Hybrid Encryption Method for Health Monitoring Systems Based on Machine Learning.

Numerous forms of disasters and vandalism can occur in transmission lines, which makes them vulnerable. As a result, the transmission pipes must be protected by a reliable monitoring system. When a wireless sensor network is built from disparate devices that are positioned at varying distances from one another, it can be used to monitor physical and environmental conditions in the surrounding environment. In addition to the built-in sensor on the exterior of a pipeline and sensors positioned to support bridge structures, wireless sensor networks have a range of other applications. Other uses include robotics, healthcare, environmental monitoring, and a variety of other areas of technology. It is feasible to use wireless sensor networks to monitor temperature and pressure, as well as leak detection and transmission line sabotage, among other applications. There are several different sorts of attacks that can be launched against wireless sensor networks. When it comes to information security in wireless sensor networks, cryptographic approaches play a critical role in ensuring the integrity of the data. Different types of cryptographic algorithms are now available for use in order to maintain network security. Specific difficulties must be addressed, though, and these are as follows: To strengthen the power of these algorithms, a unique hybrid encryption approach for monitoring energy transmission lines and increasing the security of wireless sensor networks is created in this study. While wireless sensor networks are being used to monitor transmission pipelines, the proposed hybrid encryption method ensures that data is transferred securely and promptly. The proposed method must follow three cryptographic principles: integrity, secrecy, and authenticity. All of the subtleties and underlying principles of the algorithm are explained in detail so that the algorithm can be put into action immediately after it is introduced.

Clinical Text Data Categorization and Feature Extraction Using Medical-Fissure Algorithm and Neg-Seq Algorithm.

A large amount of patient information has been gathered in Electronic Health Records (EHRs) concerning their conditions. An EHR, as an unstructured text document, serves to maintain health by identifying, treating, and curing illnesses. In this research, the technical complexities in extracting the clinical text data are removed by using machine learning and natural language processing techniques, in which an unstructured clinical text data with low data quality is recognized by Halve Progression, which uses Medical-Fissure Algorithm which provides better data quality and makes diagnosis easier by using a cross-validation approach. Moreover, to enhance the accuracy in extracting and mapping clinical text data, Clinical Data Progression uses Neg-Seq Algorithm in which the redundancy in clinical text data is removed. Finally, the extracted clinical text data is stored in the cloud with a secret key to enhance security. The proposed technique improves the data quality and provides an efficient data extraction with high accuracy of 99.6%.

Aptameric nanobiosensors for the diagnosis of COVID-19: An update.

COVID-19 pandemic has left a catastrophic effect on the world economy and human civilization. As an effective step towards controlling the transmission of viral infections during multiple waves of COVID-19, there is an urgent need to develop robust nanobiosensors for the detection of SARS-CoV-2 with high sensitivity, specificity, and fast analysis. Aptameric nanobiosensors are rapid and sensitive diagnostic platforms, capable of SARS-CoV-2 detection, which overcomes the limitations of the conventional techniques. This review article presents an outline of the aptameric nanobiosensors established for improved diagnosis of SARS-CoV-2 and the future perspectives are also covered.