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Showing 161 - 180 results of 600 for search '(( element data algorithm ) OR ((( waste processing algorithm ) OR ( level coding algorithm ))))', query time: 0.59s Refine Results
  1. 161
    Correlation matrices of handcrafted features before and after feature selection. The figure presents two heatmaps illustrating the correlation among 52 handcrafted features derived from the 3-level wavelet transform (WT) at four resolutions. These features were used as inputs for traditional ML algorithms. The left map demonstrates the correlation matrix of 52 features crafted from 4 resolutions of the 3-level WT, which are used as inputs for traditional ML algorithms. The heat map (on the right) is the correlation matrix after feature selection, in which features whose Pearson’s correlation coefficient is higher than 0.8 are considered highly correlated. We keep only one representative feature and remove its highly correlated features.

    Correlation matrices of handcrafted features before and after feature selection. The figure presents two heatmaps illustrating the correlation among 52 handcrafted features derived... by Quang Dung Dinh (20995766)

    Published 2025
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  2. 162
    Data augmentation is applied both geometrically and in terms of intensity. Geometrically, the images are mirrored, flipped, rotated, and randomly cropped. For intensity augmentation, random noise is added, and random masking is applied. Finally, a square matrix (GT matrix) is used to represent the correspondence of matching points between the two images. The GT_matrix is a square matrix of <i>N</i> × <i>N</i> dimensions. <i>GT</i> ⁡  ( <i>i</i> , <i>j</i> )  represents the element of the <i>i</i>th row and the <i>j</i>th column in the GT matrix. The shown input image pairs take optical and SAR image pairs as an example.

    Data augmentation is applied both geometrically and in terms of intensity. Geometrically, the images are mirrored, flipped, rotated, and randomly cropped. For intensity augmentatio... by Yide Di (20969124)

    Published 2025
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  3. 163
    DataSheet1_Design of a temperature-feedback controlled automated magnetic hyperthermia therapy device.pdf

    DataSheet1_Design of a temperature-feedback controlled automated magnetic hyperthermia therapy device.pdf by Anirudh Sharma (1295760)

    Published 2024
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  4. 164
    Data Sheet 2_Research on optimization of emergency supplies customs clearance strategy from the perspective of public health security based on Chinese trade data.zip

    Data Sheet 2_Research on optimization of emergency supplies customs clearance strategy from the perspective of public health security based on Chinese trade data.zip by Xia Zheng (384416)

    Published 2025
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  5. 165
    Data Sheet 1_Research on optimization of emergency supplies customs clearance strategy from the perspective of public health security based on Chinese trade data.zip

    Data Sheet 1_Research on optimization of emergency supplies customs clearance strategy from the perspective of public health security based on Chinese trade data.zip by Xia Zheng (384416)

    Published 2025
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  6. 166
    Partial polishing of experimental data.

    Partial polishing of experimental data. by Ma Haohao (22177538)

    Published 2025
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  7. 167
    Optimization results of MBB multi-material structure under different methods. (a) A level-set method for multi-material topology optimization [33]. (b) An alternating active-phase algorithm for multi-material topology optimization [34]. (c) A new mapping based interpolation function for multi-material topology optimization [35]. (d) A material-field series expansion (MFSE) model for multi-material topology optimization [36].

    Optimization results of MBB multi-material structure under different methods. (a) A level-set method for multi-material topology optimization [33]. (b) An alternating active-phase... by Zhao Li (300229)

    Published 2025
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  8. 168
    Flowchart of the study.

    Flowchart of the study. by Usman Haider (12630481)

    Published 2025
    “…Each antenna subarray connects a separate RF chain and it can only transmit one data stream. The RF chain is made to keep the beamforming weights of subarray antenna elements in a TDM fashion. …”
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  9. 169
    Channel capacity of MIMO system with variable .

    Channel capacity of MIMO system with variable . by Usman Haider (12630481)

    Published 2025
    “…Each antenna subarray connects a separate RF chain and it can only transmit one data stream. The RF chain is made to keep the beamforming weights of subarray antenna elements in a TDM fashion. …”
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  10. 170
    The <i>supply chain capitalism of AI</i>: a call to (re)think algorithmic harms and resistance through environmental lens

    The <i>supply chain capitalism of AI</i>: a call to (re)think algorithmic harms and resistance through environmental lens by Ana Valdivia (22273271)

    Published 2025
    “…This paper illustrates how the supply chain capitalism of AI is precipitating geographical asymmetries connected to contested struggles in México by focusing on a key element of these chains: data centres. In times of climate emergency, this paper calls to reconsider algorithmic harms and resistance by investigating the entire capitalist production line of the AI industry from critical and environmental lens.…”
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  11. 171
    Technological Fitness and Coherence explanation. Schematic representation of the workings of the Fitness and Complexity algorithm and Coherence diversification.

    Technological Fitness and Coherence explanation. Schematic representation of the workings of the Fitness and Complexity algorithm and Coherence diversification. by Matteo Straccamore (15083799)

    Published 2025
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  12. 172
    Execution time comparisons between DECMDR(D), HS-MMGKG(G), SEE(S), SHEIB-AGM(B) and Epi-SSA(P) on the DNME3 100 dataset.

    Execution time comparisons between DECMDR(D), HS-MMGKG(G), SEE(S), SHEIB-AGM(B) and Epi-SSA(P) on the DNME3 100 dataset. by Liyan Sun (760586)

    Published 2024
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  13. 173
    F-measure comparisons between AntEpiSeeker(A), DECMDR(D), HS-MMGKG(G), SEE(S), SHEIB-AGM(B), SNPHarvester(H), SNPRuler(R) and Epi-SSA(P) on the DNME 1000 dataset.

    F-measure comparisons between AntEpiSeeker(A), DECMDR(D), HS-MMGKG(G), SEE(S), SHEIB-AGM(B), SNPHarvester(H), SNPRuler(R) and Epi-SSA(P) on the DNME 1000 dataset. by Liyan Sun (760586)

    Published 2024
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  14. 174
    Genes of the epistastic interactions detected on the seven GWAS data using Epi-SSA.

    Genes of the epistastic interactions detected on the seven GWAS data using Epi-SSA. by Liyan Sun (760586)

    Published 2024
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  15. 175
    Power comparisons between AntEpiSeeker(A), DECMDR(D), HS-MMGKG(G), SEE(S), SHEIB-AGM(B), SNPHarvester(H), SNPRuler(R) and Epi-SSA(P) on the DME 1000 dataset.

    Power comparisons between AntEpiSeeker(A), DECMDR(D), HS-MMGKG(G), SEE(S), SHEIB-AGM(B), SNPHarvester(H), SNPRuler(R) and Epi-SSA(P) on the DME 1000 dataset. by Liyan Sun (760586)

    Published 2024
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  16. 176
    Execution time comparisons between AntEpiSeeker(A), DECMDR(D), HS-MMGKG(G), SEE(S), SHEIB-AGM(B), SNPHarvester(H), SNPRuler(R) and Epi-SSA(P) on the DME 100 dataset.

    Execution time comparisons between AntEpiSeeker(A), DECMDR(D), HS-MMGKG(G), SEE(S), SHEIB-AGM(B), SNPHarvester(H), SNPRuler(R) and Epi-SSA(P) on the DME 100 dataset. by Liyan Sun (760586)

    Published 2024
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  17. 177
    The brief description on the WTCCC dataset.

    The brief description on the WTCCC dataset. by Liyan Sun (760586)

    Published 2024
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  18. 178
    F-measure comparisons between AntEpiSeeker(A), DECMDR(D), HS-MMGKG(G), SEE(S), SHEIB-AGM(B), SNPHarvester(H), SNPRuler(R) and Epi-SSA(P) on the DME 1000 dataset.

    F-measure comparisons between AntEpiSeeker(A), DECMDR(D), HS-MMGKG(G), SEE(S), SHEIB-AGM(B), SNPHarvester(H), SNPRuler(R) and Epi-SSA(P) on the DME 1000 dataset. by Liyan Sun (760586)

    Published 2024
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  19. 179
    Power comparisons between AntEpiSeeker(A), DECMDR(D), HS-MMGKG(G), SEE(S), SHEIB-AGM(B), SNPHarvester(H), SNPRuler(R) and Epi-SSA(P) on the DNME 1000 dataset.

    Power comparisons between AntEpiSeeker(A), DECMDR(D), HS-MMGKG(G), SEE(S), SHEIB-AGM(B), SNPHarvester(H), SNPRuler(R) and Epi-SSA(P) on the DNME 1000 dataset. by Liyan Sun (760586)

    Published 2024
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  20. 180
    The overall structure and execution flow of Epi-SSA.

    The overall structure and execution flow of Epi-SSA. by Liyan Sun (760586)

    Published 2024
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