Challenges and Techniques of Social Media Analysis using Artificial Intelligence
1.This article have been chosen because of the factor that, this article provides some of the most important use of Artificial Intelligence tools have helped in analysing the social media data. The tool Crimson Hexagon is one of the major tool that uses the property of the Artificial Intelligence for the process of the social media analysis (Batrinca and Treleaven 2015).This is one of the leading article that have researched about the tools and the techniques that are been used for the purpose of the social media analytics using the concepts of the artificial intelligence (Ginsberg 2012). The paper primarily describes what social media is. According to the researchers the social media can be explained as the internet or mobile based application that uses the content of the users in a ubiquitously. Further thee researches explains the terminology and the data collection techniques that have been used in the process of the analysis. The researchers explains that the social media is especially important for the researchers to understand the data processes. Further the researchers explains about the terminology of the social media. The terminologies that are explained by the researchers are NLP or the natural language processing, the news articles, the opinion mining and scrapping. Further the researchers explains the researchers’ challenges of the paper (Korbicz et al. 2012). The scraping and the data cleaning is one of the other major challenge that is well explained in the paper. Further the researchers explains about the social media researchers and applications. (Ghahramani 2015). Thus the use of the processes of tools of Artificial Intelligence in the process of accessing these networks, is one of the most important thing that is to be done. Further explain the different concepts off the social media like the methodology that is used for the purpose of the analysis the data is explained in the paper. Also one of the other major thing that is well explained in the paper is the major social media providers. Major concerns are given to the media analytics tools that are required for the better analysis of the huge amount of the data that are produced in the social media data (Harman 2012). The researchers are unable to explain the security that the produced by the proper analysis of the data and how these have helped in the process of maintaining data integrity.
2.According to Wang, Li and Leung (2015), the mobile networks are designed with more complexity due to the fast development of internet and communication industry. This topic has been chosen as it explains the Artificial Intelligence tools and techniques (Wang and Leung 2015)..The infrastructure of the mobile networks is made complex and the devices that are used in design are diversified. The emerging technology in mobile communication is the mobile heterogeneous networks commonly known as the HetNets. The authors argue that HetNets are facing technical challenges in maintenance, management and optimization. These challenges over rules the complexity in the system resources used. The artificial intelligence tools that are being used are machine learning, fuzzy neural network, and bio-inspired algorithms. HetNets have been able to deploy effective solutions to academia and other industry. The Artificial Intelligence tools are capable of handling complex systems in a large-scale. The concept of HetNets will lead towards more intelligent and evolving AI techniques (Tong and Sriram 2012). The paper primarily describes what social media is. According to the researchers the social media can be explained as the internet or mobile based application that uses the content of the users in a ubiquitously. Further thee researches explains the terminology and the data collection techniques that have been used in the process of the analysis. This report mainly enlightens on the state-of-the-art AI based tools that will help in evolving HetNets systems and infrastructure and deal with the research issues of self- maintenance, self-management and self-optimization. The authors have been successful in explaining the advantages and disadvantages of various AI techniques that are being used. They have also successfully concluded the future challenges of the AI techniques at the end of the paper.
Emerging Technologies in Mobile Communication Industry and Use of AI
3.This article has successfully explained the working mechanisms for self- configuration, self-healing, self-maintenance and self-management. However, the article has failed to address the practical implementation of artificial tools in the networking technologies. The paper has not able to perform a critical analysis of the AI tools in the networks (Qadir et al. 2015). The implementation of the SDN technologies have been followed with proper analysis. However, it failed to implement different technologies regarding the time complexity of algorithms. Algorithms including ACO and GA has not able to provide a homogeneous complexity of emerging HelNets (Steels and Brooks 2018). This paper does not describe about the Machine to Person Communication with the help of Internet of things. M2P communication has been missing in the analysis part of the AI tools and algorithms. An effective management of the techniques used in the Internet of things have been discussed in the paper. New requirements in the address management in the bio-ecosystems have been discussed in the paper. HetNets have been able to deploy effective solutions to academia and other industry (Batrinca and Treleaven 2015). The Artificial Intelligence tools are capable of handling complex systems in a large-scale. The concept of HetNets will lead towards more intelligent and evolving AI techniques. The major thing that researchers have missed in the paper is to properly analyse the effects of Artificial Intelligence in the society. The researchers are unable to provide any information how this can affect the life of the normal people. The concept of the AI is one of the leading technology and not everyone knows about the same, this is one of the major missing in the part in the paper.
4.As stated by the authors, network anomalies are detected with the help of IDS (Intrusion detection System). It plays an important role in maintaining the security of network. One of the techniques used by IDS is Artificial Intelligence. In other words, it can be said that one of the Artificial Intelligence tool is Intrusion Detection System (Alrajeh & Lloret 2013). This paper is chosen as it elaborately describes the use of AI tools in maintaining and monitoring network security. Artificial immune system is used by IDS for monitoring traffic and internal logs in the network. The tool is also used to detect threats in the Wireless sensor networks (WSNs). It has other application like the data classification and optimizations. Neural networks that are connected like the neurons in humans are used to detect the complex trends.
Artificial intelligence enabled networking
Intrusion detection system has been successful in detecting the threats of network however, fails to solve the problem of computing. It is able to maintain the integrity, confidentiality and security of the network however, fails to explain the genetic algorithm that is used in IDS and AI. The biological concepts are also not explained clearly. WSNs continue to be vulnerable to security attacks. Network security is gaining importance as the attackers are finding new attack methods to the network.
The Artificial intelligence enabled networking is one of the major paper that that have been researched in the past. This article have been chosen because of the factor that this article provides some of the best knowledge about the use of the artificial intelligence. The paper primarily explains the AI Techniques that can be used for the purpose of understanding how the artificial intelligence works (Charniak et al. 2014. Other than this the paper provides some of the best analysis methods and tools are provided in the paper. Further the researchers explains that the Tensor voting techniques is one of the major point that can be used for the purpose of enhancing the technology for the process of the analysing data in a better manner. In order to prove the claims that that have been made the researchers provides some of the major examples in order to prove the claims that are made (Pascual 2015). The paper provides some of the major tools that have been provided in the paper. The researchers explains the Resource management is one of the best way for the process of enhancing the data in a proper manner.
In all the papers the researchers provides some of the best process of analysis of the tools that helps in the process of the analysis of the tools that are needed for the process of the data analysis (Jeansoulin and Wilson 2014). The concept of the artificial intelligence can be explained as the process of making the machines learn about the information and take decisions using these information. The paper provides some of the major examples how the artificial intelligence can help in the process of the analysis of the big data. The papers provide a huge information about the tools that are used for the process of better analysis of networking sources. The research can help in the better understanding of the different kinds of the tools that can be used in networking for the process of better analysis of the data that are created with each days.
References
Alrajeh, N. A., & Lloret, J. (2013). Intrusion detection systems based on artificial intelligence techniques in wireless sensor networks. International Journal of Distributed Sensor Networks, 9(10), 351047.
Batrinca, B. and Treleaven, P.C., 2015. Social media analytics: a survey of techniques, tools and platforms. Ai & Society, 30(1), pp.89-116.
Charniak, E., Riesbeck, C.K., McDermott, D.V. and Meehan, J.R., 2014. Artificial intelligence programming. Psychology Press.
Ghahramani, Z., 2015. Probabilistic machine learning and artificial intelligence. Nature, 521(7553), p.452.
Ginsberg, M., 2012. Essentials of artificial intelligence. Newnes.
Harman, M., 2012, June. The role of artificial intelligence in software engineering. In Proceedings of the First International Workshop on Realizing AI Synergies in Software Engineering(pp. 1-6). IEEE Press.
Jeansoulin, R. and Wilson, N., 2014. Quality of Geographic Information: Ontological approach and Artificial Intelligence Tools. arXiv preprint arXiv:1401.6679.
Korbicz, J., Koscielny, J.M., Kowalczuk, Z. and Cholewa, W. eds., 2012. Fault diagnosis: models, artificial intelligence, applications. Springer Science & Business Media.
Pascual, D.G., 2015. Artificial intelligence tools: decision support systems in condition monitoring and diagnosis. Crc Press.
Qadir, J., Yau, K.L.A., Imran, M.A., Ni, Q. and Vasilakos, A.V., 2015. IEEE access special section editorial: Artificial intelligence enabled networking. IEEE Access, 3, pp.3079-3082.
Steels, L. and Brooks, R., 2018. The artificial life route to artificial intelligence: Building embodied, situated agents. Routledge.
Tong, C. and Sriram, D. eds., 2012. Artificial Intelligence in Engineering Design: Volume III: Knowledge Acquisition, Commercial Systems, And Integrated Environments. Elsevier.
Wang, X., Li, X. and Leung, V.C., 2015. Artificial intelligence-based techniques for emerging heterogeneous network: State of the arts, opportunities, and challenges. IEEE Access, 3, pp.1379-139
Order an Essay Now & Get These Features For Free:
Turnitin Report
Formatting
Title Page
Citation
Outline
