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Fuzzy matching techniques are the presently used methods in translating the words. Neural machine translation and statistical machine translation are the methods used in MT. In machine translator tool, the strategy employed for translation needs to handle large amount of datasets and therefore the performance in retrieving correct matching output can be affected. In order to improve the matching score of MT, the advanced techniques can be presented by modifying the existing fuzzy based translator and neural machine translator. The conventional process of modifying architectures and encoding schemes are tedious process. Similarly, the preprocessing of datasets also involves more time consumption and memory utilization. In this article, a new spider web based searching enhanced translation is presented to be employed with the neural machine translator. The proposed scheme enables deep searching of available dataset to detect the accurate matching result. In addition, the quality of translation is improved by presenting an optimal selection scheme for using the sentence matches in source augmentation. The matches retrieved using various matching scores are applied to an optimization algorithm. The source augmentation using optimal retrieved matches increases the translation quality. Further, the selection of optimal match combination helps to reduce time requirement, since it is not necessary to test all retrieved matches in finding target sentence. The performance of translation is validated by measuring the quality of translation using BLEU and METEOR scores. These two scores can be achieved for the TA‐EN language pairs in different configurations of about 92% and 86%, correspondingly. The results are evaluated and compared with other available NMT methods to validate the work. [ABSTRACT FROM AUTHOR]

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Deep learning has been widely used in computer vision, natural language processing, speech recognition, and other fields. If there are errors in deep learning frameworks, such as missing module errors and GPU/CPU result discrepancy errors, it will cause many application problems. We propose a source-based fault location method, SFTL (Source File Tracking Localization), to improve the fault location efficiency of these two types of errors in deep learning frameworks. We screened 3410 crash reports on GitHub and conducted fault location experiments based on those reports. The experimental results show that the SFTL method has a high accuracy, which can help deep learning framework developers quickly locate faults and improve the stability and reliability of models. [ABSTRACT FROM AUTHOR]

For autonomous vehicles, when encountering traffic accidents or had weather and other complex situations that made the traffic signal stop working, recognizing the command of traffic police gesture was very important. A new method of traffic police gesture recognition was proposed. First, the traffic police gestures were decomposed into "key actions" and "transition actions" based on the motion characteristics, Secondly, using Faster R-CNN and fuzzy matching the gesture recognition model was build. Faster R-CNN was used to extract the features of decomposed actions for image recognition; a fuzzy matching method was built to match gestures based on the recognition of decomposed actions. Finally, the recognition experiment was executed, as for common traffic police gesture, the result showed that the new proposed method was effective. [ABSTRACT FROM AUTHOR]

Aiming at the problems of low accuracy and slow search speed of network information matching, a fuzzy matching algorithm for network information retrieval based on discrete mathematics is proposed. The interaction matrix of network information is calculated by using a tensor neural network, and the semantic matching information of word granularity and sentence granularity at different levels is obtained, and the network information is extracted. The template network information is discretized, the similarity between the direction of the discrete gradient and the magnitude of the gradient is compared, and the representative gradient vector in the template is extracted to replace all the discrete points to match, so as to realize the fuzzy matching algorithm design of network information retrieval. The experimental results show that the fuzzy matching algorithm based on discrete mathematics has higher matching accuracy. [ABSTRACT FROM AUTHOR]

IntroductionThere is increasing recognition of the value of linking food sales databases to national food composition tables for population nutrition research.ObjectivesExpanding 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).MethodsMatching 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.ResultsOf 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.ConclusionOur 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.

In community-based wellness programs, Social Security Numbers (SSNs) are rarely collected to encourage participation and protect participant privacy. One measure of program effectiveness includes changes in health care utilization. For the 65 and over population, health care utilization is captured in Medicare administrative claims data. Therefore, methods as described in this article for linking participant information to administrative data are useful for program evaluations where unique identifiers such as SSN are not available. Following fuzzy matching methodologies, participant information from the National Study of the Chronic Disease Self-Management Program was linked to Medicare administrative data. Linking variables included participant name, date of birth, gender, address, and ZIP code. Seventy-eight percent of participants were linked to their Medicare claims data. Linking program participant information to Medicare administrative data where unique identifiers are not available provides researchers with the ability to leverage claims data to better understand program effects.

With the start of the Fourth Industrial Revolution, Internet of Things (IoT), artificial intelligence (AI), and big data technologies are attracting global attention. AI can achieve fast computational speed, and big data makes it possible to store and use vast amounts of data. In addition, smartphones, which are IoT devices, are owned by most people. Based on these advantages, the above three technologies can be combined and effectively applied to navigation technology. In the case of an outdoor environment, global positioning system (GPS) technology has been developed to enable relatively accurate positioning of the user. However, due to the problem of radio wave loss because of many obstacles and walls, there are obvious limitations in applying GPS to indoor environments. Hence, we propose a method to increase the accuracy of user positioning in indoor environments using wireless-fidelity (Wi-Fi). The core technology of the proposed method is to limit the initial search region of the particle swarm optimization (PSO), an intelligent particle algorithm; doing so increases the probability that particles converge to the global optimum and shortens the convergence time of the algorithm. For this reason, the proposed method can achieve fast processing time and high accuracy. To limit the initial search region of the PSO, we first build an received signal strength indicator (RSSI) database for each sample point (SP) using a fingerprinting scheme. Then, a limited region is established through a fuzzy matching algorithm. Finally, the particles are randomly distributed within a limited region, and then the user's location is positioned through a PSO. Simulation results confirm that the method proposed in this paper achieves the highest positioning accuracy, with an error of about 1 m when the SP interval is 3 m in an indoor environment. [ABSTRACT FROM AUTHOR]

The exhaustive automatic detection of symptoms in social media posts is made difficult by the presence of colloquial expressions, misspellings and inflected forms of words. The detection of self-reported symptoms is of major importance for emergent diseases like the Covid-19. In this study, we aimed to (1) develop an algorithm based on fuzzy matching to detect symptoms in tweets, (2) establish a comprehensive list of Covid-19-related symptoms and (3) evaluate the fuzzy matching for Covid-19-related symptom detection in French tweets. The Covid-19-related symptom list was built based on the aggregation of different data sources. French Covid-19-related tweets were automatically extracted using a dedicated data broker during the first wave of the pandemic in France. The fuzzy matching parameters were finetuned using all symptoms from MedDRA and then evaluated on a subset of 5000 Covid-19-related tweets in French for the detection of symptoms from our Covid-19-related list. The fuzzy matching improved the detection by the addition of 42% more correct matches with an 81% precision. [ABSTRACT FROM AUTHOR]

Address recognition, as one of the important scenarios of natural language processing in big data applications, is an important and extremely practical technical means. Currently, the evolution of big data applications is being actively promoted, and more and more people are using big data technology to empower power address recognition. Address information in many data assets is the core area of connected devices, and the analysis and mining of core algorithms has extremely high value. This paper first analyzes the application scenarios involved in the common address information of electric power, and formulates the extraction and matching method according to the key points of the core application scenarios and the address recognition requirements; then, based on the data samples, the accuracy and calculation speed of the address recognition method are studied, and the Algorithms are analyzed and compared. Practicality; finally summarize the algorithm, and look forward to ways to improve the algorithm in the future.

Gesture recognition is an important human–computer interaction interface. This article introduces a novel hand gesture recognition system based on Leap Motion gen.2. In this system, a spatial fuzzy matching (SFM) algorithm is first presented by matching and fusing spatial information to construct a fused gesture dataset. For dynamic hand recognition, an initial frame correction strategy based on SFM is proposed to fast initialize the trajectory of test gesture with respect to the gesture dataset. A notable feature of this system is that it can run on ordinary laptops due to the small size of the fused dataset, which accelerates the calculation of recognition rate. Experimental results show that the system recognizes static hand gestures at recognition rates of 94%–100% and over 90% of dynamic gestures using our collected dataset. This can greatly enhance the usability of Leap Motion. [ABSTRACT FROM AUTHOR]

Recently, we have witnessed a rapid increase in the number of research studies in the area of brain fog, predominantly due to the fact that it is reported to be a frequent long COVID condition. However, the construct of brain fog remains ill-defined and a common method of assessment of the condition is lacking. Therefore, the main aim of the current study was to develop and validate a self-report Brain Fog Scale (BFS) for use in clinical and research settings. Participants were 1452 (n = 996, 68.6 % female) Polish university students. The data were collected anonymously through self-completion questionnaires. Results indicate that the 23-item BFS has good psychometric properties. Based on principal component analysis (PCA) and confirmatory factor analysis (CFA) results, the scale is best captured by a three-factor solution, with six items loading on the mental fatigue factor, nine items loading on the impaired cognitive acuity factor, and eight items loading on the confusion factor. We found that individuals who tested positive for COVID-19 had significantly higher mental fatigue, impaired cognitive acuity, and confusion scores than matched controls who never tested positive for COVID-19. • Brain Fog Scale (BFS) is the first validated measure of brain fog. • BFS has good psychometric properties. • BFS has three subscales: mental fatigue, impaired cognitive acuity, confusion. • Elevated brain fog symptoms were reported by individuals who had COVID-19. [ABSTRACT FROM AUTHOR]

With the start of the Fourth Industrial Revolution, Internet of Things (IoT), artificial intelligence (AI), and big data technologies are attracting global attention. AI can achieve fast computational speed, and big data makes it possible to store and use vast amounts of data. In addition, smartphones, which are IoT devices, are owned by most people. Based on these advantages, the above three technologies can be combined and effectively applied to navigation technology. In the case of an outdoor environment, global positioning system (GPS) technology has been developed to enable relatively accurate positioning of the user. However, due to the problem of radio wave loss because of many obstacles and walls, there are obvious limitations in applying GPS to indoor environments. Hence, we propose a method to increase the accuracy of user positioning in indoor environments using wireless-fidelity (Wi-Fi). The core technology of the proposed method is to limit the initial search region of the particle swarm optimization (PSO), an intelligent particle algorithm; doing so increases the probability that particles converge to the global optimum and shortens the convergence time of the algorithm. For this reason, the proposed method can achieve fast processing time and high accuracy. To limit the initial search region of the PSO, we first build an received signal strength indicator (RSSI) database for each sample point (SP) using a fingerprinting scheme. Then, a limited region is established through a fuzzy matching algorithm. Finally, the particles are randomly distributed within a limited region, and then the user’s location is positioned through a PSO. Simulation results confirm that the method proposed in this paper achieves the highest positioning accuracy, with an error of about 1 m when the SP interval is 3 m in an indoor environment.

Fuzzy matching is a widely used technique in computer-assisted translation and some other fields (it is implemented in most database engines and is used in autocompleting of data, for example). In this paper, fuzzy matching in the aspects of approximate string matching is investigated. Ba- sic algorithms like Soundex, Bitap, Boyer-Moore [1, 2] are covered. Using the results about those algorithms, several database engines are compared and a new way of handling fuzzy matching is offered. [ABSTRACT FROM AUTHOR]

User–generated content published on microblogging social networks constitutes a priceless source of information. However, microtexts usually deviate from the standard lexical and grammatical rules of the language, thus making its processing by traditional intelligent systems very difficult. As an answer, microtext normalization consists in transforming those non–standard microtexts into standard well–written texts as a preprocessing step, allowing traditional approaches to continue with their usual processing. Given the importance of phonetic phenomena in non–standard text formation, an essential element of the knowledge base of a normalizer would be the phonetic rules that encode these phenomena, which can be found in the so–called phonetic algorithms. In this work we experiment with a wide range of phonetic algorithms for the English language. The aim of this study is to determine the best phonetic algorithms within the context of candidate generation for microtext normalization. In other words, we intend to find those algorithms that taking as input non–standard terms to be normalized allow us to obtain as output the smallest possible sets of normalization candidates which still contain the corresponding target standard words. As it will be stated, the choice of the phonetic algorithm will depend heavily on the capabilities of the candidate selection mechanism which we usually find at the end of a microtext normalization pipeline. The faster it can make the right choices among big enough sets of candidates, the more we can sacrifice on the precision of the phonetic algorithms in favour of coverage in order to increase the overall performance of the normalization system. [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.

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).

Data comes in all forms, shapes, sizes and complexities. Stored in files and data sets, SAS ® users know all too well that data can be, and often is, problematic and plagued with a variety of issues. Although today's statistical software programs are extremely powerful, they are typically not designed to overcome poor quality data. This paper describes and recommends a comprehensive data preparation and fuzzy matching process to follow to enable improved statistical modeling. Statistical techniques are also available for comparing the results of the process. Most statistical software users are aware that two or more data files can be joined, or combined, without a problem when the data files have identifiers with unique and reliable values. However, many files do not have unique identifiers, or "keys", and need to be joined using character values, like names or E-mail addresses. To add to the difficulty and confusion, these identifiers might be spelled differently, or use different abbreviation or capitalization protocols. This paper describes a versatile 6-step approach to handling data preparation and fuzzy matching issues for improved statistical modeling. The steps include the identification and understanding of potential matching scenarios; exploring data values and data types; data cleaning and validation; data transformation; traditional merge and join techniques; and an assortment of techniques to successfully merge, join and match less than perfect, or "messy", data by doing phonetic matching using special-purpose character-handling functions like the SOUNDEX algorithm, and the SPEDIS, COMPLEV, and COMPGED fuzzy matching functions. Although the programming techniques described in this paper are illustrated using SAS code, many, if not most, of the techniques can be applied to any software platform that supports character-handling capabilities. [ABSTRACT FROM AUTHOR]

Quantum image processing has recently emerged as an essential problem in practical tasks, e.g. real-time image matching. Previous studies have shown that the superposition and entanglement of quantum can greatly improve the efficiency of complex image processing. In this paper, a fuzzy quantum image matching scheme based on gray-scale difference is proposed to find out the target region in a reference image, which is very similar to the template image. Firstly, we employ the proposed enhanced quantum representation (NEQR) to store digital images. Then some certain quantum operations are used to evaluate the gray-scale difference between two quantum images by thresholding. If all of the obtained gray-scale differences are not greater than the threshold value, it indicates a successful fuzzy matching of quantum images. Theoretical analysis and experiments show that the proposed scheme performs fuzzy matching at a low cost and also enables exponentially significant speedup via quantum parallel computation. [ABSTRACT FROM AUTHOR]

Operational agility, which represents the capability of the plant/facility regarding the fast detection and adaption to the new situations facing external/internal changes, is commonly regarded as one of central-properties for Smart Process Manufacturing. Clearly, operational agility significantly affects the plant/facility performance such as profit and safety. In this work, a strategy for enhancing the operational agility of petroleum refinery plants is proposed. For this strategy, the accumulated data sets from the industrial plants as well as the high-fidelity simulation activities are firstly processed to formulate the case base with a determined structure. Fuzzy matching is adopted to evaluate the similarity between the new coming case and the potential one in the formulated case base. A new criterion, namely stability number, is proposed as the performance metric for choosing an appropriate type of Fuzzy membership function (FMF). Furthermore, an optimization model is set to optimize parameters of the selected Fuzzy membership function. The application of the proposed strategy to an industrial Fluidized Catalytic Cracking Unit (FCCU) is performed to demonstrate the relevant effectiveness. [ABSTRACT FROM AUTHOR]

After 30 years of rapid development, the total mileage of China's expressways has exceeded 130 thousand kilometres, and large amounts of data are stored in the toll collection system. China's expressway toll collection is implemented in the provincial transportation network. The routes crossing provinces are divided into several records in the toll collection system. License plates are the unique identifier of a vehicle used for matching routes. However, records of license plates are not good enough, so the route matching requires some other useful auxiliary information. A fuzzy matching model based on Bayesian rules is built accordingly. Bayesian matching probability is based on license plate similarity and considers the auxiliary information. The model is of high precision and effectiveness. It is valuable in expressway toll collection data analysis using big data technology. [ABSTRACT FROM AUTHOR]

Biomedical Named Entity Recognition (Bio-NER) is the crucial initial step in the information extraction process and a majorly focused research area in biomedical text mining. In the past years, several models and methodologies have been proposed for the recognition of semantic types related to gene, protein, chemical, drug and other biological relevant named entities. In this paper, we implemented a stacked ensemble approach combined with fuzzy matching for biomedical named entity recognition of disease names. The underlying concept of stacked generalization is to combine the outputs of base-level classifiers using a second-level meta-classifier in an ensemble. We used Conditional Random Field (CRF) as the underlying classification method that makes use of a diverse set of features, mostly based on domain specific, and are orthographic and morphologically relevant. In addition, we used fuzzy string matching to tag rare disease names from our in-house disease dictionary. For fuzzy matching, we incorporated two best fuzzy search algorithms Rabin Karp and Tuned Boyer Moore. Our proposed approach shows promised result of 94.66%, 89.12%, 84.10%, and 76.71% of F-measure while on evaluating training and testing set of both NCBI disease and BioCreative V CDR Corpora. [ABSTRACT FROM AUTHOR]

In media retrieval system for intelligent transportation, media data variety and heterogeneity have been one of the most critical features. Documents with different formats may express similar semantic information, thus, searching documents reflecting users׳ intention has been a crucial and important task. For solving this problem, this paper proposes a novel semantic-based heterogeneous transportation media retrieval (TMR) approach to improve the performance. TMR supports the function of retrieving various media types such as image, video, audio and text by using a single media type. Firstly, semantic fields are extracted from the user annotating and automatic learning to express the users׳ intention. Secondly, ontology is used to represent the semantic fields of a media, and the ontology represented semantic information is saved together with the media document data. Thirdly, the semantic field adjustment process is described. Finally, fuzzy matching is employed to measure the similarity between the users׳ intention and media documents. For the returned results, we carry out the performance evaluation models in comparison with the existing approaches. Experimental result indicates the superiority of TMR in term of precision rate, computing speed, storage cost and user experience. [ABSTRACT FROM AUTHOR]

Aiming at the shortcomings of traditional methods, this paper proposed a new approach for image and electronic reconnaissance target association based on group target and multi-feature integration. The critical steps contained targets grouping, group information representation and target association, respectively. Compared with the traditional methods, there were three main differences. First, it treated the target and its nearby as a whole. The second, it introduced a fuzzy factor in the target information representation. The third was the consideration of both global and local characteristics, and the using of sequential method. Finally, it tesed the proposed approach on large datasets, and it could still acquire high association accuracy under the conditions of low target detection probability, high false alarm probability and low positioning accuracy. Experimental results indicate the validity and feasibility of the proposed image and electronic reconnaissance target association approach. [ABSTRACT FROM AUTHOR]