AIMC Topic: Algorithms

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A Robust Gaussian Process Paradigm for Predictive Modeling on Small Data sets in Environmental Science: A Case Study in Ballasted Flocculation.

Environmental science & technology
Environmental processes including ballasted flocculation (BF) present significant optimization challenges due to complex multicomponent interactions and small, heterogeneous experimental data sets that frequently lead to overfitted machine learning (...

Chemprop v2: An Efficient, Modular Machine Learning Package for Chemical Property Prediction.

Journal of chemical information and modeling
Accurate prediction of molecular properties is essential for computational design in many areas of chemistry. Deep learning has been used in these prediction tasks for a wide variety of molecular properties, and the availability of user-friendly open...

Adaptive fractional-order non-singular terminal sliding mode control for omnidirectional quadrotors based on WRBF neural network.

PloS one
This paper presents a novel robust six-degree-of-freedom trajectory tracking control strategy for tilt-rotor quadrotors operating under uncertainties and disturbances. The key contribution lies in a unified framework that synergistically co-designs a...

State estimation of multi-sensor systems based on error-state Kalman.

PloS one
With the rapid advancement of multi-sensor systems, the capabilities of robots in complex scenes are gradually improving. A multi-sensor system state estimation algorithm based on error-state Kalman filter is proposed to address the issues of noise i...

Machine learning-based prediction of glioma grading.

PloS one
OBJECTIVE: Gliomas are among the most common and heterogeneous primary tumours of the central nervous system. Accurate grading is essential for treatment planning and prognosis, yet conventional histopathological approaches are limited by subjectivit...

MaterialBrain: High-Performance Material Synthesis Extraction via Human-AI-Curated Few-Shot Large Language Models.

Journal of chemical information and modeling
The extraction of the metal-organic framework synthesis route from the literature has been crucial for the rational MOFs design with desirable functionality. The recent advent of large language models (LLMs) provides a disruptive new solution to this...

Prediction and analysis of anti-aging peptides using data augmentation and machine learning algorithms.

BMC biology
BACKGROUND: For most species, Aging is an inevitable biological process that poses significant challenges to global healthcare due to age-related diseases. Recent advances in peptide therapy have highlighted anti-aging peptides (AAPs) as a promising ...

Enhanced classification prostate cancer based on generative adversarial networks and integrated deep learning with vision transformer models.

Scientific reports
By eliminating the need to alter the source images, this paper introduces a secure technique for coverless image steganography that strengthens defense against steganalysis attacks. Our method makes use of a hybrid Generative Adversarial Network (GAN...

Harnessing hyperspectral imaging and machine learning techniques for accurate discrimination of peanut plants and weeds.

Scientific reports
Effective weed detection for precise management remains a pertinent issue in modern agriculture. In this study, hyperspectral imaging (HSI) was combined with machine learning (ML) to differentiate between peanut plants and four common weeds found in ...

AI-powered IC50 prediction for p53 inhibitors drug-target interaction via hybrid graph neural networks.

Journal of computer-aided molecular design
In recent decades, the rapid pace of digital transformation marks a transformative era for the healthcare and pharmaceutical industries. The incorporation of innovative technology, specifically Artificial Intelligence (AI) and its derivatives, has dr...