Levenshtein Distance Algorithm Research Articles

PENERAPAN ALGORITMA LEVENSHTEIN DISTANCE UNTUK PENCARIAN PADA SISTEM INFORMASI PERPUSTAKAAN FAKULTAS KEDOKTERAN UNIVERSITAS PALANGKA RAYA

The library of Faculty of Medicine of University of Palangka Raya is one of the libraries that regulates its management conventionally, and has not utilized computer technology optimally. So that, the library needs to improve its performance over the time, perticularly in delivering information on books or in terms of circulation services of book borrowing and returning transactions.
 The methodology used in making this information system is method of modified Waterfall software. This method consists of four stages; (1) Analysis; (2) Design; (3) Implementation; (4) Testing. Errors in typing the words searched for by the users could cause the searching for information cannot be found. Thus, levenshtein distance algorithm would be able to display autocorrect or provide the word suggestions to improve the title of the book that had the closest distance value to the word typed.
 Based on the results of the levenshtein distance algorithm calculation for the single target book title search, it has an accuracy of 75%; for the multi target book title with 2 words, it has an accuracy of 64.29%; for the multi target book title with 3 words, it has an accuracy of 66.75%, and; for the multi target book title with 4 words has an accuracy of 70.83%.

On the Effect of a Pilot Coding Education Support System for Complex Problem Solving Tasks

In the programming education, there is a great need of a teaching support system that can support the learner in the programming process regardless of the computer language due to instructor’s difficulty of checking the progress of learners in real-time. Its importance is especially important in lower grade coding classes such as in K-12 education because they are not used to coding and so simple problems can be regarded as complex problems. For this, a pilot coding education support system based on Levenshtein distance algorithm which shows learners’ progress to given solution in real-time was developed in order to help learners to solve complex problems easily, and the learners’ motivation and self-efficacy was measured for estimating the usefulness of developed system targeting elementary school students. When the learners use the developed system, it was found that a statistically significant difference appears in the sub-factors of learning motivation compared with traditional class teaching environments. Among the sub-factors of self-efficacy, the efficacy dimension showed statistically significant difference too.

Method for pigment spectral matching identification based on adaptive levenshtein distance

In order to solve the low accuracy of the classic spectral matching algorithms in matching the spectral data of different pigment materials in the homochromatic similar system, this paper proposed a spectral matching algorithm based on adaptive threshold levenshtein distance. The levenshtein distance was researched to improve the matching accuracy by using its characteristics of to be sensitive to the spectral reflectance difference. At the same time, by adaptively setting the judgment condition of levenshtein distance, the error of this algorithm in matching the spectral data of the same pigment material under different conditions is reduced. And the identification accuracy of different pigment materials in the homochromatic similar system is improved. The experimental results show that compared with the traditional spectral matching algorithm, the matching accuracy of the adaptive levenshtein distance algorithm is higher, and the identification result for the pigment is better.

A study on improving the quality inspection on national information by using levenshtein distance algorithm

Background/Objectives: In Korea, much effort and budget were spent to improve national R&D information management. However yet, project summaries of national R&D are not accurate enough to be utilized.Methods/Statistical analysis: To examine the accuracy of project summaries, Levenshtein Distance Algorithm (LDA) was applied. LDA is expected to extract improper project summaries of which some parts of sentences are repeatedly used. To evaluate how the algorithm performs with national R&D information in Korea, project summaries of 53,492 national R&D projects that were conducted in 2014 were used.Findings: Unlike other algorithms, LDA was able to detect project summaries consisted of repeatedly used phrases. According to the test with LDA, from 53,492 cases, 3,445 projects had inaccurate contents in project summaries. In details, 2,707 projects had improper research objective, while 712 projects and 26 projects had improper contents in research summary and expected impact, respectively. Although the algorithm allowed extracting repeatedly used phrases, it had problems of time; thus, it was only applied offline. Also, a research had to confirm once more to verify the accuracy of the result.Improvements/Applications: This paper applied LDA to detect inappropriate project summaries. The result implies that by applying LDA, the quality of the information can be improved to facilitate the utilization.

Autocomplete and Spell Checking Levenshtein Distance Algorithm To Getting Text Suggest Error Data Searching In Library

Nowadays internet technology provide more convenience for searching information on a daily. Users are allowed to find and publish their resources on the internet using search engine. Search engine is a computer program designed to facilitate a user to find the information or data that they need. Search engines generally find the data based on keywords they entered, therefore a lot of case when the user can’t find the data that they need because there are an error while entering a keyword. Thats why a search engine with the ability to detect the entered words is required so the error can be avoided while we search the data. The feature that used to provide the text suggestion is autocomplete and spell checking using Levenshtein distance algorithm. The purpose of this research is to apply the autocomplete feature and spell checking with Levenshtein distance algorithm to get text suggestion in an error data searching in library and determine the level of accuracy on data search trials. This research using 1155 data obtained from UNNES Library. The variables are the input process and the classification of books. The accuracy of Levenshtein algorithm is 86% based on 1055 source case and 100 target case.

Voice Control in Calorie Tracker Application using Levenshtein Distance Algorithm

Each food consumed by people contains a number of calories needed by the body to perform an activity. Calories can be described as fuel of engine to move and carry out tasks. Public’s indifference to the food consumed can cause some negative effects on health like, too skinny, obesity, and emergence of various diseases. Therefore, it is necessary to have an application which can provide some information about the calorie needs, so that user can control the calorie intake. This paper describes the development of an application to assist user in controlling calorie intake according to the calorie needs. This application supports voice control feature to improve user comfort in performing certain commands and inputting food consumed. In addition, this application also uses Levenshtein Distance algorithm to correct food recognition errors which is spoken by user. This application is developed using Java programming language for Android with SQLite database.

Optimasi Pencarian Kata pada Aplikasi Penerjemah Bahasa Mandarin – Indonesia Berbasis Android dengan Algoritma Levenshtein Distance

In the Chinese-Indonesian language translator application on electronic device, such as Android, input is all about pinyin. Sometimes, some errors could occured. User can not find the word which he is searching for. These errors can increase the number of processing time. A solution to solve these errors is performing optimization on the application, i.e. by providing some alternative approaches word which typed by user. Application will sort alternative words according to the smallest distance to the largest distance. Then, application will display alternative words order from top priority which has the smallest distance, to low priority which has the largest distance. In brief, translator application with optimization is useful to shorten processing time of translating from Chinese to Indonesian by providing alternative words. Optimization is done with Levenshtein Distance algorithm. Levenshtein Distance algorithm will calculate the difference between the distance of the word user typed among alternative words.
 Index Terms─Android, Chinese, Indonesian, Levenshtein Distance, optimization, translator application

A web service matching algorithm based on semantic similarity

PurposeThe purpose of this paper is to obtain more accurate matching between the request and the release of web service.Design/methodology/approachThis paper adopts Levenshtein distance algorithm to calculate the name similarity between publishing service and request service, employs cosine theorem to compute the text similarity, and uses semantic distance to count the input‐output similarity, then filters out the low similarity and bad reputation services to structure the candidate service set.FindingsThe qualitative and quantitative analysis of the scheme is given in this paper. The experimental results show that the multi‐level matching filtering algorithm can obviously improve the recall ratio and precision ratio of web service discovery.Originality/valueThis paper proposes a similarity‐based filtering algorithm for multi‐level matching.

Secure Blocking + Secure Matching = Secure Record Linkage

Performing approximate data matching has always been an intriguing problem for both industry and academia. This task becomes even more challenging when the requirement of data privacy rises. In this paper, we propose a novel technique to address the problem of efficient privacy-preserving approximate record linkage. The secure framework we propose consists of two basic components. First, we utilize a secure blocking component based on phonetic algorithms statistically enhanced to improve security. Second, we use a secure matching component where actual approximate matching is performed using a novel private approach of the Levenshtein Distance algorithm. Our goal is to combine the speed of private blocking with the increased accuracy of approximate secure matching.

Efficient Parallel Design for Edit distance algorithm in DNA Sequence Alignment

The focus of Bioinformatics research is usually on two aspects—genomics and proteomics, specifically, it’s starting from nucleic acid and protein sequences, analyzing the structural and functional biological information expressed in the sequences. Biological sequence alignment is one of the common problems, the NeedlemanWunsch algorithm based on dynamic programming is the most basic algorithm, and Edit Distance(Levenshtein Distance) algorithm is also widely used in DNA sequence alignment. Nowadays, there are large amount of improvements on the Needleman-Wunsch algorithm, while few on Edit Distance algorithm, so this paper focuses on revealing the effects of parallel design on optimizing the Edit Distance algorithm, and it also compares the two algorithms’ different significances in DNA sequence alignment objectively.

Look-alike, sound-alike drugs in oncology

Medication errors with oncology drugs can place cancer patients at risk for adverse events or death. Look-alike, sound-alike (LASA) drug names may increase the risk for errors. Published lists of LASA drug names are generally a result of voluntarily reported medication incidents. This study performed a proactive review of the oncology drug formulary from the Cancer Services of the Alberta Health Services for LASA drug pairs. The Levenshtein Distance and Bigram Similarity algorithms, same first and last letters, and Lexi-Comp(R) on-line alerts were used to review the outpatient oncology formulary to identify potential LASA generic drug name pairs. indicate there are more potential LASA generic drug name pairs in the oncology formulary than are published in the literature. The risk detection methods used in this study identified unique and common LASA drug pairs. The Bigram Similarity algorithm identified 186 LASA drug pairs from 3320 possible pairs. The Levenshtein Distance algorithm, same first and last letters, and Lexi-Comp(R) methods identified 42, 75, and 38 LASA drug pairs, respectively. Five generic LASA drug pairs were identified in common by all four of the risk determination methods. LASA drug pairs identified by three or four methods were considered to provide the highest risk for errors. A step-wise approach to risk reduction, dependent on the number of detection methods identifying a pair, is presented. For specialty areas of practice, a proactive system of reviewing LASA drug name pairs may be warranted for increasing medication safety.