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Introduction: There is increasing recognition of the value of linking food sales databases to national food composition tables for population nutrition research., Objectives: Expanding upon automated and manual database mapping approaches in the literature, our aim was to match 1,179 food products in the Canadian data subset of Euromonitor International's Passport Nutrition to their closest respective equivalents in Health Canada's Canadian Nutrient File (CNF)., Methods: Matching took place in two major steps. First, an algorithm based on thresholds of maximal nutrient difference (between Euromonitor and CNF foods) and fuzzy matching was executed to offer match options. If a nutritionally appropriate match was available among the algorithm suggestions, it was selected. When the suggested set contained no nutritionally sound matches, the Euromonitor product was instead manually matched to a CNF food or deemed unmatchable, with the unique addition of expert validation to maximize meticulousness in matching. Both steps were independently performed by at least two team members with dietetics expertise., Results: Of 1,111 Euromonitor products run through the algorithm, an accurate CNF match was offered for 65% of them; missing or zero-calorie data precluded 68 products from being run in the algorithm. Products with 2 or more algorithm-suggested CNF matches had higher match accuracy than those with one (71 vs. 50%, respectively). Overall, inter-rater agreement (reliability) rates were robust for matches chosen among algorithm options (51%) and even higher regarding whether manual selection would be required (71%); among manually selected CNF matches, reliability was 33%. Ultimately, 1,152 (98%) Euromonitor products were matched to a CNF equivalent., Conclusion: Our reported matching process successfully bridged a food sales database's products to their respective CNF matches for use in future nutritional epidemiological studies of branded foods sold in Canada. Our team's novel utilization of dietetics expertise aided in match validation at both steps, ensuring rigor and quality of resulting match selections., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2023 Gilbert, Morrison, Chen, Punian, Bernstein and Jessri.)

In this article, we describe Stata utilities that facilitate probabilistic record linkage-the technique typically used for merging two datasets with no common record identifier. While the preprocessing tools are developed specifically for linking two company databases, the other tools can be used for many different types of linkage. Specifically, the stnd compname and stnd address commands parse and standardize company names and addresses to improve the match quality when linking. The reclink2 command is a generalized version of Blasnik's reclink (2010, Statistical Software Components S456876, Department of Economics, Boston College) that allows for many-to-one matching. Finally, clrevmatch is an interactive tool that allows the user to review matched results in an efficient and seamless manner. Rather than exporting results to another file format (for example, Excel), inputting clerical reviews, and importing back into Stata, one can use the clrevmatch tool to conduct all of these steps within Stata. This helps improve the speed and flexibility of matching, which often involves multiple runs. [ABSTRACT FROM AUTHOR]

Aim at the application requirement of logo recognition in image intelligent processing, a logo recognition algorithm based on grid feature and fuzzy matching is proposed. The grid feature of the logo is extracted, and then transformed into the membership degree of fuzzy sets. at last, closeness degree is used to accomplish logo recognition. This significantly enchances adaptability and anti-interference to the poor quality image, and effectively improves the flexible processing capability of logo recognition system. Experimental results show that the recognition rate of the logo recognition algorithm can reach as high as 95.5%. [ABSTRACT FROM AUTHOR]

Nowadays, there are many queries issued to search engines targeting at finding values from structured data (e.g., movie showtime of a specific location). In such scenarios, there is often a mismatch between the values of structured data (how content creators describe entities) and the web queries (how different users try to retrieve them). Therefore, recognizing the alternative ways people use to reference an entity, is crucial for structured web search. In this paper, we study the problem of automatic generation of entity synonyms over structured data toward closing the gap between users and structured data. We propose an offline, data-driven approach that mines query logs for instances where content creators and web users apply a variety of strings to refer to the same webpages. This way, given a set of strings that reference entities, we generate an expanded set of equivalent strings (entity synonyms) for each entity. Our framework consists of three modules: candidate generation, candidate selection, and noise cleaning. We further study the cause of the problem through the identification of different entity synonym classes. The proposed method is verified with experiments on real-life data sets showing that we can significantly increase the coverage of structured web queries with good precision. [ABSTRACT FROM AUTHOR]

Mining design patterns from source code is significant for improving the intelligibility and maintainability of software. In this paper, we present a new design pattern matching method based on fuzzy, in which matrix model is used for describing both design pattern and source code, and design pattern's static and dynamic information is defined as fuzzy attribute value for measuring the matching degree. Experiments on three open-source projects demonstrate the accuracy and efficiency of the proposed methodology. [ABSTRACT FROM AUTHOR]

To set up inherent immune response of the adjustable fuzzy matching negative selection immune algorithm, based on fuzzy idea, the vaccine theory is used to pick up the vaccine during abnormal monitoring, and the algorithm performance influenced by vaccine operator and positive selection operator is studied. The simulation results show that the antibody storeroom memory function is increased by the vaccine operator and the second response period is shorten obviously, while the positive selection operator is the executant. The improved adjustable fuzzy matching negative selection algorithm has better self-study function, it has self-adaptation for the inherent immune response. [ABSTRACT FROM AUTHOR]

In order to implement Web service selection effectively, a service selection model based on multi-agent collaboration is constructed. Communication performatives in multi agent system for service selection are designed, by which agents perform collaboration. The evaluation algorithm based on fuzzy matching is proposed, which provide consistent measure for evaluation criteria. The preferences of service requestors are aggregated according to the voting protocol, by which the right value of each evaluation item it taken. Experimental results demonstrate that the mean effectiveness of service selection is increased by 29 percent; the average selection time decreased by 58 percent, and the computing complexity drops from O(n5) to O(n²) in the model. [ABSTRACT FROM AUTHOR]

We devised a novel statistical technique for the identification of the translation equivalents of source words obtained by transformation rule based translation (TRT). The effectiveness of the technique called frequency-based identification of translation equivalents (FITE) was tested using biological and medical cross-lingual spelling variants and out-of-vocabulary (OOV) words in Spanish-English and Finnish-English TRT. The results showed that, depending on the source language and frequency corpus, FITE-TRT (the identification of translation equivalents from TRT's translation set by means of the FITE technique) may achieve high translation recall. In the case of the Web as the frequency corpus, translation recall was 89.2%-91.0% for Spanish-English FITE-TRT. For both language pairs FITE-TRT achieved high translation precision: 95.0%-98.8%. The technique also reliably identified native source language words: source words that cannot be correctly translated by TRT. Dictionary-based CLIR augmented with FITE-TRT performed substantially better than basic dictionary-based CLIR where OOV keys were kept intact. FITE-TRT with Web document frequencies was the best technique among several fuzzy translation/matching approaches tested in cross-language retrieval experiments. We also discuss the application of FITE-TRT in the automatic construction of multilingual dictionaries. [ABSTRACT FROM AUTHOR]

Abstract: A uniform representation of the entire chromatographic signals in the form of a data matrix requires signals alignment. Available approaches to signals alignment (or warping) are time consuming or require time-consuming optimization of input parameters. The proposed approach to signals warping exploits a different idea, and, namely, it requires detection of a few more intense peaks in individual chromatograms only and it aligns signals, once the correspondence of the detected peaks is established. Identification of peaks is not required, as their correspondence is established, based on the fuzzy matching approach. Performance of the proposed method is evaluated for the simulated and experimental chromatograms with the number of points varying from a few hundred to several dozen thousands of points. [Copyright &y& Elsevier]

Recent MT paradigms, such as example-based MT, pattern-based MT, and hybrid MT contribute to overcoming the shortcoming of rule-based MT by improving the quality of translations. However, a problem with erroneous selection of examples or patterns also exists in these types of MTs. In this paper, we discover a matching abnormality, which we term an Over-Specification Problem that occurs when an input sentence corresponding to the pattern of general sense is actually matched to the patterns of exceptional sense, and thus is incorrectly translated. We formally define the problem and propose a solution based on the similarity of dependency trees and lexical association ratio to detect whether the input tree is dragged into the exceptional range of example trees or not. Finally, promising experimental results are provided. We take a hybrid MT as a testing MT and Chinese-to-Korean as a sample language pair. [ABSTRACT FROM AUTHOR]

The problem of automating the sensing and classification of odours is one which promises a wide range of industrial applications. During the INTESA project, a prototype electronic nose was developed, using sensors based on novel conducting polymer materials and also more traditional MOS materials. The software component of the prototype processes the transient resistance change signals recorded by the hardware, and classifies the odour sample into one of a number of "odour classes". This paper describes two of the soft computing methods investigated for learning classification rules in this domain. The first method builds on previous work done on the Fril data browser, using clustering, fuzzy matching, Fril rules and evidential logic rules. The second method uses a fuzzy extension of the ID3 decision tree induction method, called "mass assignment tree induction (MATI)". Some of the results of applying these methods to data obtained from the INTESA prototype are presented and discussed. [ABSTRACT FROM AUTHOR]

Electronic Medical Records (EMR) and Electronic Health Records (EHR) are often missing critical information about the death of a patient, although it is an essential metric for medical research in oncology to assess survival outcomes, particularly for evaluating the efficacy of new therapeutic approaches. We used open government data in France from 1970 to September 2021 to identify deceased patients and match them with patient data collected from the Institut de Cancérologie de l'Ouest (ICO) data warehouse (Integrated Center of Oncology-the third largest cancer center in France) between January 2015 and November 2021. To meet our objective, we evaluated algorithms to perform a deterministic record linkage: an exact matching algorithm and a fuzzy matching algorithm. Because we lacked reference data, we needed to assess the algorithms by estimating the number of homonyms that could lead to false links, using the same open dataset of deceased persons in France. The exact matching algorithm allowed us to double the number of dates of death in the ICO data warehouse, and the fuzzy matching algorithm tripled it. Studying homonyms assured us that there was a low risk of misidentification, with precision values of 99.96% for the exact matching and 99.68% for the fuzzy matching. However, estimating the number of false negatives proved more difficult than anticipated. Nevertheless, using open government data can be a highly interesting way to improve the completeness of the date of death variable for oncology patients in data warehouses.

We demonstrate a concatenated fuzzy matching algorithm to restrain local strong regions for the spatial beam shaping of a focused laser with quasi-near-field characteristics on the cross section in the focusing light path. Considering that the light field is redistributed according to the spatial angular spectrum after the laser beam is converged, modulations increasing with the propagation distance will decrease the smoothness of the beam. An effective algorithm for the spatial beam shaping of such focused laser is discussed by establishing the cascaded correspondence between the light field in the front stage and the later stage. The results show that the fluence contrast reduces from 37.2% to 21.4%, and the intensity modulation from 3.33:1–1.86:1. Meanwhile, the low-frequency components corresponding to local large-scale strong regions of the light field in the PSD distribution are effectively suppressed. A more regular spatial intensity profile of the focused laser is finally obtained. [ABSTRACT FROM AUTHOR]

This paper is concerned with accessing information from accident databases. It discusses the limitation of current accident databases and focuses on the issue of finding and ranking of information that relates to a query. A user or system initiates an interaction with a database by specifying what is of interest in the form of a query. The query does not have to be treated as a precise description of what is of interest, but a vague or “fuzzy” one. Fuzzy database techniques make it possible to exploit all available information by returning not only items that match the query exactly, but also items that bear some relation to the query. A domain model for accident reports in the process industries was developed. It consists of four classification hierarchies for the attributes operation , equipment, cause and consequence. A common approach for assessing how closely two terms are related is based on the number of links between the two terms on a hierarchy. This approach is not appropriate for the accident database domain. Instead, the relationship between any two nodes on a hierarchy is classified into four different types. Methods for determining similarities for the different types of relationships are discussed and have been implemented in an accident database. The ranking of the retrieved information is much more satisfactory then the “distance” based approach. [ABSTRACT FROM AUTHOR]

We identify a number of aspects that can boost the performance of Neural Fuzzy Repair (NFR), an easy-to-implement method to integrate translation memory matches and neural machine translation (NMT). We explore various ways of maximising the added value of retrieved matches within the NFR paradigm for eight language combinations, using Transformer NMT systems. In particular, we test the impact of different fuzzy matching techniques, sub-word-level segmentation methods and alignment-based features on overall translation quality. Furthermore, we propose a fuzzy match combination technique that aims to maximise the coverage of source words. This is supplemented with an analysis of how translation quality is affected by input sentence length and fuzzy match score. The results show that applying a combination of the tested modifications leads to a significant increase in estimated translation quality over all baselines for all language combinations. [ABSTRACT FROM AUTHOR]

The Semantic Web offers exciting possibilities for information retrieval (IR). In IR, we would like systems that go beyond simply matching words in documents and queries, and instead match based on topic, data type, relations among data, and many other qualities. The Semantic Web, through fuzzy matching of information spaces from different sources, will provide for much more specific information seeking than current Web-based search engines or other IR systems. In order to succeed, however, it is necessary to map between the differing schema, metadata standards, namespaces and so forth used by documents on the Semantic Web. This information space mapping may be accomplished by a simple match or table lookup when document sets come from similar or otherwise well-defined domains. When the match is less precise, sets of rules or algorithms may be employed to map between information spaces. When schema or metadata are inconsistent, though, we are left with a similar data environment as the modern Web, and must rely on the context of the documents themselves to determine the mapping between information spaces.

• This paper uses qualitative trend analysis to recognize gold-antimony rougher flotation working condition for the first time. • The proposed method establishes a new multivariable recognition framework in accordance to multiple variables' characteristics. • The improved sliding window method is used for trend extraction, which increase the recognition accuracy and reduce processing time. • This paper presents a scheme of building knowledge base for multivariable rougher flotation conditions. Rougher flotation is a complex process, making it difficult for a single variable to comprehensively and accurately reflects real-time working conditions. This paper proposes a new multivariable recognition method for rougher flotation conditions using pulp flow and froth size as parameters. It utilizes qualitative trend analysis to recognise rougher flotation condition for the first time, where an improved trend extraction method is used to improve extraction accuracy, and fuzzy logic is adopted to calculate the matching degree of trends. Considering the change in knowledge base caused by the increase in variables, this paper presents a scheme of building knowledge base for multivariable rougher flotation conditions. Experimental results of gold–antimony rougher flotation show that the proposed method can accurately recognise rougher flotation conditions. [ABSTRACT FROM AUTHOR]

Aim of Study: The use of weighed food diaries in nutritional studies provides a powerful method to quantify food and nutrient intakes. Yet, mapping these records onto food composition tables (FCTs) is a challenging, time-consuming and error-prone process. Experts make this effort manually and no automation has been previously proposed. Our study aimed to assess automated approaches to map food items onto FCTs. Methods: We used food diaries (~170,000 records pertaining to 4,200 unique food items) from the DiOGenes randomized clinical trial. We attempted to map these items onto six FCTs available from the EuroFIR resource. Two approaches were tested: the first was based solely on food name similarity (fuzzy matching). The second used a machine learning approach (C5.0 classifier) combining both fuzzy matching and food energy. We tested mapping food items using their original names and also an English-translation. Top matching pairs were reviewed manually to derive performance metrics: precision (the percentage of correctly mapped items) and recall (percentage of mapped items). Results: The simpler approach: fuzzy matching, provided very good performance. Under a relaxed threshold (score > 50%), this approach enabled to remap 99.49% of the items with a precision of 88.75%. With a slightly more stringent threshold (score > 63%), the precision could be significantly improved to 96.81% while keeping a recall rate > 95% (i.e., only 5% of the queried items would not be mapped). The machine learning approach did not lead to any improvements compared to the fuzzy matching. However, it could increase substantially the recall rate for food items without any clear equivalent in the FCTs (+7 and +20% when mapping items using their original or English-translated names). Our approaches have been implemented as R packages and are freely available from GitHub. Conclusion: This study is the first to provide automated approaches for large-scale food item mapping onto FCTs. We demonstrate that both high precision and recall can be achieved. Our solutions can be used with any FCT and do not require any programming background. These methodologies and findings are useful to any small or large nutritional study (observational as well as interventional).

Online databases of biological information offer tremendous potential for evolutionary and ecological discoveries, especially if data are combined in novel ways. However, the different names and varied spellings used for many species present major barriers to linking data. Taxonome is a software tool designed to solve this problem by quickly and reproducibly matching biological names to a given reference set. It is available both as a graphical user interface (GUI) for simple interactive use, and as a library for more advanced functionality with programs written in Python. Taxonome also includes functions to standardize distribution information to a well-defined set of regions, such as the TDWG World Geographical Scheme for Recording Plant Distributions. In combination, these tools will help biologists to rapidly synthesize disparate datasets, and to investigate large-scale patterns in species traits.