Results 91 to 100 of about 91,570 (234)

Loss‐Based Ensemble Generative Adversarial Network Model for Enhancing the Sperm Morphology Classification

Advanced Intelligent Systems, EarlyView.
A loss‐based ensemble generative adversarial network (GAN) framework is proposed to address mode collapse in sperm morphology classification. By integrating spatial augmentation and multiple GAN models, the study enhances synthetic data quality. The Shifted Window Transformer achieves 95.37% accuracy on the HuSHeM dataset, outperforming previous ...
Berke Cansiz, Hamza Osman Ilhan, Gorkem Serbes   +2 more
wiley   +1 more source

Challenges and Countermeasures of Federated Learning Data Poisoning Attack Situation Prediction

Mathematics
Federated learning is a distributed learning method used to solve data silos and privacy protection in machine learning, aiming to train global models together via multiple clients without sharing data.
Jianping Wu, Jiahe Jin, Chunming Wu
doaj   +1 more source

Federated Machine Learning

ACM Transactions on Intelligent Systems and Technology, 2019
Today’s artificial intelligence still faces two major challenges. One is that, in most industries, data exists in the form of isolated islands. The other is the strengthening of data privacy and security. We propose a possible solution to these challenges: secure federated learning.
Qiang Yang, Yang Liu, Tianjian Chen, Yongxin Tong   +3 more
openaire   +3 more sources

Roadmap on Artificial Intelligence‐Augmented Additive Manufacturing

Advanced Intelligent Systems, EarlyView.
This Roadmap outlines the transformative role of artificial intelligence‐augmented additive manufacturing, highlighting advances in design, monitoring, and product development. By integrating tools such as generative design, computer vision, digital twins, and closed‐loop control, it presents pathways toward smart, scalable, and autonomous additive ...
Ali Zolfagharian, Liuchao Jin, Qi Ge, Wei‐Hsin Liao, Andrés Díaz Lantada, Francisco Franco Martínez, Tianyu Zhang, Tao Liu, Charlie C. L. Wang, Mohammad Hossein Mosallanejad, Reza Ghanavati, Abdollah Saboori, Alejandro De Blas De Miguel, William Solórzano‐Requejo, Yi Cai, Xiangyang Dong, Huangyi Qu, Najmeh Samadiani, Guangyan Huang, Austin Downey, Yanzhou Fu, Lang Yuan, Tsz‐Kwan (Glory) Lee, Arbind Agrahari Baniya, Eisha Waseem, Abdul Rahman Sani, Abbas Z. Kouzani, Yijia Wu, Markus P. Nemitz, Masoud Shirzad, Dageon Oh, Seung Yun Nam, Amedeo Franco Bonatti, Irene Chiesa, Gabriele Maria Fortunato, Giovanni Vozzi, Carmelo De Maria, Mahdi Bodaghi   +37 more
wiley   +1 more source

Federated Learning in Data Privacy and Security

Advances in Distributed Computing and Artificial Intelligence Journal
Federated learning (FL) has been a rapidly growing topic in recent years. The biggest concern in federated learning is data privacy and cybersecurity. There are many algorithms that federated models have to work on to achieve greater efficiency, security,
Dokuru Trisha Reddy, Haripriya Nandigam, Sai Charan Indla, S. P. Raja   +3 more
doaj   +1 more source

Sparse Personalized Federated Learning

IEEE Transactions on Neural Networks and Learning Systems
Federated Learning (FL) is a collaborative machine learning technique to train a global model without obtaining clients' private data. The main challenges in FL are statistical diversity among clients, limited computing capability among clients' equipments, and the excessive communication overhead between the server and clients.
Xiaofeng Liu, Yinchuan Li, Qing Wang, Xu Zhang, Yunfeng Shao, Yanhui Geng   +5 more
openaire   +3 more sources

Multivariate Contrastive Predictive Coding with Sliding Windows for Disease Prediction from Electronic Health Records

Advanced Intelligent Systems, EarlyView.
Adaptive multi‐indicator contrastive predictive coding is introduced as a self‐supervised pretraining framework for multivariate EHR time series. An adaptive sliding‐window algorithm and 2D convolutional neural network encoder capture localized temporal patterns and global indicator dependencies, enabling label‐efficient disease prediction that ...
Hongxu Yuan, Xiaozhu Jing, Yuzheng Yan, Wuman Luo   +3 more
wiley   +1 more source

Building an Intelligent Cardiovascular System Platform: Embedding Artificial Intelligence across All Facets of Cardiovascular Medicine

Advanced Intelligent Systems, EarlyView.
This paper presents an integrated AI‐driven cardiovascular platform unifying multimodal data, predictive analytics, and real‐time monitoring. It demonstrates how artificial intelligence—from deep learning to federated learning—enables early diagnosis, precision treatment, and personalized rehabilitation across the full disease lifecycle, promoting a ...
Mowei Kong, Xingwei Zhao, Qiuting Li, Yang Yu, Chunxiang Zhang   +4 more
wiley   +1 more source

Federated Versus Central Machine Learning on Diabetic Foot Ulcer Images: Comparative Simulations

IEEE Access
This research examines the implementation of the U-Net model within a federated learning framework, focusing on the semantic segmentation of Diabetic Foot Ulcers (DFUs) images.
Mahdi Saeedi, Hamed Taheri Gorji, Fartash Vasefi, Kouhyar Tavakolian   +3 more
doaj   +1 more source

SciLitMiner: An Intelligent System for Scientific Literature Mining and Knowledge Discovery

Advanced Intelligent Systems, EarlyView.
SciLitMiner is an intelligent system that federately ingests scientific literature, filters it using advanced information retrieval methods, and applies retrieval‐augmented generation tailored to scientific domains. Demonstrated on creep deformation in γ‐TiAl alloys, SciLitMiner provides a controlled workflow for systematic knowledge discovery and ...
Vipul Gupta, Jonathan David Heaton Paul, Ingo Schmitt, Florian Pyczak   +3 more
wiley   +1 more source