IJLRET provides individual hard copy of certificates to all authors after online publication. w.e.f. 01/11/2015
Log in || Register editor@ijlret.com

Vol. 08, No. 06 [June 2022]

Paper Title :: Morphological and Canny Filter Based Algorithm for Fingerprints Recognition
Author Name :: Abdulgader Ab Sinusi
Country :: Libya
Page Number :: 01-08
One of the most reliable personal identification methods is fingerprint verification and it plays an important role in commercial and forensic applications. Designing a recognition system that will increase the accuracy is required. Yet the accuracy of fingerprint recognition systems remains a challenge This article proposes a fingerprint recognition system using canny filter and morphological operator through path analysis test case. The steps involved in the proposed recognition system include; image acquisition, pre-processing, features extraction and matching. Fast fourier transform is used to enhance the quality of the images and the features extracted efficiently to determine the minutia points in fingerprints with morphological operation, and the distribution of grey level co-occurrence matrix (GLCM) with canny filter. The proposed morphological operation can determine the bifurcation, termination of the ridges and valleys, and their corresponding angles, and Euclidean distance is recommended to be used for matching. On the other hand, features such as energy, homogeneity, entropy and correlation are extracted after canny filter is applied and Euclidean distance is again used for matching. The accuracy results from this proposed methods through path analysis cases after comparing for their success rate, false accepted rate and false rejected rate should be about 99% which is higher than any previous techniques.
Key Words: Fingerprint recognition, canny filter, morphological operator, algorithm
[1]. K. K. M. Shreyas, S. Rajeev, K. Panetta and S. S. Agaian, "Fingerprint authentication using geometric features," 2017 IEEE International Symposium on Technologies for Homeland Security (HST), Waltham, MA, 2017, pp. 1-7.
[2]. K. Ito, A. Morita, T. Aoki, T. Higuchi, H. Nakajima and K. Kobayashi, "A fingerprint recognition algorithm using phase-based image matching for low-quality fingerprints," IEEE International Conference on Image Processing, 2005, Genova, 2005, pp. II-33.
[3]. W.A. Wan Azizun, T. L. Siang, and S. Hitam, “Fingerprint recognition in wavelet domain”, Jurnal Teknologi, 41(D), Universiti Teknologi Malaysia, 2004, pp. 25-42.
[4]. M. Tico and P. Kuosmanen, "Fingerprint matching using an orientation-based minutia descriptor," in IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 25, no. 8, 2003,.pp. 1009-1014,
[5]. Kadhem, M. & Hazeem, B. T. (2010). A New Algorithm for Fingerprint Recognition. Journal of education of college,1(1), pp. 1 – 8.


Paper Title :: Exploring Models & Techniques for Ai-Driven Assessment
Author Name :: Ayse Arslan
Country :: USA
Page Number :: 09-17
Artificial intelligence (AI) has a large and increasing role for personalized training systems. This paper brings both the issues with the standard assessment paradigm and the challenges associated with AI and assessment into a deeper conversation that will ultimately improve assessment practices more generally. It highlights the need for development of actionable and personalized explanations, further incorporation of human-centric design in development of learning tools, rigorous evaluation of the impact of incorporating AI into training and ultimately advancing towards development of trustworthy training systems. The suggested platform architecture consists of an open-source Python API for specifying the workflow of an experiment (the “API”), and a Platform-as-a-Service (PaaS), which is a running instance of back-end infrastructure coupled with computational resources and a large training dataset. This study also aims to synthesize an agenda for future research on AI-driven assessmenttechniques.
[1]. Abdi, S., Khosravi, H., & Sadiq, S. (2020). Modelling learners in crowdsourcing educational systems. In International Conference on Artificial Intelligence in Education (pp. 3–9). Springer.
[2]. Abdi, S., Khosravi, H., Sadiq, S., & Darvishi, A. (2021). Open learner models for multi-activity educational systems. Artificial Intelligence in Education, 11–17. https://doi.org/10.1007/978-3-030-78270-2_2
[3]. Ahmad, N., & Bull, S. (2008). Do users trust their open learner models? In International Conference on Adaptive Hypermedia and Adaptive Web-Based Systems (pp. 255–258). Springer.
[4]. Arrieta, A. B., Díaz-Rodríguez, N., Del Ser, J., Bennetot, A., Tabik, S., Barbado, A., García, S., Gil-López, S., Molina, D., Benjamins, R., & Chatila, R. (2020). Explainable Artificial Intelligence (XAI): Concepts, taxonomies, opportunities and challenges toward responsible AI. Information Fusion, 58, 82–115.
[5]. Ashenafi, M. M. (2017). Peer-assessment in higher education-twenty-first century practices, challenges and the way forward. Assessment & Evaluation in Higher Education, 42, 226–251.













Copyright © 2015 IJLRET. All Right Reseverd Home   Editorial Board   Current Issue   Contact Us