Deep learning-based locomotive syndrome prediction method. by Junichi Kushioka (12236447)

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Deep reinforcement learning process. by Sen Cao (6017846)

“…This study introduces an improved adaptive signal control approach using an enhanced dual-layer deep Q-network (EXP-DDQN), specifically tailored for intelligent connected environments. …”

Accuracy of multiple classifiers with a deep-learning-based feature set by Afira Aslam (21433666)

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Regression analyses of the number of all ultrasonic calls identified by the experienced researcher (A.P.; “Human”) and by the DeepSqueak machine learning tool. by Piotr Popik (292215)

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Boxplot comparing Ct values for HD in flies and clothes. by Philippe Ndzomo (17706039)

Decrease in loss during the training process. by Muhammad Idrees (92773)

Inherently interpretable deep learning framework highlights clinically relevant image regions. by Kerol Djoumessi (21349992)

Dynamic Analysis of Membrane Emulsification: An In Situ Observation and Calculation with Deep Learning Model by Ke Zhou (131917)

“…In this study, a novel in situ observation device was developed, which couples a visible membrane module with a high-speed microscope system. Additionally, a deep learning model was embedded to realize the first visualization of the dynamic droplet formation process under demanding conditions. …”

Architecture of deep neural networks. by Ahmed Muqdad Alnasrallah (21647492)

“…This study proposes an IDS model for the IoMT that integrates advanced feature selection techniques and deep learning to enhance detection performance. The proposed model employs Information Gain (IG) and Recursive Feature Elimination (RFE) in parallel to select the top 50% of features, from which intersection and union subsets are created, followed by a deep autoencoder (DAE) to reduce dimensionality without losing important data. …”

S1 Data. Bibliometric analysis. S2 Data. Estrogen level analysis. S3 Data. MicroCT quantification. S4 Data. Markers of bone metabolism. by Yan She (8639796)

Comparison Of Performance at Different Optimizers and Learning Rates. by Mudhafar Jalil Jassim Ghrabat (22177655)

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Overcoming limitations to customize DeepVariant for domesticated animals with TrioTrain by Robert Schnabel (236298)

“…While exclusively training with human genomes deters transferring deep-learning-based variant calling to new species, we use the diverse ancestry within bovids to illustrate the need for advanced tools designed for comparative genomics.…”

Evaluation of the effectiveness of double task. by Fan Yang (1413)

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Visualized results of different boundary labels. by Fan Yang (1413)

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Dice scores for each tissue type in the DUKE dataset, as well as the average Dice score and average IoU for each tissue type. by Fan Yang (1413)

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Predicted results of the TCCT [4] network. by Fan Yang (1413)

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Evaluation of the effectiveness of pruning. by Fan Yang (1413)

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Evaluation of the effectiveness of boundary regression target. by Fan Yang (1413)

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Segmentation results of DUKE dataset. by Fan Yang (1413)

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Evaluation of the effectiveness of the pruning ratio (r%). by Fan Yang (1413)

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