Algorithm implementation for iris identification

Abstract Background and Importance: The dispatch of Advanced Life Support (ALS) teams in Emergency Medical Services (EMS) is still a hardly studied aspect of prehospital emergency logistics. In 2015, the dispatch algorithm of Emilia Est Emergency Operation Centre (EE-EOC) was implemented and the dispatch of ALS teams was changed from primary to secondary based on triage of dispatched vehicles for high-priority interventions when teams with Immediate Life Support (ILS) skills were dispatched. Objectives: This study aimed to evaluate the effects on the appropriateness of ALS teams’ intervention and their employment time, and to compare sensitivity and specificity of the algorithm implementation. Design: This was a retrospective before-after observational study. Settings and Participants: Primary dispatches managed by EE-EOC involving ambulances and/or ALS teams were included. Two groups were created on the basis of the years of intervention (2013-2014 versus 2017-2018). Intervention: A switch from primary to secondary dispatch of ALS teams in case of high-priority dispatches managed by ILS teams was implemented. Outcomes: Appropriateness of ALS team intervention, total task time of ALS vehicles, and sensitivity and specificity of the algorithm were reviewed. Results: The study included 242,501 emergency calls that generated 56,567 red code dispatches. The new algorithm significantly increased global sensitivity and specificity of the system in terms of recognition of potential need of ALS intervention and the specificity of primary ALS dispatch. The appropriateness of ALS intervention was significantly increased; total tasking time per day for ALS and the number of critical dispatches without ALS available were reduced. Conclusion: The revision of the dispatch criteria and the extension of the two-tiered dispatch for ALS teams significantly increased the appropriateness of ALS intervention and reduced both the global tasking time and the number of high-priority dispatches without ALS teams available.

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Entropy-Based Approach in Selection Exact String-Matching Algorithms

Entropy ◽

10.3390/e23010031 ◽

2020 ◽

Vol 23 (1) ◽

pp. 31

Author(s):

Ivan Markić ◽

Maja Štula ◽

Marija Zorić ◽

Darko Stipaničev

Keyword(s):

String Matching ◽

Quantitative Measure ◽

Search Pattern ◽

Maximum Efficiency ◽

Resource Usage ◽

Specific Domain ◽

Matching Problems ◽

Algorithm Efficiency ◽

Testing Algorithm ◽

Algorithm Implementation

The string-matching paradigm is applied in every computer science and science branch in general. The existence of a plethora of string-matching algorithms makes it hard to choose the best one for any particular case. Expressing, measuring, and testing algorithm efficiency is a challenging task with many potential pitfalls. Algorithm efficiency can be measured based on the usage of different resources. In software engineering, algorithmic productivity is a property of an algorithm execution identified with the computational resources the algorithm consumes. Resource usage in algorithm execution could be determined, and for maximum efficiency, the goal is to minimize resource usage. Guided by the fact that standard measures of algorithm efficiency, such as execution time, directly depend on the number of executed actions. Without touching the problematics of computer power consumption or memory, which also depends on the algorithm type and the techniques used in algorithm development, we have developed a methodology which enables the researchers to choose an efficient algorithm for a specific domain. String searching algorithms efficiency is usually observed independently from the domain texts being searched. This research paper aims to present the idea that algorithm efficiency depends on the properties of searched string and properties of the texts being searched, accompanied by the theoretical analysis of the proposed approach. In the proposed methodology, algorithm efficiency is expressed through character comparison count metrics. The character comparison count metrics is a formal quantitative measure independent of algorithm implementation subtleties and computer platform differences. The model is developed for a particular problem domain by using appropriate domain data (patterns and texts) and provides for a specific domain the ranking of algorithms according to the patterns’ entropy. The proposed approach is limited to on-line exact string-matching problems based on information entropy for a search pattern. Meticulous empirical testing depicts the methodology implementation and purports soundness of the methodology.

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