Kernel Metadata Declaration — on demand

What the DOI identifies

A MACHINE LEARNING FRAMEWORK FOR BIOMETRIC AUTHENTICATION USING ELECTROCARDIOGRAM (ECG)

10.46243/jst.2024.v9.i01.pp97-105 · JournalArticle — an article in a journal · Digital · Visual · Language · en

Published 2024-01-25

Part of Journal of Science & Technology · ISSN 2456-5660 · vol. 9 · no. 1 · pp. 97–105

Principal agents

• Dr.SUBBA REDDY BORRA Dr.SUBBA REDDY BORRA (author → Author)
• Longman Publishers (publisher → Publisher)

Also in the record, outside the Kernel: the abstract, the licence, 3 links, 26 references. Source: Crossref (member 25296), registered 2024-02-16, last deposited 2026-09-17.

⬇ Record (JSON) ⬇ Declaration (XML)

System metadata — ISO 26324:2025, Annex B · DOI Handbook 10.1

Each element by the standard's name and the Handbook's (in grey), read off the record.

The record

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    "abstract": {
        "value": "This paper presents a system for how with suitably embrace and modify AI (ML) techniques used to construct electrocardiogram (ECG)- based biometric authentication systems. The proposed system can assist agents and engineers in ECG-based biometric verification components to define the boundaries of required datasets and get preparing information with great quality. To determine the limits of datasets, a use case analysis is conducted. In light of different application scenarios for ECG-based verification, three distinct use cases (or validation classes) are developed. By providing more qualified preparing information given to corresponding AI models, the accuracy of ML-based ECG biometric authentication systems are expanded in result. The ECG time cutting method with the R-top mooring is utilized in this system to secure ML preparing information with great quality. In the proposed system, four new measurement metrics are acquainted with assess the quality of the ML training and testing data. Additionally, a Matlab toolkit, containing all proposed tools, metrics, and test data with exhibitions utilizing different ML techniques, is developed and made publicly available for further analysis. For developing ML-based ECG biometric authentication, the proposed system can guide experts to establish the appropriate ML solutions and the ML training datasets along with three identified user case scenarios. For analysts taking on ML techniques to design new systems in other research domains, the proposed framework",
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            "doi": "10.1109/access.2017.2707460",
            "unstructured": "Q. Zhang, D. Zhou, And X. Zeng, ‘‘Heartid: A Multiresolution Convo-Lutional Neural Network For Ecg-Based Biometric Human Identification In Smart Health Applications,’’ Ieee Access, Vol. 5, Pp. 11805–11816, 2021"
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            "key": "ref2",
            "doi": "10.1109/access.2018.2849870",
            "unstructured": "J. R. Pinto, J. S. Cardoso, And A. Lourenço, ‘‘Evolution, Current Chal-Lenges, And Future Possibilities In Ecg Biometrics,’’ Ieee Access, Vol. 6, Pp. 34746–34776, 2021"
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            "key": "ref3",
            "doi": "10.1109/tifs.2017.2784362",
            "unstructured": "E. J. Da Silva Luz, G. J. P. Moreira, L. S. Oliveira, W. R. Schwartz, And D. Menotti, ‘‘Learning Deep Off-The-Person Heart Biometrics Representa-Tions,’’ Ieee Trans. Inf. Forensics Security, Vol. 13, No. 5, Pp. 1258–1270, May 2021"
        },
        {
            "key": "ref4",
            "doi": "10.1109/access.2019.2891817",
            "unstructured": "H. Kim And S. Y. Chun, ‘‘Cancelable Ecg Biometrics Using Compres-Sive Sensing-Generalized Likelihood Ratio Test,’’ Ieee Access, Vol. 7, Pp. 9232–9242, 2022"
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            "key": "ref5",
            "doi": "10.1109/access.2018.2836950",
            "unstructured": "Y. Xin, L. Kong, Z. Liu, Y. Chen, Y. Li, H. Zhu, M. Gao, H. Hou, And Wang, ‘‘Machine Learning And Deep Learning Methods For Cybersecu-Rity,’’ Ieee Access, Vol. 6, Pp. 35365–35381, 2022"
        },
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            "key": "ref6",
            "doi": "10.1088/1757-899x/317/1/012030",
            "unstructured": "H. J. Kim And J. S. Lim, ‘‘Study On a Biometric Authentication Model Based On Ecg Using a Fuzzy Neural Network,’’ Proc. Iop Conf. Ser., Mater. Sci. Eng., Vol. 317, Mar. 2021, Art. No. 012030"
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            "doi": "10.3390/s17102228",
            "unstructured": "J. R. Pinto, J. S. Cardoso, A. Lourenço, And C. Carreiras, ‘‘Towards a Continuous Biometric System Based On Ecg Signals Acquired On The Steering Wheel,’’ Sensors, Vol. 17 No. 10, p. 2228, 2022"
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            "doi": "10.1260/2040-2295.4.4.465",
            "unstructured": "M. Sansone, R. Fusco, A. Pepino, And C. Sansone, ‘‘Electrocardiogram Pattern Recognition And Analysis Based On Artificial Neural Networks And Support Vector Machines: A Review,’’ J. Healthcare Eng., Vol. 4, No. 4, Pp. 465–504, Jun. 2023"
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            "unstructured": "A. E. Saddik, J. S. A. Falconi, And H. A. Osman, ‘‘Electrocardiogram (Ecg) Biometric Authentication,’’ U.S. Patent 9 699 182 B2, Jul. 4, 2023"
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            "key": "ref10",
            "doi": "10.1109/tsp.2016.7760964",
            "unstructured": "S. Y. Chun, J.-H. Kang, H. Kim, C. Lee, I. Oakley, And S.-P. Kim, ‘‘Ecg Based User Authentication For Wearable Devices Using Short Time Fourier Transform,’’ In Proc. 39th Int. Conf. Telecommun. Signal Process. (Tsp), Jun. 2022, Pp. 656–659"
        },
        {
            "key": "ref11",
            "unstructured": "A. F. Hussein, A. K. Alzubaidi, A. Al-Bayaty, And Q. A. Habash, ‘‘An Iot Real-Time Biometric Authentication System Based On Ecg Fiducial Extracted Features Using Discrete Cosine Transform,’’ Aug. 2021, Arxiv:1708.08189. [Online]. Available: Https://Arxiv.Org/ Abs/1708.08189 Figure 3.ECG authentication ID was predected Figure 4.ECG authentication another ID was predected Dr. SUBBA REDDY BORRA, T. SAMYUKTHA, U. KRISHNAVENI, T. KAVYA: A MACHINE LEARNING FRAMEWORK FOR BIOMETRIC AUTHENTICATION USING ELECTROCARDIOGRAM (ECG)"
        },
        {
            "key": "ref12",
            "unstructured": "Usability.Gov. Use Cases. Accessed: Feb. 1, 2019. [Online]. Available: Https://Www. Usability.Gov/How-To-And-Tools/Methods/ Use-Cases.Html"
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            "key": "ref13",
            "doi": "10.1109/iwcmc.2017.7986553",
            "unstructured": "E. K. Zaghouani, A. Benzina, And R. Attia, ‘‘Ecg Based Authentication For e-Healthcare Systems:Towards a Secured Ecg Features Transmission,’’ In Proc. 13th Int. Wireless Commun. Mobile Comput. Conf. (Iwcmc), Valencia, Spain, Jun. 2017, Pp. 1777–1783"
        },
        {
            "key": "ref14",
            "doi": "10.1007/978-3-642-04117-4_17",
            "unstructured": "F. Sufi, I. Khalil, And K. Hu, ‘‘Ecg-Based Authentication,’’ In Handbook Of Information And Communication Security. Berlin, Germany: Springer, 2020, Pp. 309–331"
        },
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            "key": "ref15",
            "doi": "10.1161/01.cir.101.23.e215",
            "unstructured": "A. L. Goldberger, L. A. N. Amaral, L. Glass, J. M. Hausdorff,P. C. Ivanov, R. G. Mark, J.Mietus, G. B. Moody, C.-K. Peng, And H. E. Stanley, ‘‘Physiobank, Physiotoolkit, And Physionet: Components Of a New Research Resource For Complex Physiologic Signals,’’ Circula-Tion"
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            "key": "ref16",
            "unstructured": "Subba Reddy Borra, G. Jagadeeswar Reddy And E. Sreenivasa Reddy, “Fingerprint Image Compression Using Wave Atom Transform”, International Journal Of Advanced Computing, Vol. 48, No. 1, 2015"
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            "doi": "10.1016/j.aci.2017.07.001",
            "unstructured": "Subba Reddyborra, G.Jagadeeswar Reddy, E.Sreenivasa Reddy, “Classification Of Fingerprint Images With The Aid Of Morphological Operation And Agnn Classifier”, Applied Computing And Informatics, 2017"
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            "unstructured": "Subba Reddy Borra, Akshaya, B. Swathi, B. Sraveena, B. Satya Sahithi. (2023). Machine Learning Algorithms-Based Prediction Of Botnet Attack For Iot Devices. Turkish Journal Of Computer And Mathematics Education (Turcomat), 14(03), 65–78. Https://Doi. Org/10.17762/Turcomat.v14i03.13938"
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            "unstructured": "Subba Reddy Borra, B Gayathri, B Rekha, B Akshitha, B. Hafeeza. (2023). K-Nearest Neighbour Classifier For Url-Based Phishing Detection Mechanism. Turkish Journal Of Computer And Mathematics Education (Turcomat), 14(03), 34–40. Https://Doi. Org/10.17762/Turcomat.v14i03.13935"
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            "unstructured": "Dr. Subba Reddy Borra, K.Harshitha, Kudithi Neha, K. Sindhusha, K. Akshaya. (2023). Block Chain Based Agriculture Crop Delivery Platform For Enforcing Transparency. Turkish Journal Of Computer And Mathematics Education (Turcomat), 14(2), 987–997. Https://Doi. Org/10.17762/Turcomat.v14i2.13925"
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            "unstructured": "Dr. B. Subba Reddy, S. Shresta, S. Sathhvika, P. Lakshmi Manasa Shreya. (2022). Role Of Machine Learning In Education: Performance Tracking And Prediction Of Students. Turkish Journal Of Computer And Mathematics Education (Turcomat), 13(03), 854–862. Https://Doi. Org/10.17762/Turcomat.v13i03.13175"
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            "unstructured": "B. Subba Reddy, V. Bhargavi., S. Samhitha, Y. Anjana, V. Saivaishnavi. (2023). Nlp-Based Supervised Learning Algorithm For Cyber Insurance Policy Pattern Prediction. Turkish Journal Of Computer And Mathematics Education (Turcomat), 14(03), 90–99. Https:// Doi.Org/10.17762/Turcomat.v14i03.13941"
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The Kernel Metadata Declaration

DOIMetadataKernel.xsd · namespace http://www.doi.org/2010/DOISchema · Attribute Value Sets 2.3 · draft — the agency's DOI name is filled in on accreditation

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