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.

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Nowadays, optical character recognition is one of the most successful automatic pattern recognition applications. Many works have been done regarding the identification of Latin and Chinese characters. However, the reason for having few investigations for the recognition of Arabic characters is the complexity and difficulty of Arabic characters identification compared to the others. In the current work, we investigate combining multiple machine learning algorithms for multi-font Arabic isolated characters recognition, where imperfect and dimensionally variable input charactersare faced. To the best of our knowledge, there is no such work yet available in this regard. Experimental results show that combined multiple classifiers can outperform each individual classifier produces by itself. The current findings are encouraging and opens the door for further research tasks in this direction.

In this paper shows a present vision of neural systems that are propelled by neural frameworks to give viable models to measurable examination. Their most essential part in neural system is the capacity to “learn”, depend in a set number of observation. With regards to neural systems, The articulation “Picking up” subsidizing that the learning picked up from the example can be outlined as tactile reconnaissance. In this regard, fake neural systems are regularly alluded to as the learning machine. In that capacity, counterfeit neural systems might be considered as images for operators who take in the reliance of their condition and make their conduct techniques subject to a predetermined number of perceptions. This exploration does not have to finish up from the natural sources of neural systems. Be that as it may, this is an absolutely scientific model and factual application. ever after the comming of PC insight, The craft of working together needs to experience uncommon changes. After some time, numerous information based registering frameworks have entered a substantial number of organizations and their utilization has turned out to be progressively across the board. With the colossal advances in innovation, the administration of data identified with cutting edge counterfeit neuroscience has turned into a basic part of business insight. In this article, we portray the key of neural systems and in addition We will audit the work done in counterfeit neural systems applications in numerous organizations. The association of this diary is as per the following. The initial segment exhibits a general prologue to neural systems. The second part features the business utilizations of neural systems. The third part takes a gander at work done in the field of insolvency determining, trailed by work in the zones of Mastercard extortion identification. The fourth part investigate the Back spread calculation – a numerical methodology and work done in the zones of securities exchange forecast, trailed by a survey of money related bookkeeping work. Area five examines The connection among ANN and Statistical strategies, Finally, we finish up this article in segment 6th pursued by references and the glossary.

Real-time image classification is one of the most challenging issues in understanding images and computer vision domain. Deep learning methods, especially Convolutional Neural Network (CNN), has increased and improved the performance of image processing and understanding. The performance of real-time image classification based on deep learning achieves good results because the training style, and features that are used and extracted from the input image. This work proposes an interesting model for real-time image classification architecture based on deep learning with fully connected layers to extract proper features. The classification is based on the hybrid GoogleNet pre-trained model. The datasets that are used in this work are 15 scene and UC Merced Land-Use datasets, used to test the proposed model. The proposed model achieved 92.4 and 98.8 as a higher accuracy.