AIMC Topic: Machine Learning

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Byzantine-robust federated learning via credibility assessment on non-IID data.

Mathematical biosciences and engineering : MBE
Federated learning is a novel framework that enables resource-constrained edge devices to jointly learn a model, which solves the problem of data protection and data islands. However, standard federated learning is vulnerable to Byzantine attacks, wh...

Application of machine learning to the monitoring and prediction of food safety: A review.

Comprehensive reviews in food science and food safety
Machine learning (ML) has proven to be a useful technology for data analysis and modeling in a wide variety of domains, including food science and engineering. The use of ML models for the monitoring and prediction of food safety is growing in recent...

The Application of Machine Learning ICA-VMD in an Intelligent Diagnosis System in a Low SNR Environment.

Sensors (Basel, Switzerland)
This paper proposes a new method called independent component analysis-variational mode decomposition (ICA-VMD), which combines ICA and VMD. The purpose is to study the application of ICA-VMD in low signal-to-noise ratio (SNR) signal processing and d...

Table2Vec-automated universal representation learning of enterprise data DNA for benchmarkable and explainable enterprise data science.

Scientific reports
Enterprise data typically involves multiple heterogeneous data sources and external data that respectively record business activities, transactions, customer demographics, status, behaviors, interactions and communications with the enterprise, and th...

Proof of concept and development of a couple-based machine learning model to stratify infertile patients with idiopathic infertility.

Scientific reports
We aimed to develop and evaluate a machine learning model that can stratify infertile/fertile couples on the basis of their bioclinical signature helping the management of couples with unexplained infertility. Fertile and infertile couples were recru...

An Ensemble Deep Learning Model for Automatic Modulation Classification in 5G and Beyond IoT Networks.

Computational intelligence and neuroscience
With rapid advancement in artificial intelligence (AI) and machine learning (ML), automatic modulation classification (AMC) using deep learning (DL) techniques has become very popular. This is even more relevant for Internet of things (IoT)-assisted ...

Hybrid pointer networks for traveling salesman problems optimization.

PloS one
In this work, we proposed a hybrid pointer network (HPN), an end-to-end deep reinforcement learning architecture is provided to tackle the travelling salesman problem (TSP). HPN builds upon graph pointer networks, an extension of pointer networks wit...

Machine learning-based patient classification system for adults with stroke: A systematic review.

Chronic illness
OBJECTIVE: To evaluate the existing evidence of a machine learning-based classification system that stratifies patients with stroke.

Physiological signal-based drowsiness detection using machine learning: Singular and hybrid signal approaches.

Journal of safety research
INTRODUCTION: Drowsiness is one of the main contributors to road-related crashes and fatalities worldwide. To address this pressing global issue, researchers are continuing to develop driver drowsiness detection systems that use a variety of measures...

Explainable Machine Learning for Property Predictions in Compound Optimization.

Journal of medicinal chemistry
The prediction of compound properties from chemical structure is a main task for machine learning (ML) in medicinal chemistry. ML is often applied to large data sets in applications such as compound screening, virtual library enumeration, or generati...