Deep learning based object detection for resource constrained devices: Systematic review, future trends and challenges ahead
Deep learning models are widely being employed for object detection due to their high
performance. However, the majority of applications that require object detection are …
performance. However, the majority of applications that require object detection are …
[HTML][HTML] Learning-based slip detection for robotic fruit grasping and manipulation under leaf interference
Robotic harvesting research has seen significant achievements in the past decade, with
breakthroughs being made in machine vision, robot manipulation, autonomous navigation …
breakthroughs being made in machine vision, robot manipulation, autonomous navigation …
[HTML][HTML] Decision-making under uncertainty for the deployment of future hyperconnected networks: A survey
N Alzate-Mejia, G Santos-Boada… - Sensors, 2021 - mdpi.com
Among the several emerging dimensioning, control and deployment of future
communication network paradigms stands out the human-centric characteristic that creates …
communication network paradigms stands out the human-centric characteristic that creates …
Multiple instance learning using 3D features for melanoma detection
This work presents a contribution to advance current solutions for the problem of melanoma
detection based on deep learning (DL) approaches. This is an active research field, which …
detection based on deep learning (DL) approaches. This is an active research field, which …
[HTML][HTML] Detecting proximal caries on periapical radiographs using convolutional neural networks with different training strategies on small datasets
X Lin, D Hong, D Zhang, M Huang, H Yu - Diagnostics, 2022 - mdpi.com
The present study aimed to evaluate the performance of convolutional neural networks
(CNNs) that were trained with small datasets using different strategies in the detection of …
(CNNs) that were trained with small datasets using different strategies in the detection of …
Two sides of miscalibration: identifying over and under-confidence prediction for network calibration
Proper confidence calibration of deep neural networks is essential for reliable predictions in
safety-critical tasks. Miscalibration can lead to model over-confidence and/or under …
safety-critical tasks. Miscalibration can lead to model over-confidence and/or under …
[HTML][HTML] Identification of characteristic points in multivariate physiological signals by sensor fusion and multi-task deep networks
Identification of characteristic points in physiological signals, such as the peak of the R wave
in the electrocardiogram and the peak of the systolic wave of the photopletismogram, is a …
in the electrocardiogram and the peak of the systolic wave of the photopletismogram, is a …
[HTML][HTML] Handling imbalanced datasets for robust deep neural network-based fault detection in manufacturing systems
Over the recent years, Industry 4.0 (I4. 0) technologies such as the Industrial Internet of
Things (IIoT), Artificial Intelligence (AI), and the presence of Industrial Big Data (IBD) have …
Things (IIoT), Artificial Intelligence (AI), and the presence of Industrial Big Data (IBD) have …
Empirical Optimal Risk to Quantify Model Trustworthiness for Failure Detection
Failure detection (FD) in AI systems is a crucial safeguard for the deployment for safety-
critical tasks. The common evaluation method of FD performance is the Risk-coverage (RC) …
critical tasks. The common evaluation method of FD performance is the Risk-coverage (RC) …