Fuzzy Matching Algorithm Research Articles

Hybrid topic modeling method based on dirichlet multinomial mixture and fuzzy match algorithm for short text clustering

Topic modeling methods proved to be effective for inferring latent topics from short texts. Dealing with short texts is challenging yet helpful for many real-world applications, due to the sparse terms in the text and the high dimensionality representation. Most of the topic modeling methods require the number of topics to be defined earlier. Similarly, methods based on Dirichlet Multinomial Mixture (DMM) involve the maximum possible number of topics before execution which is hard to determine due to topic uncertainty, and many noises exist in the dataset. Hence, a new approach called the Topic Clustering algorithm based on Levenshtein Distance (TCLD) is introduced in this paper, TCLD combines DMM models and the Fuzzy matching algorithm to address two key challenges in topic modeling: (a) The outlier problem in topic modeling methods. (b) The problem of determining the optimal number of topics. TCLD uses the initial clustered topics generated by DMM models and then evaluates the semantic relationships between documents using Levenshtein Distance. Subsequently, it determines whether to keep the document in the same cluster, relocate it to another cluster, or mark it as an outlier. The results demonstrate the efficiency of the proposed approach across six English benchmark datasets, in comparison to seven topic modeling approaches, with 83% improvement in purity and 67% enhancement in Normalized Mutual Information (NMI) across all datasets. The proposed method was also applied to a collected Arabic tweet and the results showed that only 12% of the Arabic short texts were incorrectly clustered, according to human inspection.

Using of Fuzzy Logic Matching Algorithm for Hindi Dataset

Various new computing methods based on Fuzzy Logic can be used in various systems. Numerous researchers and developers are working with Fuzzy Logic and its Applications. One such application is applied on the dataset which contains the values in Hindi Language; the major problem is that data come from different sources and when do the merge operation it will create a lot of problem; since same names are represented by different ways of writing it. Here, we apply a technique of fuzzy matching algorithm for hindi dataset. We are applying for the project of sharing bike concept which comes from different data sources. Fuzzy Logic is a very promising mathematical approach for training the dataset which being characterized by subjectivity & imprecision. More emphasis is put on the fuzzy string matching and its criteria for the performing the merge operation. This paper presents an analysis of the results achieved using fuzzy logic to model the dataset. Assessing the performance of datasets after performing matching algorithm at different levels using traditional methods.

MLD: An Intelligent Memory Leak Detection Scheme Based on Defect Modes in Software.

With the expansion of the scale and complexity of multimedia software, the detection of software defects has become a research hotspot. Because of the large scale of the existing software code, the efficiency and accuracy of the existing software defect detection algorithms are relatively low. We propose an intelligent memory leak detection scheme MLD based on defect modes in software. Based on the analysis of existing memory leak defect modes, we summarize memory operation behaviors (allocation, release and transfer) and present a state machine model. We employ a fuzzy matching algorithm based on regular expression to determine the memory operation behaviors and then analyze the change in the state machine to assess the vulnerability in the source code. To improve the efficiency of detection and solve the problem of repeated detection at the function call point, we propose a function summary method for memory operation behaviors. The experimental results demonstrate that the method we proposed has high detection speed and accuracy. The algorithm we proposed can identify the defects of the software, reduce the risk of being attacked to ensure safe operation.

Application Research and Simulation Analysis of Power Address Mining Based on Address Fuzzy Matching Algorithm

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.

Frequency and Consequences of Cervical Lymph Node Overstaging in Head and Neck Carcinoma.

Clinical lymph node staging in head and neck carcinoma (HNC) is fraught with uncertainties. Established clinical algorithms are available for the problem of occult cervical metastases. Much less is known about clinical lymph node overstaging. We identified HNC patients clinically classified as lymph node positive (cN+), in whom surgical neck dissection (ND) specimens were histopathologically negative (pN0) and in addition the subgroup, in whom an originally planned postoperative radiotherapy (PORT) was omitted. We compared these patients with surgically treated patients with clinically and histopathologically negative neck (cN0/pN0), who had received selective ND. Using a fuzzy matching algorithm, we identified patients with closely similar patient and disease characteristics, who had received primary definitive radiotherapy (RT) with or without systemic therapy (RT ± ST). Of the 980 patients with HNC, 292 received a ND as part of primary treatment. In 128/292 patients with cN0 neck, ND was elective, and in 164 patients with clinically positive neck (cN+), ND was therapeutic. In 43/164 cN+ patients, ND was histopathologically negative (cN+/pN−). In 24 of these, initially planned PORT was omitted. Overall, survival did not differ from the cN0/pN0 and primary RT ± ST control groups. However, more RT ± ST patients had functional problems with nutrition (p = 0.002). Based on these data, it can be estimated that lymph node overstaging is 26% (95% CI: 20% to 34%). In 15% (95% CI: 10% to 21%) of surgically treated cN+ HNC patients, treatment can be de-escalated without the affection of survival.

Optimizing the Retrieval of the Vital Status of Cancer Patients for Health Data Warehouses by Using Open Government Data in France.

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

Fuzzy matching algorithm of network information retrieval based on discrete mathematics

Fuzzy matching algorithm of network information retrieval based on discrete mathematics

Application Research and Simulation Analysis of Power Address Mining Based on Address Fuzzy Matching Algorithm

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.

Identification and risk prediction of potentially contaminated sites in the Yangtze River Delta

Identification and risk prediction of potentially contaminated sites (PCS) are crucial for the management of contaminated sites. However, the identification and risk prediction methods of PCS are lacking at a regional scale. Here, we established the fuzzy matching algorithm based on the site's name for identifying PCS in the Yangtze River Delta (YRD) from 2000 to 2020. The results showed that PCS in the YRD increased by over ten times, from 336 in 2000 to 4191 in 2020. Socio-economic and physical geography drive the growth of PCS and its spatiotemporal distribution, while the former has a more significant impact than the latter. We also presented a risk probability zoning strategy based on the source-pathway-receptor model, and proposed the patch-generating land-use simulation model to predict the risk probability of PCS in 2030. The results of risk probability zoning from 2000 to 2020 indicated that the local government of the YRD has started to pay attention to PCS management and risk control while developing social and economic. The results of risk prediction demonstrated that the proportion of low-risk probability pixels is 96.1% in 2030. Therefore, the planned indicator in the Action Plan on contaminated sites established by the State Council of China can be achieved in the YRD. Our experience in identifying and predicting PCS can inform how the local government worldwide manages PCS and tackles future challenges of achieving the ambition of site pollution control.

WiFi Positioning in 3GPP Indoor Office with Modified Particle Swarm Optimization

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.

Spatial beam shaping of a focused laser with quasi-near-field characteristics based on a concatenated fuzzy matching algorithm

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.

ClimActor, harmonized transnational data on climate network participation by city and regional governments

Cities and regions have become increasingly engaged in global climate change governance. They are pledging their own climate mitigation targets and participating in membership networks that typically are transnational in nature and engage thousands of subnational governments. Researching these growing trends in participation has been difficult due to the disparate and inconsistent nature of this self-reported data. To facilitate future analyses of these actors, we introduce ClimActor, the largest harmonized global dataset of more than 10,000 city and regional governments participating in networks like the Global Covenant of Mayors for Climate and Energy, C40 Cities for Climate Leadership, ICLEI Local Leaders for Sustainability, among others. We include key contextual information on each actor’s population, geographic location, and administrative jurisdiction to facilitate disambiguation of potential overlaps in actions or emissions. We also provide a series of cleaning functions based on phonetic and fuzzy string matching algorithms within an open-source R package to make it easy for anyone to immediately use the ClimActor dataset with other relevant data.

Evaluating commercially generated residential address histories in an aging population: the Ginkgo Evaluation of Memory Study

Background: Epidemiological studies of spatially varying exposures benefit from accurate long-term comprehensive residential address histories of study participants in order to accurately link locations to spatially and temporally resolved exposure information. Commercially available residential address history databases might help researchers establish historical address data retrospective to study onset. The Ginkgo Evaluation of Memory Study (GEMS) was a randomized trial in four US sites (Hagerstown MD, Pittsburgh PA, Sacramento CA, and Winston-Salem NC) from 2000-2008. We aimed to assess the accuracy and historical reach of LexisNexis residential histories for the 3,069 GEMS participants from 1980 to 2008. Methods: We submitted to LexisNexis a list of 3069 participant names and 4050 known addresses and dates of residency from 2000-2008. The query returned 10,966 addresses. We developed a set of specific rules along with a fuzzy matching algorithm, to establish a sequence of probable residential histories. The average length of histories per participant were then evaluated and compared across gender, study site, and age. Results: Almost all (97.8%) GEMS participants had at least a partial address history returned by LexisNexis for years prior to 2000. The earliest historical addresses had a median year of 1983 and 35.2% of participants had their earliest historical address going back to at least 1980. The biggest differences in average historical lengths were between study sites. Participants from Hagerstown had an average of between 2.9 and 6.0 fewer years of residential history than the other three sites. Additionally, males had between .56 and 1.23 more years on average than females. Conclusions: Commercial residential address systems have the potential to enhance health studies that require historical residential information. As these types of databases grow in accessibility and accuracy, historical databases can be a key tool of environmental epidemiologists. Demographics and geographic locations, however, could impact accuracy and address length.

Analysis and safety engineering of fuzzy string matching algorithms

In this paper we explore fuzzy string matching in an automatic ticket classification and processing system. We compare performance of the following string similarity algorithms: Longest Common Subsequence (LCS), Dice coefficient, Cosine Similarity, Levenshtein (edit) distance and Damerau distance. Through optimisation, we accomplished a 15% improvement in the ratio of false positives to true positive classifications over the existing approach used by a customer support system for free customers. To introduce greater safety; we compliment fuzzy string matching algorithms with a second layer Convolutional Neural Network (CNN) binary classifier, achieving an improved keyword classification ratio for two ticket categories by a relative 69% and 78%. Such an approach allows for classification to only be applied where a desired level of safety achieved, such as in instances where automated answers.

Automated recognition and mapping of building management system (BMS) data points for building energy modeling (BEM)

With the advance of the internet of things and building management system (BMS) in modern buildings, there is an opportunity of using the data to extend the use of building energy modeling (BEM) beyond the design phase. Potential applications include retrofit analysis, measurement and verification, and operations and controls. However, while BMS is collecting a vast amount of operation data, different suppliers and sensor installers typically apply their own customized or even random non-uniform rules to define the metadata, i.e., the point tags. This results in a need to interpret and manually map any BMS data before using it for energy analysis. The mapping process is labor-intensive, error-prone, and requires comprehensive prior knowledge. Additionally, BMS metadata typically has considerable variety and limited context information, limiting the applicability of existing interpreting methods. In this paper, we proposed a text mining framework to facilitate interpreting and mapping BMS points to EnergyPlus variables. The framework is based on unsupervised density-based clustering (DBSCAN) and a novel fuzzy string matching algorithm “X-gram”. Therefore, it is generalizable among different buildings and naming conventions. We compare the proposed framework against commonly used baselines that include morphological analysis and widely used text mining techniques. Using two building cases from Singapore and two from the United States, we demonstrated that the framework outperformed baseline methods by 25.5%, with the measurement extraction F-measure of 87.2% and an average mapping accuracy of 91.4%.

Indoor Positioning by Weighted Fuzzy Matching in Lifi Based Hospital Ward Environment

Core technologies of the fourth industrial revolution include AI (Artificial Intelligence), IoT (Internet of Things), and Big Data. The field of effective use of these technologies is the field of navigation. However, in indoor navigation, the accuracy is very low due to objects and walls placed in various positions. In this paper, we propose an indoor location tracking technology based on LiFi (Light Fidelity) communication to determine the exact location of a user using location tracking technology and communication technology. First of all, the fingerprinting database is constructed by measuring the CIR (Channel Impulse Response) value of each SP (Sample Point) using the fingerprinting technique. Then we use the Weighted Fuzzy Matching Algorithm to track the user’s location. The simulation results show that the performance of the LiFi environment is superior to that of the WiFi (Wireless Fidelity) environment.

Understanding the Users Personal Attributes Similarity Across Online Social Networks

In this modern era of technology, everyone accessing the Internet is obsessed with social media. A User accesses different social media services to fulfill his diverse needs. For instance, Instagram is mainly used for sharing personal visual content while Twitter is known for finding latest news and trends, similarly Facebook for personal posts. Such services lead to the distribution of personal information of an Internet user on these platforms. In this paper, we build a framework to discover the relationship among the attributes of a user across the social media.We use different fuzzy string matching algorithms to find the similarities between the attributes. We extract the ‘name’ and ‘username’ from a publicly shared dataset and apply two character based and token based algorithms on these features. The results are indicative of the fact that only a limited number of users share the same name and username across the sites. On further analysis, it is found that although name and username of most of the users do not exactly match, they tend to be similar with the infinitesimal difference like; underscore, period, one digit numbers, etc. This study provides an analysis of the typical variations in names and usernames, which can further be studied for the extension to other social networks This profile will help in behavior analysis of a user, which will further help us to improve recommendations and analyze for criminal behavior and similar applications.

Hand Gesture Recognition Enhancement Based on Spatial Fuzzy Matching in Leap Motion

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.

Методика виявлення просторових аномалій руху суден та її використання при оцінюванні ризиків у системі морської охорони «Гарт-12»

The article presents a method of detection of the spatial anomalies of vessel traffic based on «Hart-12» Information-Telecommunication Maritime Security System. Within this methodology, the authors propose a metric to determine the similarity of two routes of vessels based on discrete readings of their positions at different times, a clustering method for processing the data available in «Hart-12» and definitions for typical routes. When assessing risks of violation of border legislation at the maritime boundary, it is proposed to take into account the correspondence of the spatial data of the route to its descriptive part as well as average and maximum deviations from a typical route. When verifying descriptive data, the fuzzy matching algorithm based on the Levenshtein distance is used

20 GB in 10 minutes: a case for linking major biodiversity databases using an open socio-technical infrastructure and a pragmatic, cross-institutional collaboration.

Biodiversity information is made available through numerous databases that each have their own data models, web services, and data types. Combining data across databases leads to new insights, but is not easy because each database uses its own system of identifiers. In the absence of stable and interoperable identifiers, databases are often linked using taxonomic names. This labor intensive, error prone, and lengthy process relies on accessible versions of nomenclatural authorities and fuzzy-matching algorithms. To approach the challenge of linking diverse data, more than technology is needed. New social collaborations like the Global Unified Open Data Architecture (GUODA) that combines skills from diverse groups of computer engineers from iDigBio, server resources from the Advanced Computing and Information Systems (ACIS) Lab, global-scale data presentation from EOL, and independent developers and researchers are what is needed to make concrete progress on finding relationships between biodiversity datasets. This paper will discuss a technical solution developed by the GUODA collaboration for faster linking across databases with a use case linking Wikidata and the Global Biotic Interactions database (GloBI). The GUODA infrastructure is a 12-node, high performance computing cluster made up of about 192 threads with 12 TB of storage and 288 GB memory. Using GUODA, 20 GB of compressed JSON from Wikidata was processed and linked to GloBI in about 10–11 min. Instead of comparing name strings or relying on a single identifier, Wikidata and GloBI were linked by comparing graphs of biodiversity identifiers external to each system. This method resulted in adding 119,957 Wikidata links in GloBI, an increase of 13.7% of all outgoing name links in GloBI. Wikidata and GloBI were compared to Open Tree of Life Reference Taxonomy to examine consistency and coverage. The process of parsing Wikidata, Open Tree of Life Reference Taxonomy and GloBI archives and calculating consistency metrics was done in minutes on the GUODA platform. As a model collaboration, GUODA has the potential to revolutionize biodiversity science by bringing diverse technically minded people together with high performance computing resources that are accessible from a laptop or desktop. However, participating in such a collaboration still requires basic programming skills.