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.

Spatiotemporal Modeling of the Association between Neighborhood Factors and COVID-19 Incidence Rates in Scotland

This study aims to investigate the association between neighborhood-level factors and COVID-19 incidence in Scotland from a spatiotemporal perspective. The outcome variable is the COVID-19 incidence in Scotland. Based on the identification of the wave peaks for COVID-19 cases between 2020 and 2021, confirmed COVID-19 cases in Scotland can be divided into four phases. To model the COVID-19 incidence, sixteen neighborhood factors are chosen as the predictors. Geographical random forest models are used to examine spatiotemporal variation in major determinants of COVID-19 incidence. The spatial analysis indicates that proportion of religious people is the most strongly associated with COVID-19 incidence in southern Scotland, whereas particulate matter is the most strongly associated with COVID-19 incidence in northern Scotland. Also, crowded households, prepandemic emergency admission rates, and health and social workers are the most strongly associated with COVID-19 incidence in eastern and central Scotland, respectively. A possible explanation is that the association between predictors and COVID-19 incidence might be influenced by local context (e.g., people's lifestyles), which is spatially variant across Scotland. The temporal analysis indicates that dominant factors associated with COVID-19 incidence also vary across different phases, suggesting that pandemic-related policy should take spatiotemporal variations into account. © 2023 by American Association of Geographers.

Intelligent Methods and Models for Assessing Level of Student Adaptation to Online Learning

The problem of introducing online learning is becoming more and more popular in our society. Due to COVID-19 and the war in Ukraine, there is an urgent need for the transition of educational institutions to online learning, so this paper will help people not make mistakes in the process and afterward. The paper's primary purpose is to investigate the effectiveness of machine learning tools that can solve the problem of assessing student adaptation to online learning. These tools include intelligent methods and models, such as classification techniques and neural networks. This work uses data from an online survey of students at different levels: school, college, and university. The survey consists of questions such as gender, age, level of education, whether the student is in the city, class duration, quality of Internet connection, government/non-government educational institution, availability of virtual learning environment, whether the student is familiar with IT, financial conditions, type of Internet connection, a device used for studying, etc. To obtain the results on the effectiveness of online education were used the following machine learning algorithms and models: Random Forest (RF), Extra Trees (ET), Extreme, Light, and Simple Gradient Boosting (GB), Decision Trees (DT), K-neighbors (K-mean), Logistic Regression (LR), Support Vector Machine (SVM), Naїve Bayes (NB) classifier and others. An intelligent neural network model (NNM) was built to address the main issue. © 2023 Copyright for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0). CEUR Workshop Proceedings (CEUR-WS.org)

Comparison of machine learning algorithms with regression analysis to predict the COVID-19 outbreak in Thailand

Coronavirus disease (COVID-19) is a public health problem in Thailand. Currently, there are more than 5 million infected people and the rate has been increasing at some point. It is therefore important to forecast the number of new cases over a short period of time to assist in strategic planning for the response to COVID-19. The purpose of this research paper was to compare the efficiency and prediction of the number of COVID-19 cases in Thailand using machine learning of 8 models using a regression analysis method. Using the 475-day dataset of COVID-19 cases in Thailand, the results showed that the predictive accuracy model (R2 score) from the testing dataset was the random forest (RF) model, which was 99.06%, followed by K-nearest neighbor (KNN), XGBoost. And the decision tree (DT) had the precision of 98.97, 98.67, and 98.64, respectively. And the results of the comparison of the number of infected people obtained from the prediction The models that predicted the number of real infections were the decision tree, random forest, and XGBoost, which were effective at predicting the number of infections correctly in the 2-4 day period. © 2023 Institute of Advanced Engineering and Science. All rights reserved.

An Assessment of Forest Diversity: Challenges and Management

Forests have played a critical role in enriching human life's social, economic, and religious facets in several ways, both materialistic and psychological. India is one of the world's most diverse woodland habitats. Forests are valued at 20% (i.e., 3.28 million sq. km of the total land in India). Forests should be handled for the advantage of the highest number in the long term. The existence of canopies explains how forests accumulate nitrogen from the atmosphere and survive without much fertilization, unlike the agricultural fields. And organisms like insects, birds, mammals, etc. add to the biodiversity of trees and forests. India's woodland cover rose from 640, 319 sq. km (i.e., 11.2%) in 1987 to 712, 249 sq. km in 2019. In this paper, a detailed assessment of forest diversity is provided by dividing it into three segments: mangroves' significant role in affecting the woodland diversity considering it as one of the most important sources of biodiversity on the planet;wildfire is the oldest and most widespread threat in forests since it claims to threaten not just the forest resources but also the fauna and flora of the whole regime, severely disrupting biodiversity;and finally, how trees boost the environment and hence the effect of climate change on the overall destruction of forests. Due to this around 90% of the large stocks of predatory fish are gone. India is home to approximately 300 amphibian species, and about 60% of these species in India is endemic. Around 20-25% of global greenhouse gas emissions are liable for deforestation and 30% of topographical zone is influenced through land debasement. The Intergovernmental Science-Policy Forum on Biodiversity and Ecosystem Services has (IPBES) reported that 1, 000, 000 species are at present risk of elimination. Biodiversity's misfortune is attributable to a few reasons, but the methods by far the most guilty parties are natural decimation as well as over-exploitation of biodiversity, powered by our detonating numbers and undefended usage. Plastic has gotten irreplaceable during the COVID-19 pandemic, driving atmosphere champions. India creates 9.4 million tons of plastic waste each year (approx 26, 000 tons for every day). Only 5.6 million is reused, even as about 3.8 million tons are gone uncollected. Sea life biologists have cautioned that by 2050 there will be extra plastic in the Earth's oceans than fish. The four types of marine turtles that happen in India's beachfront alongside marine climate are completely jeopardized. The World Conservation Union (IUCN) states that the effects of outsider intrusive species are tremendous, unpretentious, and typically irreversible. They might be as harmful toward local species alongside biological systems on a worldwide scale of the misfortune and corruption of environments. The world is currently losing a concerning rate of ten billion trees each year. Because of these consolidated impacts of environmental change and anthropogenic activities, about 42% of the 260, 553 km2 of elephant natural surroundings is eradicated. Lately, rhino numbers have dropped drastically because of poaching for their horn which is valued inside Asian nations. And about 50% of all mammalian and bird species could go extinct in the next 200-300 years. Air defilement is one of the most distinguishably loathsome scourges to have biased India. Hence, this chapter primarily focuses on most ecosystems are under threat from several factors, and each new consequence adds to the stress already felt by ecosystems and their wildlife. © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022.

Challenging the dominant path of forest policy? Bottom-up, citizen forest management initiatives in a top-down governance context in Poland

Forest governance in Poland is characterised by the dominance of public forest ownership and hierarchical, top-down policy-making. These governance arrangements, characteristic of post-socialist countries, have traditionally been challenged by environmental NGOs, advocating stronger protection of old-growths. Recently, institutional stability of the forest policy field has been increasingly influenced by numerous citizen initiatives responding to technocratic local forest management decisions. These initiatives, so far not analysed scientifically, vary in terms of the issues addressed, actions employed, and the local actors involved. In the paper we use a data base of 274 such initiatives to explore their manifestation, actors involved, main postulates, and the responses of forest managers. Based on this, we explored whether these initiatives pose challenges to the traditional forest management and, if so, what kind. We imply that the growth of bottom-up initiatives indicates a growing diversity of beliefs and values regarding forests and the increasing determination of local people to impact local environmental decisions. Furthermore, informed by the institutional theory, we argue that the growth of local initiatives, particularly during and after Covid-19 pandemics, suggests the eroding legitimacy of dominant rules and discourses. This process is particularly visible in sub-urban forests, which are increasingly seen through a ‘well-being discourse' that highlights cultural, regulative and supportive functions of forests, while putting less emphasis on provisioning functions. We also identify a networking trend among the initiatives that unifies their discursive background and enhances their influence at the national level. Therefore, local activists can be seen as a new advocacy group in the Polish forest policy subsystem. In response to local demands public forest administration has introduced institutional changes enhancing participation but their impact is still to be assessed. We recommend establishing a monitoring programme to track new participatory practices and to identify and promote best practices.

Prediction of Corona Virus Disease Outcome and Future Forecast the Trend of COVID-19 in India: Observational Study

This work is motivated by the disease caused by the novel corona virus Covid-19, rapid spread in India. An encyclopaedic search from India and worldwide social networking sites was performed between 1 March 2020 and 20 Jun 2020. Nowadays social network platform plays a vital role to track spreading behaviour of many diseases earlier then government agencies. Here we introduced the approach to predict and future forecast the disease outcome spread through corona virus in society to give earlier warning to save from life threats. We compiled daily data of Covid-19 incidence from all state regions in India. Five states (Maharashtra, Delhi, Gujarat, Rajasthan and Madhya-Pradesh) with higher incidence and other states considered for time series analysis to construct a predictive model based on daily incidence training data. In this study we have applied the predictive model building approaches like k-nearest neighbour technique, Random-Forest technique and stochastic gradient boosting technique in COVID-19 dataset and the simulated outcome compared with the observed outcome to validate model and measure the performance of model by accuracy (ACC) and Kappa measures. Further forecast the future trends in number of cases of corona virus deceased patients using the Holt Winters Method. Time series analysis is effective tool for predict the outcome of corona virus disease. © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2022.

Potential inhibition of Melaleuca leucadendron L. compounds against the NSP5 SARS CoV-2 protein

COVID-19 is an infectious disease caused by Severe Acute Respiratory Syndrome (SARS-CoV-2), causing a global health emergency as a pandemic disease. The lack of certain drug molecules or treatment strategies to fight this disease makes it worse. Therefore, effective drug molecules are needed to fight COVID-19. Non Structural Protein (NSP5) or called Main Protease (Mpro) of SARS CoV 2, a key component of this viral replication, is considered a key target for anti-COVID-19 drug development. The purpose of this study is to determine whether the compounds in the Melaleuca leucadendron L. plant such as 1,8-cineole, terpene, guaiol, linalol, a-selinenol, beta-eudesmol and P-eudesmol are predicted to have antiviral activity for COVID-19. Interaction of compounds with NSP5 with PDB code 6WNP analyzed using molecular docking with Molegro Virtual Docker. Based on binding affinity, the highest potential as an anti-viral is Terpineol with binding energy (-119.743 kcal/mol). The results of the interaction showed that terpinol has similarities in all three amino acid residues namely Cys 145, Gly 143, and Glu 166 with remdesivir and native ligand. Melaleuca leucadendron L. may represent a potential herbal treatment to act as: COVID-19 NSP5, however these findings must be validated in vitro and in vivo.

Development and Current Situation of Forest Use by Camping Sites in Japan

After the Second World War, camping and camping sites in forests have developed and increased significantly from the 1980 s to 1990 s in Japan, relying on the laws and institutions established from the 1950 s to 1970 s across multiple administrative sectors, obtaining social approval as a legitimatized outdoor activity and forest use. Since the 2000s, the management of these camping sites has deteriorated mainly owing to economic recession, which caused the movement of camping site renewal by the private sector. This movement directed the diversification of forest use by camping sites in recent years. Camping facilities have been developed in many ways to meet the needs of campers, including organized group camps that promote education and experience in forests, solo camps, glamping, and workcations under the spread of the COVID-19 that demand relaxing or productive environment, and leisure camps that require enrichment of outdoor activities. As a result of this diversification, possibilities for effective utilization of forests and regional revitalization through the management of camping sites have been observed. Many camping sites have utilized forest lands, standing trees, and forest spaces to develop facilities and services, and there are cases where firewood production for campers has promoted the reorganization and development of local forestry and securing of personnel for forest management. In addition to securing local employment brought by reorganization, local revitalization in rural and mountainous areas has been promoted through the linkage of the needs of campers to positive economic effects, increase of the visitors who deeply connected to local people, and comprehensive and sustainable use of resources in local societies. © 2023 Nihon Ringakkai. All rights reserved.

Detecting Ransomware Attacks in Cloud Environment Using Machine Learning-Based Intelligence System in COVID-19 Chaos

2020-2022 provided nearly ideal circumstances for cybercriminals, with confusion and uncertainty dominating the planet due to COVID-19. Our way of life was altered by the COVID-19 pandemic, which also sparked a widespread shift to digital media. However, this change also increased people's susceptibility to cybercrime. As a result, taking advantage of the COVID-19 events' exceedingly unusual circumstances, cybercriminals launched widespread Phishing, Identity theft, Spyware, Trojan-horse, and Ransomware attacks. Attackers choose their victims with the intention of stealing their information, money, or both. Therefore, if we wish to safeguard people from these frauds at a time when millions have already fallen into poverty and the remaining are trying to survive, it is imperative that we put an end to these attacks and assailants. This manuscript proposes an intelligence system for identifying ransomware attacks using nature-inspired and machine-learning algorithms. To classify the network traffic in less time and with enhanced accuracy, Genetic Algorithm (GA) and Particle Swarm Optimization (PSO), two widely used algorithms are coupled in the proposed approach for Feature Selection (FS). Random Forest (RF) approach is used for classification. The system's effectiveness is assessed using the latest ransomware-oriented dataset of CIC-MalMem-2022. The performance is evaluated in terms of accuracy, model building, and testing time and it is found that the proposed method is a suitable solution to detect ransomware attacks. © 2022 IEEE.

Towards Detection of Cyberbullying in Hinglish Code Mixed Data

Covid-19 eruption and lockdown situation have increased the usages of online platforms which have impacted the users. Cyberbullying is one of the negative outcomes of using social media platforms which leads to mental and physical distress. This study proposes a machine learning-based approach for the detection of cyberbullying in Hinglish text. We use the Hinglish Code-Mixed Corpus, which consists of over 6,000 tweets, for our experiments. We use various machine learning algorithms, including Logistic regression (LR), Multinomial Naive Bayes (MNB), Support vector machine (SVM), Random Forest (RF), to train our models. We evaluate the performance of the models using standard evaluation metrics such as precision, recall, and F1-score. Our experiments show that the LR with Term Frequency-Inverse Document Frequency (TFIDF) outperforms the other models, achieving 92% accuracy. Our study demonstrates that machine learning models can be effective for cyberbullying detection in Hinglish text, and the proposed approach can help identify and prevent cyberbullying on social media platforms. © 2023 Bharati Vidyapeeth, New Delhi.

Game meat procurement market in Poland in 2009-2021

Procurement of game animals is a major source of revenue for hunting clubs in Poland. For several years, the game meat buying market has been showing an upward trend, but this situation is also influenced by random factors that negatively affect the value of the game meat buying market. For several years in our country we have been struggling with the ASF virus, and since 2020, negative effects in the economy related to the occurrence of the SARS-CoV virus have been observed, also affecting the hunting sector with its activities. The aim of the study was to analyze the dynamics of game meat procurement in Poland in the years 2009-2021. The data concerned the three most important species, namely deer, roe deer and wild boar. The analysis covered the quantity of game meat, procurement value and the average price of game meat depending on animal species. The conducted research confirmed an upward trend in the volume and value of game meat procurement for all the analysed game species. Similarly, the average procurement prices of roe dee and wild boar meat with the exception of red deer, showed an upward trend. The study confirmed the negative impact of the ASF virus and the SARS-CoV-2 virus on the game meat buying market in Poland.

4th International Conference on Natural Resources and Technology, Sumatera Utara, Indonesia, 29-30 August 2022

These proceedings, with a theme of Natural Resources and Technology for Achieving Sustainable Development Goal through Academic, Industry, and Community and a subtheme of Resilience and Innovation Research on Sustainable Natural Resources and Technology Post-Covid 19, contain 104 articles covering 6 major topics in the related fields such as (i) Natural science and natural product, (ii) Natural resource technology, (iii) Information systems of tropical resources, (iv) Tropical biodiversity, (v) Food science and food technology, and (vi) Ethnobotany and ethnozoology.

Macrolobium paulobocae (Leguminosae, Detarioideae), a new species from seasonally inundated black-water floodplain forests in the Brazilian Amazon.

Macrolobium paulobocae is presented as a new species of the legume subfamily Detarioideae. It is restricted to seasonally flooded igapó forests in the Central Amazon. We provide a description, illustration, photographs, and a distribution map of the new species, as well as a table of comparative morphology with similar, likely phylogenetically related species. The epithet is in honor of Paulo Apóstolo Costa Lima Assunção, or Paulo Boca, a great Amazonian botanist, victim of COVID-19 in January 2021.

Random forest regression on joint role of meteorological variables, demographic factors, and policy response measures in COVID-19 daily cases: global analysis in different climate zones.

Different sources of factors in environment can affect the spread of COVID-19 by influencing the diffusion of the virus transmission, but the collective influence of which has hardly been considered. This study aimed to utilize a machine learning algorithm to assess the joint effects of meteorological variables, demographic factors, and government response measures on COVID-19 daily cases globally at city level. Random forest regression models showed that population density was the most crucial determinant for COVID-19 transmission, followed by meteorological variables and response measures. Ultraviolet radiation and temperature dominated meteorological factors, but the associations with daily cases varied across different climate zones. Policy response measures have lag effect in containing the epidemic development, and the pandemic was more effectively contained with stricter response measures implemented, but the generalized measures might not be applicable to all climate conditions. This study explored the roles of demographic factors, meteorological variables, and policy response measures in the transmission of COVID-19, and provided evidence for policymakers that the design of appropriate policies for prevention and preparedness of future pandemics should be based on local climate conditions, population characteristics, and social activity characteristics. Future work should focus on discerning the interactions between numerous factors affecting COVID-19 transmission.

Method for determining predictor factor for worse outcomes in kidney transplant recipients infected with coronavirus disease 2019 in a systematic review and meta-analysis research.

The systematic review and meta-analysis were conducted for COVID-19 infections in kidney transplant patients. Recent research on this topic was still scarce and limited meta-analysis research discussion, specific to some risks or treatment in kidney transplantation patients with COVID-19 infection. Therefore, this article demonstrated the fundamental steps to conducting systematic review and meta-analysis studies to derive a pooled estimate of predictor factors of worse outcomes in kidney transplant patients with positive for the SARS-CoV- 2 test•PICOT Framework to determine the research scope•PRISMA strategy for study selection•Forest Plot for meta-analysis study.

Survival Amidst Covid-19 Pandemic: Contributions of the Forest to the Lives of the Filipino. Covid 19

has brought significant damage to the lives of the people due to extremely long lockdowns and unemployment. Thus, leaving no choice to the residents and forcing them to rely on what is available in the environment. This study was conducted to assess the contribution of the forests to the lives of the locals in Aurora through a survey on 161 respondents. Data were analyzed through descriptive statistics including frequency, mean, rank, and percentage. Results showed that 100% of the respondents depend on the forests for their food which includes fruits and vegetables in the wild and on their farms located in and along the forest. Meanwhile, 116 individuals (72%) obtained livelihood from the forest in times of the pandemic in the form of labor, farming, selling of forest goods, charcoal making, and furniture making. Generally, the individual income obtained from forest ranged from Php500.00 (8.85 USD)to Php25,000.00 (442.65 USD) and an overall mean individual monthly income of Php4,084.19 (72.32 USD). Each type of livelihood activity provided a mean monthly income ranging from Php 4350 (USD 77.02) to Php 9021 (159.73 USD) per person. However, the respondents faced challenges such as loss of products due to theft, the limited number of consumers, and struggles concerning the health of the workers especially the elders, disabled, and other high-risk individuals to COVID-19. The government must consider providing needs (financial, technical, knowledge) to the locals in obtaining products and services from the forest for sustainable utilization of the resources. This research dictate the importance of forest as a source of life to the people. Thus, the result of this study may be used as a baseline for the government in crafting policies to help ensure sustainability of the forest and the lives of the society.

Response of forest management institutions to health-related shocks. Learning from the Busitema Forest Reserve of Uganda during the COVID-19 outbreak

There is extensive literature on forest management institutional responses as a function of socio-economic and political factors, albeit limited evidence on responses triggered by health shocks. To bridge this gap, this paper analyses forest management institutional response approaches around the Busitema Forest Reserve in Uganda, using the COVID-19 pandemic as a case. Household surveys (n = 135), focus group discussions (n = 4) and key informant interviews (n = 8) provided the relevant data. The results indicate that compliance with formal and informal institutions increased during the pandemic;this was attributed to fear and uncertainty about the mode of spread of the COVID-19 virus, which was flagged by mainstream media as a zoonotic disease. Formal institutional enforcement agents, therefore, used the pandemic to forward their agenda and reinforce rules that aim to exclude local people from resource appropriation in this reserve. The response was further manifested through the transposition of existing institutions to new functions, changes in rule application and the introduction of new rules. These responses paved the way for formal institutions to tighten their control of forest resource use by allying with informal institutions. The study provides complementary evidence on institutional change with an emphasis on COVID-19 as a health-related trigger.

Wastewater-based prediction of COVID-19 cases using a random forest algorithm with strain prevalence data: A case study of five municipalities in Latvia.

Wastewater-based epidemiology (WBE) is a rapid and cost-effective method that can detect SARS-CoV-2 genomic components in wastewater and can provide an early warning for possible COVID-19 outbreaks up to one or two weeks in advance. However, the quantitative relationship between the intensity of the epidemic and the possible progression of the pandemic is still unclear, necessitating further research. This study investigates the use of WBE to rapidly monitor the SARS-CoV-2 virus from five municipal wastewater treatment plants in Latvia and forecast cumulative COVID-19 cases two weeks in advance. For this purpose, a real-time quantitative PCR approach was used to monitor the SARS-CoV-2 nucleocapsid 1 (N1), nucleocapsid 2 (N2), and E genes in municipal wastewater. The RNA signals in the wastewater were compared to the reported COVID-19 cases, and the strain prevalence data of the SARS-CoV-2 virus were identified by targeted sequencing of receptor binding domain (RBD) and furin cleavage site (FCS) regions employing next-generation sequencing technology. The model methodology for a linear model and a random forest was designed and carried out to ascertain the correlation between the cumulative cases, strain prevalence data, and RNA concentration in the wastewater to predict the COVID-19 outbreak and its scale. Additionally, the factors that impact the model prediction accuracy for COVID-19 were investigated and compared between linear and random forest models. The results of cross-validated model metrics showed that the random forest model is more effective in predicting the cumulative COVID-19 cases two weeks in advance when strain prevalence data are included. The results from this research help inform WBE and public health recommendations by providing valuable insights into the impact of environmental exposures on health outcomes.

IT CAN HAPPEN HERE!

One would expect a wildfire exploration in a region prone to wildfires to begin close to home, not Australia, but that is where it started. Lowry teaches preK in a constructivist school in an area prone to wildfires, earthquakes, winds, and floods. One of her students went back to visit relatives in Australia during the winter of the 2019-2020 school year, immediately before the initial COVID-19 quarantine. They emailed the student regularly and heard about the intense heat where he was visiting. They looked up Australia in the news and saw images of the wildfires. After a little work comparing Australia and their area, one of the students said, "This could happen here! We HAVE to do something!"