Digital watermarking has emerged as a powerful and versatile technique for safeguarding digital content through copy protection, copyright enforcement, medical applications, data authentication, fingerprinting, and more. This method involves the embedding of a specific information element, known as a watermark, within the original data, which can encompass various forms, including images, videos, audio, and text. The requirements of a watermarking system vary depending on the nature of the host media and its intended purpose. This research paper presents a comprehensive overview of digital watermarking, highlighting its fundamental characteristics, evaluation metrics, and current real-world applications. Furthermore, it conducts an extensive survey of existing literature on digital image watermarking, delving into optimization techniques. Finally, the paper offers a standardized framework for the design of watermarking methods that meet the essential design criteria for a wide range of applications.

The ultimate goal of image denoising from video is to improve the given image, which can reduce noise interference to ensure image quality. Image denoising is a problem of fundamental importance for enhancement of quality in image restoration and computer vision. Through denoising technology, image quality can have effectively optimized, signal-to-noise ratio can have increased, and the original image information can have better reflected. Image denoising is a vital preprocessing step for image based object detection, recognition, and tracking since high frequency image details are mixed with noise in most cases, most of the existing image denoising methods have difficult preserving the edge and texture information while thoroughly eliminating the noise. In this paper we review the different image denoising techniques.

Proper allocation of distributed generation (DG) units integrated with a distribution system plays a significant role in improving system performance. The voltage profile is one of the factors considered when determining the reliability and efficiency of power grid operation. This paper presents a methodology based on an analytical approach for optimally allocating (site and size) DG units in radial power distribution networks to minimize real power losses. The proposed method only requires the results of the base case load flow to determine the optimal size of DG units needed at each bus. To achieve this, suitable analytical expressions have been proposed to determine the optimal size of DG units that minimize the total real power loss in a given distribution network, along with their corresponding optimal locations. Results obtained by this proposed method validate the suitability and importance of appropriate DG allocation and the number of DG units in power distribution networks.

Childbirth is a critical event in the life of a woman. Respectful Maternity Care addresses the issue of disrespect and abuse toward women and new born, as well as provides a platform for improvement by: Raising awareness of women during pregnancy. Quantitative approach with Quasi Experimental one group, pre-test and post-test research design was conducted to assess the Effectiveness of Structured Teaching Programme on knowledge regarding Respectful Maternity Care. The Study was conducted on 47 GNM 3rd year nursing students studying in Apollo school of nursing, New Delhi selected through purposive sampling technique. Findings reveal that calculated “z” value (1.27) is more than the table value (0.205) at 0.05 level of significance which indicates that the structured teaching programme on Respectful Maternity Care was effective in increases the knowledge of GNM 3rd year Nursing students. The study reveals that structured teaching programme play a crucial role and has a positive influence on Knowledge regarding RMC among Nursing students.

A hostel is a place where students stay for pursuing formal education away from their homes. hostel life is a combination of different cultural backgrounds, in the hostel life students learns to live with different cultural background people and it can impact individual personal habits in different ways. Quantitative approach with descriptive research design was conducted to assess impact of hostel stay on the individual’s personal habits among nursing students in selected nursing hostel. The Study was conducted on 134 GNM nursing students studying in Apollo school of nursing, New Delhi selected through total enumerative sampling technique. A structured opinionnaire was developed to collect the data and shared with student nurses in the form a Google form, link https://forms.gle/t8fqxyPVEiYWCmDg8. Findings reveal that majority of the students 88.81% had positive impact of hostel stay on the individual’s personal habits and the computed chi square value to establish the association between the impact scores and age (in years) (126.47), year of study (8.2) and previous exposure of hostel stay (3.5) were statistically significant at 0.05 level of significance for the degree of freedom three, two and one respectively. This shows that there is an association between the impact of hostel stay on the individual’s personal habit with their age (in years), year of study and previous exposure to hostel stay.

Due to enormous need by the public, water has to be stored and supplied according to their needs. Water demand is not constant throughout the day. It fluctuates hour to hour. In order to supply constant amount of water, we need to store water. So to meet the public water demand, water tanks need to be constructed. They are grave elements in municipal water supply, firefighting systems and in many industrial amenities for storage of water. Intze type tank is commonly used overhead water tank in India. These tanks are designed as per IS: 3370 i.e. Code of practice for concrete structures for storage of liquids. BIS implemented the revised version of IS 3370 (part 1& 2) after a long time from its 1965 version in year 2009. Presently large number of overhead water tanks is used to distribute the water for public utility. Most of the water tanks were designed as per old IS Code: 3370-1965 without considering earthquake forces. The objective of this dissertation is to shed light on the Intze water tank designed considering the earthquake forces according to Indian standard code: 3370-2009 and draft code 1893-Part 2, (2005) considering zone-II and III. Intze tank supported on frame staging. Also this report includes analysis by STAAD Pro for seismic forces .Finally the results are validated with the results of calculation from the present study. Before taking up the design, the designer should first decide the most suitable type of staging of tanks and correct estimation of loads including statically equilibrium of structure particularly in regards to overturning of overhanging members shall be made. The design should be based on the worst possible combination of loads, moments and shears arising from vertical loads and horizontal loads acting in any direction. In this research by performing the analysis of Intze tank, what is deflection shape due to hydrostatic pressure then stresses, etc. which are analyzed.

One of the most devastating forces on earth is known to be produced by earthquakes. It has been observed that numerous buildings fell during previous earthquakes. Realistic methods of analysis and design are therefore necessary. The current method for designing earthquake-resistant structures is called performance-based design. It is an effort to forecast how structures will behave in the case of a seismic event. In the current study, the seismic activity of a G+3 multi-story hospital structure located in a separate zone is studied. It entails calculating the load and total. Building seismic weight is determined in a distinct zone based on base shear. Designing with economy, grace, and sturdiness is the art and science of structural planning and analysis. To compete in the highly competitive market, structural designing demands a thorough structural study from which the planning is expected. The present study made an effort to attain this goal. The usage of software can.

Recent years have seen an increase in the number of researchers concerned with sentiment analysis, as textual data has proven useful for a variety of real-world applications and problems. Sentiment analysis is a technique of machine learning in which a machine is trained to evaluate the sentiments, emotions, etc. of multiple text statistics, such as movie reviews and product reviews. These reviews are steadily increasing in number, which necessitates the appearance of comments in character where reviews are required to be in the form of a transcript, yielding valuable data from the vast collection of reviews. It is extremely difficult to extract positive statistics from a lengthy article or to condense it. Text analysis determines the significance of syntaxes based on their grammatical properties. In this survey, we have endeavored to provide an overview of the current and historical research on sentiment analysis, as well as exemplary research questions and methodologies for future study.

Relationships are there between objects in Wikipedia. Emphases on determining relationships are there between pairs of objects in Wikipedia whose pages can be regarded as separate objects. Two classes of relationships between two objects exists there in Wikipedia, an explicit relationship is illustrated by a single link between the two pages for the objects, and the other implicit relationship is illustrated by a link structure containing the two pages. Some of the before proposed techniques for determining relationships are cohesion-based techniques, and this technique which underestimate objects containing higher degree values and also such objects could be significant in constituting relationships in Wikipedia. The other techniques are inadequate for determining implicit relationships because they use only one or two of the following three important factor such as the distance, the connectivity and the co- citation.

When it comes to official communication in business settings, the most common method that is utilized is the electronic mail. An rising number of people are using email, despite the fact that there are other ways to communicate. A daily growth in the volume of emails necessitates the implementation of automated email management in the current environment. More than fifty-five percent of all emails are considered to be spam totals. This is a perfect example of how these spams waste the time and resources of those who use email while producing nothing of value. In light of the fact that spammers adopt ingenious and inventive strategies in order to carry out their illegal actions through spam emails, it is vital to have a solid understanding of the various classification systems for spam emails and how they function. The technique to spam classification that is based on machine learning is the primary focus of this scientific investigation. In addition to that, this study offers a complete assessment and analysis of previous research on a variety of machine learning algorithms, email properties, and methodologies. This research presents a discussion on the challenges associated with spam classification, as well as recommendations for more research that may be of value to academics.

Both industrialized and developing nations exhibit elevated prevalence rates of chronic ailments, including diabetes and Chronic Liver Disease (CLD). The prevalence of liver disease has been consistently increasing due to the escalating rates of excessive alcohol intake, exposure to harmful gases, and ingestion of tainted food, medicines, and pharmaceuticals. Due to the liver's involvement in numerous essential functions, liver illness poses significant challenges for the provision of medical treatment. Classification algorithms are commonly employed in many automatic medical diagnosis systems. The early identification of cancer has significantly enhanced the patients' prospects of survival in numerous instances. Support vector machines, or SVMs, are supervised learning models that analyze data using specific learning techniques to detect patterns. In this study, we proposed a novel methodology utilizing support vector machines, neural networks, and optimization for classification purposes. This approach, which draws inspiration from biology, aims to improve the results of classifiers in terms of performance criteria such as accuracy, precision, recall, etc. It is built upon the classification techniques of support vector machines, neural networks, and optimization. We assess and enhance all the performance metrics related to the dataset concerning liver patients. The required dataset for the study can be accessed from the UCI machine learning repository, where the dataset including information about all the patients was sourced. Our experimental results demonstrate that our method has a higher classification recognition rate for performance attributes compared to other currently employed strategies.