Results 131 to 140 of about 2,921,969 (298)
Harnessing Machine Learning to Understand and Design Disordered Solids
Advanced Intelligent Discovery, EarlyView.This review maps the dynamic evolution of machine learning in disordered solids, from structural representations to generative modeling. It explores how deep learning and model explainability transform property prediction into profound physical insight.
Muchen Wang, Yue Fan
wiley +1 more source
Cardiac disease diagnosis based on GAN in case of missing data.
PLoS ONEIn daily life, two common algorithms are used for collecting medical disease data: data integration of medical institutions and questionnaires. However, these statistical methods require collecting data from the entire research area, which consumes a ...
Xing Chen +8 more
doaj +1 more source
Probabilistic Generative Adversarial Networks
CoRR, 2017 We introduce the Probabilistic Generative Adversarial Network (PGAN), a new GAN variant based on a new kind of objective function. The central idea is to integrate a probabilistic model (a Gaussian Mixture Model, in our case) into the GAN framework which supports a new kind of loss function (based on likelihood rather than classification loss), and at ...
Hamid Eghbal-zadeh, Gerhard Widmer
openaire +3 more sources
AI‐Driven Cancer Multi‐Omics: A Review From the Data Pipeline Perspective
Advanced Intelligent Discovery, EarlyView.The exponential growth of cancer multi‐omics data brings opportunities and challenges for precision oncology. This review systematically examines AI's role in addressing these challenges, covering generative models, integration architectures, Explainable AI for clinical trust, clinical applications, and key directions for clinical translation.
Shilong Liu, Shunxiang Li, Kun Qian
wiley +1 more source
Advanced Intelligent Discovery, EarlyView.Materials informatics and autonomous experimentation are transforming the discovery of organic molecular crystals. This review presents an integrated molecule–crystal–function–optimization workflow combining machine learning, crystal structure prediction, and Bayesian optimization with robotic platforms.
Takuya Taniguchi +2 more
wiley +1 more source
Deep Convolutional Generative Adversarial Networks in Image-Based Android Malware Detection
ComputersThe recent advancements in generative adversarial networks have showcased their remarkable ability to create images that are indistinguishable from real ones.
Francesco Mercaldo +2 more
doaj +1 more source
Advanced Intelligent Discovery, EarlyView.Machine learning serves as a central engine for the intelligent characterization of two‐dimensional materials by integrating multimodal techniques, including optical microscopy, spectroscopy, electron microscopy, and scanning probe microscopy (SPM). This unified framework enables automated, high‐throughput, and quantitative extraction of structural ...
Zhi‐Long Cao, Jia‐Xu Yan
wiley +1 more source
Advanced Intelligent Systems, Volume 7, Issue 3, March 2025.This article reviews the current state of bioinspired soft robotics. The article discusses soft actuators, soft sensors, materials selection, and control methods used in bioinspired soft robotics. It also highlights the challenges and future prospects of this field.
Abhirup Sarker +2 more
wiley +1 more source
A Novel Electromagnetic Sensing Generative Adversarial Network for Uniaxial Objects
Electromagnetic imaging achieves enhanced resolution by leveraging the advanced sensing and data analysis capabilities of Internet of Things (IoT) systems.
Chiu, Chien-Ching;Chen, Po-Hsiang;Jiang, Hao;Shi, Bo-Yu
core +1 more source
Generative Adversarial Networks and Other Generative Models
, 2022 Generative networks are fundamentally different in their aim and methods compared to CNNs for classification, segmentation, or object detection. They have initially not been meant to be an image analysis tool, but to produce naturally looking images. The
Wenzel, Markus
core