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                  <text>International Journal of Interactive Mobile Technologies (iJIM)</text>
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                <text>Farah Tawfiq Abdul Hussien</text>
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                <text>Teaba Wala Aldeen Khairi</text>
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                <text>2023-05-22</text>
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                <text>I
n these days of technology, the usage of images has become increasingly high especially now that almost everyone has access to internet. Also, image helps us to learn, grabs our attention, explains complicated concepts as well as inspires us. Sharing these images is essential and therefore image encryption algorithms are proposed to secure the transmission of these image from many types of attacks such as Man-in-the-middle attack (MITM). In this paper, we proposed a hybrid security system that consist of Elliptic Curve Cryptography (ECC) and Advanced Encryption System (AES). Where ECC is used to generate private/public keys and AES is for encryption and decryption of the image using the ECC generated keys. The system works as follows; it begins by inserting the image to be encrypted which is “lena.png” of size 256*256 and passing it to AES Algorithm along with the generated public key. Then, in AES algorithm, the public key is hashed then used to encrypt the image with AES encryption algorithm. On the other hand, the decryption algorithm works as follows; inserting the encrypted image then calculating the decryption key to use it to decrypt the image using AES decryption algorithm. Finally, our experimental results shows that the National Institute of Standards and Technology (NIST) test shows that the ECC generated keys have better randomness than using only AES generated keys. Also, the encrypted image histogram show that the image pixels values are well distributed across all three channels R, G and B. This shows that the hybrid system is a step further to get a more secure image encryption system against attacks with the generated ECC keys. To get further, Logistic chaotic map has been used to encrypt images for comparison purposes with AES and ECC generated images in terms of randomness, security and histogram</text>
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                <text>https://online-journals.org/index.php/i-jim/article/view/38787</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/38787/13225</text>
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                <text>Copyright (c) 2023 Haider TH.Salim ALRikabi, Farah Tawfiq Abdul Hussien, Teaba Wala Aldeen Khairi</text>
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                <text>International Journal of Interactive Mobile Technologies (iJIM); Vol. 17 No. 10 (2023); pp. 193-211</text>
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              <elementText elementTextId="54757">
                <text>1865-7923</text>
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                <text>Man-in-the-middle attack (MITM); Elliptic Curve Cryptography (ECC); Advanced Encryption System (AES); National Institute of Standards and Technology (NIST)</text>
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                <text>Performance Evaluation of AES, ECC and Logistic Chaotic Map Algorithms in Image Encryption</text>
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                  <text>International Journal of Interactive Mobile Technologies (iJIM)</text>
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                <text>Abdallah Qusef</text>
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                <text>Murad, Sharefa</text>
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                <text>Najeh Alsalhi</text>
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                <text>Eman Shudayfat</text>
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                <text>— In application development lifecycle, specifically in test-driven development, refactoring plays a crucial role in sustaining ease. However, in-spite of bringing ease, refactoring does not ensure the desired behaviour of code after it is applied. Because refactoring tends to worsen the alignments between source code and its corresponding units. One significant solution to the aforementioned issue is the technique called unit testing. As unit testing enable the developers to confidently apply refactoring while avoiding undesired code behaviour. Unit testing provides effective preventive measures for avoiding bugs by providing immediate feedback, thus assisting to mitigate the fear of change. In this work, we present a tool called GreenRefPlus which efficiently enables the developers to maintain the veracity of code after the process of refactoring is applied. The proposed tool provides automatic recovery for the unit tests after the code is refactored. In this work, we consider Java as our target programming language and we focus on five various types of refactoring, which include Rename Method, Extract Method, Move Method, Parameter Addition and Parameter Removal. Our experiments indicate that the proposed tool GreenRefPlus enables us to consistently refactor the code and apply unit tests. The results presented in our work reveal that the proposed tool assists developers in saving approximately 43% of the total time required to manually recover from broken unit tests.</text>
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                <text>https://online-journals.org/index.php/i-jim/article/view/38785</text>
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                <text>10.3991/ijim.v17i08.38785</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/38785/13059</text>
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              <elementText elementTextId="54735">
                <text>Copyright (c) 2023 Abdallah Qusef , Najeh Alsalhi, Eman Shudayfat</text>
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                <text>International Journal of Interactive Mobile Technologies (iJIM); Vol. 17 No. 08 (2023); pp. 39-58</text>
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              <elementText elementTextId="54738">
                <text>1865-7923</text>
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              <elementText elementTextId="54739">
                <text>Unit Tests, GreenRefPlus, Code Refactoring, Automatic Recovery, JUnit, Eclipse Plug-in, Agile eXtreme Programming, Test-Driven Development.</text>
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                <text>Post-Refactoring Recovery of Unit Tests: An Automated Approach</text>
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                <text>Peer-reviewed Article</text>
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                  <text>International Journal of Interactive Mobile Technologies (iJIM)</text>
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              <elementText elementTextId="54703">
                <text>Alsaidi, Bushra Khraibet</text>
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              <elementText elementTextId="54704">
                <text>Ali,  Mariam A.</text>
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              <elementText elementTextId="54705">
                <text>Hussain, Inaam A.</text>
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                <text>2023-03-21</text>
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                <text>— In light of the pandemic that has swept the world, the use of e-learning in educational institutions has become an urgent necessity for continued knowledge communication with students. Educational institutions can benefit from the free tools that Google provide and from these applications, Google classroom which is characterized by ease of use, but the efficiency of using Google classroom is affected by several variables not studied in previous studies Clearly, this study aimed to identify the use of Google classroom as a system for managing e-learning and the factors affecting the performance of students and lecturer. The data of this study were collected from 219 members of the faculty and students at the College of Administration and Economics at the University of Baghdad in Iraq and have reached Google classroom offers all the technologies wanted by students and lecturer but there is a positive correlation between lack of knowledge of technical matters and decrease in the use of Google classroom and online education and vice versa.</text>
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                <text>https://online-journals.org/index.php/i-jim/article/view/38783</text>
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                <text>10.3991/ijim.v17i06.38783</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/38783/12897</text>
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              <elementText elementTextId="54714">
                <text>Copyright (c) 2023 Haider TH.Salim ALRikabi, Bushra khraibet Jasim , Inaam Aboud Hussain</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. 17 No. 06 (2023); pp. 20-32</text>
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              <elementText elementTextId="54717">
                <text>1865-7923</text>
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                <text>e-learning, learning management system, Google classroom, academic performance, Covid19 pandemic, data mining, deep learning.</text>
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                <text>Study the Effect of Using Google Classroom on the Academic Performance Under the Covid19 Pandemic Using Data Mining Technique</text>
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              <elementText elementTextId="54684">
                <text>Haroon Rashid Hammood Al Dallal</text>
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                <text>Wijdan Noaman Marzoog Al Mukhtar</text>
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                <text>2023-05-10</text>
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                <text>Protecting and concealing sensitive data in the modern world is challenging. Due to insufficient protection and privacy, it is feasible for critical information to be fabricated. This led to a significant financial loss for someone. The intended recipient must be trusted with sensitive information and be able to independently authenticate the accuracy of the information by independently checking the specifics. There are several driving the rise in QR codes used for information transmission. Due to their enormous capacity for storing information, QR codes are vital for dissemination. However, most currently deployed QR code solutions employ insecure data formats and never employ encryption. Secure QR Code is data protection and data concealing available technology. The Quick Response (QR) code is widely used and accessible without extensive technical training. Now, the user data stored in a QR code is effectively public knowledge and occasionally even illegally used. To address the abovementioned issues, this study's authors propose a novel QR code encryption system. Using the image's mathematical processing method, we may apply the equivalence class principle to the ordered equations of the two-dimensional code, producing the desired cryptographic result. This method exploits the unique visual properties of the QR code. Only a QR code reader can decode the code's useful information, which is too complex for standard reading methods. It will be utilised to address issues in speedy business client data protection Security, commodity anticounterfeiting, and bicycle sharing QR codes.</text>
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            <description>Information about rights held in and over the resource</description>
            <elementTextContainer>
              <elementText elementTextId="54694">
                <text>Copyright (c) 2023 Haider TH.Salim ALRikabi, Haroon Rashid Hammood Al Dallal , Wijdan Noaman Marzoog Al Mukhtar</text>
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                <text>International Journal of Interactive Mobile Technologies (iJIM); Vol. 17 No. 09 (2023); pp. 44-56</text>
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                <text>1865-7923</text>
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            <description>The topic of the resource</description>
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              <elementText elementTextId="54698">
                <text>QR code, privacy protection, Multi-directional Identity Authentication, en-cryption system, Image symmetrical processing, data processing</text>
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                <text>A QR Code Used for Personal Information Based On Multi-Layer Encryption System</text>
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                  <text>International Journal of Interactive Mobile Technologies (iJIM)</text>
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                <text>Baseem G. Nsaif</text>
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                <text>Adheed H. Sallomi</text>
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                <text>The importance of locating a mobile phone has increased significantly during last decade for security and commercial reasons. Locating the mobile phone leads to locating people. This is done by using the most common propagation models in the mobile phone network design to calculate the distance between the mobile phone and the base station, in addition to using positioning algorithms to predict the location of the mobile station. In this work, three telecommunication towers that provide mobile phone service for Zain Iraq were selected, located within the Mahmudiya area in Baghdad as a case study, and a test drive was conducted to measure the signal strength received from these base stations at more than 10 points located within the coverage area of these base stations. The Okumura-HATA model, and the UMTS propagation model were used to calculate the distances. The Gray-Wolf algorithm was used to improve mobile phone position prediction.</text>
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                <text>https://online-journals.org/index.php/i-jim/article/view/38773</text>
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                <text>https://online-journals.org/index.php/i-jim/article/view/38773/13179</text>
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          <element elementId="47">
            <name>Rights</name>
            <description>Information about rights held in and over the resource</description>
            <elementTextContainer>
              <elementText elementTextId="54675">
                <text>Copyright (c) 2023 Haider TH.Salim ALRikabi, Baseem G. Nsaif , Adheed H. Sallomi</text>
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              <elementText elementTextId="54676">
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              <elementText elementTextId="54677">
                <text>International Journal of Interactive Mobile Technologies (iJIM); Vol. 17 No. 09 (2023); pp. 127-140</text>
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              <elementText elementTextId="54678">
                <text>1865-7923</text>
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            </elementTextContainer>
          </element>
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            <name>Subject</name>
            <description>The topic of the resource</description>
            <elementTextContainer>
              <elementText elementTextId="54679">
                <text>GWO, LBS, LOS, RSSI</text>
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          <element elementId="50">
            <name>Title</name>
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                <text>Improving Mobile Location Prediction Using the Grey Wolf Optimization Algorithm</text>
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            <element elementId="50">
              <name>Title</name>
              <description>A name given to the resource</description>
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                <elementText elementTextId="15984">
                  <text>International Journal of Interactive Mobile Technologies (iJIM)</text>
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      <name>Dataset</name>
      <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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            <name>Creator</name>
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              <elementText elementTextId="54645">
                <text>Rafeef A. Hameed</text>
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              <elementText elementTextId="54646">
                <text>Wael J. Abed</text>
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              <elementText elementTextId="54647">
                <text>Ahmed T. Sadiq</text>
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            <name>Date</name>
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              <elementText elementTextId="54648">
                <text>2023-05-10</text>
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                <text>The subject of sentiment analysis through social media sites has witnessed significant development due to the increasing reliance of people on social media in advertising and marketing, especially after the Corona pandemic. There is no doubt that the prevalence of the Arabic language makes it considered one of the most important languages all over the world. Through human comments, it can know things if they are positive or negative. But in fact, the comments are many, and it takes work to evaluate the place or the product through a detailed reading of each comment. Therefore, this study applied deep learning approaches to this issue to provide final results that could be utilized to differentiate between the comments in the dataset. Arabic Sentiment Analysis was used and gave a percentage for each positive and negative commentary. This work used eight methods of deep learning techniques after using Fast Text as embedding, except Ara BERT. These techniques are the transformer (AraBERT), RNN (Long short-term memory (LSTM), Bidirectional long-short term memory (BI-LSTM), Gated recurrent units (GRUs), Bidirectional Gated recurrent units (BI-GRU)), CNN (like ALEXNET, proposed CNN), and ensemble model (CNN with BI-GRU). The Hotel Arabic Reviews Dataset was utilized to test the models. This paper obtained the following results. In the Ara BERT model, the accuracy is 96.442%. In CNN, like the Alex Net model, the accuracy is 93.78%. In the suggested CNN model, the accuracy is 94.43%. In the suggested LSTM model, the accuracy is 95%. In the suggested BI-LSTM model, the accuracy is 95.11%. The accuracy of the suggested GRU model is 95.07%. The accuracy of the suggested BI-GRU model is 95.02%. The accuracy is 94.52% in the Ensemble CNN with BI-GRU model that has been proposed. Consequently, the AraBERT outperformed the other approaches in terms of accuracy. Because the AraBERT has already been trained on some Arabic Wikipedia entries. The LSTM, BI-LSTM, GRU, and BI-GRU, on the other hand, had comparable outcomes.</text>
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              <elementText elementTextId="54651">
                <text>https://online-journals.org/index.php/i-jim/article/view/38755</text>
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              <elementText elementTextId="54652">
                <text>10.3991/ijim.v17i09.38755</text>
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              <elementText elementTextId="54654">
                <text>International Association of Online Engineering (IAOE), Vienna, Austria</text>
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              <elementText elementTextId="54655">
                <text>https://online-journals.org/index.php/i-jim/article/view/38755/13173</text>
              </elementText>
            </elementTextContainer>
          </element>
          <element elementId="47">
            <name>Rights</name>
            <description>Information about rights held in and over the resource</description>
            <elementTextContainer>
              <elementText elementTextId="54656">
                <text>Copyright (c) 2023 Haider TH.Salim ALRikabi, Rafeef A. Hameed, Wael J. Abed, Ahmed T. Sadiq</text>
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              <elementText elementTextId="54657">
                <text>https://creativecommons.org/licenses/by/4.0</text>
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              <elementText elementTextId="54658">
                <text>International Journal of Interactive Mobile Technologies (iJIM); Vol. 17 No. 09 (2023); pp. 70-87</text>
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              <elementText elementTextId="54659">
                <text>1865-7923</text>
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          <element elementId="49">
            <name>Subject</name>
            <description>The topic of the resource</description>
            <elementTextContainer>
              <elementText elementTextId="54660">
                <text>Arabic sentiment analysis, NLP, Deep Learning, embedding, CNN, RNN, AraBERT</text>
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          <element elementId="50">
            <name>Title</name>
            <description>A name given to the resource</description>
            <elementTextContainer>
              <elementText elementTextId="54661">
                <text>Evaluation of Hotel Performance with Sentiment Analysis by Deep Learning Techniques</text>
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            <description>The nature or genre of the resource</description>
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              <name>Title</name>
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                <elementText elementTextId="15984">
                  <text>International Journal of Interactive Mobile Technologies (iJIM)</text>
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      <name>Dataset</name>
      <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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            <name>Creator</name>
            <description>An entity primarily responsible for making the resource</description>
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              <elementText elementTextId="54625">
                <text>Raghad Abdulaali Azeez</text>
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              <elementText elementTextId="54626">
                <text>Abeer Salim Jamil</text>
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              <elementText elementTextId="54627">
                <text>Mohammed Salih Mahdi</text>
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            <name>Date</name>
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              <elementText elementTextId="54628">
                <text>2023-04-05</text>
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            <description>An account of the resource</description>
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                <text>User confidentiality protection is concerning a topic in control and monitoring spaces. In image, user's faces security in concerning with compound information, abused situations, participation on global transmission media and real-world experiences are extremely significant. For minifying the counting needs for vast size of image info and for minifying the size of time needful for the image to be address computationally. consequently, partial encryption user-face is picked. This study focuses on a large technique that is designed to encrypt the user's face slightly. Primarily, dlib is utilizing for user-face detection. Susan is one of the top edge detectors with valuable localization characteristics marked edges, is used to extract features vectors from user faces. Moreover, the relevance of the suggested generating key is led to a crucial role in security improvement by producing them as difficult to intruders. According to PSNR values, the recommended encryption algorithms provided an adequate outcome in the encryption, they had a lower encrypting duration and a larger encrypting impact.</text>
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                <text>application/pdf</text>
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              <elementText elementTextId="54631">
                <text>https://online-journals.org/index.php/i-jim/article/view/38753</text>
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              <elementText elementTextId="54632">
                <text>10.3991/ijim.v17i07.38753</text>
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            <description>A language of the resource</description>
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                <text>eng</text>
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            <description>An entity responsible for making the resource available</description>
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              <elementText elementTextId="54634">
                <text>International Association of Online Engineering (IAOE), Vienna, Austria</text>
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          <element elementId="46">
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            <description>A related resource</description>
            <elementTextContainer>
              <elementText elementTextId="54635">
                <text>https://online-journals.org/index.php/i-jim/article/view/38753/13011</text>
              </elementText>
            </elementTextContainer>
          </element>
          <element elementId="47">
            <name>Rights</name>
            <description>Information about rights held in and over the resource</description>
            <elementTextContainer>
              <elementText elementTextId="54636">
                <text>Copyright (c) 2023 Haider TH.Salim ALRikabi, Raghad Abdulaali Azeez, Abeer Salim Jamil, Mohammed Salih Mahdi</text>
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              <elementText elementTextId="54637">
                <text>https://creativecommons.org/licenses/by/4.0</text>
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              <elementText elementTextId="54638">
                <text>International Journal of Interactive Mobile Technologies (iJIM); Vol. 17 No. 07 (2023); pp. 69-81</text>
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              <elementText elementTextId="54639">
                <text>1865-7923</text>
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            <description>The topic of the resource</description>
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                <text>— Dlib, Partial encryption, Susan, User Face</text>
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              <elementText elementTextId="54641">
                <text>A Partial Face Encryption in Real World Experiences Based on Features Extraction from Edge Detection</text>
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                <text>Peer-reviewed Article</text>
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                  <text>International Journal of Interactive Mobile Technologies (iJIM)</text>
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                <text>Zainab Hazim Ibrahim</text>
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                <text>Ban Hassan Majeed</text>
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                <text>Lina Fouad Jawad</text>
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                <text>Computer literacy is an urgent necessity for university students, given the rapid development in the means of communication in which we live in this era, and the flow of abundant information. Mainly on the computer in all administrative and academic transactions, where first of all the registration for the semester is done through the computer. Computer culture has many characteristics and advantages that distinguish it from other sciences, including the concept of computer culture that cannot be defined absolutely, and it is difficult to define its levels, because the specifications of the computer-educated individual differ from one individual to another, and from time to time also, you find it a luxury in a country What, and you find it necessary in another country. In order to measure and know the level of computer culture among university students, a computerized scale of (40) items with five multiple-choice alternatives were built. In order to know that they have the skills of searching for information electronically, a computerized scale of (21) items were prepared, and a five-point Likert scale was adopted. Results showed that they have computer literacy and therefore they have skills of searching for information electronically at all.</text>
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                <text>https://online-journals.org/index.php/i-jim/article/view/38751</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/38751/13007</text>
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            <name>Rights</name>
            <description>Information about rights held in and over the resource</description>
            <elementTextContainer>
              <elementText elementTextId="54616">
                <text>Copyright (c) 2023 Haider TH.Salim ALRikabi, Ban Hassan Majeed , Zainab Hazim Ibrahim , Lina Fouad Jawad</text>
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              <elementText elementTextId="54617">
                <text>https://creativecommons.org/licenses/by/4.0</text>
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            <name>Source</name>
            <description>A related resource from which the described resource is derived</description>
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              <elementText elementTextId="54618">
                <text>International Journal of Interactive Mobile Technologies (iJIM); Vol. 17 No. 07 (2023); pp. 47-57</text>
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              <elementText elementTextId="54619">
                <text>1865-7923</text>
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            <name>Subject</name>
            <description>The topic of the resource</description>
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                <text>Computer Literacy; Skills; Seeking for Information; Electronically; University; Students</text>
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            <name>Title</name>
            <description>A name given to the resource</description>
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                <text>Computer Literacy with Skills of Seeking for Information Electronically among University Students</text>
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              <name>Title</name>
              <description>A name given to the resource</description>
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                <elementText elementTextId="15984">
                  <text>International Journal of Interactive Mobile Technologies (iJIM)</text>
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      <name>Dataset</name>
      <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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            <name>Creator</name>
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              <elementText elementTextId="54584">
                <text>Sabah Abdulazeez Jebur</text>
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                <text>Abbas Khalifa Nawar</text>
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                <text>Lubna Emad Kadhim</text>
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                <text>Mothefer Majeed Jahefer</text>
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            <name>Date</name>
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            <description>An account of the resource</description>
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                <text>The highest way to protect data from intruder and unauthorized persons has become a major issue. This matter led to the development of many techniques for data security, such as Steganography, Cryptography, and Watermarking to disguise data. This paper proposes an image steganography method using the Least Significant Bits (LSB) technique and XOR operator and a secret key, through which the secret key is transformed into a one-dimensional bit stream array, then these bits are XORed with the bits of the secret image. Multiple experiments have been performed to embed color and grayscale images inside cover media. In this work, the LSB technique is ideal in two ways: firstly, only the least significant one-bit (1bit) of each byte will store the embedded data, this method is named (1-LSB). Secondly, the four least significant bits of the right half-byte (4 bits) of each byte will store the embedded data, this method is named (4-LSB). Subjective and objective analyzes were performed for each LSB process. The subjective analysis is responsible for both HVS and histogram, whereas the objective analysis involved both PSNR and MSE metrics.&amp;nbsp;&amp;nbsp;&amp;nbsp;</text>
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                <text>application/pdf</text>
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              <elementText elementTextId="54591">
                <text>https://online-journals.org/index.php/i-jim/article/view/38737</text>
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              <elementText elementTextId="54592">
                <text>10.3991/ijim.v17i07.38737</text>
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            <description>A language of the resource</description>
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              <elementText elementTextId="54593">
                <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="54594">
                <text>International Association of Online Engineering (IAOE), Vienna, Austria</text>
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            <name>Relation</name>
            <description>A related resource</description>
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                <text>https://online-journals.org/index.php/i-jim/article/view/38737/13023</text>
              </elementText>
            </elementTextContainer>
          </element>
          <element elementId="47">
            <name>Rights</name>
            <description>Information about rights held in and over the resource</description>
            <elementTextContainer>
              <elementText elementTextId="54596">
                <text>Copyright (c) 2023 Haider TH.Salim ALRikabi, Sabah Abdulazeez Jebur , Abbas Khalifa Nawar, Lubna Emad Kadhim, Mothefer Majeed Jahefer</text>
              </elementText>
              <elementText elementTextId="54597">
                <text>https://creativecommons.org/licenses/by/4.0</text>
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          <element elementId="48">
            <name>Source</name>
            <description>A related resource from which the described resource is derived</description>
            <elementTextContainer>
              <elementText elementTextId="54598">
                <text>International Journal of Interactive Mobile Technologies (iJIM); Vol. 17 No. 07 (2023); pp. 167-178</text>
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              <elementText elementTextId="54599">
                <text>1865-7923</text>
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          <element elementId="49">
            <name>Subject</name>
            <description>The topic of the resource</description>
            <elementTextContainer>
              <elementText elementTextId="54600">
                <text>Image Steganography, Least Significant Bits, LSB, XOR operator.</text>
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          <element elementId="50">
            <name>Title</name>
            <description>A name given to the resource</description>
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                <text>Hiding Information in Digital Images Using LSB Steganography Technique</text>
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            <name>Type</name>
            <description>The nature or genre of the resource</description>
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          <description>The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/.</description>
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              <name>Title</name>
              <description>A name given to the resource</description>
              <elementTextContainer>
                <elementText elementTextId="15984">
                  <text>International Journal of Interactive Mobile Technologies (iJIM)</text>
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      <name>Dataset</name>
      <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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            <name>Creator</name>
            <description>An entity primarily responsible for making the resource</description>
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              <elementText elementTextId="54562">
                <text>Zakaria, Mohamad Ikram</text>
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              <elementText elementTextId="54563">
                <text>Abdul Hanid, Mohd Fadzil</text>
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              <elementText elementTextId="54564">
                <text>Hassan, Roslizam</text>
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            <name>Date</name>
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              <elementText elementTextId="54565">
                <text>2023-05-10</text>
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            <description>An account of the resource</description>
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                <text>This study was conducted to identify elements (teaching activities) that teachers can engage with involving a combination of M-learning methods with Problem-Based Learning methods (M-PBL). This study was conducted using the Nominal Group Technique (NGT) involving 11 experts with various fields of expertise such as mathematics education, educational technology, M-learning, pedagogy and the curriculum as well as primary school mathematics education teachers. The analysis of the findings was carried out using descriptive statistics (percentages) to determine the priority and ranking for each teaching activity. The findings show that overall, there are 30 relevant M-PBL teaching activities that can be carried out by teachers. The findings also show that teachers sharing the learning objectives that the pupils need to achieve using learning applications that are available on mobile devices (98%) ranked first while the teacher classifying the information obtained from each group according to priority through learning applications available on mobile devices (75%) ranked last. In conclusion, this study shows that both methods can be combined to form a new teaching method in the current 4.0 education era.</text>
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            <name>Format</name>
            <description>The file format, physical medium, or dimensions of the resource</description>
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                <text>application/pdf</text>
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            <name>Identifier</name>
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                <text>https://online-journals.org/index.php/i-jim/article/view/38663</text>
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                <text>10.3991/ijim.v17i09.38663</text>
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            <name>Language</name>
            <description>A language of the resource</description>
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              <elementText elementTextId="54570">
                <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="54571">
                <text>International Association of Online Engineering (IAOE), Vienna, Austria</text>
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            <description>A related resource</description>
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                <text>https://online-journals.org/index.php/i-jim/article/view/38663/13163</text>
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          <element elementId="47">
            <name>Rights</name>
            <description>Information about rights held in and over the resource</description>
            <elementTextContainer>
              <elementText elementTextId="54573">
                <text>Copyright (c) 2023 Mohamad Ikram Zakaria, Mohd Fadzil  Abdul Hanid, Roslizam Hassan</text>
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              <elementText elementTextId="54574">
                <text>https://creativecommons.org/licenses/by/4.0</text>
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              <elementText elementTextId="54575">
                <text>International Journal of Interactive Mobile Technologies (iJIM); Vol. 17 No. 09 (2023); pp. 4-19</text>
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              <elementText elementTextId="54576">
                <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>m-learning</text>
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                <text>problem based learning</text>
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                <text>nominal group technique</text>
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          <element elementId="50">
            <name>Title</name>
            <description>A name given to the resource</description>
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                <text>Combination of M-learning with Problem Based Learning: Teaching Activities for  Mathematics Teachers</text>
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