Data Handling
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Fahimeh Hadavimoghaddam ◽  
Saeid Atashrouz ◽  
Farzaneh Rezaei ◽  
Muhammad Tajammal Munir ◽  
Abdolhossein Hemmati-Sarapardeh ◽  

2022 ◽  
Vol 14 (1) ◽  
pp. 26
Michail Niarchos ◽  
Marina Eirini Stamatiadou ◽  
Charalampos Dimoulas ◽  
Andreas Veglis ◽  
Andreas Symeonidis

Nowadays, news coverage implies the existence of video footage and sound, from which arises the need for fast reflexes by media organizations. Social media and mobile journalists assist in fulfilling this requirement, but quick on-site presence is not always feasible. In the past few years, Unmanned Aerial Vehicles (UAVs), and specifically drones, have evolved to accessible recreational and business tools. Drones could help journalists and news organizations capture and share breaking news stories. Media corporations and individual professionals are waiting for the appropriate flight regulation and data handling framework to enable their usage to become widespread. Drone journalism services upgrade the usage of drones in day-to-day news reporting operations, offering multiple benefits. This paper proposes a system for operating an individual drone or a set of drones, aiming to mediate real-time breaking news coverage. Apart from the definition of the system requirements and the architecture design of the whole system, the current work focuses on data retrieval and the semantics preprocessing framework that will be the basis of the final implementation. The ultimate goal of this project is to implement a whole system that will utilize data retrieved from news media organizations, social media, and mobile journalists to provide alerts, geolocation inference, and flight planning.

Electronics ◽  
2022 ◽  
Vol 11 (1) ◽  
pp. 137
Abdul Razaque ◽  
Nazerke Shaldanbayeva ◽  
Bandar Alotaibi ◽  
Munif Alotaibi ◽  
Akhmetov Murat ◽  

Nowadays, cloud computing is one of the important and rapidly growing services; its capabilities and applications have been extended to various areas of life. Cloud computing systems face many security issues, such as scalability, integrity, confidentiality, unauthorized access, etc. An illegitimate intruder may gain access to a sensitive cloud computing system and use the data for inappropriate purposes, which may lead to losses in business or system damage. This paper proposes a hybrid unauthorized data handling (HUDH) scheme for big data in cloud computing. The HUDH scheme aims to restrict illegitimate users from accessing the cloud and to provide data security provisions. The proposed HUDH consists of three steps: data encryption, data access, and intrusion detection. The HUDH scheme involves three algorithms: advanced encryption standards (AES) for encryption, attribute-based access control (ABAC) for data access control, and hybrid intrusion detection (HID) for unauthorized access detection. The proposed scheme is implemented using the Python and Java languages. The testing results demonstrated that the HUDH scheme can delegate computation overhead to powerful cloud servers. User confidentiality, access privilege, and user secret key accountability can be attained with more than 97% accuracy.

S. Nickolas ◽  
K. Shobha

Data pre-processing plays a vital role in the life cycle of data mining for accomplishing quality outcomes. In this paper, it is experimentally shown the importance of data pre-processing to achieve highly accurate classifier outcomes by imputing missing values using a novel imputation method, CLUSTPRO, by selecting highly correlated features using Correlation-based Variable Selection (CVS) and by handling imbalanced data using Synthetic Minority Over-sampling Technique (SMOTE). The proposed CLUSTPRO method makes use of Random Forest (RF) and Expectation Maximization (EM) algorithms to impute missing. The imputed results are evaluated using standard evaluation metrics. The CLUSTPRO imputation method outperforms existing, state-of-the-art imputation methods. The combined approach of imputation, feature selection, and imbalanced data handling techniques has significantly contributed to attaining an improved classification accuracy (AUC curve) of 40%–50% in comparison with results obtained without any pre-processing.

2021 ◽  
Vol 14 (1) ◽  
pp. 77
Evgeny Loupian ◽  
Mikhail Burtsev ◽  
Andrey Proshin ◽  
Alexandr Kashnitskii ◽  
Ivan Balashov ◽  

Currently, when satellite data volumes grow rapidly and exceed petabyte values and their quality provides reliable analysis of long-term time series, traditional data handling methods assuming local storage and processing may be impossible to implement for small or distributed research teams. Thus, new methods based on modern web technologies providing access to very large distributed data archives are gaining increasing importance. Furthermore, these new data handling solutions should provide not just access but also analysis and processing features, similar to desktop solutions. This paper describes the VEGA-Science web GIS—an open-access novel tool for satellite data processing and analysis. The overview of its architecture and basic technical components is given, but most attention is paid to examples of actual system application for various applied and research tasks. In addition, an overview of projects using the system is given to illustrate its versatility and further development directions are considered.

2021 ◽  
Vol 11 (1) ◽  
pp. 69
Maria Efthymiou ◽  
Philip J. Lane ◽  
David Isenberg ◽  
Hannah Cohen ◽  
Ian J. Mackie

Background: Acquired activated protein C resistance (APCr) has been identified in antiphospholipid syndrome (APS) and systemic lupus erythematosus (SLE). Objective: To assess agreement between the ST-Genesia® and CAT analysers in identifying APCr prevalence in APS/SLE patients, using three thrombin generation (TG) methods. Methods: APCr was assessed with the ST-Genesia using STG-ThromboScreen and with the CAT using recombinant human activated protein C and Protac® in 105 APS, 53 SLE patients and 36 thrombotic controls. Agreement was expressed in % and by Cohen's kappa coefficient. Results: APCr values were consistently lower with the ST-Genesia® compared to the CAT, using either method, in both APS and SLE patients. Agreement between the two analysers in identifying APS and SLE patients with APCr was poor (≤65.9%, ≤0.20) or fair (≤68.5%, ≥0.29), regardless of TG method, respectively; no agreement was observed in thrombotic controls. APCr with both the ST Genesia and the CAT using Protac®, but not the CAT using rhAPC, was significantly greater in triple antiphospholipid antibody (aPL) APS patients compared to double/single aPL patients (p < 0.04) and in thrombotic SLE patients compared to non-thrombotic SLE patients (p < 0.05). Notably, the ST-Genesia®, unlike the CAT, with either method, identified significantly greater APCr in pregnancy morbidity (median, confidence intervals; 36.9%, 21.9–49.0%) compared to thrombotic (45.7%, 39.6–55.5%) APS patients (p = 0.03). Conclusion: Despite the broadly similar methodology used by CAT and ST-Genesia®, agreement in APCr was poor/fair, with results not being interchangeable. This may reflect differences in the TG method, use of different reagents, and analyser data handling.

2022 ◽  
Vol 29 (1) ◽  
Marco E. Seddon-Ferretti ◽  
Lucy M. Mottram ◽  
Martin C. Stennett ◽  
Claire L. Corkhill ◽  
Neil C. Hyatt

HERMES, a graphical user interface software tool, is presented, for pre-processing X-ray absorption spectroscopy (XAS) data from laboratory Rowland circle spectrometers, to meet the data handling needs of a growing community of practice. HERMES enables laboratory XAS data to be displayed for quality assessment, merging of data sets, polynomial fitting of smoothly varying data, and correction of data to the true energy scale and for dead-time and leakage effects. The software is written in Java 15 programming language, and runs on major computer operating systems, with graphics implementation using the JFreeChart toolkit. HERMES is freely available and distributed under an open source licence.

Liepollo Ntlhakana ◽  
Gill Nelson ◽  
Katijah Khoza-Shangase ◽  
Elton Dorkin

Background: The relevant legislation ensures confidentiality and has paved the way for data handling and sharing. However, the industry remains uncertain regarding big data handling and sharing practices for improved healthcare delivery and medical research. Methods: A semi-qualitative cross-sectional study was used which entailed analysing miners’ personal health records from 2014 to 2018. Data were accessed from the audiometry medical surveillance database (n = 480), the hearing screening database (n = 24,321), and the occupational hygiene database (n = 15,769). Ethical principles were applied to demonstrate big data protection and sharing. Results: Some audiometry screening and occupational hygiene records were incomplete and/or inaccurate (N = 4675). The database containing medical disease and treatment records could not be accessed. Ethical challenges included a lack of clarity regarding permission rights when sharing big data, and no policy governing the divulgence of miners’ personal and medical records for research. Conclusion: This case study illustrates how research can be effectively, although not maliciously, obstructed by the strict protection of employee medical data. Clearly communicated company policies should be developed for the sharing of workers’ records in the mining industry to improve HCPs.

Nanomaterials ◽  
2021 ◽  
Vol 11 (12) ◽  
pp. 3360
Roberta D’Aurelio ◽  
Ibtisam E. Tothill ◽  
Maria Salbini ◽  
Francesca Calò ◽  
Elisabetta Mazzotta ◽  

In this work we have compared two different sensing platforms for the detection of morphine as an example of a low molecular weight target analyte. For this, molecularly imprinted polymer nanoparticles (NanoMIP), synthesized with an affinity towards morphine, were attached to an electrochemical impedance spectroscopy (EIS) and a quartz crystal microbalance (QCM) sensor. Assay design, sensors fabrication, analyte sensitivity and specificity were performed using similar methods. The results showed that the EIS sensor achieved a limit of detection (LOD) of 0.11 ng·mL−1, which is three orders of magnitude lower than the 0.19 µg·mL−1 achieved using the QCM sensor. Both the EIS and the QCM sensors were found to be able to specifically detect morphine in a direct assay format. However, the QCM method required conjugation of gold nanoparticles (AuNPs) to the small analyte (morphine) to amplify the signal and achieve a LOD in the µg·mL−1 range. Conversely, the EIS sensor method was labor-intensive and required extensive data handling and processing, resulting in longer analysis times (~30–40 min). In addition, whereas the QCM enables visualization of the binding events between the target molecule and the sensor in real-time, the EIS method does not allow such a feature and measurements are taken post-binding. The work also highlighted the advantages of using QCM as an automated, rapid and multiplex sensor compared to the much simpler EIS platform used in this work, though, the QCM method will require sample preparation, especially when a sensitive (ng·mL−1) detection of a small analyte is needed.

2021 ◽  
Vol 20 ◽  
pp. e3220
Cristiane Krüger ◽  
Adriana Cristina Castanho Baldassari ◽  
Luis Felipe Dias Lopes ◽  
Lizana Ilha da Silva

Technological advances make it possible to quickly access and share personal data and information, which demands greater security and requires conscious attitudes from the different professionals who deal with these issues. Accounting professionals stand out in this universe for being responsible for customer, supplier, and employee data. The information insecurity scenario led to the creation of the General Data Protection Law (GDPL), a specific legislation for personal data handling. Driven by this context, this research aimed to analyze the GDPL compliance determinants among accounting professionals. In order to achieve this purpose, we conducted a quantitative, descriptive, survey study. For data collection, we developed and applied an online questionnaire addressed to accounting professionals. The final surveyed sample totaled 194 respondents. We performed the data analysis through Structural Equation Modeling. The validated model showed the dimensions of personal behaviors and attitudes and governance mechanisms as determinants, explaining 26.3% of GDPL compliance. This research contributes to the understanding of behavioral aspects of accounting professionals in face of the new legislation. It is an unprecedented approach and fills a gap in the accounting area, presenting useful contributions for educational institutions, class associations, and companies in the area.

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