A New UAV-Based Social Distance Detector for COVID-19 Outbreaks Reduction, Using IoT, Computer Vision and Deep Learning Technologies

Nowadays, we are living in a dangerous environment and our health system is under the threatened causes of Covid19 and other diseases. The people who are close together are more threatened by different viruses, especially Covid19. In addition, limiting the physical distance between people helps minimize the risk of the virus spreading. For this reason, we created a smart system to detect violated social distance in public areas as markets and streets. In the proposed system, the algorithm for people detection uses a pre-existing deep learning model and computer vision techniques to determine the distances between humans. The detection model uses bounding box information to identify persons. The identified bounding box centroid's pairwise distances of people are calculated using the Euclidean distance. Also, we used jetson nano platform to implement a low-cost embedded system and IoT techniques to send the images and notifications to the nearest police station to apply forfeit when it detects people's congestion in a specific area. Lastly, the suggested system has the capability to assist decrease the intensity of the spread of COVID-19 and other diseases by identifying violated social distance measures and notifying the owner of the system. Using the transformation matrix and accurate pedestrian detection, the process of detecting social distances between individuals may be achieved great confidence. Experiments show that CNN-based object detectors with our suggested social distancing algorithm provide reasonable accuracy for monitoring social distancing in public places, as well. © 2022 Lavoisier. All rights reserved.

A dynamic design strategy of epidemic spreading parameters based on data driven

When modeling and fitting various kinds of epidemic outbreaks, the value of parameters has always been an important practical problem for many scholars. In the existing studies, most of the authors select a fixed parameter by referring to the relevant literature or combined with medical experiments. With the help of Euler difference transformation and the characteristics of the solution of linear equations, we innovatively propose a dynamic update strategy of epidemic diffusion parameters based on data-driven in this study in order to overcome the above limitation. The method can help decision-makers to calculate the optimal parameters of epidemic spread by combining the real-time update data. A case study is conducted with the COVID-19 data of Wuhan. The results show that the dynamic parameter update strategy designed in this paper can effectively improve the accuracy of the evolution prediction of epidemic outbreaks, which provides an important decision support for the accurate allocation of government emergency resources. © 2023 Northeast University. All rights reserved.

Mapping of Digital Transformation Readiness, Benefits, and Barriers in Indonesian Steel Manufacturing

Indonesian steel manufacturing sector has proven able to survive and even thrive during the COVID-19 pandemic. Its post-pandemic potential is also very large, considering that Indonesia's steel consumption per capita is the lowest in ASEAN. However, the highly volatile business situation and competition mean that it needs to reduce process costs, reduce processing time, and increase productivity;all of which could potentially be achieved by digital transformation. However, there is very limited research on digital transformation in Indonesian steel manufacturing. This research aims to map the readiness, potential benefit, and barriers to digital transformation in Indonesian steel manufacturing using MICMAC matrix method, with validation and verification carried out directly by experts involved in decision-making process on several steel manufacturing entities in Indonesia. Result of this study shown that to prepare digital transformation readiness, Indonesian steel manufacturing should emphasize its effort on preparing strategy and leadership, investment for industry 4.0, innovation policy, competency development, connectivity, and cyber security. The main benefits that should be pursued first are the capability of monitoring in real time and assisting in communication and data flow;whereas the main barrier that should be taken care of first is preparing its organizational change. © 2022 ACM.

Attribute Analysis and Diagnosis of LUNG CT Images of COVID-19

Computed Tomography (CT) is an authoritative verification standard for patients with Corona Virus Disease 2019 (COVID-19). Automatic detection of lung infection through CT is of great significance for epidemic prevention and control and prevention of cross-infection. The accuracy of existing lung CT image segmentation methods is not high, and due to the privacy protection measures of hospitals, the number of COVID-19 lung CT data sets is too small, which is prone to over-fitting during training. In this paper, we propose a qualitative mapping model for the diagnosis and localization of COVID-19 lesions. The binary image processed by U-net network is used as input, and lung CT is segmented as four attributes, and attribute diagnosis is carried out with the help of correlation matrix and transformation degree function. Experiments show that this method not only avoids the over-fitting risk of data sets, but also increases the robustness of data. Experiments also prove that this design has higher accuracy than the simple neural network learning. © 2022 IEEE.

Mechatronics Design and Kinematic Simulation of UTP-ISR01 Robot with 6-DOF Anthropomorphic Configuration for Flexible Wall Painting

In the year 2020, countries were in a race against the spread of Covid-19, leading to major deficiencies in the areas of health, economy, and construction. For this reason, the robotics industry emerged as a viable and safe option to perform important and critical tasks in different sectors, one of them is the real estate. For this reason, a robotic arm was designed to wall painting, this study is supported by the mechatronics engineering department of the Universidad Tecnológica del Perú. The designed robot called: 'UTP-ISR01' has 6 axes and a linear displacement of 2.8 m with turns of 0.24 sec/60°. For the calculation of the forward kinematics the Denavit Hartenberg method was used, then the homogeneous transformation matrices were used to calculate the rotation and translation movements of the robotic manipulator. With the equations identified in the inverse kinematics, the positions and orientations of the robot were plotted, as well as the dimensions of the working area. The CAD design was carried out with engineering software, such as Autodesk Inventor for the mechanical design and assembly of the parts. In addition, with RoboDK software, kinematic simulations and analysis were performed. In conclusion, the robotic arm will reduce the delivery times of the apartments built by the real estate companies. © 2022 IEEE.

A Nonlinear Transformation Methods Using Covid-19 Data in the Kurdistan Region

Ordinary Least squares (OLS) are the most widely used due to tradition and their optimal properties to estimate the parameters of linear and nonlinear regression models. Nevertheless, in the presence of outliers in the data, estimates of OLS become inefficient, and even a single unusual point can have a significant impact on the estimation of parameters. In the presence of outliers is the use of robust estimators rather than the method of OLS. They are finding a suitable nonlinear transformation to reduce anomalies, including non-Additivity, heteroscedasticity, and non-normality in multiple nonlinear regression. It might be beneficial to transform the response variable or predictor variable, or both together to present the equation in a simple, functional form that is linear in the transformed variables. To illustrate the superior transformation function, we compare the squared correlation coefficient (coefficient of determination), Breusch-Pagan test, and Shapiro-Wilk test between the transformation functions. © 2022 IEEE.

Texture Analysis of Ultrasound Images to Differentiate Pneumonia and Covid-19

Lung ultrasound can potentially diagnose lung abnormalities such as pneumonia and covid-19, but it requires high experience. Covid-19, as a global pandemic, has similar common symptoms as pneumonia. The proper diagnosis of covid-19 and pneumonia necessitates clinicians' high expertise and skill to classify Covid-19 disease. This paper presents an approach to differentiate pneumonia and covid-19 based on texture analysis of ultrasound images. The proposed scheme is based on the Gray Level Co-occurrence Matrix (GLCM) features computing with Contrast Limited Adaptive Histogram Equalization (CLAHE) and gamma transformation for image enhancement. The results of the feature extraction analysis for lung ultrasound images suggest that differentiating pneumonia and Covid-19 is possible based on image texture features. © 2021 IEEE.

Hybrid Classifiers Based on CNN, LSOF, GMDH in COVID-19 Pneumonic Lesions Types Classification Task

The paper proposes new network tools for solving classification problems based on the hybridization of convolutional networks, self-organized forests, and Group Method of Data Handling classifiers. To determine the types of lung lesions in COVID-19 on computed tomography slices the new solutions have been applied. The features of the texture obtained from the matrices of GLCM, GLRLM, GLSZM, GLDM, NGTDM statistics are used here as classification features. The mechanism of transformation of texture matrices into class-oriented features based on hybrid architectures of neural networks is developed for this. The concept development of self-organized forest according to the principles of GMDH with the use of logistical mechanisms and optimization of voting functions (LSOF) is proposed. Classifiers based on CNN, LSOF, and GMDH are used to determine the type of lung lesions 'ground-glass', 'crazy-paving', 'consolidation'on CT images of patients. The classification results obtained by the proposed algorithms have been compared with other modern methods. Data for the research were provided by the State Institution 'F.G. Y anovsky National Institute of Phthisiology and Pulmonology of the NAMS of Ukraine'. © 2021 IEEE.

Express Diagnosis of COVID-19 on Cough Audiograms with Machine Learning Algorithms from Scikit-learn library and GMDH Shell tool

The ongoing COVID-19 pandemic and necessity of mass control of population makes to create inexpensive rapid diagnostic methods that could replace or complement existing methods based on clinical studies. In response to this challenge, at the end of 2020 MIT scientists proposed a way to detect COVID-19 sick patients using audio recordings of their cough. They build a binary classifier based on a trained deep neural network that provides 99% precision in detecting sick patients on a dataset of 5000 people (the precision of detecting the healthy ones is not reported). In our study, we propose another technology, which uses: (a) a simple transformation of digital audiograms being matrices 'fequency-time'and (b) typical machine learning algorithms from the popular scikit-learn Python library and the platform GMDH Shell. Objects of consideration are: a large unbalanced dataset (282 sick and 1595 healthy) and a small balanced dataset (174 sick and 193 healthy). In total GMDH-based algorithms demonstrated some better results with both datasets. The winners provides the following precisions of detecting sick/healthy patients [%]: (a) 92/95 on the small dataset and 78/95 on the large data set for the algorithm SVM with a Gaussian kernel;(b) 95/97 on the small dataset and 82/96 on the large data set for the algorithm Random Forest based on GMDH. We suppose these results are promising. © 2021 IEEE.

On Determining the Position of Expert Confidence Concentration Points When Modifying Membership Functions

The apparatus of fuzzy sets membership functions is a powerful tool widely used to express expert preferences when solving problems in various subject areas. Algorithmic ways to modify membership functions without additional involvement of experts are common modern means of obtaining new functions on the basis of the existing ones. This paper investigates methods of possible resolution of ambiguity in determining the position of the middle point of expert confidence concentration of triangular membership functions in cases of their modification with the application of algorithms that use the reference functions. The obtained membership functions of fuzzy sets can be used in modeling the behavior of agents under conditions of substantial uncertainty caused by COVID–19. © 2021, The Author(s), under exclusive license to Springer Nature Switzerland AG.