DEEP LEARNING BASED AN EFFICIENT HYBRID PREDICTION MODEL FOR COVID-19 CROSS-COUNTRY SPREAD AMONG E7 AND G7 COUNTRIES

The COVID-19 pandemic has caused the death of many people around the world and has also caused economic problems for all countries in the world. In the literature, there are many studies to analyze and predict the spread of COVID-19 in cities and countries. However, there is no study to predict and analyze the cross-country spread in the world. In this study, a deep learning based hybrid model was developed to predict and analysis of COVID-19 cross-country spread and a case study was carried out for Emerging Seven (E7) and Group of Seven (G7) countries. It is aimed to reduce the workload of healthcare professionals and to make health plans by predicting the daily number of COVID-19 cases and deaths. Developed model was tested extensively using Mean Squared Error (MSE), Root Mean Squared Error (RMSE), Mean Absolute Error (MAE) and R Squared (R2). The experimental results showed that the developed model was more successful to predict and analysis of COVID-19 cross-country spread in E7 and G7 countries than Linear Regression (LR), Random Forest (RF), Support Vector Machine (SVM), Multilayer Perceptron (MLP), Convolutional Neural Network (CNN), Recurrent Neural Network (RNN) and Long Short-Term Memory (LSTM). The developed model has R2 value close to 0.9 in predicting the number of daily cases and deaths in the majority of E7 and G7 countries. © 2023 by the authors.

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)

A Classification Approach to Detect Public Sentiments towards COVID-19 Vaccines

In this paper, team University of Botswana Computer Science (UBCS) investigate the opinions of Twitter users towards vaccine uptake. In particular, we build three different text classifiers to detect people's opinions and classify them as provax-for opinions that are for vaccination, antivax for opinions against vaccination and neutral-for opinions that are neither for or against vaccination. Two different datasets obtained from Twitter, 1 by Cotfas and the other by Fire2022 Organizing team were merged to and used for this study. The dataset contained 4392 tweets. Our first classifier was based on the basic BERT model and the other 2 were machine learning models, Random Forest and Multinomial Naive Bayes models. Naive Bayes classifier outperformed other classifiers with a macro-F1 score of 0.319. © 2022 Copyright for this paper by its authors.

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.

An Exploratory Analysis of Delhi Air Quality Using Statistics and Machine Learning Models

Air pollution is one of the most significant concerns of the present era, which has severe and alarming effects on human health and the environment, thereby escalating the climate change issue. Hence, in-depth analysis of air pollution data and accurate air quality forecasting is crucial in controlling the growing pollution levels. It also aids in designing appropriate policies to prevent exposure to toxic pollutants and taking necessary precautionary measures. Air quality in Delhi, the capital of India, is inferior compared to other major cities in the world. In this study, daily and hourly concentrations of air pollutants in the Delhi region were collected and analyzed using various methods. A comparative analysis is performed based on months, seasons, and the topography of different stations. The effect of the Covid-19 lockdown on the reduction of pollutant levels is also studied. A correlation analysis is performed on the available data to show the relationships and dependencies among different pollutants, their relationship with weather parameters, and the correlations between the stations. Various machine learning models were used for air quality forecasting, like Linear Regression, Vector Auto Regression, Gradient Boosting Machine, Random Forest, and Decision Tree Regression. The performance of these models was compared using RMSE, MAE, and MAPE metrics. This study is focused on the dire state of air pollution in Delhi, the primary reasons behind it, and the efficacy of calculated lockdowns in bringing down pollution levels. It also highlights the potential of Linear Regression and Decision Tree Regression models in predicting the air quality for different time intervals. © 2022 IEEE.

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.

Comparison of Predictive Machine Learning Models to Predict the Level of Adaptability of Students in Online Education

With the onset of the COVID-19 pandemic, online education has become one of the most important options available to students around the world. Although online education has been widely accepted in recent years, the sudden shift from face-to-face education has resulted in several obstacles for students. This paper, aims to predict the level of adaptability that students have towards online education by using predictive machine learning (ML) models such as Random Forest (RF), K-Nearest-Neighbor (KNN), Support vector machine (SVM), Logistic Regression (LR) and XGBClassifier (XGB).The dataset used in this paper was obtained from Kaggle, which is composed of a population of 1205 high school to college students. Various stages in data analysis have been performed, including data understanding and cleaning, exploratory analysis, training, testing, and validation. Multiple parameters, such as accuracy, specificity, sensitivity, F1 count and precision, have been used to evaluate the performance of each model. The results have shown that all five models can provide optimal results in terms of prediction. For example, the RF and XGB models presented the best performance with an accuracy rate of 92%, outperforming the other models. In consequence, it is suggested to use these two models RF and XGB for prediction of students' adaptability level in online education due to their higher prediction efficiency. Also, KNN, SVM and LR models, achieved a performance of 85%, 76%, 67%, respectively. In conclusion, the results show that the RF and XGB models have a clear advantage in achieving higher prediction accuracy. These results are in line with other similar works that used ML techniques to predict adaptability levels. © 2023, International Journal of Advanced Computer Science and Applications. All Rights Reserved.

Polymer Materials & Waste Industrial Products might be Dangerous for Destroying the Forests in Türkiye: An Overview of Climate Changing in Viewpoint of Bio Thermochemical Analysis

Cleaning and saving the forest is related to managing and utilizing forests' biodiversity, productivity, and considering the relevant environment. Plastic waste management is now seen as an important goal for sustainable forest use as well as incorporating recycled plastics into products. Another way that industries impact wildlife and forests is by releasing additives such as industrial gases, organic & inorganics materials, plastic & resins, fertilizers & alkalies, and chlorine. Polymer wastes manage to enter into and harm numerous biological functions of animals. In this work, the concept of sustainable Türkiye's forest practices and addressing the impact of plastic waste on the forests and environment before and during the COVID-19 epidemic are discussed along with current sources of those mentioned materials wastes that manage to enter the environment. Through this work, the data of bio thermochemistry and thermodynamics calculations of such polymers have been investigated to exhibit the range of sustainability and unsustainability of those mentioned polymers and resins in the environment due to forest climate change. Since the Türkiye chemical industry is one of the most important industrial factors, their production contributes in similar proportions to greenhouse gas emissions. Interestingly, low data on energy usage in the factories and chemical industry is available in the public domain. Ethylene production is the major product in terms of the production volume of the petrochemical industry. Nitrogenous fertilizer production is a very energy-intensive industry, producing a variety of fertilizers and other nitrogen compounds. In addition, ammonia, chlorine, and caustic soda are the most important mediator chemical material used as the main compound for almost all products. © 2023 by the authors.

Accurate Machine Learning Algorithm for Monkey Pox Based on Covid-19

The Covid-19 virus is still marching all over the world. Many people are getting infected and a few are fatal to death. This research paper expressed that supervised learning has revealed supreme results than unsupervised learning in machine learning. Within supervised learning, random forest regression outplays all other algorithms like logistic regression (LR), support vector machine (SVM), decision tree (DT), etc. Now monkeypox is escalating in other countries at present. This virus is allied to human orthopox viruses. It can expand from one to one through contact person having rash or body fluids etc. The symptoms of monkeypox are much similar to covid19 virus-like fever, cold, fatigue, and body pains. Herewith we concluded that random forest regression shows possible foremost (97.15%) accuracy. © 2023 IEEE.

A Garden of Marvels: Tales of Wonder from Early Medieval China by Robert Ford Campany (review)

The category of zhiguai (accounts of the strange) texts is diverse, encompassing a wide variety of anecdotes, historical records, memoirs, letters, temple inscriptions, and biographies, among others, that recount encounters with sacred, ordinary, and apotropaic objects, shapeshifting animals, ghosts, demons, local gods, and numinous beings such as Daoist transcendents or the Buddha, Buddhist practitioners, deities and supernatural creatures;visits to otherworldly places such as the court of judgment in the afterlife, hidden villages of immortals or enlightened beings à la James Hilton's Shangri-la or the Tibetan mythical kingdom of Shambhala, or even heaven or hell;and unaccountable phenomena such as bizarre dreams, premonitions, and miraculous occurrences, including surviving entombment and the return from death (xxviii). Mordicai Gerstein's children's book Carolinda Clatter (2005), with its description of a giant's sleeping body becoming a mountain with forests, caves, and waterfalls, mirrors the cosmogonic myth of Pangu, whose body parts become the world in item 85 (58 and 59). The eerie feel of the scene in C. S. Lewis's The Magician's Nephew (1955), where Digory Kirke enters the Garden to pluck an apple from the Tree of Knowledge to protect Narnia, is highly reminiscent of item 47 (35), where uninvited intruders eat their fill of otherworldly fruit from a remote orchard but are admonished by an unseen voice in midair to drop the fruit they intended to take with them.

Comparative Analysis of Classification Techniques on Olympic Games Datasets

Olympic game is a prestigious ceremony that occurs after every four years. However, due to the spread of coronavirus in 2020, the game was held in 2021, which is post-Covid. The main aim of this research is to find out if there was a difference in the performance of nations in Rio 2016 Olympics (pre-Covid) and Tokyo 2020 Olympics (post-Covid). Statistical analysis is carried out to find the correlation between the different variables. One of the highly correlated variables (Gold Tally) is removed while performing the classification analysis. The idea is to see if the classifiers are able to do the comparative analysis without it or not. The classification algorithms utilized in this research are Decision Table, Decision Tree, Naïve Bayes, and Random Forest. The datasets used in this research are imbalanced sets, which were later transformed to balance sets through under-sampling. Random Forest was able to give 100% accuracy in both datasets whereas the True Positive Rate (TPR) was also 100%. After doing the comparative analysis it was found that irrespective of pre and post-Covid, the performance of athletes did not change. This paves the way for other researchers to investigate if Covid had any impact on the performance of the athletes or not. In the future, more vast variables will be investigated to do a more detailed comparative analysis. © 2022 IEEE.

Automatic Diagnosis of COVID-19 Based on Machine Learning

The lives and health of individuals are significantly threatened by the extremely infectious and dangerous Corona Virus Disease 2019 (COVID-19). For the containment of the epidemic, quick and precise COVID-19 detection and diagnosis are essential. Currently, artificial diagnosis based on medical imaging and nucleic acid detection are the major approaches used for COVID-19 detection and diagnosis. However, nucleic acid detection takes a long time and requires a dedicated test box, while manual diagnosis based on medical images relies too much on professional knowledge, and analysis takes a long time, and it is difficult to find hidden lesions. Thanks to the rapid development of pattern recognition algorithms, building a COVID-19 diagnostic model based on machine learning and clinical symptoms has become a feasible rapid detection solution. In this paper, support vector machines and random forest algorithms are used to build a COVID-19 diagnostic model, respectively. Based on the quantitative comparison of the performance of the two methods, the future development trends in this field are discussed. © 2023 IEEE.

A Machine Learning-driven IoT Architecture for Predicting the Growth and Trend of Covid-19 Epidemic Outbreaks to Identify High-risk Locations

Covid-19 has had a destructive influence on global economics, social life, education, and technologies. The rise of the Covid-19 pandemic has increased the use of digital tools and technologies for epidemic control. This research uses machine learning (ML) models to identify populated areas and predict the disease's risk and impact. The proposed system requires only details about mask utilization, temperature, and distance between individuals, which helps protect the individual's privacy. The gathered data is transferred to an ML engine in the cloud to determine the risk probability of public areas concerning Covid-19. Extracted data are input for multiple ML techniques such as Random Forest (RF), Decision tree (DT), Naive Bayes classifier(NBC), Neural network(NN), and Support vector machine (SVM). Expectation maximization (EM), K-means, Density, Filtered, and Farthest first (FF) clustering algorithms are applied for clustering. Compared to other algorithms, the K-means produces better superior accuracy. The regression technique is utilized for prediction. The outcomes of several methods are compared, and the most suitable ML algorithms utilized in this study are used to identify high-risk locations. In comparison to other identical architectures, the suggested architecture retains excellent accuracies. It is observed that the time taken to build the model using locally weighted learning(LWL) was 0.02 seconds, and the NN took more time to build, which is 0.90 seconds. To test the model, an LWL algorithm took more time which is 1.73 seconds, and the NN took less time to test, which is 0.02 seconds. The NBC has a 99.38 percent accuracy, the RF classifier has a 97.33 percent accuracy, and the DT has a 94.51 percent accuracy for the same data set. These algorithms have significant possibilities for predicting the likelihood of crowd risks of Covid-19 in a public space. This approach generates automatic notifications to concerned government authorities in any aberrant detection. This study is likely to aid researchers in modeling healthcare systems and spur additional research into innovative technology. © 2023 IEEE.

World forests, global change, and emerging pests and pathogens. (Special Issue: Re-assessing the challenges of emerging pests and pathogens for sustainable development post-COVID.)

Global changes play today an important role in altering patterns of human, animal, and plant host-pathogen interactions and invasive pest species. With rapid development in sequencing technology, there is also an increase in pathogen and pest studies adopting a macroscale, biogeographical perspective, and we present the most recent elements on existing ecological and biogeographical trends. We also compare the results on the one hand on emerging infectious diseases of animals and humans, and on the other hand on plant pathogens and pests. International exchanges of people, animals, and plant products currently contribute to their geographical extension but with notable differences across disease and pest systems, and regions. This review highlights that the subject of pathogens and plant pests, traditionally rooted in agronomic approaches, lacks work on macroecology and biogeography. We discuss the research orientations to better anticipate their ecological and economic impacts in order to better achieve environmental sustainability.

INVESTIGATION OF COVID-19 IMPACT ON AEROSOL OPTICAL DEPTH OVER MALAYSIA AND INDONESIA

Lockdown or movement control order (MCO) was implemented all over the world, including Malaysia and Indonesia, during the coronavirus disease 2019 (COVID-19) pandemic. During the lockdown period, human activities were restricted. The restriction led to the reduction of human-made particulate matter released to the atmosphere. One of the indicators that could be used to estimate the concentration of particulate matter in the atmosphere is aerosol optical depth (AOD). The aim of this study is to investigate the variation in AOD level over the Malaysia and Indonesia region during this restriction period. This study has utilized monthly and daily Moderate Resolution Imaging Spectroradiometer (MODIS) Terra AOD product that can be accessed through National Aeronautics and Space Administration (NASA)'s Geospatial Interactive Online Visualization and Analysis Infrastructure (GIOVANNI) system. The developed long-term time-averaged map showed a high AOD level over Sumatera and South Kalimantan, with the maximum value being 0.4. The comparison among during, pre- and post-lockdown periods showed a reduction in the AOD level. The maximum AOD level decreased to 0.3 during the lockdown period compared to 0.4 in the pre- (2019) and post-lockdown periods (2021 and 2022). Average monthly time series showed no spike in the AOD level in 2020 and 2021. Hovmöller diagram showed low AOD throughout the latitude and longitude during lockdown compared to the pre- and post-lockdown periods. Analyses of the yearly AOD level showed reduction in the AOD level from +11.31% in 2019 to -18.17% and -18.01% in 2020 and 2021, respectively. The result also showed that the average daily AOD percentage during the lockdown period in 2020 had decreased to -5.34% from -3.18% in 2019 and had increased to +1.26 in 2021.

The Ogres Zilie kalni park urban forest management

The impact of the Covid-19 pandemic demonstrated the importance of urban forests for human well-being at a time of tight constraints, when large forests close to urban areas were in high demand. Increased use affects the management of territories. Urban forests play an important role in providing ecosystem services. Urban forests show a close link between ecosystem services and forest functions. A literature review was carried out, exploring the ecosystem services and specific urban forest services provided by such territories. This article examines the experience of the Ogres Zilie kalni during the Covid-19 pandemic, taking into account the peculiar functions of urban forests. Different types of recreation that take place in the Ogres Zilie kalni, and their impact on park management are discussed. The aim of the article is to analyse and present the challenges of urban forest governance and management under the influence of Covid-19, looking through the functions of urban forests. Taking into account the classifications of ecosystem services available in Zilie kalni, zoning and assessment of the territory have been carried out. Cartographic material has been created based on practical experience and employee interviews.The practical experience of territory management gained during Covid-19 is important and should be taken into account in the future development of green spaces, respecting the new habits of visitors potentially affected by the pandemic, where one of the most important proposals is to develop more small localised recreation areas on smaller

Connected Conservation: Rethinking conservation for a telecoupled world

The convergence of the biodiversity and climate crises, widening of wealth inequality, and most recently the COVID-19 pandemic underscore the urgent need to mobilize change to secure sustainable futures. Centres of tropical biodiversity are a major focus of conservation efforts, delivered in predominantly site-level interventions often incorporating alternative-livelihood provision or poverty-alleviation components. Yet, a focus on site-level intervention is ill-equipped to address the disproportionate role of (often distant) wealth in biodiversity collapse. Further these approaches often attempt to ‘resolve' local economic poverty in order to safeguard biodiversity in a seemingly virtuous act, potentially overlooking local communities as the living locus of solutions to the biodiversity crisis. We offer Connected Conservation: a dual-branched conservation model that commands novel actions to tackle distant wealth-related drivers of biodiversity decline, while enhancing site-level conservation to empower biodiversity stewards. We synthesize diverse literatures to outline the need for this shift in conservation practice. We identify three dominant negative flows arising in centres of wealth that disproportionately undermine biodiversity, and highlight the three key positive, though marginalized, flows that enhance biodiversity and exist within biocultural centres. Connected Conservation works to amplify the positive flows, and diminish the negative flows, and thereby orientates towards desired states with justice at the centre. We identify connected conservation actions that can be applied and replicated to address the telecoupled, wealth-related reality of biodiversity collapse while empowering contemporary biodiversity stewards. The approach calls for conservation to extend its collaborations across sectors in order to deliver to transformative change.