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                  <text>Journal of Naval Architecture and Marine Engineering</text>
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                <text>Chanda, Ratan Kumar</text>
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                <text>Hasan, Mohammad Sanjeed</text>
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                <text>Investigation on fluid flow and energy distribution in a rotating coiled rectangular duct (CRD) with differentially heated horizontal walls has been analyzed numerically by using a spectral-based numerical scheme. The system is rotated around the vertical axis in the clockwise direction over the Taylor number (Tr) ranging from 0 to 2000 keeping the other parameters constant as aspect ratio Ar =3, curvature ratio BETA=0.5 the Dean number Dn = 1000 and the Prandtl number Pr = 7.0 (water). To reveal steady solution (SS) curves, we applied path continuation technique and obtained five asymmetric SS curves comprising with 2- to 8-pair cell. A bar diagram is also drawn to visualize, at a glance, longitudinal vortex generation on various curves of steady solutions. To explore unsteady behavior, time-progression analysis is performed and flow characteristics are precisely determined by obtaining phase space trajectory of the solutions. The transient flow demonstrates various stages of physically realizable solutions including chaotic, multi-periodic, periodic and steady-state; and it is found that the number of secondary vortices declines as Tr is increased. Convective heat transfer (CHT) is computed and the corresponding dependence on the flow stages is discussed accurately. Finally, a comparison has been made between the numerical computation and experimental investigations which shows a benchmark agreement. &amp;nbsp;</text>
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                <text>Association of Naval Architects and Marine Engineers</text>
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                <text>Copyright (c) 2021 Journal of Naval Architecture and Marine Engineering</text>
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                <text>Journal of Naval Architecture and Marine Engineering; Vol. 18 No. 2 (2021); 187-205</text>
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                <text>A computational study on flow characteristics and energy distribution in a rotating coiled rectangular duct with longitudinal vortex generation</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>https://online-journals.org/index.php/i-jim/article/view/6568</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/6568/4357</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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            <name>Subject</name>
            <description>The topic of the resource</description>
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              <elementText elementTextId="24663">
                <text>Keywords Extraction</text>
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                <text>Multimedia</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>International Journal of Interactive Mobile Technologies (iJIM)</text>
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              <elementText elementTextId="39954">
                <text>Khan, Ijaz Muhammad</text>
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                <text>Ahmad, Abdul Rahim</text>
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                <text>Jabeur, Nafaa</text>
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                <text>Mahdi, Mohammed Najah</text>
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              <elementText elementTextId="39958">
                <text>2021-08-11</text>
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                <text>One of the important key applications of learning analytics is offering an opportunity to the institutions to track the student’s academic activities and provide them with real-time adaptive consultations if the student academic performance diverts towards the inadequate outcome. Still, numerous barriers exist while developing and implementing such kind of learning analytics applications. Machine learning algorithms emerge as useful tools to endorse learning analytics by building models capable of forecasting the final outcome of students based on their available attributes. The machine learning algorithm’s performance demotes with using the entire attributes and thus a vigilant selection of predicting attributes boosts the performance of the produced model. Though, several constructive techniques facilitate to identify the subset of productive attributes, however, the challenging task is to evaluate if the prediction attributes are meaningful, explicit, and controllable by the students. This paper reviews the existing literature to come up with the student’s attributes used in developing prediction models. We propose a conceptual framework which demonstrates the classification of attributes as either latent or dynamic. The latent attributes may appear significant but the student is not able to control these attribute, on the other hand, the student has command to restrain the dynamic attributes. Each of the major class is further categorized to present an opportunity to the researchers to pick constructive attributes for model development.</text>
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                <text>https://online-journals.org/index.php/i-jim/article/view/20019</text>
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                <text>10.3991/ijim.v15i15.20019</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/20019/9655</text>
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                <text>Copyright (c) 2021 Ijaz Muhammad Khan, Abdul Rahim Ahmad, Nafaa Jabeur, Mohammed Najah Mahdi</text>
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                <text>International Journal of Interactive Mobile Technologies (iJIM); Vol. 15 No. 15 (2021); pp. 4-19</text>
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              <elementText elementTextId="39968">
                <text>1865-7923</text>
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                <text>Learning analytics</text>
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                <text>Student performance prediction</text>
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                <text>Academic analytics</text>
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                <text>Machine learning</text>
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                <text>A Conceptual Framework to Aid Attribute Selection in Machine Learning Student Performance Prediction Models</text>
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              <elementText elementTextId="38008">
                <text>Baker, Esraa Jaffar</text>
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                <text>Abu Bakar, Juliana Aida</text>
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                <text>Zulkifli, Abdul Nasir</text>
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                <text>2020-10-13</text>
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                <text>Many studies have begun to consider how to ensure a pleasant experience during visits to cultural heritage sites and museums. Although, when considering the populace of the visitors to these sites, the hearing impaired (HI) visitors which made up of a smaller percentage, have not been in the literature limelight as much as the normal hearing visitors. Thus, the hearing impaired tends to endure certain unpalatable experiences leading to dissatisfaction of their visits. Literature has shown that Mobile Augmented Reality (MAR) can improve the experiences of visitors to the museum in terms of engagement, enjoyment and learning. This is evident in a number of articles tailored towards normal hearing visitors. However, a recent study has taken into consideration the hearing impaired visitors by identifying the engagement elements of MAR for the HI museum visitors. The identified elements include; aesthetics, interaction, interest, usability, satisfaction, motivation, curiosity, enjoyment, perceived control, self-efficacy, and focused attention. This article thus takes a step further by introducing the MAR for the HI museum visitors’ engagement (MARHIME) conceptual model. These elements are derived from a review of literature which has been done comprehensively and are validated by a panel of experts. Altogether eleven elements went through the expert review process and only six elements were validated to be used for the construction of the MARHIME model. This article also further grounds the justification of these selected six elements in relation to engagement. Future work will include the development of the MARHIME prototype which will be used to validate the model among the hearing impaired visitors at a museum.</text>
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                <text>International Journal of Interactive Mobile Technologies (iJIM); Vol. 14 No. 17 (2020); pp. 79-96</text>
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                <text>Artha Journal of Social Sciences; Vol. 15 No. 4 (2016): Artha Journal of Social Sciences; 87-116</text>
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                <text>A Conceptual Review of the Odd-Even Policy on Delhi’s Urban Environment</text>
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                <text>Context-awareness is becoming a key aspect of mobile learning systems. In fact, an efficient mobile learning system has to be sensitive to the context that characterizes the interactions between humans, applications and the surrounding environment. Researches in context-aware mobile learning have concentrated on how to adapt application to context.  In this paper, we present an innovative approach for modeling bijective adaptations between learning activities and context because they influence each others in learning processes. First, we identify contextual elements and their features relevant for mobile and collaborative learning.  Then, we propose a multi-layer middleware that supports tasks for managing and adapting context. The originality of this middleware comes especially from its top-most layer which is an adaptor that defines two classes of functionalities: the adaptation of learning activities to context and the adaptation and updating of context to learning activities. Finally, we present a simulator that implements this adaptor and our bijective adaptation approach. This simulator based on context-aware and mobile learning scenario, makes possible the interactions of mobile learning applications with context (environment) in such a way that they can detect the contextual elements values changes in order to, on the one side, adapt their learning activities to provide services more appropriate to new values of the environment.   On the other side, adapt and update contextual elements to the needs of their learning activities and preferences of the mobile user to create more adequate learning environment which helps him/her concentrate better on his/her learning activities.</text>
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                <text>Copyright (c) 2017 Jihene Malek, Mona Laroussi, Alain Derycke, Henda Ben Ghezala</text>
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                <text>International Journal of Interactive Mobile Technologies (iJIM); Vol. 2 No. 1 (2008)</text>
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                <text>A Context-Aware Approach for Modeling Bijective Adaptations between Context and Activity in a Mobile and Collaborative learning</text>
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                <text>In this paper, we propose a cooking recipe recommendation system which runs on a consumer smartphone as an interactive mobile application. The proposed system employs real-time visual object recognition of food ingredients, and recommends cooking recipes related to the recognized food ingredients. Because of visual recognition, by only pointing a built-in camera on a smartphone to food ingredients, a user can get to know a related cooking recipes instantly. The objective of the proposed system is to assist people who cook to decide a cooking recipe at grocery stores or at a kitchen. In the current implementation, the system can recognize 30 kinds of food ingredient in 0.15 seconds, and it has achieved the 83.93% recognition rate within the top six candidates. By the user study, we confirmed the effectiveness of the proposed system.</text>
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                <text>Copyright (c) 2017 Keiji Yanai, Takuma Maruyama, Yoshiyuki Kawano</text>
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                <text>International Journal of Interactive Mobile Technologies (iJIM); Vol. 8 No. 2 (2014); pp. 28-34</text>
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                <text>The University of Queensland Law Journal; Vol. 41 No. 3 (2022): The University of Queensland Law Journal; 243-276</text>
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