AIMC Topic: Neural Networks, Computer

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Computer aided detection of tuberculosis using two classifiers.

Biomedizinische Technik. Biomedical engineering
Tuberculosis caused by Mycobacterium tuberculosis have been a major challenge for medical and healthcare sectors in many underdeveloped countries with limited diagnosis tools. Tuberculosis can be detected from microscopic slides and chest X-ray but a...

Convolutional Neural Networks Quantization with Double-Stage Squeeze-and-Threshold.

International journal of neural systems
It has been proven that, compared to using 32-bit floating-point numbers in the training phase, Deep Convolutional Neural Networks (DCNNs) can operate with low-precision during inference, thereby saving memory footprint and power consumption. However...

Temporal deep learning framework for retinopathy prediction in patients with type 1 diabetes.

Artificial intelligence in medicine
The adoption of electronic health records in hospitals has ensured the availability of large datasets that can be used to predict medical complications. The trajectories of patients in real-world settings are highly variable, making longitudinal data...

An interpretable deep learning model for classifying adaptor protein complexes from sequence information.

Methods (San Diego, Calif.)
Adaptor proteins (APs) are a family of proteins that aids in intracellular membrane trafficking, and their impairments or defects are closely related to various disorders. Traditional methods to identify and classify APs require time and complex tech...

Waste-to-energy as a tool of circular economy: Prediction of higher heating value of biomass by artificial neural network (ANN) and multivariate linear regression (MLR).

Waste management (New York, N.Y.)
Circular economy is a global trend as a promising strategy for the sustainable use of natural resources. In this context, waste-to-energy presents an effective solution to respond to the ever-increasing waste generation and energy demand duality. How...

DENVIS: Scalable and High-Throughput Virtual Screening Using Graph Neural Networks with Atomic and Surface Protein Pocket Features.

Journal of chemical information and modeling
Computational methods for virtual screening can dramatically accelerate early-stage drug discovery by identifying potential hits for a specified target. Docking algorithms traditionally use physics-based simulations to address this challenge by estim...

Data-driven emergence of convolutional structure in neural networks.

Proceedings of the National Academy of Sciences of the United States of America
Exploiting data invariances is crucial for efficient learning in both artificial and biological neural circuits. Understanding how neural networks can discover appropriate representations capable of harnessing the underlying symmetries of their input...

Spiking Neural P Systems With Enzymes.

IEEE transactions on nanobioscience
The neurotransmitter is a chemical substance that transmits information between neurons. Its metabolic process includes four links: synthesis, storage, release and inactivation. As one of the important chemical components of neurotransmitters, acetyl...

N⁴ Sim: The First Nervous NaNoNetwork Simulator With Synaptic Molecular Communications.

IEEE transactions on nanobioscience
The unconventional nature of molecular communication necessitates contributions from a host of scientific fields making the simulator design for such systems to be quite challenging. The nervous system is one of the largest and most important nanonet...