A three-stage learning algorithm for deep multilayer perceptron (DMLP) with effective weight initialisation based on sparse auto-encoder is proposed in this paper, which aims to overcome difficulties in training deep neural networks with limited training data in high-dimensional feature space. At the first stage, unsupervised learning is adopted using sparse auto-encoder to obtain the initial weights of the feature extraction layers of the DMLP. At the second stage, error back-propagation is used to train the DMLP by fixing the weights obtained at the first stage for its feature extraction layers. At the third stage, all the weights of the DMLP obtained at the second stage are refined by error back-propagation. Network structures an

The Coronavirus Disease 2019 (COVID-19) pandemic has caused an unprecedented disruption in medical education and healthcare systems worldwide. The disease can cause life-threatening conditions and it presents challenges for medical education, as instructors must deliver lectures safely, while ensuring the integrity and continuity of the medical education process. It is therefore important to assess the usability of online learning methods, and to determine their feasibility and adequacy for medical students. We aimed to provide an overview of the situation experienced by medical students during the COVID-19 pandemic, and to determine the knowledge, attitudes, and practices of medical students regarding electronic medical education.

The current problem is summarized in what is called the development failing experience
in comprehencing the studying materials , so the students will feel worry of repeating failure
in he future , so he would seek blind keeping on heart for the studying material bond this isbad due to the forgetting in the future , one side of thesis research problem is that there is
many contradictory researches result in relation to the learning styles which impose the
nessicity to find results lessen this contradiction . the importance of the research is
summarized in the importance of the subject under the study , in that the researcher ( as in
her knowledge ) did not find a thesrs tackling the subject of the distinguished students

The  adopted method in the teaching of history is conservation and indoctrination in all grades, and this will lead to a lack of students interact with teachers in the course of the lesson, and poor use of teachers to questions that raise students' thinking during the lesson, which leads to a lack of interest in the topic of the lesson and wasting opportunities contribution making it the teacher at the center of the educational process, and to provide arrogating the researcher to contribute to teaching style with the belief that the use of this method of teaching could lead to overcome the difficulties and problems faced by the teaching material.

And there are educational complexes integrated approac

     In the last few years, the literature conferred a great interest in studying the feasibility of using memristive devices for computing. Memristive devices are important in structure, dynamics, as well as functionalities of artificial neural networks (ANNs) because of their resemblance to biological learning in synapses and neurons regarding switching characteristics of their resistance. Memristive architecture consists of a number of metastable switches (MSSs). Although the literature covered a variety of memristive applications for general purpose computations, the effect of low or high conductance of each MSS was unclear. This paper focuses on finding a potential criterion to calculate the conductance of each MMS rather t

This study aimed to assess orthodontic postgraduate students’ use of social media during the COVID-19 lockdown. Ninety-four postgraduate students (67 master’s students and 27 doctoral students) were enrolled in the study and asked to fill in an online questionnaire by answering questions regarding their use of social media during the COVID-19 lockdown. The frequency distributions and percentages were calculated using SPSS software. The results showed that 99% of the students used social media. The most frequently used type of social media was Facebook, 94%, followed by YouTube, 78%, and Instagram, 65%, while Twitter and Linkedin were used less, and no one used Blogger. About 63% of the students used elements of social media to l

The intelligent buildings provided various incentives to get highly inefficient energy-saving caused by the non-stationary building environments. In the presence of such dynamic excitation with higher levels of nonlinearity and coupling effect of temperature and humidity, the HVAC system transitions from underdamped to overdamped indoor conditions. This led to the promotion of highly inefficient energy use and fluctuating indoor thermal comfort. To address these concerns, this study develops a novel framework based on deep clustering of lagrangian trajectories for multi-task learning (DCLTML) and adding a pre-cooling coil in the air handling unit (AHU) to alleviate a coupling issue. The proposed DCLTML exhibits great overall control and is

This research aims to examine the relationship between learning organization and behavior of work teams. The variable of the learning organization took four dimensions depending on the study (sudhartna & Li, 2004): Common cultural values ​​, communication, knowledge transfer and the characteristics of workers. The behavior of teams was identified on the basis of realizing of the respondents of their organization to work as a team where the research relied concepts applied in the study (Hakim , 2005) , and chose to research the case of a service organization for the study and relied on four dimensions of coordination , cooperation , sharing of information , the performance of the team, and was a curriculum approach and des

The emergence of SARS-CoV-2, the virus responsible for the COVID-19 pandemic, has resulted in a global health crisis leading to widespread illness, death, and daily life disruptions. Having a vaccine for COVID-19 is crucial to controlling the spread of the virus which will help to end the pandemic and restore normalcy to society. Messenger RNA (mRNA) molecules vaccine has led the way as the swift vaccine candidate for COVID-19, but it faces key probable restrictions including spontaneous deterioration. To address mRNA degradation issues, Stanford University academics and the Eterna community sponsored a Kaggle competition.This study aims to build a deep learning (DL) model which will predict deterioration rates at each base of the mRNA