A survey on agents applications in healthcare: Opportunities, challenges and trends.

BACKGROUND AND OBJECTIVE: The agent abstraction is a powerful one, developed decades ago to represent crucial aspects of artificial intelligence research. The meaning has transformed over the years and now there are different nuances across research communities. At its core, an agent is an autonomous computational entity capable of sensing, acting, and capturing interactions with other agents and its environment. This review examines how agent-based techniques have been implemented and evaluated in a specific and very important domain, i.e. healthcare research. METHODS: We survey key areas of agent-based research in healthcare, e.g. individual and collective behaviours, communicable and non-communicable diseases, and social epidemiology. We propose a systematic search and critical review of relevant recent works, introduced by an exploratory network analysis. RESULTS: Network analysis enables to devise out 5 main research clusters, the most active authors, and 4 main research topics. CONCLUSIONS: Our findings support discussion of some future directions for increasing the value of agent-based approaches in healthcare.

Key Contributory Factors Influencing Cycling Safety: Comprehensive Review Based on Accident Data and Literature Survey

In the last years, it is evident that cycling is becoming an alternative transportation mode to driving and has gained more popularity among all age groups, particularly in metropolitan cities, due to COVID-19. Although cycling is beneficial to individuals and urban cities (i.e., reduction of traffic congestion and promotion of a healthy lifestyle), it could also expose cyclists to risky situations, resulting in serious consequences. Therefore, this research aims at conducting a comprehensive analysis of the key contributory factors by using data derived from cycling accident and literature reports. More specifically, the accident data are first used to prioritize contributory factors contributing to a high level of cycling risk, and then the results guide the development of the literature review. The literature review analysis emphasized the characteristics, relationships, and control measures against different selected contributory factors identified from cycling accident reports. The in-depth analysis aids to figure out and better understand what the characteristics and relationships of these factors are, how they affect the safety of cyclists individually and jointly, and what to do to control their negative effects. The findings will not only provide practical insights for transport authorities to control contributory factors influencing cycling safety, but also engage more research for the improvement of cycling popularity, prevention of cycling risks, and enhancement of cycling safety in future. © 2023 American Society of Civil Engineers.

Vision Transformers in medical computer vision-A contemplative retrospection

Vision Transformers (ViTs), with the magnificent potential to unravel the information contained within images, have evolved as one of the most contemporary and dominant architectures that are being used in the field of computer vision. These are immensely utilized by plenty of researchers to perform new as well as former experiments. Here, in this article, we investigate the intersection of vision transformers and medical images. We proffered an overview of various ViT based frameworks that are being used by different researchers to decipher the obstacles in medical computer vision. We surveyed the applications of Vision Transformers in different areas of medical computer vision such as image-based disease classification, anatomical structure segmentation, registration, region-based lesion detection, captioning, report generation, and reconstruction using multiple medical imaging modalities that greatly assist in medical diagnosis and hence treatment process. Along with this, we also demystify several imaging modalities used in medical computer vision. Moreover, to get more insight and deeper understanding, the self-attention mechanism of transformers is also explained briefly. Conclusively, the ViT based solutions for each image analytics task are critically analyzed, open challenges are discussed and the pointers to possible solutions for future direction are deliberated. We hope this review article will open future research directions for medical computer vision researchers.

COVID-19 detection on chest x-ray image using yolo based architecture

The whole world has been facing the problem of novel Coronavirus (COVID-19) since 2020. Over 88 million cases are confirmed and around 5 lacks deaths are accounted. Using the Lung-Computed Tomography (CT) Lesion Segmentation dataset, deep learning techniques may be used to quickly identify COVID-19 and the exact region that is infected. Based on CT, it is easy to identify the problem and the infected area, then assisting treatment of COVID-19. In the literature survey, research study has considered many research papers worked done work on identification of COVID-19 using chest/lungs X-ray image, and with that identified what are the deep learning-based models or methodology they have used for detecting COVID-19 result. To overcome their result, Authors have proposed a latest methodology of deep learning with the YOLO variant 7x to get optimum result of COVID -19 detection from lungs X-ray image. To identify COVID-19, Authors have applied proposed methodology on publically avail X-ray image-based dataset of COVID-19, proposed methodology has achieved good performance to detect COVID infection from lungs. © 2023 IEEE.

Evaluation of Various Machine Learning Based Existing Stress Prediction Support Systems (SPSSs) for COVID-19 Pandemic

COVID-19 profoundly impacts human beings in various ways, i.e., psychological, socioeconomic, fear, social isolation, etc., augmenting the prevailing inequalities in mental health. The role of machine learning (ML) can be understood through its various potential applications in Stress Prediction in mental health. This literature survey uncovered various related articles, which were utilized to determine the essential structure for analysis. The gathered information helped in providing the new ideas and the concepts, which were incorporated with the support of literature and classified under broad themes based on mental health during the pandemic COVID-19. This study emphasized assessing various existing "Stress Prediction Support Systems” based on machine learning. This article also addresses the mental health issues that were emerged due to COVID-19 pandemic, further;also analysed the previously available stress prediction Machine Learning based models. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

A Systematic Literature Survey of Emotion Detection of Students in E-learning

Affective computing is a part of artificial intelligence, which is becoming more important and widely used in education to process and analyze large amounts of data. Consequently, the education system has shifted to an E-learning format because of the COVID-19 epidemic. Then, e-learning is becoming more common in higher education, primarily through Massive Open Online Courses (MOOCs). This study reviewed many prior studies on bolstering educational institutions using AI methods, including deep learning, machine learning, and affective computing. According to the findings, these methods had a very high percentage of success. These studies also helped academic institutions, as well as teachers, understand the emotional state of students in an e-learning environment. © 2022 IEEE.

Epidemiological disaster management: Literature survey and analysis

Epidemiological disasters can cause significant suffering and change lives, but how they are handled can have just as much of an impact. This research aims to shed light on epidemiological disaster management literature from multidisciplinary perspectives and analyze its development and trends. A total of 365 scholarly articles were analyzed for this study using a number of databases from various academic disciplines. Search Keywords included "pandemic disaster management,” "pandemic planning,” "pandemic preparedness,” "pandemic response,” and "pandemic recovery.” Consequently, this paper surveys the literature and presents a brief background on epidemiological disasters and their management, a descriptive and inferential analysis of studies on the subject matter, a discussion of relevant issues, and suggested potential research directions for those interested. The analysis reveals that traditional methods for managing epidemiological disasters primarily rely on medical principles and policies, with medical sciences accounting for the great majority of studies, followed by social sciences. Moreover, the majority of the research has focused on response and preparedness, while recovery has gotten relatively little attention in favor of these earlier phases. Accordingly, based on various strategies/approaches exploited by different countries to deal with the COVID-19 pandemic and the trend of the existing body of research identified in this study, a paradigm shift in epidemiological disaster management is inevitable. © 2023 by the author.

Epidemiological disaster management: Literature survey and analysis

Epidemiological disasters can cause significant suffering and change lives, but how they are handled can have just as much of an impact. This research aims to shed light on epidemiological disaster management literature from multidisciplinary perspectives and analyze its development and trends. A total of 365 scholarly articles were analyzed for this study using a number of databases from various academic disciplines. Search Keywords included "pandemic disaster management,” "pandemic planning,” "pandemic preparedness,” "pandemic response,” and "pandemic recovery.” Consequently, this paper surveys the literature and presents a brief background on epidemiological disasters and their management, a descriptive and inferential analysis of studies on the subject matter, a discussion of relevant issues, and suggested potential research directions for those interested. The analysis reveals that traditional methods for managing epidemiological disasters primarily rely on medical principles and policies, with medical sciences accounting for the great majority of studies, followed by social sciences. Moreover, the majority of the research has focused on response and preparedness, while recovery has gotten relatively little attention in favor of these earlier phases. Accordingly, based on various strategies/approaches exploited by different countries to deal with the COVID-19 pandemic and the trend of the existing body of research identified in this study, a paradigm shift in epidemiological disaster management is inevitable. © 2023 by the author.

Does Social Innovation Matter for Government? An Exploratory Study

Social innovation scholars and sociologists regard shocks and crises that impact heavily on social systems as opportunities for self-reflection and as windows of opportunity for the emergence of new ideas and possibilities. In this sense, the social systems recovery in the new normal post-Covid19 era can open new opportunities for the spreading of the transformational impact of social innovation. This will concern also public administration organizations since social innovation can also be seen as a particular perspective on how the public sector should be reformed. Hence, social innovation should be a topic of particular interest for public administration scholars. The aim of this exploratory study is to investigate whether and how social innovation has been considered in the top academic public administration journals. The study confirms that the topic is still underexplored in this literature and highlights some possible research directions that can contribute to bridge this gap. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Uncertainties in Liner Shipping and Ship Schedule Recovery: A State-of-the-Art Review

Each shipping line is expected to establish a reliable operating model, and the design of ship schedules is a key operational consideration. Long-term profits for shipping lines can be expected from a well-designed ship schedule. In today’s liner service design, managing the time factor is critical. Shipping schedules are prone to different unexpected disruptions. Such disruptions would necessitate a near-real-time analysis of port capacity and re-design of the original ship schedule to offset the negative externalities. Ship schedule recovery strategies should be implemented to mitigate the effects caused by disruptions at ports or at sea, which may include, but are not limited to, ship sailing speed adjustment, handling rate adjustment at ports, port skipping, and port skipping with container diversion. A proper selection of ship schedule recovery strategies is expected to minimize deviations from the original ship schedule and reduce delays in the delivery of cargoes to the destination ports. This article offers a thorough review of the current liner shipping research primarily focusing on two major themes: (1) uncertainties in liner shipping operations;and (2) ship schedule recovery in response to disruptive events. On the basis of a detailed review of the available literature, the obtained results are carefully investigated, and limitations in the current state-of-the-art are determined for every group of studies. Furthermore, representative mathematical models are provided that could be further used in future research efforts dealing with uncertainties in liner shipping and ship schedule recovery. Last but not least, a few prospective research avenues are suggested for further investigation.

Gender Segregation in Tourism: A Comprehensive Literature Survey and Policy Recommendations for the Post-COVID Era

Gender segregation in the tourism industry is a critical issue from the lens of the sustainable development goals in the contemporary world. Although it's not an emerging phenomenon, COVID-19 pandemic has worsened the situation. In this context, this study has two main purposes. First, it intends to examine the gender segregation in the tourism labor force with a comprehensive literature survey. And second, it aims to develop policy implications for the post-COVID era. By means of these purposes, it's intended to fill the gap in the literature in the axis of post-COVID foresights and policy options. In this respect, after a detailed introduction, the first section is devoted to different dimensions of gender segregation in the tourism industry. Following this background, the second section is attributed to a comprehensive literature review. Then, the third section is assigned to policy implications for the post-COVID era. Lastly, the conclusion gives a synthesis for the gender segregation in tourism labor force and its near future. © 2022. Journal of International. All Rights Reserved.

Highly Skilled Migrant Workers as a Vulnerability of Small Island Developing States During the COVID-19 Pandemic: Cases of Three Countries in Micronesia

Several small island developing states (SIDS) in the Pacific managed to avoid the COVID-19 pandemic by implementing measures to ensure national isolation. Primarily due to being ordered to leave by their respective organizations, e.g., overseas development administration (ODA) in the developed world, many highly skilled migrant workers left these countries. This sudden exodus of highly skilled foreigners created a number of problems in these countries;for example, schools suffered from teacher staffing shortages and hospitals had reduced capacity to offer medical services due to the paucity of nurses and doctors. This study aims to examine the situations in the Federated State of Micronesia (FSM), Palau, and the Republic of the Marshall Islands (RMI), where many foreign workers have left their duty stations to return home under COVID-19, to elicit lessons learned and possible ways and means to alleviate the observed problems. To this end, literature surveys and interviews were conducted with informants. Results indicated that developing and maintaining a remote work environment is a promising method to fill the gaps caused by the sudden absence of foreign workers in management posts, even under non-emergency situation. This is because in the case that highly skilled migrant workers are forced to vacate their duty stations suddenly, immediately hiring replacements is often not possible. Promoting distance education also proved effective for COVID-19-free nations such as the FSM, Palau, and the RMI, not only during emergencies, but also during normal times. Similarly, the daily use of telemedicine is likely to be effective in coping with emergencies, as shown in the case of FSM’s Pohnpei State Hospital. We found both distance education and telemedicine to be effective measures to address the sudden departure of highly skilled migrant workers in the fields of education and medical services. Moreover, other forms of remote work should prove useful in other sectors such as industry and administration. These systems should be progressively developed during non-emergency times and integrated into the daily operations of relevant sectors. © Fuji Technology Press Ltd. Creative Commons CC BY-ND: This is an Open Access article distributed under the terms of the Creative Commons Attribution-NoDerivatives 4.0 International License (http://creativecommons.org/licenses/by-nd/4.0/).

Literature Survey: Computational Models for Analyzing and Predicting the Spread of the Coronavirus Pandemic

Viral diseases are extremely widespread infections caused by viruses, which is a type of microorganism. Some of the common curable viral diseases are common cold, flu, pneumonia mumps, measles, etc. In addition to this, there are also some deadly viral diseases are human immunodeficiency virus (HIV), human pappilomavirus (HPV), SARS, Ebola, etc., which is incurable. The recent coronavirus has also taken its place in this latter list for which the vaccine is yet to be discovered. As early diagnosis is the only option as of now which could control the death rate of this disease, several researchers are in the process of inventing drugs and vaccines for the same. At this stage, it is vital to develop some automated systems that could possibly detect the virus’s presence at an early stage. Numerous scholarly articles concerning proposing computational models encompassing the spread of the coronavirus disease have been studied, analyzed, and juxtaposed with an aim to determine the optimality and accuracy of various models. This work aims to develop a collective study on the models developed so far for the prediction and spread of coronavirus. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

Review of classifiers used for novel corona virus foot-print detection

Development in the field of Machine Learning and Artificial Intelligence are greatly simplifying the critical bio-medical engineering applications. The first outbreak of Covid’19 pandemic was observed in Mainland China and soon it spread over to remaining 214 countries. World Health Organization (WHO) came to forefront and named it as Corona Virus Disease 2019. This highly contagious disease causes serious impact due to Severe Acute Respiratory Syndrome (SARS)-COV Virus. In this article, we are about to disclose the detailed literature survey around how Machine Learning and Artificial Intelligence are momentously assisting the biomedical engineering segment to tackle with the situation created due to Covid’19 Pandemic. Subsequently, different classifiers, which are used by the researchers for effective diagnosis of Covid’19 infection, are studied for projecting the research in effective diagnosis of different strains of the Corona Virus. © 2021 IEEE.

Application of Artificial Intelligence in Human Resource Management of T Insurance Company in the Post-epidemic Era

With the advent of the post-epidemic era, human resource management is the most urgent issue faced by small and medium-sized entrepreneurs during the epidemic. This paper analyzes the influence of artificial intelligence technology on the human resource management of T Insurance company by using literature and market survey method. As an emerging technology field, the popularization and application of ARTIFICIAL intelligence technology will still face some difficulties. Finally, the paper puts forward specific strategies to promote the extensive application of artificial intelligence technology in human resource management in the post-epidemic era. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

2019 Novel coronavirus: where we are and what we know.

There is a current worldwide outbreak of a new type of coronavirus (2019-nCoV), which originated from Wuhan in China and has now spread to 17 other countries. Governments are under increased pressure to stop the outbreak spiraling into a global health emergency. At this stage, preparedness, transparency, and sharing of information are crucial to risk assessments and beginning outbreak control activities. This information should include reports from outbreak sites and from laboratories supporting the investigation. This paper aggregates and consolidates the virology, epidemiology, clinical management strategies from both English and Chinese literature, official news channels, and other official government documents. In addition, by fitting the number of infections with a single-term exponential model, we report that the infection is spreading at an exponential rate, with a doubling period of 1.8 days.