Face Mask And Body Temperature Detection In Sensor Enabled Automated Door Control System

Developing an automatic door-opening system that can recognize masks and gauge body temperature is the aim of this project. The new Corona Virus (COVID-19) is an unimaginable pandemic that presents the medical industry with a serious worldwide issue in the twenty-first century. How individuals conduct their lives has substantially changed as a result. Individuals are reluctant to seek out even the most basic healthcare services because of the rising number of sick people who pass away, instilling an unshakable terror in their thoughts.This paper is about the Automatic Health Machine (AHM). In this dire situation, the government provided the people with a lot of directions and information. Apart from the government, everyone is accountable for his or her own health. The most common symptom of corona infection is an uncontrollable rise in body temperature. In this project, we create a novel device to monitor people's body temperatures using components such as an IR sensor and temperature sensor. © 2023 IEEE.

Lung Lesion Images Classification Based on Deep Learning Model and Adaboost Techniques

Today, the medical industry is promoting the research and application of artificial intelligence in disease diagnosis and treatment. The development of diagnostic methods with the support of electronic devices and information technology can help doctors save time in diagnosing and treating diseases, especially medical images. Diagnosis of lung lesions based on lung images is a case study. This paper proposed a method for lung lesion images classification based on modified U-Net and VGG-19 combined on adaboost techniques. The modified U-Net architecture with 5 pooling and 5 unpooling. It has the unpooling layer with kernels of size 2 × 2, stride 2 × 2 to get output consistent with the adaboost. The result of the proposed method is about 97.61% and better results than others in the Covid-19 radiography dataset. © 2023, ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering.

Research and Application of 5G Edge AI in Medical Industry

With the development of 5G and AI technology, the infectious virus detection framework system based on the combination of 5G MEC and medical sensors can effectively assist in the intelligent detection and control of influenza viruses such as COVID-19. Employing the edge computing and 5G+MEC model, the virus AI model is trained for the collected influenza virus data. Then the virus AI model can be used to evaluate the virus patients on the local edge computing service platform. Therefore, this paper introduces an algorithm and resource allocation, which uses 5G functions (especially, low latency, high bandwidth, wide connectivity, and other functions) to achieve local chest X-ray or CT scan images to detect COVID-19. Meanwhile, this paper also compares the computational efficiency of different algorithms in the 5G edge AI-based infectious virus detection framework, in this way to select the best algorithm and resource allocation. © 2022 IEEE.

Evaluation of the Improvements of Transformer Prediction with Convolution

Proposed and developed 5 years ago, Transformer has been a prevailing machine learning method and is widely used to solve various kinds of practical problems [1]. According to relevant works, Transformer has performed well in both natural language processing and computer vision tasks, so we would like to test its effectiveness in prediction, specifically, time series prediction. Over the past two years, COVID-19 is no doubt one of the major factors that influences the changes in the stock prices, and the medical industry should be among the most significantly affected, which would provide an ideal sample for us to study transformer on time series prediction. In this paper, we not only construct a machine learning model using Transformer to predict the stock prices of one medical company but also add a convolution layer to try to optimize the predictions. The comparison of the outcome from the two models suggests that the convolution layer could improve the performance of the naive transformer in several ways. © 2022 IEEE.

Comparative Analysis of Deep Learning Models for Covid-19 Detection from Chest X-rays

Lungs are the organs which play key role in human respiratory system. The severity of infections caused to the lungs might vary from mild to moderate. Chest X-Ray is a principal diagnostic tool used in detecting various types of lung diseases. The whole world is struggling due to a pandemic arised in 2019, known as Coronavirus disease or Covid-19, a severe respiratory infection. The medical industry demanded the use of computer aided techniques for analysing extremity of the disease. This work aims to examine the effectiveness of pretrained deep learning models in classifying chest X-rays as Covid, Viral pneumonia and Healthy cases. We have used largest publicly accessible Covid dataset, QaTa Cov-19 for conducting experiments. Out of six fine tuned deep learning pretrained network models, Densenet 201 outperformed with highest accuracy of 98.6% and AUC of 0.9996. © 2022 IEEE.

Speciesism and Preference of Human–Artificial Intelligence Interaction: A Study on Medical Artificial Intelligence

Artificial intelligence (AI) has revolutionized the medical industry in the decade. It is critical to integrate human–computer interaction into daily clinic service and further increase the public acceptance of medical AI. Based on self-categorization theory, our research draws on speciesism as a vital cognitive factor to examine how patients' speciesism affects their acceptance of medical AI in different roles. The study adopted a positivist research paradigm by examining 249 samples of data collected during COVID-19 in China. The results indicate that patients with higher speciesism tend to have lower acceptance of medical AI in an independent role but higher acceptance in an assistive role. Furthermore, we verified the mediating effect of human–computer trust and the positive moderating role of human uniqueness perception. This article expands the practicality of speciesism from human–animal relationships into human–AI relationships and contributes to human–computer interaction from the perspective of medical AI acceptance. © 2023 Taylor & Francis Group, LLC.

Internet of Wearable Medical Things for COVID-19 Diagnostics

Wearable devices have played a key role in the medical industry, especially since the COVID-19 pandemic spread. The need for a self-monitoring system increased since the spread of the virus. With the development of semiconductor technology and the increased research and development in medical wearable devices, wearable devices have been able to detect the medical condition of patients. This paper presents a biomedical wearable device to monitor the vital signs of patients. The device can be used to detect the patient COVID-19 infection. Data were extracted using different sensors and other components, and results were displayed on a mobile application that showed the health status of the patient. A PCB (Printed Circuit Board) design was made for the purpose of making the system a wearable device. The system power consumption ranged from 5-37.5mW. © 2022 IEEE.

A Deep Learning based System for Covid-19 Positive Cases Detection Using Chest X-ray Images

Recent technology advancements open the door for the employment of deep learning-based methods in practically all spheres of human endeavor. Deep learning algorithms can be employed in the medical industry for the categorization and identification of various diseases because of their accuracy. The recent coronavirus (COVID-19) pandemic has significantly strained the global health system. By using medical imaging and PCR testing, COVID-19 can be diagnosed. Since COVID-19 is very communicable, chest X-ray diagnosis is frequently regarded as safe. In this report, a deep learning-based method is suggested for differentiating COVID-19 infections from other illnesses that aren't COVID-19. A pre-trained model, Densenet121 is employed to categorize COVID-19. © 2022 IEEE.

COVID-19 Article Classification Using Word-Embedding and Different Variants of Deep-Learning Approach

The COVID-19 pandemic has changed the way we go about our everyday lives, and we will continue to see its impact for a long time. These changes especially apply to the business world, where the market is very volatile as a result. Requirements of the people are changing rapidly, as are the restrictions on transport and trade of goods. Due to the intense competition and struggles brought about due to the pandemic, acting first on profit opportunities is crucial to businesses doing well in the current climate. Thus, getting the relevant news in time, out of the huge number of COVID-19 related articles published daily is of utmost importance. The same applies to other industries, like the medical industry, where innovations and solutions to managing COVID-19 can save lives, and money in other parts of the world. Manually combing through the massive number of articles posted every day is both impractical and laborious. This task has the potential to be automated using Natural Language Processing (NLP) with Deep Learning based approaches. In this paper, we conduct exhaustive experiments to find the best combination of word-embedding, feature selection, and classification techniques;and find the best structure for the Deep Learning model for article classification in the COVID-19 context. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Development Status and Trend Analysis of Internet of Medical Things Industry in China

This article analyzes the development status, development trend and prospects of China's Internet of Medical Things (IoMT) industry from a macro perspective. Our survey mainly includes: analyzing the necessity and urgency of China's medical system reform from the various dilemmas faced by China's medical system, and analyzing the development of the IoMT industry based on the current basic conditions of development of the Internet of Things (IoT), information technology and background of COVID-19 epidemic. Opportunities and the evolution of China's IoMT policy were also analyzed. Moreover, from the five aspects of medical industry informatization, Internet hospitals, smart wearable devices, medical AI industry and medical industry digitization, the development status and trends of China's IoMT industry are analyzed. Finally, it looks forward to the development prospects and directions of IoMT industry for health care in China. © 2022 The authors and IOS Press.

Secure e-Prescription Management System: Mitigating Blended Threat in IoBE

New information and communication technologies (ICT) are being applied in various industries to upgrade the value of the major service items. Moreover, data collection, storage, processing, and security applications have led to the creation of an interrelated ICT environment in which one industry can directly influence the other. This is called the "internet of blended environments" (IoBE), as it is an interrelated data environment based on internet-ofthings collection activities. In this environment, security incidents may increase as size and interconnectivity of attackable operations grow. Consequently, preemptive responses to combined security threats are needed to securely utilize IoBE across industries. For example, the medical industry has more stringent information protection measures than other industries. Consequently, it has become a major target of attackers, as more clinician-patient interactions occur over the internet owing to COVID-19. Therefore, this study aims to acquire security for IoBE while focusing on the medical industry. Among the various types of medical ICT services, this study analyzes data flow and potential security threats from the e-prescription lifecycle perspective, which is highly utilized, strongly data-centric, and has numerous security issues. Based on our analysis, we propose a secure authentication and data-sharing scheme. © 2023 CRL Publishing. All rights reserved.

Contactless Covid-19 Monitoring System Using IOT

The main purpose of this study is to track down corona virus interactions using the Internet of Things. The sickness is reported to be very contagious when it comes into touch with sick people. High fever, cough, and trouble breathing are the most common signs of COVID19. They've demonstrated how the sickness has evolved to conceal its signs. Because this sickness is highly contagious, it has the potential to spread rapidly, killing thousands of people. And the transmission chain must be identified as a top concern. The Internet of Things are collection that work together to accomplish a goal. Every object has its own identity, which will be used to record main Occurrences serve as a springboard for future learning and judgments. In the medical industry, IoT plays an indisputable role in disease identification and surveillance. A new epidemic is spreading across the globe. Amid a slew of other life-threatening illnesses Despite tight lockdown procedures, COVID-19, a respiratory syndrome virus discovered in 2019, is now posing a significant threat to countries. Conclusions - The authors of this study created a design for an IoT system that collects data from individuals via sensors and sends it to clinicians via mobile phones, computers, and other devices to predict the Covid-19 sickness. The main goal is to predict COVID-19 so that early health surveillance may be provided. Therefore, the writers are able to distinguish between the two. © 2022 IEEE.

Design and Development of Machine Learning Techniques for Disease Prediction

Machine Learning is ever advancing field and as more and more research is being done in the field, more applications are being developed for this field and it is now being used in all fields. Also, nowadays people are facing multiples diseases posing danger to human life. This prompted researchers to critically analyse and work to apply Machine learning in the use of prediction of these diseases and using this analysis to assist the medical industry. The idea is to find various datasets of different Diseases like Dengue, Covid-19. Perform analysis on the datasets of these diseases to understand more about them and how much they affect us. There are various models available like KNN, SVM, etc. The task is to work with different models and find out how they perform with data of different diseases and which models are most affective and accurate. © 2022 IEEE.

An APT Malware Classification Method Based on Adaboost Feature Selection and LightGBM

Advanced Persistent Threat (APT) attack activities with the theme of COVID-19 and vaccine are also growing rapidly. The target of APT attack has gradually expanded from government agencies to vaccine manufacturers, medical industry and so on. What's more, APT groups have a strict organizational structure and professional division of labor and malware delivered by the same APT groups are similar. Classifying malware samples into known APT groups in time can minimize losses as soon as possible and keep relevant industries vigilant. In our paper, we proposed a multi-classification method of APT malware based on Adaboost and LightGBM. We collect real APT malware samples that have been delivered by 12 known APT groups. The API call sequence of each APT malware is obtained through the sandbox. For the relationship between adjacent APIs, we use TF-IDF algorithm combined with bi-gram. Then, Adaboost algorithm is used to select out the important API features, which form the target feature subset. Finally, we use the above subset combined with LightGBM ensemble algorithm to train multiple classifiers, named Ada-LightGBM. The experimental results show that our method is superior to the single Adaboost and LightGBM method. The classifier has good recognition performance for the test samples. © 2021 IEEE.

Comparing Technical Trends between Industrial Leaders via Video Data

IT (Information technology) has been rapidly growing. Until early 2000s, IT mainly exists for IT industry. However, IT expands their fields into the outer fields of IT industry such as medical and agricultural industries. It means that the cutting-edge technologies have increased to a level that humanity cannot grasp all. It is difficult for even industrial leaders and followers to grasp all. Thus, in order to grasp and create the cutting-edge technologies, this research provide the latest states from keynotes of some events. These text data for this analysis could be gained because a lot of events have shifted from in-person to online by the impact of COVID-19 (the coronavirus disease 2019). Consequently, this analysis measured the closeness between industries that have available data and found that the changes of topics become more frequent after COVID-19. This analysis is to evaluate the potential in order to compare with confidential data and to discover the gap between international trends and in-company competences in the future. © 2021 IEEE.