Fuzzy Search Research Articles

Size and layout optimization of truss structures with dynamic constraints using the interactive fuzzy search algorithm

Optimizing truss structures considering natural frequency constraints can fundamentally enhance their dynamic behaviour under transient loadings. In this regard, the current investigation assesses the search capability of the interactive fuzzy search algorithm (IFSA) in solving this class of problem. Since the natural frequencies are very sensitive to configuration changes in the system, throughout the optimization process the algorithm should be able to adaptively shift its search behaviour. To meet this aim, the IFSA utilizes two nine-rule fuzzy modules which permanently monitor the optimization process and try to balance the exploration and exploitation search behaviours of the algorithm. Then, the search capability of the proposed method is tested on five structural benchmark problems. The numeric results reveal that the IFSA, by systematically reducing the number of ineffective iterations, considerably decreases the required computational cost. In addition, considering solution accuracy and process stability, IFSA provides promising results in solving structural optimization problems.

ALGORITHM OF CROSS LANGUAGE FUZZY SEARCH BASED ON HASH-VECTORS FOR AUTOMATIC COMPARISON OF PERSONAL NAMES

The algorithm of cross language fuzzy search based on hash vectors for automatic matching of personal names is proposed. In the response mode for an input request, names in Latin spelling and a given value for the similarity measure, the algorithm determines the set of output Cyrillic names contained in the database of the information search system. The principal feature of the proposed algorithm is the rejection of the direct translation of personal names. Instead, the hashing mechanism of personal names is used, followed by mapping them into the same hidden vector space where the computational procedures of the decision-making system are built. In the process of research, it was solved a number of actual intermediate tasks. Thus, the decomposition algorithms of the explored database, the generation and clustering of the dictionary of basic morphemes are an instrument that is of independent value in solving the problem of automatically translating names from a foreign language, the translation rules of which are unknown – the socalled generalized transcription. After mapping names into a vector space, the matching operation is reduced to assessing the similarity between vectors. As a measure of similarity, several quantities were considered in the study. The most convenient measure of similarity is the cosine similarity, the critical value of which was obtained by plotting the FMR (False Match Rate) and FNMR (False Non-Match Rate) graphs. The developed algorithm is universal with respect to the languages used, that is, it does not depend on a specific alphabet. In the practical implementation of the developed algorithm, a series of experimental studies was carried out using a database containing more than 2.5 million names.

Large-scale structural optimization using a fuzzy reinforced swarm intelligence algorithm

In contrast with conventional structural optimization benchmark problems real size structures mostly contain a large number of members and their optimal design provide a serious challenging area for optimization methods. In this regard, current study deals with assessing the search performance of the recently developed fuzzy reinforced metaheuristic technique so called Interactive Fuzzy Search Algorithm on the optimization of large-scale structures. This method is a self-adaptive and parameter-free algorithm which applies a dual-module fuzzy decision mechanism to adjust its search behavior during the optimization process. This mechanism employs two nine rule fuzzy modules which permanently monitor the agents updating process and based on the governing conditions of the problem emphasize their exploration or exploitation search behavior. Attained results show that proposed method can adopt itself with the extensive search space of the studied problems. Form both accuracy and stability aspects Interactive Fuzzy Search Algorithm provides promising results on solving large-scale structural optimization problems.

A fuzzy adaptive gravitational search algorithm for two-dimensional multilevel thresholding image segmentation

Two-dimensional (2D) multilevel thresholding is an important technique for noisy image segmentation which has drawn much attention during the past few years. The conventional image segmentation methods are efficient for 2D bi-level thresholding. However, the computational complexity grows exponentially when extended to 2D multilevel thresholding since they search the optimal thresholds by exhaustive strategy. To tackle this problem, a fuzzy adaptive gravitational search algorithm (FAGSA) using Tsallis entropy as its objective function has been presented to find the optimal 2D multilevel thresholds in this paper. In the FAGSA, fuzzy logic controllers are designed to tune the control parameters. The state-of-the-art heuristic algorithms are compared with this proposed algorithm. Both test images and noisy images are utilized in the experiments to evaluate the performance of the involved algorithms. The experimental results significantly demonstrate the superiority of our algorithm in terms of the objective function value, image quality measures and time consumption.

Adaptive inertia weight Bat algorithm with Sugeno-Function fuzzy search

Bat algorithm (BA) turns into the most generally utilized meta-heuristic algorithm to solve the different sort of global optimization problems. In the optimization of continuous data, BA experiences one of the prominent difficulties called premature convergence. In order to tackle premature convergence, this study exhibits a new version of BA called Adaptive inertia weight Bat algorithm with Sugeno-Function Fuzzy Search (ASF-BA). The proposed algorithm ASF-BA brings two major adjustments in the standard BA. Firstly, we incorporated an adaptive inertia weight to boost up the velocity rate of bats effectively. Secondly, we replaced the random searching method of standard BA with Sugeno-Function fuzzy search, which used Sugeno-Function decline curves to dynamically adjust the fitness of each bat according to their own experience and experience of their neighbour bats. We compared ASF-BA with several old fashioned and new fashioned optimization algorithms. ASF-BA is also tested against top hybridized and enhanced version of DE algorithms. The CEC 2017 benchmark (30 real parameter numerical optimization problems ), CEC 2017 ( 28 constrained optimization problems) and 19 additional benchmark problems have been used to examine and compare the performance of ASF-BA with other state of the art variants. Contrasted with the existing BA and other leading variants of BA, DE, and PSO on CEC 2017 constrained and numerical benchmarks, the ASF-BA is excellent to the state-of-art variants of BA, DE, and PSO in terms of stability, convergence speed and solution quality. The ASF-BA sets stable support for resolving optimization problems of intelligent and expert systems. Furthermore, we also examined the performance of proposed ASF-BA for the weight optimization of Feed Forward Neural Networks (FFNN) and compared ASF-BA with Back Propagation Algorithm (BPA), BA and PSO. ASF-BA achieved 94 % of maximum accuracy. The experimental outcomes reveal that the suggested algorithm executed especially reliable and effective than the existing state of the art variants.

FSS-SDD: fuzzy-based semantic search for secure data discovery from outsourced cloud data

In current decade, cloud computing has become more prevalent and utilized for managing more sensitive data that are stored over cloud storage. While outsourcing the sensitive and private data on to the cloud, security is the major factor to be concentrated in efficient data search using keywords. As is auspicious, security of selective keyword search can be achieved using the powerful cryptographic technique called encryption. That is, the data that are to be outsourced have to be encrypted before storing it on cloud. Symmetric encryption is used between the data owner and the customer, whereas asymmetric encryption is used between the data owner and the cloud server. In such cases, the data discovery and retrieval have become more stimulating process. However, there are many methodologies developed for searching the encrypted outsourced data from the cloud. In this paper, the efficient data discovery process is focused and enhanced with the developed model called fuzzy-based semantic search for secure data discovery (FSS-SDD). A multi-valued logic called fuzzy logic is used, which has truth values of variables ranging between 0 and 1. It is used where the truth values range between completely true and completely false. The fuzzy logic-based semantic search improves the searching experience of the end user, by finding and retrieving the exact matching files for corresponding search files given by the user. Moreover, the model discovers the closely relevant matches using the semantic similarities, in cases, when the exact matches are not avail. For reducing the false positive rates, grading mechanism is enforced. Using the proposed FSS-SDD model, the processing overhead on new updates is effectively reduced and security in data retrieval is guaranteed.

A Privacy-preserving Fuzzy Search Scheme Supporting Logic Query over Encrypted Cloud Data

Fuzzy keyword search is a necessary and important feature of information retrieval in modern cloud storage services since users with insufficient knowledge may input typos or keywords with inconsistent formats. However, most of the existing fuzzy search schemes adopt bloom filter and locality sensitive hashing which cannot resist Sparse Non-negative Matrix Factorization based attack (SNMF attack). To this end, we propose a new secure multi-keyword fuzzy search scheme for encrypted cloud data, our scheme leverages random redundancy method to handle the deterministic of bloom filter to resist SNMF attack. Besides the privacy, our scheme uses tree-based index construction to improve search efficiency and allows users to conduct complicated fuzzy search with logic operations “AND”, “OR” and “NOT”, which can meet more flexible and fine-grained query demands. The theoretical analysis and experiments on real-world data show the security and high performance of our scheme.

Paillier-based Fuzzy Multi-Keyword Searchable Encryption Scheme with Order-Preserving

Efficient multi-keyword fuzzy search over encrypted data is a desirable technology for data outsourcing in cloud storage. However, the current searchable encryption solutions still have deficiencies in search efficiency, accuracy and multiple data owner support. In this paper, we propose an encrypted data searching scheme that can support multiple keywords fuzzy search with order preserving (PMS). First, a new spelling correction algorithm-(Possibility-Levenshtein based Spelling Correction) is proposed to correct user input errors, so that fuzzy keywords input can be supported. Second, Paillier encryption is introduced to calculate encrypted relevance score of multiple keywords for order preserving. Then, a queue-based query method is also applied in this scheme to break the linkability between the query keywords and search results and protect the access pattern. Our proposed scheme achieves fuzzy matching without expanding the index table or sacrificing computational efficiency. The theoretical analysis and experiment results show that our scheme is secure, accurate, error-tolerant and very efficient.

Hybrid fuzzy logic and gravitational search algorithm-based multiple filters for image restoration

In this paper, we present a multiple image filters for removal of impulse noises from test images. It utilises fuzzy logic (FL) approach to design a noise detector (ND) optimised by gravitational search algorithm (GSA) and utilises median filter (MF) for restoring. The proposed multiple filters used the FL approach to detect each pixels of a tests image are noise corrupted or not. If it is considered as noise-corrupted, the multiple filters restore it with the MF filter. Otherwise, it remains unchanged. We split the image into number of windows and each window apply the multiple filters. The filter output is used for the rule generation. The optimal rules are selected using GSA and given to the fuzzy logic system to detect the noise pixel. The experimental results are carried out using different noise level and different methods. The performance measured in terms of PSNR, MSE and visual quality.

Blockchain-Based Searchable Encryption Scheme With Fair Payment

In order to solve the problem that most of the existing blockchain-based searchable encryption schemes only support exact keyword search and mutual distrust between the cloud server and the user, a searchable encryption scheme based on blockchain, that has functions such as file update dynamically, search results verification, fuzzy keyword search, and fair payment, is proposed. We use edit distance to generate the fuzzy keywords set. According to the un-modification characteristic of Ethereum blockchain and the security of the RSA accumulator, our scheme can verify the search results by the smart contract and realize the service-payment fairness between the user and the cloud server. The security analysis shows that the proposed scheme achieves non-adaptive semantic security. Performance analysis and experiment show that our scheme is efficient and meets the demand of search application in the cloud environment.

Fuzzy Keywords-Driven Public Sports Resource Allocation Strategies Retrieval With Privacy-Preservation

Currently, the government proposes various public sports resource allocation strategies to promote the construction of public sports in the local. Thus, searching for the document database becomes an economic and efficient way for government officials to understand the contents of various public sports resource allocation strategies. As the government officials lack background knowledge of public sports resource allocation, the government officials are hard to retrieve appropriate documents. What's more, the document database is generally unwilling to share all text document information with the government official as some documents contain sensitive data information. Considering the above drawbacks, we put forward a fuzzy keywords-driven public sports resource allocation strategies retrieval approach based on the Simhash (named PSRAS <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">k+S</sub> ) in the paper. The retrieval approach can accurately recommend appropriate documents to the government officials, by using the text description mining of documents and the fuzzy keywords search technology. Furthermore, the retrieval process is not revealing the sensitive data information of the irrelevant public sports resource allocation strategy document to the government officials, so the retrieval process can achieve local information protection mechanism. Finally, a case study explains the feasibility and effectiveness of our retrieval approach step by step.

An Efficient Anomaly Intrusion Detection Method With Feature Selection and Evolutionary Neural Network

Recently, with the technological and digital revolution, the security of data is very crucial as a massive amount of data is generated from various networks. Intrusion Detection System (IDS) has been observed to be perhaps the best solution because of its capability to distinguish between attacks that originate within or outside a corporate network. In this study, the most significant features for enhancing the IDS efficiency and creating a smaller dataset in order to reduce the execution time for detecting attacks are selected from the sizeable network dataset. This research designed an anomaly-based detection, by adopting the modified Cuckoo Search Algorithm (CSA), called Mutation Cuckoo Fuzzy (MCF) for feature selection and Evolutionary Neural Network (ENN) for classification. The proposed search algorithm uses mutation to more accurately examine the search space, to allow candidates to escape local minima. Moreover, the value of the solution is evaluated based on the objective function and the Fuzzy C Means (FCM) clustering method used to provide the best results for the overlapping dataset and create the fuzzy membership search domain which includes all possible compromise solutions. A proposed model has been practically used to the problem of intrusion detection as well as been validated using the NSL-KDD dataset. The experimental results reveal that reducing features by selecting and utilizing the most relevant features can improve execution time and at the same time enhance the efficiency and performance of IDS.

Fault estimation and compensation for hypersonic flight vehicle via type-2 fuzzy technique and cuckoo search algorithm

This study proposes a fault estimation and compensation scheme for hypersonic flight vehicle (HFV) attitude system with body flap fault. To achieve the fault compensation capability, an interval type-2 fuzzy estimator is designed to approximate the unknown nonlinear function caused by modeling uncertainties and the fault. The fault-tolerant controller via the terminal sliding mode and dynamic surface techniques is developed to ensure the tracking accuracy of attitude angles for HFV and the desired control torque can be achieved. Then, a novel control allocation scheme with a cuckoo search algorithm and linear programming method is proposed to distribute the desired torque to aerodynamic surfaces and reaction control system jets. The stability of the proposed scheme is analyzed using the Lyapunov stability theory. The validity of the method is verified by a series of comparisons on numerical simulation results.

FSSE: An Effective Fuzzy Semantic Searchable Encryption Scheme Over Encrypted Cloud Data

Currently, searchable encryption has attracted considerable attention in the field of cloud computing. The existing research mainly focuses on keyword-based search schemes, most of which support the exact matching of keywords. However, keyword-based search schemes ignore spelling errors and semantic expansions of keywords. The significant drawback makes the existing techniques unsuitable in cloud computing as it greatly affects system usability and can not completely satisfy the users' search intentions. In this paper, we propose an effective fuzzy semantic searchable encryption scheme (FSSE) that supports multi-keyword search over encrypted data in cloud computing. In our scheme, we exploit a keyword fingerprint generation algorithm to generate a fingerprint set of the keyword dictionary and a fingerprint of the query keywords, and employ Hamming distance to quantify keywords similarity. Based on the proposed fingerprint generation algorithm and Hamming distance, we realize fuzzy search. Furthermore, we utilize the semantic expansion technique to expand query keywords and calculate the semantic similarity between the query keywords and the expanded word of the query keywords to achieve the semantic search. To improve the search efficiency, we construct an inverted index structure and use the vector intersection matching as well as short-circuit matching operations to effectively filter irrelevant documents. The theoretical analysis and experimental results demonstrate that our proposed scheme satisfies the security guarantee of searchable encryption, enhances system usability, and is more efficient in comparison with the state of the art schemes.

Privacy-preserving Navigation Supporting Similar Queries in Vehicular Networks

Traffic-sensitive navigation systems in vehicular networks help drivers avoid traffic jams by providing several realtime navigation routes. However, drivers still encounter privacy concerns because their sensitive locations, i.e., their start point and endpoint, are submitted to an honest-but-curious navigation service provider (NSP). Previous privacy-preserving studies exhibit serious deficiencies under similar queries: if a driver makes several similar queries, i.e., periodically makes requests for the same start point and endpoint to the NSP, these requests will eventually reveal the areas of the two points as well as the route. In this paper, we present a novel privacy-preserving navigation scheme PiSim, which supports similar queries in navigation services. Intuitively, we transform the typical navigation approach into a traffic congestion querying approach. Instead of sending two locations to the NSP and awaiting a navigation route, drivers query the traffic congestion along the navigation route. Specifically, PiSim is characterized by extending anonymous authentication, facilitating privacy-preserving multi-keyword fuzzy search, and constructing weighted proximity graphs. Our scheme protects location privacy and route privacy, and defends against multiple requesting, spurious reporting, and collusion attacks from malicious drivers. Finally, a detailed analysis confirms the privacy and security properties of PiSim. Extensive experiments are conducted to demonstrate the feasibility, performance, and privacy protection level.

Unbiased fuzzy global optimization of Lennard-Jones clusters for N ≤ 1000.

We propose a fuzzy global optimization (FGO) algorithm to identify the lowest-energy structure of nanoclusters. In contrast to traditional methods implemented in the real space, FGO utilizes mostly the discrete space in a fuzzy search framework. Starting from random initial configurations, we carry out directed Monte Carlo and surface Monte Carlo in the discrete space to obtain low-energy candidate clusters and make real-space local optimizations finally to get the real global minimum structure. The performance of FGO is demonstrated in a large set of standard Lennard-Jones (LJ) clusters with up to 1000 atoms. All the putative global minima reported in the literature are successfully obtained with a low scaling of CPU time with cluster size, and new global minimum structures for LJ clusters with 894, 974, and 991 atoms are identified. Due to the unbiased nature, FGO can potentially deal with the global optimization of other nanomaterials with high efficiency and reliability.

EliMFS: Achieving Efficient, Leakage-Resilient, and Multi-Keyword Fuzzy Search on Encrypted Cloud Data

Motivated by privacy preservation requirements for outsourced data, keyword searches over encrypted cloud data have become a hot topic. Compared to single-keyword exact searches, multi-keyword fuzzy search schemes attract more attention because of their improvements in search accuracy, typo tolerance, and user experience in general. However, existing multi-keyword fuzzy search solutions are not sufficiently efficient when the file set in the cloud is large. To address this, we propose an Efficient Leakage-resilient Multi-keyword Fuzzy Search (EliMFS) framework over encrypted cloud data. In this framework, a novel two-stage index structure is exploited to ensure that search time is independent of file set size. The multi-keyword fuzzy search function is achieved through a delicate design based on the Gram Counting Order, the Bloom filter, and the Locality-Sensitive Hashing. Furthermore, considering the leakages caused by the two-stage index structure, we propose two specific schemes to resist these potential attacks in different threat models. Extensive analysis and experiments show that our schemes are highly efficient and leakage-resilient.

Rough fuzzy cuckoo search for triclustering microarray gene expression data

Analyzing time series microarray gene expression data is a computational challenge due to its three-dimensional characteristics. Triclustering techniques are applied to three-dimensional data for mining similarly expressed genes under a subset of conditions and time points. In this work, a novel rough fuzzy cuckoo search algorithm is proposed for triclustering genes across samples and time points simultaneously. By applying the upper and lower approximation of rough set theory and the objective function of fuzzy k-means, rough fuzzy k-means was incorporated into a cuckoo search to handle the uncertainty of the data. The proposed method was applied to three real-life time series gene expression datasets. This work was evaluated using four validation indices and correlation analysis was performed to indicate the cluster quality. The proposed work was also compared with the existing triclustering algorithms and it outperformed the other methods.

Efficient dynamic multi-keyword fuzzy search over encrypted cloud data

Multi-keyword search of encrypted cloud data has attracted extensive attention worldwide in the recent years due to the increasing concern for data security and privacy in Cloud Computing. Fault-tolerance is important for multi-keyword fuzzy search which can provide accurate results even with the presence of minor spelling and typographical errors in the search keywords. But, existing fuzzy search schemes lack efficiency due to their high computational overhead and do not support file dynamic updates. This paper proposes an efficient dynamic multi-keyword fuzzy search scheme for encrypted cloud data to support dynamic file updates. Locality sensitive hashing (LSH) and Bloom filters are employed to generate index vectors and query vectors. Based on the generated vectors, a balanced binary tree is constructed as the index for the entire file set, and a Top-k search algorithm is developed to search k files that are most relevant to a given query with the help of the index tree. Extensive experiments conducted on real-world datasets demonstrate that our scheme is more efficient than existing similar schemes.

Advancement of taxonomic searches in the European search catalogue for plant genetic resources

AbstractGenebanks play an important role in the conservation of global plant biodiversity. The European Search Catalogue for Plant Genetic Resources (EURISCO) was created as a central entry point to provide information on these collections. However, a major challenge lies in the heterogeneity of scientific plant names. This makes the selection of suitable plant material, e.g. for research or breeding purposes, significantly more difficult. For this reason, the taxonomic backbone of EURISCO has been completely revised. Search terms entered by users are now automatically checked against taxonomic reference repositories, allowing a variety of synonyms to be identified. In addition, a fuzzy search has been implemented, which makes the search function tolerant of erroneous data (e.g. caused by typing errors). Besides improvements of the search interface, more support will be given to EURISCO's data providers. The new developments provide a tool that makes it easier to identify problem cases within the data, such as accepted/non-accepted taxonomic names, and will successively improve the quality of taxonomic information in EURISCO.