Research, Development, and Comparative Characterization of Methods for Invisible Embedding Digital Watermarks in Electronic Text Documents Maxim Martemyanov, Maria Lapina, Mary Anita E. A. Navigating Technological Advancement in the Vuca and Bani World, 2025 This article discusses the concept of digital watermark (DWС), its classification, the main file formats for storing textual information electronically, types of attacks on DWC, and methods of embedding digital watermarks in electronic text documents based on formatting changes, using metadata, structural and linguistic changes. The practical development of each method is carried out, their advantages, disadvantages and resistance to various attacks are identified, and their comparative characterization is presented. On the basis of the analysis, we experimentally developed our own methods of introducing DWC into text documents, including the approach using zero-width spaces (ZWSP) (U+200B), which provides high resistance to various attacks and imperceptibility for the user. The results of the work demonstrate the effectiveness of each implementation and provide recommendations for their application depending on the requirements for copyright protection and authentication of text files.
Automated Detection Model (ADM) for Glaucoma, Exudate and Diabetic Retinopathy (DR) Diagnosis Using Fundus Images M P Karthikeyan, E.A. Mary Anita, D. Mohana Geetha 2nd International Conference on Research Methodologies in Knowledge Management Artificial Intelligence and Telecommunication Engineering Rmkmate 2025, 2025 A total of 15 million people in India suffer from blindness yet statistical analysis shows 75% of these cases can be treated. The research shows DR and Glaucoma lead to blindness in India. Long-term diabetes mainly causes diabetic retinopathy which stands as the primary cause of blindness. Glaucoma damages the optic nerve until blindness develops. The digitized format of fundus images provides useful diagnostic information about infected retinas for proper eye disease detection. Eye defect diagnosis at an early stage enables medical care that greatly decreases patient vision loss risk. An ophthalmologist conducted the disease screening process through examination of fundus image abnormalities. Higher rates of DR and glaucoma prevalence do not affect the number of available ophthalmologists for evaluating fundus images so the prevention of diseases has been delayed. An automated analytical system should be developed presently to help ophthalmologists enhance their diagnostic process efficiency. The paper introduces an artificial learning methodology that utilizes concatenate systems to detect input fundus images in three categories namely ND and GI and EI and DRI. No Diseases (ND), ii. Glaucoma (GI) iii. The classification groups include Exudate infected Images (EI) along with two other categories namely Glaucoma (GI) and DR Images (DRI). The proposed model Automated Detection Model (ADM) starts by analyzing input samples with histogram-based model and employs DenseNet121 and Inception-ResNetV2to facilitate further processing. The Convolution Neural Networks (CNN) function gathers and sorts the feature extraction data obtained from both models. The proposed approach demonstrates improved accuracy and recall plus average precision when used instead of a solitary model. The proposed machine-learning approach using fundus images proves successful for Glaucoma, Exudate and DR diagnosis according to this experiment.
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