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 authors failed to mention the location of the study. 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.

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.

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. This research presents better and more secure ways of accessing user data in the face of current technological risks.

Objective Summary 2

Introduction

Ingale et al. (2020) are the researchers who worked on developing new opportunities for previous research concerning biometrics. These authors perform research on various topics that are relevant under the theme of biometrics. The researchers were from San Jose State University, and some were qualified members of the IEEE. The authors did not mention the timespan when they conducted the research or the location of the experiment.

Summary

           The study design was an experimental setup where the authors engaged several ECG databases. Five of these databases were on-the-person, while the remaining one was in-house off-the-person. The researchers use the model databases to analyze several metrics pertaining to the topic of discussion, to validate the performance of the model. The authors further used fivefold cross-validation of performance to assess the effectiveness of the model. Ingale et al. (2020) revealed that manufacturers could achieve 100% accuracy on fixed window segmentation with FIR when they combine 1.86% FAR. Also, the study assesses 1,694 subjects and found an EER of 2.11%. The study shows that the ECG biometric authentication model surpasses the contemporary models currently in use but has insufficient abilities to filter, segment, match, and extract features.

Conclusion

           The authors of this paper came up with several datasets that pertain to biometric safety. They do an exemplary job of presenting both the pros and cons of the field of biometric safety. The authors, through this work, present a new approach that improves the performance of biometric security measures.

Objective Summary 3

Introduction

               This paper highlights the concept of modern type of computing and the influence of storage on the performance of edge computing. Edge computing has been quite revolutionary in several areas of current computing and technology in that field, including elements like artificial intelligence (AI). Mahadevappa et al. (2021) researched identifying an attack and isolating data that has been infected without interfering with the other nodes nearby. The authors of this paper should have mentioned the research date, and the study area was also included.

Summary

               This research had the methodology of separating various data so that modern safety of computers becomes optimum for users and other stakeholders. The study focuses on the possible ways to separate infected data from the rest of the ones that have not suffered interference. The edge nodes for data acquisition detected the intruders and quarantined other devices suspected by dimensionality reduction. The quarantine stage of the concept creates reputation scores that identify and sanitize false alarms on devices. The preliminary investigation that the authors performed in the research was effective in reducing obstacles to improve the efficiency of biometric security. The LDA helps increase the accuracy of quarantine by 72.83% in 0.9 seconds of training time.

Conclusion

               The study above helped to show that the biometric technology can be modified to help reduce the possible attacks on computer systems that companies use often. The safety of biometrics and computer systems is dependent on several factors as the authors present in their study. The authors provide a solution for data quarantine in edge computing. The model uses lightweight dimensionality reduction to quarantine data that has encountered intrusion. These researchers propose a future model to validate the findings and improve estimation efficiency.

References

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

Ingale, M., Cordeiro, R., Thentu, S., Park, Y., & Karimian, N. (2020). ECG biometric authentication: A comparative analysis. IEEE Access, 8, 117853-117866. Retrieved from https://ieeexplore.ieee.org/iel7/6287639/8948470/09123339.pdf

Mahadevappa, P., & Murugesan, R. K. (2021). A data quarantine model to secure data in edge computing. arXiv preprint arXiv:2111.07672. Retrieved from https://arxiv.org/pdf/2111.07672

Objective Summary 1

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.

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. 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. 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.

Conclusion

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.

Objective Summary 2

Introduction

Fashami et al. (2022) researched the factors that impact digital marketing through digital content. The research was also about how much marketing strategies can attract and influence consumers fond of social media. The authors reckon that digital marketing is very important, and more research on the area is compulsory. The authors are duly qualified to partake in the study because they are members of Alzahra University, located in Tehran, Iran.

Summary

The authors did not mention the specific location where they conducted the research. The methodology of this study was a systematic bibliometric review that was used to analyze modern marketing using content and promotion of the brand. The authors used a Bibliometrix package with both software of R and VOSviewer. The authors chose bibliometric methodology because it helps to reveal how knowledge evolves and how resources provide a quantitative view of facts. Fashami et al. (2022) performed a bibliometric analysis in two ways: performance analysis and mapping the science behind the knowledge. The authors presented the findings of the first part, which was the performance analysis. This research section showed that the countries with the most citations were the United Kingdom, the United States, and China. The authors also presented the five main clusters alongside each cluster's corresponding theories, contexts, and methods.

Conclusion

           The paper used ADO-TCM as a framework to present the five main clusters impacting digital content marketing. These clusters were characteristics of sources, social media, content, consumers, and the general online community. The choice of consumers to participate or not in social media marketing depends on the process that a company uses to market its digital content. The process of digital marketing, furthermore, relies on other contexts like culture and artificial intelligence.

Objective Summary 3

Introduction

               Rahmani et al. (2023) researched how small online businesses in Iran use the model of sustainable integration to benefit from digital marketing. The authors were determined to reveal the most relevant components to integrate sustainability in the field of digital marketing. The authors should have mentioned both the location and the timespan of the research. However, the researchers mentioned that the population of participants came from Islamic Azad University.

Summary

The methodology of the research was a descriptive survey that incorporated qualitative study. The authors used Strauss and Corbin model to research data theory and combined this with the data from the in-depth interviews of the research participants. Rahmani et al. (2023) collected research data using semi-structured interviews with 11 participants from the North Tehran Branch of Islamic Azad University. The group of experts who were participants in the study was found through snowball sampling. The interview sessions were codded using three methods of axial, open, and selective coding. The authors used MAXQDA software with grounded theory to analyze the study's qualitative data.

               The authors presented the research findings by extracting duplicate and synonymous codes. The findings of the relationships were presented under six headings of central phenomena, strategies and action, intervening conditions, calm conditions and consequences, and contextual conditions. The main categories from the study were digital marketing, social performance, environmental function, economic performance, the competitiveness of firms, commercialization strategies, and quality of service delivery and development. Each of these main categories was presented with its subcategories.

Conclusion

               The major categories in the causal conditions were social and environmental performance. Businesses can enjoy digital marketing by increasing their focus on environmental performance. Such efforts improve not only the state of the environment but also the business's reputation. The researchers recommend that small businesses use targeted marketing to focus on specific market segments. Iranian companies should emulate small businesses which have good environmental performance.

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://doi.org/10.1016/j.techfore.2019.119794

Fashami, R., Z., Haghighinasab, M., Seyyedamiri, N., & Ahadi, P. (2022). Designing a Digital Content Marketing Framework to Engage Consumers with Brands on Social Media: A Bibliometric Review. Journal of Business Management, 14(4), 573-601. Retrieved from https://jibm.ut.ac.ir/article_90590.html?lang=en

Rahmani, N., Vahabzadeh Munshi, S., & Mehrani, H. (2023). Integration of Sustainability in Small Business Digital Marketing: A Qualitative Study. International Journal of Digital Content Management, 4(6), 287-309. Retrieved from https://journals.atu.ac.ir/article_14943_57e1132bed944a4b13d0a9d794238cff.pdf