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.
Every day, the estimated volume of data which is generated per day is 2.6 quintillion bytes. From the last two years, there is a lot of data generation and execution is taking rise due to feasible technologies and devices. To make the information accessible with ease, we need to classify the information data and predict an accurate or at least an approximate expected result which is forwarded to the end user client. To achieve the said process, the information technology industries are more concerned with machine learning and edge computing. Machine learning is a integral subset of artificial intelligence. In machine learning, the foremost step towards achieving the above task is to observe the data which is produced in large amount, later classify the data to make the system learn (train) from the old data (experience) that is stored at the server level and finally predict an estimation as a result. The obtained result is been transformed onto the devices which have made a request for a particular data. These devices are remotely located at the corner of the central data center. The process in which the execution of the information data is done at the corner of the data center is called as edge computing. In today’s world of high computation, these two technologies i.e machine learning and edge computing are creating an overwhelming significance for its usage in the business market and end user clients. Here, we try to explain few possibilities of integrating the two technologies.
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.
The rapid progression of health care technologies systems and transmission strategies makes it reliable to gain, dispense and manage data over medical devices and as well improves conventional hospital information systems (HIS) to deliver effective health care services. When the medical information is communicated through wireless network, there exists a high chance of modifying the information. Before examining the patient, the physician has to check for the integrity of received medical image. A futuristic tele healthcare framework has been proposed to ensure the security, for offering complete healthcare services at reduced cost. In this paper, the proposed framework encompasses the integration of three modules viz. Prediction, Padding and Chaotic map encryption. This work offers an enhanced security for both patient medical data as well as medical scan images to a great extent. Two processes are involved in the padding sequence namely: forward and backward snail tour. After the snail tour process, the adjacent pair of pixels in the original image is XORing in order to make it ready for further process using the chaotic map encryption algorithm. Three operations have been carried out in chaotic approach namely: permutation, diffusion and substitution. This encryption method arises, a valid confusion and diffusion in the image pixels such that, the security of the images/data is enhanced. The performance of the proposed approach is assessed and compared with existing schemes such as ECC and Chaotic map encryption on a set of MRI/CT medical images. Experimental outcomes demonstrate that the proposed framework offers robustness in terms of security, quality and reliability which alleviate misdiagnosis at the physician end in telemedicine applications.
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