Table shows the Feature Fusion Results achieved by concatenating different data modalities, taken in pairs and all three together. It also highlights the percentage increase in F1 score of fused features compared to the best single modality result among the fused features.

Table shows the Feature Fusion Results achieved by concatenating different data modalities, taken in pairs and all three together. It also highlights the percentage increase in F1 score of fused features compared to the best single modality result among the fused features.

<p>Table shows the Feature Fusion Results achieved by concatenating different data modalities, taken in pairs and all three together. It also highlights the percentage increase in F1 score of fused features compared to the best single modality result among the fused features.</p>

محفوظ في:

التفاصيل البيبلوغرافية
المؤلف الرئيسي:	Durgesh Ameta (21439753) (author)
مؤلفون آخرون:	Surendra Kumar (33312) (author), Rishav Mishra (21439756) (author), Laxmidhar Behera (14341372) (author), Aniruddha Chakraborty (394000) (author), Tushar Sandhan (21439759) (author)
منشور في:	2025
الموضوعات:	Space Science Biological Sciences not elsewhere classified Chemical Sciences not elsewhere classified Information Systems not elsewhere classified use machine learning systematic study compares sensitive model outperform selected representation method random resampling techniques proposed resampling techniques mld )&# 8212 deep learning approaches multimodal features obtained exploring feature performance based features compared direct comparative studies odor classification multi label classification models three mentioned features large novel dataset explainable model sheds molecular fingerprint features odor classification molecular fingerprint three kinds predictive performance label datasets different features recent studies previous studies molecular fingerprints xlink "> vibrational spectra usually represented two gaps sensory modality second objective research delves remains complex odour relations multifaceted approach inherent challenge inadequately understood inadequately documented fp ). first one electric nose data imbalance csmlp ). class imbalance also report
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مواد مشابهة

Feature Importance plots for Subset-IGD_MS:
حسب: Durgesh Ameta (21439753)
منشور في: (2025)

Feature Importance plots for Subset-IGD_VS: (a)
حسب: Durgesh Ameta (21439753)
منشور في: (2025)

Visual Analysis of Feature Importance in Subset-IGD_FP Dataset: (a)
حسب: Durgesh Ameta (21439753)
منشور في: (2025)

Overview of feature generation from VS for molecule Carveol (shown in inset).
حسب: Durgesh Ameta (21439753)
منشور في: (2025)

Overview of feature generation from MS for molecule Carveol (shown in inset). (a)
حسب: Durgesh Ameta (21439753)
منشور في: (2025)

This table compares the performance of RF, BR, CC, and CSMLP algorithms across various datasets and features. The evaluation uses Multi-Label Random Under-Sampling (MLRUS) and Multi-Label Random Over-Sampling (MLROS) techniques with sampling ratios of 10%, 20%, and 30%. Micro-averaged F1-scores, Precision and Recall are reported for each algorithm-dataset pair; each cell has F1 score on the top, then Precision in the middle and Recall at the bottom. The last column shows percentage increases from the baseline compared to our best result to provide insights into handling class imbalance and improving classification accuracy. Additionally, the RF, BR, and CC results are compared with the findings from Saini <i>et al</i>. [8] on the IGD_FP dataset.
حسب: Durgesh Ameta (21439753)
منشور في: (2025)

SHAP Analysis Hyper-Parameters with Descriptions and Impact
حسب: Durgesh Ameta (21439753)
منشور في: (2025)

Table shows values of imbalance measures for Integrated Dataset (IGD) and Subset of Integrated Dataset (Subset-IGD) for original data, and resampled data using ML-RUS (10%) and ML-ROS (10%).
حسب: Durgesh Ameta (21439753)
منشور في: (2025)

Subset-IGD dataset split into training and test sets using random and iterative sampling.
حسب: Durgesh Ameta (21439753)
منشور في: (2025)

Plot shows the proportion of samples associated with each odor class in Integrated Dataset (IGD) and Subset of Integrated Dataset(Subset-IGD).
حسب: Durgesh Ameta (21439753)
منشور في: (2025)

Supplementary File 1 is an Excel spreadsheet containing a list of molecules found in IGD, along with the molecules of Subset-IGD.
حسب: Durgesh Ameta (21439753)
منشور في: (2025)

Previously literatures on feature evaluation for relevance based on linear transformation.
حسب: Jia Liu (41593)
منشور في: (2025)

Characteristics of the sensors recommended by IFE-FGS under limit of eight features.
حسب: Jia Liu (41593)
منشور في: (2025)

Initial performance of the e-nose with the use of all sensors and single feature extraction method.
حسب: Jia Liu (41593)
منشور في: (2025)

The UMAP visualization of the fused features extracted in this work.
حسب: Hui Yang (91136)
منشور في: (2025)

The t-SNE visualization of the fused features extracted in this work.
حسب: Hui Yang (91136)
منشور في: (2025)

Distribution of Category Feature Representation Weights in Partial Category Feature Vectors.
حسب: Ming Gao (115719)
منشور في: (2025)

14 Category Feature Vectors.
حسب: Ming Gao (115719)
منشور في: (2025)

Example of Feature Vectors for Text.
حسب: Ming Gao (115719)
منشور في: (2025)

Example of Feature Layer Waveforms for Text.
حسب: Ming Gao (115719)
منشور في: (2025)

Example of Class-typical Feature Vectors.
حسب: Ming Gao (115719)
منشور في: (2025)

Weight Distribution of Feature Words for Two Categories.
حسب: Ming Gao (115719)
منشور في: (2025)

Toward Metabolic Similarity in Read-Across: A Case Study Using Graph Convolutional Networks to Predict Genotoxicity Outcomes from Simulated Metabolic Networks
حسب: Brett Hagan (13923937)
منشور في: (2025)

Toward Metabolic Similarity in Read-Across: A Case Study Using Graph Convolutional Networks to Predict Genotoxicity Outcomes from Simulated Metabolic Networks
حسب: Brett Hagan (13923937)
منشور في: (2025)

Toward Metabolic Similarity in Read-Across: A Case Study Using Graph Convolutional Networks to Predict Genotoxicity Outcomes from Simulated Metabolic Networks
حسب: Brett Hagan (13923937)
منشور في: (2025)

Toward Metabolic Similarity in Read-Across: A Case Study Using Graph Convolutional Networks to Predict Genotoxicity Outcomes from Simulated Metabolic Networks
حسب: Brett Hagan (13923937)
منشور في: (2025)

Toward Metabolic Similarity in Read-Across: A Case Study Using Graph Convolutional Networks to Predict Genotoxicity Outcomes from Simulated Metabolic Networks
حسب: Brett Hagan (13923937)
منشور في: (2025)

Toward Metabolic Similarity in Read-Across: A Case Study Using Graph Convolutional Networks to Predict Genotoxicity Outcomes from Simulated Metabolic Networks
حسب: Brett Hagan (13923937)
منشور في: (2025)

Results on individual and fused features.
حسب: Tee Connie (20501253)
منشور في: (2025)

Example of a typical feature layer waveform for Category .
حسب: Ming Gao (115719)
منشور في: (2025)

Known volatile organic compounds and gases emanated from the metabolite of the bacteria.
حسب: Jia Liu (41593)
منشور في: (2025)

The experimental results (Max) of all the involved algorithms on six public datasets.
حسب: Jia Liu (41593)
منشور في: (2025)

The experimental results (Mean) of all the involved algorithms on six public datasets.
حسب: Jia Liu (41593)
منشور في: (2025)

Experiment results of all the involved algorithms on the two bacteria datasets and six famous public datasets.
حسب: Jia Liu (41593)
منشور في: (2025)

Structure description of the eight datasets.
حسب: Jia Liu (41593)
منشور في: (2025)

Algorithm flow of the IFE method.
حسب: Jia Liu (41593)
منشور في: (2025)

Description of the two custom bacteria odor datasets.
حسب: Jia Liu (41593)
منشور في: (2025)

Specifications of the involved sensors of the custom e-nose.
حسب: Jia Liu (41593)
منشور في: (2025)

Heatmap matrix (Max accuracy) for rank statistics of the validation results on the eight datasets.
حسب: Jia Liu (41593)
منشور في: (2025)

Heatmap matrix (Mean accuracy) for rank statistics of the validation results on the eight datasets.
حسب: Jia Liu (41593)
منشور في: (2025)

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