Using Lucene And Clustering Libraries

PSO Clustering with Semantic based Approach 0.90 S.No. Method Prediction Rate 1. Web Page Prefetching 0.65 2. AR,FS, and FGS 0.69 3. Sequential Association Rule 0.72 4.

This section discuss about the common traits or ideas observed in the three research topics. Although, each of the three articles discuss a unique idea, all of them are aimed at utilizing the web data to produce better results. Web data mining is a hot research topic in the current realm of big data. These papers discuss about the utilization of the valuable user generated data from the social media or the browser cookies to provide the best user experience in order to maintain the user interest in the company's product or to take effective decisions by an individual. All the three articles propose an idea to solution the problem stated, compared their results to the existing models and showed significant improvement.

Here we discuss about the common traits or ideas observed in the three research topics. Although, these three papers discuss about different ideas, they all fall under the web data mining domain. web data mining is a hot research topic in the current realm of big data. These papers discuss about the utilisation of the valuable user generated data from the social media or the the browser cookies to provide the best user experience in order to maintain the user interest in the company's product or to take effective decisions by the individual.

With the advent of computer technology in 1990’s the need to search large databases was increasingly becoming vital. The search engines prior to PageRank had limitations, the then most widely used algorithm used text based indexes to provide search results on World Wide Web however had limitations of improper search results as the logic used by the search engines looked at the number of occurrences of the search word in webpage which sometimes resulted in improper search results. Another technique used during the time was based on variations of standard vector space model – i.e. search based on how recent the webpage was updated and/or how close the search terms are to the

Also enforced by OCR, the Patient Safety and Quality Improvement Act of 2005 (PSQIA) established a voluntary reporting system where data is analyzed and used to enhance the safety and quality of healthcare delivery. PSQIA provides confidentiality protections to healthcare providers who were previously concerned about the use of patient safety event reports in criminal, civil, and administrative proceedings. By limiting the use of event reports the fear report medical errors has decreased among many healthcare providers (Medical Errors and Patient Safety, 2008).

Patient safety is of utmost importance in healthcare. As healthcare providers we are all human, and therefore we all error, having a system that will double check our actions for said errors would be of great value. Technology in healthcare is developed with the intent to combat exactly that. The role of technology continues beyond the detection of human error and plays an overall significant part in keeping and maintaining patients’ safety.

Error can occur daily with the public safety or criminal justice organization, especially the ones that have to be manually input into a system like the Uniform Crime Reports (UCR). The errors are not done purposely, but humans are in charge of inputting the data it is possible to make errors which tend to show up as gaps within the reporting systems. An error can happen when officers record the actualities of an incident correctly, but misclassify the type of crime (Nolan et al. 2006). Hypothetically, categorization of errors can happen for various reasons. For example, simple mistakes made by law enforcement officers (LEOs), bad writing habits that were passed down from police officer to police officer,

As of May 31, 2018 there have been only 12 near misses reported companywide, but during the same time period, we had 31 first aid injuries and 19 records reported. Comparatively, injury statistics suggest for the 31 first aids and 19 records, we probably had at least 450 near misses that were not reported. In fact, that number could be

In October of 2015, ETS began an effort to extrapolate more meaning from the data. The need for advanced analytics, and proactive solutions to problems became a central focus for the organization. Over 150,000 tickets were analyzed for textual similarities. Alerts that automatically generate tickets were a key finding, since each type of alert had similar ticket descriptions. Tickets were then categorized and grouped by the type of alert the ticket was representative of. As of 2016, ETS has successfully categorized over 200,000 incidents into over 80 distinct categories.

I think that LUCES has a great potential to reaching so many womxn of color on campus. I can first speak on the spaces and communities that I belong to. I think that sometimes that there are not as many Black womxn apart of LUCES as there could be. I think that there is a gap between these two communities. As a LUCES Scholar one has to establish this relationship immediately within the school year because if that relationship isn’t already established the students get into their routine and LUCES ends up not being a priority. This can be very difficult to achieve, but I believe visibility can be a toll to expand. I know as a first year the SDMA office can be very intimating to the point of not even going inside. In order to make oneself visible

It is also important to record and investigate 'near-miss ' incidents to learn from mistakes and prevent workers being injured or becoming ill in future.

The advent of records management systems (RMS) has solved several problems associated with the “paper tiger.” Records management systems are used to input and organize information from different types of reports in one easy-to-access format (Police in America). These systems have helped with the police officers workload (not needing to file written reports). The database and information advancements have made the use of card file indexes all but extinct. Information that was once used on these card files, such as the person’s name, date of birth, criminal record and case numbers are now entered into a computerized system that is accessible to the officers with the touch of a button. This works as a check for police officers as well. Through the use of early intervention (EI) systems are a collection of personnel records for the use of increasing accountability of

Reporting of an adverse event plays a vital role in the maintenance of patient safety and quality of care. There are two types of reporting system in the health care setting. The process which ensure the liability of Committed action is called “Mandatory reporting” (Lambton College in Toronto, 2017). On the other hand, the process which emphasized on patient safety is called “voluntary reporting” (Lambton College in Toronto,2017). However, there are some obstacles in the timely and efficiently reporting of errors and near-misses by personnel (Douglas & Peter,

Check sheets are tools which contains a collection of items and an indicator which shows how often each item on the list occurs. It makes the data collection process easier by categorizing events that are likely to occur. The performance of check sheet can be amplified if it is used with other tools such as Histograms and Pareto analysis. “Ishikawa estimated that 80% to 90% of all workplace problems could be solved using only the simple quality improvement tools”. (Thomas Pyzdek 2015)

This project helps the common man to take safety and preventionery measures when an accident takes place. This project is to take the existing requirements and convert them to Work light mobile application. The benefits of this project are to be able to use a mobile device to make the tools more assessable.