Query expansion – Hybrid framework using fuzzy logic and PRF

Sharma, Dilip Kumar; Pamula, Rajendra; Chauhan, D. S.

doi:10.1016/j.measurement.2022.111300

Cited by 6 publications

(7 citation statements)

References 47 publications

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“…It is important to note that, right after model development, various performance metrics including Adj.R 2 , NS, PI, R 2 , RMSE, RSR, VAF, and WI, were used to evaluate hybrid LSSVMs. Note that these indices are frequently used [ 94 , 95 , 96 , 97 , 98 , 99 , 100 , 101 , 102 , 103 , 104 , 105 , 106 , 107 , 108 , 109 , 110 , 111 , 112 , 113 , 114 , 115 , 116 ] to evaluate the generalization capabilities of any prediction model from a variety of perspectives, including correlation accuracy, related error, variance, and so on. The expressions of these indices can be given as follows:

where p and

represent the total number of input parameters and observations, respectively; …”

Section: Resultsmentioning

confidence: 99%

Modelling Compression Strength of Waste PET and SCM Blended Cementitious Grout Using Hybrid of LSSVM Models

et al. 2022

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Nowadays, concretes blended with pozzolanic additives such as fly ash (FA), silica fume (SF), slag, etc., are often used in construction practices. The utilization of pozzolanic additives and industrial by-products in concrete and grouting materials has an important role in reducing the Portland cement usage, the CO2 emissions, and disposal issues. Thus, the goal of the present work is to estimate the compressive strength (CS) of polyethylene terephthalate (PET) and two supplementary cementitious materials (SCMs), namely FA and SF, blended cementitious grouts to produce green mix. For this purpose, five hybrid least-square support vector machine (LSSVM) models were constructed using swarm intelligence algorithms, including particle swarm optimization, grey wolf optimizer, salp swarm algorithm, Harris hawks optimization, and slime mold algorithm. To construct and validate the developed hybrid models, a sum of 156 samples were generated in the lab with varying percentages of PET and SCM. To estimate the CS, five influencing parameters, namely PET, SCM, FLOW, 1-day CS (CS1D), and 7-day CS (CS7D), were considered. The performance of the developed models was assessed in terms of multiple performance indices. Based on the results, the proposed LSSVM-PSO (a hybrid model of LSSVM and particle swarm optimization) was determined to be the best performing model with R2 = 0.9708, RMSE = 0.0424, and total score = 40 in the validation phase. The results of sensitivity analysis demonstrate that all the input parameters substantially impact the 28-day CS (CS28D) of cementitious grouts. Among them, the CS7D has the most significant effect. From the experimental results, it can be deduced that PET/SCM has no detrimental impact on CS28D of cementitious grouts, making PET a viable alternative for generating sustainable and green concrete. In addition, the proposed LSSVM-PSO model can be utilized as a novel alternative for estimating the CS of cementitious grouts, which will aid engineers during the design phase of civil engineering projects.

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where p and

represent the total number of input parameters and observations, respectively; …”

Section: Resultsmentioning

confidence: 99%

Modelling Compression Strength of Waste PET and SCM Blended Cementitious Grout Using Hybrid of LSSVM Models

et al. 2022

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“…Sharma et al 35 presented a hybrid model using fuzzy logic and Pseudo‐Relevance Feedback (PRF) to select expanded medical user query terms. Fuzzy similarity reasoning is used to select optimal terms using WordNet semantic filtering and the BERT score.…”

Section: Background Of the Workmentioning

confidence: 99%

“…GOA is one main class of population‐based algorithms inspired by the lifestyle of grasshoppers. Fuzzy Logic Controller gains wide popularity due to its application in different complicated design mechanisms such as the Wireless Sensor Networks (WSN), 40 ground motion prediction, 41 Medical image fusion, 42 disease dataset feature selection, 43 big data sonar classification, 44 and vibration control of complex systems 35 …”

Section: Background Of the Workmentioning

confidence: 99%

“…These authors 35–37,40–44 mainly applied the GOA algorithm due to its ability to preserve the feasibility of newly generated solutions. When compared to the PSO and BI algorithms, the GOA algorithm improves the capability of the FLC in handling high‐dimensional problems and overcoming local minima issues.…”

Section: Background Of the Workmentioning

confidence: 99%

“…Sharma et al 35 presented a hybrid model using fuzzy logic and Pseudo-Relevance Feedback (PRF) to select expanded medical user query terms.…”

Section: Background Of the Workmentioning

confidence: 99%

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An automation query expansion strategy for information retrieval by using fuzzy based grasshopper optimization algorithm on medical datasets

Srivel¹,

Kalaiselvi

Shanthi

et al. 2022

Concurrency and Computation

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Due to the technical words employed, which are primarily recognized by medical specialists, information retrieval in the medical area is sometimes described as sophisticated. Because of this, users frequently have trouble coming up with queries utilizing these medical phrases. However, this problem may be readily fixed by an information retrieval system that finds the pertinent terms that fit the user's query and automatically creates a ranking document using these keywords. To enhance the IR performance, the Automatic Query expansion method is applied by appending additional query terms for the medical domain. We propose a novel fuzzy-based Grasshopper Optimization Algorithm (GOA) based on automatic query expansion. This work is mainly focused on filtering the most relevant augmented query by utilizing the synchronization score of IR evidence like normalized term frequency, inverse document frequency, and normalization of document length. The main aim of this work is to identify the medical terms that appropriately match the user's queries. The GOA algorithm ranks the terms based on relevance and then identifies the terms with the maximum synchronization value. The documents formed using the optimal expanded query are classified into three types, namely totally relevant, moderately relevant, and marginally relevant. Besides, the comparison of the proposed work is carried out for different performance metrics like Mean-Average Precision, F-measure, Precision-recall, and Precision rank are evaluated and analyzed by using TREC-COVID, TREC Genomics 2007, and MEDLARs medical datasets for the proposed and some of the state-of-art works. For a total of 60 queries, the proposed model offers an F1-Score of 0.964, 0.959, and 0.968 for the MEDLARS, TREC Genomics, and TREC COVID19 datasets, respectively. The E1-score and Mean Reciprocal Rate (MRR) of the proposed model is 0.8 and 0.9 when evaluated using the TREC COVID19 dataset. Performance analyses show that the proposed approach outperforms the other automatic keyword expansion approaches in the medical domain.

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