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Application of unsupervised clustering algorithm and heat-map analysis for selection of lactic acid bacteria isolated from dairy samples based on desired probiotic properties

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LWT-FOOD SCIENCE AND TECHNOLOGY 2020年 118卷 108839-000页

作者： Nami, Yousef Panahi, Bahman Jalaly, Hossein Mohammadzadeh Bakhshayesh, Reza Vaseghi Hejazi, Mohammad Amin AREEO Agr Biotechnol Res Inst Iran Branch Northwest & West Reg Dept Food Biotechnol Tabriz Iran AREEO Agr Biotechnol Res Inst Iran Branch Northwest & West Reg Dept Genom Tabriz Iran

Phenotypic and genotypic characterization of candidate probiotic strains are indispensable steps to the nourishments utilization. Hence, in this study, sixty-three lactic acid bacteria (LAB) isolates of traditional dairy origin already distinguished using pyrosequence-based 16S-rRNA profiling were characterized for their probiotic properties. The unsupervised clustering algorithm and heat-map analysis efficiency for selection of candidate probiotic strains by oppressing their phenotypic characteristics were tested. Results demonstrated that YP9, YPB, TP15, and ShP1 isolates show highest acid and bile tolerance, and Cell surface hydrophobicity. Moreover, CP3, CP12, TP15, TP16, TP17, ShP1, YPS, YP9, and CuP3 showed the highest BSH activity. Interestingly, the inhibitory effects of YP8, ShP1, and YP9 against Yersinia, Listeria, and Yersinia, respectively, were confirmed. clustering and heat-map analysis effectively selected and distinguished nine Lactobacillus isolates (YPB, YP9, CP3, TP15-17, ShP1, CP12, and CuP3) as candidate probiotic strains. This study opens a new avenue on the selection and characterization of new isolates of bacteria based on their probiotics properties for future application in functional foods.

关键词： Probiotics LAB Dairy samples Heat-map analysis unsupervised clustering algorithm

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Deep feature clustering for multi-class industrial image anomaly detection

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KNOWLEDGE-BASED SYSTEMS 2025年 311卷

作者： Wang, Rongxiang Li, Zhi Zheng, Long Wang, Weidong Li, Shuyun Guizhou Univ Coll Comp Sci & Technol State Key Lab Publ Big Data Guiyang 550025 Peoples R China

Existing unsupervised multi-class anomaly detection algorithms usually train unified reconstruction networks to capture the distribution of all classes simultaneously. However, under such a challenging setting, popular reconstruction networks need to be elaborately designed to avoid the "identical shortcut''. In addition, the distribution of each category is different, which may mean different requests for expression ability. To solve these problems, built on the intuitive "classification-then-detection"idea, we utilize clustering algorithm to expose the category information hidden in the pre-trained deep features, then propose a simple and application- friendly approach for multi-class anomaly detection. The proposed approach consists of Category Anchor Construction (CAC), Category Information Mining (CIM) and Local Feature Routing (LFR). Firstly, CAC is proposed to extract the corresponding pre-trained features from a small subset of training images to construct category anchors, preserving the valuable category information provided by the training set. Then, CIM is introduced to mine category information embedded in pre-trained features by category anchors voting and acquires the category labels. Finally, to achieve multi-class anomaly detection, we propose LFR, splitting multi-class distribution into multiple single-class distributions according to category labels so that separate single-class anomaly detection heads can be trained to express them. In spite of simplicity, the proposed method outperforms state-of-the-art algorithms in terms of accuracy and stability on the widely used MVTec-AD, VisA, MVTec-LOCO, MPDD and BTAD datasets.

关键词： Multi-class anomaly detection Industrial image anomaly detection unsupervised clustering algorithm Deep feature clustering Application-friendly one-for-all model

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Self-Supervised Self-Organizing clustering Network: A Novel unsupervised Representation Learning Method

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IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS 2024年第2期35卷 1857-1871页

作者： Li, Shuo Liu, Fang Jiao, Licheng Chen, Puhua Li, Lingling Xidian Univ Joint Int Res Lab Intelligent Percept & Computat Key Lab Intelligent Percept & Image Understanding Int Res Ctr Intelligent Percept & ComputatMinist Xian 710071 Shaanxi Peoples R China

Deep learning-based clustering methods usually regard feature extraction and feature clustering as two independent steps. In this way, the features of all images need to be extracted before feature clustering, which consumes a lot of calculation. Inspired by the self-organizing map network, a self-supervised self-organizing clustering network (S(3)OCNet) is proposed to jointly learn feature extraction and feature clustering, thus realizing a single-stage clustering method. In order to achieve joint learning, we propose a self-organizing clustering header (SOCH), which takes the weight of the self-organizing layer as the cluster centers, and the output of the self-organizing layer as the similarities between the feature and the cluster centers. In order to optimize our network, we first convert the similarities into probabilities which represents a soft cluster assignment, and then we obtain a target for self-supervised learning by transforming the soft cluster assignment into a hard cluster assignment, and finally we jointly optimize backbone and SOCH. By setting different feature dimensions, a Multilayer SOCHs strategy is further proposed by cascading SOCHs. This strategy achieves clustering features in multiple clustering spaces. S(3)OCNet is evaluated on widely used image classification benchmarks such as Canadian Institute For Advanced Research (CIFAR)-10, CIFAR-100, Self-Taught Learning (STL)-10, and Tiny ImageNet. Experimental results show that our method significant improvement over other related methods. The visualization of features and images shows that our method can achieve good clustering results.

关键词： Deep learning-based clustering self-organizing map self-supervised learning unsupervised clustering algorithm unsupervised representation learning

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The involvement of the posterior parietal cortex in promotion and prevention focus

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CURRENT PSYCHOLOGY 2023年第30期42卷 26115-26124页

作者： Han, Hyun Jung Lee, Sangil Jung, Wi Hoon Gachon Univ Dept Psychol 1342 Seongnam Daero Seongnam Si 13120 Gyeonggi Do South Korea Univ Calif Berkeley Social Sci Matrix Berkeley CA 94720 USA

Motivation plays a critical role in human cognitive function, while acting as a driving force for the necessary behavior to achieve a desired goal and success (i.e., achievement motivation). Based on the theoretical background of achievement motivation, this study designed an incentive delay task with four motivational orientations (i.e., promotion, prevention, mastery/self, and performance/other). To investigate whether people would have their behavioral patterns toward achievement motivation orientation, we applied an unsupervised clustering algorithm to classify individuals' behavioral responses acquired from the task by categorizing certain behavioral similarities. As a result, this hierarchical clustering approach classified subjects into two distinctive subgroups: Group#1 (i.e., the pro/pre group, n = 52) and Group#2 (i.e., the self/other group, n = 48). Based on clustering, Group#1 showed significantly better performance with promotion/prevention orientations, whereas Group#2 exhibited significantly higher performance with self/other orientations. Structural brain analyses discovered increased gray matter volume and sulcal depth in the posterior parietal cortex (PPC) in the pro/pre group compared to the self/other group. With resting-state functional magnetic resonance imaging data, we found higher local brain fluctuations in the medial prefrontal cortex (mPFC) in the self/other group compared to the pro/pre group. Furthermore, mPFC seed-based functional connectivity showed significantly increased functional coupling with the posterior cingulate cortex in the self/other group relative to the pro/pre group. Taken together, these results shed light on structural and functional neural mechanisms related to achievement motivation and, furthermore, provide novel insights regarding PPC's role in motivational processing toward promotion- and prevention-focused orientation.

关键词： Achievement motivation unsupervised clustering algorithm Posterior parietal cortex Medial prefrontal cortex Posterior cingulate cortex

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Efficient computing technique for reliability analysis of high-dimensional and low-failure probability problems using active learning method

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PROBABILISTIC ENGINEERING MECHANICS 2024年 77卷

作者： Rajak, Pijus Roy, Pronab Natl Inst Technol Durgapur Dept Civil Engn Durgapur 713209 India

In spite of recent advancements in reliability analysis, high-dimensional and low-failure probability problems remain challenging because many samples and function calls are required for an accurate result. Function calls lead to a sharp increase in computational cost in terms of time. For this reason, an active learning algorithm is proposed using Kriging metamodel, where an unsupervised algorithm is used to select training samples from random samples for the first and second iterations. Then, the metamodel is improved iteratively by enriching the concerned domain with samples near the limit state function and samples obtained from a space-filling design. Hence, rapid convergence with the minimum number of function calls occurs using this active learning algorithm. An efficient stopping criterion has been developed to avoid premature or late-mature terminations of the metamodel and to regulate the accuracy of the failure probability estimations. The efficacy of this algorithm is examined using relative error, number of function calls, and coefficient of efficiency in five examples which are based on high-dimensional and low-failure probability with random and interval variables.

关键词： unsupervised clustering algorithm Kriging metamodel Sample selection strategy Space-filling design Active learning algorithm Stopping criterion

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Image clustering algorithm using superpixel segmentation and non-symmetric Gaussian-Cauchy mixture model

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IET IMAGE PROCESSING 2020年第16期14卷 4132-4143页

作者： Ji, Sifan Zhu, Hongqing Wang, Pengyu Ling, Xiaofeng East China Univ Sci & Technol Sch Informat Sci & Engn Shanghai 200237 Peoples R China

In this study, an unsupervised clustering algorithm is proposed to label superpixel density images. Firstly, the authors propose a novel superpixel segmentation algorithm driven by a modified fuzzy C-means objective function, Kullback-Leibler (KL) divergence, and an entropy term, which generate superpixels with good boundary adherence and intensity homogeneity. In this model, the logarithm of Gaussian distribution as a new distance metric is used to improve the accuracy of boundary pixel classification, the KL divergence is applied to regularise the fuzzy objective function. Based on this model, the generated superpixel intensity images with a highly distinctive background colour from the colour of the target are obtained. Grouping cues generated by superpixels can affect the performance of image clustering greatly. Next, according to the small amount of clustering data generated by the superpixel intensity images, they construct a non-symmetric mixture model based on a mixture of Gaussian distribution and Cauchy distribution for implementing image clustering. Thus, clustering of colour images is transformed into clustering of these newly generated data. The advantage of this model is its well adaption to different shapes of observed data. Experimental results on publicly available data sets are provided to demonstrate the effectiveness of the proposed algorithm.

关键词： image colour analysis Gaussian distribution image segmentation pattern clustering mixture models image classification computer vision fuzzy set theory entropy unsupervised learning image clustering algorithm-based superpixel segmentation nonsymmetric Gaussian-Cauchy mixture model computer vision research unsupervised clustering algorithm superpixel density images novel superpixel segmentation algorithm Kullback-Leibler divergence good boundary adherence intensity homogeneity Gaussian distribution KL divergence fuzzy objective function generated superpixel intensity images clustering data nonsymmetric mixture model natural colour images newly generated data clustering model

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An image segmentation method based on a modified local-information weighted intuitionistic Fuzzy C-means clustering and Gold-panning algorithm

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ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE 2021年 101卷 104209-104209页

作者： Wei, Dong Wang, Zhongbin Si, Lei Tan, Chao Lu, Xuliang China Univ Min & Technol Sch Mechatron Engn 1 Daxue Rd Xuzhou Jiangsu Peoples R China China Univ Min & Technol Jiangsu Key Lab Mine Mech & Elect Equipment 1 Daxue Rd Xuzhou Jiangsu Peoples R China

The image segmentation method based on clustering analysis has the advantages of small sample space constraints and strong universality. As an unsupervised clustering algorithm, the fuzzy C-means clustering algorithm is widely used in practical engineering. However, it is still some shortcomings: the fuzzy C-means clustering algorithm is difficult to interpret the noise effectively, which makes it more sensitive to the noise, and the selection of key parameters has to be made by trial and error experiments, reducing the adaptability of the algorithm. Besides, its iteration process is heavily influenced by the initial clustering centers and easy to fall into local optimum. Therefore, an intuitionistic Fuzzy C-means clustering method, based on local-information weight, is proposed in this paper. By introducing the local-information weight, the proposed algorithm adjusts the local-information influence weight adaptively in fuzzy partition, which enhances its robustness to noisy images. Furthermore, a novel swarm intelligence algorithm, called the Gold-Panning algorithm, is proposed to optimize the initial clustering centers and key parameters in the clustering algorithm. By utilizing the Gold-Panning algorithm, the adaptability of the proposed clustering algorithm is further improved. In this paper, the proposed methods are explained in detail and compared with the existing methods to demonstrate its superior performance.

关键词： Image segmentation unsupervised clustering algorithm Local-information weight Swarm intelligence

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Multi-object Cosegmentation Using Density-based clustering

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电脑学刊 2020年第1期31卷 148-165页

作者： Tzu-Chiang Wang I-Cheng Chang Chun-Man Lin

Cosegmentation is one of the interesting and popular topics in computer vision. The goal of cosegmentation is to extract the common foreground objects from an image set with minimum additional information. The existing cosegmentation algorithms could be classified into two categories. One is to extract one kind of foreground objects in the image set under unsupervised approaches; the other one is to find different kinds of common foreground objects in the image set under supervised approaches of which the number of kinds should be predefined. In this paper, we propose an unsupervised cosegmentation method for multiple foreground objects, which need not preset the number of object kinds. Moreover, most of the existing cosegmentation algorithms assume that the common foreground objects should appear in all images of the image set. However, if the foreground object only appears in a few images, the object is often misclassified. Our proposed algorithm can segment different kinds of common objects and have a higher segmentation rate for some foreground objects not appearing in all images. In the proposed work, an image is considered as the combination of several objects, and each object is composed of object elements. The image set could be decomposed into lots of object elements, and then object elements with similar features could be clustered into one sub-object class representing one part of an object. According to the class distribution of elements, common objects are extracted by the selection criteria. The concept of independent object elements is also proposed to increase the segmentation rate. In the experimental results, we demonstrate that the proposed approach could get better segmentation results compared with other methods.

关键词： cosegmentation algorithm density-based clustering algorithm multi-class cosegmentation algorithm multiple object segmentation unsupervised clustering algorithm

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Multi-criteria inventory ABC classification using Gaussian Mixture Model

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IFAC-PapersOnLine 2019年第13期52卷 1925-1930页

作者： Zowid, F.M. Babai, M.Z. Douissa, M.R. Ducq, Yves Univ. of Bordeaux IMS UMR 5218 CNRS Talence France Kedge Business School Talence France University of Tunis Tunis Tunisia

ABC classification is a technique widely used by companies to deal with inventories consisting of very large numbers of distinct stock keeping units. Single-criterion ABC classification methods are often used in practice and recently multi-criteria methods have attracted the attention of academics and practitioners. Several models have been developed to deal with the multi-criteria ABC inventory classification (MCIC). To the best of our knowledge, very few researches have used the unsupervised machine learning methods to address the MCIC problem despite their attractive theoretical and practical properties. Therefore, in this paper, the Gaussian mixture model (GMM) is proposed to deal with the multi-criteria ABC inventory classification problem. GMM is a simple optimization model that can be used for classification purposes with a low computational time. A numerical investigation of the cost-service inventory of the GMM model is presented in this paper. The performance of the model is also compared to some mathematical programming-based MCIC models. The numerical study is conducted by means of a theoretical dataset, consisting of 47 stock keeping units, which has been commonly used in the literature. The numerical results show that the proposed model can have a promised performance along with the existing MCIC classification models in the literature in terms of the cost-service inventory efficiency. © 2019, IFAC (International Federation of Automatic Control) Hosting by Elsevier Ltd. All rights reserved.

关键词： Learning systems clustering algorithms Gaussian distribution Inventory control Mathematical programming ABC inventory classification Gaussian Mixture Model Inventory classification Multi criteria method Numerical investigations Optimization modeling unsupervised clustering algorithm unsupervised machine learning

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Multi-criteria inventory ABC classification using Gaussian Mixture Model 9

Multi-criteria inventory ABC classification using Gaussian M...

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9th IFAC/IFIP/IFORS/IISE/INFORMS Conference on Manufacturing Modelling, Management and Control (IFAC MIM)

作者： Zowid, F. M. Babai, M. Z. Douissa, M. R. Ducq, Yves Univ Bordeaux IMS CNRS UMR 5218 Talence France Kedge Business Sch Talence France Univ Tunis Tunis Tunisia

ABC classification is a technique widely used by companies to deal with inventories consisting of very large numbers of distinct stock keeping units. Single-criterion ABC classification methods are often used in practice and recently multi-criteria methods have attracted the attention of academics and practitioners. Several models have been developed to deal with the multi-criteria ABC inventory classification (MCIC). To the best of our knowledge, very few researches have used the unsupervised machine learning methods to address the MCIC problem despite their attractive theoretical and practical properties. Therefore, in this paper, the Gaussian mixture model (GMM) is proposed to deal with the multi-criteria ABC inventory classification problem. GMM is a simple optimization model that can be used for classification purposes with a low computational time. A numerical investigation of the cost-service inventory of the GMM model is presented in this paper. The performance of the model is also compared to some mathematical programming-based MCIC models. The numerical study is conducted by means of a theoretical dataset, consisting of 47 stock keeping units, which has been commonly used in the literature. The numerical results show that the proposed model can have a promised performance along with the existing MCIC classification models in the literature in terms of the cost-service inventory efficiency. (C) 2019, IFAC (International Federation of Automatic Control) Hosting by Elsevier Ltd. All rights reserved.

关键词： inventory classification multi-criteria inventory classification GMM unsupervised clustering algorithm

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