[Data Entry Error Checking Software Free Download
Gildo Santiago
I am attempting to create a Java program that will allow the user to enter any desired amount of numerical grades and then calculate the MIN, MAX & Average. Handling input errors is where it gets confusing for me.
I've come up with two different approaches, the first allowing the user to enter their desired number of grades until the user enters sentinel -1. The second approach prompts the user to enter the number of grades beforehand.
I need to incorporate messages to alert user when numbers outside parameters are entered- lettesr, words, etc. When I try to implement these error handling loops, I can't figure out how to get the loops to ignore errored responses.
I can't figure how to get it to recognize the -1, display error, and then prompt for one more entry to total 3. Perhaps Approach One would be easier since there is no set number of grades that is predetermined.
To give warning messages to users you probably want to experiment with try/catch statements (exception handling) and also include booleans as controls for while loops. Also seeing that your code requires the user to enter a few numbers I assume you would need to use some sort of array/arraylist to help calculate min/max/avg. Hence your code may turn out to be something like this:
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Background: The objective of this study was to develop and validate machine learning models for data entry error detection in a national out-of-hospital cardiac arrest (OHCA) prehospital patient care report database.Methods: Adult OHCAs of presumed cardiac etiology were included. Data entry errors were defined as discrepancies between the coded data and the free-text note documenting the intervention or event; for example, information that was recorded as "absent" in the coded data but "present" in the free-text note. Machine learning models using the extreme gradient boosting, logistic regression, extreme gradient boosting outlier detection, and K-nearest neighbor outlier detection algorithms for error detection within nine core variables were developed and then validated for each variable.Results: Among 12,100 OHCAs, the proportion of cases with at least one error type was 16.2%. The area under the receiver operating characteristic curve (AUC) of the best-performing model (model with the highest AUC for each outcome variable) was 0.71-0.95. Machine learning models detected errors most efficiently for outcome place and initial rhythm errors; 82.6% of place errors and 93.8% of initial rhythm errors could be detected while checking 11 and 35% of data, respectively, compared to the strategy of checking all data.Conclusion: Machine learning models can detect data entry errors in care reports of emergency medical services (EMS) clinicians with acceptable performance and likely can improve the efficiency of the process of data quality control. EMS organizations that provide more prehospital interventions for OHCA patients could have higher error rates and may benefit from the adoption of error-detection models.
Once the analyst is satisfied that the CORSIM model is fully coded, the analyst must then examine the model for completeness and errors. The error correction step is essential in developing a working model so that the calibration process does not result in parameters that are distorted to compensate for overlooked coding errors. There is a distinct difference between the error checking stage and the calibration stage. The error checking stage examines and removes the errors produced during model development. Conversely, the calibration stage involves adjusting the model parameters to assure the model will accurately reproduce local traffic conditions. The calibration process relies on the elimination of all major errors in demand and network coding before calibration begins. Error checking involves various reviews of the coded network, coded demands, and default parameters.
The practitioner should manage the error checking step by controlling the resources assigned to error checking, ensuring a consistent process is followed, staying on task (i.e., not rushing into calibration or alternatives analysis), and completing the task on schedule. The analyst should document the errors and the approach and techniques used for resolving them.
Software errors can be tested by coding simple test problems (such as a single link or intersection) where the result (such as capacity or mean speed) can be computed manually and compared to the model. Some software errors can only be resolved by working with the software distributor or software developer.
It is good practice to have an analyst familiar with the project, but not involved in the base model coding, perform the review of the input data. Subtle coding errors may be easier to detect from someone that has not been personally involved in the base model development. The analyst should check for data coding errors that may be causing the simulation model to represent travel behavior incorrectly. Subtle data coding errors are the most frequent cause of unrealistic vehicle behavior in a CORSIM model. Subtle coding errors include apparently correctly coded input that is incorrect because of how it is used in the model to determine vehicle behavior. Input data errors may happen for different reasons including:
There are many ways to review the input data. For example, it may be easier to use other tools than those built into TSIS. Many companies and individuals have created scripts that perform data entry and data transfer tasks. Reviewing the input data using the TSIS provided tools are described below.
A network does not have to run in CORSIM to be able to be viewed in TRAFVU. This is very useful for checking errors produced during a CORSIM run. TRAFVU can show links that do not connect correctly or the placement of network objects like bus stations or parking zones that produce errors.
The TRF file is a text file that can be viewed with any text editor including the text editor built into TSIS. Comparing the TRF file parameters to the data collected during the data collection phase (stored in a spreadsheet format) is important to ensuring the correct data is being modeled. Unfortunately, reading the file is not the most straight-forward approach to data error checking and it does require knowledge of the TRF file record types. The records in the TRF file can be sorted so they are easier to review. Turn on sorting in the TRAFED preferences. The records will be sorted by their unique identifier (normally the first entry or the first two entries).
CORSIM itself can be used to check the network for errors and warnings. CORSIM checks all the network parameters to ensure they fall between the minimum and maximum values. It also checks against many other rules that exist. TRAFED checks many of these same ranges and rules to catch data input errors as they are input. However, TRAFED does not check every single rule that CORSIM checks so there may be error messages generated when the network is run through CORSIM. TRAFED can use CORSIM to do a diagnostic check of the input data by using the Network/Check menu item. Input errors will be displayed in the Output Window.
When running CORSIM there may be many errors and warnings. Some of these messages are due to input data errors, or they may be due to situations during the run that cause errors to occur. Error messages stop the execution of CORSIM. The errors must be corrected prior to continuing. It is advisable to fix the first error first and work down the list. Correcting the first error will sometimes correct subsequent errors.
Warning messages may indicate significant potential problems that should be corrected or they may indicate conditions that should be evaluated and thereafter ignored. Each warning message should be investigated. If it is decided to allow the warning to remain, the reason for allowing it should be documented. A few such warnings are discussed below:
The importance of a comparison of model animation to field design and operations cannot be overemphasized. More than just a presentation tool, animation is a great debugging tool. The network has not yet been calibrated so the network may not operate exactly as it does in the real world, but the animation should at least be viewed in this step to make sure the modeled network is in a reasonable range with the real world.
Animation output enables the analyst to observe the vehicle behavior that is being modeled and assess the reasonableness of CORSIM. Running the simulation model and reviewing the animation, even with artificial demands, can be useful to identify input coding errors. A two-stage process can be followed in reviewing the animation output:
Run the animation at an extremely low demand level (so low that there is no congestion). The analyst should then trace single vehicles through the network and see where they unexpectedly slow down. Uncharacteristic vehicle behavior (such as unexpected braking or stops) is a quick indicator of possible coding errors. These will usually be locations of minor network coding errors that disturb the movement of vehicles over the link or through the node. This test should be repeated along selected links in the network.
Once the extremely low demand level tests have been completed, then run the simulation at 50 percent of the existing demand level. At this level, demand is usually not yet high enough to cause congestion. If congestion appears, it may be the result of some more subtle coding errors that affect the distribution of vehicles across lanes or their headways. Check entry and exit link flows to verify that all demand is being correctly loaded and moved through the network.
The animation should be observed in close detail at key congestion points to determine if the animated vehicle behavior is realistic. If the observed vehicle behavior appears to be unrealistic, the analyst should explore the following potential causes of the unrealistic animation in the order shown below:
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