ABSTRACT
With the development and progress of intelligent algorithms, more and more social robots are used to interfere with the information transmission and direction of international public opinion. This paper takes the agenda of COVID-19 in Twitter as the breakthrough point, and through the methods of web crawler, Twitter robot detection, data processing and analysis, aims at the agenda setting of social robots for China issues, that is, to carry out data visualization analysis for the stigmatized China image. Through case analysis, concrete and operable countermeasures for building the international communication system of China image were provided. © 2022 IEEE.
ABSTRACT
During the Covid-19 pandemic, the insurance industry's digital shift quickened, resulting in a surge in insurance fraud. To combat insurance fraud, a system that securely manages and monitors insurance processes must be built by combining a machine learning classification framework with a web application. Examining and identifying fraudulent features is a frequent method of detecting fraud, but it takes a long time and can result in false results. One of these issues is addressed by the proposed solution. By digitalizing the paper-based workflow in insurance firms, this paper intends to improve the efficiency of the existing approach. This method also aimed to improve the present approach's data management by integrating a web application with a machine learning stacking classifier framework experimented on a linear regression-based iterative imputed data for detecting fraud claims and making the entire claim processing and documentation process more robust and agile. © 2022 IEEE.
ABSTRACT
The pandemic of the coronavirus disease 2019 has shown weakness and threats in various fields of human activity. In turn, the World Health Organization has recommended different preventive measures to decrease the spreading of coronavirus. Nonetheless, the world community ought to be ready for worldwide pandemics in the closest future. One of the most productive approaches to prevent spreading the virus is still using a face mask. This case has required staff who would verify visitors in public areas to wear masks. The aim of this paper was to identify persons remotely who wore masks or not, and also inform the personnel about the status through the message queuing telemetry transport as soon as possible using the edge computing paradigm. To solve this problem, we proposed to use the Raspberry Pi with a camera as an edge device, as well as the TensorFlow framework for pre-processing data at the edge. The offered system is developed as a system that could be introduced into the entrance of public areas. Experimental results have shown that the proposed approach was able to optimize network traffic and detect persons without masks. This study can be applied to various closed and public areas for monitoring situations. © 2022 Copyright for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).
ABSTRACT
We demonstrate the feasibility of a generalized technique for semantic deduplication in temporal data domains using graph-based representations of data records. Structured data records with multiple timestamp attributes per record may be represented as a directed graph where the nodes represent the events and the edges represent event sequences. Edge weights are based on elapsed time between connecting nodes. In comparing two records, we may merge these directed graphs and determine a representative directed acyclic graph (DAG) inclusive of a subset of nodes and edges that maintain the transitive weights of the original graphs. This DAG may then be evaluated by weighting elapsed time equivalences between records at each node and measuring the fraction of nodes represented in the DAG versus the union of nodes between the records being compared. With this information, we establish a duplication score and use a specified threshold requirement to assert duplication. This method is referred to as Temporal Deduplication using Directed Acyclic Graphs (TD:DAG). TD:DAG significantly outperformed established ASNM and ASNM+LCS methods for datasets rep-resenting two disparate domains, COVID-19 government policy data and PlayStation Network (PSN) trophy data. TD:DAG produced highly effective and comparable F1 scores of 0.960 and 0.972 for the two datasets, respectively, versus 0.864/0.938 for ASNM+LCS and 0.817/0.708 for ASNM. © 2023 IEEE.
ABSTRACT
In recent years, there has been a significant growth in the development of machine learning algorithms towards better experience in patient care. In this paper, a contemporary survey on the deep learning and machine learning techniques used in multimodal signal processing for biomedical applications is presented. Specifically, an overview of the preprocessing approaches and the algorithms proposed for five major biomedical applications are presented, namely detection of cardiovascular diseases, retinal disease detection, stress detection, cancer detection and COVID-19 detection. In each case, processing on each multimodal data type, such as an image or a text is discussed in detail. A list of various publicly available datasets for each of these applications is also presented. © 2023 IEEE.
ABSTRACT
The analysis of current trends in the implementation of effective socio-economic solutions and their development under the influence of COVID-19 is made. The prospects of using innovative and telecommunication technologies, robotics, big data processing methods and knowledge management methods in the formation and management of global economic clusters were noted. The clustering of delivery robots under pandemic conditions by methods of machine learning was carried out. The peculiarities of COVID-19 assessment as the main formative factor influencing socio-economic decision-making on a global scale are disclosed. The necessity and possible consequences of adopting and implementing new decisions designed to minimize the negative effects of COVID-19 on Russian and global economies are discussed. It is noted that the design and development of innovations in the system of management and transfer of knowledge is an indispensable condition for the successful development of future socio-economic relations. On the basis of the obtained results conclusions are made about the background of the applied solutions, about the vector of their direction and makes it clear what should be paid special attention to when assessing the current situation in society and determine which solutions are most effective and how the social order should be transformed to successfully withstand the new challenges. © 2023 SPIE.
ABSTRACT
The multiple comparison method refers to the hypothesis test of whether there is a significant difference between the means of each sample after the analysis of variance is performed on the samples of each group to be tested. In data analysis, the multiple comparison method can perform a more precise difference analysis based on the analysis of variance. Therefore, this study will select the LSD (Least significant difference) test method in the multiple comparison method to analyze the physical fitness test scores of males and females in the three grades from 2019 to 2021 in the investigated schools. In this way, we can understand the substantial impact of students' home-based learning on students' physical health during the new crown epidemic, so as to make targeted development plans for students' physical health in the current special period, and minimize the adverse impact of the new crown epidemic on students' physical health. © 2023 SPIE.
ABSTRACT
Concerns about the effects of global warming and predicted rising sea levels are radically changing government policies to lower carbon emissions using sustainable green technologies. The United Kingdom aims to reduce its carbon emissions by 78% by 2035 and achieve net zero by 2050. This is a major driver for energy management and is influencing development of buildings which use autonomous smart technologies to assist in lowering carbon footprints. These Smart Buildings use digital technologies by connecting sensor data with intelligent systems which can be monitored remotely to provide more efficient facilities management. The data harvested and transmitted from the IoT sensors provides a key component for Big Data Analytics using techniques such as Association rule mining for intelligent interpretation which can assist facilities management becoming more agile regarding office space utilization. The shift toward hybrid working particularly instigated by the COVID-19 pandemic and recent energy supply concerns caused by the Ukraine crisis presents facilities management with opportunities to optimize their space, reduce energy consumption, and allow them to identify commercial opportunities for the unused space throughout the building. This chapter discusses the use of association rules for data mining derived from a simulated dataset for an investigative analysis of office workflow patterns for facilities management operations, resource conservation, and sustainability. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.
ABSTRACT
Deduplication is a key component of the data preparation process, a bottleneck in the machine learning (ML) and data mining pipeline that is very time-consuming and often relies on domain expertise and manual involvement. Further, temporal data is increasingly prevalent and is not well suited to traditional similarity and distance-based deduplication techniques. We establish a fully automated, domain-independent deduplication model for temporal data domains, known as TemporalDedup, that infers the key attribute(s), applies a base set of deduplication techniques focused on value matches for key, non-key, and elapsed time, and further detects duplicates through inference of temporal ordering requirements using Longest Common Subsequence (LCS) for records of a shared type. Using LCS, we split each record's temporal sequence into constrained and unconstrained sequences. We flag suspicious (errant) records that are non-adherent to the inferred constrained order and we flag a record as a duplicate if its unconstrained order, of sufficient length, matches that of another record. TemporalDedup was compared against a similarity-based Adaptive Sorted Neighborhood Method (ASNM) in evaluating duplicates for two disparate datasets: (1) 22,794 records from Sony's PlayStation Network (PSN) trophy data, where duplication may be indicative of cheating, and (2) emergency declarations and government responses related to COVID-19 for all U.S. states and territories. TemporalDedup (F1-scores of 0.971 and 0.954) exhibited combined sensitivities above 0.9 for all duplicate classes whereas ASNM (0.705 and 0.732) exhibited combined sensitivities below 0.2 for all time and order duplicate classes. © 2023 World Scientific Publishing Company.
ABSTRACT
The closure of schools and urgent importance of distance education during the Covid 19 pandemic revealed a necessity to find suitable teaching methods that would help to maintain the continuity level of educational system in case of different possible critical periods of society's survival, such as viral and bacterial pandemics, war, climate, meteorological and other disasters. In this context, the need for possibilities of distance-oriented further education of teachers has also emerged. Moreover, it was very important to create a great amount of suitable teaching materials. All mentioned facts require serious didactic research. In fact, it would also help to identify the positives and negatives of previous teaching methods applied in each individual school subject before the Covid 19 pandemic in Slovakia. During the Covid 19 pandemic, the authors of this paper have implemented a natural science-technologically oriented university course for future teachers in the period of Covid 19 pandemic. The research was conducted by method of pedagogical experiment. Paper consists of 9 chapters altogether, also including introduction, conclusion, acknowledgement, and bibliography. The first chapters are theoretical. They directly present the process of creating FVCOVIDE = Distance forms of education (during Covid 19 pandemic, having the character of experimental forms of teaching). Chapter 3 is of pedagogical experiment nature, applying the above-mentioned models into school practice. It also includes research hypotheses, statistical data processing and results interpretation © 2023, International Journal of Emerging Technologies in Learning.All Rights Reserved.
ABSTRACT
In this paper, we present and demonstrate that the implementation of an efficient Project Management Strategy has effectively contributed in a safe and successful completion of a very complex 3D OBN Seismic Survey in congested Oil fields. Thus, delivering high quality data on schedule and within the predetermined budget at the full satisfaction of all involved parties and stakeholders. Strong commitment to HSSE Standards and working as an integrated One-Team with full collaboration and continuous communication between all the Team members are among the main Success Factors of the 3D seismic survey which was carried out during the critical period of COVID-19. Moreover, the deployment of experienced personnel, advanced and reliable Technologies with adequate equipment have also extended the efficiency of this OBN 3D seismic survey. Preliminary results of 3D seismic data processing, interpretation and reservoir characterization are also briefly presented and discussed as a clear enhancement of data quality was already observed compared to the legacy 3D OBC data set. A fast track small 3D cube was successfully processed as an utmost and urgent priority for appraisal well selection, design and drilling. Copyright © 2023, Society of Petroleum Engineers.
ABSTRACT
Nowadays, the usage of social media platforms is rapidly increasing, and rumours or false information are also rising, especially among Arab nations. This false information is harmful to society and individuals. Blocking and detecting the spread of fake news in Arabic becomes critical. Several artificial intelligence (AI) methods, including contemporary transformer techniques, BERT, were used to detect fake news. Thus, fake news in Arabic is identified by utilizing AI approaches. This article develops a new hunter-prey optimization with hybrid deep learning-based fake news detection (HPOHDL-FND) model on the Arabic corpus. The HPOHDL-FND technique undergoes extensive data pre-processing steps to transform the input data into a useful format. Besides, the HPOHDL-FND technique utilizes long-term memory with a recurrent neural network (LSTM-RNN) model for fake news detection and classification. Finally, hunter prey optimization (HPO) algorithm is exploited for optimal modification of the hyperparameters related to the LSTM-RNN model. The performance validation of the HPOHDL-FND technique is tested using two Arabic datasets. The outcomes exemplified better performance over the other existing techniques with maximum accuracy of 96.57% and 93.53% on Covid19Fakes and satirical datasets, respectively. © 2023 Tech Science Press. All rights reserved.
ABSTRACT
This article presents the analysis of the demand and the characterization of mobility using public transportation in Montevideo, Uruguay, during the COVID-19 pandemic. A urban data-analysis approach is applied to extract useful insights from open data from different sources, including mobility of citizens, the public transportation system, and COVID cases. The proposed approach allowed computing significant results to determine the reduction of trips caused by each wave of the pandemic, the correlation between the number of trips and COVID cases, and the recovery of the use of the public transportation system. Overall, results provide useful insights to quantify and understand the behavior of citizens in Montevideo, regarding public transportation during the COVID-19 pandemic. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.
ABSTRACT
Scholars have carried out a lot of research in the field of using data processing methods to analyze the evolution characteristics and development trends of infectious diseases. The research on data model method is more in-depth, that is, according to the specific characteristics of infectious diseases, suitable data models are designed and combined with different parameters to analyze infectious diseases, mainly including infectious disease data models based on statistical theory or dynamic theory. The former is mostly used in the case of insufficient initial data. Local analysis is carried out by means of a priori or assumptions to achieve global prediction. The latter mainly includes SIR model, complex network model, and cellular automata model. SIR model is the most in-depth research. Scholars have constructed or optimized Si model, SIS model, SEIR model, IR model, and other derivative models based on SIR model in combination with the characteristics of viruses. In this paper, the data source is Wuhan epidemic information released by Health Commission of Hubei Province. Combined with the specific characteristics of COVID-19, the traditional dynamic propagation model is optimized, and an improved SEIR model is constructed. The results of the improved SEIR model are in good agreement with the actual epidemic trend in Wuhan. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
ABSTRACT
Coronavirus disease 2019, commonly known as COVID-19, is an extremely contagious disease caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Computerised Tomography (CT) scans based diagnosis and progression analysis of COVID-19 have recently received academic interest. Most algorithms include two-stage analysis where a slice-level analysis is followed by the patient-level analysis. However, such an analysis requires labels for individual slices in the training data. In this paper, we propose a single-stage 3D approach that does not require slice-wise labels. Our proposed method comprises volumetric data pre-processing and 3D ResNet transfer learning. The pre-processing includes pulmonary segmentation to identify the regions of interest, volume resampling and a novel approach for extracting salient slices. This is followed by proposing a region-of-interest aware 3D ResNet for feature learning. The backbone networks utilised in this study include 3D ResNet-18, 3D ResNet-50 and 3D ResNet-101. Our proposed method employing 3D ResNet-101 has outperformed the existing methods by yielding an overall accuracy of 90%. The sensitivity for correctly predicting COVID-19, Community Acquired Pneumonia (CAP) and Normal class labels in the dataset is 88.2%, 96.4% and 96.1%, respectively. © 2013 IEEE.
ABSTRACT
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.
ABSTRACT
We collect a total of 1830 data from January 2020 to June 2022 and use R for data processing and wavelet analysis. Moreover, we analyze the interactions between the COVID-19 pandemic, the Russian-Ukrainian war, crude oil price, the S&P 500 and economic policy uncertainty within a time-frequency frame work. As a result that the COVID-19 pandemic and the Russian-Ukrainian war has the extraordinary effects on the three indexes and the effect of the Russian- Ukrainian war on the crude oil price and US stock price higher than on the US economic uncertainty. © COPYRIGHT SPIE.
ABSTRACT
The aim of the study is to create a dashboard framework to monitor the spread of the Covid-19 pandemic based on quantitative and qualitative data processing. The theoretical part propounds the basic assumptions underlying the concept of the dashboard framework. The paper presents the most important functions of the dashboard framework and examples of its adoption. The limitations related to the dashboard framework development are also indicated. As part of empirical research, an original model of the Dash-Cov framework was designed, enabling the acquisition and processing of quantitative and qualitative data on the spread of the SARS-CoV-2 virus. The developed model was pre-validated. Over 25,000 records and around 100,000 tweets were analyzed. The adopted research methods included statistical analysis and text analysis methods, in particular the sentiment analysis and the topic modeling. © 2022 IEEE Computer Society. All rights reserved.
ABSTRACT
One of which machine learning data processing problems is imbalanced classes. Imbalanced classes could potentially cause bias towards the majority classes due to the nature of machine learning algorithms that presume that the object cardinality in classes is around similar number. Oversampling or generating new objects in minority class are common approaches for balancing the dataset. In text oversampling method, semantic meaning loses often occur when deep learning algorithms are used. We propose synonym-based text generation for restructuring the imbalanced COVID-19 online-news dataset. Three deep learning models (MLP, CNN, and LSTM) using TF/IDF and word embedding (WE) feature are tested with the original and balanced dataset. The results indicate that the balance condition of the dataset and the use of text representative features affect the performance of the deep learning model. Using balanced data and deep learning models with WE greatly affect the classification significantly higher performances as high as 4%, 5%, and 6% in accuracy, precision, recall, and f1-score, respectively. © 2022 IEEE.
ABSTRACT
The purpose of this article is to discuss the purchasing behavior (PB), influencing factors, and personal factors of Chinese consumers through literature review, questionnaire survey, and research results. The frequency and amount of purchasing behavior (FPB and APB) are studied in the consumption behavior. In the influencing factors, channel cognitions (CC), product cognitions (PC), and the influence of personal factors on consumer consumption behavior of fresh agricultural products. The FPB and APB are significantly and positively correlated with the CC, PC, and advantage cognition (AC). The FPB and APB are significantly and negatively correlated with disadvantage cognitions (DC). People's CC, PC, and DC significantly affect their FPB and APB. © 2022 IEEE.