Algorithm performance in different operational phases. by Dongqing Zhang (423941)

Comparative prediction performance analysis of different algorithms. by Dongqing Zhang (423941)

Controlling FDR with multiple different <i>α</i> values. by Aapo E. Korhonen (22530674)

Trainable parameters for different algorithms. by Anwar Hossain Efat (19942283)

Notations and definitions used in the algorithm. by Nazym Alimbekova (22766158)

An illustration of the Deepwalk algorithm operation involves a random walk on the graph, resulting in a “walk” sequence, which is then utilized to construct a graph. by Xiaobing Deng (1622233)

Results from Cell-TRACTR and model overview for object detection. by Owen M. O’Connor (11965460)

Revised 1: Capitalizing on the differences of prediction between multiple scRNA-seq clustering methods. by Yanis Asloudj (19711636)

“…To address this issue, ensemble algorithms detect cell populations by integrating multiple clustering methods, and minimizing the differences of their predictions. …”

Distributed and explainable GHSOM for anomaly detection in sensor networks by Paolo Mignone (9760832)

Results of Canny edge detector. by Lei Liang (326409)

Flow of complete face detection method. by Yingying Mei (13817440)

“…To solve these problems, based on the improvement of adaptive boosting to improve the accuracy of face detection, the study proposes a residual network 18-layer face feature extraction algorithm based on hybrid domain attention mechanism algorithm. …”

Predicting AMI in PTBXL. by Axel Nyström (15220943)

“…We find a considerable improvement from transfer learning, consistent across multiple state-of-the-art ResNet architectures and data sizes, with the best performing model improving from 0.79 AUC to 0.85 AUC. …”

Patient characteristics. by Axel Nyström (15220943)

“…We find a considerable improvement from transfer learning, consistent across multiple state-of-the-art ResNet architectures and data sizes, with the best performing model improving from 0.79 AUC to 0.85 AUC. …”

Main results. by Axel Nyström (15220943)

“…We find a considerable improvement from transfer learning, consistent across multiple state-of-the-art ResNet architectures and data sizes, with the best performing model improving from 0.79 AUC to 0.85 AUC. …”

Model summary. by Axel Nyström (15220943)

“…We find a considerable improvement from transfer learning, consistent across multiple state-of-the-art ResNet architectures and data sizes, with the best performing model improving from 0.79 AUC to 0.85 AUC. …”

AMI incidence. by Axel Nyström (15220943)

“…We find a considerable improvement from transfer learning, consistent across multiple state-of-the-art ResNet architectures and data sizes, with the best performing model improving from 0.79 AUC to 0.85 AUC. …”

Pre-training results. by Axel Nyström (15220943)

“…We find a considerable improvement from transfer learning, consistent across multiple state-of-the-art ResNet architectures and data sizes, with the best performing model improving from 0.79 AUC to 0.85 AUC. …”

Comparison of facial feature extraction effect. by Yingying Mei (13817440)

“…To solve these problems, based on the improvement of adaptive boosting to improve the accuracy of face detection, the study proposes a residual network 18-layer face feature extraction algorithm based on hybrid domain attention mechanism algorithm. …”

Results of ablation experiment. by Yingying Mei (13817440)

“…To solve these problems, based on the improvement of adaptive boosting to improve the accuracy of face detection, the study proposes a residual network 18-layer face feature extraction algorithm based on hybrid domain attention mechanism algorithm. …”

Face feature extraction results. by Yingying Mei (13817440)

“…To solve these problems, based on the improvement of adaptive boosting to improve the accuracy of face detection, the study proposes a residual network 18-layer face feature extraction algorithm based on hybrid domain attention mechanism algorithm. …”