AIMC Topic: Neural Networks, Computer

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Explainable Molecular Sets: Using Information Theory to Generate Meaningful Descriptions of Groups of Molecules.

Journal of chemical information and modeling
Algorithmically identifying the meaningful similarities between an assortment of molecules is a critical chemical problem, and one which is only gaining in relevance as data-driven chemistry continues to progress. Effectively addressing this challeng...

Remaining Useful Life Prediction from 3D Scan Data with Genetically Optimized Convolutional Neural Networks.

Sensors (Basel, Switzerland)
In the current industrial landscape, increasingly pervaded by technological innovations, the adoption of optimized strategies for asset management is becoming a critical key success factor. Among the various strategies available, the "Prognostics and...

Antenna Impedance Matching Using Deep Learning.

Sensors (Basel, Switzerland)
We propose a deep neural network (DNN) to determine the matching circuit parameters for antenna impedance matching. The DNN determines the element values of the matching circuit without requiring a mathematical description of matching methods, and it...

A Deep-Learning Based Visual Sensing Concept for a Robust Classification of Document Images under Real-World Hard Conditions.

Sensors (Basel, Switzerland)
This paper's core objective is to develop and validate a new neurocomputing model to classify document images in particularly demanding hard conditions such as image distortions, image size variance and scale, a huge number of classes, etc. Document ...

Multi-Classification of Complex Microseismic Waveforms Using Convolutional Neural Network: A Case Study in Tunnel Engineering.

Sensors (Basel, Switzerland)
Due to the complexity of the various waveforms of microseismic data, there are high requirements on the automatic multi-classification of such data; an accurate classification is conducive for further signal processing and stability analysis of surro...

Adaptive Attention Memory Graph Convolutional Networks for Skeleton-Based Action Recognition.

Sensors (Basel, Switzerland)
Graph Convolutional Networks (GCNs) have attracted a lot of attention and shown remarkable performance for action recognition in recent years. For improving the recognition accuracy, how to build graph structure adaptively, select key frames and extr...

Personality Classification of Social Users Based on Feature Fusion.

Sensors (Basel, Switzerland)
Based on the openness and accessibility of user data, personality recognition is widely used in personalized recommendation, intelligent medicine, natural language processing, and so on. Existing approaches usually adopt a single deep learning mechan...

A Novel Intelligent Fault Diagnosis Method for Rolling Bearings Based on Wasserstein Generative Adversarial Network and Convolutional Neural Network under Unbalanced Dataset.

Sensors (Basel, Switzerland)
Rolling bearings are widely used in industrial manufacturing, and ensuring their stable and effective fault detection is a core requirement in the manufacturing process. However, it is a great challenge to achieve a highly accurate rolling bearing fa...

Quantum Artificial Neural Network Approach to Derive a Highly Predictive 3D-QSAR Model for Blood-Brain Barrier Passage.

International journal of molecular sciences
A successful passage of the blood-brain barrier (BBB) is an essential prerequisite for the drug molecules designed to act on the central nervous system. The logarithm of blood-brain partitioning (LogBB) has served as an effective index of molecular B...

FoldHSphere: deep hyperspherical embeddings for protein fold recognition.

BMC bioinformatics
BACKGROUND: Current state-of-the-art deep learning approaches for protein fold recognition learn protein embeddings that improve prediction performance at the fold level. However, there still exists aperformance gap at the fold level and the (relativ...