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Modeling Social Networks using Data Mining Approaches-Review
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     Getting knowledge from raw data has delivered beneficial information in several domains. The prevalent utilizing of social media produced extraordinary quantities of social information. Simply, social media delivers an available podium for employers for sharing information. Data Mining has ability to present applicable designs that can be useful for employers, commercial, and customers. Data of social media are strident, massive, formless, and dynamic in the natural case, so modern encounters grow. Investigation methods of data mining utilized via social networks is the purpose of the study, accepting investigation plans on the basis of criteria, and by selecting a number of papers to serve as the foundation for this article. Afterward a watchful evaluation of these papers, it has beeniscovered that numerous data extraction approaches were utilized with social media data to report a number of various research goals in several fields of industrial and service. Though, implementations of data mining are still raw and require more work via industry and academic world to prepare the work sufficiently. Bring this analysis to a close. Data mining is the most important rule for uncovering hidden data in large datasets, especially in social network analysis, and it demonstrates the most important social media technology.

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Publication Date
Mon Dec 25 2023
Journal Name
Ieee Access
ITor-SDN: Intelligent Tor Networks-Based SDN for Data Forwarding Management
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Tor (The Onion Routing) network was designed to enable users to browse the Internet anonymously. It is known for its anonymity and privacy security feature against many agents who desire to observe the area of users or chase users’ browsing conventions. This anonymity stems from the encryption and decryption of Tor traffic. That is, the client’s traffic should be subject to encryption and decryption before the sending and receiving process, which leads to delay and even interruption in data flow. The exchange of cryptographic keys between network devices plays a pivotal and critical role in facilitating secure communication and ensuring the integrity of cryptographic procedures. This essential process is time-consuming, which causes del

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Publication Date
Wed Nov 24 2021
Journal Name
2021 Ieee Asia Pacific Conference On Postgraduate Research In Microelectronics And Electronics (primeasia)
Review of 3D Networks-On-Chip Simulators and Plugins
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A comprehensive review focuses on 3D network-on-chip (NoC) simulators and plugins while paying attention to the 2D simulators as the baseline is presented. Discussions include the programming languages, installation configuration, platforms and operating systems for the respective simulators. In addition, the simulator’s properties and plugins for design metrics evaluations are addressed. This review is intended for the early career researchers starting in 3D NoC, offering selection guidelines on the right tools for the targeted NoC architecture, design, and requirements.

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Publication Date
Sun Oct 30 2022
Journal Name
Iraqi Journal Of Science
Ensemble Approach for Detecting COVID-19 Propaganda on Online Social Networks
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     COVID-19 affected the entire world due to the unavailability of the vaccine. The social distancing was a contributing factor that gave rise to the usage of Online Social Networks. It has been seen that people share the information that comes to them without verifying its source . One of the common forms of information that is disseminated that have a radical purpose is propaganda. Propaganda is organized and conscious method of molding conclusions and impacting an individual's contemplations to accomplish the ideal aim of proselytizer. For this paper, different propagandistic tweets were shared in the COVID-19 Era. Data regarding COVID-19 propaganda was extracted from Twitter. Labelling of data was performed manually using diffe

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Publication Date
Wed Sep 23 2020
Journal Name
Artificial Intelligence Research
Hybrid approaches to feature subset selection for data classification in high-dimensional feature space
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This paper proposes two hybrid feature subset selection approaches based on the combination (union or intersection) of both supervised and unsupervised filter approaches before using a wrapper, aiming to obtain low-dimensional features with high accuracy and interpretability and low time consumption. Experiments with the proposed hybrid approaches have been conducted on seven high-dimensional feature datasets. The classifiers adopted are support vector machine (SVM), linear discriminant analysis (LDA), and K-nearest neighbour (KNN). Experimental results have demonstrated the advantages and usefulness of the proposed methods in feature subset selection in high-dimensional space in terms of the number of selected features and time spe

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Publication Date
Thu Oct 01 2015
Journal Name
Al–bahith Al–a'alami
The Role of Social Networks Sites in Prioritizing Political Reform among University Professors
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With the development of modern mass media and the prevalence of use continues to both researchers and practitioners their efforts to understand how the media affect Hzha on both the individual and the institutions, society and culture as a whole, which means that the need to develop models and theories explain and predict the effects of the use of such means, therefore, the study of modern technologies of communication and information as an area of ​​research has become mature to establish the intellectual base cohesive, but they are not mature enough, which calls for more research developments therefore become social networking sites online, (Facebook, and YouTube, and straining) known today as the new social media, which is witness

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Publication Date
Sun Jan 01 2023
Journal Name
2nd International Conference On Mathematical Techniques And Applications: Icmta2021
Review of clustering for gene expression data
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Publication Date
Sat Jan 01 2022
Journal Name
Indonesian Journal Of Electrical Engineering And Computer Science
Construct an efficient distributed denial of service attack detection system based on data mining techniques
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<span>Distributed denial-of-service (DDoS) attack is bluster to network security that purpose at exhausted the networks with malicious traffic. Although several techniques have been designed for DDoS attack detection, intrusion detection system (IDS) It has a great role in protecting the network system and has the ability to collect and analyze data from various network sources to discover any unauthorized access. The goal of IDS is to detect malicious traffic and defend the system against any fraudulent activity or illegal traffic. Therefore, IDS monitors outgoing and incoming network traffic. This paper contains a based intrusion detection system for DDoS attack, and has the ability to detect the attack intelligently, dynami

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Publication Date
Tue Mar 25 2014
Journal Name
Sensors
Minimal Camera Networks for 3D Image Based Modeling of Cultural Heritage Objects
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Publication Date
Sat Dec 02 2017
Journal Name
Al-khwarizmi Engineering Journal
Direction Finding Using GHA Neural Networks
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 This paper adapted the neural network for the estimating of the direction of arrival (DOA). It uses an unsupervised adaptive neural network with GHA algorithm to extract the principal components that in turn, are used by Capon method to estimate the DOA, where by the PCA neural network we take signal subspace only and use it in Capon (i.e. we will ignore the noise subspace, and take the signal subspace only).

 

 

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Publication Date
Mon Aug 01 2022
Journal Name
Baghdad Science Journal
Perceptually Important Points-Based Data Aggregation Method for Wireless Sensor Networks
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The transmitting and receiving of data consume the most resources in Wireless Sensor Networks (WSNs). The energy supplied by the battery is the most important resource impacting WSN's lifespan in the sensor node. Therefore, because sensor nodes run from their limited battery, energy-saving is necessary. Data aggregation can be defined as a procedure applied for the elimination of redundant transmissions, and it provides fused information to the base stations, which in turn improves the energy effectiveness and increases the lifespan of energy-constrained WSNs. In this paper, a Perceptually Important Points Based Data Aggregation (PIP-DA) method for Wireless Sensor Networks is suggested to reduce redundant data before sending them to the

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