scholarly journals Self-Learning Approaches for Real Optical Networks

Author(s):  
Marc Ruiz ◽  
Fabien Boitier ◽  
Patricia Layec ◽  
Luis Velasco
Procedia CIRP ◽  
2019 ◽  
Vol 79 ◽  
pp. 313-318 ◽  
Author(s):  
Benjamin Lindemann ◽  
Fabian Fesenmayr ◽  
Nasser Jazdi ◽  
Michael Weyrich

Curationis ◽  
1996 ◽  
Vol 19 (2) ◽  
Author(s):  
B. Majumdar

The rapid pace at which biological health breakthroughs and advancements in technology occur is creating unique challenges to health care programmes. The curricula of all health care programmes will need to be set in learning environments where students will be able to develop learning skills that are transportable across situations, over a whole lifetime. This article attempts to focus on self-directed learning (SDL) concepts and the development of a learning contract/plan, including the roles of both the student and faculty in self-learning approaches and contractual development.


2021 ◽  
pp. 307-327
Author(s):  
Hussein Joumaa ◽  
Khoder Jneid ◽  
Mireille Jacomino

2015 ◽  
pp. 1784-1804
Author(s):  
Natalia Kushik ◽  
Jeevan Pokhrel ◽  
Nina Yevtushenko ◽  
Ana Cavalli ◽  
Wissam Mallouli

This paper is devoted to the problem of evaluating the quality of experience (QoE) for a given multimedia service based on the values of service parameters such as QoS indicators. This paper proposes to compare two self learning approaches for predicting the QoE index, namely the approach based on logic circuit learning and the approach based on fuzzy logic expert systems. Experimental results for comparing these two approaches with respect to the prediction ability and the performance are provided.


2020 ◽  
Author(s):  
João Pedro Serrasqueiro Martins ◽  
Filipe Martins Rodrigues ◽  
Nuno Paulo Ferreira Henriques

Industry 4.0 must respond to some challenges such as the flexibility and robustness of unexpected conditions, as well as the degree of system autonomy, something that is still lacking. The evolution of Industry 4.0 aims at converting purely mechanical machines into machines with self-learning capacity in order to improve overall performance  and contribute to the optimization of maintenance. An important contribution of Industry 4.0 in the industrial sector is predictive maintenance and prescriptive maintenance. This article should be analysed as a methodology proposal to implement an automatic forecasting model in a test bench for the recognition of a machine’s failure and contribute to the development of algorithms for preventive and descriptive maintenance. Keywords: Industry 4.0, Artificial intelligence, Machine learning, Predictive maintenance, Prescriptive maintenance


2018 ◽  
Vol 6 (4) ◽  
pp. 1
Author(s):  
Azza Fathi Ibrahim ◽  
Azza Anwar Aly

Every day and every moment, nurses have to deal with a wide variety of patient’s issues and problems, with multiple difficulties and conflicts. Nurse’s judgment considers the core component of healthcare activities. This judgment directs her/his achievement and choices, not for her/him only, but for other healthcare professionals. Thus, nurses have to be competent observers and decision makers with reasoning and sound regarding their intervention and practice. Clinical judgment skills are essential aptitudes in nursing practice, predominantly, in nursing internship intermediary period, in which, a graduate nurse faced several predicaments and obstacles in such transitory experience from academic work to real labor. The present study aimed to develop a clinical judgment model to guide nursing interns in their nursing practice and assess its effectiveness on nursing interns’ clinical judgment knowledge and skills. The study passed through Quasi-experimental pretest-posttest research design. A stratified random sampling approach was used to recruit 50 nursing interns as an experimental group out of 305. The collection of data was carried out in the following hospitals of nurses’ intern’s training: Damanhur Medical National Institute, El Raee El Saleh, El Farok and Kafer El Dawar Hospitals, in Damanhur, Egypt. The Clinical Judgment Evaluation Sheet (CJES) was employed to collect necessary data. It included two parts: the Clinical Judgment Knowledge Test that was developed by Fathi & Aly in 2018, beside the Lasater Clinical Judgment Rubric (LCJR) that was developed by Kathie Lasater et al., in 2009. Results before using the developed model demonstrated that there were observed lacking in knowledge and skills about clinical judgment in nursing practice among experimental group. Then improvements were noticed after using the developed clinical judgment model. These results confirm that the use of an educational and self-learning reference such as the developed clinical judgment model is a successful tool for Egyptian nursing interns in nursing practice. Conclusion and recommendations: There is an understandable deficiency of nursing interns’ clinical judgment knowledge and skills in nursing practice. But, after using the developed clinical judgment model with them as a self-learning reference, it was confirmed that it is a helpful approach to develop and improve clinical judgment knowledge and skills of nursing interns. Creativity in using instructional aides and self-learning approaches is an essential ability that is important among nurse educators and preceptors who direct nursing intern’s performance. For further sturdies, replicate the study using the developed model with different subjects in nursing practice or develop new instructive models about creative, reflective, discovery, and decision-making models among nursing interns.


Author(s):  
Shi-Chung Chang ◽  
Jennifer Wen-Shya Lee ◽  
Kun-You Lin ◽  
Ho-Lin Chen ◽  
Jiun-Peng Chen ◽  
...  

International higher education policies and literature have called for students and faculty to collaborate effectively in the co-designing and co-teaching of curricula. In the fall of 2017, the Department of Electrical Engineering of National Taiwan University launched the “Creative Cornerstone Course Design for ICT+ and Engineering Education” course, which is a co-design course, to engage higher division and graduate students in co-creating and co-teaching the curriculum of a “Cornerstone EECS Design and Implementation” freshman course, which was a cornerstone course to be conducted in the spring of 2018. This paper presents the educational practice and learning outcomes of the co-design course. The implementation of the co-design course involved the following activities: (a) project- and team-based learning approaches, (b) active student partnership with teachers for designing the cornerstone course curriculum, and (c) preparatory cultivation of the students as teaching assistants for co-teaching. Learning outcome analysis indicated that freshman students significantly benefited in terms of their self-exploration of ICT-related subjects, basic professional knowledge, operational techniques, and confidence in self-learning when the cornerstone course was developed through co-designing.+ICT: Information and Communication Technology


Author(s):  
Natalia Kushik ◽  
Jeevan Pokhrel ◽  
Nina Yevtushenko ◽  
Ana Cavalli ◽  
Wissam Mallouli

This paper is devoted to the problem of evaluating the quality of experience (QoE) for a given multimedia service based on the values of service parameters such as QoS indicators. This paper proposes to compare two self learning approaches for predicting the QoE index, namely the approach based on logic circuit learning and the approach based on fuzzy logic expert systems. Experimental results for comparing these two approaches with respect to the prediction ability and the performance are provided.


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