AIMC Topic: Neural Networks, Computer

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Task-agnostic exoskeleton control via biological joint moment estimation.

Nature
Lower-limb exoskeletons have the potential to transform the way we move, but current state-of-the-art controllers cannot accommodate the rich set of possible human behaviours that range from cyclic and predictable to transitory and unstructured. We i...

Drug Sensitivity Prediction Based on Multi-stage Multi-modal Drug Representation Learning.

Interdisciplinary sciences, computational life sciences
Accurate prediction of anticancer drug responses is essential for developing personalized treatment plans in order to improve cancer patient survival rates and reduce healthcare costs. To this end, we propose a drug sensitivity prediction model based...

Developing physics-informed neural networks for model predictive control of periodic counter-current chromatography.

Journal of chromatography. A
The applications of continuous manufacturing technology in biopharmaceuticals require advanced design, monitoring, and control due to its complexity. Traditional mechanistic models, which rely on numerical solutions, suffer from long computational ti...

UMS-ODNet: Unified-scale domain adaptation mechanism driven object detection network with multi-scale attention.

Neural networks : the official journal of the International Neural Network Society
Unsupervised domain adaptation techniques improve the generalization capability and performance of detectors, especially when the source and target domains have different distributions. Compared with two-stage detectors, one-stage detectors (especial...

Explaining deep learning models for age-related gait classification based on acceleration time series.

Computers in biology and medicine
BACKGROUND: Gait analysis holds significant importance in monitoring daily health, particularly among older adults. Advancements in sensor technology enable the capture of movement in real-life environments and generate big data. Machine learning, no...

Expert level of detection of interictal discharges with a deep neural network.

Epilepsia
OBJECTIVE: Deep learning methods have shown potential in automating the detection of interictal epileptiform discharges (IEDs) in electroencephalography (EEG). We compared IED detection using our previously trained deep neural network with a group of...

Double-Condensing Attention Condenser: Leveraging Attention in Deep Learning to Detect Skin Cancer from Skin Lesion Images.

Sensors (Basel, Switzerland)
Skin cancer is the most common type of cancer in the United States and is estimated to affect one in five Americans. Recent advances have demonstrated strong performance on skin cancer detection, as exemplified by state of the art performance in the ...

An Emotion Recognition Method for Humanoid Robot Body Movements Based on a PSO-BP-RMSProp Neural Network.

Sensors (Basel, Switzerland)
This paper mainly explores the computational model that connects a robot's emotional body movements with human emotion to propose an emotion recognition method for humanoid robot body movements. There is sparse research directly carried out from this...

Enhancing image-based diagnosis of gastrointestinal tract diseases through deep learning with EfficientNet and advanced data augmentation techniques.

BMC medical imaging
The early detection and diagnosis of gastrointestinal tract diseases, such as ulcerative colitis, polyps, and esophagitis, are crucial for timely treatment. Traditional imaging techniques often rely on manual interpretation, which is subject to varia...

Facial Image expression recognition and prediction system.

Scientific reports
Facial expression recognition system is an advanced technology that allows machines to recognize human emotions based on their facial expressions. In order to develop a robust prediction model, this research work proposes three distinct architectural...