AIMC Topic: Neural Networks, Computer

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Exploration and exploitation in continual learning.

Neural networks : the official journal of the International Neural Network Society
Continual learning (CL) has received a surge of interest, particularly in parameter isolation approaches, aiming to prevent catastrophic forgetting by assigning a disjoint parameter set to each task. Despite their effectiveness, existing approaches o...

Self-Referencing Agents for Unsupervised Reinforcement Learning.

Neural networks : the official journal of the International Neural Network Society
Current unsupervised reinforcement learning methods often overlook reward nonstationarity during pre-training and the forgetting of exploratory behavior during fine-tuning. Our study introduces Self-Reference (SR), a novel add-on module designed to a...

Joint identification of hydraulic conductivity and groundwater pollution sources using unscented Kalman smoother with multiple data assimilation and deep learning.

Ecotoxicology and environmental safety
Identification of groundwater pollution sources (IGPSs) is a prerequisite for pollution remediation and pollution risk prediction. Data assimilation approaches have been used extensively in IGPSs field in recent years. A data assimilation approach-un...

Utilisation of Deep Neural Networks for Estimation of Cajal Cells in the Anal Canal Wall of Patients with Advanced Haemorrhoidal Disease Treated by LigaSure Surgery.

Cells
Interstitial cells of Cajal (ICCs) play a key role in gastrointestinal smooth muscle contractions, but their relationship with anal canal function in advanced haemorrhoidal disease (HD) remains poorly understood. This study uses deep neural network (...

Explainable artificial intelligence to diagnose early Parkinson's disease via voice analysis.

Scientific reports
Parkinson's disease (PD) is a neurodegenerative disorder affecting motor control, leading to symptoms such as tremors and stiffness. Early diagnosis is essential for effective treatment, but traditional methods are often time-consuming and expensive....

Scale selection and machine learning based cell segmentation and tracking in time lapse microscopy.

Scientific reports
Monitoring and tracking of cell motion is a key component for understanding disease mechanisms and evaluating the effects of treatments. Time-lapse optical microscopy has been commonly employed for studying cell cycle phases. However, usual manual ce...

CGLoop: a neural network framework for chromatin loop prediction.

BMC genomics
BACKGROUND: Chromosomes of species exhibit a variety of high-dimensional organizational features, and chromatin loops, which are fundamental structures in the three-dimensional (3D) structure of the genome. Chromatin loops are visible speckled patter...

Boosting semi-supervised federated learning by effectively exploiting server-side knowledge and client-side unconfident samples.

Neural networks : the official journal of the International Neural Network Society
Semi-supervised federated learning (SSFL) has emerged as a promising paradigm to reduce the need for fully labeled data in training federated learning (FL) models. This paper focuses on the label-at-server scenario, where clients' data are entirely u...

Grain protein function prediction based on improved FCN and bidirectional LSTM.

Food chemistry
With the development of high-throughput sequencing technologies, predicting grain protein function from amino acid sequences based on intelligent model has become one of the significant tasks in bioinformatics. The soybean, maize, indica, and japonic...

Exploring quantum neural networks for binary classification on MNIST dataset: A swap test approach.

Neural networks : the official journal of the International Neural Network Society
In this study, we propose a novel modularized Quantum Neural Network (mQNN) model tailored to address the binary classification problem on the MNIST dataset. The mQNN organizes input information using quantum images and trainable quantum parameters e...