Automated Grading and Classifying Tear Ferning Images Using a Novel Computer-Based Approach | Chapter 6 | Mathematics and Computer Science: Contemporary Developments Vol. 6

In the current endeavor, the major purpose is to develop a novel computer-based approach for determining the properties of tear ferning (TF). Through the utilization of the newly designed five-point grading system, it is possible to automatically analyze each TF image by utilizing the original TF photographs. This study aims to develop an automated system for grading tear-ferning images to improve the accuracy and efficiency of diagnosing dry eye conditions. A novel approach was introduced, constructing vector characteristics (VC) for each grade using a combination of texture analysis with gray-level co-occurrence matrix (GLCM), power spectrum (PS) analysis, and line segment counting. Three distinct power frequencies were utilized since the VC possessed the ability to differentiate between different frequencies. The differences in likeness that were seen between the pictures served as a source of inspiration for the choosing of line segments. Based on the findings of analysis, it was discovered that each grade of TF reference image contained a unique vector cloud (VC) that displayed notable distinctions from the other grades. Key features from GLCM, PS at specific frequencies and the number of line segments were used to build the VC. The results showed significant differences between the VCs for each grade, indicating the potential for accurate automatic grading. This advancement represents a crucial step towards creating more objective and reliable computer-based diagnostic tools for dry eye conditions.

 

Author (s) Details

Ali S. Saad

Department of Biomedical Technology, College of Applied Medical Sciences, King Saud University Riyadh 11433, Saudi Arabia.

 

Gamal A. El-Hiti
Department of Optometry, College of Applied Medical Sciences, King Saud University Riyadh 11433, Saudi Arabia.

 

Ali M. Masmali
Department of Optometry, College of Applied Medical Sciences, King Saud University Riyadh 11433, Saudi Arabia.

 

Please see the book here:- https://doi.org/10.9734/bpi/mcscd/v6/2693

Fine Grained Access Control on Information Retrieval | Chapter 2 | Research and Developments in Engineering Research Vol. 1

 Enterprise computer data storage and retrieval is more difficult than web-located information retrieval due to recovery from heterogeneous distributed dossier sources and the need for higher recovery accuracy [1]. In [2], collaborative mixture (CF) guided by active response (AF) is proposed for information retrieval in energy environment. However, this work did not allow for possibility enforcing fine-grained approach control on retrieval, which is a fault-finding requirement in an enterprise atmosphere. This paper proposes fine-grained approach controlled information retrieval in an resourcefulness environment with nothing leakage assurance by way of direct or inference. Concept masking utilizing CP-ABE is done to guarantee fine grained access control. The precision of search results has raised due to fine-grained approach control.

Author(s) Details:

L. Dinesha,
Department of Computer Science and Engineering, Mangalore Institute of Technology & Engineering, Moodabirdi, Karnataka, India.

B. S. Pradeep,
Department of Computer Science and Engineering, Mangalore Institute of Technology & Engineering, Moodabirdi, Karnataka, India.

Sukhwinder Sharma,
Department of Computer Science and Engineering, Mangalore Institute of Technology & Engineering, Moodabirdi, Karnataka, India.

Please see the link here: https://stm.bookpi.org/RADER-V1/article/view/10087