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                <text>G. Baby Saral</text>
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                <text>R. Priya</text>
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                <text>Mobile Screen spent by teenage who use lot of time on screen experience psychiatric problems such as frustration, fatigue, depression, anxiety, and so on. Matplotlib pyplot with histogram figure is used to evaluate the psychiatric condition data (CSV file). For the purpose of rating the condition data, the standard deviation and mean are determined. For data analysis, there is a correlation matrix that compares every row and column. Gradient Booster, Logistic Regression, Knn algorithm is used to compare the precision of the machine learning process, which merged the characteristics of addictive and non-addictive data to find rating disorders.</text>
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                <text>Copyright (c) 2023 G. Baby Saral, R. Priya</text>
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                <text>International Journal of Interactive Mobile Technologies (iJIM); Vol. 17 No. 04 (2023); pp. 144-151</text>
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                <text>1865-7923</text>
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                <text>Data Science, Gradien Booster, logistic regression, Pyplot.</text>
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                <text>Mobile Screen Addiction Using Addictive Attribute by Combination of Algorithm-In Addiction Disorder</text>
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                <text>N. Chitra</text>
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                <text>Safinaz. S</text>
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                <text>K. Bhanu Rekha</text>
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                <text>The Feature Selection concept is the procedure where in the data is simplified removing the irrelevant features. Divergence method is another strategy of method where in the relation among the attributes and class is measured to understand their contribution to performance. Nowadays the mobile network is&amp;nbsp; integrated with the other networks like the computer networks transmitting all kinds of data leading to attacks in&amp;nbsp; networks&amp;nbsp; known as intrusion in computer networks equally applicable to mobile communications.&amp;nbsp; So in this paper the intrusion detection method involving the other other mode of communication is considered in mobile interaction network. The proposed algorithm performs feature selection using the divergence evaluation method to reduce the feature set. A 10% KDDCUP99 data set was used for the evaluation of the proposed algorithm, and performance metrics were evaluated using the C4.5 classifier. The metrics TPR, FPR and consistency were compared with the mutual information based DMIFSA, RMIFSA, MMIFSA methods and the proposed method is implemented on Python 3.8 that proved to achieve the high accuracy of 99.94% as compared to other methods and also reduce the redundant features. The consistency in Accuracy is maintained almost from 4 features to 10 features in proposed method as compared to other methods that indicates the stability of the system is achieved.</text>
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                <text>https://online-journals.org/index.php/i-jim/article/view/37733</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/37733/12761</text>
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                <text>Copyright (c) 2023 N. Chitra, Safinaz. S, K. Bhanu Rekha</text>
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                <text>International Journal of Interactive Mobile Technologies (iJIM); Vol. 17 No. 04 (2023); pp. 75-88</text>
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                <text>1865-7923</text>
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                <text>Divergence, Feature Selection, C4.5 Classifier, mobile communications, TPR (True Positive Rate), FPR (False positive Rate), Accuracy, stability</text>
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                <text>Divergence Based Feature Selection for Pattern Recognizing of the Performance of Intrusion Detection in Mobile Communications Merged with the Computer Communication Networks</text>
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              <elementText elementTextId="54043">
                <text>Ghorab, Ahmed S.</text>
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                <text>Rasheed, Raed S.</text>
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                <text>Hamad, Hatem M.</text>
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                <text>2023-04-26</text>
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                <text>Vehicle capacities and intelligence are rapidly increasing, which will likely support a wide range of novel and interesting applications. However, these resources are not effectively utilized. To take advantage of these invaluable capabilities in smart vehicles, they can be used in the cloud environment and can be operated through distributed computing platforms in order to benefit from their combined processing power, storage capacity, and memory resources. Vehicular edge computing (VEC) is a promising field that allows computing tasks to be transferred from cloud servers to vehicular edge servers for processing, allowing data and apps to be placed closer to vehicles (users).
This paper proposes a framework that combines two modules, the first one for managing microservice caching in vehicle-mounted edge networks, such that we use cluster-based caching technique to deal with the case where similar microservices are frequently requested in VEC. The second one integrates the computational capabilities of the edge servers with the capabilities of vehicles to perform task offloading in a collaborative manner.
Our solution addresses the limitations of existing edge computing platforms during peak times by combining microservices caching with computational task offloading to improve overall system performance.
&amp;nbsp;</text>
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                <text>https://online-journals.org/index.php/i-jim/article/view/37729</text>
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                <text>10.3991/ijim.v17i08.37729</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/37729/13063</text>
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                <text>Copyright (c) 2023 Ahmed S. Ghorab, Hatem Hammad, Raed Rasheed</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. 08 (2023); pp. 78-99</text>
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                <text>1865-7923</text>
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                <text>Mobile Edge Computing (MEC)</text>
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                <text>Task Offloading</text>
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                <text>Microservice Caching</text>
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                <text>Joint Microservices Caching and Task Offloading Framework in VEC Based on Deep Reinforcement Learning</text>
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                <text>Abdalilah. G. I. Alhalangy</text>
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                <text>Group’s communications using Mobile Ad hoc Networks (MANETs) have recently drawn a lot of interest. Information is delivered to a group of receivers simultaneously through a process known as multipath. Therefore, to enable team communication activities, an effective and efficient multipath routing protocol is essential. Enhancing multicast routing has been the subject of numerous initiatives. However, they don't account for scalability. A scalable geographic multicast routing protocol was proposed in this research (SGMRP). This protocol's primary goal is to create a compact, adaptable multipath routing strategy that works regardless of the number of broadcast participants or the magnitude of the network. Using a prediction model and cross-validation techniques, the model performance evaluation tool assesses model effectiveness. The satisfaction with the service was also evaluated using a question. In addition to reducing the number of participating nodes in localization, the proposed method also gets rid of duplicate packets between clusters. The article introduces the framework's fundamental idea, details its transparent implementations, and finishes with a preliminary assessment of the framework based on typical mobile software kinds.</text>
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                <text>https://online-journals.org/index.php/i-jim/article/view/37675</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/37675/12765</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="54034">
                <text>Copyright (c) 2023 Abdalilah. G. I. Alhalangy</text>
              </elementText>
              <elementText elementTextId="54035">
                <text>https://creativecommons.org/licenses/by/4.0</text>
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            <description>A related resource from which the described resource is derived</description>
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              <elementText elementTextId="54036">
                <text>International Journal of Interactive Mobile Technologies (iJIM); Vol. 17 No. 04 (2023); pp. 101-116</text>
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              <elementText elementTextId="54037">
                <text>1865-7923</text>
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            <description>The topic of the resource</description>
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                <text>Scalable Geographic Multicast Routing Protocol (SGMRP); Mobile Ad hoc Networks (MANETs); GPS; localization; multicast routing;</text>
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                <text>SGMRP-Utilizing a Mobile Ad Hoc Network to Develop a Scalable Geographic Multicast Routing Protocol for Localization</text>
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                  <text>International Journal of Interactive Mobile Technologies (iJIM)</text>
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          <element elementId="39">
            <name>Creator</name>
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              <elementText elementTextId="54007">
                <text>Adil. O. Y. Mohamed</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 rate of smartphone purchases is rising daily, and mobile payments are now frequently accepted in various areas. It is essential to transfer money completely safe as well as quick. The encrypted distributed ledgers function provides verified real-time transaction confirmation without the necessity for intermediaries like banks and clearinghouses; blockchain provide quick, secure, decentralized, and inexpensive transaction services. Blockchain technology makes money transfer simpler with transparency and financial data security. Through these capabilities, blockchain has attracted interest from around the world. However, some challenges arise while completing some financial security needs. This work proposes a framework for secure mobile payments based on blockchain technology. The advantages of blockchain are discussed and how blockchain technology provides multi-level authentication to secure mobile based financial transactions. Due to the increased safety and confidentiality of users on mobile payment apps, the suggested system takes into account the need of developing a safe application for the mobile transactions. We have also addressed the security related challenges of blockchain based payment applications and provided potential solutions.</text>
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                <text>application/pdf</text>
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                <text>https://online-journals.org/index.php/i-jim/article/view/37671</text>
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                <text>International Association of Online Engineering (IAOE), Vienna, Austria</text>
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              <elementText elementTextId="54015">
                <text>https://online-journals.org/index.php/i-jim/article/view/37671/12755</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="54016">
                <text>Copyright (c) 2023 Adil. O. Y. Mohamed</text>
              </elementText>
              <elementText elementTextId="54017">
                <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>
            <elementTextContainer>
              <elementText elementTextId="54018">
                <text>International Journal of Interactive Mobile Technologies (iJIM); Vol. 17 No. 04 (2023); pp. 37-46</text>
              </elementText>
              <elementText elementTextId="54019">
                <text>1865-7923</text>
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            </elementTextContainer>
          </element>
          <element elementId="49">
            <name>Subject</name>
            <description>The topic of the resource</description>
            <elementTextContainer>
              <elementText elementTextId="54020">
                <text>Mobile Payment; Blockchain; Secure; transaction; fast</text>
              </elementText>
            </elementTextContainer>
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            <name>Title</name>
            <description>A name given to the resource</description>
            <elementTextContainer>
              <elementText elementTextId="54021">
                <text>Intelligent Blockchain-Based Secure Framework for Transaction in Mobile Electronic Payment System</text>
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            <element elementId="50">
              <name>Title</name>
              <description>A name given to the resource</description>
              <elementTextContainer>
                <elementText elementTextId="15984">
                  <text>International Journal of Interactive Mobile Technologies (iJIM)</text>
                </elementText>
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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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          <element elementId="39">
            <name>Creator</name>
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              <elementText elementTextId="53988">
                <text>Rusul H. Al_taie</text>
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              <elementText elementTextId="53989">
                <text>Hawraa abd Al_kadum hassan</text>
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            </elementTextContainer>
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            <name>Date</name>
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            <elementTextContainer>
              <elementText elementTextId="53990">
                <text>2023-02-23</text>
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          <element elementId="41">
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              <elementText elementTextId="53991">
                <text>These days, We know that mobile phones with camera are very common to&amp;nbsp; used by most of the people because of their ease of use and carry . So I figured I need for mobile phone images processing. These images are often subjected to a variety of types of distortion, such as noise, poor illumination, and blur due to transmission , image acquisition and mobile movement during capturing scene. This Degradation leads to produce distorted images are not suitable for viewing or application. To overcome this degradation, this paper presents image improvement in mobile phone by distortion removal using Band Reject Filter and DFT to obtain on the best clear image. Periodic noise is sinusoidal of several of certain frequencies and periodic in nature. It looks identical to the bars that the image is covered. The proposed method is subjected to eliminate this noise from a set of images using Band Reject Filter using discrete Fourier transform that attenuate and replace a number of degradation bands. Then, computes the peak signal to noise ratio metric for each band. After that, select the largest value of each band for the purpose the making one clear image. Finally, the bands that carry value large are merged to produce a one image. The final image is converted to the spatial domain using an inverse Fourier transform. The experimental outcomes indicate that the proposed method improves the degraded image by a greater percentage without using the better value for metric and makes image more clear and&amp;nbsp; informative compare with the input image.</text>
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            <name>Identifier</name>
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              <elementText elementTextId="53993">
                <text>https://online-journals.org/index.php/i-jim/article/view/37669</text>
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              <elementText elementTextId="53994">
                <text>10.3991/ijim.v17i04.37669</text>
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            <description>A language of the resource</description>
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              <elementText elementTextId="53995">
                <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="53996">
                <text>International Association of Online Engineering (IAOE), Vienna, Austria</text>
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              <elementText elementTextId="53997">
                <text>https://online-journals.org/index.php/i-jim/article/view/37669/12769</text>
              </elementText>
            </elementTextContainer>
          </element>
          <element elementId="47">
            <name>Rights</name>
            <description>Information about rights held in and over the resource</description>
            <elementTextContainer>
              <elementText elementTextId="53998">
                <text>Copyright (c) 2023 Rusul H. Al_taie, Hawraa abd Al_kadum hassan</text>
              </elementText>
              <elementText elementTextId="53999">
                <text>https://creativecommons.org/licenses/by/4.0</text>
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            </elementTextContainer>
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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="54000">
                <text>International Journal of Interactive Mobile Technologies (iJIM); Vol. 17 No. 04 (2023); pp. 134-143</text>
              </elementText>
              <elementText elementTextId="54001">
                <text>1865-7923</text>
              </elementText>
            </elementTextContainer>
          </element>
          <element elementId="49">
            <name>Subject</name>
            <description>The topic of the resource</description>
            <elementTextContainer>
              <elementText elementTextId="54002">
                <text>Band Reject Filter (BRF) ; Periodic Noise; Image Enhancement; DFT; Mobile Phone images.</text>
              </elementText>
            </elementTextContainer>
          </element>
          <element elementId="50">
            <name>Title</name>
            <description>A name given to the resource</description>
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              <elementText elementTextId="54003">
                <text>Enhance Mobile Phone Digital Camera Images Using Band Reject Filter and Discrete Fourier Transform</text>
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            </elementTextContainer>
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            <name>Type</name>
            <description>The nature or genre of the resource</description>
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              <elementText elementTextId="54004">
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              <elementText elementTextId="54006">
                <text>Non-refereed Article</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>
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          <element elementId="39">
            <name>Creator</name>
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              <elementText elementTextId="53968">
                <text>Ahdi Hassan</text>
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              <elementText elementTextId="53969">
                <text>Abdalilah. G. I. Alhalangy</text>
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              <elementText elementTextId="53970">
                <text>Fahad Alzahrani</text>
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          <element elementId="40">
            <name>Date</name>
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            <elementTextContainer>
              <elementText elementTextId="53971">
                <text>2023-02-23</text>
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              <elementText elementTextId="53972">
                <text>Fake accounts on online social networks are increasing today with an increase in the number of active social network users. Social media websites allow users to share thoughts, facts, views and re-sharing these into various networks. Social media platforms provide users with enormous valuable information but with this great amount of information in social media, many issues like fake profile, online hacking have also grown. The fake profiles in online social sites create fake news and share unwanted material which contains spam links that affect natural users. The massive issue in social media communication networks is spam and it is necessary to identify fake profiles to stop spam. In this paper, a supervised machine learning algorithm called support vector machine (SVM) is used to identify fake accounts on social media effectively. In order to automatically identify fake online profiles, Random Forest classifier is used with SVM. With this concept, it can be applied online easily to identify millions of accounts that cannot be examined manually. The result of this model is compared with other identification techniques and the results show that the proposed algorithm performs better with high precision and recall. This method efficiently safeguards social media networks from online threats and attacks.</text>
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            <name>Format</name>
            <description>The file format, physical medium, or dimensions of the resource</description>
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              <elementText elementTextId="53973">
                <text>application/pdf</text>
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              <elementText elementTextId="53974">
                <text>https://online-journals.org/index.php/i-jim/article/view/37645</text>
              </elementText>
              <elementText elementTextId="53975">
                <text>10.3991/ijim.v17i04.37645</text>
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            <name>Language</name>
            <description>A language of the resource</description>
            <elementTextContainer>
              <elementText elementTextId="53976">
                <text>eng</text>
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          <element elementId="45">
            <name>Publisher</name>
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            <elementTextContainer>
              <elementText elementTextId="53977">
                <text>International Association of Online Engineering (IAOE), Vienna, Austria</text>
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              <elementText elementTextId="53978">
                <text>https://online-journals.org/index.php/i-jim/article/view/37645/12759</text>
              </elementText>
            </elementTextContainer>
          </element>
          <element elementId="47">
            <name>Rights</name>
            <description>Information about rights held in and over the resource</description>
            <elementTextContainer>
              <elementText elementTextId="53979">
                <text>Copyright (c) 2023 Ahdi Hassan, Abdalilah. G. I. Alhalangy , Fahad Alzahrani</text>
              </elementText>
              <elementText elementTextId="53980">
                <text>https://creativecommons.org/licenses/by/4.0</text>
              </elementText>
            </elementTextContainer>
          </element>
          <element elementId="48">
            <name>Source</name>
            <description>A related resource from which the described resource is derived</description>
            <elementTextContainer>
              <elementText elementTextId="53981">
                <text>International Journal of Interactive Mobile Technologies (iJIM); Vol. 17 No. 04 (2023); pp. 64-74</text>
              </elementText>
              <elementText elementTextId="53982">
                <text>1865-7923</text>
              </elementText>
            </elementTextContainer>
          </element>
          <element elementId="49">
            <name>Subject</name>
            <description>The topic of the resource</description>
            <elementTextContainer>
              <elementText elementTextId="53983">
                <text>Social Media, SVM, Random Forest Classifier, Fake profile, Mobile network.</text>
              </elementText>
            </elementTextContainer>
          </element>
          <element elementId="50">
            <name>Title</name>
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            <elementTextContainer>
              <elementText elementTextId="53984">
                <text>Fake Accounts Identification in Mobile Communication Networks Based on Machine Learning</text>
              </elementText>
            </elementTextContainer>
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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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          <element elementId="39">
            <name>Creator</name>
            <description>An entity primarily responsible for making the resource</description>
            <elementTextContainer>
              <elementText elementTextId="53947">
                <text>Aljammal, Ashraf H.</text>
              </elementText>
              <elementText elementTextId="53948">
                <text>taamneh , Salah</text>
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              <elementText elementTextId="53949">
                <text>Qawasmeh, Ahmad</text>
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              <elementText elementTextId="53950">
                <text>Bani Salameh, Hani</text>
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                <text>Nowadays, individuals and organizations are&amp;nbsp;increasingly&amp;nbsp;targeted by phishing attacks,&amp;nbsp;so&amp;nbsp;an accurate phishing detection&amp;nbsp;system&amp;nbsp;is&amp;nbsp;required. Therefore, many phishing detection techniques have been proposed as well as phishing datasets have been collected. In this paper, three datasets have been used to train and test machine learning classifiers. The datasets have been archived by Phish-Tank and UCI Machine Learning Repository. Furthermore, Information Gain algorithm have been used for features reduction and selection purpose. In addition, six machine learning classifiers have been evaluated, namely NaiveBayes, ANN, DecisionStump, KNN, J48 and RandomForest. However, the classifiers have been trained and tested over the three datasets in two stages. The first stage is using all features included in each dataset while the second stage using selected features by IG algorithm. At the first stage RandomForest classifier has shown the best performance over Dataset-1 and Dataset-2, while J48 has shown the best performance over Dataset-3. On the other hand, after features selection, the RandomForest classifier was the superior among the other five classifiers over Dataset-1 and Dataset-2 with accuracy of 98% and 93.66% respectively. While ANN classifier has shown the best performance with accuracy of 88.92% over Dataset-3. Because of the few number of instances as well as features in Dataset-3 comparing to the other two dataset; the performance of the classifiers has been affected.</text>
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                <text>https://online-journals.org/index.php/i-jim/article/view/37575</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/37575/12813</text>
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            <name>Rights</name>
            <description>Information about rights held in and over the resource</description>
            <elementTextContainer>
              <elementText elementTextId="53959">
                <text>Copyright (c) 2023 Ashraf Hamdan Aljammal</text>
              </elementText>
              <elementText elementTextId="53960">
                <text>https://creativecommons.org/licenses/by/4.0</text>
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                <text>International Journal of Interactive Mobile Technologies (iJIM); Vol. 17 No. 05 (2023); pp. 71-83</text>
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              <elementText elementTextId="53962">
                <text>1865-7923</text>
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            <name>Subject</name>
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                <text>Phishing attack, phishing attack detection, Cybersecurity, Machine Learning, Web security</text>
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                <text>Machine Learning Based Phishing Attacks Detection Using Multiple Datasets</text>
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                  <text>International Journal of Interactive Mobile Technologies (iJIM)</text>
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      <name>Dataset</name>
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              <elementText elementTextId="53924">
                <text>Tran, Thi Ngoc Anh</text>
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                <text>Nguyen Thanh, Phong</text>
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            <name>Date</name>
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            <description>An account of the resource</description>
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                <text>In the educational digital transformation, the effective use of technological platform has been proved to support formative assessment and improve student learning outcomes. This research focuses on Socrative – a student response system (SRS) that has been appreciated by many recent studies. The quasi-experimental design is used to evaluate the impact of completing Socrative-based Physics online homework on students’ learning outcomes. Data were collected from 305 grade 10 students at two high schools in central Vietnam. The results showed that 152 students have participated in the homework on Socrative (experimental group), with an average score of 7.73/10 in the final exam; while the score of 153 students who did not participate in the activity (control group) was 5.31/10. The results of the survey indicated that students have positive attitudes toward the use of Socrative as an online homework platform. In addition, the students recommended that Socrative should be applied frequently in many subjects and exams in order to enhance their learning outcomes.</text>
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                <text>https://online-journals.org/index.php/i-jim/article/view/37513</text>
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                <text>10.3991/ijim.v17i05.37513</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="53932">
                <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/37513/12825</text>
              </elementText>
            </elementTextContainer>
          </element>
          <element elementId="47">
            <name>Rights</name>
            <description>Information about rights held in and over the resource</description>
            <elementTextContainer>
              <elementText elementTextId="53934">
                <text>Copyright (c) 2023 Thi Ngoc Anh Tran, Phong Nguyen Thanh</text>
              </elementText>
              <elementText elementTextId="53935">
                <text>https://creativecommons.org/licenses/by/4.0</text>
              </elementText>
            </elementTextContainer>
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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="53936">
                <text>International Journal of Interactive Mobile Technologies (iJIM); Vol. 17 No. 05 (2023); pp. 182-199</text>
              </elementText>
              <elementText elementTextId="53937">
                <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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                <text>Socrative</text>
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                <text>online homework</text>
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                <text>learning outcomes</text>
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                <text>physics education</text>
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                <text>student response system (SRS)</text>
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            <name>Title</name>
            <description>A name given to the resource</description>
            <elementTextContainer>
              <elementText elementTextId="53943">
                <text>The  Effects of Socrative-Based Online Homework on Learning Outcomes  in Vietnam: A Case Study</text>
              </elementText>
            </elementTextContainer>
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            <description>The nature or genre of the resource</description>
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          <elementContainer>
            <element elementId="50">
              <name>Title</name>
              <description>A name given to the resource</description>
              <elementTextContainer>
                <elementText elementTextId="15984">
                  <text>International Journal of Interactive Mobile Technologies (iJIM)</text>
                </elementText>
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    <itemType itemTypeId="14">
      <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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        <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>
        <elementContainer>
          <element elementId="39">
            <name>Creator</name>
            <description>An entity primarily responsible for making the resource</description>
            <elementTextContainer>
              <elementText elementTextId="53901">
                <text>Ayo, Eliza B.</text>
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              <elementText elementTextId="53902">
                <text>Jotic, Rommel N.</text>
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              <elementText elementTextId="53903">
                <text>Raqueño, Avelina</text>
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              <elementText elementTextId="53904">
                <text>Loresca,  John Vincent G.</text>
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              <elementText elementTextId="53905">
                <text>Mendoza, Irene F.</text>
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              <elementText elementTextId="53906">
                <text>Baroña, Princess Vanessa M.</text>
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                <text>2023-05-22</text>
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                <text>Abstract— This document presents the design and development of an integrated library management system based on the needs assessment conducted in response to technology, infrastructure, existing condition, facilities, technology readiness of human resources, automation sufficiency level, and support of technical staff among ninety-eight (98) respondents using the descriptive developmental method.  
Developing a system based on research is crucial in ensuring the accuracy and reliability of the system. This approach addresses specific problems or challenges by incorporating evidence-based practices and the latest advancements in the field.
The findings include the need to mentor the respondents on how to use technology and the influence of others as a factor in using technology. There is a difficulty encountered in navigating schools’ virtual libraries, unavailability of materials, and visiting it is not enticing anymore since information is available from the internet. The rarity of introducing new technology and the absence of 3D printers are common pain points. However, improvements on the library website, email/chat with a librarian, online research guides, online book reservations, and book delivery service were noticed. Research gaps and recommendations from the selected Library Management System were also employed as additional input in the development of the system. A deeper understanding of the Internet of Things, Artificial Intelligence, Big Data, Robotics, and Embedded Systems through training, webinars, and workshops are necessary for the librarian. These technologies could be applied in library operations and transactions. Procurement of the latest equipment which includes 3D printers and the development of integrated library systems are steps to take to improve the library's services and operation. By combining the results of assessments and incorporating new knowledge derived from research gaps and recommendations, provided a stronger foundation for the system. It also provides validation and grounding in scientific principles, ensuring that the system is well-informed and effective in its implementation. Ultimately, developing a system based on research leads to more informed and effective solutions to pain points and challenges.</text>
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                <text>https://online-journals.org/index.php/i-jim/article/view/37509</text>
              </elementText>
              <elementText elementTextId="53911">
                <text>10.3991/ijim.v17i10.37509</text>
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            <name>Language</name>
            <description>A language of the resource</description>
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                <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="53913">
                <text>International Association of Online Engineering (IAOE), Vienna, Austria</text>
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              <elementText elementTextId="53914">
                <text>https://online-journals.org/index.php/i-jim/article/view/37509/13231</text>
              </elementText>
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            <name>Rights</name>
            <description>Information about rights held in and over the resource</description>
            <elementTextContainer>
              <elementText elementTextId="53915">
                <text>Copyright (c) 2023 Eliza Bundoc Ayo, Dr. Rommel N. Jotic, Dr. Avelina Raqueno, Mr. John Vincent Loresca, Ms. Princess Vanessa M. Baroña, Ms. Irene F. Mendoza</text>
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                <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>
            <elementTextContainer>
              <elementText elementTextId="53917">
                <text>International Journal of Interactive Mobile Technologies (iJIM); Vol. 17 No. 10 (2023); pp. 242-256</text>
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              <elementText elementTextId="53918">
                <text>1865-7923</text>
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            <name>Subject</name>
            <description>The topic of the resource</description>
            <elementTextContainer>
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                <text>library management system, software development, assessment</text>
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                <text>Development of an Integrated Library Management System (ILMS)</text>
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