scholarly journals Crash data reporting systems in fourteen Arab countries: challenges and improvement

2020 ◽  
Vol 56 (4) ◽  
pp. 73-88
Author(s):  
Zahira Abounoas ◽  
Wassim Raphael ◽  
Yarob Badr ◽  
Rafic Faddoul ◽  
Anne Guillaume
Keyword(s):  
Data Mining ◽  
Data Collection ◽  
Information Gain ◽  
Arab Countries ◽  
Road User ◽  
Handheld Devices ◽  
Data Reporting ◽  
Electronic Data Collection ◽  
Crash Data ◽  
Reporting Systems

Traffic crash fatalities and serious injuries still represent a big burden for most Arab countries because the actual policies, strategies, and interventions are based on poorly collected data. Through this paper, we assessed the crash data reporting systems in Fourteen Arab countries via a survey conducted to identify the fundamental dysfunctions at the management and data collection levels. Then, to address some of the dataset problems, we had applied data mining technics to select a minimum of variables (crash, vehicle, and road user) that should be collected for a better understanding of crash circumstances. For this raison, three methods of selection (correlation, information gain, and gain ratio) and seven classifiers (naive Bayes, nearest neighbour, random forest, random tree, J48, reduced error pruning tree, and bagging) were tested and compared to identify the variables that affect significantly the crashes severity. Decision trees family of classifiers showed the best performance based on the analysis of the area under the curve. The explanatory variables obtained from the data mining process were combined with other descriptive variables to maintain traceability. As a result, we produced hybrid lists of variables for the crash, vehicle, and road user, each contains 25 variables. Finally, in order to propose a cost-effective solution to switch from manual to electronic data collection, we got inspired by a tool used to track animals to create and customize a unified e-form for handheld devices, in order to ensure easy entering of the harmonized data for the entire region based on our selected lists of variables. The tool verified the countries requirements especially by enabling data collection and transfer with and without the internet, and by allowing data analysis thought its built-in Geographic Information System (GIS) capabilities.

Internal and External Threat Analysis of Anonymized Dataset

2020 ◽  
pp. 172-185
Author(s):  
Saurav Jindal ◽  
Poonam Saini
Keyword(s):  
Data Mining ◽  
Data Collection ◽  
Information Gain ◽  
Threat Analysis ◽  
External Threat ◽  
Utility Of Information ◽  
Cost Penalty ◽  
Computational Processes ◽  
Different Sources ◽  
Unusual Situation

In recent years, data collection and data mining have emerged as fast-paced computational processes as the amount of data from different sources has increased manifold. With the advent of such technologies, major concern is exposure of an individual's self-contained information. To confront the unusual situation, anonymization of dataset is performed before being released into public for further usage. The chapter discusses various existing techniques of anonymization. Thereafter, a novel redaction technique is proposed for generalization to minimize the overall cost (penalty) of the process being inversely proportional to utility of generated dataset. To validate the proposed work, authors assume a pre-processed dataset and further compare our algorithm with existing techniques. Lastly, the proposed technique is made scalable thus ensuring further minimization of generalization cost and improving overall utility of information gain.

scholarly journals Data missingness in the Michigan NEMSIS (MI-EMSIS) dataset: a mixed-methods study

2021 ◽  
Vol 14 (1) ◽  
Author(s):  
Mahshid Abir ◽  
Rekar K. Taymour ◽  
Jason E. Goldstick ◽  
Rosalie Malsberger ◽  
Jane Forman ◽  
...  
Keyword(s):  
Mixed Methods ◽  
Data Collection ◽  
Prehospital Care ◽  
Care Quality ◽  
Data Mapping ◽  
Data Reporting ◽  
Electronic Data Collection ◽  
Trends Over Time ◽  
National Emergency ◽  
Over Time

Abstract Objective The study was done to evaluate levels of missing and invalid values in the Michigan (MI) National Emergency Medical Services Information System (NEMSIS) (MI-EMSIS) and explore possible causes to inform improvement in data reporting and prehospital care quality. Methods We used a mixed-methods approach to study trends in data reporting. The proportion of missing or invalid values for 18 key reported variables in the MI-EMSIS (2010–2015) dataset was assessed overall, then stratified by EMS agency, software platform, and Medical Control Authorities (MCA)—regional EMS oversight entities in MI. We also conducted 4 focus groups and 10 key-informant interviews with EMS participants to understand the root causes of data missingness in MI-EMSIS. Results Only five variables of the 18 studied exhibited less than 10% missingness, and there was apparent variation in the rate of missingness across all stratifying variables under study. No consistent trends over time regarding the levels of missing or invalid values from 2010 to 2015 were identified. Qualitative findings indicated possible causes for this missingness including data-mapping issues, unclear variable definitions, and lack of infrastructure or training for data collection. Conclusions The adoption of electronic data collection in the prehospital setting can only support quality improvement if its entry is complete. The data suggest that there are many EMS agencies and MCAs with very high levels of missingness, and they do not appear to be improving over time, demonstrating a need for investment in efforts in improving data collection and reporting.

scholarly journals Autonomous Vehicles and Vulnerable Road-Users—Important Considerations and Requirements Based on Crash Data from Two Countries

2021 ◽  
Vol 11 (7) ◽  
pp. 101
Author(s):  
Andrew Paul Morris ◽  
Narelle Haworth ◽  
Ashleigh Filtness ◽  
Daryl-Palma Asongu Nguatem ◽  
Laurie Brown ◽  
...  
Keyword(s):  
Autonomous Vehicles ◽  
Red Light ◽  
Road User ◽  
Red Light Running ◽  
Crash Data ◽  
Passenger Vehicles ◽  
Road Users ◽  
Vulnerable Road User ◽  
Vulnerable Road Users ◽  
Road Intersections

(1) Background: Passenger vehicles equipped with advanced driver-assistance system (ADAS) functionalities are becoming more prevalent within vehicle fleets. However, the full effects of offering such systems, which may allow for drivers to become less than 100% engaged with the task of driving, may have detrimental impacts on other road-users, particularly vulnerable road-users, for a variety of reasons. (2) Crash data were analysed in two countries (Great Britain and Australia) to examine some challenging traffic scenarios that are prevalent in both countries and represent scenarios in which future connected and autonomous vehicles may be challenged in terms of safe manoeuvring. (3) Road intersections are currently very common locations for vulnerable road-user accidents; traffic flows and road-user behaviours at intersections can be unpredictable, with many vehicles behaving inconsistently (e.g., red-light running and failure to stop or give way), and many vulnerable road-users taking unforeseen risks. (4) Conclusions: The challenges of unpredictable vulnerable road-user behaviour at intersections (including road-users violating traffic or safe-crossing signals, or taking other risks) combined with the lack of knowledge of CAV responses to intersection rules, could be problematic. This could be further compounded by changes to nonverbal communication that currently exist between road-users, which could become more challenging once CAVs become more widespread.

scholarly journals Role of The School Administration in Applying the Quality Standards in Teaching Among Islamic Education Teachers Perspectives in The Directorate of Education in The Zarqa Region: دور الإدارة المدرسية في تطبيق معايير جودة التدريس من وجهة نظر معلمي التربية الإسلامية في مديرية تعليم منطقة الزرقاء بالأردن

2020 ◽  
Vol 4 (30) ◽  
Author(s):  
Omar AbdelAziz Mosa Yassen
Keyword(s):  
Data Collection ◽  
School Administration ◽  
Quality Standards ◽  
Arab Countries ◽  
Islamic Education ◽  
Male And Female ◽  
Female Teachers ◽  
Descriptive Approach ◽  
Teachers Perspectives

This study aimed to identify the role of the school administration in applying the quality standards in teaching among Islamic education teachers Perspectives in the Directorate of Education in the Zarqa Region study sample consisted of (72) male teachers and (88) female teachers. To achieve the objectives of the study,  The descriptive approach was used a questionnaire was used as a tool for data collection; The result showed that the level of the role of the school administration in applying the quality standards in teaching among Islamic education teachers was High with (3.71) score the stander of planning gets the highest score with (4.85) degree then the stander of implementation the lesson was also high with (3.83) score finally the stander of the evaluation was moderate with (3.56) score The finding also showed there are no statistically significant differences between the level of Islamic Education teachers sex (male and female) in addition there were statistically significant differences refers to years of experience in favor of the five and less. - In light of these results, there are a set of recommendations and suggestions to maintain quality standards in teaching and to improve evaluation quality in Zarqa School and in the whole of the kingdom of Jordan and Arab countries.

scholarly journals Privacy Preservation using (L, D) Inference Model Based on Dependency Identification Information Gain

2019 ◽  
Vol 8 (6S3) ◽  
pp. 1170-1173
Keyword(s):  
Data Mining ◽  
Information Gain ◽  
Original Data ◽  
Perturbation Approach ◽  
Sensitive Information ◽  
Functional Dependencies ◽  
Inference Model ◽  
Data Set ◽  
Data Mining Techniques ◽  
Original Dataset

The improvement of an information processing and Memory capacity, the vast amount of data is collected for various data analyses purposes. Data mining techniques are used to get knowledgeable information. The process of extraction of data by using data mining techniques the data get discovered publically and this leads to breaches of specific privacy data. Privacypreserving data mining is used to provide to protection of sensitive information from unwanted or unsanctioned disclosure. In this paper, we analysis the problem of discovering similarity checks for functional dependencies from a given dataset such that application of algorithm (l, d) inference with generalization can anonymised the micro data without loss in utility. [8] This work has presented Functional dependency based perturbation approach which hides sensitive information from the user, by applying (l, d) inference model on the dependency attributes based on Information Gain. This approach works on both categorical and numerical attributes. The perturbed data set does not affects the original dataset it maintains the same or very comparable patterns as the original data set. Hence the utility of the application is always high, when compared to other data mining techniques. The accuracy of the original and perturbed datasets is compared and analysed using tools, data mining classification algorithm.

scholarly journals Analysis of erroneous data entries in paper based and electronic data collection

2019 ◽  
Author(s):  
Benedikt Ley ◽  
Komal Raj Rijal ◽  
Jutta Marfurt ◽  
Nabaraj Adhikari ◽  
Megha Banjara ◽  
...  
Keyword(s):  
Data Collection ◽  
Data Entry ◽  
Categorical Variables ◽  
Data Sets ◽  
Continuous Variables ◽  
Electronic Data ◽  
Suitable Alternative ◽  
Electronic Data Collection ◽  
Data Points ◽  
Time Variables

Abstract Objective: Electronic data collection (EDC) has become a suitable alternative to paper based data collection (PBDC) in biomedical research even in resource poor settings. During a survey in Nepal, data were collected using both systems and data entry errors compared between both methods. Collected data were checked for completeness, values outside of realistic ranges, internal logic and date variables for reasonable time frames. Variables were grouped into 5 categories and the number of discordant entries were compared between both systems, overall and per variable category. Results: Data from 52 variables collected from 358 participants were available. Discrepancies between both data sets were found in 12.6% of all entries (2352/18,616). Differences between data points were identified in 18.0% (643/3,580) of continuous variables, 15.8% of time variables (113/716), 13.0% of date variables (140/1,074), 12.0% of text variables (86/716), and 10.9% of categorical variables (1,370/12,530). Overall 64% (1,499/2,352) of all discrepancies were due to data omissions, 76.6% (1,148/1,499) of missing entries were among categorical data. Omissions in PBDC (n=1002) were twice as frequent as in EDC (n=497, p<0.001). Data omissions, specifically among categorical variables were identified as the greatest source of error. If designed accordingly, EDC can address this short fall effectively.

Usability Evaluation of an Offline Electronic Data Capture App in a Prospective Multicentre Dementia Registry – digiDEM Bayern: a Mixed Method Study (Preprint)

2021 ◽  
Author(s):  
Michael Reichold ◽  
Miriam Hess ◽  
Peter L. Kolominsky-Rabas ◽  
Elmar Gräßel ◽  
Hans-Ulrich Prokosch
Keyword(s):  
Data Collection ◽  
Technology Acceptance ◽  
Mobile Devices ◽  
Mixed Method ◽  
User Group ◽  
Electronic Data ◽  
Thinking Aloud ◽  
Electronic Data Collection ◽  
Usability Problems ◽  
Dementia Registry

BACKGROUND Digital registries have shown to provide an efficient way better to understand the clinical complexity and long-term progression of diseases. The paperless way of electronic data collection during a patient interview saves both: time and resources. In the prospective multicenter 'Digital Dementia Registry Bavaria - digiDEM Bayern', interviews are also conducted on-site in rural areas with unreliable internet connectivity. It must be ensured that electronic data collection can still be performed there, and it is no need to fall back on paper-based questionnaires. Therefore, the EDC system REDCap offers, in addition to a web-based data collection solution, the option to collect data offline via an app and synchronize it afterward. OBJECTIVE This study evaluates the usability of the REDCap app as an offline electronic data collection option for a lay user group and examines the necessary technology acceptance using mobile devices for data collection. Thereby, the feasibility of the app-based offline data collection in the dementia registry project was evaluated before going live. METHODS The study was conducted with an exploratory mixed-method in the form of an on-site usability test with the 'Thinking Aloud' method combined with a tailored semi-standardized online questionnaire including System Usability Score (SUS). The acceptance of mobile devices for the data collection was surveyed based on the technology acceptance model (TAM) with five categories. RESULTS Using the Thinking Aloud method, usability problems were identified and solutions were derived therefore. The evaluation of the REDCap app resulted in a SUS score of 74, which represents 'good' usability. After evaluating the technology acceptance questionnaire, it can be stated that the lay user group is open to mobile devices as interview tools. CONCLUSIONS The usability evaluation results show that a lay user group like the data collecting partners in the digiDEM project can handle the REDCap app well overall. The usability test provided statements about positive aspects and was able to identify usability problems of the REDCap app. In addition, the current technology acceptance in the sample showed that heterogeneous groups of different ages with different experiences in handling mobile devices are also ready for the use of app-based EDC systems. Based on the results, it can be assumed that the offline use of an app-based EDC system on mobile devices is a viable solution to collect data in a registry-based research project.

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