GAEMTBD: Genetic algorithm based entity matching techniques for bibliographic databases

Mishra, Sumit; Saha, Sriparna; Mondal, Samrat

doi:10.1007/s10489-016-0874-z

Cited by 15 publications

(4 citation statements)

References 43 publications

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“…Thus, their biological heredity can be transmitted to new generations through biologically inspired operators, such as crossover and mutation [24]. In this evolutionary process, a fitness value is used to measure the degree of goodness of a chromosome, and the process continues for a fixed number of generations or until a specified performance is reached [25].…”

Section: Determining a Set Of Part Lens Cavity Number Combinations Ba...mentioning

confidence: 99%

Determining Optimal Assembly Condition for Lens Module Production by Combining Genetic Algorithm and C-BLSTM

Min,

Son,

Choi

2024

Processes

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Mobile camera modules are manufactured by aligning and assembling multiple differently shaped part lenses. Therefore, selecting the part lenses to assemble from candidates (called cavities) and determining the directional angle of each part lens for assembly have been important issues to maximize production yield. Currently, this process is manually conducted by experts at the manufacturing site, and the manual assembly condition optimization carries the risk of reduced production yield and increased failure cost as it largely depends on one’s expertise. Herein, we propose an AI framework that determines the optimal assembly condition including the combination of part lens cavities and the directional angles of part lenses. To achieve this, we combine the genetic algorithm with convolutional bidirectional long-term short-term memory (C-BLSTM). To the best of our knowledge, this is the first study on lens module production finding the optimal combination of part lens cavities and directional angles at the same time using machine learning methods. Based on experimental results using real-world datasets collected by lens module manufacturers, the proposed framework outperformed existing algorithms with an F1 score of 0.89. Moreover, the proposed method (S2S-AE) for predicting the directional angles exhibited the best performance compared to existing algorithms with an accuracy of 78.19%.

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Section: Determining a Set Of Part Lens Cavity Number Combinations Ba...mentioning

confidence: 99%

Determining Optimal Assembly Condition for Lens Module Production by Combining Genetic Algorithm and C-BLSTM

Min,

Son,

Choi

2024

Processes

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“…It learns linkage rules from a set of existing reference links. Mishra et al [14] proposed an unsupervised method for entity matching problems using genetic algorithm. Being dedicated specifically to biblio-graphic databases is one of the major limitations of this approach.…”

Section: Related Workmentioning

confidence: 99%

“…The first approaches [12,13] were supervised in nature. Next, an unsupervised approach [14] is proposed using the search capabilities of the genetic algorithm to solve the entity resolution problem, where the problem is considered as a partitioning problem. However the limitation of this work is that it targets only bibliographic databases which are quite different from other databases.…”

Section: Introductionmentioning

confidence: 99%

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PSOBER: PSO based entity resolution

Aassem¹,

Hafidi²,

Khalfi³

et al. 2021

Math. Model. Comput.

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Entity Resolution is the task of mapping the records within a database to their corresponding entities. The entity resolution problem presents a lot of challenges because of the absence of complete information in records, variant distribution of records for different entities and sometimes overlaps between records of different entities. In this paper, we have proposed an unsupervised method to solve this problem. The previously mentioned problem is set as a partitioning problem. Thereafter, an optimization algorithm-based technique is proposed to solve the entity resolution problem. The presented approach enables the partitioning of records across entities. A comparative analysis with the genetic algorithm over datasets proves the efficiency of the considered approach.

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