Comparative Analysis of Different Systematic Odometry Error Correction Methods on a Differential Drive Indoor Service Robot

The COVID-19 pandemic has led to a surge of interest in research work involving the development of robotic systems that reduce human-to-human interaction, as such a technology can greatly benefit healthcare industries in preventing the spread of highly infectious diseases. An indoor service robot is built and equipped with wheel odometry and a 2D LiDAR. However, the presence of the systematic odometry errors is evident during field testing. Hence, the possibility of minimizing systematic odometry errors is inspected using various methods of calculation, namely: UMBmark, Lee's and Jung's. The methods all use the Bidirectional Square Path test, performed together with ROS. It is found that Jung's method is the most appropriate method showing a 20.4% improvement compared to the uncalibrated dead reckoning accuracy. Moreover, it is found that the presence of slippage, a nonsystematic error, greatly affects the return position errors of the robot. Consequently, it is recommended to improve the design of the wheelbase to minimize the effects of nonsystematic errors. © 2022 IEEE.

Comparative Analysis of Linear Regression and Logistic Regression for the COVID-19 Detection from CT-Scans

The new emerging Coronavirus disease (COVID-19) is a pandemic disease due to its enormous infectious capability. Generally affecting the lungs, COVID-19 engenders fever, dry cough, and tiredness. However, some patients may not show symptoms. An imaging test, such as a chest X-ray or a chest CT scan, is therefore requested for reliable detection of this pneumonia type. Despite the decreasing trends both in the new and death reported cases, there is an extent need for quick, accurate, and inexpensive new methods for diagnosis. In this framework, we propose two machine learning (ML) algorithms: linear regression and logistic regression for effective COVID-19 detection in the abdominal Computed Tomography (CT) dataset. The ML methods proposed in this paper, effectively classify the data into COVID-19 and normal classes without recourse to image preprocessing or analysis. The effectiveness of these algorithms was shown through the use of the performance measures: accuracy, precision, recall, and F1-score. The best classification accuracy was obtained as 96% with logistic regression using the saga solver with no added penalty against 95.3% with linear regression. As for precision, recall, and F1-score the value of 0.89 was reached by logistic regression for all these metrics, as well as the value of 0.87 by linear regression. © 2022 IEEE.

Comparative Analysis of Laboratory and Radiation Methods of Studies with the Degree of Severity of Patients with COVID-19

The study is devoted to a comparative analysis and retrospective evaluation of laboratory and instrumental data with the severity of lung tissue damage in COVID-19 of patients with COVID-19. An improvement was made in the methodology for interpreting and analyzing dynamic changes associated with COVID-19 on CT images of the lungs. The technique includes the following steps: pre-processing, segmentation with color coding, calculation and evaluation of signs to highlight areas with probable pathology (including combined evaluation of signs). Analysis and interpretation is carried out on the emerging database of patients. At the same time the following indicators are distinguished: the results of the analysis of CT images of the lungs in dynamics;the results of the analysis of clinical and laboratory data (severity course of the disease, temperature, saturation, etc.). The results of laboratory studies are analyzed with an emphasis on the values of the main indicator - interleukin-6. This indicator is a marker of significant and serious changes characterizing the severity of the patient's condition. © 2023 IEEE.

Comparison Analysis of Prediction the Number of Covid-19 Cases Using Support Vector Regression and Long Short-Time Memory

Covid-19 emerged as a pandemic outbreak that spread almost worldwide at the end of December 2019. While this research was carried out, the Covid-19 pandemic was still ongoing. Many countries have made various attempts to overcome Covid-19. In Indonesia, the government and stakeholders, including researchers, have made many activities to reduce the number of positive patients. One of many activities that the government made is the vaccination program. The vaccination program is believed to be the most effective in reducing the number of positive cases of Covid-19. But nobody knows when the Covid-19 pandemic will end. Stakeholder has to know how the trend of Covid-19 cases in Indonesia to make a better decision for facing Covid-19 cases. This study aims to predict the number of positive Covid-19 cases in Indonesia by conducting a comparative analysis performance of Support Vector Regression (SVR) method and Long Short-Term Memory (LSTM) method in machine learning to the prediction of the number of Covid-19 cases. This study was conducted using the dataset Covid-19 in Indonesia from Control Team from 13 January 2021 until 08 November 2021 and with 300 records. The evaluation has been conducted to know the performance of the model prediction number of Covid-19 with Support Vector Regression method and Long Short-Term Memory method based on values of R-Square (R2), the value of Mean Absolute Error (MAE) and Mean Square Error (MSE). The research found that the method Support Vector Regression has better performance than Long Short-Term Memory method for making a prediction of the number Covid-19 using Machine Learning model based on the value of accuracy and error rate based with the value of R-Squared, MAE, and MSE are consecutively 0.902, 0.163, and 0.072. © 2022 IEEE.

A Comparative Analysis of IoT Protocols for Resource Constraint Devices and Networks

Communication standards and protocols are detrimental to the success of any Internet of Things (IoT) system or application. Selecting a communication standard and a suitable middleware or messaging protocol for IoT connectivity is challenging due to the heterogeneous resource-constrained IoT devices and their messaging requirements. Recently, several messaging/middleware protocols in the IoT field were developed and adopted in the industry. However, to date, there is no specific messaging protocol that can support all messaging use cases and fulfil the overall requirements of IoT systems. Therefore, it is critical to understand the application layer messaging and communication protocols of IoT systems to identify the most appropriate protocol that could fit and be applied in various contexts. This paper provides a comparative analysis of the MQTT, CoAP, and AMQP messaging protocols including their security. © 2022 ACM.

Analysis of the Features of Sustainable Development of the Mercosur Countries in the Context of the Covid-19 Pandemic

Purpose: The purpose of this article is to identify the features of sustainable development of the MERCOSUR countries in the context of the COVID-19 pandemic. Design/methodology/approach: The authors use comparative and retrospective analysis to identify the distinguishing characteristics of countries meeting the sustainable development goals. The objects of research are the MERCOSUR countries. Findings: It has been established that Uruguay is the undisputed leader in sustainable development in the context of COVID-19. Two countries: Brazil and Venezuela slowed down the pace of implementation of national sustainable development strategies due to the pandemic and other reasons. Originality/value: According to the results of the analysis, it was revealed that countries that have long-term national strategies for sustainable development are more stable in achieving sustainable development goals. The size of a national economy does not guarantee that it can successfully overcome an external shock such as the lockdown caused by the COVID-19 pandemic. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

A novel fuzzy group decision making approach for buying a house in pandemic process

After the detection of the first official case diagnosed with COVID-19 on March 2020, the new package called as "Economic Stability Shield” have been implemented in Turkey to minimize the effects of the pandemic. As in all sectors, the epidemic caused various changes in the real estate sector. The demands of citizens who stayed in homes for a long time during the epidemic period have also changed when buying a house. This study aims to investigate the importance of selection criteria for buying a house criterion in the real estate sector and to determine the most suitable house of via a Multi-Criteria Decision Making (MCDM) approach. By integrating Fuzzy Analytic Hierarchy Process (FAHP) and Multi-Attribute Ideal Real Comparative Analysis (FMAIRCA), the assessment criteria of buying house and suitable houses are evaluated. While making this evaluation, the weights of the criteria are obtained by pairwise comparisons under fuzzy environment through collected from five households living in Konya. Next, the FMAIRCA method is used to rank the candidate houses according to the views of the households. As a result of the study, it has been determined that the criteria of eligibility for credit are the most important criteria. For the case study, we perform a comparative analysis on the performance of fuzzy TOPSIS and fuzzy VIKOR in buying a house. Our comparative analysis for this case study shows that the three fuzzy MCDM methods achieve the identical rankings. © 2023 Gazi Universitesi Muhendislik-Mimarlik. All rights reserved.

Impact of COVID-19-Related Travel Restrictions on the Environment and Travel Time Reliability

This study analyzes the effect of the restrictions in traffic movement enforced in order to combat the spread of coronavirus on air quality and travel time reliability under heterogeneous and laneless traffic conditions. A comparative analysis was conducted to examine quantity of pollutants, average travel time distributions (TTD), and their associated travel time reliability (TTR) metrics during the COVID-19 pandemic, postpandemic, and during partial restrictions. Pollutants data (PM2.5, NO2, and NOX) and travel time data for selected locations from Chennai City in India were collected for a sample period of one week using Wi-Fi sensors and state-run air quality monitoring stations. It was observed that the average quantity of PM2.5, NO2, and NOX were increased by 433.1%, 681.4%, and 99.2%, respectively, during the postlockdown period. Correlation analysis also indicated that all considered air pollutants are moderately correlated to Wi-Fi hits, albeit to varied degrees. From the analysis, it was also found that average TTD mean and interquartile range values were increased by 47.2% and 105.2%. In addition, the buffer time index, planning time index, travel index, and capacity buffer index associated with these TTD metrics were increased by 148.1%, 63.7%, 42.8%, and 202.9%, respectively, soon after relaxing travel restrictions. © 2023 American Society of Civil Engineers.

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.

Comparative Analysis of Various Models for Potato Leaf Disease Classification using Deep Learning

Potato is one among the most extensively consumed staple foods, ranking fourth on the global food pyramid. Moreover, because of the global coronavirus outbreak, global potato consumption is expanding dramatically. Potato diseases, on the other hand, are the primary cause of crop quality and quantity decline. Plant conditions will be dramatically worsened by incorrect disease classification and late identification. Fortunately, leaf conditions can help identify various illnesses in potato plants. Potato (Solanum tuberosum L) is one of the majorly farmed vegetable food crops in worldwide. The output of potato crops in both quality and quantity is affected majorly due to fungal blight infections, which causes a severe impact on the global food yield. The most severe foliar diseases for potato crops are early blight and late blight. The causes of these diseases are Alternaria solani and Phytophthora infestants respectively. Farmers suspect such problems by focusing on the color change or transformation in potato leaves, which is effortless due to subjectivity and lengthy time commitment. In such circumstances, it is critical to develop computer models that can diagnose those diseases quickly and accurately, even in their early stages. © 2023 IEEE.

Retrospective Study and Evaluation of School Failure (Junior High School) in Scientific Subjects

This article aims, on the one hand, to theoretically analyze the fact of school failure by identifying and describing its extent, specifically in the province of Ouezzane in northern Morocco, on the other hand, it aims to describe the effect of hybrid education caused by the COVID-19 health crisis on student results in the 2020/2021 school year as well as to make a comparative analysis of school failure rates following an exploratory approach for previous school years;namely, the years 2015/2016, 2016/2017, 2017/2018 and 2018/2019. In order to carry out this study, we proceeded with an in-depth analysis of the marks of the students relating to the scientific subjects, in particular: mathematical sciences, sciences of life and earth and physics, resulting from the school curriculum obtained at the regional examination for the third year of college. Finally, we have suggested some recommendations regarding the technology plan that aim to reduce the rate of this failure in this province in particular and can be generalized in the other parts of the kingdom. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Comparative retrieval approaches for concentrated Suspended Particulate Matter (CSPM) detection in the Ganges using Sentinel-2 and Cloud Computing

In this paper, there are four distinct models utilized for the retrieval of CSPM from the Sentinel 2A/2B satellite imageries by using cloud computing techniques. In this study, a comparative analysis of different CSPM models was carried out at three different sites (Haridwar, Varanasi, and Hooghly). The study reveals that there are significant changes in CSPM in the Ganges in three different periods such as pre, during, and post-COVID. Noteworthy, fewer anthropogenic activities have generated important transformations in aquatic environments during the COVID. © 2023 IEEE.

HYBRID AUDIT AS AN INNOVATIVE APPROACH IN THE AUDIT PRACTICE OF FORESTRY

Auditing has undergone many changes in the context of globalization and digitalization. Under the influence of the COVID-19 health crisis, additional restrictions are introduced, which are related to the impossibility of physical inspections (desk cheks or on-site ones). This has a significant effect on the activities of sectors and enterprises where on-site inspections are a priority, like in forestry. In order to answer the current challenges, the Forest StewardshipCouncil introduces a new audit approach, called "hybrid audit". The main goal of the study is to analyze the benefits and risks of hybrid audits in forestry. This will be done by (1) performing a comparative analysis with other types of audit (particularly financial and IT audit) and (2) deriving the characteristics and requirements of the hybrid audit. The study is conducted based on the scientific methods of analogy, analysis and synthesis, induction, deduction, and logical approach. The results of the study outlined the need for specific, local forestry-related rules and methodology of the procedures for the hybrid audit to be developed. Additionally, there is a risk of gaps in the check and verification of the forestry documentation and resources when using the hybrid audit. © 2022 15th International Scientific Conference WoodEMA 2022 - Crisis Management and Safety Foresight in Forest-Based Sector and SMES Operating in the Global Environment. All rights reserved.

Foreign Language Anxiety in Multilingual Students Before and After Corona-Lockdown: A Comparative Analysis

The study is devoted to linguistic anxiety of multilingual students in the conditions of adaptation to foreign-language educational environment. The work was carried out in two stages covering the period of study before COVID and after COVID. The main goal was to reveal structural and contents differences of the phenomenon of linguistic anxiety depending on changes in educational conditions and general psychological atmosphere in Lockdown conditions. A total of 120 foreign students studying humanities were interviewed. A comparative analysis of the data obtained showed that the samples did not differ significantly in terms of the overall index of linguistic anxiety and resilience. However, there were differences in the basic constructs. Thus, bilingual students before Corona-Lockdown had the highest test anxiety score. Anxiety about communication apprehension was lower. The situation changed after Corona-Lockdown. Multilingual respondents revealed the maximum anxiety concerning live communication and the minimum concerning testing. Foreign language anxiety and resilience appeared to correlate. Factor analysis of the data for both samples revealed differences in the number of components and their compounds. In the first sample, the components with a communicative orientation prevailed, while in the second sample, the components with an evaluation-test orientation dominated. The study showed the variability of the components that make up the basic subconstructs of linguistic anxiety. The influence of individual cognitive characteristics and styles on linguistic anxiety was suggested. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

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

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

Comparative Analysis of Machine Learning Models to Predict Depression, Anxiety and Stress

Mental Health Issues are a hidden pandemic which will emerge in the upcoming years. As the world witnessed COVID-19 pandemic and went into lockdown, the cases of Depression, Anxiety and Stress skyrocketed than ever before. This has given rise to the need for exploring the interdisciplinary field of Artficial Intelligence and Psychometry. In this paper, we propose compare various machine learning and ensemble learning methods, on the survey dataset comprising of the DASS-42 Psychometric Test Results and Demographic information. Random Forest, Decision Tree, Support Vector Machine (SVM), AdaBoost, CatBoost, and Extreme Gradient Boosting (XGBoost) are used to classify the level of Depression, Anxiety and Stress into normal, mild, moderate, severe and extremely severe categories. In our experiments on the dataset, Support Vector Machine outperformed and reached a final F1-measure of 94%, 95% and 91% in the prediction of Depression, Anxiety and Stress, respectively. © 2022 IEEE.

Making Online English Learning More Engaging for Chinese College Students: A Comparative Analysis of MOOCs and Bilibili

Due to the COVID-19, Chinese universities have moved their English courses online. Students took lessons through various online learning platforms, especially MOOCs. However, MOOCs' problems such as low course completion rate, poor learning engagement, and learning efficiency harm the education quality. This study explores whether Bilibili, an online entertainment platform providing English videos, has better affordances in online English learning. Through a comparative analysis of the two platforms using questionnaires and SPSS software, the results show that compared to MOOCs, respondents perceived Bilibili to have a higher level of interactivity. In addition, respondents regarded Bilibili's screen bullets to have more significant positive effects on their English learning than MOOCs' discussion forums, especially in triggering their English learning interest. Still, the difference is not prominent in learning English grammar. Based on the results, the study argues that universities should choose between MOOCs and Bilibili according to the different characteristics of English skills. © 2022 Owner/Author.

Comparative analysis of different scene for face mask detection based on YOLOv4

The 2019-nCoV can be transmitted through respiratory droplets and other methods, which greatly endangers public health security. Wearing masks correctly has been proven to be one of the effective means to prevent virus infection, but limited by the complexity of practical application scenarios, the wearing of masks still relies heavily on manual supervision. Therefore, a fast and accurate face mask wearing detection method is urgently needed. In this paper, a mask detection algorithm based on improved YOLO-v4 is proposed as a solution to the problems of low accuracy, poor real-time performance, and poor robustness caused by complicated environments. In addition, a number of different training approaches, such as mosaic data augment, CIOU, label smoothing, cosine annealing, etc., are introduced. These techniques help to increase the training speed of the model as well as the accuracy of its detection. With a fast-training model, the model will be able to detect and compare the results of samples from different scenarios. The experiment will compare front and side faces, different colored masks, scenes of varying complexity and other perspectives in a systematic way. The experiment's result was able to reach 99.38 % accuracy after the model was trained using data from a variety of face masks being worn. Experiment results, both quantitative and qualitative, indicate that the method can be adapted to most scenarios and offers effective ideas for improvement. © 2023 SPIE.

Parallel coordinate visualization of edge bundling based on comprehensive index evaluation of media platform: Take the comparative analysis of industry big data in the post-epidemic era as an example

The outbreak of Covid-19 in specific areas characterized with global industrial chain reconstruction, grasps the epidemic prevention and control, while the growth of economics is the key point of government management in the post-epidemic time scale. In the last two years, various industries were affected by the outbreak of the new champions league experience "determination to keep factories and workers idle - to return to work and production - not to return to work and production”, which significantly changes the industrial performances. Both industry change and the development of the pattern of economic evaluation are equally important. This study used media platform composite index and edge parallel coordinates visualization method of combining the bundle, selected in 2020 and 2021, 11 industry information volume in 7 platform, through the medium of agenda building industry change from comparative analysis of two developing and development situation. This paper indicated that the major factors influencing the change of media attention in the course of the epidemic were the degree of social risk and the basic needs of the public. The epidemic had a great impact on the development of all industries, and the continuous improvement of the industry's ability to deal with major risks became the main trend of future development. © 2022 SPIE.

Comparative Analysis of COVID 19 Detection from Cough Speech Using Machine Learning Classifiers

Corona Virus Disease-2019, or COVID-19, has been on the rise since its emergence, so its early detection is necessary to stop it from spreading rapidly. Speech detection is one of the best ways to detect it at an early stage as it exhibits variations in the nasopharyngeal cavity and can be performed ubiquitously. In this research, three standard databases are used for detection of COVID-19 from speech signal. The feature set includes the baseline perceptual features such as spectral centroid, spectral crest, spectral decrease, spectral entropy, spectral flatness, spectral flux, spectral kurtosis, spectral roll off point, spectral skewness, spectral slope, spectral spread, harmonic to noise ratio, and pitch. 05 ML based classification techniques have been employed using these features. It has been observed that Generalized Additive Model (GAM) classifier offers an average of 95% and a maximum of 97.55% accuracy for COVID-19 detection from cough signals. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.