Posted: February 18th, 2023
1. Apply the individual feedback the Professor provided for the sample objective summary to have objective summary #1.
2. Create 2 additional objective summaries.
3. Submit 3 objective summaries.
SAMPLE OBJECTIVE SUMMARY 1
SAMPLE OBJECTIVE SUMMARY 2
Artificial Intelligence in Social Media and its role in Digital Marketing
Venkatanaresh Tadoju
Department of Computer Science, Monroe College, King Graduate School
KG604: Graduate Research & Critical Analysis
Dr. Donna Tennyson
2/11/2023
Feedback from Dr. Tennyson
· Include information suggested in comments.
· Revise before included with Assignment 3.
· Excellent job!
Objective Summary
Introduction
Capatina et al. (2020) joined hands to perform research on the way modern technology affects how businesses use marketing on social media platforms. The researchers performed the study to determine whether the future capabilities of AI in social media match the expectations of stakeholder owners and users. The research was conducted on the owners and employees of digital agencies in the year 2020 in Romania, Italy, and France. The researchers use different approaches, starting with a focus group followed by an online survey. The paper proposes a casual model to discover AI software’s capabilities to match the needs of potential users. The paper aims to identify variables that explain how well respondents are knowledgeable about SMM. Additionally, Capatina et al. (2020) set out to find which of the four capabilities the users are likely to consider for a test. The researchers used the research to explain how different variables affect the likelihood of users to engage modern technology when it comes to social media marketing. Comment by Donna Tennyson: Who (OK) Comment by Donna Tennyson: Why (OK) Comment by Donna Tennyson: How (OK)
Summary of the findings
The paper is subdivided into a total of four sections which are the introduction, theoretical background, methodology, and findings. The research uses 150 owners and marketers in three countries to find the value of AI technology in SMM. SMM relies on various customer data like purchases, sales, and behavior to recommend the best products for customers. Marketers can also consider key data like images and sentiments as they contain information that is likely to go unnoticed. The focus group of digital marketing professionals helps build on the knowledge and awareness of AI. Capatina et al. (2020) completed the research with an online questionnaire with three experts in each of the three nations. According to the study, it is conceivable in Romania to pinpoint consumption locations and times and link sales to a brand’s social media usage frequency. Comment by Donna Tennyson: What (OK)
Conclusion
The researcher also presented findings for the two remaining countries, France and Italy. In France, conversely, users’ interest in responding to new products on social media is the main element that motivate them to engage in social media marketing testing using the proposed software. The software’s capacity to categorize content generated from user activity impacted consumer interest in the new program. Finally, the capacity to classify social media postings based on where they are in the customer purchasing cycle and the capacity to choose the material for use regularly. Thus, each country had specific factors affecting the participation of users. Comment by Donna Tennyson: What (OK) Move the findings to the same section of the paper.
Reference
Capatina, A., Kachour, M., Lichy, J., Micu, A., Micu, A. E., & Codignola, F. (2020). Matching the future capabilities of an artificial intelligence-based software for social media marketing with potential users’ expectations. Technological Forecasting and Social Change, 151, 119794. https://do
SAMPLE OBJECTIVE SUMMARY 1 1
SAMPLE OBJECTIVE SUMMARY 1 2
Biometric Data Integrity and its Impact on Biometric Security Technologies
Rajdip Rathod
Department of Computer Science, Monroe College, King Graduate School
KG604: Graduate Research & Critical Analysis
Dr. Donna Tennyson
2/11/2023
Feedback from Dr. Tennyson
· Summary section of objective summary should be identified with 2nd level heading.
· Revise to include information suggested in comments.
· Make revisions before included with Assignment 3.
· Good job!
Objective Summary 1
Introduction
Ahmed et al. (2018) are the researchers who wanted to reveal the kind of cyberattacks happening in various cyber systems. The six authors of the work are qualified since they presented their work through a conference paper with a good global reputation. Ahmed et al. (2018) conducted the research in two areas comprising water treatment and distribution facilities, SWaT and WADI. The study shows that the noise fingerprint can uniquely identify many sensors with an accuracy greater than 90%. The vulnerability of the systems to cyberattacks depends on the system’s understanding and the profile of noise it detects. NoisePrint is scalable, as seen by the wide variety of tools, procedures, and categorization methods. On two separate testbeds, the authors examined the suggested scheme’s viability, highlighting the generalizability and scalability of the NoisePrint. Comment by Donna Tennyson: Who (OK) Why (OK) Add: Where research conducted. Add: When research conducted if stated in the article. If not included in article, state that in objective summary. Comment by Donna Tennyson: What (OK) Comment by Donna Tennyson: Move detailed information to draft research paper.
Fingerprint sensors and noise detection are two technologies that help identify attacks on technological devices. Ahmed et al. (2018) researched two sample testbeds that dealt with water. The SWaT testbed helped the authors to develop a model with the right components and physics, using concepts of first principles (Ahmed et al., 2018). The authors easily established a model used in the second testbed. According to the findings, the suggested approach can identify zero-alarm assaults, whereas statistical reference methods cannot. Furthermore, scientists have demonstrated that sensors may be recognized uniquely with more than 90% accuracy. The authors need more accuracy, which is possible through the distinction between the sensor’s noise and the process. Comment by Donna Tennyson: How (OK)
Conclusion
Biometrics improves online security and protects users from potential data breaches. These technological systems operate while producing data for both the fingerprint and the noise. This study assesses NoisePrint using testbeds for water delivery and treatment. The authors performed not one but two tests of the NoisePrint system, each on a separate testbed. The often-used industrial sensors are analyzed, but the research is broadly relevant to other industrial applications. I am particularly impressed with this research as it presents better and more secure ways of accessing user data in the face of current technological risks. Comment by Donna Tennyson: Delete personal pronoun. Just state “this research…” because, since you are the author, statements in the paper are written by you.
Reference
Ahmed, C. M., Ochoa, M., Zhou, J., Mathur, A. P., Qadeer, R., Murguia, C., & Ruths, J. (2018, May). Noiseprint: Attack detection using sensor and process noise fingerprint in cyber physical systems. In Proceedings of the 2018 on Asia Conference on Computer and Communications Security (pp. 483-497). https://doi.org/10.1145/3196494.3196532
SOLUTION
Biometric data integrity refers to the accuracy, reliability, and consistency of biometric data over time. In biometric security technologies, biometric data is used to authenticate individuals based on their unique physiological or behavioral characteristics such as fingerprints, iris patterns, face recognition, voice recognition, and others.
The integrity of biometric data is essential for the effective functioning of biometric security technologies. If the biometric data is compromised or corrupted, it can lead to false positives or false negatives, which can result in security breaches, identity theft, or other forms of fraud.
The impact of biometric data integrity on biometric security technologies is significant. Biometric systems rely on accurate and reliable data to accurately identify individuals and grant or deny access. If the biometric data is compromised or corrupted, it can render the system useless, leading to security vulnerabilities and breaches.
To maintain the integrity of biometric data, biometric security technologies must ensure that the data is collected and stored securely, and that appropriate measures are taken to protect the data from unauthorized access or tampering. Biometric systems must also regularly update and verify the biometric data to ensure its accuracy and prevent data degradation.
In conclusion, biometric data integrity is critical for the effective functioning of biometric security technologies. The accuracy, reliability, and consistency of biometric data must be maintained to prevent security breaches and identity theft. As such, biometric security technologies must prioritize data integrity in their design, implementation, and maintenance.
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