ABSTRACT
Including ethical concepts and considerations in engineering education has attracted significant interest in recent years, mainly due to the impact of some AI applications in different areas of our life. The use of case studies in teaching ethics is a well-known and useful approach. The debate related with a given case study helps students think about the implications, motivations and foreseeable impact of the technologies. This fact is in contrast with the common easy-thinking that technologies are neutral and that an engineer should not bother about ethics and does not have any responsibility at all. While many basic technologies may be considered neutral, more developed and complex systems are not so neutral;they have a motivation and some foreseeable impact and consequences. Thence, the main message is that engineers have a responsibility when developing these systems. This paper presents a case study used in a course for Ph.D. students in a Technical University to introduce the concept of ethics by design and to stress the idea of responsible conduct in engineering. The case under study is the design and development of tracing applications for fighting against the Covid-19 pandemic in 2020. The analysis of the case requires to understand the basic technologies proposed, the different alternatives considered at that time, the basic facts related with the contagion chain and the main factors to be addressed, the consideration of the balance between public health rights and individual privacy rights, and the social aspects related with the acceptability by citizens. © 2022 SEFI 2022 - 50th Annual Conference of the European Society for Engineering Education, Proceedings. All rights reserved.
ABSTRACT
This chapter discusses an important topic in factory management, that of improving the understandability of AI applications for group multi-criteria decision making in manufacturing systems. Due to its long-term and cross-functional impact, decision making may be more critical to the competitiveness and sustainability of manufacturing systems than production planning and control. This chapter uses the example of choosing the right smart and automation technologies for factories during the COVID-19 pandemic. This topic is of particular importance as many factories are forced to close or operate on a smaller scale (using a smaller workforce), thus pursuing further automation. Artificial intelligence and Industry 4.0 technologies have many applications in this area, most of which can also be applied for other decision-making purposes in manufacturing systems. First, a systematic procedure was established to guide the group multi-criteria decision-making process. Applications of AI and XAI to identify targets are first reviewed. Subsequently, the application of AI and XAI to selection factors and development of criteria is presented. Artificial intelligence techniques are widely used to derive criteria priorities. Therefore, it is particularly important to explain XAI techniques and tools for such AI applications. Aggregating the judgments of multiple decision makers is the next focus, followed by the introduction of AI and XAI applications to evaluate the overall performance of each alternative. Taking fuzzy ranking preference based on similarity to ideal solution (FTOPSIS) as an example, the application of XAI techniques and tools in explaining comparison results using FTOPSIS is illustrated. Another AI technology used for the same purpose is fuzzy VIKOR. XAI techniques and tools for interpreting fuzzy VIKOR are also presented. Finally, several metrics are proposed to evaluate the effectiveness of XAI techniques or tools for decision making in the manufacturing domain. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.
ABSTRACT
Human potential is being expanded with the development of AI. Increases in both the quantity and quality of medically-verified reports of successful treatments, as well as the rate at which our understanding of these treatments is expanding, are providing a fresh perspective on human administration. This research was driven by the need to bring attention to the need of using AI to combat the COVID-19 Crisis, and it focuses on the current state of AI applications in clinical administration while treating COVID-19. Deciphering this pathogen also requires the use of Big Data, which we stress. We also give an overview of several intelligence methodologies and procedures that can be employed for various medical information-based pandemics. We categorise the various forms of artificial intelligence now employed in health care research, such as neural networks, conventional support vector machines, and cutting-edge knowledge acquisition. Areas that use Intelligence cloud computing to combat other viruses like COVID-19 have also received attention. This study was conducted to assist medical practitioners and scientists in overcoming challenges they have encountered in processing COVID-19 large data sets. Some of the researched methodologies have proposed important advances in medical research methodology, however their accuracy is only around 90%. We wrap off with a deep dive into how AI could give security researchers a leg up in the struggle against this type of malware. © 2022 IEEE.
ABSTRACT
Machine learning algorithms immediately became critical in the battle against the COVID-19 outbreak. Diagnoses, medicine research, an illness spread predictions, and population surveillance all required the use of artificial intelligence (AI) methods as the epidemic grew in scope. To combat COVID-19, screening procedures that are both effective and rapid are required. At COVID-19, AI developers took a chance to show how AI can benefit all mankind. It was only after the employment of AI in the battle against COVID-19. AI's various and diverse applications in the epidemic are documented in this study. It is the purpose of this study to help shape the future development and usage of these technologies, whether in the present or future health crises. © 2023, Institute of Advanced Engineering and Science. All rights reserved.
ABSTRACT
The new coronavirus disease (COVID-19) comprises the public health systems around the world. The number of infected people and deaths are escalating day-to-day, which puts enormous pressure on healthcare systems. COVID-19 symptoms include fatigue, cough, and fever. These symptoms are also diagnosed for other pneumonia, which creates complications in identifying COVID-19, especially throughout the influenza season. The rise of the COVID-19 pandemic among individuals has made it essential to improve medical image screening of this pneumonia. Rapid identification is a necessary step to stop the spread of this virus and plays a vital role in early detection. With this as a motivator, we applied deep learning techniques to diagnose the coronavirus using chest X-ray images and to implement a robust AI application to classify COVID-19 pneumonia from non-COVID-19 for the respiratory system in these images. This paper proposes different deep learning algorithms, including classification and segmentation methods. By taking advantage of convolutional neural network models, we exploited different pre-trained deep learning models such as (ResNet50, ResNet101, VGG-19, and U-Net architectures) to extract features from chest X-ray images. Four datasets of chest X-ray images have been employed to assess the performance of the proposed methods. These datasets have been split into 80% for training and 20% for validation of the architectures. The experimental results showed an overall accuracy of 99.42% for the classification and 93% for segmentation approaches. The proposed approaches can help radiologists and medical specialists to identify the insights of infected regions for the respiratory system in the early stages. © 2022 SPIE.
ABSTRACT
Online mode during COVID-19 has raised mental health issues among students along with the stigma around it that continues to exist. As a result of which, there are people who are not comfortable in disclosing their personal details to outsiders for help. This brings in the use of AI applications that can help in this critical issue requiring regular monitoring. In this paper, a survey is conducted to know about awareness about this topic. A review about many conversational AI chatbots is provided that are helpful for handling mental health issues such as stress, anxiety, and depression in a number of ways. These include voice and text based chatbots developed in the last decade. The strengths and limitations of these are also discussed. © 2022 IEEE.
ABSTRACT
Artificial intelligence (AI) is a computer-based technology that has the ability to learn and intelligently perform given tasks. AI-based services have already become a natural part of human life. As the significance of using these technologies is getting more recognized over time, they continuously give promising results to not only solve the current problems but also identify the possible problems of future. If these services can be used in this way, they may even surpass human solutions in different landscapes. Managing pandemic is one of them. At present, the new issues arising from the spread of coronavirus disease 2019 (COVID-19) in the world have become an important part of AI services for social good. These services have already shown promising results regarding healthcare in different landscapes such as early diagnosis, tracking the spread of virus, monitoring patients, relieving the overload on healthcare workers, identifying high-risk groups, and raising awareness about self-hygiene. However, most of the issues related to fighting against COVID-19 at a global level show that use of AI technologies in such disease prevention or public health management is still very limited. AI technologies could be more functional in the management of such crisis when they become part of intelligent healthcare systems and a requirement for the management systems of any potential pandemic in the future. The purpose of this chapter is to provide consistent knowledge to stakeholders regarding the critical issues and to analyze how AI-based solutions could be used for fighting against pandemics. Therefore using AI services in the management of COVID-19 pandemic is examined in four main phases: prevention, preparation, response, and recovery. Next, critical issues, ethical uses of those services, and the latest learned lessons were discussed. Then the existing gaps in fighting pandemics are presented together with the concluding remarks, where suggestions of relevant AI systems and how AI can help in fighting against pandemics are summarized. © 2022 Elsevier Inc.
ABSTRACT
Advanced healthcare technologies, including artificial intelligence (AI), the Internet of Things (IoT), big data, and deep learning, are required to counter and even prepare for new illnesses. As a result, we are examining IA's capacity to control and manage COVID-19 (Coronavirus) and other emerging pandemics. Using COVID-19 or Coronavirus and Artificial Intelligence or AI keywords, the material may be quickly found in the PubMed database. COVID-19 AI's existing understanding was analyzed to see how it may be used to increase COVID-19 AI's overall usefulness. Seven COVID-19 pandemic-related AI applications have been documented. The technology has the potential to locate the infection, track it through the system, and make forecasts about when the virus will infiltrate the whole system again. Decision-making tools are desperately needed to help combat this outbreak and allow healthcare institutions to gather enough information in real time to halt its spread. The primary objective of AI is to mimic human thinking using an expert methodology. COVID-19 vaccination production may also play a critical part in making sense of and advocating a similar project. This kind of technology is helpful in screening because of its emphasis on discoveries. © 2021 IEEE.
ABSTRACT
In order to effectively prevent the spread of COVID19, people from different parts of the world were supposed to be wearing face masks after the WHO put it as a primordial instruction to stop its propagation. Researchers from different backgrounds gathered their efforts to ensure the respect of wearing face mask, namely AI field researchers. In this research, we are interested on the AI applications that were done from the beginning of the pandemic to prevent the COVID 19 contamination, especially those related to the mask wearing detection. The detection of wearing mask is classified as a computer vision problem, more specifically, an object detection one. Besides, with the evolution of the computational power and the availability of huge number of datasets, deep learning models using image and video processing techniques were proposed in order to detect people transgressing the wearing mask rule. In this paper we introduce a literature review of object detection, a case study of this problem which consists in the wearing mask detection, the related works as well as the different proposed solutions, and the suggested general pipeline for the treatment of this problem. © 2021 IEEE.
ABSTRACT
Artificial intelligence (AI) is playing a key supporting role in the fight against COVID-19 and perhaps will contribute to solutions quicker than we would otherwise achieve in many fields and applications. Since the outbreak of the pandemic, there has been an upsurge in the exploration and use of AI, and other data analytic tools, in a multitude of areas. This paper addresses some of the many considerations for managing the development and deployment of AI applications, including planning; unpredictable, unexpected, or biased results; repurposing; the importance of data; and diversity in AI team membership. We provide implications for research and for practice, according to each of the considerations. Finally we conclude that we need to plan and carefully consider the issues associated with the development and use of AI as we look for quick solutions.
ABSTRACT
BACKGROUND AND AIMS: Healthcare delivery requires the support of new technologies like Artificial Intelligence (AI), Internet of Things (IoT), Big Data and Machine Learning to fight and look ahead against the new diseases. We aim to review the role of AI as a decisive technology to analyze, prepare us for prevention and fight with COVID-19 (Coronavirus) and other pandemics. METHODS: The rapid review of the literature is done on the database of Pubmed, Scopus and Google Scholar using the keyword of COVID-19 or Coronavirus and Artificial Intelligence or AI. Collected the latest information regarding AI for COVID-19, then analyzed the same to identify its possible application for this disease. RESULTS: We have identified seven significant applications of AI for COVID-19 pandemic. This technology plays an important role to detect the cluster of cases and to predict where this virus will affect in future by collecting and analyzing all previous data. CONCLUSIONS: Healthcare organizations are in an urgent need for decision-making technologies to handle this virus and help them in getting proper suggestions in real-time to avoid its spread. AI works in a proficient way to mimic like human intelligence. It may also play a vital role in understanding and suggesting the development of a vaccine for COVID-19. This result-driven technology is used for proper screening, analyzing, prediction and tracking of current patients and likely future patients. The significant applications are applied to tracks data of confirmed, recovered and death cases.