Custom Validation Procedure for Tesys Recommender System

Abstract The paper presents the validation procedure of the model used in the analysis of the composite blade for the rotor of the ILX-27 rotorcraft, designed and manufactured in the Institute of Aviation, by means of numerical analyses and tests of composite elements. Numerical analysis using finite element method and experimental studies of three research objects made of basic materials comprising the blade structure – carbon-epoxy laminate, glass-epoxy composite made of roving and foam filler – were carried out. The elements were in the form of four-point bent beams, and for comparison of the results the deflection arrow values in the middle of the beam and axial deformations on the upper and lower surfaces were selected. The procedure allowed to adjust the discrete model to real objects and to verify and correct the material data used in the strength analysis of the designed blade.

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A behavior-based recommender system of university students' daily diets

Advances in Communication Technology and Systems ◽

10.2495/iccts140521 ◽

2014 ◽

Author(s):

Kewen Tang ◽

Wan Yi ◽

Qin Yin ◽

Zhaoxing Li

Keyword(s):

University Students ◽

Recommender System ◽

Behavior Based

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An Approach to Design and Development Recommender System

International Journal of Computer Sciences and Engineering ◽

10.26438/ijcse/v6i3.431433 ◽

2018 ◽

Vol 6 (3) ◽

pp. 431-433

Author(s):

Samir N Ajani ◽

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Lokesh M Heda ◽

Santosh Kumar Sahu ◽

Manish M Motghare ◽

...

Keyword(s):

Recommender System ◽

Design And Development

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Toward a Hybrid Recommender System for E-learning Personnalization Based on Data Mining Techniques

JOIV International Journal on Informatics Visualization ◽

10.30630/joiv.2.4.158 ◽

2018 ◽

Vol 2 (4) ◽

pp. 271 ◽

Cited By ~ 3

Author(s):

Outmane Bourkoukou ◽

Essaid El Bachari

Keyword(s):

Learning Style ◽

Recommender System ◽

Adaptive Learning ◽

Learning Objects ◽

Research Field ◽

Learning System ◽

K Nearest Neighbors ◽

E Learning ◽

Hybrid Recommender System ◽

Hybrid Recommender

Personalized courseware authoring based on recommender system, which is the process of automatic learning objects selecting and sequencing, is recognized as one of the most interesting research field in intelligent web-based education. Since the learner’s profile of each learner is different from one to another, we must fit learning to the different needs of learners. In fact from the knowledge of the learner’s profile, it is easier to recommend a suitable set of learning objects to enhance the learning process. In this paper we describe a new adaptive learning system-LearnFitII, which can automatically adapt to the dynamic preferences of learners. This system recognizes different patterns of learning style and learners’ habits through testing the psychological model of learners and mining their server logs. Firstly, the device proposed a personalized learning scenario to deal with the cold start problem by using the Felder and Silverman’s model. Next, it analyzes the habits and the preferences of the learners through mining the information about learners’ actions and interactions. Finally, the learning scenario is revisited and updated using hybrid recommender system based on K-Nearest Neighbors and association rule mining algorithms. The results of the system tested in real environments show that considering the learner’s preferences increases learning quality and satisfies the learner.

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