As e-commerce is increasingly becoming popular, the number of customer reviews that a product receives grows rapidly. However, for popular products, many online product reviews exist but for other reviews product reviews are very few. These online discussions about particular products may help other online users to make a decision in buying/ not buying those products, like in amazon.com and ebay.com. Since an enormous number of unstructured and ungrammatical reviews on a product exist, opinion mining is getting a crucial research area for better decision making of buying products. In this paper, we apply an opinion mining approach to summarize the unstructured and ungrammatical users' reviews, based on Support Vector Machine (SVM). Two levels of classification is applied: 1)Features classification and 2) Polarity classification for every feature class. Our approach has been tested on Amazon data with dataset of 535 sentences, where a summary is obtained and analysis of precision (93.15%) and recall (92.41%) illustrate the accuracy of the proposed system.

As e-commerce is increasingly becoming popular, the number of customer reviews that a product receives grows rapidly. However, for popular products, many online product reviews exist but for other reviews product reviews are very few. These online discussions about particular products may help other online users to make a decision in buying/ not buying those products, like in amazon.com and ebay.com. Since an enormous number of unstructured and ungrammatical reviews on a product exist, opinion mining is getting a crucial research area for better decision making of buying products. In this paper, we apply an opinion mining approach to summarize the unstructured and ungrammatical users' reviews, based on Support Vector Machine (SVM). Two levels of classification is applied: 1)Features classification and 2) Polarity classification for every feature class. Our approach has been tested on Amazon data with dataset of 535 sentences, where a summary is obtained and analysis of precision (93.15%) and recall (92.41%) illustrate the accuracy of the proposed system.

As e-commerce is increasingly becoming popular, the number of customer reviews that a product receives grows rapidly. However, for popular products, many online product reviews exist but for other reviews product reviews are very few. These online discussions about particular products may help other online users to make a decision in buying/ not buying those products, like in amazon.com and ebay.com. Since an enormous number of unstructured and ungrammatical reviews on a product exist, opinion mining is getting a crucial research area for better decision making of buying products. In this paper, we apply an opinion mining approach to summarize the unstructured and ungrammatical users' reviews, based on Support Vector Machine (SVM). Two levels of classification is applied: 1)Features classification and 2) Polarity classification for every feature class. Our approach has been tested on Amazon data with dataset of 535 sentences, where a summary is obtained and analysis of precision (93.15%) and recall (92.41%) illustrate the accuracy of the proposed system.

Different types of e-assessment systems that are recognized at universities and based on the campus wireless have been developed. These systems help the students to use their Mobile Phones as learning media to access the information more easily from anywhere and at anytime. Seppala and Alamaki developed a mobile learning project for teacher training. Their study compared the effectiveness of internet, face-to-face and mobile based instructions. Al Masri has proposed a study to compare the effective strategy in paper-based assessment with mobile-based assessment for assessing university students in English literature. It has been found that students gained better scores in mobile phone-based test than in paper-based test. This paper aims to determine and measure the effects of mobile-based assessments on the perception, achievement levels and performance of the students in internet-assisted courses. The main functionalities and features of this paper are: Knowledge evaluation, automatic generation of exams, exam grading, communication, course management, and questions-bank database.

Web-based learning tools provide integrated environments of various technologies to support diverse educators’ and learners’ needs via the Internet. Motivation strategies in online distance learning and e-learning should be identified early in the process so as to enhance the learning outcomes. Online learning can be more flexible and often involves more technologies that can give learners the opportunity to interact with instructors and other learners effectively and flexibly. Recent studies indicate that university students who have been enrolled on e-learning courses outperform those being taught on traditional courses. An example of this can be found at Carnegie Mellon University (CMU) in America where student exam results have shown improvement as a result of e-learning techniques. It is therefore imperative that an education system is created which is capable of rapid adaption to its technological, social, cultural and political environment. This paper shows that the use of interactive features of e-learning increases the motivation of undergraduate students for the learning process.

Abstract Developing telepresence robots is one of the most important trends in the robotics research area, where the user acts as he/she is located in a remote location. In 2010, telepresence robots became a noticeable trend after the robot “QB” that introduced by Silicon Valley start-up Anybots (Robotics trends for 2012. IEEE Robot. Autom. Mag. 19(1):119–123, 2012). Although, the availability of “QB” as a commercial telepresence robot, its cost made it unavailable for most users. In this work, a low-cost telepresence robot is presented using iRobot-Create, Microsoft Kinect sensor, and video glasses. The proposed system makes the user feels like he/she is located in a different location and acting as in the normal life (walking, stop, rotating his/her head). The user takes feedback via a streaming video from the remote location to a pair of video glasses worn by him. The remote unit consists of three components: a single iRobot-Create, a laptop, and two web-cams. In the user side, the user’s movements are recognized using Microsoft Kinect sensor. We use the RGB camera in Microsoft Kinect sensor for streaming the video of the user to the remote side. So, People in the remote side see the user, as he/she is located with them. The results of the proposed system show that the user is integrated in another environment using low-cost hardware components.

Abstract Developing telepresence robots is one of the most important trends in the robotics research area, where the user acts as he/she is located in a remote location. In 2010, telepresence robots became a noticeable trend after the robot “QB” that introduced by Silicon Valley start-up Anybots (Robotics trends for 2012. IEEE Robot. Autom. Mag. 19(1):119–123, 2012). Although, the availability of “QB” as a commercial telepresence robot, its cost made it unavailable for most users. In this work, a low-cost telepresence robot is presented using iRobot-Create, Microsoft Kinect sensor, and video glasses. The proposed system makes the user feels like he/she is located in a different location and acting as in the normal life (walking, stop, rotating his/her head). The user takes feedback via a streaming video from the remote location to a pair of video glasses worn by him. The remote unit consists of three components: a single iRobot-Create, a laptop, and two web-cams. In the user side, the user’s movements are recognized using Microsoft Kinect sensor. We use the RGB camera in Microsoft Kinect sensor for streaming the video of the user to the remote side. So, People in the remote side see the user, as he/she is located with them. The results of the proposed system show that the user is integrated in another environment using low-cost hardware components.

Abstract Developing telepresence robots is one of the most important trends in the robotics research area, where the user acts as he/she is located in a remote location. In 2010, telepresence robots became a noticeable trend after the robot “QB” that introduced by Silicon Valley start-up Anybots (Robotics trends for 2012. IEEE Robot. Autom. Mag. 19(1):119–123, 2012). Although, the availability of “QB” as a commercial telepresence robot, its cost made it unavailable for most users. In this work, a low-cost telepresence robot is presented using iRobot-Create, Microsoft Kinect sensor, and video glasses. The proposed system makes the user feels like he/she is located in a different location and acting as in the normal life (walking, stop, rotating his/her head). The user takes feedback via a streaming video from the remote location to a pair of video glasses worn by him. The remote unit consists of three components: a single iRobot-Create, a laptop, and two web-cams. In the user side, the user’s movements are recognized using Microsoft Kinect sensor. We use the RGB camera in Microsoft Kinect sensor for streaming the video of the user to the remote side. So, People in the remote side see the user, as he/she is located with them. The results of the proposed system show that the user is integrated in another environment using low-cost hardware components.

Abstract The field of Optical Character Recognition (OCR) has gained more attention in the recent years because of its importance and applications. Some examples of OCR are: video indexing, references archiving, car-plate recognition, and data entry. In this work a robust system for OCR is presented. The proposed system recognizes text in poor quality images. Characters are extracted from the given poor quality image to be recognized using chain-code representation. The proposed system uses Google online spelling to suggest replacements for words which are misspelled during the recognition process. For evaluating the proposed system, the born-digital dataset ICDAR is used. The proposed system achieves 74.02 % correctly recognized word rate. The results demonstrate that the proposed system recognizes text in poor quality images efficiently.

Abstract The field of Optical Character Recognition (OCR) has gained more attention in the recent years because of its importance and applications. Some examples of OCR are: video indexing, references archiving, car-plate recognition, and data entry. In this work a robust system for OCR is presented. The proposed system recognizes text in poor quality images. Characters are extracted from the given poor quality image to be recognized using chain-code representation. The proposed system uses Google online spelling to suggest replacements for words which are misspelled during the recognition process. For evaluating the proposed system, the born-digital dataset ICDAR is used. The proposed system achieves 74.02 % correctly recognized word rate. The results demonstrate that the proposed system recognizes text in poor quality images efficiently.