Perspective Chapter: Text Watermark Analysis - Concept, Technique, and Applications

Watermarking is a modern technology in which identifying information is embedded in a data carrier. It is not easy to notice without affecting data usage. A text watermark is an approach to inserting a watermark into text documents. This is an extremely complex undertaking, especially given the scarcity of research in this area. This process has proven to be very complex, especially since there has only been a limited amount of research done in this field. Conducting an in-depth analysis, analysis, and implementation of the evaluation, is essential for its success. The overall aim of this chapter is to develop an understanding of the theory, methods, and applications of text watermarking, with a focus on procedures for defining, embedding, and extracting watermarks, as well as requirements, approaches, and linguistic implications. Detailed examination of the new classification of text watermarks is provided in this chapter as are the integration process and related issues of attacks and language applicability. Research challenges in open and forward-looking research are also explored, with emphasis on information integrity, information accessibility, originality preservation, information security, and sensitive data protection. The topics include sensing, document conversion, cryptographic applications, and language flexibility

Using machine learning architecture to optimize and model the treatment process for saline water level analysis

Water is a vital resource that makes it possible for human life forms to exist. The need for freshwater consumption has significantly increased in recent years. Seawater treatment facilities are less dependable and efficient. Deep learning systems have the potential to increase the efficiency as well as the accuracy of salt particle analysis in saltwater, which will benefit water treatment plant performance. This research proposed a novel method for optimization and modelling of the treatment process for saline water based on water level data analysis using machine learning (ML) techniques. Here, the optimization and modelling are carried out using molecular separation-based reverse osmosis Bayesian optimization. Then the modelled water saline particle analysis has been carried out using back propagation with Kernelized support swarm machine. Experimental analysis is carried out based on water salinity data in terms of accuracy, precision, recall, and specificity, computational cost, and Kappa coefficient. The proposed technique attained an accuracy of 92%, precision of 83%, recall of 78%, specificity of 81%, computational cost of 59%, and Kappa coefficient of 78%. HIGHLIGHTS A novel method in optimization and modelling of the treatment process for saline water based on water level data analysis using machine learning techniques is proposed.; Here the optimization and modelling are carried out using molecular separation-based reverse osmosis Bayesian optimization.; The modelled water saline particle analysis has been carried out using back propagation with a Kernelized support swarm machine.