Intelligent medicine and beyond

With the developments of medical artificial intelligence (AI), meta-data analysis, intelligence-aided drug design and discovery, surgical robots and image-navigated precision treatments, intelligent medicine (IM) as a new era evolved from ancient medicine and biomedical medicine, has become an emerging topic and important criteria for clinical applications. It is fully characterized by fundamental research-driven, new-generation technique-directed as well as state-of-the-art paradigms for advanced disease diagnosis and therapy leading to an even broader future of modern medicine. As a fundamental subject and also a practice-oriented field, intelligent medicine is highly trans-disciplinary and cross-developed, which has emerged the knowledge of modern medicine, basic sciences and engineering. Basically, intelligent medicine has three domains of intelligent biomaterials, intelligent devices and intelligent techniques. Intelligent biomaterials derive from traditional biomedical materials, and currently are endowed with multiple functionalities for medical uses. For example, micro-/nanorobots, smart responsive biomaterials and digital drugs are representative intelligent biomaterials which have been already commercialized and applied to clinical uses. Intelligent devices, such as surgical robots, rehabilitation robots and medical powered exoskeleton, are an important majority in the family of intelligent medicine. Intelligent biomaterials and intelligent devices are more and more closely integrated with each other especially on the occasions of intelligence acquisition, remote transmission, AI-aided analysis and management. In comparison, intelligent techniques are internalized in the former two domains and are playing a critical role in the development of intelligent medicine. Representative intelligent techniques of telemedicine, image-navigated surgery, virtual/augmented reality and AI-assisted image analysis for early-stage disease assessments have been employed in nowadays clinical operations which to a large extent relieved medical labors. In the past decades, China has been in the leading groups compared to international colleagues in the arena of intelligent medicine, and a series of eminent research has been clinically translated for practical uses in China. For instance, the first 5G-aided remote surgery has been realized in Fujian Province in January 2019, which for the first time validated their applicability for human uses. The surgical robots have found China as the most vigorous market, and more than 10 famous Chinese companies are developing versatile surgical robots for both Chinese people and people all over the world. China also applied AI techniques to new drug developments especially in early 2020 when COVID-19 epidemic roared, and several active molecules and drug motifs have been discovered for early-stage COVID-19 screening and treatments. Based on the significance of intelligent medicine and its rapid developments in both basic research and industrials, this review summarized the comprehensive viewpoints of the Y6 Xiangshan Science Conferences titled with Fundamental Principles and Key Technologies of Intelligent Medicine, and gave an in-depth discussion on main perspectives of future developments of the integration of biomaterial and devices, the integration of bioinformatics and medical hardware, and the synergy of biotechnology and intelligence information. It is expected that this featuring article will further promote intelligent medicine to an even broader community not only for scientists but also for industrials, and in the long run embrace a perspective future for its blooming and rich contributions in China in the coming 5 years. © 2023 Chinese Academy of Sciences. All rights reserved.

EMOTIONAL ANALYSIS OF SAFENESS AND RISK PERCEPTION OF DIFFERENT PAYMENT SYSTEMS IN ITALY AND THE UK DURING THE COVID-19 PANDEMIC

Public safety, security and risk perception is an important aspect considered in opinion mining and sentiment analysis typically carried out on social networks. This involves considering each individual's opinion and determining a sense of what the public feels about an incident, event or place. In that sense, social networks play an important role in capturing the emotions of people. Security and safety managers can employ opinion mining and sentiment analysis as a tool to discover any unforeseen vulnerabilities in a precise manner and thereby plan and manage any associated risks. Furthermore, a continuous evaluation of risk perception can be carried out for timely and planned interventions in a seamless, effective manner to reduce or avoid any panic amongst communities. Without such advance techniques, safety and security of people, infrastructures and specific contexts can be easily compromised. Recent work in this direction has shown promising results in managing risks, especially during the COVID-19 pandemic. The purpose of the present work is to investigate the perception of risk associated with different payment systems, in Italy and the UK, during the COVID-19 pandemic, from 10 November 2020 to 13 May 2021, by means of the semantic analysis of the textual contents existing in Twitter. © 2022 WIT Press.

Emotional analysis of safeness and risk perception of drones during the COVID-19 pandemic in Italy

The estimation of perceived safeness and risk by individuals is very significant for security and safety managing. Every person is based on the judgments of other persons to do a choosing and the Internet personifies the location where these judgments are mostly tracked, obtained, and assessed. From this point of view, social networks have a significant influence. For this reason, Opinion Mining and Sentiment Analysis have found remarkable utilizes in a multiplicity of circumstances and one of the most notable is represented by public safety and security. The purpose of the present work is to investigate the perception of safeness and risk of drones during COVID-19 pandemic in Italy. It was considered the period from Aprile 15 to December 10, 2020, properly divided into 4 significant phases, highlighting the emotional components using the semantic analysis of the textual contents present in Twitter. © 2021 IEEE.

Emotional analysis of safeness and risk perception of London and Rome railway stations during the COVID-19 pandemic

The evaluation of perceived safeness and risk by persons is particularly valuable for safety and security handling. Each individual is influenced by others’ opinions on safety and security and the Internet embodies the place where these opinions are mostly pursued, acquired, and estimated. From this point of view, social networks play a considerable influential role. For this reason, Opinion Mining and Sentiment Analysis have found noteworthy uses in a variety of situations and one of the most interesting is embodied by public safety and security. The goal of this work is to examine the perception of safety and risk within the railway stations of London (UK) and Rome (Italy) during COVID-19 pandemic. In particular, the railway stations of London Victoria and Waterloo and the railway stations of Rome Termini and Tiburtina were considered, from March 23 to July 9, 2020, highlighting the emotional components in three distinct pandemic phases of the considered period in the two countries, by means of the semantic analysis of the textual contents present in Twitter. © 2021 IEEE.

Emotional analysis of safeness and risk perception of London and Rome airports during the COVID-19 pandemic

The evaluation of perceived safeness and risk by individuals is really useful for security and safety managing. Every individual is founded on the opinion of other individuals to get a selection and the Internet personifies the location where these judgments are mainly sought, obtained, and evaluated. From this point of view, social networks are characterized by a significant effect. Due to this reason, Opinion Mining and Sentiment Analysis have found remarkable uses in various environments and one of the most interesting is embodied by public security and safety. The aim of this work is to study the perception of risk of aircraft passengers and users of airports of London (UK) and Rome (Italy) during COVID-19 pandemic. In particular, the airports of London Heathrow and Gatwick and the airports of Rome Fiumicino and Ciampino were studied, from March 23 to July 9, 2020, highlighting the emotional components in three distinct pandemic phases of the considered period in the two countries, by means of the semantic analysis of the textual contents existing in Twitter. © 2021 IEEE.

Emotional analysis of safeness and risk perception of transports and travels by car and motorcycle in London and Rome during the COVID-19 pandemic

The estimation of perceived safeness and risk by people is especially invaluable for security and safety managing. Everyone is based on the opinion of other persons to do selections of any kind and the Internet personifies the location where these opinions are primarily tracked, obtained, and assessed. From this point of view, social networks are considered of huge impact. For this reason, Opinion Mining and Sentiment Analysis reached remarkable applications in a diversity of circumstances and one of the most remarkable is expressed by public security and safety. The purpose of the present work is to investigate the perception of risk with transports and travels by car and motorcycles in London (UK) and Rome (Italy) during COVID-19 pandemic, from March 23 to July 9, 2020, underlighting the emotional components in three distinct pandemic phases of the considered period in the two countries, via the semantic analysis of the textual contents existing in Twitter. © 2021 IEEE.

The Development of Energy Efficient Routing Protocol Based on SDN Architecture And Intelligent Technique For HIHC-IoT

Due to the COVID-19 virus infections that have occurred recently, the development of an intelligent healthcare protocol that considers emergent heart cases becomes indispensable. This protocol is based on the method that aims to monitor patients remotely by using Internet of Thing (IoT) devices, which do not select the nodes that are nearby the patient's or in the room to choose as a Clusters Head (CH). So on, the energy consumption of these devices will be reduced, because of their highest importance than the other non-medical ones. Accordingly, this paper proposes a method called High Importance Healthcare-Internet of Things (HIHC-IoT), which is suitable for the emergent healthcare conditions of the patient and the caregiver. Furthermore, WSNs have some issues that reduce system performance, such as resource limits for sensors that may affect power supply, memory, communication capacity, and processing units. In the proposed work, the optimum set of CHs has been selected depending on the residual energy, the distance between the nodes, and the HI nodes. In addition, cloud technology, SDN architecture, and an efficient intelligent algorithm called High Importance-Future Search Algorithm (HI-FSA) have been used. Finally, the compered result of normal protocols with the proposed intelligent protocol, showed an increase in network life by about 40% and about 22% for an optimized routing protocol and increasing the number of packets delivered between nodes. © 2021 IEEE.

Machine Intelligent Techniques for COVID-19 Detection: A Critical Review and Analysis

Every human being is discussing a highly addressed topic in the current days which is about the COrona VIrus Disease (COVID) in 2019-2020. The outbreak of corona has affected the human race all over the world, the patient count is increasing day by day, and doctors are in a critically need of computer-aided diagnosis with machine learning (ML) algorithms that will discover and diagnose the coronavirus for a large number of patients. Also, it is more complicated to estimate the discharge time and the criticalness of the patient during treatment. Chest computed tomography (CT) scan was the best tool for the corona diagnosis. Also survival analysis methods in ML outperform better in predicting discharge time. In this, we survey on the COVID 19 diagnosis with a chain of CT scan pictures mined from the COVID-19 data set by using ML algorithms like marine predator, simplified suspected infected recovered (SIR), image acquisition, and some more techniques and also survival analysis techniques of ML. The survey clearly explains the models used up to now which are highly defined for the diagnosis of COVID-19 Virus. © 2021 IEEE.

A Review of Intelligent Techniques for Implementing SMART Learning Environments

The world is facing an unprecedented situation with the rapid rise in Covid-19, impacting on all spheres of life. The world of Education has not been spared of this phenomenon, with millions of learners not being able to attend schools and universities. It is clear that e-learning and technology-enhanced-learning remains the way forward in this difficult situation. However, the concept of one-size-fits-all is ever-present and the teaching and learning process does not consider the individualities of the learners which might be in terms of prior knowledge, learning style, level of maturity, pace of learning and emotional state. This research advocates the usage of SMART Learning Environments which are able to consider the individualities of the learners and are able to provide personalised learning. This research highlights key technologies that can be used to develop SMART Learning Environments which the researchers perceive as being the way forward given the actual context. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.