Nowadays the multimedia data easily available to most people. It is the main cause of illegal access to multimedia content, theft of the intellectual property, easily copying and manipulate the data over the internet, and spreading fake news. With the increase in the availability of the internet to a common man, it is observed that most of the multimedia data misused. Nowadays telemedicine, tele diagnosis, tele consultation, teleradiology, telematic services are necessary. Electronic Patient Record is essential to provide these services. It is necessary to secure the multimedia data by using a suitable watermarking technique. Due to the huge availability of high-speed internet, digital movie piracy was increased rapidly and it causes revenue loss and affects the employment of the people. The high demand for protecting digital videos or movies from unauthorized access. So, it is necessary to protect the intellectual property of the owner, stop unauthorized access of data, also protect and stop the spreading of fake news. In this article, we presented a survey of digital watermarking, bolding its key concepts, embedded and extracted features, state-of-the-art implementation, and research challenges. The primary goal of this article is to provide an improved understanding of the embedding-extracting of watermarking challenges of watermarking and identify important research paths in health care and multimedia applications.

Nowadays, digital watermark protects intellectual property in the digital world. In this article, a new singular value decomposition (SVD) based robust unsighted video watermarking technique is used for protection of copyright or owns individual work. The Singular Value Decomposition is used in this method to embed the information in a video file. In the embedded process the information generally referred to as watermark, which is of the form either audio, text, video, or any binary image or digital image. The embedded information can be identified in this process without considering the mechanisms of the original video, or any other knowledge about the encoded video ‘s main singular values. Singular value decomposition is used in embedding the information as well as retrieving the information from the embedded watermarked video. SVD based robust unsighted video technique shows that it bears required robustness on various attacks like MPEG-2 compressed video, median filtered, small rescaled, and rotational videos. In the experimental result, it is noticed that the unsighted video watermarking technique has visible quality and robustness for various attacks. In which watermark is installed in uniquely chose particular estimations of the video, and the first request can be kept up to recovering the data in our method and it has been indicated roust to different attacks.

Invasive Ductal Carcinoma Breast Cancer (IDC-BC) is the most common type of cancer and its asymptomatic nature has led to an increased mortality rate globally. Advancements in artificial intelligence and machine learning have revolutionized the medical field with the development of AI-enabled computer-aided diagnosis (CAD) systems, which help in determining diseases at an early stage. CAD systems assist pathologists in their decision-making process to produce more reliable outcomes in order to treat patients well. In this work, the potential of pre-trained convolutional neural networks (CNNs) (i.e., EfficientNetV2L, ResNet152V2, DenseNet201), singly or as an ensemble, was thoroughly explored. The performances of these models were evaluated for IDC-BC grade classification using the DataBiox dataset. Data augmentation was used to avoid the issues of data scarcity and data imbalances. The performance of the best model was compared to three different balanced datasets of Databiox (i.e., 1200, 1400, and 1600 images) to determine the implications of this data augmentation. Furthermore, the effects of the number of epochs were analysed to ensure the coherency of the most optimal model. The experimental results analysis revealed that the proposed ensemble model outperformed the existing state-of-the-art techniques in relation to classifying the IDC-BC grades of the Databiox dataset. The proposed ensemble model of the CNNs achieved a 94% classification accuracy and attained a significant area under the ROC curves for grades 1, 2, and 3, i.e., 96%, 94%, and 96%, respectively.

Now a days we see more people are dying because of heart attacks and cardiac arrest. The Electrocardiogram is mostly used for identifying the heart diseases and Now a days we see more people are dying because of heart attacks and cardiac arrest. The Electrocardiogram is mostly used for identifying the heart diseases and

Satellite images mainly utilized in the events of a natural disaster management, identifying geographical information, viz land cover classes namely, buildings, Satellite images mainly utilized in the events of a natural disaster management, identifying geographical information, viz land cover classes namely, buildings,