AIMC Topic: Neural Networks, Computer

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RNA secondary structure prediction using deep learning with thermodynamic integration.

Nature communications
Accurate predictions of RNA secondary structures can help uncover the roles of functional non-coding RNAs. Although machine learning-based models have achieved high performance in terms of prediction accuracy, overfitting is a common risk for such hi...

Transfer Extreme Learning Machine with Output Weight Alignment.

Computational intelligence and neuroscience
Extreme Learning Machine (ELM) as a fast and efficient neural network model in pattern recognition and machine learning will decline when the labeled training sample is insufficient. Transfer learning helps the target task to learn a reliable model b...

A deep learning-based method for grip strength prediction: Comparison of multilayer perceptron and polynomial regression approaches.

PloS one
The objective of this study was to accurately predict the grip strength using a deep learning-based method (e.g., multi-layer perceptron [MLP] regression). The maximal grip strength with varying postures (upper arm, forearm, and lower body) of 164 yo...

Neural Networks with Emotion Associations, Topic Modeling and Supervised Term Weighting for Sentiment Analysis.

International journal of neural systems
Automated sentiment analysis is becoming increasingly recognized due to the growing importance of social media and -commerce platform review websites. Deep neural networks outperform traditional lexicon-based and machine learning methods by effective...

SAM-GAN: Self-Attention supporting Multi-stage Generative Adversarial Networks for text-to-image synthesis.

Neural networks : the official journal of the International Neural Network Society
Synthesizing photo-realistic images based on text descriptions is a challenging task in the field of computer vision. Although generative adversarial networks have made significant breakthroughs in this task, they still face huge challenges in genera...

Whole-brain modelling of resting state fMRI differentiates ADHD subtypes and facilitates stratified neuro-stimulation therapy.

NeuroImage
Recent advances in non-linear computational and dynamical modelling have opened up the possibility to parametrize dynamic neural mechanisms that drive complex behavior. Importantly, building models of neuronal processes is of key importance to fully ...

Isogeometric finite element-based simulation of the aortic heart valve: Integration of neural network structural material model and structural tensor fiber architecture representations.

International journal for numerical methods in biomedical engineering
The functional complexity of native and replacement aortic heart valves (AVs) is well known, incorporating such physical phenomenons as time-varying non-linear anisotropic soft tissue mechanical behavior, geometric non-linearity, complex multi-surfac...

Discrimination of malignant from benign thyroid lesions through neural networks using FTIR signals obtained from tissues.

Analytical and bioanalytical chemistry
The current gold standard in cancer diagnosis-the microscopic examination of hematoxylin and eosin (H&E)-stained biopsies-is prone to bias since it greatly relies on visual examination. Hence, there is a need to develop a more sensitive and specific ...

Resting-state functional network models for posttraumatic stress disorder.

Journal of neurophysiology
Four recent articles were examined for their use of resting-state functional magnetic resonance imaging on participants with posttraumatic symptoms. Theory-driven computations were complemented by the novel use of network metrics, which revealed redu...

Intelligent Brushing Monitoring Using a Smart Toothbrush with Recurrent Probabilistic Neural Network.

Sensors (Basel, Switzerland)
Smart toothbrushes equipped with inertial sensors are emerging as high-tech oral health products in personalized health care. The real-time signal processing of nine-axis inertial sensing and toothbrush posture recognition requires high computational...