Levenshtein Distance Research Articles

A novel method leveraging time series data to improve subphenotyping and application in critically ill patients with COVID-19

Computational subphenotyping, a data-driven approach to understanding disease subtypes, is a prominent topic in medical research. Numerous ongoing studies are dedicated to developing advanced computational subphenotyping methods for cross-sectional data. However, the potential of time-series data has been underexplored until now. Here, we propose a Multivariate Levenshtein Distance (MLD) that can account for address correlation in multiple discrete features over time-series data. Our algorithm has two distinct components: it integrates an optimal threshold score to enhance the sensitivity in discriminating between pairs of instances, and the MLD itself. We have applied the proposed distance metrics on the k-means clustering algorithm to derive temporal subphenotypes from time-series data of biomarkers and treatment administrations from 1039 critically ill patients with COVID-19 and compare its effectiveness to standard methods. In conclusion, the Multivariate Levenshtein Distance metric is a novel method to quantify the distance from multiple discrete features over time-series data and demonstrates superior clustering performance among competing time-series distance metrics.

Retracted: Mitigation Impact of Energy and Time Delay for Computation Offloading in an Industrial IoT Environment Using Levenshtein Distance Algorithm

Retracted: Mitigation Impact of Energy and Time Delay for Computation Offloading in an Industrial IoT Environment Using Levenshtein Distance Algorithm

Scientific reference style using rule-based machine learning

Regular Expressions (RegEx) can be employed as a technique for supervised learning to define and search for specific patterns inside text. This work devised a method that utilizes regular expressions to convert the reference style of academic papers into several styles, dependent on the specific needs of the target publication or conference. Our research aimed to detect distinctive patterns of reference styles using RegEx and compare them with a dataset including various reference styles. We gathered a diverse range of reference format categories, encompassing seven distinct classes, from various sources such as academic papers, journals, conference proceedings, and books. Our approach involves employing RegEx to convert one referencing format to another based on the user's specific preferences. The proposed model demonstrated an accuracy of 57.26% for book references and 57.56% for journal references. We used the similarity ratio and Levenshtein distance to evaluate the dataset's performance. The model achieved a 97.8% similarity ratio with a Levenshtein distance of 2. Notably, the APA style for journal references yielded the best results. However, the effectiveness of the extraction function varies depending on the reference style. For APA style, the model showed a 99.97% similarity ratio with a Levenshtein distance of 1. Overall, our proposed model outperforms baseline machine learning models in this task. This study introduces an automated program that utilizes regular expressions to modify academic reference formats. This will enhance the efficiency, precision, and adaptability of academic publishing.

Emotional vocabulary in immigrants’ L2 written discourse: is linguistic distance a proxy for L2 emotionality?

ABSTRACT Emotional vocabulary is an important element in daily conversations, and knowledge and teaching of this vocabulary in a second language (L2) should be a primary goal in migration contexts. This study aimed to identify the emotional words used in the written productions of 288 adult immigrants from different countries of origin who were beginner-level learners of Spanish and to analyse the affective dimensions of valence and arousal of these words. The study also investigated whether the linguistic distance between the first language (L1) of these immigrants and their L2 (Spanish) – as assessed with the normalised and divided Levenshtein distance – constituted a proxy for emotionality in L2 written discourse. Multiple regression models and mediation analysis revealed that the effect of linguistic distance on the number of high-arousal words was mediated by L2 proficiency level, and that L2 proficiency level had a positive influence on the number of emotional (positive/negative) words. The results also revealed that these immigrants used a greater number of positive words in their L2 written productions.

Correcting spelling mistakes in Persian texts with rules and deep learning methods

This study aims to develop a system for automatically correcting spelling errors in Persian texts using two approaches: one that relies on rules and a common spelling mistake list and another that uses a deep neural network. The list of 700 common misspellings was compiled, and a database of 55,000 common Persian words was used to identify spelling errors in the rule-based approach. 112 rules were implemented for spelling correction, each providing suggested words for misspelled words. 2500 sentences were used for evaluation, with the word with the shortest Levenshtein distance selected for evaluation. In the deep learning approach, a deep encoder-decoder network that utilized long short-term memory (LSTM) with a word embedding layer was used as the base network, with FastText chosen as the word embedding layer. The base network was enhanced by adding convolutional and capsule layers. A database of 1.2 million sentences was created, with 800,000 for training, 200,000 for testing, and 200,000 for evaluation. The results showed that the network's performance with capsule and convolutional layers was similar to that of the base network. The network performed well in evaluation, achieving accuracy, precision, recall, F-measure, and bilingual evaluation understudy (Bleu) scores of 87%, 70%, 89%, 78%, and 84%, respectively.

A novel approach for standardizing clinical laboratory categorical test results using machine learning and string distance similarity

Standardizing clinical laboratory test results is critical for conducting clinical data science research and analysis. However, standardized data processing tools and guidelines are inadequate. In this paper, a novel approach for standardizing categorical test results based on supervised machine learning and the Jaro-Winkler similarity algorithm is proposed. A supervised machine learning model is used in this approach for scalable categorization of the test results into predefined groups or clusters, while Jaro-Winkler similarity is used to map text terms into standard clinical terms within these corresponding groups. The proposed method is applied to 75062 test results from two private hospitals in Bangladesh. The Support Vector Classification algorithm with a linear kernel has a classification accuracy of 98%, which is better than the Random Forest algorithm when categorizing test results. The experiment results show that Jaro-Winkler similarity achieves a remarkable 99.93% success rate in the test result standardization for the majority of groups with manual validation. The proposed method outperforms previous studies that concentrated on standardizing test results using rule-based classifiers on a smaller number of groups and distance similarities such as Cosine similarity or Levenshtein distance. Furthermore, when applied to the publicly available MIMIC-III dataset, our approach also performs excellently. All these findings show that the proposed standardization technique can be very beneficial for clinical big data research, particularly for national clinical research data hubs in low- and middle-income countries.

Methodology for Analyzing Bitstreams Based on the Use of the Damerau–Levenshtein Distance and Other Metrics

Methodology for Analyzing Bitstreams Based on the Use of the Damerau–Levenshtein Distance and Other Metrics

Examining Workflow for Simulated Pediatric Emergency Medical Services Care

Purpose: Workflow analysis is an important approach for understanding clinical care in complex systems. This is particularly true for low frequency events such as the care of children in the out-of-hospital setting. The purpose of this study was to characterize emergency medical service (EMS) workflow in the care of children during simulated emergency, prehospital encounters.
 Methods: This is a secondary analysis exploring high-fidelity videorecorded simulations, performed by emergency personnel. Two scenarios were used in 19 simulations which consisted of a 15-month and a 1-month old with respiratory decompensation and shock requiring intravenous fluid, respiratory support, and medication administration. One trained investigator performed review of the videos of teams EMS practitioners, quantifying the sequence and number of tasks performed and time to completion of the simulated intervention. The variance in sequence of tasks was quantified using the Levenshtein distance. We quantified the proportion of time with no activity (idle time) and temporal overlap (team multitasking time).
 Results: We identified 17 types of distinct tasks performed during the simulation. There was high variability across simulations in the sequences, types, and number of tasks performed. Team multitasking was noted in all scenarios, with a mean of 99% multitasking ratio (range: 52-202%). Mean proportion of idle time was 4% (range: 0-11%). Weight estimate, intravenous or intraosseous access, radio report, blood glucose level (BGL), medication administration, pulse check and respiration check were observed in all videos. Other tasks were observed in only a proportion of scenarios with varying frequencies. Median number of differences in sequence of tasks between scenarios was 15.
 Conclusions: Our analysis found a high percentage of team multitasking and significant variability in frequency and sequence of task completion. Further research is needed to assess the reasons and effect of this degree of variability on the efficiency and effectiveness of prehospital interventions.

Search Optimization of PIP Scholarship Recipients In Web-Based Student Data Application Using The Levenshtein Distance Algorithm

Realizing that education is very important, the government supports every citizen to get education. One of the government programs is the Smart Indonesia Program. PlP is a scholarship designed to help school-age children from poor/vulnerable families to continue to receive education services, both through formal elementary to high school/vocational schools and non-formal pathways from package a to package c and special education. SDN II Babakanloa has not been touched by technology for processing student data. So that the student section has difficulties in recording and updating student data. Student names have unique identities and errors often occur in typing the keywords to be searched. This results in an information that is desired or sought can not be found. Therefore we need a web-based data application that can provide keyword corrections in searching for student names. This study aims to create a web-based student data application by optimizing corrections to typing keywords searched by implementing the Levenshtein Distance Algorithm and also making it easier to process and search student data. The development method used is the Rational Unified Process (RUP) with the stages of Inception, Elaboration, Construction, and Transition. Designed using the CodeIgniter Framework with the PHP and JavaScript programming languages. The application of the Levenshtein Distance Algorithm can optimize the search for student data and reduce the occurrence of search errors by School Operators. The application of the Levenshtein Distance Algorithm produces a very good accuracy rate of 94% of the results of student data correction. accordance with the expectations of the School Operator. So it shows that the application of the Levenshtein Distance Algorithm is appropriate to use in optimizing the search.

Classification of Severe Maternal Morbidity from Electronic Health Records Written in Spanish Using Natural Language Processing

One stepping stone for reducing the maternal mortality is to identify severe maternal morbidity (SMM) using Electronic Health Records (EHRs). We aim to develop a pipeline to represent and classify the unstructured text of maternal progress notes in eight classes according to the silver labels defined by the ICD-10 codes associated with SMM. We preprocessed the text, removing protected health information (PHI) and reducing stop words. We built different pipelines to classify the SMM by the combination of six word-embeddings schemes, three different approaches for the representation of the documents (average, clustering, and principal component analysis), and five well-known machine learning classifiers. Additionally, we implemented an algorithm for typos and misspelling adjustment based on the Levenshtein distance to the Spanish Billion Word Corpus dictionary. We analyzed 43,529 documents constructed by an average of 4.15 progress notes from 22,937 patients. The pipeline with the best performance was the one that included Word2Vec, typos and spelling adjustment, document representation by PCA, and an SVM classifier. We found that it is possible to identify conditions such as miscarriage complication or hypertensive disorders from clinical notes written in Spanish, with a true positive rate higher than 0.85. This is the first approach to classify SMM from the unstructured text contained in the maternal EHRs, which can contribute to the solution of one of the most important public health problems in the world. Future works must test other representation and classification approaches to detect the risk of SMM.

Crop Information Retrieval Framework Based on LDW-Ontology and SNM-BERT Techniques

Currently, on the Internet, the information about agriculture is augmenting extremely; thus, searching for precise, relevant data of various details is highly complicated. To deal with particular difficulties like lower relevancy rate, false detection of retrieval resources, poor similarity rate, unstructured data format, multivariate data, irrelevant spelling, and higher computation time, an intelligent Information Retrieval (IR) system is required. An IR Framework centered on Levenshtein Distance Weight-centric Ontology (LDW-Ontology) and Sutskever Nesterov Momentum-centred Bidirectional Encoder Representation from Transformer (SNM-BERT) methodologies is presented here to overcome the complications as mentioned earlier. Firstly, the data is pre-processed, transmuting the unstructured data into a structured format, thus mitigating the error probabilities. Then, the LDW-Crop Ontology construction is done regarding the structured data. In the methodology presented, significance, frequency,and the suggestion of word in mind are considered to build Crop ontology. In the MongoDB database, the data being constructed are amassed. Then, by utilizing SNM-BERT, the data is trained for IR regarding clustered input produced by Inter Quartile Pruning Range-centred Hierarchical Divisive Clustering (IQPR-HDC) model. The LDW is computed for the provided user query; subsequently, the similarity evaluation outcomes are obtained from the database. The experiential evaluation displays that when analogized with the prevailing methodologies, a better accuracy of 94 % for simple queries and 92% for complex queries is achieved. Along with retrieval rate with lower computation time is achieved by the proposed methodology.

Comparing Levenshtein distance and dynamic time warping in predicting listeners’ judgments of accent distance

Listeners attend to variation in segmental and prosodic cues when judging accent strength. The relative contributions of these cues to perceptions of accentedness in English remains open for investigation, although objective accent distance measures (such as Levenshtein distance) appear to be reliable tools for predicting perceptual distance. Levenshtein distance, however, only accounts for phonemic information in the signal. The purpose of the current study was to examine the relative contributions of phonemic (Levenshtein) and holistic acoustic (dynamic time warping) distances from the local accent to listeners’ accent rankings for nine non-local native and nonnative accents. Listeners (n = 52) ranked talkers on perceived distance from the local accent (Midland American English) using a ladder task for three sentence-length stimuli. Phonemic and holistic acoustic distances between Midland American English and the other accents were quantified using both weighted and unweighted Levenshtein distance measures, and dynamic time warping (DTW). Results reveal that all three metrics contribute to perceived accent distance, with the weighted Levenshtein slightly outperforming the other measures. Moreover, the relative contribution of phonemic and holistic acoustic cues was driven by the speaker's accent. Both nonnative and non-local native accents were included in this study, and the benefits of considering both of these accent groups in studying phonemic and acoustic cues used by listeners is discussed.

Unveiling the Wave: Unraveling Sales Trends and Unlocking Market Basket Insights

This article presents sales trends and market basket analysis for Food and Beverages (F&B) items recommendation using linear regression time series and association rules. The purpose of this study is to help businesses to make better informed decisions by understanding their customers’ purchasing behaviors and the sales trends of the F&B items. This includes the study of both cross- selling and up-selling patterns using market basket analysis. To improve the market basket analysis, food name clustering has been carried out to group those foods having similarity ratio higher or equal to 80 based on the Levenshtein distances between the food names. This study analyzes sales data from a mobile app platform that register the Malaysian F&B hawkers (or merchants) and app users (or buyers) who sell and purchase F&B respectively. This study is based on sales trends analysis and association rules of product recommendations by examining FP-Growth and the relationship between product items bought. This study provides valuable insights for local Malaysian hawkers who are looking to improve their F&B items recommendations and to make better data-driven decisions while planning their marketing activities. It helps to improve sales by either up or cross-sell their products more effectively.

Rancang Bangun Chatbot Pengaduan Kekerasan Perempuan Anak Dengan Metode Fuzzy String Matching Dan Enhanced Confix Stripping Stemmer

Violence against women is a serious issue affecting many individuals in society. In Indonesia, including Kota Probolinggo, the number of cases of violence against women remains high. This research aims to design a chatbot application as a means for reporting violence against women and children (VAWC) in Kota Probolinggo. The application provides an anonymous and safe space for victims to report their complaints without revealing their identities. Additionally, the chatbot delivers information and education about different types of violence against women and preventive measures. The development of the chatbot application utilizes Fuzzy String Matching, Enhanced Confix Stripping Stemmer, and preprocessing methods to enhance data search accuracy and provide appropriate responses to users. The testing results show that the chatbot offers responses with satisfactory accuracy. Leveraging AI technology in the form of a chatbot is expected to provide an effective solution in addressing and preventing violence against women, offering a secure and anonymous platform for victims to report incidents. The application is envisioned to offer better support and assistance to victims of violence.
 The chatbot application implements fuzzy string matching, the Levenshtein distance algorithm, and Enhanced Confix Stripping Stemmer to process user input before matching it with the database. During the Enhanced Confix Stripping Stemmer process, sentences are transformed into an array of basic words, enhancing the accuracy of data matching when using fuzzy string matching and the Levenshtein distance algorithm. Testing results indicate a 4.77% difference between manual calculations and the chatbot system. This difference may be attributed to variations in text processing and other factors.

Testing The Accuracy of Fingerprint Recognition using Levenshtein Distance and Hamming Distance Methods

The presence or evidence of attendance is crucial in monitoring the presence of every individual working in a particular field. Developing an employee attendance system using fingerprints can expedite the processing of data of employees who have or have not attended. One brand of machine used as a fingerprint attendance tool is Fingerspot Flexcode. The data obtained from the machine comes in the form of bitmap images that are converted into strings using encoding. Although the resulting string sequences are different, there is a possibility of similarity in fingerprint data among employees because the system cannot distinguish data precisely. Therefore, the comparison between the Levenshtein Distance and Hamming Distance methods is used to determine which method has the highest accuracy in processing the system's calculation. The method with the highest accuracy will determine the level of compatibility of the method with the tested tool. For example, 6 fingerprint data are taken from each of the 7 different employees, resulting in a total of 42 data as test data. The calculation results show that the accuracy of the Levenshtein Distance method is 80,76 % with a precision of 46,43 %, while the Hamming Distance method is 78,34 % with a precision of 30,50 % in processing string similarity in fingerprint data. Based on these results, it can be concluded that the Levenshtein Distance method is better in calculating similarity in fingerprint data compared to the Hamming Distance method because it has a higher level of accuracy and precision compared to the Hamming Distance method.

Optical High-Speed Rolling Mark Detection Using Object Detection and Levenshtein Distance

This paper presents an automated high-speed rolling mark recognition system for railroad rails utilizing image processing techniques. Rolling marks, which consist of numbers, letters, and special characters, were engraved into the rail web as 3D information. These rolling marks provide crucial details regarding the rail manufacturer, steel quality, year of production, and rail profile. As a result, they empower rail infrastructure managers to gain valuable insights into their infrastructure. The rolling marks were captured using a standard color camera under dark field illumination. The recognition of individual numbers, letters, and special characters was achieved through state-of-the-art deep neural network object detection, specifically employing the YOLO architecture. By leveraging reference rolling marks, the detected characters can then be accurately interpreted and corrected. This correction process involves calculating a weighted Levenshtein distance, ensuring that the system can identify and rectify partially misidentified rolling marks. Through the proposed system, the accurate and reliable identification of rolling marks was achieved, even in cases in which there were partial errors in the detection process. This novel system thus has the potential to substantially improve the management and maintenance of railroad infrastructure.

The cognate continuum

Abstract Cognates, words that are similar in form and meaning across two languages, form compelling test cases for bilingual access and representation. Overwhelmingly, cognate pairs are subjectively selected in a categorical either- or manner, often with criteria and modality unspecified. Yet the few studies that take a more nuanced approach, selecting cognate pairs along a continuum of overlap, show interesting, albeit somewhat divergent results. This study compares three measures that quantify cognateness continuously to obtain modality-specific cognate scores for the same set of Norwegian-English word-translation pairs: (1) Researcher Intuitions – bilingual researchers rate the degree of overlap between the paired words, (2) Levenshtein Distance – an algorithm that computes overlap between word pairs, and (3) Translation Elicitation – English-speaking monolinguals guess what Norwegian words mean. Results demonstrate that cognateness can be ranked on a continuum and reveal measure and modality-specific effects. Orthographic presentation yields higher cognateness status than auditory presentation overall. Though all three measures intercorrelated moderately to highly, Researcher Intuitions demonstrated a bimodal distribution, yielding scores on the high and low end of the spectrum, consistent with the common categorical approach in the field. Levenshtein Distance would be preferred for fine-grained distinctions along the continuum of form overlap.

Volume formula and growth rates of the balls of strings under the edit distances

Analogs of balls in n-dimensional Euclidean space are defined in the monoid of strings composed of letters of an alphabet if a metric is defined on the monoid. These analogs are not only balls in the monoid of strings but also regular languages. Since the Levenshtein distance, a reasonable metric between strings, was introduced in 1965, researchers have aimed to determine a formula for computing the volumes of balls of strings under this distance and their growth rate. However, this problem remains open. In this paper, we first derive a formula for the volumes of balls of strings under a metric obtained by extending the Hamming distance as a metric on the whole monoid and subsequently reveal the growth rate of balls of strings under the Levenshtein distance using this formula. Furthermore, we describe a conjecture on the formula for the volumes of spheres of strings under the Levenshtein distance. Next, we construct an efficient randomized algorithm that computes estimates of the volumes of the balls whose estimation errors are less than a given value with a probability greater than a specified value. Finally, we examine the validity of the proposed conjecture, using the (true) volumes obtained by exhaustive searches for small spheres and estimates of the volumes obtained by this algorithm for large ones.

The impact of interlingual correspondences on cognate recognition in Slavic intercomprehension

The present paper is devoted to the problem of cognate recognition in an intercomprehension setting, in which the target language is not directly known to the readers, but is closely related to their native language. Previous research has shown that once context is accounted for, intelligibility is partially predicted by measures of interlingual proximity such as Levenshtein distance. Taken alone, however, such measures are insufficient to fully explain empirical data. It has thus been suggested that specific interlingual correspondences may have a more conspicuous impact than others. To verify this hypothesis, 163 East Slavic-speaking learners of L2 Polish were asked to translate a set of non-words, obtained by manipulating a set of Russian words with respect to a single segment (e.g. initial consonant). All non-words were consistent with Polish phonotactics; however, some modifications matched existing phonological correspondences between Polish and Russian, while others did not. Target items were presented in writing through an online survey, initially in isolation, subsequently within a meaningful sentence.The results show that the presence of a meaningful context significantly improves the chances of cognate recognition. Further, non-words containing systematic sound correspondences resulted in a higher recognition rate than non-words comprising arbitrary modifications. Within the latter group, finally, modifications to consonants in word-initial and word-medial position as well as diverging positions of the stress resulted in significantly lower recognition scores.

Linguistic and semantic characteristics of articles and peer review reports in Social Sciences and Medical and Health Sciences: analysis of articles published in Open Research Central

We aimed to examine the differences in articles, peer review and editorial processes in Medical and Health Sciences vs. Social Sciences. Our data source was Open Research Central (ORC) portal, which hosts several journal platforms for post-publication peer review, allowing the analysis of articles from their submission, regardless of the publishing outcome. The study sample included 51 research articles that had Social Sciences tag only and 361 research articles with Medical and Health Sciences tag only. Levenshtein distance analysis showed that text changes over article versions in social science papers were statistically significant in the Introduction section. Articles from Social Sciences had longer Introduction and Conclusion sections and higher percentage of articles with merged Discussion and Conclusion sections. Articles from Medical and Health Sciences followed the Introduction-Methods-Results-Discussion (IMRaD) structure more frequently and contained fewer declarations and non IMRaD sections, but more figures. Social Sciences articles had higher Word Count, higher Clout, and less positive Tone. Linguistic analysis revealed a more positive Tone for peer review reports for articles in Social Sciences and higher Achievement and Research variables. Peer review reports were significantly longer for articles in Social Sciences but the two disciplines did not differ in the characteristics of the peer review process at all stages between the submitted and published version. This may be due to the fact that they were published on the same publication platform, which uses uniform policies and procedures for both types of articles.