AIMC Topic: Neural Networks, Computer

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Smart self-propelled particles: a framework to investigate the cognitive bases of movement.

Journal of the Royal Society, Interface
Decision-making and movement of single animals or group of animals are often treated and investigated as separate processes. However, many decisions are taken while moving in a given space. In other words, both processes are optimized at the same tim...

Frequency constraint-based adversarial attack on deep neural networks for medical image classification.

Computers in biology and medicine
The security of AI systems has gained significant attention in recent years, particularly in the medical diagnosis field. To develop a secure medical image classification system based on deep neural networks, it is crucial to design effective adversa...

Thermal Time Constant CNN-Based Spectrometry for Biomedical Applications.

Sensors (Basel, Switzerland)
This paper presents a novel method based on a convolutional neural network to recover thermal time constants from a temperature-time curve after thermal excitation. The thermal time constants are then used to detect the pathological states of the ski...

Scene context is predictive of unconstrained object similarity judgments.

Cognition
What makes objects alike in the human mind? Computational approaches for characterizing object similarity have largely focused on the visual forms of objects or their linguistic associations. However, intuitive notions of object similarity may depend...

EPC-DARTS: Efficient partial channel connection for differentiable architecture search.

Neural networks : the official journal of the International Neural Network Society
With weight-sharing and continuous relaxation strategies, the differentiable architecture search (DARTS) proposes a fast and effective solution to perform neural network architecture search in various deep learning tasks. However, unresolved issues, ...

ROOD-MRI: Benchmarking the robustness of deep learning segmentation models to out-of-distribution and corrupted data in MRI.

NeuroImage
Deep artificial neural networks (DNNs) have moved to the forefront of medical image analysis due to their success in classification, segmentation, and detection challenges. A principal challenge in large-scale deployment of DNNs in neuroimage analysi...

Predicting Critical Properties and Acentric Factors of Fluids Using Multitask Machine Learning.

Journal of chemical information and modeling
Knowledge of critical properties, such as critical temperature, pressure, density, as well as acentric factor, is essential to calculate thermo-physical properties of chemical compounds. Experiments to determine critical properties and acentric facto...

Packets-to-Prediction: An Unobtrusive Mechanism for Identifying Coarse-Grained Sleep Patterns with WiFi MAC Layer Traffic.

Sensors (Basel, Switzerland)
A good night's sleep is of the utmost importance for the seamless execution of our cognitive capabilities. Unfortunately, the research shows that one-third of the US adult population is severely sleep deprived. With college students as our focused gr...

A nystagmus extraction system using artificial intelligence for video-nystagmography.

Scientific reports
Benign paroxysmal positional vertigo (BPPV), the most common vestibular disorder, is diagnosed by an examiner changing the posture of the examinee and inducing nystagmus. Among the diagnostic methods used to observe nystagmus, video-nystagmography ha...

Mapping, intensities and future prediction of land use/land cover dynamics using google earth engine and CA- artificial neural network model.

PloS one
Mapping of land use/ land cover (LULC) dynamics has gained significant attention in the past decades. This is due to the role played by LULC change in assessing climate, various ecosystem functions, natural resource activities and livelihoods in gene...