AIMC Topic: Neural Networks, Computer

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A Deep Learning System for Recognizing and Recovering Contaminated Slider Serial Numbers in Hard Disk Manufacturing Processes.

Sensors (Basel, Switzerland)
This paper outlines a system for detecting printing errors and misidentifications on hard disk drive sliders, which may contribute to shipping tracking problems and incorrect product delivery to end users. A deep-learning-based technique is proposed ...

Deep Learning and Its Application to Function Approximation for MR in Medicine: An Overview.

Magnetic resonance in medical sciences : MRMS : an official journal of Japan Society of Magnetic Resonance in Medicine
This article presents an overview of deep learning (DL) and its applications to function approximation for MR in medicine. The aim of this article is to help readers develop various applications of DL. DL has made a large impact on the literature of ...

Neural network representation of electronic structure from ab initio molecular dynamics.

Science bulletin
Despite their rich information content, electronic structure data amassed at high volumes in ab initio molecular dynamics simulations are generally under-utilized. We introduce a transferable high-fidelity neural network representation of such data i...

Automated assignment of cell identity from single-cell multiplexed imaging and proteomic data.

Cell systems
A major challenge in the analysis of highly multiplexed imaging data is the assignment of cells to a priori known cell types. Existing approaches typically solve this by clustering cells followed by manual annotation. However, these often require sev...

Combining STDP and binary networks for reinforcement learning from images and sparse rewards.

Neural networks : the official journal of the International Neural Network Society
Spiking neural networks (SNNs) aim to replicate energy efficiency, learning speed and temporal processing of biological brains. However, accuracy and learning speed of such networks is still behind reinforcement learning (RL) models based on traditio...

Interpreting neural computations by examining intrinsic and embedding dimensionality of neural activity.

Current opinion in neurobiology
The ongoing exponential rise in recording capacity calls for new approaches for analysing and interpreting neural data. Effective dimensionality has emerged as an important property of neural activity across populations of neurons, yet different stud...

A Scalable Artificial Neuron Based on Ultrathin Two-Dimensional Titanium Oxide.

ACS nano
A spiking neural network consists of artificial synapses and neurons and may realize human-level intelligence. Unlike the widely reported artificial synapses, the fabrication of large-scale artificial neurons with good performance is still challengin...

Improved Self-Organizing Map-Based Unsupervised Learning Algorithm for Sitting Posture Recognition System.

Sensors (Basel, Switzerland)
As the intensity of work increases, many of us sit for long hours while working in the office. It is not easy to sit properly at work all the time and sitting for a long time with wrong postures may cause a series of health problems as time goes by. ...

A Real-Time Electrical Load Forecasting in Jordan Using an Enhanced Evolutionary Feedforward Neural Network.

Sensors (Basel, Switzerland)
Power system planning and expansion start with forecasting the anticipated future load requirement. Load forecasting is essential for the engineering perspective and a financial perspective. It effectively plays a vital role in the conventional monop...

GenNet framework: interpretable deep learning for predicting phenotypes from genetic data.

Communications biology
Applying deep learning in population genomics is challenging because of computational issues and lack of interpretable models. Here, we propose GenNet, a novel open-source deep learning framework for predicting phenotypes from genetic variants. In th...