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                  <text>International Journal of Interactive Mobile Technologies (iJIM)</text>
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                <text>Alhomaidhi, Shrowg</text>
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                <text>2021-11-09</text>
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                <text>This research attempts to find out the impact of using cellphones on the students’ pronunciation with an emphasis on consonant clusters. The sample of the study was 40 female secondary school students divided into two groups control and experimental. The study attempted to answer one question: Will the use of pronunciation videos on cell phones help to improve Saudi female secondary students' pronunciation of English consonant clusters occurring in the onset position? To achieve this, the researcher conducted a pre-test and a post-test to test students’ pronunciation of selected vocabulary from the English Inventory of consonant clusters. After the pre-test, the experimental group used video to practice these sounds while the control group practiced that in traditional classes. The findings showed that both groups improved. However, the excremental group, having utilised cell phone videos, made significant improvement by 7.05% in the correct production of consonant clusters.</text>
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                <text>https://online-journals.org/index.php/i-jim/article/view/26909</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/26909/10185</text>
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                <text>Copyright (c) 2021 Shrowg Alhomaidhi</text>
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                <text>International Journal of Interactive Mobile Technologies (iJIM); Vol. 15 No. 21 (2021); pp. 158-171</text>
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                <text>1865-7923</text>
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                <text>consonant cluster</text>
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                <text>The Impact of Using Cell Phones on the Pronunciation of English Consonant Clusters among Saudi Female Secondary School Students</text>
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                <text>Nguyen, Nga Thuy</text>
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                <text>Tran, Huong Thi Thu</text>
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                <text>2022-01-18</text>
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                <text>Since 2020, Covid 19 has completely changed the daily activities of almost all nations, and education has been heavily affected. Because of school closures, face-to-face classrooms were halted or replaced with online classes in which both lecturers and learners had to adjust their teaching and learning styles to cope with unexpected situations. The ‘new normal’ of learning from homes, spending hours staring at screens, and struggling with piles of online tasks has somehow demotivated students to continue learning. This study explores factors affecting students’ desire to take online courses after experiencing e-learning during Covid 19. Nine hundred fifty-five students of Vietnam National University took part in the survey via an online questionnaire. Data were analyzed using SPSS20; correlation, hierarchical regression was employed to examine how online factors influence students’ decision. The research results showed that skill enhancement, self-regulated learning, lecturer interaction during the course were among the most important predictors of students’ desire to take more online courses. In contrast, student interaction imposed no significant influence. This study gives the theoretical background for other studies in the same field and suggests practical implications for governments and universities to implement online training better to cope with the pandemic...</text>
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                <text>https://online-journals.org/index.php/i-jim/article/view/26777</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/26777/10545</text>
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            <description>Information about rights held in and over the resource</description>
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                <text>Copyright (c) 2021 Nga Thuy Nguyen, Huong Thi Thu Tran</text>
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                <text>https://creativecommons.org/licenses/by/4.0</text>
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                <text>International Journal of Interactive Mobile Technologies (iJIM); Vol. 16 No. 01 (2022); pp. 22-37</text>
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              <elementText elementTextId="45585">
                <text>1865-7923</text>
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                <text>mobile learning</text>
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                <text>online learning</text>
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                <text>student satisfaction</text>
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                <text>technical support</text>
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                <text>Factors Affecting Students’ Desire to Take Upcoming Online Courses after E-learning Experience During Covid 19</text>
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                  <text>International Journal of Interactive Mobile Technologies (iJIM)</text>
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              <elementText elementTextId="45546">
                <text>Achmad, Andani</text>
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                <text>Muliadi</text>
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                <text>Areni, Intan Sari</text>
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                <text>Palantei, Elyas</text>
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                <text>Hadis, Muhammad Sabirin</text>
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                <text>Achmad, Andini Dani</text>
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                <text>The consumption of electrical energy always increases every year where one of the causes is lifestyle. A bad lifestyle such as forgetting to turn off the unused electronic devices when they are leaving home is one of the factors for increasing consumption of electrical energy which results in waste. This can occur because the device control system is still done manually. One solution to solve this problem is to implement a remote device control system. Several studies have developed a remote-control system but it is not equipped with features that can determine the condition of the device in normal or damaged conditions, so it is difficult to identify the success of the control. In this paper, an Internet of Things-based electronic device control system has been designed and built which is equipped with a device condition monitoring feature. Light, temperature, thermal and electric current sensors are embedded in the system to determine the condition of the device. The control test on the system was successful with an average response time of 3.32 seconds and the accuracy of sensor readings above 89%. With the implementation of this system, users can easily monitor and control electronic devices anywhere and anytime so that the opportunities for efficient and effective use of electrical energy can be further increased.</text>
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                <text>https://online-journals.org/index.php/i-jim/article/view/26737</text>
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                <text>10.3991/ijim.v16i05.26737</text>
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                <text>eng</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/26737/10893</text>
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                <text>Copyright (c) 2022 Andani Achmad, Muliadi Muliadi, Intan Sari Areni, Elyas Palantei, Muhammad Sabirin Hadis</text>
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                <text>International Journal of Interactive Mobile Technologies (iJIM); Vol. 16 No. 05 (2022); pp. 139-152</text>
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              <elementText elementTextId="45563">
                <text>1865-7923</text>
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            <name>Subject</name>
            <description>The topic of the resource</description>
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                <text>smart electrical device control</text>
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                <text>internet of things</text>
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                <text>smart monitoring</text>
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                <text>Smart Electrical Devices Control with Intrusion Detection Alert</text>
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                <text>Hanafi, Hafizul Fahri bin</text>
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                <text>Wong, Kung-Teck</text>
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                <text>Muhamad Adnan, Muhamad Hariz Bin</text>
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                <text>Selamat, Abu Zarrin Bin</text>
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                <text>Zainuddin, Nur Azlan Bin</text>
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                <text>Abdullah, Mohd Faizal Nizam Lee Bin</text>
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                <text>This study developed and used a mobile Augmented Reality (AR) reading kit to help preschool students recognize alphabets and read simple words more effectively. This study was based on the quantitative approach involving an experimental methodology that used a one-group pretest-posttest design. In this study, the variables of interest to be measured were students’ reading skills, motivation, and self-learning. The learning treatment involved a series of reading sessions using the novel learning application that spanned three weeks, with each session lasting for two hours. The sample of this study comprised 60 preschool students, whose ages ranged from four to six, who were selected from three different preschools. The effectiveness of the novel-reading kit was evaluated in terms of students’ learning performance, learning motivation in reading, and self-learning. The data for the former were gathered from pre-testing and post-testing. At the same time, a survey was administered to the students to elicit their opinions and feedback on the last two factors. Furthermore, all descriptive and inferential statistical procedures have been selected to analyze the data. Specifically, a one-way analysis of variance (ANOVA) was applied to analyze the data, which demonstrated significant differences in the students’ reading skills, motivation, and self-learning before and after the learning interventions. These findings showed the students could recognize alphabets more accurately, read simple words more intelligently, become more motivated to read and be highly engaged in self-learning using the mobile AR reading kit.</text>
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                <text>Copyright (c) 2021 HAFIZUL FAHRI BIN HANAFI</text>
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                <text>International Journal of Interactive Mobile Technologies (iJIM); Vol. 15 No. 24 (2021); pp. 94-107</text>
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              <elementText elementTextId="45536">
                <text>1865-7923</text>
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                <text>Mobile augmented reality</text>
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                <text>Utilizing Animal Characters of a Mobile Augmented Reality (AR) Reading Kit to Improve Preschoolers’ Reading Skills, Motivation, and Self-Learning: An Initial Study</text>
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                <text>Wheeb, Ali H.</text>
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                <text>Al-jamali, Nadia Adnan</text>
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                <text>Optimized Link State Routing Protocol (OLSR) is an efficient routing protocol used for various Ad hoc networks. OLSR employs the Multipoint Relay (MPR) technique to reduce network overhead traffic. A mobility model's main goal is to realistically simulate the movement behaviors of actual users. However, the high mobility and mobility model is the major design issues for an efﬁcient and effective routing protocol for real Mobile Ad hoc Networks (MANETs). Therefore, this paper aims to analyze the performance of the OLSR protocol concerning various random and group mobility models. Two simulation scenarios were conducted over four mobility models, specifically the  Random Waypoint model (RWP), Random Direction model (RD), Nomadic Community model (NC), and the Reference Point Group Model (RPGM) with a low as well as high random range mobility of the nodes.  Moreover, BonnMotion Software and Network simulator NS-3 used to implement the simulation scenarios. Further, the performance of the OLSR protocol analyzed and evaluated based on latency, routing overhead, and packet loss ratio metrics. According to the results, the OLSR protocol provides the best performance over the RWP model in a low mobility environment, whereas the Nomadic mobility model is suitable for OLSR protocol in a high mobility environment.</text>
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                <text>https://online-journals.org/index.php/i-jim/article/view/26663</text>
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                <text>https://online-journals.org/index.php/i-jim/article/view/26663/10555</text>
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              <elementText elementTextId="45506">
                <text>Copyright (c) 2021 Ali Hussein Wheeb, Nadia Adnan Al-jamali</text>
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                <text>International Journal of Interactive Mobile Technologies (iJIM); Vol. 16 No. 01 (2022); pp. 106-119</text>
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                <text>1865-7923</text>
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            <name>Subject</name>
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                <text>olsr routing protocol</text>
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                <text>random mobility</text>
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                <text>network simulation.</text>
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                <text>Performance Analysis of OLSR Protocol in Mobile Ad Hoc Networks</text>
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                  <text>International Journal of Interactive Mobile Technologies (iJIM)</text>
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              <elementText elementTextId="45472">
                <text>Hidayati, Abna</text>
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                <text>Bentri, Alwen</text>
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                <text>Eldarni</text>
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                <text>The aim of this research is to determine the supporting factors for the implementation of mobile learning for elementary school students in Indonesia using an authentic approach and real-world activities. This is significant because various approaches to mobile learning have been used in elementary institutions. However, this educational model is not ideal because the data shows that 92% of students have learning difficulties, and 38% state they do not receive teachers’ guidance.The method used was descriptive quantitative with the population of elementary school students and a purposively selected sample of 26 of those pupils. Samples were taken based on the characteristics of the coastal area and city center. To assess their entry behavior, questionnaires and test sheets were used. Moreover, data was gathered through home visits and the Whatsapp application. It was then processed using the percentage technique and the SmartPLS application, to obtain accurate results. In addition, a qualitative approach was used to develop the analysis.According to the results, the supporting factors for the implementation of mobile learning using an authentic approach and real-world activities are as follows: 1) students already have the concrete and logical thinking skills, 2) the entry behavior of students are still low, and 3) visual and auditory learning styles affect meaningful learning. Furthermore, research suggests that teachers and learning designers should create mobile learning exercises which support authentic learning and real-world pursuits. </text>
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                <text>https://online-journals.org/index.php/i-jim/article/view/26551</text>
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                <text>International Association of Online Engineering (IAOE), Vienna, Austria</text>
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            <description>Information about rights held in and over the resource</description>
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              <elementText elementTextId="45483">
                <text>Copyright (c) 2022 abna hidayati hidayati</text>
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                <text>International Journal of Interactive Mobile Technologies (iJIM); Vol. 16 No. 05 (2022); pp. 107-120</text>
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                <text>1865-7923</text>
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                <text>mobile learning</text>
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                <text>Supporting Factors for the Implementation of Mobile Learning for Elementary School Students Using an Authentic Approach and Real-World Activities</text>
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                <text>Abuaddous, Hayfa Y.</text>
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                <text>Saleh, Ashraf Mousa</text>
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                <text>Enaizan, Odai</text>
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                <text>User Experience (UX) evaluation has a significant importance for any interactive application. Mobile device applications have additional limitations to convey good user experiences (UX) due to the usage and features of mobile devices in ubiquitous computing contexts. There are a lot of automated tools available for evaluating, such applications particularly developed for Android and iOS. Automated UX evaluation provides a number of strengths which are able to overcome the limitations of non-automated (moderated) counterparts. Automated UX evaluation is regarded as unexplored field, especially when it is adopted for the UX evaluation of complex mobile application. Despite these, automated UX evaluation tools are unexplored and should only be used in coupling with traditional UX evaluation techniques. Hence, this study aims to discuss the literature in automated UX evaluation and to highlight the UX measurements that merit further investigation. This paper presents the most frequently used automated UX evaluation tools for mobile applications. Therefore, the study analyzes existing automated UX tools and frameworks and identifies which UX attributes and measurements of automated UX evaluation are likely to be of use in future research. Moreover, we discuss the strengths and limitations of automated UX evaluation methods for mobile applications. Finally, the paper provides an overview of automated UX evaluation techniques and tools. It also presents the strengths and limitations for comparing various evaluation methods.</text>
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                <text>International Journal of Interactive Mobile Technologies (iJIM); Vol. 16 No. 04 (2022); pp. 30-45</text>
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                <text>1865-7923</text>
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                <text>Manalang, Jomari C.</text>
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                <text>The corona-virus disease (COVID)-19 pandemic has caused extreme disruption in the delivery of instruction in many educational institutions all over the world. However, this circumstance may not only be a challenge but also an opportunity to foster learning through exploration of various technologies that can be developed, more specifically in a course characterized with a strong skills-based orientation such as physical education (PE). Therefore, the aim set in this study was to develop a mobile application for Movement Competency Training (MCT) grounded on the User-Centered Design Model. This descriptive mixed-method research started with a preliminary needs assessment analysis which was participated by 121 Filipino students enrolled in the MCT course and 10 PE teachers. Results revealed the skills that require a higher degree of proficiency such as non-locomotor skills, locomotor, and mobility skills. Also, the majority of the respondents are mobile phone users which justified the adoption of mobile instruction and the development of an application for MCT. Moreover, indicated in this research was the preference of the intended users in terms of design and interface that led to the development of the mobile application and had specified the requirements based on the users' needs. The initial assessment of the developed application indicated a highly acceptable level of functional suitability, usability, and portability by the student-respondents. A field testing of the application may be recommended to determine its effectiveness in mastering the skills in MCT independently.</text>
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            <description>A language of the resource</description>
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            <description>Information about rights held in and over the resource</description>
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              <elementText elementTextId="45433">
                <text>Copyright (c) 2022 Rodel B. Punzalan, Julius Ceazar G. Tolentino, John Paul P. Miranda, Jomari C. Manalang, Lovely Kate S. Hermogenes, Jayson T. Mallari</text>
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              <elementText elementTextId="45435">
                <text>International Journal of Interactive Mobile Technologies (iJIM); Vol. 16 No. 03 (2022); pp. 92-103</text>
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                <text>1865-7923</text>
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            <description>The topic of the resource</description>
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                <text>mobile application</text>
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                <text>user-centered design</text>
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                <text>physical education</text>
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                <text>movement competency training</text>
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                <text>flexible learning</text>
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                <text>Towards the Development of a Mobile Application in Movement Competency Training Grounded on the User-Centered Design Model: The Case of a State University in the Philippines</text>
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      <description>Data encoded in a defined structure. Examples include lists, tables, and databases. A dataset may be useful for direct machine processing.</description>
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              <elementText elementTextId="45397">
                <text>Lakshmanarao, A.</text>
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                <text>Shashi, M.</text>
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                <text>Android is the most widely used operating system in smartphones. Mobile users can download and access apps easily from the play store. Due to lack of security awareness and risk associated with mobile apps, malware apps would be downloaded by normal users in general. The consequences after installing a malware app are unpredictable. Malware apps can gather user personal data, browsing history, user profiles, user sensitive data like passwords. Hence, android malware detection is essential for providing security to mobile users. Android malware detection using machine learning is done either by extracting static features (opcodes, permissions, intents, system commands) or by extracting dynamic features (log behavior, system calls, dataflow). In this paper, opcode sequences are extracted from malware and benign apps, and Recurrent Neural Networks are proposed on extracted sequences. Benign apps are collected from the play store, apkpure.com and malware apps are collected from the virus share website. The proposed Recurrent Neural Network model could achieve 96% accuracy for android malware detection.</text>
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                <text>https://online-journals.org/index.php/i-jim/article/view/26433</text>
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                <text>10.3991/ijim.v16i01.26433</text>
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            <description>A language of the resource</description>
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                <text>eng</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/26433/10561</text>
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            <name>Rights</name>
            <description>Information about rights held in and over the resource</description>
            <elementTextContainer>
              <elementText elementTextId="45407">
                <text>Copyright (c) 2021 A Lakshmanarao, M Shashi</text>
              </elementText>
              <elementText elementTextId="45408">
                <text>https://creativecommons.org/licenses/by/4.0</text>
              </elementText>
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                <text>International Journal of Interactive Mobile Technologies (iJIM); Vol. 16 No. 01 (2022); pp. 145-157</text>
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              <elementText elementTextId="45410">
                <text>1865-7923</text>
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          <element elementId="49">
            <name>Subject</name>
            <description>The topic of the resource</description>
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                <text>Android</text>
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                <text>Malware</text>
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                <text>Opcodes</text>
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                <text>Recurrent Neural Networks</text>
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                <text>Android Malware Detection with Deep Learning using RNN from Opcode Sequences</text>
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                <elementText elementTextId="15984">
                  <text>International Journal of Interactive Mobile Technologies (iJIM)</text>
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      <name>Dataset</name>
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          <element elementId="39">
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              <elementText elementTextId="45370">
                <text>Faisal, Tarig</text>
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                <text>Awawdeh, Moath</text>
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                <text>Habte, Daniel</text>
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                <text>Berhanu, Hala</text>
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                <text>Kifle, Kibrom</text>
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                <text>Misghina, Lia</text>
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                <text>Bashir, Anees</text>
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                <text>Consumerism has constantly been growing, and visiting marketplaces and purchasing is a key development factor for the economic development of a city. The spending habits of people are a reflector of many development indices. As people go out for purchasing in malls for shopping essential items, they often tend to find long queues at the billing counter, and the queues are more longer during weekends and during sale seasons. This has been deterring people from going out shopping and instead resort to online shopping. This has affected the sales and revenue of departmental chains. Also, people spend quite a considerable amount of time searching through rows in order to find the items of their choice. Proposing a solution to these above-mentioned problems using technology is a real boon.  Installing a payment option on the cart itself would result in cutting down the waiting times in queues. Moreover, the customers can also benefit from navigation through the virtual maps that would be displayed on the screens of the carts. This reduces the time wasted by customers in searching for products. Hence the customers are self-dependent during their shopping. For the retail group, their transactions and management are truly simplified with these developments. Additionally, the counter labor will decrease if not cease to zero, which in turn reduces the labor cost drastically. In times of pandemic like now, people have been sticking with online shopping due to various reasons, but they long to go out and spend time in the malls as they would in normal times. The proposed solution would definitely assist in the normalization of lives and bring back on-ground retail back to life.</text>
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                <text>https://online-journals.org/index.php/i-jim/article/view/26397</text>
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                <text>10.3991/ijim.v15i24.26397</text>
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            <description>A language of the resource</description>
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              <elementText elementTextId="45382">
                <text>eng</text>
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            <name>Publisher</name>
            <description>An entity responsible for making the resource available</description>
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              <elementText elementTextId="45383">
                <text>International Association of Online Engineering (IAOE), Vienna, Austria</text>
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              <elementText elementTextId="45384">
                <text>https://online-journals.org/index.php/i-jim/article/view/26397/10487</text>
              </elementText>
            </elementTextContainer>
          </element>
          <element elementId="47">
            <name>Rights</name>
            <description>Information about rights held in and over the resource</description>
            <elementTextContainer>
              <elementText elementTextId="45385">
                <text>Copyright (c) 2021 Tarig Faisal, Moath Awawdeh, Daniel Habte Kifle, Hala Berhanu Mohamed, Kibrom Kifle Tesfagabeir, Lia Misghina Yebiyo, Anees Bashir</text>
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                <text>https://creativecommons.org/licenses/by/4.0</text>
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              <elementText elementTextId="45387">
                <text>International Journal of Interactive Mobile Technologies (iJIM); Vol. 15 No. 24 (2021); pp. 77-93</text>
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              <elementText elementTextId="45388">
                <text>1865-7923</text>
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            </elementTextContainer>
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          <element elementId="49">
            <name>Subject</name>
            <description>The topic of the resource</description>
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              <elementText elementTextId="45389">
                <text>Barcode Reader</text>
              </elementText>
              <elementText elementTextId="45390">
                <text>Radio Frequency Identification</text>
              </elementText>
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                <text>Smart Gate</text>
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                <text>Smart Shopping Gate</text>
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                <text>Smart Cart With Multi-shopping Solutions</text>
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      </elementSet>
    </elementSetContainer>
  </item>
</itemContainer>
