Elementary effects (EE) results for chorionic vessel generation employing the input parameter ranges from Table 1. by Diana C. de Oliveira (9220729)

Elementary effects (EE) results for the generation of villous vessels within a placentone employing the input parameter ranges from Table 1. by Diana C. de Oliveira (9220729)

Attack and defense behaviors with the sensory–motor algorithm between a single predator and a single prey. by Hiroyuki Ichijo (13891153)

Mobility Analysis: Home and Work Detection from Network Signaling Data (NSD) by Alexe Vlad (20556224)

“…Researchers can adapt the code to similar mobility datasets to investigate commuting trends and urban mobility structures.…”

The simulation of OSCNN involves loading data from a laser and SLM and simulating different optical free space components. by Reyhane Ahmadi (14857363)

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Outline of OSCNN. by Reyhane Ahmadi (14857363)

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Accuracy of different electrical and optical neural networks for various input noise levels. by Reyhane Ahmadi (14857363)

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Gabor filters are created as sine wave filters in four different directions, namely 0, <i>π</i>/2, <i>π</i>, and 3<i>π</i>/4 degrees. by Reyhane Ahmadi (14857363)

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Accuracy of different electrical and optical neural network on Caltech and ETH-80 data sets. by Reyhane Ahmadi (14857363)

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OSCNN accuracy with different feature extractors. by Reyhane Ahmadi (14857363)

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Comparison of power consumption in optical and silicon-based neural network implementations. by Reyhane Ahmadi (14857363)

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Comparison of optical and silicon-based neural network implementations. by Reyhane Ahmadi (14857363)

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Attack and defense behaviors between a single predator and a single prey using the sensory–motor algorithm. by Hiroyuki Ichijo (13891153)

Data and codes for the paper "Machine-Learning-Enabled Spatial Pattern Mining: Evaluating the Impact of Imperfect Inputs". by Zhili Li (18836803)

Seismic-Acoustic Dataset of Coastal Bryde’s Whales in the Beibu Gulf by Yue Wang (22435645)

Quantitative results on WEDU dataset. by Dunlu Lu (19964225)

“…In comparative experiments on four plant datasets, MAR-YOLOv9 improved the mAP@0.5 accuracy by 39.18% compared to seven mainstream object detection algorithms, and by 1.28% compared to the YOLOv9 model. …”

Counting results on DRPD dataset. by Dunlu Lu (19964225)

“…In comparative experiments on four plant datasets, MAR-YOLOv9 improved the mAP@0.5 accuracy by 39.18% compared to seven mainstream object detection algorithms, and by 1.28% compared to the YOLOv9 model. …”

Quantitative results on RFRB dataset. by Dunlu Lu (19964225)

“…In comparative experiments on four plant datasets, MAR-YOLOv9 improved the mAP@0.5 accuracy by 39.18% compared to seven mainstream object detection algorithms, and by 1.28% compared to the YOLOv9 model. …”

Main module structure. by Dunlu Lu (19964225)

“…In comparative experiments on four plant datasets, MAR-YOLOv9 improved the mAP@0.5 accuracy by 39.18% compared to seven mainstream object detection algorithms, and by 1.28% compared to the YOLOv9 model. …”

Counting results on MTDC-UAV dataset. by Dunlu Lu (19964225)

“…In comparative experiments on four plant datasets, MAR-YOLOv9 improved the mAP@0.5 accuracy by 39.18% compared to seven mainstream object detection algorithms, and by 1.28% compared to the YOLOv9 model. …”