Case Retrieval Algorithm Using Similarity Measure and Adaptive Fractional Brain Storm Optimization for Health Informaticians

Yadav, Poonam

doi:10.1007/s13369-015-1928-y

Cited by 14 publications

(12 citation statements)

References 24 publications

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“…Fractional brain storm optimization is used for case retrieval [1]. Here, a similarity measure is proposed known as PESM measure, which is used in retrieving the patient information.…”

Section: Resultsmentioning

confidence: 99%

“…Also, an improved kernel possibilistic c-means algorithm (IKPCM) is proposed. Below Table [1] briefly list out some optimization processes used in big data analytics. Recursive chunk division(RCD) for optimal pipelining, optimal parallelism-concurrency pipelining(PCP)…”

Section: International Journal Of Engineering and Advanced Technologymentioning

confidence: 99%

“…Researchers have used various optimization techniques. For example, PESM measure is use in retrieving the patient information [1] in case-based reasoning using similarity measure and adaptive fractional brain storm optimization, population based optimization technique is used in looking for the optima of optimization problems [2], biogeography-based optimization(BBO) is used which works under some natural phenomena, Jayaoptimization algorithm[JOA] is used for constrained and unconstrained optimal problems [3], genetic algorithm(GA) is used in optimization which works under chromosomes and genes [2]. This research includes a comparative study of different indexing and optimization techniques along with a taxonomy which includes Artificial Intelligence (AI), Non-Artificial Intelligence(NAI), and Collaborative Artificial Intelligence(CAI) based indexing techniques.…”

Section: Introductionmentioning

confidence: 99%

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Indexing and Optimization Techniques in Biomedical Industry

N.R*¹,

Prem²

2019

IJEAT

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The unpredictable amount of data generated everyday by smart phones, social networks, health care systems etc. is really mind blowing. Smart phones alone generate 335exabytes of data ineveryyear that is really big data.Thus, the storage industry is facing several challenges in providing high magnitude of storage and retrieval devices at lowest costs which help to fulfill the requirements of big data and even technologies like de-duplication on storage devices are also becoming very important. Similarly, in recent days storing and retrieving the health care information in biomedical area is also becoming a great challenge in providing the best optimum data because of its huge amount of biomedical datasets. In order to achieve efficiency in providing highest quality health care information, an optimized index scheme is needed for big data which is based on accuracy and timelines. The existing indexing and optimization solutions are not enough to meet the emerging grow of index size and seek time. The objective of this paper is to identify better indexing solutions by investigating the basic big data requirements on indexing and optimization. This also includes a comparative study of various indexing and optimization techniques along with a taxonomy which contains Artificial Intelligence (AI) and Non Artificial Intelligence (NAI) based indexing techniques, optimization enhancement techniques which improves the performance efficiency of big data health care informatics.

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“…Fractional brain storm optimization is used for case retrieval [1]. Here, a similarity measure is proposed known as PESM measure, which is used in retrieving the patient information.…”

Section: Resultsmentioning

confidence: 99%

Section: International Journal Of Engineering and Advanced Technologymentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Indexing and Optimization Techniques in Biomedical Industry

N.R*¹,

Prem²

2019

IJEAT

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“…is section describes the novel optimization method, namely, AFBS-WOA, for the optimal key coefficient generation. e developed AFBS-WOA algorithm is designed by integrating AFBSO [18] and WOA. e developed AFBS-WOA algorithm selects the key matrix coefficient without changing the characteristics of the original database.…”

Section: Optimal Key Coefficient Generation Using Developed Afbs-woamentioning

confidence: 99%

Cloud-Assisted Privacy-Preserving Method for Healthcare Using Adaptive Fractional Brain Storm Integrated Whale Optimization Algorithm

Revathi

Gayathri

Kalaivani

et al. 2021

Security and Communication Networks

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The security of medical data in the cloud is the key consideration of cloud customers. While publishing the medical data, the cloud distributor may suffer from data leakages and attacks such that the data may leak. In order to resolve this, this article devises the developed Adaptive Fractional Brain Storm Integrated Whale Optimization Algorithm (AFBS_WOA), which is the hybridization of Adaptive Fractional Brain Storm Optimization (AFBSO) and Whale Optimization algorithm (WOA). The developed AFBS_WOA algorithm generates the key matrix coefficient for retrieving the perturbed database in order to preserve the privacy of healthcare data in the cloud. The developed AFBS-WOA scheme utilized the fitness function involving utility and privacy measures for calculating the secret key. Here, the privacy-preserved database is obtained by multiplying the input database with a key matrix based on developed AFBS-WOA using the Tracy–Singh product. For data retrieval, the secret key is shared with the service provider in order to retrieve the database, and then the data are accessed. Moreover, the experimental result demonstrates that the developed AFBS_WOA model attained the maximum utility and privacy measure of 0.1872 and 0.8755 using the Hungarian dataset.

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“…If the two datasets contain similar categories of images, the two datasets are considered to belong to similar domains, that is, they belong to similar domains. For example, the ALOT data set is a public data set containing 250 kinds of texture images [20]. The Fabric1000 data set collected independently in this paper is a data set of 1000 kinds of cloth pictures with rich texture information.…”

Section: Using Transfer Learning To Solve the Problem Of Missing Traimentioning

confidence: 99%

A security risk plan search assistant decision algorithm using deep neural network combined with two-stage similarity calculation

Fang

et al. 2019

Pers Ubiquit Comput

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In view of the nonlinearity and uncertainty of safety accident risk assessment, firstly, based on the deep neural network, the training criterion of the network is changed, and the triplet convolutional neural network with the similarity measure as the cost function is proposed. The inactive multi-scale set features are extracted from them, so that the semantic features obtained by learning are suitable for security risk image retrieval. In the image retrieval application, the training samples of the retrieved data set are not enough to train a large network, and the innovative application of migration learning to security risk image retrieval proposes to train the network with data sets similar to the retrieved data sets. Then based on the traditional nearest neighbor algorithm, this paper proposes a case similarity calculation method based on two-dimensional structure of structural similarity and attribute similarity, input the characteristic attribute value of the current emergency event, and conduct similar case retrieval. The final calculation returns the historical case and its solution that the user is most similar to the currently entered incident feature. The experiment proves that the maximum relative error between the output of the network and the expected output value is 5.17%, and the minimum relative error is 1.38%, which has high accuracy.

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Case Retrieval Algorithm Using Similarity Measure and Adaptive Fractional Brain Storm Optimization for Health Informaticians

Cited by 14 publications

References 24 publications

Indexing and Optimization Techniques in Biomedical Industry

Indexing and Optimization Techniques in Biomedical Industry

Cloud-Assisted Privacy-Preserving Method for Healthcare Using Adaptive Fractional Brain Storm Integrated Whale Optimization Algorithm

A security risk plan search assistant decision algorithm using deep neural network combined with two-stage similarity calculation

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