AIMC Topic: Machine Learning

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Inspecting the Running Process of Horizontal Federated Learning via Visual Analytics.

IEEE transactions on visualization and computer graphics
As a decentralized training approach, horizontal federated learning (HFL) enables distributed clients to collaboratively learn a machine learning model while keeping personal/private information on local devices. Despite the enhanced performance and ...

Deep Learning Hybrid Techniques for Brain Tumor Segmentation.

Sensors (Basel, Switzerland)
Medical images play an important role in medical diagnosis and treatment. Oncologists analyze images to determine the different characteristics of deadly diseases, plan the therapy, and observe the evolution of the disease. The objective of this pape...

A Novel Data Augmentation Method for Improving the Accuracy of Insulator Health Diagnosis.

Sensors (Basel, Switzerland)
Performing ultrasonic nondestructive testing experiments on insulators and then using machine learning algorithms to classify and identify the signals is an important way to achieve an intelligent diagnosis of insulators. However, in most cases, we c...

Predicting Chemical Carcinogens Using a Hybrid Neural Network Deep Learning Method.

Sensors (Basel, Switzerland)
Determining environmental chemical carcinogenicity is urgently needed as humans are increasingly exposed to these chemicals. In this study, we developed a hybrid neural network (HNN) method called HNN-Cancer to predict potential carcinogens of real-l...

Understanding basic principles of Artificial Intelligence: a practical guide for intensivists.

Acta bio-medica : Atenei Parmensis
BACKGROUND AND AIM: Artificial intelligence was born to allow computers to learn and control their environment, trying to imitate the human brain structure by simulating its biological evolution. Artificial intelligence makes it possible to analyze l...

Artificial intelligence-based methods for fusion of electronic health records and imaging data.

Scientific reports
Healthcare data are inherently multimodal, including electronic health records (EHR), medical images, and multi-omics data. Combining these multimodal data sources contributes to a better understanding of human health and provides optimal personalize...

Investigation of the Binding Fraction of PFAS in Human Plasma and Underlying Mechanisms Based on Machine Learning and Molecular Dynamics Simulation.

Environmental science & technology
More than 7000 per- and polyfluorinated alkyl substances (PFAS) have been documented in the U.S. Environmental Protection Agency's CompTox Chemicals database. These PFAS can be used in a broad range of industrial and consumer applications but may pos...

Self-fulfilling prophecies and machine learning in resuscitation science.

Resuscitation
INTRODUCTION: Growth of machine learning (ML) in healthcare has increased potential for observational data to guide clinical practice systematically. This can create self-fulfilling prophecies (SFPs), which arise when prediction of an outcome increas...

Current understanding of biological interactions and processing of DNA origami nanostructures: Role of machine learning and implications in drug delivery.

Biotechnology advances
DNA origami has emerged as an exciting avenue that provides a versatile two and three-dimensional DNA-based platform for nanomedicine and drug delivery applications. Their incredible programmability, custom synthesis, efficiency, biocompatibility, an...

Machine learning algorithms to identify cluster randomized trials from MEDLINE and EMBASE.

Systematic reviews
BACKGROUND: Cluster randomized trials (CRTs) are becoming an increasingly important design. However, authors of CRTs do not always adhere to requirements to explicitly identify the design as cluster randomized in titles and abstracts, making retrieva...