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  1. Article ; Online: Weighted Mutual Information for Aggregated Kernel Clustering.

    Kachouie, Nezamoddin N / Shutaywi, Meshal

    Entropy (Basel, Switzerland)

    2020  Volume 22, Issue 3

    Abstract: Background: ...

    Abstract Background:
    Language English
    Publishing date 2020-03-18
    Publishing country Switzerland
    Document type Journal Article
    ZDB-ID 2014734-X
    ISSN 1099-4300 ; 1099-4300
    ISSN (online) 1099-4300
    ISSN 1099-4300
    DOI 10.3390/e22030351
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  2. Article ; Online: Silhouette Analysis for Performance Evaluation in Machine Learning with Applications to Clustering.

    Shutaywi, Meshal / Kachouie, Nezamoddin N

    Entropy (Basel, Switzerland)

    2021  Volume 23, Issue 6

    Abstract: Grouping the objects based on their similarities is an important common task in machine learning applications. Many clustering methods have been developed, among them k-means based clustering methods have been broadly used and several extensions have ... ...

    Abstract Grouping the objects based on their similarities is an important common task in machine learning applications. Many clustering methods have been developed, among them k-means based clustering methods have been broadly used and several extensions have been developed to improve the original k-means clustering method such as k-means ++ and kernel k-means. K-means is a linear clustering method; that is, it divides the objects into linearly separable groups, while kernel k-means is a non-linear technique. Kernel k-means projects the elements to a higher dimensional feature space using a kernel function, and then groups them. Different kernel functions may not perform similarly in clustering of a data set and, in turn, choosing the right kernel for an application could be challenging. In our previous work, we introduced a weighted majority voting method for clustering based on normalized mutual information (NMI). NMI is a supervised method where the true labels for a training set are required to calculate NMI. In this study, we extend our previous work of aggregating the clustering results to develop an unsupervised weighting function where a training set is not available. The proposed weighting function here is based on Silhouette index, as an unsupervised criterion. As a result, a training set is not required to calculate Silhouette index. This makes our new method more sensible in terms of clustering concept.
    Language English
    Publishing date 2021-06-16
    Publishing country Switzerland
    Document type Journal Article
    ZDB-ID 2014734-X
    ISSN 1099-4300 ; 1099-4300
    ISSN (online) 1099-4300
    ISSN 1099-4300
    DOI 10.3390/e23060759
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  3. Article ; Online: Silhouette Analysis for Performance Evaluation in Machine Learning with Applications to Clustering

    Meshal Shutaywi / Nezamoddin N. Kachouie

    Entropy, Vol 23, Iss 759, p

    2021  Volume 759

    Abstract: Grouping the objects based on their similarities is an important common task in machine learning applications. Many clustering methods have been developed, among them k-means based clustering methods have been broadly used and several extensions have ... ...

    Abstract Grouping the objects based on their similarities is an important common task in machine learning applications. Many clustering methods have been developed, among them k-means based clustering methods have been broadly used and several extensions have been developed to improve the original k-means clustering method such as k-means ++ and kernel k-means. K-means is a linear clustering method; that is, it divides the objects into linearly separable groups, while kernel k-means is a non-linear technique. Kernel k-means projects the elements to a higher dimensional feature space using a kernel function, and then groups them. Different kernel functions may not perform similarly in clustering of a data set and, in turn, choosing the right kernel for an application could be challenging. In our previous work, we introduced a weighted majority voting method for clustering based on normalized mutual information (NMI). NMI is a supervised method where the true labels for a training set are required to calculate NMI. In this study, we extend our previous work of aggregating the clustering results to develop an unsupervised weighting function where a training set is not available. The proposed weighting function here is based on Silhouette index, as an unsupervised criterion. As a result, a training set is not required to calculate Silhouette index. This makes our new method more sensible in terms of clustering concept.
    Keywords k-means ; kernel k-means ; machine learning ; nonlinear clustering ; silhouette index ; weighted clustering ; Science ; Q ; Astrophysics ; QB460-466 ; Physics ; QC1-999
    Subject code 006
    Language English
    Publishing date 2021-06-01T00:00:00Z
    Publisher MDPI AG
    Document type Article ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

    Full text online

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  4. Article ; Online: Association Factor for Identifying Linear and Nonlinear Correlations in Noisy Conditions.

    Kachouie, Nezamoddin N / Deebani, Wejdan

    Entropy (Basel, Switzerland)

    2020  Volume 22, Issue 4

    Abstract: Background: In data analysis and machine learning, we often need to identify and quantify the correlation between variables. Although Pearson's correlation coefficient has been widely used, its value is reliable only for linear relationships and ... ...

    Abstract Background: In data analysis and machine learning, we often need to identify and quantify the correlation between variables. Although Pearson's correlation coefficient has been widely used, its value is reliable only for linear relationships and Distance correlation was introduced to address this shortcoming.
    Methods: Distance correlation can identify linear and nonlinear correlations. However, its performance drops in noisy conditions. In this paper, we introduce the Association Factor (AF) as a robust method for identification and quantification of linear and nonlinear associations in noisy conditions.
    Results: To test the performance of the proposed Association Factor, we modeled several simulations of linear and nonlinear relationships in different noise conditions and computed Pearson's correlation, Distance correlation, and the proposed Association Factor.
    Conclusion: Our results show that the proposed method is robust in two ways. First, it can identify both linear and nonlinear associations. Second, the proposed Association Factor is reliable in both noiseless and noisy conditions.
    Language English
    Publishing date 2020-04-13
    Publishing country Switzerland
    Document type Journal Article
    ZDB-ID 2014734-X
    ISSN 1099-4300 ; 1099-4300
    ISSN (online) 1099-4300
    ISSN 1099-4300
    DOI 10.3390/e22040440
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  5. Article ; Online: Association Factor for Identifying Linear and Nonlinear Correlations in Noisy Conditions

    Nezamoddin N. Kachouie / Wejdan Deebani

    Entropy, Vol 22, Iss 440, p

    2020  Volume 440

    Abstract: Background: In data analysis and machine learning, we often need to identify and quantify the correlation between variables. Although Pearson’s correlation coefficient has been widely used, its value is reliable only for linear relationships and Distance ...

    Abstract Background: In data analysis and machine learning, we often need to identify and quantify the correlation between variables. Although Pearson’s correlation coefficient has been widely used, its value is reliable only for linear relationships and Distance correlation was introduced to address this shortcoming. Methods: Distance correlation can identify linear and nonlinear correlations. However, its performance drops in noisy conditions. In this paper, we introduce the Association Factor (AF) as a robust method for identification and quantification of linear and nonlinear associations in noisy conditions. Results: To test the performance of the proposed Association Factor, we modeled several simulations of linear and nonlinear relationships in different noise conditions and computed Pearson’s correlation, Distance correlation, and the proposed Association Factor. Conclusion: Our results show that the proposed method is robust in two ways. First, it can identify both linear and nonlinear associations. Second, the proposed Association Factor is reliable in both noiseless and noisy conditions.
    Keywords association factor ; Pearson’s correlation ; distance correlation ; maximal information coefficient (MIC) ; detrended fluctuation analysis (DFA) ; nonlinear relation ; Science ; Q ; Astrophysics ; QB460-466 ; Physics ; QC1-999
    Subject code 306
    Language English
    Publishing date 2020-04-01T00:00:00Z
    Publisher MDPI AG
    Document type Article ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

    Full text online

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    Inter-library loan at ZB MED

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  6. Article ; Online: Weighted Mutual Information for Aggregated Kernel Clustering

    Nezamoddin N. Kachouie / Meshal Shutaywi

    Entropy, Vol 22, Iss 3, p

    2020  Volume 351

    Abstract: Background: A common task in machine learning is clustering data into different groups based on similarities. Clustering methods can be divided in two groups: linear and nonlinear. A commonly used linear clustering method is K-means. Its extension, ... ...

    Abstract Background: A common task in machine learning is clustering data into different groups based on similarities. Clustering methods can be divided in two groups: linear and nonlinear. A commonly used linear clustering method is K-means. Its extension, kernel K-means, is a non-linear technique that utilizes a kernel function to project the data to a higher dimensional space. The projected data will then be clustered in different groups. Different kernels do not perform similarly when they are applied to different datasets. Methods: A kernel function might be relevant for one application but perform poorly to project data for another application. In turn choosing the right kernel for an arbitrary dataset is a challenging task. To address this challenge, a potential approach is aggregating the clustering results to obtain an impartial clustering result regardless of the selected kernel function. To this end, the main challenge is how to aggregate the clustering results. A potential solution is to combine the clustering results using a weight function. In this work, we introduce Weighted Mutual Information (WMI) for calculating the weights for different clustering methods based on their performance to combine the results. The performance of each method is evaluated using a training set with known labels. Results: We applied the proposed Weighted Mutual Information to four data sets that cannot be linearly separated. We also tested the method in different noise conditions. Conclusions: Our results show that the proposed Weighted Mutual Information method is impartial, does not rely on a single kernel, and performs better than each individual kernel specially in high noise.
    Keywords weighted mutual information ; aggregated clustering ; kernel k-means ; conditional entropy ; Science ; Q ; Astrophysics ; QB460-466 ; Physics ; QC1-999
    Subject code 006
    Language English
    Publishing date 2020-03-01T00:00:00Z
    Publisher MDPI AG
    Document type Article ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

    Full text online

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    Inter-library loan at ZB MED

    Your chosen title can be delivered directly to ZB MED Cologne location if you are registered as a user at ZB MED Cologne.

  7. Article ; Online: Statistical Modeling of Fine Sediments Dredged Using a Variable Area Dredging Suction Head to Improve Water Quality

    Leigh A. Provost / Robert Weaver / Nezamoddin N. Kachouie

    Hydrology, Vol 8, Iss 98, p

    2021  Volume 98

    Abstract: The changing climate affects the agricultural lands, and, in turn, the changes in agricultural lands alter the watershed. A major concern regarding waterbodies is the increased sedimentation rates due to climate change. To improve the water quality, it ... ...

    Abstract The changing climate affects the agricultural lands, and, in turn, the changes in agricultural lands alter the watershed. A major concern regarding waterbodies is the increased sedimentation rates due to climate change. To improve the water quality, it is crucial to remove fine sediments. Using current environmental dredging methods is challenging because of the sediment volumes that must be dredged, the absence of nearby disposal sites, and the shoreline infrastructure at the dredging locations. To address these issues, we used a surgical dredging method with a variable area suction head that can easily maneuver around the docks, pilings, and other infrastructures. It can also isolate the fine grain material to better manage the dredged volumes in the seabed where nutrients are typically adhered. To this end, a statistical analysis of the dredged samples is essential to improve the design efficiency. In this work, we collected several samples using a variable area suction head with different design settings. The collected samples using each design setting were then used to model the distributions of the different grain sizes in the dredged sediments. The proposed statistical model can be effectively used for the prediction of sediment sampling outcomes to improve the gradation of the fine sediments.
    Keywords dredging ; water quality ; statistical modeling ; log-normal distribution ; beta distribution ; bootstrap method ; Science ; Q
    Subject code 550 ; 310
    Language English
    Publishing date 2021-06-01T00:00:00Z
    Publisher MDPI AG
    Document type Article ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

    Full text online

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    Inter-library loan at ZB MED

    Your chosen title can be delivered directly to ZB MED Cologne location if you are registered as a user at ZB MED Cologne.

  8. Article ; Online: Discriminant Analysis of Lung Cancer Using Nonlinear Clustering of Copy Numbers.

    Kachouie, Nezamoddin N / Shutaywi, Meshal / Christiani, David C

    Cancer investigation

    2020  Volume 38, Issue 2, Page(s) 102–112

    Abstract: Background: ...

    Abstract Background:
    MeSH term(s) Carcinoma, Non-Small-Cell Lung/blood ; Carcinoma, Non-Small-Cell Lung/diagnosis ; Carcinoma, Non-Small-Cell Lung/genetics ; Cluster Analysis ; DNA Copy Number Variations ; Discriminant Analysis ; Early Detection of Cancer/methods ; Humans ; Lung Neoplasms/blood ; Lung Neoplasms/diagnosis ; Lung Neoplasms/genetics ; Neoplasm Recurrence, Local ; Polymorphism, Single Nucleotide ; Reproducibility of Results ; Sensitivity and Specificity
    Language English
    Publishing date 2020-01-30
    Publishing country England
    Document type Journal Article
    ZDB-ID 604942-4
    ISSN 1532-4192 ; 0735-7907
    ISSN (online) 1532-4192
    ISSN 0735-7907
    DOI 10.1080/07357907.2020.1719501
    Database MEDical Literature Analysis and Retrieval System OnLINE

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    In stock of ZB MED Cologne/Königswinter

    Zs.A 1862: Show issues Location:
    Je nach Verfügbarkeit (siehe Angabe bei Bestand)
    bis Jg. 1994: Bestellungen von Artikeln über das Online-Bestellformular
    Jg. 1995 - 2021: Lesesall (1.OG)
    ab Jg. 2022: Lesesaal (EG)

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  9. Article ; Online: Identifying Similarities and Disparities Between DNA Copy Number Changes in Cancer and Matched Blood Samples.

    Kachouie, Nezamoddin N / Deebani, Wejdan / Christiani, David C

    Cancer investigation

    2019  Volume 37, Issue 10, Page(s) 535–545

    Abstract: Background: ...

    Abstract Background:
    MeSH term(s) Biomarkers, Tumor/genetics ; Carcinoma, Non-Small-Cell Lung/genetics ; Carcinoma, Non-Small-Cell Lung/pathology ; DNA Copy Number Variations/genetics ; Female ; Humans ; Lung Neoplasms/genetics ; Lung Neoplasms/pathology ; Male ; Neoplasm Recurrence, Local/genetics ; Neoplasm Recurrence, Local/pathology ; Neoplasm Staging/methods
    Chemical Substances Biomarkers, Tumor
    Language English
    Publishing date 2019-10-10
    Publishing country England
    Document type Journal Article
    ZDB-ID 604942-4
    ISSN 1532-4192 ; 0735-7907
    ISSN (online) 1532-4192
    ISSN 0735-7907
    DOI 10.1080/07357907.2019.1667368
    Database MEDical Literature Analysis and Retrieval System OnLINE

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    Kategorien

    In stock of ZB MED Cologne/Königswinter

    Zs.A 1862: Show issues Location:
    Je nach Verfügbarkeit (siehe Angabe bei Bestand)
    bis Jg. 1994: Bestellungen von Artikeln über das Online-Bestellformular
    Jg. 1995 - 2021: Lesesall (1.OG)
    ab Jg. 2022: Lesesaal (EG)

    Order via subito

    This service is chargeable due to the Delivery terms set by subito. Orders including an article and supplementary material will be classified as separate orders. In these cases, fees will be demanded for each order.

  10. Article: Statistical Modeling of Fine Sediments Dredged Using a Variable Area Dredging Suction Head to Improve Water Quality

    Provost, Leigh A. / Weaver, Robert / Kachouie, Nezamoddin N.

    Hydrology. 2021 June 28, v. 8, no. 3

    2021  

    Abstract: The changing climate affects the agricultural lands, and, in turn, the changes in agricultural lands alter the watershed. A major concern regarding waterbodies is the increased sedimentation rates due to climate change. To improve the water quality, it ... ...

    Abstract The changing climate affects the agricultural lands, and, in turn, the changes in agricultural lands alter the watershed. A major concern regarding waterbodies is the increased sedimentation rates due to climate change. To improve the water quality, it is crucial to remove fine sediments. Using current environmental dredging methods is challenging because of the sediment volumes that must be dredged, the absence of nearby disposal sites, and the shoreline infrastructure at the dredging locations. To address these issues, we used a surgical dredging method with a variable area suction head that can easily maneuver around the docks, pilings, and other infrastructures. It can also isolate the fine grain material to better manage the dredged volumes in the seabed where nutrients are typically adhered. To this end, a statistical analysis of the dredged samples is essential to improve the design efficiency. In this work, we collected several samples using a variable area suction head with different design settings. The collected samples using each design setting were then used to model the distributions of the different grain sizes in the dredged sediments. The proposed statistical model can be effectively used for the prediction of sediment sampling outcomes to improve the gradation of the fine sediments.
    Keywords climate ; climate change ; infrastructure ; prediction ; sediments ; shorelines ; statistical analysis ; statistical models ; surface water ; water quality ; watersheds
    Language English
    Dates of publication 2021-0628
    Publishing place Multidisciplinary Digital Publishing Institute
    Document type Article
    ZDB-ID 2777964-6
    ISSN 2306-5338
    ISSN 2306-5338
    DOI 10.3390/hydrology8030098
    Database NAL-Catalogue (AGRICOLA)

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