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resampling approaches for the quantitative analysis of spatially distributed cells

Data Intelligence 2024年第1期6卷 104-119页

作者： Giorgio Bertolazzi Michele Tumminello Gaia Morello Beatrice Belmonte Claudio Tripodo Department of Economics Business and StatisticsUniversity of PalermoPalermoItaly Tumor Immunology Unit Department of Health SciencesUniversity of PalermoPalermoItaly Histopathology Unit Institute of Molecular Oncology Foundation(IFOM)ETS-The AIRC Institute of Molecular OncologyMilanItaly

Image segmentation is a crucial step in various image analysis pipelines and constitutes one of the cutting-edge areas of digital pathology.The advent of quantitative analysis has enabled the evaluation of millions of individual cells in tissues,allowing for the combined assessment of morphological features,biomarker expression,and spatial context.The recorded cells can be described as a point pattern process.However,the classical statistical approaches to point pattern processes prove unreliable in this context due to the presence of multiple irregularly-shaped interstitial cell-devoid spaces in the domain,which correspond to anatomical features(e.***.vessels,lipid vacuoles,glandular lumina)or tissue artefacts(e.***.tissue fractures),and whose coordinates are unknown.These interstitial spaces impede the accurate calculation of the domain area,resulting in biased clustering measurements.Moreover,the mistaken inclusion of empty regions of the domain can directly impact the results of hypothesis testing.The literature currently lacks any introduced bias correction method to address interstitial cell-devoid spaces.To address this gap,we propose novel resampling methods for testing spatial randomness and evaluating relationships among different cell populations.Our methods obviate the need for domain area estimation and provide non-biased clustering measurements.We created the SpaceR software(https://***/GBertolazzi/SpaceR)to enhance the accessibility of our methodologies.

关键词： Digital pathology spatial cytometry spatial clustering spatial randomness resampling

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Credit Card Fraud Detection Using Improved Deep Learning Models

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Computers, Materials & Continua 2024年第1期78卷 1049-1069页

作者： Sumaya S.Sulaiman Ibraheem Nadher Sarab M.Hameed Computer Science Department Al-Mustansiriya UniversityBaghdad10001Iraq Computer Science Department University of BaghdadBaghdad10001Iraq Faculty of Basic Education Al-Mustansiriya UniversityBaghdad10001Iraq

Fraud of credit cards is a major issue for financial organizations and individuals.As fraudulent actions become more complex,a demand for better fraud detection systems is rising.Deep learning approaches have shown promise in several fields,including detecting credit card fraud.However,the efficacy of these models is heavily dependent on the careful selection of appropriate hyperparameters.This paper introduces models that integrate deep learning models with hyperparameter tuning techniques to learn the patterns and relationships within credit card transaction data,thereby improving fraud detection.Three deep learning models:AutoEncoder(AE),Convolution Neural Network(CNN),and Long Short-Term Memory(LSTM)are proposed to investigate how hyperparameter adjustment impacts the efficacy of deep learning models used to identify credit card fraud.The experiments conducted on a European credit card fraud dataset using different hyperparameters and three deep learning models demonstrate that the proposed models achieve a tradeoff between detection rate and precision,leading these models to be effective in accurately predicting credit card fraud.The results demonstrate that LSTM significantly outperformed AE and CNN in terms of accuracy(99.2%),detection rate(93.3%),and area under the curve(96.3%).These proposed models have surpassed those of existing studies and are expected to make a significant contribution to the field of credit card fraud detection.

关键词： Card fraud detection hyperparameter tuning deep learning autoencoder convolution neural network long short-term memory resampling

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resampling in neural networks with application to spatial analysis

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Geo-Spatial Information Science 2022年第3期25卷 413-424页

作者： Bruno Póvoa Rodrigues Vinicius Francisco Rofatto Marcelo Tomio Matsuoka Talita Teles Assunção Graduate Program in Agriculture and Geospatial Information Federal University of UberlândiaMonte CarmeloBrazil Institute of Geography Federal University of UberlândiaMonte CarmeloBrazil

In developing Artificial Neural Networks(ANNs),the available dataset is split into three categories:training,validation and testing.However,an important problem arises:How to trust the predic-tion provided by a particular ANN?Due to the randomness related to the network itself(architecture,initialization and learning procedure),there is usually no best choice.Considering this issue,we provide a framework,which captures the randomness related to the network itself.The idea is to perform several training and test trials based on the Jackknife resampling method.Jackknife consists of iteratively deleting a single observation each time from the sample and recomputing the ANN on the rest of the sample data.Consequently,interval prediction is available instead of point prediction.The proposed method was applied and tested using pH,Ca and P data obtained by analyzing 118 georeferenced soil points.The results,based on the dataset size simulation,showed that 60%reduction in available dataset offers compatible accuracy in relation to full dataset,and therefore a higher cost of sampling in the field would not be necessary.The re-sampling method spatially characterizes the points of greater or lesser accuracy and uncertainty.The re-sampling method increased the success rate by using interval prediction instead of using the mean as the most probable value.Although we restrict it to the regression neural network model,the resampling method proposed can also be extended to other modern statistical tools,such as Kriging,Least Squares Collocation(LSC),Convolutional Neural Network(CNN),and so on.

关键词： Artificial Neural Network(ANN) data splitting resampling delete-1 Jackknife spatial analysis

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resampling TESTS OF STATISTICAL HYPOTHESES

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Acta Mathematica Scientia 1996年第3期16卷 279-286页

作者：石坚 Institute of Systems Science Academia Sinica Beijing 100080 China

In this paper, large sample properties of resampling tests of hypotheses on the population mean resampled according to the empirical likelihood and the Kullback-Leibler criteria are investigated. It is proved that und... 详细信息

关键词： test of hypothesis resampling empirical likelihood Kullback-Leibler distance

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ESR-PINNs:Physics-informed neural networks with expansion-shrinkage resampling selection strategies

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Chinese Physics B 2023年第7期32卷 337-346页

作者：刘佳楠侯庆志魏建国孙泽玮 College of Intelligence and Computing Tianjin UniversityTianjin 300350China State Key Laboratory of Hydraulic Engineering Simulation and Safety Tianjin UniversityTianjin 300350China

Neural network methods have been widely used in many fields of scientific research with the rapid increase of computing power.The physics-informed neural networks(PINNs)have received much attention as a major breakthrough in solving partial differential equations using neural networks.In this paper,a resampling technique based on the expansion-shrinkage point(ESP)selection strategy is developed to dynamically modify the distribution of training points in accordance with the performance of the neural networks.In this new approach both training sites with slight changes in residual values and training points with large residuals are taken into account.In order to make the distribution of training points more uniform,the concept of continuity is further introduced and incorporated.This method successfully addresses the issue that the neural network becomes ill or even crashes due to the extensive alteration of training point distribution.The effectiveness of the improved physics-informed neural networks with expansion-shrinkage resampling is demonstrated through a series of numerical experiments.

关键词： physical informed neural networks resampling partial differential equation

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Deep Learning Based Sentiment Analysis of COVID-19 Tweets via resampling and Label Analysis

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Computer Systems Science & Engineering 2023年第10期47卷 575-591页

作者： Mamoona Humayun Danish Javed Nz Jhanjhi Maram Fahaad Almufareh Saleh Naif Almuayqil Department of Information Systems College of Computer and Information SciencesJouf UniversitySakakah72311Saudi Arabia School of Computer Science SCSTaylor’s UniversitySubang Jaya47500SelangorMalaysia

Twitter has emerged as a platform that produces new data every day through its users which can be utilized for various purposes.People express their unique ideas and views onmultiple topics thus providing vast knowledge.Sentiment analysis is critical from the corporate and political perspectives as it can impact decision-making.Since the proliferation of COVID-19,it has become an important challenge to detect the sentiment of COVID-19-related tweets so that people’s opinions can be tracked.The purpose of this research is to detect the sentiment of people regarding this problem with limited data as it can be challenging considering the various textual characteristics that must be analyzed.Hence,this research presents a deep learning-based model that utilizes the positives of random minority oversampling combined with class label analysis to achieve the best results for sentiment analysis.This research specifically focuses on utilizing class label analysis to deal with the multiclass problem by combining the class labels with a similar overall sentiment.This can be particularly helpful when dealing with smaller datasets.Furthermore,our proposed model integrates various preprocessing steps with random minority oversampling and various deep learning algorithms including standard deep learning and bi-directional deep learning algorithms.This research explores several algorithms and their impact on sentiment analysis tasks and concludes that bidirectional neural networks do not provide any advantage over standard neural networks as standard Neural Networks provide slightly better results than their bidirectional counterparts.The experimental results validate that our model offers excellent results with a validation accuracy of 92.5%and an F1 measure of 0.92.

关键词： Bi-directional deep learning resampling random minority oversampling sentiment analysis class label analysis

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Dynamic resampling particle filter adaptive to changes in system model

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控制理论与应用（英文版） 2011年第4期9卷 584-588页

作者： Ling WU Faxing LU Electronic Engineering College Naval University of Engineering Wuhan Hubei 430033 China

A dynamic resampling particle filter （DRPF）, integrated with scatteriflg and migrating operations, is proposed for the flexibility in nonlinear systems subject to changes in the system model without a considerable increase in computational cost. Under the change detection with the tracking error, the scattering and migration are alternatively adopted where the scattering can boost the diversity in the particle population when no change, or slow change occurs, while the migrating updates particles with new observations if the change comes abruptly. Simulation results validate the proposed PF as a promising alternative to the existing PFs.

关键词： Particle filter Bayesian estimation Change detection resampling

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Adaptive Spectral Clustering Ensemble Selection via resampling and Population-Based Incremental Learning Algorithm

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Wuhan University Journal of Natural Sciences 2011年第3期16卷 228-236页

作者： XU Yuanchun JIA Jianhua School of Information Engineering Jingdezhen Ceramic Institute Jingdezhen 333002 Jiangxi China

In this paper, we explore a novel ensemble method for spectral clustering. In contrast to the traditional clustering ensemble methods that combine all the obtained clustering results, we propose the adaptive spectral clustering ensemble method to achieve a better clustering solution. This method can adaptively assess the number of the component members, which is not owned by many other algorithms. The component clusterings of the ensemble system are generated by spectral clustering （SC） which bears some good characteristics to engender the diverse committees. The selection process works by evaluating the generated component spectral clustering through resampling technique and population-based incremental learning algorithm （PBIL）. Experimental results on UCI datasets demonstrate that the proposed algorithm can achieve better results compared with traditional clustering ensemble methods, especially when the number of component clusterings is large.

关键词： spectral clustering clustering ensemble selective ensemble resampling population-based incremental learning algorithm （PBIL） data clustering

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resampling Simulator for the Probability of Detecting Invasive Species in Large Populations

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Journal of Software Engineering and Applications 2014年第6期7卷 498-505页

作者： David E. Legg Jeffrey G. Fidgen Krista L. Ryall Department of Ecosystem Science and Management University of Wyoming Laramie USA Great Lakes Forestry Centre Sault Ste. Marie Canada

This paper proposes a resampling simulator that will calculate probabilities of detecting invasive species infesting hosts that occur in large numbers. Different methods were examined to determine the bias of observed cumulative distribution functions (c.d.f.s), generated from prototype resampling simulators. One involved seeing if they matched theoretical c.d.f.s, which were generated using formulae for calculating the probability of the union of many events (union formulae), which are known to be correct. Others involved assessing the bias of observed c.d.f.s, generated from using prototype resampling simulators operating on much larger simulated populations, when computation of theoretical c.d.f.s from the union formulae was not practical. Examples are given for using the proposed resampling simulator for detecting an invasive insect pest within the context of an invasive species management system.

关键词： resampling Simulator Detection of Invasive Species Invasive Species Management System Large Populations

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Fast Forgery Detection with the Intrinsic resampling Properties

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Journal of Information Security 2010年第1期1卷 11-22页

作者： Cheng-Chang Lien Cheng-Lun Shih Chih-Hsun Chou 不详

With the rapid progress of the image processing software, the image forgery can leave no visual clues on the tampered regions and make us unable to authenticate the image. In general, the image forgery technologies often utilizes the scaling, rotation or skewing operations to tamper some regions in the image, in which the resampling and interpolation processes are often demanded. By observing the detectable periodic distribution properties generated from the resampling and interpolation processes, we propose a novel method based on the intrinsic properties of resampling scheme to detect the tampered regions. The proposed method applies the pre-calculated resampling weighting table to detect the periodic properties of prediction error distribution. The experimental results show that the proposed method outperforms the conventional methods in terms of efficiency and accuracy.

关键词： Image Forgery resampling Forgery Detection Intrinsic Properties of resampling

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