internship requirements are not met. Many factors contribute to this issue making the prediction of employability an important challenge for researchers in the higher education field. In this paper, our aim is to introduce an effective method to predict student employability based on context and using Gradient Boosting classifiers. Our contributions consist of harnessing the power of gradient boosting algorithms to perform context-aware employability status prediction processes. Student employability prediction relies on identifying the most predictive features impacting the hiring opportunity of graduates. Hence, we define two context models, which are the student context based on the student features and the internship context based on internship features. Experiments are conducted using three gradient boosting classifiers: eXtreme Gradient Boosting (XGBoost), Category Boosting (CatBoost) and Light Gradient Boosted Machine (LGBM). The results obtained showed that applying LGBM classifiers over the internship context performs the best compared to student context. Therefore, this study provides strong evidence that the employability of graduates is predictable from the internship context.

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Section: Introductionmentioning

confidence: 99%

Predicting Student Employability Through the Internship Context Using Gradient Boosting Models

et al. 2022

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“…However, their performance capabilities are limited because of the semantic gap between the low level descriptors and the user understanding [7] [8] [9] . Besides, various types of social features [10][II] [12] [13](tags, locations, users, ... ) tend to effectively describe the semantic of images with the help of content features. Hence, it seems useful to benefit from complex connections that may exist among photos and other features in social media to improve search effectiveness [14].…”

Section: Introductionmentioning

confidence: 99%

Semantic-aware framework for Mobile Image Search

Bouhlel

Ksibi

Ammar

et al. 2015

2015 15th International Conference on Intelligent Systems Design and Applications (ISDA)

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Social and Mobile are the two very characterizing trends of the Internet. Subsequently, the volume of photos with rich social, textual and contextual information increases exponentially either on mobile devices or social networks. Performing an efficient and effective mobile Image Search over social photo collection is therefore a crucial challenge. Indeed, capture the complex connections among social photos is as important as speeding up similarity search at large scale. This paper present a generic Mobile Image Search framework with hypergraph hashing. On the mobile side, users are enabled to formulate whether visual, textual or vocal queries. On the server side, we start by modeling complex connections that may exist among photos and social features using an hypergraph. To accelerate the nearest neighbor search over the hypergraph, a spectral hashing is performed. Namely, each hypergraph vertex is mapped to a binary string without loss of similarity. For unseen items in the hypergraph, a query-adaptive supervised learning is carried out to learn binary strings based on the query type. We report the initial results over NUS-WIDE collection which show that the proposed framework is promising in the field of Mobile Image Search.

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