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                <text>Classifying bubbles in liquids is a crucial problem that is demanded within multiple fields. This paper discusses a new method for classifying bubble sizes in non-contact and inexpensive approach using ultrasound analysis. Exploiting the principle of buoyancy, free rising bubbles with larger volumes elevate faster to the surface compared to the smaller ones, given that they have the same densities. An envelope detector is proposed which tracks the changes in the ultrasound signals reflected by the bubbles when they cross the ultrasound field. These changes in the reflected signals are distinctive for the sizes under consideration. Relevant spectral and linear predictive coding features that represent the distinct characteristics are extracted. These features are fed to a feed-forward artificial neural network to successfully classify air bubbles according to their sizes with an accuracy of 98.8%. This method provides promising applications to be implemented in industrial, biomedical and environmental fields.</text>
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                <text>Copyright (c) 2017 Walid B. Hussein, Sarah A. Essmat, Nestor Yoma</text>
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                <text>International Journal of Interactive Mobile Technologies (iJIM); Vol. 11 No. 2 (2017); pp. 128-138</text>
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                <text>1865-7923</text>
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                <text>On Bubble Sizing in Water by Ultrasound</text>
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                <text>M-learning has the potential to take education out of classroom boundaries. Based on the device used, any student/learner can access a vast area of content. Varying from podcasts to videos, participate in virtual lessons or just ask a mentor over the net directly for answers to his problems. These new technological capabilities create demand for rethinking pedagogy and school system education. For that reason our examples of mobile applications within education (formal/informal) been made in correlation with technology. Usability details for each app are being discussed through the ISO9241 part11 standard. While describing current trends by taking in consideration curriculum prescribed directions and differentiated instruction’s theoretical suggestions.</text>
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                <text>Copyright (c) 2017 Athanasios Drigas, Pantelis Angelidakis</text>
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                <text>International Journal of Interactive Mobile Technologies (iJIM); Vol. 11 No. 4 (2017); pp. 17-29</text>
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                <text>1865-7923</text>
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                <text>m-learning</text>
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                <text>intelligent tutoring systems</text>
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                <text>Mobile Applications within Education: An Overview of Application Paradigms in Specific Categories</text>
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                <text>Bahaa-Eldin, Ayman M.</text>
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                <text>El-Seoud, Samir Abou</text>
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                <text>Copyright (c) 2017 Ayman M. Bahaa-Eldin, Samir Abou El-Seoud</text>
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                <text>International Journal of Interactive Mobile Technologies (iJIM); Vol. 11 No. 2 (2017); pp. 4-5</text>
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                <text>1865-7923</text>
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                <text>BUE International Conference on Sustainable Vital Technologies in Engineering and Informatics</text>
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                <text>Ahmed, Eman</text>
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                <text>Sorrour, Amin A.</text>
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                <text>Sobh, Mohamed A.</text>
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                <text>Bahaa-Eldin, Ayman M.</text>
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                <text>Malwares are increasing rapidly. The nature of distribution and effects of malwares attacking several applications requires a real-time response. Therefore, a high performance detection platform is required. In this paper, Hadoop is utilized to perform static binary search and detection for malwares and viruses in portable executable files deployed mainly on the cloud. The paper presents an approach used to map the portable executable files to Hadoop compatible files. The Boyer–Moore-Horspool Search algorithm is modified to benefit from the distribution of Hadoop. The performance of the proposed model is evaluated using a standard virus database and the system is found to outperform similar platforms.</text>
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                <text>https://online-journals.org/index.php/i-jim/article/view/6577</text>
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                <text>Copyright (c) 2017 Eman Ahmed, Amin A. Sorrour, Mohamed A. Sobh, Ayman M. Bahaa-Eldin</text>
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                <text>International Journal of Interactive Mobile Technologies (iJIM); Vol. 11 No. 2 (2017); pp. 113-127</text>
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                <text>1865-7923</text>
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                <text>Since cloud computing technology is growing day by day, it comes with many security problems, especially from threats such as malware. As more services migrate to the cloud architecture, the cloud will become a more appealing target for cyber criminals. In this paper, we present the current threats to the cloud environment, and the proposed detection systems for malware in the cloud environment. We then present a multiple detection system that is aimed at combating the spread of malware by cloud environment.</text>
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                <text>International Journal of Interactive Mobile Technologies (iJIM); Vol. 11 No. 2 (2017); pp. 139-145</text>
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                <text>1865-7923</text>
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                <text>We describe a SMS-based information system called CATS, which allows posting and searching through free Arabic text using Information Extraction (IE) technology. We discuss the challenges of applying IE technology for unedited real Arabic text. In addition, we describe the structure of this system and our approach to produce an open robust system capable of including more sub domains with the minimum effort.</text>
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                <text>Copyright (c) 2017 Daoud M. Daoud, Samir Abou El-Seoud</text>
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                <text>International Journal of Interactive Mobile Technologies (iJIM); Vol. 11 No. 2 (2017); pp. 99-112</text>
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                <text>1865-7923</text>
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                <text>Arabic Language Processing</text>
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                <text>Classified Ads</text>
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                <text>Attribute Based Searching.</text>
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                <text>Employing Information Extraction for Building Mobile Applications</text>
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                <text>Mwinyi, Masoud U.</text>
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                <text>Alja'am, Jihad M.</text>
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                <text>Abou El-Seoud, Samir</text>
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                <text>Children with special needs face major difficulties to understand the script of stories. They cannot comprehend the words’ meaning properly. Instructors use different techniques to explain them. They read the script several times, use glossy images, symbols, and short clips. The instructors spend a lot of time searching for these elements. The preparation of every tutorial requires several hours of hard work. The objective of this work is to build a system to assist the instructor finding multimedia elements in real time that can be used during the learning sessions. The proposed system is based on a domain-oriented architecture focusing on animals and food. We use different techniques for text processing including concept analysis, key words extraction and sentence ranking. We can then determine the most significant words and sentences and generate the corresponding multimedia elements. Instructors and children can use smart devices in learning inside and outside the classroom.</text>
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                <text>Copyright (c) 2017 Masoud U. Mwinyi, Jihad M. Alja'am, Samir Abou El-Seoud</text>
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                <text>International Journal of Interactive Mobile Technologies (iJIM); Vol. 11 No. 2 (2017); pp. 53-65</text>
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                <text>1865-7923</text>
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              <elementText elementTextId="24663">
                <text>Keywords Extraction</text>
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                <text>Ontology</text>
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                <text>Special Education.</text>
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                <text>A Concept Analysis Scheme of Simple Stories for Learning Resources through Extraction of Domain-based Multimedia Elements</text>
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                <text>Marwan, Samiha Abdelrahman Mohammed</text>
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                <text>Shawish, Ahmed</text>
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                <text>Nagaty, Khaled</text>
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                <text>There are continuous threats to network technologies due to its rapidly-changing nature, which raises the demand for data-safe transmission. As a result, the need to come up with new techniques for securing data and accommodating the growing quantities of information is crucial. From nature to science, the idea that genes themselves are made of information stimulated the research in molecular deoxyribonucleic acid (DNA). DNA is capable of storing huge amounts of data, which leads to its promising effect in steganography. DNA steganography is the art of using DNA as an information carrier which achieves high data storage capacity as well as high security level. Currently, DNA steganography techniques utilize the properties of only one DNA strand, since the other strand is completely dependent on the first one. This paper presents a DNA-based steganography technique that hides data into both DNA strands with respect to the dependency between the two strands. In the proposed technique, a key of the same length of the reference DNA sequence is generated after using the second DNA strand. The sender sends both the encrypted DNA message and its reference DNA sequence together into a microdot. If the recipient receives this microdot uncontaminated, the sender can safely send the generated key afterwards. The proposed technique doubles the amount of data stored and guarantees a secure transmission process as well, for even if the attacker suspects the first-sent DNA sequence, they will never receive the key, and hence full data extraction is nearly impossible. The conducted experimental study confirms the effectiveness of the proposed.</text>
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                <text>eng</text>
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                <text>Copyright (c) 2017 Samiha Abdelrahman Mohammed Marwan, Ahmed Shawish, Khaled Nagaty</text>
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                <text>International Journal of Interactive Mobile Technologies (iJIM); Vol. 11 No. 2 (2017); pp. 88-98</text>
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                <text>1865-7923</text>
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                <text>DNA Steganography</text>
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                <text>Data Hiding</text>
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                <text>Utilizing DNA Strands for Secured Data-Hiding  with High Capacity</text>
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                <text>El-Seoud, Samir Abou</text>
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                <text>Mohamed, Reham Fouad</text>
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                <text>Much of our scientific, technological, and economic future depends on the availability of an ever-increasing supply of computational power. However, the increasing demand for such power has pushed electronic technology to the limit of physical feasibility and has raised the concern that this technology may not be able to sustain our growth in the near future. It became important to consider an alternative means of achieving computational power. In this regard, DNA computing was introduced based on the usage of DNA and molecular biology hardware instead of the typical silicon based technology. The molecular computers could take advantage of DNA's physical properties to store information and perform calculations. These include extremely dense information storage, enormous parallelism and extraordinary energy efficiency. One of the main advantages that DNA computations would add to computation is its self - parallel processing while most of the electronic computers now use linear processing. In this paper, the DNA computation is reviewed and its state of the art challenges and applications are presented. Some of these applications are those require fast processing, at which DNA computers would be able to solve the hardest problems faster than the traditional ones. For example, 10 trillion DNA molecules can fit in one cubic centimeter that would result in a computer that holds 10 terabytes of data. Moreover, this work focuses on whether a large scale molecular computer can be built.</text>
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                <text>https://online-journals.org/index.php/i-jim/article/view/6564</text>
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                <text>International Association of Online Engineering (IAOE), Vienna, Austria</text>
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                <text>https://online-journals.org/index.php/i-jim/article/view/6564/4358</text>
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                <text>Copyright (c) 2017 Samir Abou El-Seoud, Reham Fouad Mohamed, Samy Ghoneimy</text>
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              <elementText elementTextId="24617">
                <text>International Journal of Interactive Mobile Technologies (iJIM); Vol. 11 No. 2 (2017); pp. 74-87</text>
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                <text>1865-7923</text>
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                <text>DNA Computing</text>
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                <text>Quantum Computation</text>
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                <text>Biocomputer</text>
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                <text>Molecular Computation.</text>
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                <text>DNA Computing: Challenges and Application</text>
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                <elementText elementTextId="15984">
                  <text>International Journal of Interactive Mobile Technologies (iJIM)</text>
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                <text>AbdelFattah, Mohamed</text>
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                <text>Galal, Dahab</text>
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                <text>Hassan, Nada</text>
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                <text>Elzanfaly, Doaa</text>
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                <text>Tallent, Greg</text>
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                <text>Sentiment Analysis (SA) or Opinion Mining is the process of analysing natural language texts to detect an emotion or a pattern of emotions towards a certain product to make a decision about that product. SA is a topic of text mining, Natural Language Processing (NLP) and web mining disciplines. Research in SA is currently at its peak given the amount of data generated from social media networks. The concept is that consumers are expressing exactly what they need, want and expect from a product but on the other hand the companies don’t have the tools to analyse and understand these feelings to satisfy these consumers accordingly. One of the applications that generate a high rate of reactions and sentiments in social networks is Instagram. This study focuses on analysing the reactions generated by the top 50 fashion houses on Instagram given their top 20 images with the highest number of likes. The approach taken in this study is to qualify the visual aesthetics of fashion images and to establish why some succeed on social media more than others. The basic question asked in this paper is whether there are certain visual aesthetics that appeal more to the user and are therefore more successful on social media than others as determined by a measure we introduce, ‘Social Value’. To do so, a sentiment analysis tool is developed to measure the proposed social value of each image. An input of comments from each image will be processed. Each comment will go through a pre-processing phase; each word will be placed through a lexicon to identify if it is positive or negative. The output of the lexicon is a score value assigned to each comment to identify its degree of positivity, negativity, or it has no effect on the social value. Adding to these results, the number of likes and shares would also be taken into consideration quantifying the image’s value. A cumulative result is then produced to determine the social value of an image.</text>
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                <text>International Association of Online Engineering (IAOE), Vienna, Austria</text>
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                <text>https://online-journals.org/index.php/i-jim/article/view/6563/4355</text>
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                <text>Copyright (c) 2017 Mohamed AbdelFattah, Dahab Galal, Nada Hassan, Doaa Elzanfaly, Greg Tallent</text>
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                <text>International Journal of Interactive Mobile Technologies (iJIM); Vol. 11 No. 2 (2017); pp. 66-73</text>
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                <text>1865-7923</text>
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                <text>Sentiment Analysis</text>
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                <text>Instagram</text>
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                <text>A Sentiment Analysis Tool for Determining the Promotional Success of Fashion Images on Instagram</text>
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