With the advent of technology, electronic learning (e-learning) has become a key aspect of distance learning, making it easier, faster, and more global. With e-learning, teachers have found it easy to deliver education, while students have found new opportunities to learn. There have been several advances to e-learning as a resource for distance learning, with its major features being the use of electronic mediums to facilitate learning. This study is the first of its kind in Iraq that aims to explore the instructional needs of teachers with regards to e-learning for computer science at the secondary school level in Iraq. The study aims to identify the perceptions of secondary school teachers of computer science towards the use of e- learning methods and to also investigate the training needs of teachers for the use of e-learning for teaching of computer science in Iraq. Results indicated that e-learning resources available in the schools surveyed are not enough and the teachers are not making adequate use of e-learning resources. Also, while teachers were aware of the efficiency of e-learning for pedagogy, the schools do not have adequate support mechanisms in place. Teachers were also aware of the competitive edge that e-learning offers them, but did not have adequate training to enable them to use it effectively. Therefore, authorities have to establish a training plan to get teachers comfortable with technology. Such training should start with the basics of e-learning, so that teachers with no knowledge of technology can be carried along.
Extracting synonyms from textual corpora using computational techniques is an interesting research problem in the Natural Language Processing (NLP) domain. Neural techniques (such as Word2Vec) have been recently utilized to produce distributional word representations (also known as word embeddings) that capture semantic similarity/relatedness between words based on linear context. Nevertheless, using these techniques for synonyms extraction poses many challenges due to the fact that similarity between vector word representations does not indicate only synonymy between words, but also other sense relations as well as word association or relatedness. In this paper, we tackle this problem using a novel 2-step approach. We first build distributional word embeddings using Word2Vec then use the induced word embeddings as an input to train a feed-forward neutral network using annotated dataset to distinguish between synonyms and other semantically related words
Artificial intelligent and application of computer vision are an exciting topic in last few years, and its key for many real time applications like video summarization, image retrieval and image classifications. One of the most trend method in deep learning is a convolutional neural network, used for many applications of image processing and computer vision. In this work convolutional neural networks CNN model proposed for color image classification, the proposed model build using MATLAB tools of deep learning. In addition, the suggested model tested on three different datasets, with different size. The proposed model achieved highest result of accuracy, precision and sensitivity with the largest dataset and it was as following: accuracy is 0.9924, precision is 0.9947 and sensitivity is 0.9931, compare with other models.
Background: In recent years, innovation is considered necessary for organizations to adapt to the changing environment and increase need for effective nurse leader for enhancing leadership qualities, supportive work environment that is critical for staff nurses' innovation.
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