AIMC Topic: Neural Networks, Computer

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Multi-task convolutional neural networks for predicting in vitro clearance endpoints from molecular images.

Journal of computer-aided molecular design
Optimization of compound metabolic stability is a highly topical issue in pharmaceutical research. Accordingly, application of predictive in silico models can potentially reduce the number of design-make-test-analyze iterations and consequently speed...

Pruning recurrent neural networks replicates adolescent changes in working memory and reinforcement learning.

Proceedings of the National Academy of Sciences of the United States of America
Adolescent development is characterized by an improvement in multiple cognitive processes. While performance on cognitive operations improves during this period, the ability to learn new skills quickly, for example, a new language, decreases. During ...

MobilePrune: Neural Network Compression via Sparse Group Lasso on the Mobile System.

Sensors (Basel, Switzerland)
It is hard to directly deploy deep learning models on today's smartphones due to the substantial computational costs introduced by millions of parameters. To compress the model, we develop an ℓ0-based sparse group lasso model called MobilePrune which...

TimeREISE: Time Series Randomized Evolving Input Sample Explanation.

Sensors (Basel, Switzerland)
Deep neural networks are one of the most successful classifiers across different domains. However, their use is limited in safety-critical areas due to their limitations concerning interpretability. The research field of explainable artificial intell...

Hybrid and Deep Learning Approach for Early Diagnosis of Lower Gastrointestinal Diseases.

Sensors (Basel, Switzerland)
Every year, nearly two million people die as a result of gastrointestinal (GI) disorders. Lower gastrointestinal tract tumors are one of the leading causes of death worldwide. Thus, early detection of the type of tumor is of great importance in the s...

Cost-Sensitive Learning for Anomaly Detection in Imbalanced ECG Data Using Convolutional Neural Networks.

Sensors (Basel, Switzerland)
Arrhythmia detection algorithms based on deep learning are attracting considerable interest due to their vital role in the diagnosis of cardiac abnormalities. Despite this interest, deep feature representation for ECG is still challenging and intrigu...

Predicting Energy Consumption Using LSTM, Multi-Layer GRU and Drop-GRU Neural Networks.

Sensors (Basel, Switzerland)
With the steep rise in the development of smart grids and the current advancement in developing measuring infrastructure, short term power consumption forecasting has recently gained increasing attention. In fact, the prediction of future power loads...

Energy and thermal modelling of an office building to develop an artificial neural networks model.

Scientific reports
Nowadays everyone should be aware of the importance of reducing CO emissions which produce the greenhouse effect. In the field of construction, several options are proposed to reach nearly-Zero Energy Building (nZEB) standards. Obviously, before unde...

A deep learning-based radiomics approach to predict head and neck tumor regression for adaptive radiotherapy.

Scientific reports
Early regression-the regression in tumor volume during the initial phase of radiotherapy (approximately 2 weeks after treatment initiation)-is a common occurrence during radiotherapy. This rapid radiation-induced tumor regression may alter target coo...

Neural networks enable efficient and accurate simulation-based inference of evolutionary parameters from adaptation dynamics.

PLoS biology
The rate of adaptive evolution depends on the rate at which beneficial mutations are introduced into a population and the fitness effects of those mutations. The rate of beneficial mutations and their expected fitness effects is often difficult to em...