Multifactor data analysis to forecast an individual's severity over novel COVID-19 pandemic using extreme gradient boosting and random forest classifier algorithms

AI and machine learning are increasingly often applied in the medical industry. The COVID-19 epidemic will start to spread quickly over the planet around the start of 2020. At hospitals, there were more patients than there were beds. It was challenging for medical personnel to identify the patient who needed treatment right away. A machine learning approach is used to predict COVID-19 pandemic patients at high risk. To provide input data and output results that execute the machine learning model on the backend, a straightforward Python Flask web application is employed. Here, the XGBoost algorithm, a supervised machine learning method, is applied. In order to predict high-risk patients based on their current underlying health issues, the model uses patient characteristics as well as criteria like age, sex, health issues including diabetes, asthma, hypertension, and smoking, among others. The XGBoost model predicts the patient's severity with an accuracy of about 98% after data pre-processing and training. The most important factors to the models are chosen to be age, diabetes, sex, and obesity. Patients and hospital personnel will benefit from this project's assistance in making timely choices and taking appropriate action. This will let medical personnel decide how much time and space to devote to the COVID-19 high-risk patients. providing a treatment that is both efficient and ideal. With this programme and the necessary patient data, hospitals may decide whether a patient need immediate care or not.

Readwell: A Book Recommender Android Application with a Point-of-Sales System

The COVID-19 pandemic has given people much free time. With this, the researchers want to encourage these people to read instead of scrolling through social media. A barrier to reading for many people is not knowing what to read and disinterest in popular books that they would find when they search online. The existing websites that encourage book reading rely on social networking for their recommendations, while the collaborative filtering algorithms applied to books do not exist in the mobile application form. Readwell is a book recommender Android app with a Point-of-Sales System created using Java, Python, and SQLite databases. The information regarding the books was web scraped from the Goodreads website. It aims to apply the more efficient collaborative filtering algorithm to an accessible mobile application that allows users to directly buy the books they are interested in, thus encouraging the reading and buying of books. The researchers created unit test cases to validate the different functionalities of the application. © 2022 IEEE.

Visual analysis of mask sales data based on crawler

The outbreak of COVID-19 makes people feel distant from each other, and masks have become one of the indispensable articles in People's Daily life. At present, there are many brands of masks with various types and uneven quality. In order to understand the current market of masks and the sales of different brands, users can choose masks with perfect quality. This paper uses Python web crawler technology, based on the input of the word "mask", crawl JD website sales data, through data visualization technology drawing histogram, pie chart, the word cloud, etc., for goods compared with the relationship between price, average price of all brands, brands, average distribution of analysis and evaluation of user information, In this way, the sales situation, price distribution and quality evaluation of each store of the product can be visually displayed. At the same time, it also provides some reference for other users who need to buy the product. © The Authors. Published under a Creative Commons Attribution CC-BY 3.0 License.

A Long Short Term Memory Machine Learning Technique for Covid-19 Patient Count Prediction

The Corona-virus H19 pandemic is quickly spreading throughout the globe. Every three to four times, waves occur and have a major effect on people's lives. Other illnesses including covid disorders are misdiagnosed in this setting. There is no reliable statistics on the total number of covid patients in the nation, and no system exists to track them. This prevents the patients from receiving the necessary care and treatment. The number of patients in a given dataset may be determined with more precision using AI methods. In this article, we show how to forecast how many patients will be included in the Covid-19 database by using an adaptive method. Python spyder is used to run the simulation. . © 2023 IEEE.

An approach for Face Detection and Face Recognition using OpenCV and Face Recognition Libraries in Python

With the extraordinary growth in images and video data sets, there is a mind-boggling want for programmed understanding and evaluation of data with the assistance of smart frameworks, since physically it is a long way off. Individuals, unlike robots, have a limited capacity to distinguish unexpected expressions. As a result, the programmed face proximity frame- work is important in face identification, appearance recognition, head-present evaluation, human-PC cooperation, and other applications. Software that uses facial recognition for face detection and identification is regarded as biometric. This study converts the mathematical aspects of a person's face into a face print, which is then stored in a database to verify an individual's identification. A deep learning system compares a digital image or an image taken quickly to a previously stored image(which is saved in the database). The face has a significant function in interpersonal communication for identifying oneself. Face recognition technology determines the size and placement of a human face in a digital picture. Facial recognition software has a wide range of uses in the consumer market and in the security and surveillance sectors. The COVID pandemic has brought facial recognition into greater focus lately than ever before. Face detection and recognition play a vital part in security systems that people need to interact with without making physical contact. The pattern of online exam proctoring is employing face detection and recognition. Facial recognition is used in the airline sector to enable rapid, accurate identification and verification at every stage of the passenger trip. In this research, we focused on image quality because it is the major drawback in existing algorithms and used OPEN CV, Face Recognition, and designed algorithms using libraries in python. This study discusses a method for facial recognition along with its implementation and applications. © 2023 IEEE.

Development of Data Logger for COVID Vaccine Temperature Monitoring System

The Data Logger (DL) is a unique tool created to carry out the typical duty of gathering data in a specific area. This common task can include measuring humidity, temperature, pressure or any other physical quantities. Due to the current pandemic situation, its use in temperature monitoring of Covid vaccine will be crucial. According to World Health Organization (WHO) guidelines, COVID vaccine can be stored and transported at -80 °C, -20°C and +2-8°C and shelf life is reduced as vaccine is transferred from one storage temperature to another. So cost effective, efficient and standalone Data Logger (DL) is the need of the hour. The Data logger is proposed to be developed with the use of ESP8266 Node MCU microcontroller. It takes power from a 5V Battery. DS18B20 sensor will be used for temperature sensing. Here we will use Wi-Fi module of ESP8266 Node MCU to send the temperature data from sensor to the Google Sheet over the internet. This real time data will be stored in the format of time and month/date/year. Data logged in Google Sheet will be displayed to the user with the help of graphical user interface (GUI) which is developed using PYTHON scripting language. GUI will allow user to interact with Data Logger through visual graphs. The Data Logger components are mounted on a double layered PCB. © 2022 IEEE.

Gesture Based Computer Control System

The whole world has been witnessing the gigantic enemy in the form of COVID-19 since March 2020. With its super-fast spread, it has devastated a major part of the world and found to be the most dangerous virus of the 21st Century. All countries went into a lockdown to control the spread of the virus, and the economy dropped down to an all- time low index. The major guideline to avoid the spread of diseases like COVID- 19 at work is avoiding contact with people and their belongings. It is not safe to use computing devices because it may result in the spread of the virus by touching them. This paper presents an Artificial Intelligence- based virtual mouse that detects or recognizes hand gestures to control the various functions of a personal computer. The virtual mouse Algorithm uses a webcam or a built-in camera of the system to capture hand gestures, then uses an algorithm to detect the palm boundaries similar to that of the face detection model of the media pipe face mesh algorithm. After tracing the palm boundaries, it uses a regression model and locates the 21 3D hand-knuckle coordinate points inside the recognized hand/palm boundaries. Once the Hand Landmarks are detected, they are used to call windows Application Programming Interface (API) functions to control the functionalities of the system. The proposed algorithm is tested for volume control and cursor control in a laptop with the Windows operating system and a webcam. The proposedsystem took only 1ms to identify the gestures and control the volume and cursor in real-time. © 2023 IEEE.

Impact of Regional Mobility on Air Quality during COVID-19 Lockdown in Mississippi, USA Using Machine Learning.

Social distancing measures and shelter-in-place orders to limit mobility and transportation were among the strategic measures taken to control the rapid spreading of COVID-19. In major metropolitan areas, there was an estimated decrease of 50 to 90 percent in transit use. The secondary effect of the COVID-19 lockdown was expected to improve air quality, leading to a decrease in respiratory diseases. The present study examines the impact of mobility on air quality during the COVID-19 lockdown in the state of Mississippi (MS), USA. The study region is selected because of its non-metropolitan and non-industrial settings. Concentrations of air pollutants-particulate matter 2.5 (PM2.5), particulate matter 10 (PM10), ozone (O3), nitrogen oxide (NO2), sulfur dioxide (SO2), and carbon monoxide (CO)-were collected from the Environmental Protection Agency, USA from 2011 to 2020. Because of limitations in the data availability, the air quality data of Jackson, MS were assumed to be representative of the entire region of the state. Weather data (temperature, humidity, pressure, precipitation, wind speed, and wind direction) were collected from the National Oceanic and Atmospheric Administration, USA. Traffic-related data (transit) were taken from Google for the year 2020. The statistical and machine learning tools of R Studio were used on the data to study the changes in air quality, if any, during the lockdown period. Weather-normalized machine learning modeling simulating business-as-scenario (BAU) predicted a significant difference in the means of the observed and predicted values for NO2, O3, and CO (p < 0.05). Due to the lockdown, the mean concentrations decreased for NO2 and CO by -4.1 ppb and -0.088 ppm, respectively, while it increased for O3 by 0.002 ppm. The observed and predicted air quality results agree with the observed decrease in transit by -50.5% as a percentage change of the baseline, and the observed decrease in the prevalence rate of asthma in MS during the lockdown. This study demonstrates the validity and use of simple, easy, and versatile analytical tools to assist policymakers with estimating changes in air quality in situations of a pandemic or natural hazards, and to take measures for mitigating if the deterioration of air quality is detected.

Doc-Handler: Document Scanner, Manipulator, and Translator based on Image and Natural language processing

Physical documents may easily be converted into digital versions in the modern digital era by employing scanning software and the internet. The day when this activity needed printers and scanners is long gone. Nowadays, even our smartphones and cameras may be used to quickly convert paper documents into digital ones. This is especially useful in the wake of the COVID-19 pandemic, where the ability to share and access documents online is more important than ever. This study proposes an application for illiterate people to quickly translate scanned papers or photos into their native language and save them in a digital format. The Application makes use of image processing methods and has capabilities including PDF conversion, image colour adjustment, cropping, and Optical Character Recognition (OCR). A user-friendly application, developed using the Flutter Framework and programmed in Python and Dart, serves as the interface for the system. The proposed application is cross-platform and works with a variety of gadgets. This method intends to increase accessibility and productivity for illiterate people in the digital age by integrating image processing with language translation. © 2023 IEEE.

Teaching Python programming for bioinformatics with Jupyter notebook in the Post-COVID-19 era.

The COVID-19 pandemic has forced the Bioinformatics course to switch from on-site teaching to remote learning. This shift has prompted a change in teaching methods and laboratory activities. Students need to have a basic understanding of DNA sequences and how to analyze them using custom scripts. To facilitate learning, we have modified the course to use Jupyter Notebook, which offers an alternative approach to writing custom scripts for basic DNA sequence analysis. This approach allows students to acquire the necessary skills while working remotely. It is a versatile and user-friendly platform that can be used to combine explanations, code, and results in a single document. This feature enables students to interact with the code and results, making the learning process more engaging and effective. Jupyter Notebook provides a hybrid approach to learning basic Python scripting and genomics that is effective for remote teaching and learning during the COVID-19 pandemic.

COVID-19: Boredom in the family

This research is focused on the subject of boredom in the families during the stay-at-home process forced by coronavirus disease 2019 pandemic. The literature on boredom was reviewed, and then the qualitative research was designed with the open-ended questions appropriate for the subject and purpose. The research was conducted between April 20 and 29, 2020, in Istanbul, Turkey, with the participation of 264 families. The most significant findings of the research showed that family members accustomed to active life experienced boredom more during the stay-at-home process, they utilized information technologies very often to overcome boredom, the importance of time spent at home increased, involuntary behaviors such as overeating and snacking became common, the livelihood difficulties and fear of unemployment increased boredom, nevertheless, no conflict occurred between the family members, and the process taught to be patient and strong. (PsycInfo Database Record (c) 2023 APA, all rights reserved)

Bibliometric overview of reactivity in research on environmental response to COVID-19

COVID-19 has been a worldwide emergency and continues to spread in the environment. It is crucial to keep following up on current solutions to this pandemic and think about future epidemic prevention. Herein, a comprehensive bibliometric analysis was performed to examine different facets of research output on the environmental response against COVID-19. The relevant bibliographic dataset was queried in PubMed for literature published since the COVID-19 outbreak. Python program was used to extract the metadata information from the dataset toward the research production in environmental response to the pandemic. Key points covered in the analysis included contribution of authorship and country to the scientific output, strength of collaborative network, and main topics of research themes. Regarding contributions, the USA was the most productive country in terms of publications and authorships, followed by China, the UK, Italy, and India. Using activity index as a relative indicator for research reactivity, Pakistan, Saudi Arabia, and India, followed by the USA and the UK, were highly reactive to the environmental and COVID-19 studies. For research collaboration, the USA demonstrated the highest level of domestic independence and Saudi Arabia had an extremely high level of international collaborations. The global research production could be covered in 20 major topics and grouped into four themes as control and prevention, public healthcare, disease research, and COVID-19 impacts. Overall, this study visualized global research reactivity and interactive networks in environmental response to COVID-19 and provided a basis of utilizing Python program in rapid literature review for strategizing scientific solutions to future epidemic prevention.Copyright © 2023 John Libbey Eurotext. All rights reserved.

Forecasting Commercial Vehicle Production Using Quantitative Techniques

Firms selling commercial vehicles often face difficulties due to recessions in the globalized economy. Manufacturers are keen to anticipate demand in future quarters to optimize their production schedules. In this study, commercial vehicle production data from a leading Indian automotive manufacturer were analyzed using moving averages, exponential smoothing, seasonal decomposition and autoregressive integrated moving average (ARIMA) models with the goal of forecasting. The results reveal that the ARIMA (0,1,1) model effectively predicts the sectoral downturn coinciding with the global financial crisis of 2008. As life returns to normal after the financial crisis caused by COVID-19, such models may be used to strategically move past the disruption.

Python optimization code for solving a mathematical modeling of COVID_19

In recent years, mathematical modeling has played a key role in many life applications such as computer science, physics, chemistry, and genetics. Actually, in this paper, our focus is on the classifications and the importance of mathematical programming and its applications in health problems especially the Mathematical Modeling of COVID_19. According to the era of the Corona pandemic, it has been using mathematical equations to employ mathematical programming in epidemics and the mechanism of spreading in urban areas. The solution of the problem is presented in two directions;the first was by graphic representation and the other by using computational software via the Python language. © 2023 Author(s).

Mathematical optimization software for solving differential equation model of COVID-19 numerically

Mathematical modeling is critical and crucial in a variety of fields, including medicine, physics and economics. This paper uses mathematical modeling to analyze coronavirus data in Iraq, specifically in Karbala province. Our basic idea involves applying two approaches using software. The first is to apply the SIR model to obtain forecasts for the spread of the corona epidemic over the entire year, using November 2021 data from Karbala province as a starting point. The second direction is to use the Euler approach to predict the development of the pandemic and finally, optimization techniques have been introduced for the spread of the coronavirus and methods to prevent its spread. © 2023 Author(s).

Model of Visualization and Analytics for Open Data (Case: Election Voters & Kids Disability Category)

Several regional head elections had to be postponed due to the pandemic, including in Indonesia because of the COVID-19 pandemic. Several big cities in Indonesia are of concern because of their large population and GDP. This study conducts analysis and testing of datasets taken from Open Data in a city in Indonesia. In addition to conducting research on regional head elections, we also present information on voters from the category of kids with disabilities. The steps used in this research are using regional mapping data of the city of Surabaya in the Election of the Regional Head. Download the data or dataset for the Regional Head Election ampersand Categories of kids with disabilities. Based on the dataset voters from the category of children with disabilities are more than 5 percent.In this research, we use Python to process our datasets & Big Data technology. Data cleaning or cleansing, Exploratory Data Analysis, and Empirical Cumulative Distribution Functions (ECDF) in python are also needed. Result from ECDF chart with steady increase (increment of 0.1). The highest variance value is in Electoral District 5 = 6.090 and the lowest value is in Electoral District 4 = 0.90. The result of Open Data is graphical data visualization and candidate scores to help as an alternative for the 2024 Regional Head Election and the Category of kids with disabilities. © 2023 IEEE.

Construction of Tibetan Medicine Prescription Database for Treating "Gnyan-rims" and Analysis of Their Medication Law Based on Python Language.

Objective: To construct the database of Tibetan medicine prescriptions for "Gnyan-rims" disease,and to explore the invisible medication law of Tibetan medicine in the treatment of "Gnyan-rims" disease,such as prescription compatibility and combination of drug properties. Method: The prescriptions for treating "Gnyan-rims" were retrieved from four Tibetan medical literatures such as The Four Medical Tantras,Kong-sprul-zin-tig, Phyag-rdor-gso-rig-phyogs-bsgrigs and Sman-sbyor-lag-len-phyogs-bsgrigs, and the database was constructed under Python code,and the Apriori algorithm and the vector structure model of taste property flavor transformation were used for analysis. Result:According to the characteristics of Tibetan medicine prescription data,with six fields of prescription name,formula,dosage,efficacy,source and original text as the core,a Tibetan medicine treatment "Gnyan-rims" prescription database with functions of cleaning, searching and exporting was established. A total of 7 602 prescriptions were included in the database,among which 598 prescriptions had therapeutic effects of "Gnyan" and "Rims". The results of compatibility analysis showed that Shexiang,Hezi,Honghua,Mukuer Moyao,Tiebangchui,Tianzhuhuang and Bangga were the most frequently used drugs,while the correlation degrees of Shexiang-Mukuer Moyao,Honghua-Tianzhuhuang,Shexiang-Hezi and Shexiang-Tiebangchui were the strongest,and all the drug composition of Wuwei Shexiang pills appeared in the top ten correlations. According to the property analysis of 40 prescriptions containing high-frequency drugs,19 prescriptions were found to have excessive bitter taste,followed by 9 prescriptions such as Sanchen powders with excessive sweetish taste,and the ratios of sweetish and bitter tastes in six tastes were >35%. The total of sweetish and bitter prescriptions accounted for 70% of the total prescriptions. Among the three flavors,the bitter flavor was the most abundant. The cool effect,dull effect and heavy effect were prominent among the seventeen effects. Conclusion: The prescription database of Tibetan medicine for "Gnyan-rims" can promote the high-quality development of research on prevention and treatment of plague with ethnic medicine. Tibetan medicine treatment of "Gnyan-rims" focuses on the composition of Wuwei Shexiang pills,with the property combination of "cool-bitter and sweet-bitter flavor-cool,dull and heavy", which mainly treats diseases such as "heat sharp light-mkhris pa-heat". These studies can provide data basis and theoretical reference for the selection of Tibetan medicine prescription and its composition for treating plague.Copyright © 2021, China Academy of Chinese Medical Sciences Institute of Chinese Materia Medica. All rights reserved.

Energy Prediction under Changed Demand Conditions: Robust Machine Learning Models and Input Feature Combinations

Deciding on a suitable algorithm for energy demand prediction in a building is non-trivial and depends on the availability of data. In this paper we compare four machine learning models, commonly found in the literature, in terms of their generalization performance and in terms of how using different sets of input features affects accuracy. This is tested on a data set where consumption patterns differ significantly between training and evaluation because of the Covid-19 pandemic. We provide a hands-on guide and supply a Python framework for building operators to adapt and use in their applications. © International Building Performance Simulation Association, 2022

Management of an Automatic System to Generate Reports on the Attendance Control of Teachers in a Educational Center

Many countries were affected by the appearance of SARS-COV-2 that was spreading rapidly, causing damage to humanity and causing a global crisis, this generated a generalized quarantine to avoid the physical approaches recommended by the health system, affecting all students in the world, since it was wanted to avoid forming foci of contagion in educational centers, for this reason, some automated systems that are marketed in the markets were applied to combat the pandemic in educational centers, but they are inefficient when registering the work attendance of teachers, causing loss of time in the registration process and causing an agglomeration of people due to the failure in the registration process, in addition to not allowing to manage the reports of the teacher's work attendance. In view of this problem, in this article the management of an automatic system was carried out to generate reports on the attendance control of the teaching staff in the educational center and control the working hours of each teacher to be visualized through a user interface, being able to control the labor discipline of each teacher since all the records will be stored in a database. Through the development of the system, it was observed that the system works effectively since an efficiency of 98.87% was obtained in its operation to control the time of entry and exit of each teacher, being an accepted value since the process is conducted safely. © 2023 International Journal on Recent and Innovation Trends in Computing and Communication. All rights reserved.

Artificial Intelligence in Medical Sciences and Psychology: With Application of Machine Language, Computer Vision, and NLP Techniques

Get started with artificial intelligence for medical sciences and psychology. This book will help healthcare professionals and technologists solve problems using machine learning methods, computer vision, and natural language processing (NLP) techniques. The book covers ways to use neural networks to classify patients with diseases. You will know how to apply computer vision techniques and convolutional neural networks (CNNs) to segment diseases such as cancer (e.g., skin, breast, and brain cancer) and pneumonia. The hidden Markov decision making process is presented to help you identify hidden states of time-dependent data. In addition, it shows how NLP techniques are used in medical records classification. This book is suitable for experienced practitioners in varying medical specialties (neurology, virology, radiology, oncology, and more) who want to learn Python programming to help them work efficiently. It is also intended for data scientists, machine learning engineers, medical students, and researchers. What You Will Learn • Apply artificial neural networks when modelling medical data • Know the standard method for Markov decision making and medical data simulation • Understand survival analysis methods for investigating data from a clinical trial • Understand medical record categorization • Measure personality differences using psychological models • Who This Book Is For Machine learning engineers and software engineers working on healthcare-related projects involving AI, including healthcare professionals interested in knowing how AI can improve their work setting. © 2022 by Tshepo Chris Nokeri.