AIMC Topic: Machine Learning

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Using machine learning as a surrogate model for agent-based simulations.

PloS one
In this proof-of-concept work, we evaluate the performance of multiple machine-learning methods as surrogate models for use in the analysis of agent-based models (ABMs). Analysing agent-based modelling outputs can be challenging, as the relationships...

Is Artificial Intelligence (AI) a Pipe Dream? Why Legal Issues Present Significant Hurdles to AI Autonomy.

AJR. American journal of roentgenology
Proponents of artificial intelligence (AI) technology have suggested that in the near future, AI software may replace human radiologists. Although assimilation of AI into the specialty has occurred more slowly than predicted, developments in machine ...

Hybrid machine learning classification scheme for speaker identification.

Journal of forensic sciences
Motivated by the requirement to prepare for the next generation of "Automatic Spokesperson Recognition" (ASR) system, this paper applied the fused spectral features with hybrid machine learning (ML) strategy to the speech communication field. This st...

Prediction of the disease causal genes based on heterogeneous network and multi-feature combination method.

Computational biology and chemistry
At present, the prediction of disease causal genes is mainly based on heterogeneous. Research shows that heterogeneous network contains more information and have better prediction results. In this paper, we constructed a heterogeneous network includi...

Machine Learning Predicts Biogeochemistry from Microbial Community Structure in a Complex Model System.

Microbiology spectrum
Microbial community structure is influenced by the environment and in turn exerts control on many environmental parameters. We applied this concept in a bioreactor study to test whether microbial community structure contains information sufficient to...

Comparative Study of Machine-Learning Frameworks for the Elaboration of Feed-Forward Neural Networks by Varying the Complexity of Impedimetric Datasets Synthesized Using Eddy Current Sensors for the Characterization of Bi-Metallic Coins.

Sensors (Basel, Switzerland)
A suitable framework for the development of artificial neural networks is important because it decides the level of accuracy, which can be reached for a certain dataset and increases the certainty about the reached classification results. In this pap...

Perspectives in machine learning for wildlife conservation.

Nature communications
Inexpensive and accessible sensors are accelerating data acquisition in animal ecology. These technologies hold great potential for large-scale ecological understanding, but are limited by current processing approaches which inefficiently distill dat...

Identification of Synovial Fibroblast-Associated Neuropeptide Genes and m6A Factors in Rheumatoid Arthritis Using Single-Cell Analysis and Machine Learning.

Disease markers
OBJECTIVES: Synovial fibroblasts (SFs) play an important role in the development and progression of rheumatoid arthritis (RA). However, the pathogenic mechanism of SFs remains unclear. The objective of this study was to investigate how neuropeptides ...

Compound W-Net with Fully Accumulative Residual Connections for Liver Segmentation Using CT Images.

Computational and mathematical methods in medicine
Computed tomography (CT) is a common modality for liver diagnosis, treatment, and follow-up process. Providing accurate liver segmentation using CT images is a crucial step towards those tasks. In this paper, we propose a stacked 2-U-Nets model with ...