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Articoli di riviste sul tema "Alert algorithm"

Autore: Grafiati

Pubblicato: 4 giugno 2021

Ultima modifica: 5 febbraio 2022

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1

Horn, John, and Stephen Ueng. "The Effect of Patient-Specific Drug-Drug Interaction Alerting on the Frequency of Alerts: A Pilot Study." Annals of Pharmacotherapy 53, no. 11 (2019): 1087–92. http://dx.doi.org/10.1177/1060028019863419.

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Background: False-positive drug-drug interaction alerts are frequent and result in alert fatigue that can result in prescribers bypassing important alerts. Development of a method to present patient-appropriate alerts is needed to help restore alert relevance. Objective: The purpose of this study was to assess the potential for patient-specific drug-drug interaction (DDI) alerts to reduce alert burden. Methods: This project was conducted at a tertiary care medical center. Seven of the most frequently encountered DDI alerts were chosen for developing patient-specific, algorithm-based DDI alerts

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Jain, Nikita, Deepali Virmani, and Ajith Abraham. "Overlap Function Based Fuzzified Aquatic Behaviour Information Extracted Tsunami Prediction Model." International Journal of Distributed Systems and Technologies 10, no. 1 (2019): 56–81. http://dx.doi.org/10.4018/ijdst.2019010105.

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Past natural hazards have produced numerous biological and physical indicators that can be used to predict similar instances in the future. These indicators can be sensed dynamically underwater or on land to generate real time alerts. This article proposes the first validated fuzzified system to predict tsunamis (FABETP) using an overlap-based algorithm. This proposed algorithm can predict seismicity based on underwater marine animal's anomalous behavior, characterized and implemented as biological indicators (i.e., aquatic animal behavioral attributes). Relevant information is extracted from

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Oppenheimer, Julia, Oluwafemi Ojo, Annalee Antonetty, et al. "Timely Interventions for Children with ADHD through Web-Based Monitoring Algorithms." Diseases 7, no. 1 (2019): 20. http://dx.doi.org/10.3390/diseases7010020.

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The aim of this study was to evaluate an automated trigger algorithm designed to detect potentially adverse events in children with Attention-Deficit/Hyperactivity Disorder (ADHD), who were monitored remotely between visits. We embedded a trigger algorithm derived from parent-reported ADHD rating scales within an electronic patient monitoring system. We categorized clinicians’ alert resolution outcomes and compared Vanderbilt ADHD rating scale scores between patients who did or did not have triggered alerts. A total of 146 out of 1738 parent reports (8%) triggered alerts for 98 patients. One h

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XIONG, Li-qiong. "Alert aggregation algorithm based on genetic clustering algorithm." Journal of Computer Applications 28, no. 4 (2008): 896–98. http://dx.doi.org/10.3724/sp.j.1087.2008.00896.

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Ziewacz, John E., Sigurd H. Berven, Valli P. Mummaneni, et al. "The design, development, and implementation of a checklist for intraoperative neuromonitoring changes." Neurosurgical Focus 33, no. 5 (2012): E11. http://dx.doi.org/10.3171/2012.9.focus12263.

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Object The purpose of this study was to provide an evidence-based algorithm for the design, development, and implementation of a new checklist for the response to an intraoperative neuromonitoring alert during spine surgery. Methods The aviation and surgical literature was surveyed for evidence of successful checklist design, development, and implementation. The limitations of checklists and the barriers to their implementation were reviewed. Based on this review, an algorithm for neurosurgical checklist creation and implementation was developed. Using this algorithm, a multidisciplinary team

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6

Her, Qoua L., Mary G. Amato, Diane L. Seger, et al. "The frequency of inappropriate nonformulary medication alert overrides in the inpatient setting." Journal of the American Medical Informatics Association 23, no. 5 (2016): 924–33. http://dx.doi.org/10.1093/jamia/ocv181.

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Abstract Background Experts suggest that formulary alerts at the time of medication order entry are the most effective form of clinical decision support to automate formulary management. Objective Our objectives were to quantify the frequency of inappropriate nonformulary medication (NFM) alert overrides in the inpatient setting and provide insight on how the design of formulary alerts could be improved. Methods Alert overrides of the top 11 ( n = 206) most-utilized and highest-costing NFMs, from January 1 to December 31, 2012, were randomly selected for appropriateness evaluation. Using an em

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Chen, Ji, Sara Chokshi, Roshini Hegde, et al. "Development, Implementation, and Evaluation of a Personalized Machine Learning Algorithm for Clinical Decision Support: Case Study With Shingles Vaccination." Journal of Medical Internet Research 22, no. 4 (2020): e16848. http://dx.doi.org/10.2196/16848.

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Background Although clinical decision support (CDS) alerts are effective reminders of best practices, their effectiveness is blunted by clinicians who fail to respond to an overabundance of inappropriate alerts. An electronic health record (EHR)–integrated machine learning (ML) algorithm is a potentially powerful tool to increase the signal-to-noise ratio of CDS alerts and positively impact the clinician’s interaction with these alerts in general. Objective This study aimed to describe the development and implementation of an ML-based signal-to-noise optimization system (SmartCDS) to increase

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8

Biswas, Aditya, Chirag R. Parikh, Harold I. Feldman, et al. "Identification of Patients Expected to Benefit from Electronic Alerts for Acute Kidney Injury." Clinical Journal of the American Society of Nephrology 13, no. 6 (2018): 842–49. http://dx.doi.org/10.2215/cjn.13351217.

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Background and objectivesElectronic alerts for heterogenous conditions such as AKI may not provide benefit for all eligible patients and can lead to alert fatigue, suggesting that personalized alert targeting may be useful. Uplift-based alert targeting may be superior to purely prognostic-targeting of interventions because uplift models assess marginal treatment effect rather than likelihood of outcome.Design, setting, participants, & measurementsThis is a secondary analysis of a clinical trial of 2278 adult patients with AKI randomized to an automated, electronic alert system versus usual

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Fan, Lidan, Weili Wu, Kai Xing, and Wonjun Lee. "Precautionary rumor containment via trustworthy people in social networks." Discrete Mathematics, Algorithms and Applications 08, no. 01 (2016): 1650004. http://dx.doi.org/10.1142/s179383091650004x.

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In a social network, rumor containment is vital, as the diffusion of a rumor will bring terrible results. Precautionary measure can be used to control rumor propagation: Anticipating the spread of a rumor, one can (1) select a set of trustworthy people (TP) in the network, (2) alert the TP about the rumor, and (3) ask the TP to protect their neighbors by sending out alerts. In this paper, we study the problem of how to select the least number of TP, satisfying the requirement that the entire network is protected by the alerts that the TP send. We propose an asymmetric trust (AT) information pr

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Soewito, Benfano, Irwan, Anna Antonyová, and Fergyanto E. Gunawan. "Fall Detection Algorithm to Generate Security Alert." Procedia Computer Science 59 (2015): 350–56. http://dx.doi.org/10.1016/j.procs.2015.07.532.

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Reich, Nicholas G., Derek A. T. Cummings, Stephen A. Lauer, et al. "Triggering Interventions for Influenza: The ALERT Algorithm." Clinical Infectious Diseases 60, no. 4 (2014): 499–504. http://dx.doi.org/10.1093/cid/ciu749.

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Soto, Ignacio, Felipe Jimenez, Maria Calderon, Jose Naranjo, and Jose Anaya. "Reducing Unnecessary Alerts in Pedestrian Protection Systems Based on P2V Communications." Electronics 8, no. 3 (2019): 360. http://dx.doi.org/10.3390/electronics8030360.

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There are different proposals in the literature on how to protect pedestrians using warning systems to alert drivers of their presence. They can be based on onboard perception systems or wireless communications. The evaluation of these systems has been focused on testing their ability to detect pedestrians. A problem that has received much less attention is the possibility of generating too many alerts in the warning systems. In this paper, we propose and analyze four different algorithms to take the decision on generating alerts in a warning system that is based on direct wireless communicati

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Kirkendall, Eric S., Michal Kouril, Judith W. Dexheimer, et al. "Automated identification of antibiotic overdoses and adverse drug events via analysis of prescribing alerts and medication administration records." Journal of the American Medical Informatics Association 24, no. 2 (2016): 295–302. http://dx.doi.org/10.1093/jamia/ocw086.

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Objectives: Electronic trigger detection tools hold promise to reduce Adverse drug event (ADEs) through efficiencies of scale and real-time reporting. We hypothesized that such a tool could automatically detect medication dosing errors as well as manage and evaluate dosing rule modifications. Materials and Methods: We created an order and alert analysis system that identified antibiotic medication orders and evaluated user response to dosing alerts. Orders associated with overridden alerts were examined for evidence of administration and the delivered dose was compared to pharmacy-derived dosi

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Pfistermeister, Barbara, Brita Sedlmayr, Andrius Patapovas, et al. "Development of a Standardized Rating Tool for Drug Alerts to Reduce Information Overload." Methods of Information in Medicine 55, no. 06 (2016): 507–15. http://dx.doi.org/10.3414/me16-01-0003.

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Summary Background: A well-known problem in current clinical decision support systems (CDSS) is the high number of alerts, which are often medically incorrect or irrelevant. This may lead to the so-called alert fatigue, an over -riding of alerts, including those that are clinically relevant, and underuse of CDSS in general. Objectives: The aim of our study was to develop and to apply a standardized tool that allows its users to evaluate the quality of system-generated drug alerts. The users’ ratings can subsequently be used to derive recommendations for developing a filter function to reduce i

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Gwak, Jongseong, Akinari Hirao, and Motoki Shino. "An Investigation of Early Detection of Driver Drowsiness Using Ensemble Machine Learning Based on Hybrid Sensing." Applied Sciences 10, no. 8 (2020): 2890. http://dx.doi.org/10.3390/app10082890.

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Drowsy driving is one of the main causes of traffic accidents. To reduce such accidents, early detection of drowsy driving is needed. In previous studies, it was shown that driver drowsiness affected driving performance, behavioral indices, and physiological indices. The purpose of this study is to investigate the feasibility of classification of the alert states of drivers, particularly the slightly drowsy state, based on hybrid sensing of vehicle-based, behavioral, and physiological indicators with consideration for the implementation of these identifications into a detection system. First,

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Lu, Xianguang, Xuehui Du, and Wenjuan Wang. "An Alert Aggregation Algorithm Based on K-means and Genetic Algorithm." IOP Conference Series: Materials Science and Engineering 435 (November 5, 2018): 012031. http://dx.doi.org/10.1088/1757-899x/435/1/012031.

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Chen, Yuzhong, Zhenyu Liu, Yulin Liu, and Chen Dong. "Distributed Attack Modeling Approach Based on Process Mining and Graph Segmentation." Entropy 22, no. 9 (2020): 1026. http://dx.doi.org/10.3390/e22091026.

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Attack graph modeling aims to generate attack models by investigating attack behaviors recorded in intrusion alerts raised in network security devices. Attack models can help network security administrators discover an attack strategy that intruders use to compromise the network and implement a timely response to security threats. However, the state-of-the-art algorithms for attack graph modeling are unable to obtain a high-level or global-oriented view of the attack strategy. To address the aforementioned issue, considering the similarity between attack behavior and workflow, we employ a heur

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Cochran, Elizabeth S., Julian Bunn, Sarah E. Minson, et al. "Event Detection Performance of the PLUM Earthquake Early Warning Algorithm in Southern California." Bulletin of the Seismological Society of America 109, no. 4 (2019): 1524–41. http://dx.doi.org/10.1785/0120180326.

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Abstract We test the Japanese ground‐motion‐based earthquake early warning (EEW) algorithm, propagation of local undamped motion (PLUM), in southern California with application to the U.S. ShakeAlert system. In late 2018, ShakeAlert began limited public alerting in Los Angeles to areas of expected modified Mercalli intensity (IMMI) 4.0+ for magnitude 5.0+ earthquakes. Most EEW systems, including ShakeAlert, use source‐based methods: they estimate the location, magnitude, and origin time of an earthquake from P waves and use a ground‐motion prediction equation to identify regions of expected st

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19

Chayadevi, M. L., Sujith Madhyastha, K. N. Nisarga, H. Charitha, and B. Susharan. "Automated Teller Machine Security with Image Processing and Machine Learning Techniques." Journal of Computational and Theoretical Nanoscience 17, no. 9 (2020): 4473–81. http://dx.doi.org/10.1166/jctn.2020.9100.

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There are many scenarios in society with thefts and crimes involved in Automated Teller Machines (ATM). These events are increasing day-by-day and which is also increasing the complexities on the crime investigation agencies. In order to deal with these situations, we have proposed an automated security method inside ATMs using image processing techniques which can alert the concerned authorities immediately whenever these types of situations arise. Hybrid method with Viola-Jones algorithm has been used for face recognition along with the Haar-cascade features. In the case of objects such as k

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20

Jayakumar, M., and T. Christopher. "Alert Message Creation based Advanced Encryption Standard Algorithm." International Journal of Computer Applications 85, no. 7 (2014): 43–47. http://dx.doi.org/10.5120/14857-3224.

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21

Osuo-Genseleke, Macarthy, and Ojekudo Nathaniel. "Hybridized Design For Feature Optimization and Reduction of Intrusion Detection Systems Alert in a Correlation Framework." International Journal of Innovative Science and Research Technology 5, no. 7 (2020): 1051–55. http://dx.doi.org/10.38124/ijisrt20jul783.

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The Intrusion Detection System (IDS) produces a large number of alerts. Many large organizations deploy numerous IDSs in their network, generating an even larger quantity of these alerts, where some are real or true alerts and several others are false positives. These alerts cause very severe complications for IDS and create difficulty for the security administrators to ascertain effective attacks and to carry out curative measures. The categorization of such alerts established on their level of attack is necessary to ascertain the most severe alerts and to minimize the time required for respo

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Austrian, Jonathan S., Catherine T. Jamin, Glenn R. Doty, and Saul Blecker. "Impact of an emergency department electronic sepsis surveillance system on patient mortality and length of stay." Journal of the American Medical Informatics Association 25, no. 5 (2017): 523–29. http://dx.doi.org/10.1093/jamia/ocx072.

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Abstract Objective The purpose of this study was to determine whether an electronic health record–based sepsis alert system could improve quality of care and clinical outcomes for patients with sepsis. Materials and Methods We performed a patient-level interrupted time series study of emergency department patients with severe sepsis or septic shock between January 2013 and April 2015. The intervention, introduced in February 2014, was a system of interruptive sepsis alerts triggered by abnormal vital signs or laboratory results. Primary outcomes were length of stay (LOS) and in-hospital mortal

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Jing, Yong Wen, and Li Fen Li. "SOM and PSO Based Alerts Clustering in Intrusion Detection System." Applied Mechanics and Materials 401-403 (September 2013): 1453–57. http://dx.doi.org/10.4028/www.scientific.net/amm.401-403.1453.

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With the growing deployment of host and network intrusion detection systems (IDSs), thousands of alerts are generally generated from them per day. Managing these alerts becomes critically important. In this paper, a hybrid alert clustering method based on self-Organizing maps (SOM) and particle swarm optimization (PSO) is presented. We firstly select the important features through binary particle swarm optimization (BPSO) and mutual information (MI) and get a dimension reduced dataset. SOM is used to cluster the dataset. PSO is used to evolve the weights for SOM to improve the clustering resul

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Gren, Lisa H., Christina A. Porucznik, Elizabeth A. Joy, Joseph L. Lyon, Catherine J. Staes, and Stephen C. Alder. "Point-of-Care Testing as an Influenza Surveillance Tool: Methodology and Lessons Learned from Implementation." Influenza Research and Treatment 2013 (April 11, 2013): 1–10. http://dx.doi.org/10.1155/2013/242970.

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Objectives. Disease surveillance combines data collection and analysis with dissemination of findings to decision makers. The timeliness of these activities affects the ability to implement preventive measures. Influenza surveillance has traditionally been hampered by delays in both data collection and dissemination. Methods. We used statistical process control (SPC) to evaluate the daily percentage of outpatient visits with a positive point-of-care (POC) influenza test in the University of Utah Primary Care Research Network. Results. Retrospectively, POC testing generated an alert in each of

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Man, Dapeng, Wu Yang, Wei Wang, and Shichang Xuan. "An Alert Aggregation Algorithm Based on Iterative Self-Organization." Procedia Engineering 29 (2012): 3033–38. http://dx.doi.org/10.1016/j.proeng.2012.01.435.

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Punia, Divya, and Rajender Kumar. "Experimental Characterization of Routing Protocols in Urban Vehicular Communication." Transport and Telecommunication Journal 20, no. 3 (2019): 229–41. http://dx.doi.org/10.2478/ttj-2019-0019.

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Abstract Vehicular communication is a cynosure in automotive industry these days. V2V communication is one of the types of vehicular communication which render lane change warning, emergency vehicle alert, intersection alert, congestion alert, payment at tolls etc. A simulation research on the utilization of vehicle to vehicle connectivity via different routing protocols in VANET, contemplating the instance of a city situation has been introduced in our paper in order to resolve the issue of traffic routing. A routing algorithm is proposed which firstly selects optimal path via carry and forwa

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Dewi Paramitha, Ida Ayu Shinta, Gusti Made Arya Sasmita, and I. Made Sunia Raharja. "Analisis Data Log IDS Snort dengan Algoritma Clustering Fuzzy C-Means." Majalah Ilmiah Teknologi Elektro 19, no. 1 (2020): 95. http://dx.doi.org/10.24843/mite.2020.v19i01.p14.

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Snort is one of open source IDS to detect intrusion or potentially malicious activity on network traffic. Snort will give alert for every detected intrusion and write the alerts in log. Log data in IDS Snort will help network administrator to analyze the vulnerability of network security system. Clustering algorithm such as FCM can be used to analyze the log data of IDS Snort. Implementation of the algorithm is based on Python 3 and aims to cluster alerts in log data into 4 risk categories, such as low, medium, high, and critical. The outcome of this analysis is to show cluster results of FCM

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Umakirthika, D., P. Pushparani, and M. Valan Rajkumar. "Internet of Things in Vehicle Safety – Obstacle Detection and Alert System." International Journal Of Engineering And Computer Science 7, no. 02 (2018): 23540–51. http://dx.doi.org/10.18535/ijecs/v7i2.05.

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Recent development of the Internet of Things (IoT) and Smart Things provides an opportunity for the informationization for the automotive industry. This article describes Obstacle Detection and Alert System (ODAS) both incorporated as a single system for the obstacles such as speed breakers, barricades on the road using Internet of Things. Obstacle detection system uses in-built algorithm to detect an obstacle on the road using minimal vehicle parameters such as vehicle speed, steering angle. Obstacles locations thus marked by the detection system are stored locally and uploaded to cloud from

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Devi, D. Gayatri. "COVID Safety Measures Alert System." International Journal for Research in Applied Science and Engineering Technology 9, no. VII (2021): 269–76. http://dx.doi.org/10.22214/ijraset.2021.36288.

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The corona virus COVID-19 pandemic is causing a global health crisis so the effective protection method is wearing a face mask and maintaining social distance in public areas according to the World Health Organization (WHO). The COVID-19 pandemic forced governments across the world to impose lockdowns to prevent virus transmissions. Reports Indicate that wearing facemasks and maintaining social distance while at work clearly reduces the risk of transmission. An efficient and economic approach of using AI to create a safe environment in a manufacturing setup. So we are doing a Project on detect

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Zhang, Dong Sheng. "Generalization Privacy Protection Method for Alarm Data." Applied Mechanics and Materials 543-547 (March 2014): 3646–49. http://dx.doi.org/10.4028/www.scientific.net/amm.543-547.3646.

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To resolve conflicts between share and collaborative analysis requirements of security alarm and alert data holders worries about privacy, it firstly probes into the anonymized protection method Incognito. Based on that, it improves the algorithm to solve existing problems by extending common data like privacy protection targets to alert data. The generalized anonymous processing model for alert data is developed and the quantitative evaluation is realized between the level of alert datas secret protection and data quality. With authoritative data set of intrusion detection attack scenario as

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Ma, Ru Hong, Xiao Hui Dong, and Jian Lin Wang. "Application of Sliding Window-Genetic Programming Algorithm in Alert and Forecast for Mine Safety Monitoring." Advanced Materials Research 605-607 (December 2012): 855–58. http://dx.doi.org/10.4028/www.scientific.net/amr.605-607.855.

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SW-GP (Sliding Window-Genetic Programming) algorithm is provided to implement dynamic forecast of monitoring data in order to more effectively utilize coal mine monitoring data to alert and forecast safety accident. In the program, sampling data is obtained by sliding window technology and model is founded automatically by GP algorithm. The result of instance shows that forecasting values from the model well agree with the real values, which explains that employing SW-GP modeling can settle problem for alert and forecast for mine safety monitoring data satisfactorily.

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Politovich, Marcia K., R. Kent Goodrich, Corrinne S. Morse, Alan Yates, Robert Barron, and Steven A. Cohn. "The Juneau Terrain-Induced Turbulence Alert System." Bulletin of the American Meteorological Society 92, no. 3 (2011): 299–313. http://dx.doi.org/10.1175/2010bams3024.1.

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Abstract The Juneau, Alaska, airport vicinity experiences frequent episodes of moderate and severe turbulence, which affect arriving and departing air traffic. The Federal Aviation Administration funded the National Center for Atmospheric Research to develop a warning system, consisting of carefully placed anemometers and wind profilers, along with data communications, an algorithm, and display, to warn pilots of potentially hazardous situations. The system uses regressions based on comparisons of research aircraft data with measurements from the ground-based sensors to estimate the turbulence

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Savrasov, F. V., and R. V. Meyta. "Algorithm of Route Position's Detecting for Service Transport." Applied Mechanics and Materials 770 (June 2015): 495–500. http://dx.doi.org/10.4028/www.scientific.net/amm.770.495.

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The problem associated with the current location’s determination of the transport unit moving on the pre-planned route is observed. Criteria to be followed when solving this problem are designated. An algorithm allowing the operational monitoring of the movement with the possibility of immediate alert the dispatcher about timetable's adherence is proposed.

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Eskandarian, Azim, and Ce Zhang. "A Brain Wave-Verified Driver Alert System for Vehicle Collision Avoidance." SAE International Journal of Transportation Safety 9, no. 1 (2021): 105–22. http://dx.doi.org/10.4271/09-09-01-0002.

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Collision alert and avoidance systems (CAS) could help to minimize driver errors. They are instrumental as an advanced driver-assistance system (ADAS) when the vehicle is facing potential hazards. Developing effective ADAS/CAS, which provides alerts to the driver, requires a fundamental understanding of human sensory perception and response capabilities. This research explores the premise that external stimulation can effectively improve drivers’ reaction and response capabilities. Therefore this article proposes a light-emitting diode (LED)-based driver warning system to prevent potential col

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Jiang, Song, Minjie Lian, Caiwu Lu, Qinghua Gu, Shunling Ruan, and Xuecai Xie. "Ensemble Prediction Algorithm of Anomaly Monitoring Based on Big Data Analysis Platform of Open-Pit Mine Slope." Complexity 2018 (August 1, 2018): 1–13. http://dx.doi.org/10.1155/2018/1048756.

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With the diversification of pit mine slope monitoring and the development of new technologies such as multisource data flow monitoring, normal alert log processing system cannot fulfil the log analysis expectation at the scale of big data. In order to make up this disadvantage, this research will provide an ensemble prediction algorithm of anomalous system data based on time series and an evaluation system for the algorithm. This algorithm integrates multiple classifier prediction algorithms and proceeds classified forecast for data collected, which can optimize the accuracy in predicting the

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Nof, Ran N., and Ittai Kurzon. "TRUAA—Earthquake Early Warning System for Israel: Implementation and Current Status." Seismological Research Letters 92, no. 1 (2020): 325–41. http://dx.doi.org/10.1785/0220200176.

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Abstract Following a governmental decision to build a national earthquake early warning system (EEWS) named TRUAA, the Geological Survey of Israel has upgraded the national Israeli Seismic Network with more than 100 stations countrywide. The stations are spread mainly along the main hazardous fault systems of the Dead Sea and Carmel-Zfira, which potentially may produce Mw 7.5 earthquakes. Currently the system is shifting from the deployment phase into a testing phase in which the earthquake point-source integrated code (EPIC) EEW algorithm is used. During the deployment phase, real-time perfor

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Haeffelin, Martial, Quentin Laffineur, Juan-Antonio Bravo-Aranda, et al. "Radiation fog formation alerts using attenuated backscatter power from automatic lidars and ceilometers." Atmospheric Measurement Techniques 9, no. 11 (2016): 5347–65. http://dx.doi.org/10.5194/amt-9-5347-2016.

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Abstract. Radiation fog occurs over many locations around the world in stable atmospheric conditions. Air traffic at busy airports can be significantly disrupted because low visibility at the ground makes it unsafe to take off, land and taxi on the ground. Current numerical weather prediction forecasts are able to predict general conditions favorable for fog formation, but not the exact time or location of fog occurrence. A selected set of observations available in near-real time at strategic locations could also be useful to track the evolution of key processes and key parameters that drive f

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Jidin, Aiman Zakwan, Lim Siau Li, and Ahmad Fauzan Kadmin. "Implementation of Algorithm for Vehicle Anti-Collision Alert System in FPGA." International Journal of Electrical and Computer Engineering (IJECE) 7, no. 2 (2017): 775. http://dx.doi.org/10.11591/ijece.v7i2.pp775-783.

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<span lang="EN-US">Vehicle safety has becoming one of the important issues nowadays, due to the fact the number of road accidents, which cause injuries, deaths and also damages, keeps on increasing. One of the main factors which contribute to these accidents are human's lack of awareness and also carelessness. This paper presents the development and implementation of an algorithm to be utilized for vehicle anti-collision alert system, which may be useful to reduce the occurrence of accidents. This algorithm, which is to be deployed with the front sensors of the vehicle, is capable of ale

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Wang, Ruiping, Yonggen Jiang, Xiaoqin Guo, Yiling Wu, and Genming Zhao. "Influence of infectious disease seasonality on the performance of the outbreak detection algorithm in the China Infectious Disease Automated-alert and Response System." Journal of International Medical Research 46, no. 1 (2017): 98–106. http://dx.doi.org/10.1177/0300060517718770.

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Objective The Chinese Center for Disease Control and Prevention developed the China Infectious Disease Automated-alert and Response System (CIDARS) in 2008. The CIDARS can detect outbreak signals in a timely manner but generates many false-positive signals, especially for diseases with seasonality. We assessed the influence of seasonality on infectious disease outbreak detection performance. Methods Chickenpox surveillance data in Songjiang District, Shanghai were used. The optimized early alert thresholds for chickenpox were selected according to three algorithm evaluation indexes: sensitivit

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Liu, De Fang, Ming Deng, and Hai Yan Chen. "Application of Motion Detection Algorithm in Patient Monitoring System." Applied Mechanics and Materials 333-335 (July 2013): 646–49. http://dx.doi.org/10.4028/www.scientific.net/amm.333-335.646.

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The paper proposes a smart, reliable and robust algorithm for motion detection, tracking and activity analysis. Background subtraction is considered intelligent algorithms for the same. Mount the web camera focused to the patient. PC should have a unique external Internet IP Address. Android mobile phone should be GPRS enabled. GSM technology is used for sending SMS. It is a client-server technology wherein client captures the images, checks for motion if any, discards the packets until motion is detected. Use background subtraction algorithm to check the motion. The surveillance camera does n

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D’Arinzo, Lauren, Krisha Patel, Tevin Carrington, Miatta Goba, and Jeffrey Gerber. "1120. Reliability of Parent-Reported Pediatric Antibiotic Use in a Longitudinal Birth Cohort." Open Forum Infectious Diseases 6, Supplement_2 (2019): S398. http://dx.doi.org/10.1093/ofid/ofz360.984.

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Abstract Background Depending on the population of interest, it is not always feasible to acquire electronic health record (EHR) data for antibiotic prescribing in longitudinal outpatient studies. Even when available, EHR algorithms are limited to only capturing in-network prescriptions. Thus, there is value in learning more about the reliability of parent-reported data to see whether this approach can be validated for epidemiologic research. Methods We examined antibiotic prescribing in the MAGIC (Microbiome, Antibiotics, and Growth Infant Cohort) Study, a longitudinal birth cohort of healthy

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Harvey, David, Wessel Valkenburg, and Amara Amara. "Predicting malaria epidemics in Burkina Faso with machine learning." PLOS ONE 16, no. 6 (2021): e0253302. http://dx.doi.org/10.1371/journal.pone.0253302.

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Accurately forecasting the case rate of malaria would enable key decision makers to intervene months before the onset of any outbreak, potentially saving lives. Until now, methods that forecast malaria have involved complicated numerical simulations that model transmission through a community. Here we present the first data-driven malaria epidemic early warning system that can predict the 13-week case rate in a primary health facility in Burkina Faso. Using the extraordinarily high-fidelity data of infant consultations taken from the Integrated e-Diagnostic Approach (IeDA) system that has been

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Prakash, Surya. "Alert System of Earthquake Detection." International Journal for Research in Applied Science and Engineering Technology 9, no. 8 (2021): 1774–78. http://dx.doi.org/10.22214/ijraset.2021.37614.

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Abstract: Global warning shows unpredictablenature that changes in subsurface geologicfeatures. Due to the complex nature ofseismic events, it is challengeable task to efficiently identify the prominent features that leads to seismic events. Taking the advantage of availability of Seismic dataset, AI using machine learning is a powerful statistical tools to mitigate these practical challenges for earthquake prediction. The paper focuses on the alert and prediction model of an earthquake using machine learning algorithm. The alert time is a function of distance from epicenter and most alert tim

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Uljon, Sacha N., Daimon P. Simmons, Joseph W. Rudolf, et al. "Validation and Implementation of an Ordering Alert to Improve the Efficiency of Monoclonal Gammopathy Evaluation." American Journal of Clinical Pathology 153, no. 3 (2019): 396–406. http://dx.doi.org/10.1093/ajcp/aqz180.

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Abstract Objectives To evaluate the use of a provider ordering alert to improve laboratory efficiency and reduce costs. Methods We conducted a retrospective study to assess the use of an institutional reflex panel for monoclonal gammopathy evaluation. We then created a clinical decision support (CDS) alert to educate and encourage providers to change their less-efficient orders to the reflex panel. Results Our retrospective analysis demonstrated that an institutional reflex panel could be safely substituted for a less-efficient and higher-cost panel. The implemented CDS alert resulted in 79% o

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Schaefer, Jordan K., Rajiv K. Pruthi, Jennifer S. Shin, Mark W. Dobie, and Pedro J. Caraballo. "Improving Recognition, Diagnosis, and Management Of Heparin Induced Thrombocytopenia By Implementing a Computer-Based Clinical Decision Support System." Blood 122, no. 21 (2013): 2966. http://dx.doi.org/10.1182/blood.v122.21.2966.2966.

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Abstract Introduction Heparin induced thrombocytopenia (HIT) is often not considered as a potential etiology of thrombocytopenia. The gradual 5 to 14 day decline in platelet count (PC) may not be easily recognized by busy clinicians. The life-threatening complications of HIT are potentially preventable with prompt recognition and management. Implementation of computer-based clinical decision support systems (CDSS) may aid in addressing these issues. Such systems have shown promise in increasing provider recognition, appropriate testing, and management of a variety of conditions. Methods We dev

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Bae, Ihn-Han. "An Intelligent Broadcasting Algorithm for Early Warning Message Dissemination in VANETs." Mathematical Problems in Engineering 2015 (2015): 1–8. http://dx.doi.org/10.1155/2015/848915.

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Vehicular ad hoc network (VANET) has gained much attention recently to improve road safety, reduce traffic congestion, and enable efficient traffic management because of its many important applications in transportation. In this paper, an early warning intelligence broadcasting algorithm is proposed, EW-ICAST, to disseminate a safety message for VANETs. The proposed EW-ICAST uses not only the early warning system on the basis of time to collision (TTC) but also the intelligent broadcasting algorithm on the basis of fuzzy logic. Thus, the EW-ICAST resolves effectively broadcast storm problem an

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Singha, Jaikhomba, Avishek Basu, M. A. Krishnakumar, Bhal Chandra Joshi, and P. Arumugam. "A real-time automated glitch detection pipeline at Ooty Radio Telescope." Monthly Notices of the Royal Astronomical Society 505, no. 4 (2021): 5488–96. http://dx.doi.org/10.1093/mnras/stab1640.

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ABSTRACT Glitches are the observational manifestations of superfluidity inside neutron stars. The aim of this paper is to describe an automated glitch detection pipeline, which can alert the observers on possible real-time detection of rotational glitches in pulsars. Post alert, the pulsars can be monitored at a higher cadence to measure the post-glitch recovery phase. Two algorithms, namely median absolute deviation and polynomial regression, have been explored to detect glitches in real time. The pipeline has been optimized with the help of simulated timing residuals for both the algorithms.

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Naijit, Kittimasak. "Emergency Notification Using Combination Algorithm with Recognition ECG Signal." International Journal of Online and Biomedical Engineering (iJOE) 16, no. 05 (2020): 15. http://dx.doi.org/10.3991/ijoe.v16i05.12707.

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<p class="p1">Intensive Care Unit (ICU) Rooms usually have several detectors attached to each patient providing intensive care, and several processors control and interpret. If the processor detects an abnormality, the medical professional office will be alerted. Nevertheless, many patients with heart disease are concerned with day-to-day behaviors such as hard work, battle, exercise, shock, fight, and war. Become due to clinical depression and erectile impotence this induces anxiety and fear. The boundaries of your heart muscle and coronary strength are unclear. They want a warning that

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Sangari, Siva, and K. Baskaran. "Secured Token Based Handover (STBH) Algorithm for Traffic Collision alert in VANETs." Asian Journal of Research in Social Sciences and Humanities 6, no. 6 (2016): 1858. http://dx.doi.org/10.5958/2249-7315.2016.00332.4.

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Lee, Young-Ha, Sung-Tae Kim, and Guk-Boh Kim. "Design and Evaluation of an Early Intelligent Alert Broadcasting Algorithm for VANETs." Journal of Korean Society for Internet Information 13, no. 4 (2012): 95–102. http://dx.doi.org/10.7472/jksii.2012.13.4.95.

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