Assign Priority Levels Research Articles

Probabilistic Fuzzy System for Evaluation and Classification in Failure Mode and Effect Analysis

Failure Mode and Effect Analysis (FMEA) is an essential risk analysis tool that is widely applicable in various industrial sectors. This structured technique allows us to identify and assign priority levels to potential failures that violate the reliability of a system or process. Failure evaluation occurs in a decision-making environment with uncertainty. This study proposes a probabilistic fuzzy system that integrates linguistic and stochastic uncertainty based on a Mamdani-type model to strengthen the FMEA technique. The system is based on analyzing the frequency of failures and obtaining the parameters to determine the probability of occurrence through the Poisson distribution. In addition, the severity and detection criteria were evaluated by the experts and modeled using the Binomial distribution. The evaluation result is a discrete value analogous to the process of obtaining the success or failure of the expert generating the evaluation of 10 Bernoulli experiments. Three fuzzy inference expert systems were developed to combine multiple experts’ opinions and reduce linguistic subjectivity. The case study was implemented in the knitting area of a textile company in the south of Guanajuato to validate the proposed approach. The potential failure of the knitting machinery, which compromises the top tension subsystem’s performance and the product’s quality, was analyzed. The proposed system, which is based on a robust mathematical model, allows for reliable fault evaluation with a simple scale. The classification performed by the system and the one performed by the experts has similar behavior. The results show that the proposed approach supports decision-making by prioritizing failure modes.

Auto Timetable Management Mobile Application

In this fast-paced lifestyle, we play multiple roles and bear different responsibilities. This kind of lifestyle needs proper time management skills to look after the tasks and duties that have been entrusted. In order to do time management effectively, tools such as timetables are commonly adopted. Rescheduling of a timeslot in a timetable could happen due to certain unforeseen events that require immediate action. The rearrangement of one timeslot could possibly affect another timeslot in a timetable, essentially, it needs extra time and effort to draw new planning. In this paper, we proposed to develop an auto timetable scheduling mobile application to combat the hustle of manual timetable management. By assigning priority levels and constraints such as the preferred time and date of a task, the proposed application can reschedule and generate a new timetable automatically. With the proposed application, we claim that users can reduce the hustle of creating and managing their timetables in a manual way.
 

Minimum relevant features to obtain AI explainable system for predicting breast cancer in WDBC

The potential to explain why a machine learning model produces a certain prediction in incomprehensible terms is becoming increasingly crucial, as it provides accountability and confidence in the algorithm's decision-making process. The interpretation of complex models is difficult. Various approaches to dealing with this issue are being offered. These problems are typically handled in tree ensemble methods by assigning priority levels to input features globally or for a specific prediction. We show that current feature attribution approaches are inconclusive, and develop solutions using SHAP (SHapley Additive Explanation) values, LIME (Local Interpretable Model-Agnostic Explanations), and the Skope Rules package. We employ feature selection methods from SHAP and LIME in this work, which uses the Breast cancer Wisconsin data sets. In the suggested method, features are chosen at the first level of feature selection using Decision tree entropy values. Based on the SHAP and LIME reports, level 2 features are chosen from fewer options. The features are tested on a Decision Tree (DT) model and a DT and Support Vector Machine (SVM) ensemble. Experiments suggest that the ensemble works better as compared to DT. We have also used the Skope Rules package to generate global rules for generalization.

Fog-Computing-Based Energy Storage in Smart Grid: A Cut-Off Priority Queuing Model for Plug-In Electrified Vehicle Charging

Electric vehicles (EVs) are likely to become very popular within the next few years. With possibly millions of such vehicles operating across the smart cities, smart grid energy providers can be directly impacted by the charging of EV batteries. In order to reduce this impact and optimize energy saving, in this article, we propose a coordinated model for scheduling the plug-in of EVs for charging and discharging energy. The model is based on a new decentralized Fog architecture for smart grid in order to reduce the completion and communication delay of EV energy demand scheduling. To enhance the scheduling of EV demands and predict the future energy flows, we propose a plug-in system of EVs based on calendar planning. We develop a mathematical formalism based on Markov chains using a multipriority queuing theory with cut-off discipline in order to reduce the waiting time to plug-in. We implement three planning algorithms in order to assign priority levels and then optimize the plug-in time into each EV public supply station. To the best of our knowledge, this is the first article that proposes a model that tries to save energy by planning the plug-in of EVs using a cut-off priority queuing model and a decentralized Fog architecture. We evaluate the performances of our solution via extensive simulations using a realistic energy loads from the city of Toronto, and we compare it with other recent works.

Interrater Reliability Testing of the Maternal Fetal Triage Index

ObjectiveTo conduct interrater reliability testing of the Maternal Fetal Triage Index (MFTI), a standardized tool forobstetric triage. DesignObservational study of a convenience sample of nurses’ triage assessments of hospital-based obstetricpatients. Setting:A birth unit of a suburban hospital located in a large metropolitan region with approximately 5,200 birthsannually. ParticipantsTen registered nurses provided triage assessments of 211 pregnant women. Methods:Using blinded paired triage assessments, we assessed the reproducibility of the triage priority levels assignedusing the MFTI. Results:Priority levels assigned by the MFTI research nurse and the study nurses were in agreement for 154 of the211 (72.9%) triage assessments. The strength of agreement was classified as good based upon the weighted kappascore of 0.65. There was no statistically significant difference in the accuracy of assigning priority levels between theday and night shifts Conclusion:The interrater reliability of the MFTI met the minimum strength of agreement threshold goal of 0.60 whenused by nurses in a large birth unit to assign priority for evaluation. Based on this finding, the MFTI can be recommendedfor use in obstetric triage settings. Additional testing should be done to measure how this standardized tool improvescare processes and outcomes.

Method for Assigning Priority Levels in Acute Care (MAPLe-AC) predicts outcomes of acute hospital care of older persons - a cross-national validation

BackgroundAlthough numerous risk factors for adverse outcomes for older persons after an acute hospital stay have been identified, a decision making tool combining all available information in a clinically meaningful way would be helpful for daily hospital practice. The purpose of this study was to evaluate the ability of the Method for Assigning Priority Levels for Acute Care (MAPLe-AC) to predict adverse outcomes in acute care for older people and to assess its usability as a decision making tool for discharge planning.MethodsData from a prospective multicenter study in five Nordic acute care hospitals with information from admission to a one year follow-up of older acute care patients were compared with a prospective study of acute care patients from admission to discharge in eight hospitals in Canada. The interRAI Acute Care assessment instrument (v1.1) was used for data collection. Data were collected during the first 24 hours in hospital, including pre-morbid and admission information, and at day 7 or at discharge, whichever came first. Based on this information a crosswalk was developed from the original MAPLe algorithm for home care settings to acute care (MAPLe-AC). The sample included persons 75 years or older who were admitted to acute internal medical services in one hospital in each of the five Nordic countries (n = 763) or to acute hospital care either internal medical or combined medical-surgical services in eight hospitals in Ontario, Canada (n = 393). The outcome measures considered were discharge to home, discharge to institution or death. Outcomes in a 1-year follow-up in the Nordic hospitals were: living at home, living in an institution or death, and survival. Logistic regression with ROC curves and Cox regression analyses were used in the analyses.ResultsLow and mild priority levels of MAPLe-AC predicted discharge home and high and very high priority levels predicted adverse outcome at discharge both in the Nordic and Canadian data sets, and one-year outcomes in the Nordic data set. The predictive accuracy (AUC's) of MAPLe-AC's was higher for discharge outcome than one year outcome, and for discharge home in Canadian hospitals but for adverse outcome in Nordic hospitals. High and very high priority levels in MAPLe-AC were also predictive of days to death adjusted for diagnoses in survival models.ConclusionMAPLe-AC is a valid algorithm based on risk factors that predict outcomes of acute hospital care. It could be a helpful tool for early discharge planning although further testing for active use in clinical practice is still needed.

A Controlled Skip Parser

Real-world natural language sentences are often long and complex, and contain unexpected grammatical constructions. They even include noise and ungrammaticality. This paper describes the Controlled Skip Parser, a program that parses such real-world sentences by skipping some of the words in the sentence. The new feature of this parser is that it controls its behavior by finding out which words to skip, without using domain-specific knowledge. The parser is a priority-based chart parser. By assigning appropriate priority levels to the constituents in the chart, the parser's behavior is controlled. Statistical information is used for assigning priority levels. The statistical information (n-grams) can be thought of as a generalized approximation of the grammar learned from past successful experiences. The control mechanism gives a great speed-up and reduction in memory usage. Experiments on real newspaper articles are shown, and our experience with this parser in a machine translation system is described.