AIMC Topic: Neural Networks, Computer

Clear Filters Showing 4511 to 4520 of 31376 articles

Seizure Sources Can Be Imaged from Scalp EEG by Means of Biophysically Constrained Deep Neural Networks.

Advanced science (Weinheim, Baden-Wurttemberg, Germany)
Seizure localization is important for managing drug-resistant focal epilepsy. Here, the capability of a novel deep learning-based source imaging framework (DeepSIF) for imaging seizure activities from electroencephalogram (EEG) recordings in drug-res...

PREPRINT Machine Learning for the Sensitivity Analysis of a Model of the Cellular Uptake of Nanoparticles for the Treatment of Cancer.

International journal for numerical methods in biomedical engineering
Experimental studies on the cellular uptake of nanoparticles (NPs), useful for the investigation of NP-based drug delivery systems, are often difficult to interpret due to the large number of parameters that can contribute to the phenomenon. It is th...

Integration of SPEI and machine learning for assessing the characteristics of drought in the middle ganga plain, an agro-climatic region of India.

Environmental science and pollution research international
Drought, as a natural and intricate climatic phenomenon, poses challenges with implications for both natural ecosystems and socioeconomic conditions. Evaluating the characteristics of drought is a significant endeavor aimed at mitigating its impact o...

Synchronization-Inspired Interpretable Neural Networks.

IEEE transactions on neural networks and learning systems
Synchronization is a ubiquitous phenomenon in nature that enables the orderly presentation of information. In the human brain, for instance, functional modules such as the visual, motor, and language cortices form through neuronal synchronization. In...

Clinical Prompt Learning With Frozen Language Models.

IEEE transactions on neural networks and learning systems
When the first transformer-based language models were published in the late 2010s, pretraining with general text and then fine-tuning the model on a task-specific dataset often achieved the state-of-the-art performance. However, more recent work sugg...

Lifelong Learning With Cycle Memory Networks.

IEEE transactions on neural networks and learning systems
Learning from a sequence of tasks for a lifetime is essential for an agent toward artificial general intelligence. Despite the explosion of this research field in recent years, most work focuses on the well-known catastrophic forgetting issue. In con...

GeSeNet: A General Semantic-Guided Network With Couple Mask Ensemble for Medical Image Fusion.

IEEE transactions on neural networks and learning systems
At present, multimodal medical image fusion technology has become an essential means for researchers and doctors to predict diseases and study pathology. Nevertheless, how to reserve more unique features from different modal source images on the prem...

MGCNRF: Prediction of Disease-Related miRNAs Based on Multiple Graph Convolutional Networks and Random Forest.

IEEE transactions on neural networks and learning systems
Increasing microRNAs (miRNAs) have been confirmed to be inextricably linked to various diseases, and the discovery of their associations has become a routine way of treating diseases. To overcome the time-consuming and laborious shortcoming of tradit...

Biologically Plausible Sparse Temporal Word Representations.

IEEE transactions on neural networks and learning systems
Word representations, usually derived from a large corpus and endowed with rich semantic information, have been widely applied to natural language tasks. Traditional deep language models, on the basis of dense word representations, requires large mem...

A Collaborative Multimodal Learning-Based Framework for COVID-19 Diagnosis.

IEEE transactions on neural networks and learning systems
The pandemic of coronavirus disease 2019 (COVID-19) has led to a global public health crisis, which caused millions of deaths and billions of infections, greatly increasing the pressure on medical resources. With the continuous emergence of viral mut...