Neural Network Used for the Fusion of Predictions Obtained by the K-Nearest Neighbors Algorithm Based on Independent Data Sources

Przybyła–Kasperek, Małgorzata; Marfo, Kwabena Frimpong

doi:10.3390/e23121568

Cited by 10 publications

(3 citation statements)

References 21 publications

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“…The relative distance between instances is more important than their absolute position within a given region [ 19 ]. The k -NN algorithm is suitable for analyzing large, multidimensional datasets [ 41 , 44 ], and is the optimal method when prior knowledge of the data distribution is lacking [ 17 , 45 ]. Furthermore, there is no requirement for off-line training when using the k -NN algorithm, so it is also time efficient [ 14 ].…”

Section: Discussionmentioning

confidence: 99%

Deep Learning Artificial Intelligence to Predict the Need for Tracheostomy in Patients of Deep Neck Infection Based on Clinical and Computed Tomography Findings—Preliminary Data and a Pilot Study

Chen

Chin

2022

Diagnostics

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Background: Deep neck infection (DNI) can lead to airway obstruction. Rather than intubation, some patients need tracheostomy to secure the airway. However, no study has used deep learning (DL) artificial intelligence (AI) to predict the need for tracheostomy in DNI patients. Thus, the purpose of this study was to develop a DL framework to predict the need for tracheostomy in DNI patients. Methods: 392 patients with DNI were enrolled in this study between August 2016 and April 2022; 80% of the patients (n = 317) were randomly assigned to a training group for model validation, and the remaining 20% (n = 75) were assigned to the test group to determine model accuracy. The k-nearest neighbor method was applied to analyze the clinical and computed tomography (CT) data of the patients. The predictions of the model with regard to the need for tracheostomy were compared with actual decisions made by clinical experts. Results: No significant differences were observed in clinical or CT parameters between the training group and test groups. The DL model yielded a prediction accuracy of 78.66% (59/75 cases). The sensitivity and specificity values were 62.50% and 80.60%, respectively. Conclusions: We demonstrated a DL framework to predict the need for tracheostomy in DNI patients based on clinical and CT data. The model has potential for clinical application; in particular, it may assist less experienced clinicians to determine whether tracheostomy is necessary in cases of DNI.

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Section: Discussionmentioning

confidence: 99%

Deep Learning Artificial Intelligence to Predict the Need for Tracheostomy in Patients of Deep Neck Infection Based on Clinical and Computed Tomography Findings—Preliminary Data and a Pilot Study

Chen

Chin

2022

Diagnostics

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“…Evidence theory, also known as Dempster-Shafer evidence theory [28,29], is able to distinguish between 'uncertainty' and 'not knowing' and is able to deal with uncertainty arising from 'not knowing' with a high degree of flexibility, and thus has gained wide application in uncertainty inference and data fusion [30,31].…”

Section: Introduction To Ds Evidence Theorymentioning

confidence: 99%

Research of ordinal model fusion based apple grading

Bi,

Qu,

Shen

et al. 2024

Meas. Sci. Technol.

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Near Infrared Spectrum (NIR) has been applied for the rapid non-destructive prediction and classification of the internal soluble solids content (SSC) of apples, due to its rapid, non-destructive and non-polluting nature. However, current apple grading methods do not make full use of the orderliness relationship existing in the apple grading labels. Therefore, ordinal model is introduced in the issue of apples grading based on the internal SSC. In details, the orderliness in the classification model is considered and ordinal regression is combined with apple classification model to establish ordered partitioning neural network and ordinal regression extreme learning machine, respectively. Meanwhile, to address the problems of poor applicability of single prediction model and the grading uncertainty associated with compulsory segmentation of grading boundaries, a Gaussian mass function generating method is proposed based on the distance between the predicted ordinal class labels and the real grading boundaries, and the multiple models can be fused through the Dempster combination rule, making a fuller description of the uncertainty problem on ordinal class labels prediction, providing a research basis for online non destructive testing (NDT) grading of apples. Finally, the multiple model fusion process is fully demonstrated by simulation examples, the grading accuracy of Yantai Red Fuji apples is also improved than single ordinal model.

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“…Neural networks have been considered for dispersed data in various applications. The papers [9,10] considered neural networks as a model for aggregating prediction vectors generated by local classifiers. In the paper [11], neural networks were used in a federated learning approach.…”

Section: Introductionmentioning

confidence: 99%

Study on the Use of Artificially Generated Objects in the Process of Training MLP Neural Networks Based on Dispersed Data

Marfo

Przybyła–Kasperek

2023

Entropy

Self Cite

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This study concerns dispersed data stored in independent local tables with different sets of attributes. The paper proposes a new method for training a single neural network—a multilayer perceptron based on dispersed data. The idea is to train local models that have identical structures based on local tables; however, due to different sets of conditional attributes present in local tables, it is necessary to generate some artificial objects to train local models. The paper presents a study on the use of varying parameter values in the proposed method of creating artificial objects to train local models. The paper presents an exhaustive comparison in terms of the number of artificial objects generated based on a single original object, the degree of data dispersion, data balancing, and different network structures—the number of neurons in the hidden layer. It was found that for data sets with a large number of objects, a smaller number of artificial objects is optimal. For smaller data sets, a greater number of artificial objects (three or four) produces better results. For large data sets, data balancing and the degree of dispersion have no significant impact on quality of classification. Rather, a greater number of neurons in the hidden layer produces better results (ranging from three to five times the number of neurons in the input layer).

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Neural Network Used for the Fusion of Predictions Obtained by the K-Nearest Neighbors Algorithm Based on Independent Data Sources

Cited by 10 publications

References 21 publications

Deep Learning Artificial Intelligence to Predict the Need for Tracheostomy in Patients of Deep Neck Infection Based on Clinical and Computed Tomography Findings—Preliminary Data and a Pilot Study

Deep Learning Artificial Intelligence to Predict the Need for Tracheostomy in Patients of Deep Neck Infection Based on Clinical and Computed Tomography Findings—Preliminary Data and a Pilot Study

Research of ordinal model fusion based apple grading

Study on the Use of Artificially Generated Objects in the Process of Training MLP Neural Networks Based on Dispersed Data

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