Fuzzy Search Research Articles

Secure query processing for smart grid data using searchable symmetric encryption

In the last decade, the smart grid has utilized many modern technologies and applications compared with the conventional grid. Cloud computing offers numerous managed services and storage solutions for smart grid data. To balance security, privacy, efficiency, and utility, more efforts should be made to keep up with the rapid evolution of technology. Searchable encryption techniques are widely considered an intelligent solution to ensure data privacy and security while maintaining the functionality to search over encrypted data. In this paper, we propose a more reliable and efficient searchable symmetric encryption scheme for smart grid data. It is a dynamic keyword searchable scheme that uses a symmetric cipher based on a key hashing algorithm (DKS-SCKHA) to generate a secure index. This scheme eliminates the false-positive issue associated with the use of bloom filters and narrows the scope of the retrieved search results. Additionally, it efficiently supports both partial and exact query processing on the encrypted database. Both theoretical and security analyses demonstrate the efficiency and security of the DKS-SCKHA scheme compared to other previous schemes. Comprehensive experiments on a smart grid dataset showed that the DKS-SCKHA scheme is 35–68% more efficient than the schemes compared in this paper. The DKS-SCKHA scheme supports three keyword search scenarios: single, conjunctive, and disjunctive. Furthermore, the DKS-SCKHA scheme is extended to support dynamic fuzzy keyword search on the encrypted database (DEFKS-SCKHA). We evaluated the security and efficiency of the DEFKS-SCKHA scheme through security analysis and experimental evaluation. Theoretical analysis shows that the proposed scheme is secure against known-ciphertext and known-background attacks.

Enhancing Control Systems through Type-3 Fuzzy Logic Optimization

The advancement of new tools in the field of control systems is a contemporary development. This work introduces the utilization of Type-3 fuzzy logic, a relatively recent concept that has been applied across various disciplines. In our case, a Type-3 fuzzy system is designed to enhance the optimization of parameters within the harmony search algorithm, specifically tailored for a control problem. Through a series of experiments, the efficacy of this novel Type-3 fuzzy logic tool is put to the test. Previous studies have primarily explored Type-1 and Type-2 fuzzy logic. To assess the performance of this new Type-3 fuzzy logic tool, a comparative analysis of results is conducted using statistical testing. The introduction of Type-3 fuzzy logic in the control domain represents a novel and innovative approach. This approach extends beyond the conventional Type-1 and Type-2 fuzzy logic, showcasing the dynamic evolution in the field. Results obtained through experimentation are analyzed, and statistical tests are employed to determine whether the Type-3 fuzzy logic tool yields superior outcomes compared to its predecessors. By doing so, this study contributes to the growing body of research that explores the potential benefits of Type-3 fuzzy logic and its application in control systems, offering new perspectives and opportunities for further advancements in the field. We have to mention that the utilization of Type-3 fuzzy logic in enhancing metaheuristics is a relatively new trend, and in this work, this research has extended this to the realm of harmony search. In addition, the application of the optimal design of the ball-and-beam fuzzy controllers has not been previously carried out with the Type-3 fuzzy harmony search algorithm, which is the novelty of this study.

USING A DICTIONARY OF PARONYMS FOR PERSONAL NAMES COMPONENTS TO IDENTIFY PERSONAL DATA

The use of fuzzy search methods when identifying input messages inevitably leads to the appearance of quite a lot of information noise in the output data. It is proposed to solve the current problem of cutting off information noise in the output data by analyzing the results of a fuzzy search using a dictionary of paronyms for nominal components. In relation to nominal components, paronyms are those nominal components that, on the one hand, satisfy the criteria of similarity of an information retrieval system, but, in fact, differ in meaning (they relate to completely different individuals). For the first time, the problem of reducing information noise in the output message of information retrieval systems was solved using an information filter, the structure-forming element of which is a dictionary of paronyms of nominal components. When compiling a dictionary of paronyms, a mechanism was used to categorize paronyms of nominal components depending on the number of distorted characters. The proposed approach to reducing information entropy depending on the category and number of paronyms of nominal components in family-name groups can be applied not only for information search by family-name groups, but also in a broad sense – for text data frequently used in queries, which are subject to various distortions.

A comparison of two user-friendly methods to identify and support correction of misspelled medications

ObjectiveTo identify and support correction of misspelled medication names recorded as free text, we compared the relative effectiveness of two user-friendly methods, used without reliance on clinical knowledge. MethodsLeveraging the SAS® COMPGED function, fuzzy string search programs examined 1.8 million medication records from 183,600 World Trade Center General Responder Cohort monitoring visits conducted in New York and New Jersey between 7/16/2002 and 3/31/2021, producing replicable generalized edit distance scores between the reported and correct spelling. Scores < 120 were selected as optimal and compared to Stedman’s 2020 Plus Medical/Pharmaceutical Spell Checker first suggested word, used as the comparative standard because it employs both spelling and phonetic similarities to suggest matching words. We coded each methods’ results as identifying or not identifying the medications within each visit. ResultsMost types of medications (94.4 % anxiety, 98.4 % asthma and 94.6 % ulcer/gastroesophageal reflux disease) were correctly spelled. Cross tabulations assessed the agreement (anxiety 99.9 %, asthma 99.6 % and 98.4 % ulcer/ gastroesophageal reflux disease), false positive (respectively 0.02 %, 0.03 % and 2.0 %) and false negative (respectively 1.9 %, 0.5 % and 1.0 %) values. Scores < 120 occasionally correctly identified medications missed by the spell checker. We observed no difference in medication misspellings across socio-economically and culturally diverse patient characteristics. ConclusionsBoth methods efficiently identified most misspelled medications, greatly minimizing the review and rectification needed. The fuzzy method is more universally applicable for condition-specific medications identification, but requires more programming skills. The spell checker is inexpensive, but benefits from modest programming skills and is only available in some languages.

Improved Maximum Power Point Tracking Control for D-PMSG Systems: Fuzzy Gradient Step Approach

Improved Maximum Power Point Tracking Control for D-PMSG Systems: Fuzzy Gradient Step Approach

A search tool based on language modelling developed for The Index of Middle English Prose

Non-standardised early vernaculars present a problem for search tools due to the high degree of variation. The challenge lies in the variation found in orthography, syntax, and lexicon between titles, incipits, and explicits in manuscript copies of the same work. Traditional search methods relying on exact string matching or regular expressions fail to address these variations comprehensively. This project presents a web-based search tool specifically designed to handle linguistic and textual variation. The software is made available as a part of the Index of Middle English Prose (IMEP). The search tool addresses the issue of variation by utilizing a database of incipits and explicits, character-based n-gram language models (LMs) built with the Stanford Research Institute Language Modelling (SRILM) toolkit, and a fuzzy search script (IMEP: FSS) written in Python. The tool optimizes for recall, retrieving multiple potential matches for a search string, without attempting to identify the ‘correct’ one. The search process involves looking up exact matches in the database while simultaneously using the fuzzy search script to evaluate the incipits and explicits against a model of the search string, followed by a match of the search string against models of the incipits and explicits. This two-step process shortens the processing time, which would otherwise be unreasonably long, because while using SRILM to match the search string against each incipit or explicit in the IMEP for precision could be time-consuming, running a first step where all texts are matched against a single LM built from the search string allows for faster processing. A web application, built using Django and Docker, combines the results of the direct database lookup and the fuzzy search script, presenting them as a list with exact matches followed by fuzzy matches ordered by increasing model perplexity. The tool is made available Open Access and can be adapted to other datasets.

A predictive modelling strategy for warpage and shrinkage defects in plastic injection molding using fuzzy logic and pattern search optimization

A predictive modelling strategy for warpage and shrinkage defects in plastic injection molding using fuzzy logic and pattern search optimization

LSCO: Light spectrum chimp optimization based spinalnet for live face detection and recognition

Face detection (FD) is considered to be of utmost significance in monitoring and safety systems. Finding concealed faces is a crucial research issue, particularly in populated or high-security settings like events and retail stores, where they may pose a security danger. To bridge this gap, a Light Spectrum Chimp Optimization (LSCO) enabled SpinalNet is introduced for live face detection and recognition. At first, an input image is fed up into the pre-processing phase to enhance the contrast image and remove the unwanted noises utilizing the Type 2 Fuzzy and Cuckoo Search (T2FCS) filter. Thereafter, the face is detected utilizing Viola-Jones which classifies the images based on the simple feature values to accurately identify and analyze human faces. Moreover, the detected image is passed to the feature extraction to acquire the face component features like eyes, nose, mouth, etc from the human face image. Here, the features employed are Local Directional Pattern Variance (LDPv), Local Directional Number Pattern (LDNP), Local Gabor Binary Pattern (LGBP) and statistical features like, mean, variance, kurtosis, skewness, and Convolutional Neural Networks (CNN) features. At last, face recognition is accomplished by the proposed LSCO which is tuned by SpinalNet. Here, LSCO is the integration of Light Spectrum Optimizer (LSO) and Chimp optimization (ChOA). The suggested technique has produced accuracy, True positive rate (TPR), True Negative rate (TNR) and execution time obtained 93.5%, 90.7%, 87.5% and 0.236 sec respectively.

Development of fuzzy search method for creating an efficient information search system in text data

The object of research is the processes of effective search for information in a set of textual data. The subject of the research is the fuzzy search method, which will allow to effectively solve the problem of searching for information in a set of textual data. The paper considers the process of developing a fuzzy search method, which consists of 9 consecutive steps and is required for a quick search for matches in a large set of text data. Based on this method, it is proposed to create a fuzzy search system that will solve the problem of finding the most relevant documents from a set of such documents. The proposed fuzzy search method combines the advantages of algorithms based on deterministic finite automata and algorithms based on dynamic programming for calculating the Damerau-Levenshtein distance. Such a combination allows to implement the symbol similarity table in an optimal way. As part of the work, an approach for creating a symbol similarity table was proposed and an example of such a table was created for symbols from the English alphabet, which allows to find the degree of similarity between two symbols with constant asymptotics and to convert the current symbol into its basic counterpart. For document filtering, a metric was developed to evaluate the correspondence of text data to a search phrase, which simultaneously takes into account the number of found and not found characters and the number of found and not found words. The Damerau-Levenstein algorithm allows to find the edit distance between two words, taking into account the following types of errors: substitution, addition, deletion, and transposition of characters. The work proposed a modification of this algorithm by using a similarity table to more accurately estimate the editing distance between two words. The developed method makes it possible to create a fuzzy search system that will help find the desired results faster and increase the relevance of the obtained results by sorting them according to the values of the proposed test data similarity metric.

Blind CFO estimation based on weighted subspace fitting criterion with fuzzy adaptive gravitational search algorithm

AbstractThis paper deals with the blind carrier frequency offset (CFO) estimation based on weighted subspace fitting (WSF) criterion with fuzzy adaptive gravitational search algorithm (GSA) for the interleaved orthogonal frequency-division multiplexing access (OFDMA) uplink system. For the CFO estimation problem, it is well known that the WSF has superior statistical characteristics and better estimation performance. However, the type of CFO estimation must pass through the high-dimensional space problem. Optimizing complex nonlinear multimodal functions requires a large computational load, which is difficult and not easy to maximize or minimize nonlinear cost functions in large parameter spaces. This paper firstly presents swarm intelligence (SI) optimization algorithms such as GSA, particle swarm optimization (PSO), and hybrid PSO and GSA (PSOGSA) to improve estimation accuracy and reduce the computational load of search. At the same time, this paper also integrates a fuzzy inference system to WSF-GSA to dynamically adjust the gravitational constant, which can not only reduce the searching computational load, but also improve the performance of GSA in the global optimization and solution accuracy. Finally, several simulation results are provided for illustrating the effectiveness of the proposed estimator.

BC-RFMS: blockchain-based rankable fuzzy multi-keyword search scheme

BC-RFMS: blockchain-based rankable fuzzy multi-keyword search scheme

A novel verifiable chinese multi-keyword fuzzy rank searchable encryption scheme in cloud environments

As an important cryptographic primitive, searchable encryption (SE) plays a crucial role in performing keyword searching on encrypted texts. However, in order to realize fuzzy keyword search, most fuzzy search encryption schemes utilize wildcards and gram technology to construct fuzzy sets, which consumes a lot of storage and computational resources. Therefore, in this paper, we propose a new verifiable Chinese multi-keyword fuzzy rank searchable encryption (VCMKFRSE) scheme. Firstly, we take advantage of the Yongzi Ba method to convert Chinese keywords into stroke strings, and employ chinese keywords vector generation algorithm to convert the stroke string into a keyword vector. Secondly, we utilize inverted index tables to establish the relationship between keywords and documents. In particular, the relevance score between keywords and documents is calculated by using the three-factor algorithm. Also, we adopt the MinHash function to construct a fuzzy index table for each keyword vector. Thirdly, in order to realize the authentication of search results and avoid receiving useless search results, we build an authentication tag table by using an authentication tag generation function. Afterwards, we apply probabilistic trapdoors to resist distinguishability attacks. At last, our scheme achieves IND-CCA secure and is more efficient comparing with the state of the art. Overall, our proposal achieves fuzzy multi-keyword search and more accurate search result ranking, while ensuring data security and higher efficiency.

A Survey on Searchable Symmetric Encryption

Outsourcing data to the cloud has become prevalent, so Searchable Symmetric Encryption (SSE), one of the methods for protecting outsourced data, has arisen widespread interest. Moreover, many novel technologies and theories have emerged, especially for the attacks on SSE and privacy-preserving. But most surveys related to SSE concentrate on one aspect (e.g., single keyword search, fuzzy keyword search) or lack in-depth analysis. Therefore, we revisit the existing work and conduct a comprehensive analysis and summary. We provide an overview of state-of-the-art in SSE and focus on the privacy it can protect. Generally, (1) we study the work of the past few decades and classify SSE based on query expressiveness. Meanwhile, we summarize the existing schemes and analyze their performance on efficiency, storage space, index structures, and so on.; (2) we complement the gap in the privacy of SSE and introduce in detail the attacks and the related defenses; (3) we discuss the open issues and challenges in existing schemes and future research directions. We desire that our work will help novices to grasp and understand SSE comprehensively. We expect it can inspire the SSE community to discover more crucial leakages and design more efficient and secure constructions.

A search tool based on language modelling developed for The Index of Middle English Prose.

Non-standardised early vernaculars present a problem for search tools due to the high degree of variation. The challenge lies in the variation found in orthography, syntax, and lexicon between titles, incipits, and explicits in manuscript copies of the same work. Traditional search methods relying on exact string matching or regular expressions fail to address these variations comprehensively. This project presents a web-based search tool specifically designed to handle linguistic and textual variation. The software is made available as a part of the Index of Middle English Prose (IMEP). The search tool addresses the issue of variation by utilizing a database of incipits and explicits, character-based n-gram language models (LMs) built with the Stanford Research Institute Language Modelling (SRILM) toolkit, and a fuzzy search script (IMEP: FSS) written in Python. The tool optimizes for recall, retrieving multiple potential matches for a search string, without attempting to identify the 'correct' one. The search process involves looking up exact matches in the database while simultaneously using the fuzzy search script to evaluate the incipits and explicits against a model of the search string, followed by a match of the search string against models of the incipits and explicits. This two-step process shortens the processing time, which would otherwise be unreasonably long, because while using SRILM to match the search string against each incipit or explicit in the IMEP for precision could be time-consuming, running a first step where all texts are matched against a single LM built from the search string allows for faster processing. A web application, built using Django and Docker, combines the results of the direct database lookup and the fuzzy search script, presenting them as a list with exact matches followed by fuzzy matches ordered by increasing model perplexity. The tool is made available Open Access and can be adapted to other datasets.

ORFanID: A web-based search engine for the discovery and identification of orphan and taxonomically restricted genes.

With the numerous genomes sequenced today, it has been revealed that a noteworthy percentage of genes in a given taxon of organisms in the phylogenetic tree of life do not have orthologous sequences in other taxa. These sequences are commonly referred to as "orphans" or "ORFans" if found as single occurrences in a single species or as "taxonomically restricted genes" (TRGs) when found at higher taxonomic levels. Quantitative and collective studies of these genes are necessary for understanding their biological origins. However, the current software for identifying orphan genes is limited in its functionality, database search range, and very complex algorithmically. Thus, researchers studying orphan genes must harvest their data from many disparate sources. ORFanID is a graphical web-based search engine that facilitates the efficient identification of both orphan genes and TRGs at all taxonomic levels, from DNA or amino acid sequences in the NCBI database cluster and other large bioinformatics repositories. The software allows users to identify genes that are unique to any taxonomic rank, from species to domain, using NCBI systematic classifiers. It provides control over NCBI database search parameters, and the results are presented in a spreadsheet as well as a graphical display. The tables in the software are sortable, and results can be filtered using the fuzzy search functionality. The visual presentation can be expanded and collapsed by the taxonomic tree to its various branches. Example results from searches on five species and gene expression data from specific orphan genes are provided in the Supplementary Information.

ESOMIR: a curated database ofbiomarker genes andmiRNAs associated with esophageal cancer.

'Esophageal cancer' (EC) is a highly aggressive and deadly complex disease. It comprises two types, esophageal adenocarcinoma (EAC) and esophageal squamous cell carcinoma (ESCC), with Barrett's esophagus (BE) being the only known precursor. Recent research has revealed that microRNAs (miRNAs) play a crucial role in the development, prognosis and treatment of EC and are involved in various human diseases. Biological databases have become essential for cancer research as they provide information on genes, proteins, pathways and their interactions. These databases collect, store and manage large amounts of molecular data, which can be used to identify patterns, predict outcomes and generate hypotheses. However, no comprehensive database exists for EC and miRNA relationships. To address this gap, we developed a dynamic database named 'ESOMIR (miRNA in esophageal cancer) (https://esomir.dqweilab-sjtu.com)', which includes information about targeted genes and miRNAs associated with EC. The database uses analysis and prediction methods, including experimentally endorsed miRNA(s) information. ESOMIR is a user-friendly interface that allows easy access to EC-associated data by searching for miRNAs, target genes, sequences, chromosomal positions and associated signaling pathways. The search modules are designed to provide specific data access to users based on their requirements. Additionally, the database provides information about network interactions, signaling pathways and region information of chromosomes associated with the 3'untranslated region (3'UTR) or 5'UTR and exon sites. Users can also access energy levels of specific miRNAs with targeted genes. A fuzzy term search is included in each module to enhance the ease of use for researchers. ESOMIR can be a valuable tool for researchers and clinicians to gain insight into EC, including identifying biomarkers and treatments for this aggressive tumor. Database URL https://esomir.dqweilab-sjtu.com.

Repeated patterns of gamma-ray flares suggest structured jets of blazars as likely neutrino sources

ABSTRACT Fermi-Large Area Telescope (LAT) observations provide continuous and regularly sampled measurements of gamma-ray photon flux for hundreds of blazars. Many of these light curves, spanning almost 15 yr, have been thoroughly examined for periodicity in multiple studies. However, the possibility that blazars may exhibit irregularly repeating flaring patterns in their gamma-ray light curves has not been systematically explored. In this study, we aim to find repeating episodes of flaring activity in the 100 brightest blazars using Fermi-LAT light curves with various integration times. We use a Bayesian Blocks representation to convert the time series into strings of symbols and search for repeating sub-strings using a fuzzy search algorithm. As a result, we identify 27 repeated episodes in the gamma-ray light curves of 10 blazars. We find that the patterns are most likely produced in structured jets composed of a fast spine and a slower sheath. When individual emission features propagate in the spine, they scatter seed photons produced in the non-uniform sheath through the inverse Compton mechanism, resulting in a set of gamma-ray flares with a similar profile every such passage. Additionally, we explore the theoretically predicted possibility that the spine-sheath structure facilitates the production of high-energy neutrinos in blazar jets. Using the catalogue of track-like events detected by the IceCube neutrino telescope, we find evidence supporting this hypothesis at a 2.8σ significance level.

Blockchain-assisted multi-keyword fuzzy search encryption for secure data sharing

Cloud-based data sharing has become a significant technology trend thanks for its convenience and economy. Data owners are suggested to encrypt data before outsourcing for data security and users’ privacy. This approach makes it a challenging problem to implement the retrieval of encrypted data while enforcing fine-grained access control over the ciphertext. In this paper, we propose a blockchain-assisted multi-keyword fuzzy search encryption (BCFSE) scheme for secure data sharing which supports fine-grained access control and attribute revocation. The BCFSE scheme maps the attributes in the ciphertext policy attribute-based encryption (CP-ABE) to attribute tokens on the blockchain, which provides lightweight, secure and trusted attribute management. Moreover, we construct a keyword search scheme based on CP-ABE by using searchable symmetric encryption algorithm in the keyword encryption and retrieval phases, effectively reducing computational cost. BCFSE combines the advantages of CP-ABE and searchable encryption based on blockchain, which implements authorization and search with the assistance of smart contracts to ensure the reliability of the search process. In addition, the BCFSE scheme supports multi-keyword fuzzy search with both AND and OR semantics. Extensive experiments have been performed on real datasets to validate the effectiveness and efficiency of our BCFSE scheme.

The 3D Modules of Enzyme Catalysis: Deconstructing Active Sites into Distinct Functional Entities

Enzyme catalysis is governed by a limited toolkit of residues and organic or inorganic co-factors. Therefore, it is expected that recurring residue arrangements will be found across the enzyme space, which perform a defined catalytic function, are structurally similar and occur in unrelated enzymes. Leveraging the integrated information in the Mechanism and Catalytic Site Atlas (M-CSA) (enzyme structure, sequence, catalytic residue annotations, catalysed reaction, detailed mechanism description), 3D templates were derived to represent compact groups of catalytic residues. A fuzzy template-template search, allowed us to identify those recurring motifs, which are conserved or convergent, that we define as the “modules of enzyme catalysis”. We show that a large fraction of these modules facilitate binding of metal ions, co-factors and substrates, and are frequently the result of convergent evolution. A smaller number of convergent modules perform a well-defined catalytic role, such as the variants of the catalytic triad (i.e. Ser-His-Asp/Cys-His-Asp) and the saccharide-cleaving Asp/Glu triad. It is also shown that enzymes whose functions have diverged during evolution preserve regions of their active site unaltered, as shown by modules performing similar or identical steps of the catalytic mechanism. We have compiled a comprehensive library of catalytic modules, that characterise a broad spectrum of enzymes. These modules can be used as templates in enzyme design and for better understanding catalysis in 3D.

FSSSA: A Fuzzy Squirrel Search Algorithm Based on Wide-Area Search for Numerical and Engineering Optimization Problems

The Squirrel Search Algorithm (SSA) is widely used due to its simple structure and efficient search ability. However, SSA exhibits relatively slow convergence speed and imbalanced exploration and exploitation. To address these limitations, this paper proposes a fuzzy squirrel search algorithm based on a wide-area search mechanism named FSSSA. The fuzzy inference system and sine cosine mutation are employed to enhance the convergence speed. The wide-area search mechanism is introduced to achieve a better balance between exploration and exploitation, as well as improve the convergence accuracy. To evaluate the effectiveness of the proposed strategies, FSSSA is compared with SSA on 24 diverse benchmark functions, using four evaluation indexes: convergence speed, convergence accuracy, balance and diversity, and non-parametric test. The experimental results demonstrate that FSSSA outperforms SSA in all four indexes. Furthermore, a comparison with eight metaheuristic algorithms is conducted to illustrate the optimization performance of FSSSA. The results indicate that FSSSA exhibits excellent convergence speed and overall performance. Additionally, FSSSA is applied to four engineering problems, and experimental verification confirms that it maintains superior performance in realistic optimization problems, thus demonstrating its practicality.