Titlebook: Machine Learning and Knowledge Extraction; 5th IFIP TC 5, TC 12 Andreas Holzinger,Peter Kieseberg,Edgar Weippl Conference proceedings 2021

Irrepressible

Airbnb Price Prediction Using Machine Learning and Sentiment Analysis,so used to select the most representative features for predicting the price of the listings. Experimentation shows that SVR model can achieve an . score of 69% and a MSE of 0.147 (defined on ln(price)) on the test set, outperforming the other models considered in the paper. [Link to the repository:

subordinate

死亡

Deep Convolutional Neural Network (CNN) Design for Pathology Detection of COVID-19 in Chest X-Ray Isubsequently the images were used for training. After consolidation, COVID-19 images comprised only 5% of the dataset. To address the class imbalance, we have used dynamic image augmentation technique to reduce the bias. We have then explored custom CNN and VGG-16, InceptionNet-V3, MobileNet-V2, Res

閑聊

Meander

On the Trustworthiness of Tree Ensemble Explainability Methods,he accuracy and stability of global feature importance methods through comprehensive experiments done on simulations as well as four real-world datasets. We focus on tree-based ensemble methods as they are used widely in industry and measure the accuracy and stability of explanations under two scena

minaret

音樂學(xué)者

Mining Causal Hypotheses in Categorical Time Series by Iterating on Binary Correlations,ficantly high correlations are then combined and can be validated for causalities by means of plausibility and semantic criteria. Our experimental results are presented on anonymised real production state time series and a simple representational concept for further causal interpretation is introduced.

煩擾

Rice Seed Image-to-Image Translation Using Generative Adversarial Networks to Improve Weedy Rice Im from a typical mobile phone cameras into the closed environment setting. Our GAN architecture can translate mobile phone images and achieves 90.06% weedy rice recognition accuracy, as compared to 58.10% without the translation.

少量

直覺沒有

On the Overlap Between Grad-CAM Saliency Maps and Explainable Visual Features in Skin Cancer Imageshich is almost 50% of the performance of state-of-the-art architectures specialized in feature mask prediction at pixel-level, such as U-Net. Finally, a breakdown test between malignancy and classification correctness shows that higher resolution saliency maps could help doctors in spotting wrong classifications.