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Telemedicine and online consultations with doctors has become very popular during the pandemic and involves the transmission of medical data through the internet. Thus this raises concern about the security of the medical data of the patient as the records to contain sensitive and confidential information. A Secure multimedia transformation approach is proposed in this paper using a deep learning-based chaotic logistic map. The proposed work achieves novelty by the integration of a lightweight encryption function using a chaotic logistic map. It also uses the ResNet model to perform classification for identifying the fake medical multimedia data. A linear feedback shift register operations and an interactive user interface facilitate ease of usage to secure the medical multimedia data. The chaotic map provides the security properties such as confusion and diffusion necessary for the encryption ciphers. At the same time, they are highly sensitive to input conditions, thus making the proposed encryption algorithm more secure and robust. The proposed encryption mechanism helps in securing the medical image and video data. On the receiver side, Multilayer perceptions (MLP) of the deep learning approach are used to classify the medical data according to the features required to make other processes. When tested, the proposed work proves efficient in securing medical data against various cyber-attacks and exhibits high entropy levels.

There has been an astonished growth in the evolving technology. When it comes to the word technology, the most predominant usage by the society is Internet. There are many channels in the Internet through which data is transmitted. Having such rapid and fast growth, wide communication media increases the exposure to security threats causing unintellectual ingress of information. Steganography is the main aspect of communicating in an aspect which hides the extent of the communication. Steganalysis is another important concern in data concealing which is the art of identifying the existence of steganography. Critical review has been presented on steganalysis and detecting the steganography in images without embedding and extracting the payloads in the images. RS steganalysis and a classifier are used in this paper to detect the hidden payload in bulk images.

The use of advanced technologies has increased drastically to maintain any sensitive records related to education, health, or finance. It helps to protect the data from unauthorized access by attackers. However, all the existing advanced technologies face some issues because of their uncertainties. These technologies have some lapses to provide privacy, attack-free, transparency, reliability, and flexibility. These characteristics are essential while managing any sensitive data like educational certificates or medical certificates. Hence, we designed an Industry 5.0 based blockchain application to manage medical certificates using Remix Ethereum blockchain in this paper. This application also employs a distributed application (DApp) that uses a test RPC-based Ethereum blockchain and user expert system as a knowledge agent. The main strength of this work is the maintenance of existing certificates over a blockchain with the creation of new certificates that use logistic Map encryption cipher on existing medical certificates while uploading into the blockchain. This application helps to quickly analyze the birth, death, and sick rate as per certain features like location and year.

The COVID-19 pandemic has affected people’s lives in all aspects. This pandemic has raised the usage of ubiquitous networks such as mobile ad hoc networks (MANETs) for information exchange in various domains. MANET is a group of versatile nodes that communicates with each other without relying on a fixed physical framework. One of the prominent features of MANET is its versatile topology. Because of this striking feature, MANETs are employed in various domains like defense and combat operations, disaster management, healthcare, and environmental monitoring. In this paper, we enlighten the significance of MANET in the smart healthcare system. The COVID-19 pandemic outbreak demanded the reshaping of the healthcare systems to combat the pandemic and similar cataclysms. Existing healthcare systems are proved inefficient in dealing with pandemic situations, because they are not fully automated and also vulnerable to various security attacks. Therefore, it is vital to empower the healthcare sectors by integrating ubiquitous networks and other emerging technologies. In this paper, we proposed a MANET-based secure healthcare system to exchange medical data among portable nodes. Ensuring secure communication in the MANET-based healthcare system is one of the challenging issues. Healthcare system demands the sharing of confidential medical data among mobile nodes. So it is essential to provide secure information exchange in healthcare system by using strong cryptographic schemes. In this paper, we proposed a hybrid cryptographic algorithm for secure medical information exchange among mobile healthcare nodes. The proposed cryptographic scheme uses logistic map for key generation. Logistic map exhibits high security with less computational power. Our simulation results show that the proposed hybrid cryptographic scheme exhibits better security against various attacks in MANET-based healthcare systems.

Augmented Reality (AR) is an innovation that empowers us in coordinating computerized data into the client's real-world space. It offers an advanced and progressive methodology for medicines, providing medication training. AR aids in surgery planning, and patient therapy discloses complex medical circumstances to patients and their family members. With accelerated upgrades in innovation, the ever-increasing number of medical records get accessible, which contain a lot of sensitive medical data, similar to medical substances and relations between them. To exploit the clinical texts in these data, it is important to separate significant data from these texts. Drugs, along with some kind of the fundamental clinical components, additionally should be perceived. Drug name recognition (DNR) tries to recognize drugs specified in unstructured clinical texts and order them into predefined classifications, which is utilized to deliver a connected 3D model inside the present reality client space. This work shows the utilization of AR to give an active and visual representation of data about medicines and their applications. The proposed method is a mobile application that uses a native camera and optical character recognition algorithm (OCR) to extract the text on the medicines. The extracted text is over and above processed using natural language processing (NLP) tools which are then used to identify the generic name and category of the drug using the dedicated DNR database. The database used for the system is scraped using various resources of medical studies and is named a medi-drug database from a development standpoint. 3D model prepared particularly for the drug is then presented in AR using ArCore. The results obtained are encouraging. The proposed method can detect the text with an average time of 0.005 s and can produce the visual representation of the output with an average time of 1.5 s.

Currently, blockchain technology has evolved into a reliable and secure platform to provide security for the data. This technology's main adoption areas are agriculture, supply chain, food sector, energy industries, Health care, Real estate, Voting system, and education sector. This paper reviewed existing related works and applications using blockchain technology. Forgery or fraud on medical documents is a significant challenge in the health care sector. Hence this paper proposed a Blockchain-based framework using Ehterum that is a public blockchain. Users required medical documents generated and issued by officials authentically in the way of a unique blockchain-based ID. Users can submit this Blockchain-based ID anywhere instead of a physical paper. Attacks or forgery on the medical document can reduce using advanced featured technology, Blockchain. Blockchain technology has the features such as immutable, distributive, secure, reliable, and transparent. Remix Etherum IDE, solidity programming, and Metamask Wallet were used to implement the proposed application. The main strength of this work is designing and deploying results and comparisons with the existing applications.

Due to the rapid technological advancements in recent years, humans have gained the ability to design and implement the knowledge into machines and it also allows them to perform various functions such as autonomous thinking ability, understanding skills, and problem-solving. Moreover, machine learning [ML] plays an important role in developing the image-processing models and application. In real-time applications, the labels of objects may be unfamiliar to those who are unaware of them, or there may be several identical objects but are labeled differently. In this paper, the approach that would be useful to detect and classify various objects is presented with a trial study by utilizing various datasets. This method can be used to improve accuracy in finding the classification of similar objects. In the process of classifying real-time objects, this system uses a supervised learning technique, where various datasets are trained and compared with the queried object. Here, the Support Vector Machine (SVM) algorithm is utilized for performing analysis and decision making in object classification domain. The result of this project is to read the given objects with the help of computer vision and allowing the machine to perform the prediction or classification for the given object. More than 800 images obtained from standard datasets of trained labeled classes are implemented in our application to classify the objects.