EigenSample: A non-iterative technique for adding samples to small datasets

Jayadeva,; Soman, Sumit; Saxena, Sandeep K.

doi:10.1016/j.asoc.2017.08.017

Cited by 15 publications

(4 citation statements)

References 44 publications

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“…For example, Jayadeva et al. [54] , [55] introduced a non-iterative method for adding samples to small datasets. This technique estimates the data in a sub-space of its eigenvectors, clusters them in the sub-space, and produces additional samples within the clusters.…”

Section: Proposed Methodologymentioning

confidence: 99%

A CNN-LSTM network with multi-level feature extraction-based approach for automated detection of coronavirus from CT scan and X-ray images

Naeem

Bin-Salem

2021

Applied Soft Computing

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Auto-detection of diseases has become a prime issue in medical sciences as population density is fast growing. An intelligent framework for disease detection helps physicians identify illnesses, give reliable and consistent results, and reduce death rates. Coronavirus (Covid-19) has recently been one of the most severe and acute diseases in the world. An automatic detection framework should therefore be introduced as the fastest diagnostic alternative to avoid Covid-19 spread. In this paper, an automatic Covid-19 identification in the CT scan and chest X-ray is obtained with the help of a combined deep learning and multi-level feature extraction methodology. In this method, the multi-level feature extraction approach comprises GIST, Scale Invariant Feature Transform (SIFT), and Convolutional Neural Network (CNN) extract features from CT scans and chest X-rays. The objective of multi-level feature extraction is to reduce the training complexity of CNN network, which significantly assists in accurate and robust Covid-19 identification. Finally, Long Short-Term Memory (LSTM) along the CNN network is used to detect the extracted Covid-19 features. The Kaggle SARS-CoV-2 CT scan dataset and the Italian SIRM Covid-19 CT scan and chest X-ray dataset were employed for testing purposes. Experimental outcomes show that proposed approach obtained 98.94% accuracy with the SARS-CoV-2 CT scan dataset and 83.03% accuracy with the SIRM Covid-19 CT scan and chest X-ray dataset. The proposed approach helps radiologists and practitioners to detect and treat Covid-19 cases effectively over the pandemic.

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Section: Proposed Methodologymentioning

confidence: 99%

A CNN-LSTM network with multi-level feature extraction-based approach for automated detection of coronavirus from CT scan and X-ray images

Naeem

Bin-Salem

2021

Applied Soft Computing

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“…[ 119 ] In some existing studies, the EigenSample technique was adopted to augment the data, which may have biased the evaluation results to some extent. [ 102 , 147 ]…”

Section: Platelet Rnas Are Informative On the Cancer Diagnostics Journeymentioning

confidence: 99%

“…[119] In some existing studies, the EigenSample technique was adopted to augment the data, which may have biased the evaluation results to some extent. [102,147] Primary data and diagnostic parameters: Determination of diagnostic parameters requires strict adherence to the principle of blindness, and each parameter should be documented in detail. A shared database of platelet RNAs and detailed demographic and clinical characteristics spanning both healthy individuals and patients with different diseases, including neoplasms, is urgently needed to explore the clinical value of platelet RNA.…”

Section: Patients Controlsmentioning

confidence: 99%

A narrative review for platelets and their RNAs in cancers: New concepts and clinical perspectives

Xiang

Xiang²,

Zhang

et al. 2022

Medicine

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Recent years have witnessed a growing body of evidence suggesting that platelets are involved in several stages of the metastatic process via direct or indirect interactions with cancer cells, contributing to the progression of neoplastic malignancies. Cancer cells can dynamically exchange components with platelets in and out of blood vessels, and directly phagocytose platelets to hijack their proteome, transcriptome, and secretome, or be remotely regulated by metabolites or microparticles released by platelets, resulting in phenotypic, genetic, and functional modifications. Moreover, platelet interactions with stromal and immune cells in the tumor microenvironment lead to alterations in their components, including the ribonucleic acid (RNA) profile, and complicate the impact of platelets on cancers. A deeper understanding of the roles of platelets and their RNAs in cancer will contribute to the development of anticancer strategies and the optimization of clinical management. Encouragingly, advances in high-throughput sequencing, bioinformatics data analysis, and machine learning have allowed scientists to explore the potential of platelet RNAs for cancer diagnosis, prognosis, and guiding treatment. However, the clinical application of this technique remains controversial and requires larger, multicenter studies with standardized protocols. Here, we integrate the latest evidence to provide a broader insight into the role of platelets in cancer progression and management, and propose standardized recommendations for the clinical utility of platelet RNAs to facilitate translation and benefit patients.

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“…Der-Chiang Li et al proposed a method of estimating the sample first and then reconstructing the sample [31]. Soman, Sumit et al proposed a non-iterative technique to add small samples and samples [32]. Yan-Lin He et al proposed a nonlinear interpolation virtual sample generation method to enhance sample information [13].…”

Section: Introductionmentioning

confidence: 99%

A Virtual Sample Generation Method Based on Differential Evolution Algorithm for Overall Trend of Small Sample Data: Used for Lithium-ion Battery Capacity Degradation Data

Kang

Guan

et al. 2019

IEEE Access

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Considering the wide application of lithium-ion battery in life, the prediction of the remaining life of lithium-ion battery has become a research hotspot. Studies show, due to the improvement of the technology level of lithium-ion battery, its life is getting longer and longer. Even under the condition of accelerated life test, it is difficult to obtain enough available data for research in a short term. In order to solve the problem of how to accurately predict the residual life with the data-driven method under the condition of small sample size, an overall trend virtual sample generation method based on differential evolution (OT-DEVSG) is proposed. This method uses a differential evolution algorithm with better optimization performance, and improves the original mega-trend-diffusion (MTD) method, the range of virtual samples is effectively constrained and the trend of samples can be estimated more accurately. The method can effectively generate a virtual sample data sequence with time parameters, and adapt the virtual sample to the real-life sample trend, which solves the problem of insufficient degradation data of the lithium-ion batteries. Finally, we validate the effectiveness of the OT-DEVSG method with three existing data sets. The experimental results show that the proposed OT-DEVSG method is effective for solving the problem of long-term life prediction of lithium-ion batteries.INDEX TERMS Lithium-ion battery, overall trend virtual sample generation method based on differential evolution (OT-DEVSG), small data.

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EigenSample: A non-iterative technique for adding samples to small datasets

Cited by 15 publications

References 44 publications

A CNN-LSTM network with multi-level feature extraction-based approach for automated detection of coronavirus from CT scan and X-ray images

A CNN-LSTM network with multi-level feature extraction-based approach for automated detection of coronavirus from CT scan and X-ray images

A narrative review for platelets and their RNAs in cancers: New concepts and clinical perspectives

A Virtual Sample Generation Method Based on Differential Evolution Algorithm for Overall Trend of Small Sample Data: Used for Lithium-ion Battery Capacity Degradation Data

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