Forward feature extraction from imbalanced microarray datasets using wrapper based incremental genetic algorithm

Priya, R. Devi; Sivaraj, R.; Anitha, N.; Devisurya, V.

doi:10.1504/ijbic.2020.111275

Cited by 6 publications

(2 citation statements)

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“…The feature extraction on the basic of the GA needs to be on the basic of the text, not the feature extraction algorithm on the basic of the entire text set. Therefore, when extracting text features, the feature words in the same text can be put into a feature vector representing the text, so as to avoid ignoring the connection between feature items [15][16]. On this basis, this paper proposes a text feature vector on the basic of X 2 statistics, which can not only preserve the correlation between text features, but also distinguish the correlation between features and classes; and uses this vector as the initial population, through multiple rounds of genetic vectors are obtained to improve classification accuracy; through the coordination of crossover operation and mutation operation, global search can be realized and local minima can be avoided [17][18]; according to the characteristics of feature extraction, the fitness function and intersection rules are designed to solve the problem of inappropriate processing of low-frequency words in statistical analysis [19][20].…”

Section: Feature Extraction Technology On the Basic Of Genetic Algorithmmentioning

confidence: 99%

Platform Design of Project Bidding System on the Basic of TCM-KNN and Genetic Algorithm

Sun

2024

Proceedings of the First International Conference on Science, Engineering and Technology Practices for Sustainable Development,

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Project bidding is a widely used method of project transactions in the world. It promotes fairness and justice in the field of project construction, and also prevents and overcomes corruption in the field of project construction. Nowadays, electronic bidding has gradually replaced traditional bidding methods, and the big date (BD) brought by electronic bidding has also provided sufficient convenience for the establishment and improvement of engineering enterprise credit evaluation systems. Therefore, this paper is to solve the problem of insufficient application of BD in the current engineering electronic bidding. This paper combines the TCM-KNN classification algorithm with the genetic algorithm(GA), collects data information related to bidding transactions from multiple channels, and conducts research on the internal correlation and mutual influence between these data information, so as to determine the evaluation indicators suitable for engineering enterprise credit evaluation. And on the basic of the BD analysis of engineering electronic bidding, this paper establishes the corresponding engineering enterprise credit evaluation(ECE) index system and ECE model. Finally, the experimental results show that the GA is used to optimize the TCM-KNN model, and the parameters that are more in line with the actual situation are obtained.

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Section: Feature Extraction Technology On the Basic Of Genetic Algorithmmentioning

confidence: 99%

Platform Design of Project Bidding System on the Basic of TCM-KNN and Genetic Algorithm

Sun

2024

Proceedings of the First International Conference on Science, Engineering and Technology Practices for Sustainable Development,

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“…The input samples chosen for analysis should always be balanced, containing the appropriate number of samples belonging to each class label. Handling an imbalanced dataset is important for better classification [25,26]. According to the papers described above, deep learning architectures are rapidly being applied to facemask detection to prevent COVID-19 spread using a transfer learning-based deep neural network [27][28][29][30][31][32][33][34].…”

Section: Literature Reviewmentioning

confidence: 99%

Real-Time Facemask Detection for Preventing COVID-19 Spread Using Transfer Learning Based Deep Neural Network

Ali

Shanmugam²,

Muthusamy³

et al. 2022

Electronics

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The COVID-19 pandemic disrupted people’s livelihoods and hindered global trade and transportation. During the COVID-19 pandemic, the World Health Organization mandated that masks be worn to protect against this deadly virus. Protecting one’s face with a mask has become the standard. Many public service providers will encourage clients to wear masks properly in the foreseeable future. On the other hand, monitoring the individuals while standing alone in one location is exhausting. This paper offers a solution based on deep learning for identifying masks worn over faces in public places to minimize the coronavirus community transmission. The main contribution of the proposed work is the development of a real-time system for determining whether the person on a webcam is wearing a mask or not. The ensemble method makes it easier to achieve high accuracy and makes considerable strides toward enhancing detection speed. In addition, the implementation of transfer learning on pretrained models and stringent testing on an objective dataset led to the development of a highly dependable and inexpensive solution. The findings provide validity to the application’s potential for use in real-world settings, contributing to the reduction in pandemic transmission. Compared to the existing methodologies, the proposed method delivers improved accuracy, specificity, precision, recall, and F-measure performance in three-class outputs. These metrics include accuracy, specificity, precision, and recall. An appropriate balance is kept between the number of necessary parameters and the time needed to conclude the various models.

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Multi‐level learning based on3D CTimage integrated medical clinic information for accurate diagnosis of pulmonary nodules

Zhang

Zhao

et al. 2022

Concurrency and Computation

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The diagnosis of pulmonary nodules by clinicians depends not only on radiological imaging but also on the patient's own clinical record information and other factors. However, exploring the guiding role of clinical information is a major challenge. In this article, an intelligent personalized diagnosis decision‐making model is proposed, which combines radiology images with patient information. First, the 3D image cube of the pulmonary nodule is constructed. Then, a 3D multi‐level fusion ResNet is designed to extract the features of the nodule by making full use of the spatial context information. Finally, a kind of classification model based on feature‐related analysis was proposed, which fused clinical information features and image features and realized a nonlinear radial basis feature mapping. We tested this method on the public dataset and a cooperation hospital dataset. Experiments show that this method can effectively improve the classification accuracy of unstable nodules at the classification boundary. Our model showed significant improvements in sensitivity, specificity, and accuracy. Meanwhile, compared with other deep learning diagnosis methods, our method achieves better discriminative results and is highly suited to be used for pulmonary nodule diagnosis.

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Forward feature extraction from imbalanced microarray datasets using wrapper based incremental genetic algorithm

Cited by 6 publications

References 0 publications

Platform Design of Project Bidding System on the Basic of TCM-KNN and Genetic Algorithm

Platform Design of Project Bidding System on the Basic of TCM-KNN and Genetic Algorithm

Real-Time Facemask Detection for Preventing COVID-19 Spread Using Transfer Learning Based Deep Neural Network

Multi‐level learning based on3D CTimage integrated medical clinic information for accurate diagnosis of pulmonary nodules

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