1 10 5 https://repository.horizon.ac.id/files/original/2950e7127f9e4a025a5731888678857f.pdf 02a80264c62a2916cc8caf2b4e349b02 Dublin Core 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/. Title A name given to the resource VOL. 11 NO. 2 (2022) APRIL 2022 Dublin Core 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/. Title A name given to the resource Analysis of Deep-Fake Technology Impacting Digital World Credibility: A Comprehensive Literature Review Subject The topic of the resource Deep learning; Encoder; Decoder Generative adversarial networks (GANs). Question Generation, NLP, Intelligent Tutoring System (ITS. Description An account of the resource Deep-Fake Technique is a new scientific method that uses Artificial-Intelligince to make fake videos with an affect of facial expressions and coordinated movement of lips. This technology is frequently employed in a variety of contexts with various goals. Deep-Fake technology is being used to generate an extremely realistic fake video that can be widely distributed to promote false information or fake news about any celebrity or leader that was not created by them. Because of the widespread use of social media, these fraudulent videos can garner billions of views in under an hour and have a significant impact on our culture. Deep-Fakes are a threat to our celebrities, democracy, religious views, and commerce, according to the findings, but they can be managed through rules and regulations, strong company policy, and general internet user awareness and education. We need to devise a process for examining such video and distinguishing between actual and fraudulent footage. Creator An entity primarily responsible for making the resource Mohd Akbar, Mohd Suaib, Mohd Shahid Hussain Source A related resource from which the described resource is derived www.ijcit.com Date A point or period of time associated with an event in the lifecycle of the resource April 2022 Contributor An entity responsible for making contributions to the resource peri irawan Format The file format, physical medium, or dimensions of the resource pdf Language A language of the resource english Type The nature or genre of the resource text https://repository.horizon.ac.id/files/original/88df98d65e8e213c7c91bb7aba2cd1fa.pdf a61c4be84fd9e4eb55bbffc42c83581b Dublin Core 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/. Title A name given to the resource VOL. 11 NO. 2 (2022) APRIL 2022 Dublin Core 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/. Title A name given to the resource A Protection Layer over MapReduce Framework for Big Data Privacy Subject The topic of the resource Data Analytics; HADOOP; HDFS; Map Reduce Protection Layer (MRPL); Privacy-preserving. Description An account of the resource In many organizations, big data analytics has become a trend in gathering valuable data insights. The framework MapReduce, which is generally used for this purpose, has been accepted by most organizations for its exceptional characteristics. However, because of the availability of significant processing resources, dispersed privacy-sensitive details can be collected quickly, increasing the widespread privacy concerns. This article reviews some of the existing research articles on the MapReduce framework's privacy issues and proposes an additional layer of privacy protection over the adopted framework. The data is split into bits and processed in the clouds, and two other steps are taken. Hadoop splits the file into bits of a smaller scale. The task tracker then allocates these bits to several mappers. First, the data is split up into key-value pairs, and the intermediate data sets are generated. The efficiency of the suggested approach may then be effectively interpreted. Overall, the proposed method provides improved scalability. The following figures compare execution time with relation to file size and the number of partitions. As privacy protection technique is used, the loss of data content can be appropriately handled. It has been demonstrated that MRPL outperforms current methods in terms of CPU optimization, memory usage, and reduced information loss. Research reveals that the suggested strategy creates significant advantages for Big Data by enhancing privacy and protection. MRPL can considerably solve the privacy issues in Big Data. Creator An entity primarily responsible for making the resource Hidayath Ali Baig Source A related resource from which the described resource is derived www.ijcit.com Date A point or period of time associated with an event in the lifecycle of the resource April 2022 Contributor An entity responsible for making contributions to the resource peri irawan Format The file format, physical medium, or dimensions of the resource pdf Language A language of the resource english Type The nature or genre of the resource text https://repository.horizon.ac.id/files/original/ee470bccc20d3904257bace445911b4d.pdf 4be7bc4510f1243d506823f416f4f5b9 Dublin Core 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/. Title A name given to the resource VOL. 11 NO. 2 (2022) APRIL 2022 Dublin Core 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/. Title A name given to the resource A Discrete Mathematical Model of the Variable State of the Pandemic Subject The topic of the resource pandemic; mathematical modeling Covid-19; open systems; equation of the discrete variable state, superposition of epidemic waves, prediction. Description An account of the resource The paper describes and analyzes a discrete mathematical model of the variable state of the pandemic, which is important for determining production quantities of vaccines and antiviral drugs, predicting the number of infected persons, and the intensity of the process of disseminating information or new ideas to the public. According to the system of differential equations of the pandemic, a discrete mathematical model in vector-matrix form was developed and the equilibrium of the model in the space state was proved. As a result of the implementation of the pandemic model, the discrete dynamic curves of the variable state were obtained in a Matlab package. Creator An entity primarily responsible for making the resource Avtandil Bradvelidze, János Sztrik, Khatuna Bradvelidze, Irakli Basheleishvili Source A related resource from which the described resource is derived www.ijcit.com Date A point or period of time associated with an event in the lifecycle of the resource April 2022 Contributor An entity responsible for making contributions to the resource peri irawan Format The file format, physical medium, or dimensions of the resource pdf Language A language of the resource english Type The nature or genre of the resource text https://repository.horizon.ac.id/files/original/e4c30e0169c059f9c69a77bcdcec7e4c.pdf 490cabca8f196e0c7ab99b8207a1c75f Dublin Core 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/. Title A name given to the resource VOL. 11 NO. 2 (2022) APRIL 2022 Dublin Core 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/. Title A name given to the resource Feature Extraction using Histogram of Oriented Gradients for Image Classification in Maize Leaf Diseases Subject The topic of the resource Feature extraction, ORB, HOG, KAZE, Image classification, machine learning, and classifier Description An account of the resource The paper presents feature extraction methods and classification algorithms used to classify maize leaf disease images. From maize disease images, features are extracted and passed to the machine learning classification algorithm to identify the possible disease based on the features detected using the feature extraction method. The maize disease images used include images of common rust, leaf spot, and northern leaf blight and healthy images. An evaluation was done for the feature extraction method to see which feature extraction method performs best with image classification algorithms. Based on the evaluation, the outcomes revealed Histogram of Oriented Gradients performed best with classifiers compared to KAZE and Oriented FAST and rotated BRIEF. The random forest classifier emerged the best in terms of image classification, based on four performance metrics which are accuracy, precision, recall, and F1-score. The experimental outcome indicated that the random forest had 0.74 accuracy, 0.77 precision, 0.77 recall, and 0.75 F1-score. Creator An entity primarily responsible for making the resource Vincent Mbandu Ochango, Geoffrey Mariga Wambugu, John Gichuki Ndia Source A related resource from which the described resource is derived www.ijcit.com Date A point or period of time associated with an event in the lifecycle of the resource April 2022 Contributor An entity responsible for making contributions to the resource peri irawan Format The file format, physical medium, or dimensions of the resource pdf Language A language of the resource english Type The nature or genre of the resource text https://repository.horizon.ac.id/files/original/d3d20d25e1d3d092bc646da2dc04ffa5.pdf e0fc29104958376c295eeb048afd8d7f Dublin Core 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/. Title A name given to the resource VOL. 11 NO. 2 (2022) APRIL 2022 Dublin Core 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/. Title A name given to the resource ENMSJ : An Efficiency Filtering Technique using Bitmap Vectors for n-way Joins in Wireless Sensor Networks Subject The topic of the resource communication cost, in-network join, n-way join, wireless sensor networks. Description An account of the resource In wireless sensor networks, join queries execution introduces a high energy consumption. While energy is an important factor for sensors survival, several techniques were developed to reduce it. Sensors energy is affected by the number of transferred messages whereas query is performed. The aim of the proposed techniques was then to decrease the communicated data volume. So, the exchanged data volume is soaring when joins are performed between many data tables. This joins type is called: n-way join query. In this paper, we present an efficiency technique to treat n-way join queries in wireless sensor networks. This technique is named: Enhanced N-way Mediated Semi-Join (ENMSJ). ENMSJ is an improvement of a precedent strategie that we proposed: N-way Mediated Semi-Join (NMSJ). ENMSJ uses bitmap tables to more reduce transferred messages quantity. We compared the two techniques to test their performance. Obtained results are very hopeful. Creator An entity primarily responsible for making the resource Boubekeur DJAIL Date A point or period of time associated with an event in the lifecycle of the resource April 2022 Contributor An entity responsible for making contributions to the resource peri irawan Format The file format, physical medium, or dimensions of the resource pdf Language A language of the resource english Type The nature or genre of the resource text