CP70011E Research Method Assignment Sample 2023
Introduction
Artificial intelligence is a priority in machine learning systems as it improves the performance of the security system as well as provides better support. Further, this study material is prepared to take approval to conduct a dissertation on the impact of biometric technology on the machine learning system that is used to develop security systems in organisations. There are different types of benefits and impacts are observed in information security.
Research rationale
The current situation in the whole world is to manage data processing through digital technology and store it in a digital form. Thus, there are chances of data hacking as well as data batching by an unauthorized person. AI and different types of digital frameworks are used for maintaining information security and improving machine learning systems in any organisation. On the other hand, this method assists business organisations to improve stakeholder engagement through improving the safety of data of different types of stakeholders in the organisation.
Aims and Objectives
Research aim
Any study paper has certain aims to prepare as well as gives impact on different participants of that study paper. As opined by Hamidi, (2019), thus this study paper has main aims to analyze the actual impact of biometric technology for improving the security system of data of any organisation. On the other hand, the author of the research has another main issue that identifies different issues that improve risks of digital data stored at cloud bases sites. This research aims to develop a biometric system using MATLAB.
Research question
Q1. How is a biometric system implemented to improve information security?
Q2. How many types of biometric systems are used by organisations in machine learning?
Q3. How is the accuracy of data security compared with traditional methods of data storage?
Research objectives
- To determine the role of artificial intelligence in the data protection process.
- To evaluate the process of data security systems and uses of artificial intelligence.
- To identify the impact of a biometric system on different stakeholders of an organisation.
Analysis of different aims and objectives
Impact on stakeholder’s management
Stakeholders refer to those people and members of organisations as well as industry who play a significant role to improve organisation growth and their process at a global level. In this context, an organisation has gathered information about their different types of stakeholders in terms of their investment and due to organisation. As opined by Das et al. (2018), an organisation faces different types of problems while their management has a traditional method to perform business processes. Management has lost important data of different stakeholders by fire or maybe theft. Different organisations focus on implementing biometric technology in their data management system to improve the safety of data.
There are two main impacts of artificial and biometric data security systems. As opined by Wu and Li (2020), the first impact is that data management of any organisation has a low chance of data losing data breaching. This is necessary for any organisation to man-machine the data of stakeholders for better relationships between them as well as provide better satisfaction for them. Whereas stakeholders also feel safe about their important information in the organisation. As opined by Howard et al. (2018), AI not only makes safe data nevertheless also develops a cloud-based platform to interchange important data between stakeholders and organisations that boost data performance and HRM in an organisation. Besides, employees of any organisation as stakeholders are able to acquire skills and knowledge about digital technology that is necessary for developing a cloud-based organisation structure and improving the efficiency of employees.
Consumers’ satisfaction level of any organisation is also improved after implementation of biometric technology at different processes of an organisation. AI is a part of biometric technology and organisations develop DSC and digital manufacturing processes in organizations. Both processes of any business are significant to improve consumers’ satisfaction level as well as provide a better experience. As opined by Bragazzi et al. (2020), as a result, consumers of any organisation are more interested to be involved in the business. As a result, organisations are also able to manage big data in cloud-based digital platforms.
The negative impact of biometric application on the organisation
Biometric applications have different types of advantages and benefits nevertheless also have some negative impacts as per the maturity and size of an organisation. This is to protect the data and information of organisations at a large level. Thus, it is required large investments for the installation of AI and biometric technology in their organisation. As argued by McAteer et al. (2019), the main reason for this investment is the different features of the biometric system. As stated by Haenlein and Kaplan (2019), biometrics is performed through “fingertips, face, voice and other sensors’ ‘ that have massive costs and expenses at different stages for installation as well as maintenance process for safety. Besides, this system is only suitable for a large level of business organisations and organisations that maintain their business at a global level. The cost of acquisition for biometric application for any industry is high as compared with other security systems as the whole process is maintained through sensors. On the other hand, up-gradation of AI and digital technology that is used for maintaining all steps is necessary for an organisation. As argued by North, (2020), that operation is to boost the performance of the security system in the organisation as well as give new features to maintain data on the digital platforms. The maintenance cost is also acquired by heavy investments. These are the main basic negative impacts of the biometric application
Literature Review
Introduction
Humans are entering an era of artificial reasoning along with massive volumes of data and in this way, computers are turning out to be shrewder with execution even to a human level in restricted applications. It has been additionally associated all aspects of the globe with ultrahigh-speed Internet to share data in practically ongoing, and creatively make changes on the way of life of individuals. At the centre of computerized reasoning, AI calculations add to semi consequently or naturally foster exceptionally clever frameworks by conquering existing troubles for different fields, remembering applications for designing, business, science, and unadulterated craftsmanship. Biometrics are arising as fundamental innovations for Internet-period smart frameworks to guarantee both PC and organization protections just as security for independent gear.
The literature review section of the research paper of part which evaluated various previous research on the topic to enhance understanding of the research topic. This enables researchers to identify various variables in the research paper to increase the quality of research. This section allows research to cover various aspects of the research topic through which a clear vision of the research paper can be achieved.
Empirical study
According to Ortiz et al. 2018, Biometrics in current software engineering is characterized as the robotized utilization of organic properties to distinguish an individual. These properties permit people to distinguish a few people contingent upon their physical and social qualities just as their right use permits PC situations to perceive designs for security assignments. These particular sorts of errands have turned into another examination field and, in the outcome; its applications have been radically ventured into numerous new areas (Awad, 2018). This was normal due to the incremental interest for security and the upsides of biometric frameworks; biometric highlights can’t be taken, lost or disregarded. In this sense, any subtleties of the human body which varies from one human to other will be utilized as remarkable biometric information to fill in as that individual’s remarkable (ID), one might say that these frameworks give security in light of what you own rather than what you know (secret key/PIN) for sure you have (savvy card). In this sense, a few frameworks have been created in view of different physiological and conduct characteristics, which incorporate unique finger impression, face, iris, retina, voice, keystroke, ear, hand math, signature and step. In such a manner, machine learning strategies are valuable in choosing proper component portrayals that will work with the occupation of the choice capacity, in managing worldly data, and in melding multi-modular data.
According to Krishnamoorthy et al. 2018, biometric authentication can be defined as the verification of a user’s validity with the help of some kind of identification method. Some aspects in terms of user identity require taking into consideration that is further verified with machine learning technology. The machines are provided with particular languages where the identities of users are provided. The ML technologies of recent gadgets find similarities between these identity aspects if this identification is matched for a particular user. This is how the authentication process works. Every one of the investigations conveyed before contain fewer clients.
Furthermore, highlights when contrasted with this review. In order to record the biometric example of the client, all potential elements should be recorded to get high arrangement results. This upgrades to extraordinarily recognize every client; highlighting choice after this will assist with distinguishing the main elements to play out the ID of clients precisely. While understanding the impact of various highlights in the dataset, this analysis was completed, where the gadget explicit highlights like equipment, producer, SDK variant, country code, language, and the number of CPU centres were eliminated. The outcomes were without gadget information on the whole dataset with thirty occurrences for each client. The ideal number of elements was chosen to be 88 to yield better order execution.
According to Al Alkeem et al. 2019, Conventional biometric frameworks utilize alphanumeric or graphical passwords or token-based strategies that require ”something you know and something you have”. The weaknesses of these frameworks incorporate the dangers of absent-mindedness, misfortune, and robbery. To address these deficiencies, biometric validation is quickly supplanting conventional validation techniques and is turning into a piece of regular daily existence. The electrocardiogram (ECG) is perhaps the latest trait considered for biometric purposes. In this work, an ECG-based confirmation framework is depicted as reasonable for security checks and clinic conditions. The proposed framework will help specialists concentrate on ECG-based biometric confirmation procedures to characterize dataset limits and to secure excellent preparation information.
In order to identify the sustainability of the process, an aggregate of 90 ECG information tests to produce the reference work information base was established. The reference work for every ECG information substance (that means recognizable proof) was then, at that point, produced utilizing a common data-based Designated Time relapse approach. The validation execution of the proposed framework was assessed with a disarray lattice as well as likewise by utilizing the AMGCG tool kit in MATLAB to investigate two key boundaries: the ECG cutting time (sliding window) and the inspecting time span. It has been tracked down that a sliding window of 0.4s accomplished the best presentation and that the ideal testing term is 37s. Taking everything into account, utilizing these upgraded boundaries, the proposed validation framework is capable of accomplishing precise outcomes.
According to (Stamp, 2018), AI occupies a significant part in software engineering research, and many examinations of machine learning technology affect true applications. Inside the field of data security, it is hard to exaggerate the expected applications for AI. A wide assortment of AI methods has been introduced that are outside of the neural organization worldview (Thomas et al. 2020). For every method examined, an outline is provided, trailed by a delegate test of safety-related applications where the procedure has demonstrated helpfulness. The data introduced here is expected to give a delicate prologue to the field, and to provide the pursuers with a feeling of the wide assortment of uses where AI can assume a valuable part. It is actually significant that practising and assisting the algorithm may not accomplish very noteworthy outcomes. Lamentably, supporting calculations overall will more often than not be amazingly delicate to a commotion. Additionally, since a remarkable weighing capacity is utilized in AdaBoost software, exceptions can cause trouble.
According to Kim et al. 2019, most application frameworks support Internet access for general clients, distinguishing people with their own bodies has turned into a pattern for clients to get to application frameworks. In the end, biometric confirmation has become a hot examination point lately. Among different biometric confirmation plans, for example, unique finger impression checking and facial acknowledgement, electrocardiogram confirmation has the benefit of taking on live client body signals during verification. As a general rule, AI procedures are taken on to build a check model for client recognizable proof by getting the client’s live ECG information. As of late, there are a number of ECG models present on the basis of biometric stability of condition-of-craftsmanship written works on ECG based biometrics. Notwithstanding, a few ECG biometrics challenges actually require further examination like verification order, pre-handling for information quality upgrade, information acquisitions, a choice on Deep Learning
(DL) and other Machine Learning characterization draws near.
According to Pirbhulal et al. 2019, new ECG location gadgets will become versatile, embeddable, lightweight with cell phones and wearable gadgets, and connectable with far off waiters through remote advances sooner rather than later, ECG based biometric validation will be conveyed on gigantic application frameworks from one side of the planet to the other. To get high precision on client verification, ML strategies are for the most part embraced to assemble a more vigorous assessment model for ECG based biometric confirmation. In this paper a summed up AI structure for ECG based biometric validation is presented.
The proposed structure depicts the overall information handling stream of an ML-based ECG validation system alongside different capacity highlights to assist researchers with effectively planning and assessing an ML-based ECG client verification system. To accomplish an exceptionally significant level presentation and be keener as planned, late AI calculations with best-in-class structures can be applied to those biometric frameworks. In this research, researchers have presented some agent biometrics, talk about significant qualities of tests from comparing biometrics, and furthermore portray their successful elements and descriptors. It has additionally presented the notable directed AI calculations and profound learning in discrete sections alongside their applications to biometric studies (Baynath et al. 2019). Those capacities incorporate three general validation classes for ECG client confirmation, three new information pre-handling strategies, a period cutting method to produce great ECG datasets, four new information quality measurements, and an openly accessible Mat lab Toolbox.
Concept of variable
Variables are those aspects of a research paper on which whole research is based. These variables are mainly two kinds, one dependent variable and another is an independent variable. Dependent variables are those which vary on the independent variables and change in the independent variable affected on the dependent variable. However, on the other hand, independent variables refer to those variables that don’t vary on any aspects and change in this variable causes various changes. In The current scenario, independent variables are tools and techniques used in the biometric process. On the other hand, independent variables of the workforce and organisational culture that’s activities are dependent on this.
Usage of the biometric application
Based on the empirical study it has been noticed that through the use of biometric applications an organisation can be able to manage its activities in an effective way. Apart from this, the application of this software boosts organisational culture and employee engagement. Through this application, transparency is properly maintained which increases the morals of employees. Other than this, it has also been noted that the application of biometrics in information security boosts the skills of employees and workers which is significantly beneficial for the management to achieve its organisational goals. In the current era of the market where technology is changing rapidly, it is necessary for the business and organisation to have an effective tool by which data and information are properly maintained. As stated by Deliversky and Deliverska (2018), in this situation biometric application in information management systems provides a wider range of scopes through which better management of data and confidential information can be ensured.
On the other hand, analysis of the biometric application in an organisation also reflects future scopes for sustainable growth and development. In the last two decades technology has emerged as a leading factor for businesses and organisations through which great quality of service can be provided. Other than this, employee engagement is also effectively managed through the application of these tools and technology (Shokishalov and Wang, 2019). In this situation, biometric applications in organisations boost management capabilities to minimise errors and enhance decision-making ability. This reflects that the implementation of his technology positively impacts the management system to secure its information in an effective way.
An issue faced while a biometric application
Technology advancement is rapidly changing in the business environment; this leads to demand for various changes in organisational culture and workflow management. As opined by Ross et al. (2019), due to this, various organisations and industries hesitate to choose this. On the other hand, implementation of this technology leads to incurred various costs which affect business profitability. Furthermore, on analysis of various issues of organisational while implications of the biometric system it has been noted that this application requires various skills and knowledgeable employees through which smooth running of this system in data management can be ensured. However, organisations sometimes fail to meet these criteria that affect business management to a great extent (Barra et al. 2018). Moreover, the high risk of data breaches and data manipulation is increasing in the implementation of biometric applications and the chance of a cyber attack is high. Due to this, various organisations and industries faced issues in better management of information that affected business brand value.
Literature gap
On analysis of various previous research not chosen topics, it has been found that very little research has been conducted on the current issues and challenges faced by the business industry while implications of the biometric system. This reflects a scope to conduct and research. Apart from this, as this technology is new to world market research on various aspects of the biometric application such as the impact on stakeholders engagement, information security management remains untouched (Chamikara et al. 2020). In this situation, this research highlights these aspects of the biometric system through which a better understanding of this system can be possible.
Conclusion
As it gives a few procedures and numerous sorts of calculations, AI offers a few benefits over different methodologies for biometric design acknowledgement. Along these lines, this capacity fulfils an expanding need for security and more brilliant applications. Likewise, it may very well be valued that all the given unaided, managed and supported learning calculations meet the essential attributes. From this research, it can be expected that the references given will serve the pursuer in making novel AI answers for testing biometrics issues dependent on original methodologies. Overall researchers’ perception regarding machine learning technology of biometric solutions has been depicted here that will provide a clear concept. Moreover, Biometric implementation in information security may require further investigation regarding the security issues. Several types of research have been conducted for reducing security issues. But there are still some investigations required in terms of algorithm improvement that can be further researched.
Methodology
The methodology is a core part of a research paper. This provides necessary guidance to researchers through which effective tools and techniques application can be possible. This part of the research paper highlights different research strategies, approaches and research designs that provide opportunities for researchers to choose the most suitable tool for the whole research process. Other than this, this provides a roadmap for the successful conditions of the whole research process. Based on this, research will be able to provide logical conclusions on the whole research topic.
Research philosophy, design and approach
Research philosophy:
Research philosophy is a way through which different data and information can be collected analysed and evaluated. This plays a significant role in the research paper through which detailed analysis can be possible. Various research philosophies are there such as positivism, realism and interpretivism (Stubley, 2021). In this research paper, interpretivism research philosophy is being selected. The principal of this research philosophy provided scopes to observe the social world in a specific way. This will boost the reliability of research.
Research Design:
Research design is a framework that is chosen by researchers for the purpose of data collection, measurements of data with different tools. It is an integral part of a research paper that enables researchers to analyse data in an effective way. Various types of research design are there such as descriptive, causal, correlational and experimental (Sovacool et al.2018). Here descriptive research design opted which will enable researchers to analyse various aspects of biometric system in an effective way and provide opportunities to give a logical conclusion on a research topic.
Research approach:
The research approach is a plan and procedures through which wide assumptions of detailed methods of data collection, analysis and interpretation. Research approaches can be of different kinds such as deductive, inductive and mixed approaches. A deductive approach is a test of theory and phenomenon in respect of current research topics (Grinchenko and Shchapova, 2020). On the other hand, an inductive research approach is a way in which theory is developed on the basis of the current scenario and tests while conducting whole research. The mixed approach is the combination of both deductive and inductive research approaches.
The multivariable regression algorithm is used here for developing the security application. It has used multiple indefinite variables as the input data of the system. These input variables have provided several dependent variables as the output result of the system.
In this research paper, a deductive approach of research is chosen that will enable researchers to know the current scenario of the biometric system through testing with theory and framework. This increases research reliability and its effectiveness.
Machine learning algorithm:
“The Machine Learning algorithm” will help here to get the outcomes with the most accuracy.
The multivariable regression algorithm will be used in this research. It will create the relationship between dependent variables and independent variables. It may be more than one in number. The “multivariable regression will help to get the values of the variables using the values of one or more than one indefinite variable. The time period of sampling time should not be greater than 20 seconds. The authentication procedure starts with the “use case” process. It helps to check digital security.
Data collection and data analysis
Data collection:
Data collection plays a significant role in the whole research process. Research quality and reliability vary on data collection methodology. It is necessary for researchers to opt for an effective data collection approach through which research quality can be increased. Collection of Data can be from two major resources first is primary resources and the second is secondary data resources. In the primary data resources interview, a survey is conducted. On the other hand, in secondary data collection resources various journals, books, articles and government records are collected.
The data collection method will help to get the input data so that the output can be identified. The input variables are also noted in such a way that the output variation for each input data can be noticed. For this, the input variables are also represented in a graphical way. The input data are denoted across three different planes to get the exact graphical representation.
In this research paper, a secondary data collection method is used. Choosing this data collection method research guided through various resources which increase the quality of the research paper and its authenticity.
Data analysis:
Data analysis enables researchers to establish relationships between different concepts and variables. Through processing in a research paper, a researcher is able to increase the evaluated collected data in an effective way. Apart from this, research objectives are also being evaluated through this process. Data analysis is considered as the process of collecting, analysing and interpreting data through which research is able to find relationships between various aspects of a research topic. As stated by Lowe et al.(2018), Data analysis varies depending on the data collection method. As in the current scenario where a secondary data collection method is selected, thematic data analysis is suitable for this research paper. By choosing this method of data analysis research is able to establish a relationship between the variable of the biometric system and analyse the various challenge and their benefits. This method will provide a wider range of scopes to provide necessary recommendations on the effective application of biometric systems by which better data management can be possible.
Ethical consideration: Ethical considerations are quite necessary ingredients to conduct research in an effective manner. As opined by Ako et al. (2020), it is necessary for the research to ensure that ethics has been properly maintained in the research process. This research will maintain all the ethics in an effective manner that ensures less harm to developing research in future. This research will take permission from the publisher to collect data for the research paper. Other than this, this research will collect all data and resources from authentic websites that boost research authenticity. Moreover, data collection and data management security will be prioritised in the whole research process that reduces chances of data manipulation and data breaches.
Outcomes
Expected outcome
There are some outcomes of this study paper and dissertation that is conducted in future time as per proposal approval. As any study paper gives some impact on the environment as well as different factors of study materials. While data collection for different thematic analyses actual value or importance of AI and digital gadgets is identified. On the other hand, there are some positive outcomes if anyone cannot access the data of any organisation without performing that organisation. As opined by Ogbanufe and Kim (2018), besides, the users of data must agree to the positive use of data and not represent data in research papers with changes. These are significant outcomes that are maintained while preparing research papers as well as noticed at different digital sources of data.
The image has previewed the graphical representation of the sampling time and the accuracy. The X-axis is denoted as sampling time and the Y-axis is imagined as the rate of accuracy.
The plot has represented the graphical relation between the slicing time and the rate of accuracy. The slicing time is measured in seconds.
Potential contribution
A potential outcome of any research paper indicates different types of rules and regulation that is maintained for consent research. Besides, these shared stepmother factors that assist to improve the privacy of research papers are also considered as a potential outcome of research papers. Further, there are some key factors maintained that assist any business organisation to understand the value and possible significance of biometric application. On the other hand, AI plays a prime role in developing an effective biometric application in any organisation that’s the main role is to protect data from any unauthorized person as well as access. These outcomes may be also considered in the preparation of the final dissertation paper.
Project Plan
A project plan refers to different steps that may assist the researcher to conduct the whole research paper as well as the achievable aims and objectives of the research paper. The researcher is already given the first 9 weeks to complete element 1. Further, the author is considering element 2 on the same topic that is conducted in element 1. As stated by Ioannou and Tussyadiah, (2020), the author is in Spain for an additional 6 weeks to prepare this study paper. Week 10 may be considered for setting up different plans and steps that are necessary for preparing a quality research paper. Whereas week 12 may be required for the methodology section of the dissertation paper and week 13 may be taken for analysis of different data and consider actual results from analysis of data. That data may be collected by researchers in week 11 through different sources of data collection. Further week 14 may be performed to submit for approval and take approval. Finally submit the study paper in week 15. These are the whole project plan and timetable for taking steps.
Conclusion
This research paper is conducted to analyze the latest biometric process in business organisations to maintain information security. On the other hand, the biometric process consists of AI and different types of the latest software that are designed as per risks consist of different types of data processes at different levels of business organisation. These are basic considerations for preparing a dissertation in the future time. Thus suitable methodology is considered for collecting data on this topic as well as other factors that are relevant for analysis. Finally, it is considered that all significant factors for maintaining ethics while collecting data from different resources.
References
Journals
Ako, T., Plugge, E., Mhlanga-Gunda, R. and Van Hout, M.C., 2020. Ethical guidance for health research in prisons in low-and middle-income countries: a scoping review. Public Health, 186, pp.217-227. Available at: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7449980/
Al Alkeem, E., Kim, S.K., Yeun, C.Y., Zemerly, M.J., Poon, K.F., Gianini, G. and Yoo, P.D., 2019. An enhanced electrocardiogram biometric authentication system using machine learning. IEEE Access, 7, pp.123069-123075.
Awad, A.I., 2018. Introduction to information security foundations and applications.
Barra, S., Choo, K.K.R., Nappi, M., Castiglione, A., Narducci, F. and Ranjan, R., 2018. Biometrics-as-a-service: Cloud-based technology, systems, and applications. IEEE Cloud Computing, 5(4), pp.33-37. Available at: https://ieeexplore.ieee.org/iel7/6509491/8436068/08436083.pdf
Baynath, P., Soyjaudah, K.S. and Khan, M.H.M., 2019, September. Machine learning algorithm on keystroke dynamics fused pattern in biometrics. In 2019 Conference on Next Generation Computing Applications (NextComp) (pp. 1-6). IEEE.
Bragazzi, N.L., Dai, H., Damiani, G., Behzadifar, M., Martini, M. and Wu, J., 2020. How big data and artificial intelligence can help better manage the COVID-19 pandemic. International journal of environmental research and public health, 17(9), p.3176. Available at: https://www.mdpi.com/1660-4601/17/9/3176/pdf
Chamikara, M.A.P., Bertók, P., Khalil, I., Liu, D. and Camtepe, S., 2020. Privacy-preserving face recognition utilizing differential privacy. Computers & Security, 97, p.101951. Available at: https://arxiv.org/pdf/2005.10486
Das, A., Galdi, C., Han, H., Ramachandra, R., Dugelay, J.L. and Dantcheva, A., 2018, October. Recent advances in biometric technology for mobile devices. In 2018 IEEE 9th international conference on biometrics theory, applications and systems (BTAS) (pp. 1-11). IEEE. Available at: https://hal.inria.fr/hal-01894140/file/DasGaldiHanRamachandraDugelayDantcheva_BTAS_18.pdf
Deliversky, J. and Deliverska, M., 2018. Ethical and legal considerations in biometric data usage—Bulgarian perspective. Frontiers in public health, 6, p.25. Available at: https://www.frontiersin.org/articles/10.3389/fpubh.2018.00025/full?report=reader
Grinchenko, S. and Shchapova, Y.L., 2020. The deductive approach to Big History’s Singularity. In The 21st Century Singularity and Global Futures (pp. 201-210). Springer, Cham. Available at: https://www.academia.edu/download/62374223/2020_Big_History_Singularity20200315-18964-5xj2s5.pdf#page=211
Haenlein, M. and Kaplan, A., 2019. A brief history of artificial intelligence: On the past, present, and future of artificial intelligence. California management review, 61(4), pp.5-14. Available at: https://www.researchgate.net/profile/Ishaq-Azhar-Mohammed/publication/353888035_The_Interaction_Between_Artificial_Intelligence_and_Identity_Access_Management_An_Empirical_study/links/6116a4731ca20f6f861e49df/The-Interaction-Between-Artificial-Intelligence-and-Identity-Access-Management-An-Empirical-study.pdf
Hamidi, H., 2019. An approach to develop the smart health using Internet of Things and authentication based on biometric technology. Future generation computer systems, 91, pp.434-449. Available at: http://muhammetbaykara.com/wp-content/uploads/2018/10/ymhgece_bio.pdf
Howard, J.J., Blanchard, A.J., Sirotin, Y.B., Hasselgren, J.A. and Vemury, A.R., 2018, October. An investigation of high-throughput biometric systems: Results of the 2018 department of homeland security biometric technology rally. In 2018 IEEE 9th International Conference on Biometrics Theory, Applications and Systems (BTAS) (pp. 1-7). IEEE. Available at: https://mdtf.org/publications/rally-results.pdf
Ioannou, A., Tussyadiah, I. and Lu, Y., 2020. Privacy concerns and disclosure of biometric and behavioral data for travel. International Journal of Information Management, 54, p.102122. Available at: https://www.researchgate.net/profile/Iis-Tussyadiah/publication/341340368_Privacy_concerns_and_disclosure_of_biometric_and_behavioral_data_for_travel/links/5f9178c792851c14bcdb7e36/Privacy-concerns-and-disclosure-of-biometric-and-behavioral-data-for-travel.pdf
Journals
Kim, S.K., Yeun, C.Y., Damiani, E. and Lo, N.W., 2019. A machine learning framework for biometric authentication using electrocardiogram. IEEE Access, 7, pp.94858-94868.
Krishnamoorthy, S., Rueda, L., Saad, S. and Elmiligi, H., 2018, May. Identification of user behavioral biometrics for authentication using keystroke dynamics and machine learning. In Proceedings of the 2018 2nd International Conference on Biometric Engineering and Applications (pp. 50-57).
Lowe, A., Norris, A.C., Farris, A.J. and Babbage, D.R., 2018. Quantifying thematic saturation in qualitative data analysis. Field Methods, 30(3), pp.191-207. Available at: https://www.researchgate.net/profile/Andrew-Lowe-10/publication/322659061_Quantifying_Thematic_Saturation_in_Qualitative_Data_Analysis/links/5c914a0c92851cf0ae8991ec/Quantifying-Thematic-Saturation-in-Qualitative-Data-Analysis.pdf
McAteer, I., Ibrahim, A., Zheng, G., Yang, W. and Valli, C., 2019. Integration of biometrics and steganography: A comprehensive review. Technologies, 7(2), p.34. Available at: https://www.mdpi.com/2073-431X/7/3/37/pdf
North-Samardzic, A., 2020. Biometric Technology and Ethics: Beyond Security Applications. Journal of Business Ethics, 167(3), pp.433-450. Available at: http://search.ebscohost.com/login.aspx?direct=true&profile=ehost&scope=site&authtype=crawler&jrnl=01674544&AN=147018593&h=T93TvO3SmOax9VFMEkwhVvFfXaCAXyoX%2BaBhPq1f3h3jIKAph2j2ofsIAE3tORxopHRxMFrFTUGT4zVT2UF0HQ%3D%3D&crl=f&casa_token=GL-0sgqzTPIAAAAA:qGyfixxbY7wD5mXvQkaORssfL3bpOZZYxpYiWlfQOU_GypBpCgD5bRnne40547Yn_UmUMCejbbg_1ng
Ogbanufe, O. and Kim, D.J., 2018. Comparing fingerprint-based biometrics authentication versus traditional authentication methods for e-payment. Decision Support Systems, 106, pp.1-14. Available at: https://www.researchgate.net/profile/Obi-Ogbanufe-2/publication/321200649_Comparing_fingerprint-based_biometrics_authentication_versus_traditional_authentication_methods_for_e-payment/links/602d464a299bf1cc26d21f41/Comparing-fingerprint-based-biometrics-authentication-versus-traditional-authentication-methods-for-e-payment.pdf
Ortiz, N., Hernández, R.D., Jimenez, R., Mauledeoux, M. and Avilés, O., 2018. Survey of biometric pattern recognition via machine learning techniques. Contemporary Engineering Sciences, 11(34), pp.1677-1694.
Pirbhulal, S., Pombo, N., Felizardo, V., Garcia, N., Sodhro, A.H. and Mukhopadhyay, S.C., 2019, December. Towards machine learning enabled security framework for IoT-based healthcare. In 2019 13th International Conference on Sensing Technology (ICST) (pp. 1-6). IEEE.
Reference
Ross, A., Banerjee, S., Chen, C., Chowdhury, A., Mirjalili, V., Sharma, R., Swearingen, T. and Yadav, S., 2019, June. Some research problems in biometrics: The future beckons. In 2019 International Conference on Biometrics (ICB) (pp. 1-8). IEEE. Available at: https://arxiv.org/pdf/1905.04717
Shokishalov, Z.H. and Wang, H., 2019. Applying eye tracking in information security. Procedia Computer Science, 150, pp.347-351. Available at: https://www.sciencedirect.com/science/article/pii/S1877050919304089/pdf?md5=3af0191bbf13261f23049245cbe016f9&pid=1-s2.0-S1877050919304089-main.pdf
Sovacool, B.K., Axsen, J. and Sorrell, S., 2018. Promoting novelty, rigor, and style in energy social science: towards codes of practice for appropriate methods and research design. Energy Research & Social Science, 45, pp.12-42. Available at: https://www.sciencedirect.com/science/article/pii/S2214629618307230
Stamp, M., 2018. A survey of machine learning algorithms and their application in information security. In Guide to Vulnerability Analysis for Computer Networks and Systems (pp. 33-55). Springer, Cham.
Stubley, E.V., 2021. Philosophy as a Method of Inquiry. Visions of Research in Music Education, 16(1), p.91. Available at: https://opencommons.uconn.edu/cgi/viewcontent.cgi?article=1384&context=vrme
Thomas, T., Vijayaraghavan, A.P. and Emmanuel, S., 2020. Machine learning approaches in cyber security analytics. Springer.
Torres, J., de los Santos, S., Alepis, E. and Patsakis, C., 2019. Behavioral Biometric Authentication in Android Unlock Patterns through Machine Learning. In ICISSP (pp. 146-154).
Wu, W., Pirbhulal, S. and Li, G., 2020. Adaptive computing-based biometric security for intelligent medical applications. Neural Computing and Applications, 32(15), pp.11055-11064. Available at: https://www.researchgate.net/profile/Wanqing-Wu/publication/329195826_Adaptive_computing-based_biometric_security_for_intelligent_medical_applications/links/5e70350f299bf14570f284c9/Adaptive-computing-based-biometric-security-for-intelligent-medical-applications.pdf
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