AIMC Topic: Neural Networks, Computer

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Advancements in Fluidic Ionic Devices: Implications for Neuromorphic Integrated Circuit Design.

ACS sensors
Biological brains that use ions as signaling carriers exhibit powerful computing and learning capabilities. Inspired by this, ion-based neuromorphic devices have gained widespread attention. As a neuromorphic device, the fluidic ionic memristor can s...

Machine Learning Models Based on Enlarged Chemical Spaces for Screening Carcinogenic Chemicals.

Chemical research in toxicology
Machine learning (ML) models for screening carcinogenic chemicals are critical for the sound management of chemicals. Previous models were built on small-scale datasets and lacked applicability domain (AD) characterization that is necessary for regul...

AmesFormer: State-of-the-Art Mutagenicity Prediction with Graph Transformers.

Chemical research in toxicology
The Ames mutagenicity test is a gold standard assay for the safety assessment of new chemicals. However, many in silico models rely on challenging-to-interpret ensemble strategies and molecular fingerprint data, which neglects gestalt molecular struc...

[A multi-feature fusion-based model for fetal orientation classification from intrapartum ultrasound videos].

Nan fang yi ke da xue xue bao = Journal of Southern Medical University
OBJECTIVES: To construct an intelligent analysis model for classifying fetal orientation during intrapartum ultrasound videos based on multi-feature fusion.

DCSLK: Combined large kernel shared convolutional model with dynamic channel Sampling.

NeuroImage
This study centers around the competition between Convolutional Neural Networks (CNNs) with large convolutional kernels and Vision Transformers in the domain of computer vision, delving deeply into the issues pertaining to parameters and computationa...

Automated Electro-construction waste Sorting: Computer vision for part-level segmentation.

Waste management (New York, N.Y.)
The global generation of construction, demolition, and renovation (CDR) waste has surged, increasing the demand for efficient recycling solutions. Emerging technologies can automate the sorting of CDR waste, which is crucial for specialised categorie...

A Transformer-Based Framework for Counterfactual Estimation of Antihypertensive Treatment Effect on COVID-19 Infection Risk - A Proof-of-Concept Study.

American journal of hypertension
BACKGROUND: Transformer-based neural networks excel in modelling high-dimensional, time-series data with complex dependencies. This proof-of-concept study applies a transformer-X-learner framework to estimate treatment effects using real-world data, ...

Artificial intelligence in bacterial diagnostics and antimicrobial susceptibility testing: Current advances and future prospects.

Biosensors & bioelectronics
Recently, artificial intelligence (AI) has emerged as a transformative tool, enhancing the speed, accuracy, and scalability of bacterial diagnostics. This review explores the role of AI in revolutionizing bacterial detection and antimicrobial suscept...

Application of artificial intelligence in the rapid determination of moisture content in medicine food homology substances.

Food chemistry
Moisture content is crucial in quality testing of medicine food homology substances. This study aimed to present a new modeling method for moisture content based on near-infrared spectroscopy. When comparing three methods of partial least squares reg...

A Diagnosis-Based Siamese Network for Fault Detection Through Transfer Learning.

Journal of chemical information and modeling
Traditional deep-learning-based approaches often struggle with data imbalance and variability across fault conditions and normal scenarios, especially in industrial processes. Besides, inconsistent feature distributions from combining different fault...