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Human–computer interaction (HCI) is deployed in various real-time applications, including healthcare, for automated patient response. In such applications, robot-assisted interactive scenarios are modeled to handle patient queries and provide precise information. Timely query sensing and accurate data analysis are required to achieve accurate patient responses. In this study, responsive policy decision (RPD) using manifold mediator learning (MML) is introduced to improve data detection accuracy and accuracy in robot-assisted HCI applications. The initial decision-making process in data analytics is based on interaction stages and medical data detection. After identifying the most appropriate policy, respondents are provided with time-based responses based on the patient's queries. When it comes to improving the accuracy of data analysis decisions, machine learning uses policies based on interaction stages and previous state efficiency of HCI responses. The experimental analysis proves the reliability of the proposed method by improving the accuracy of data analysis and reducing its complexity and response time for the varying queries and time intervals. [ABSTRACT FROM AUTHOR]

Thrombosis is one of the leading causes of death worldwide. Out of four, one person is dying of thrombosis; yet, the seriousness of this disease is underappreciated. Its early prediction and prevention continue to be a dilemma that confuses researchers. Nevertheless, a light can be seen at the end of the tunnel; thanks to nanoscience which has led to the development of new generations of nanostructure with different applications in bio-medicine and bio-engineering. The key paradigm for the Internet of Nano Things (IoNT) has allowed for new medical data to be collected which potentially helps achieve more accurate disease prediction. It has enabled real-time health services and turned the physical space of a patient into a smart space. While an enabler for several applications, the artificial nature of Internet of Nano Things devices can be harmful where the implementation of Nano Things may lead to unintended health effects. To overcome this issue, researchers have suggested the novel paradigm of the IoBNT that combines nanotechnology with tools from synthetic biology to provide reengineering of biological embedded computing devices. IoBNT promises many medical applications, such as intra-body sensing and actuation networks, based on biological cells and their characteristics in the biochemical field. In this paper, a novel IoBNT-based model with an optimized Bio-Cyber communication interface that helps predict and analyze blood vessel clots is introduced. The model utilizes a bio-interface to collect information on the blood vessels and convert it into an electrical equivalent format. Furthermore, the optical or thermal responsiveness excites the release of definite nano-carrier molecules such as liposomes which may be devised across the bloodstream and enter the targeted area passively to stimulate suitable nano-devices to predict the clots. The Bio-Cyber interface is used for linking the traditional electromagnetic wave to the Bio-Signaling Network based on the bioluminescence concept. Lab-scale simulation analysis shows prominent outcomes in the prediction of blood vessel clots with 97.66% accuracy and 12.22% tolerance level in error rate. [ABSTRACT FROM AUTHOR]

Provision of higher throughput without sacrificing consistency guarantees in replication systems is a critical problem. In this paper, we propose a novel approach called Bidirectional Chain Replication (BCR) to improve throughput in traditional Chain Replication (CR) through better utilization of computing and communication resources of the chain. Unlike CR where the whole replicated data store is treated as a single unit, in BCR the replicated shared data at each server in the chain is split into two disjoint Logical Partitions (LP1, LP2). This forms two chains running concurrently on the same hardware in two opposite directions; the first chain (CR1) exclusively manipulates data objects in LP1, while the second chain (CR2) exclusively manipulates data objects in LP2, therefore, conflict is avoided and concurrency is guaranteed. The simultaneous employment of these two chains results in better utilization of hardware in the sense that the two chains can evenly share the workload, hence, throughput can be improved without sacrificing consistency. Experimental results showed an improvement of approximately 85% in throughput of BCR over CR. [ABSTRACT FROM AUTHOR]