Results 71 to 80 of about 10,022,386 (288)

Is There a Market for Organic Milk in Serbia? Insights From Integrated Choice and Latent Variable Model

Agribusiness, EarlyView.
ABSTRACT Past growth in the global organic market has been concentrated in high‐income countries, while in middle‐income countries such as Serbia the organic market remains nascent and characterized by a sparse assortment of organic products, high retail premia and limited evidence on consumer preferences and their drivers.
Milan Tatic, Sanja Dzever, Bojan Matkovski, Danilo Djokic   +3 more
wiley   +1 more source

Communication Efficiency and Non-Independent and Identically Distributed Data Challenge in Federated Learning: A Systematic Mapping Study

Applied Sciences
Federated learning has emerged as a promising approach for collaborative model training across distributed devices. Federated learning faces challenges such as Non-Independent and Identically Distributed (non-IID) data and communication challenges.
Basmah Alotaibi, Fakhri Alam Khan, Sajjad Mahmood   +2 more
doaj   +1 more source

Consumer Demand and Market Response to Added Sugar Labeling: Evidence From the Updated Nutrition Facts Panel

Agribusiness, EarlyView.
ABSTRACT This paper examines how mandatory disclosure of added sugar content on the updated U.S. Nutrition Facts Panel (NFP) affects consumer demand and market outcomes. Using NielsenIQ Retail Scanner Data (2015–2020) and a random coefficient discrete choice model, we estimate how added sugar labeling influences purchasing behavior in yogurt and cookie
Yuxiang Zhang, Yizao Liu
wiley   +1 more source

Nonlinear Distortion-Aware Channel Estimation Strategies for IRS-Aided MIMO Systems

IEEE Access
The article addresses the challenges posed by nonlinear distortions in Intelligent Reflecting Surface (IRS)-assisted Multiple-Input Multiple-Output (MIMO) systems, which are often overlooked in conventional channel estimation strategies.
D. L. Sharini, Ravilla Dilli, M. Kanthi, G. D. Goutham Simha   +3 more
doaj   +1 more source

Multi-Level Coupling Network for Non-IID Sequential Recommendation

IEEE Access, 2019
Sequential recommendation has been recently attracting a lot attention to suggest users with next items to interact. However, most of the traditional studies implicitly assume that users and items are independent and identically distributed (IID) and ...
Yatong Sun, Guibing Guo, Xiaodong He, Xiaohua Liu   +3 more
doaj   +1 more source

Phase Coherence Induced by Additive Gaussian and Non-gaussian Noise in Excitable Networks With Application to Burst Suppression-Like Brain Signals

Frontiers in Applied Mathematics and Statistics, 2020
It is well-known that additive noise affects the stability of non-linear systems. Using a network composed of two interacting populations, detailed stochastic and non-linear analysis demonstrates that increasing the intensity of iid additive noise ...
Axel Hutt, Jérémie Lefebvre, Darren Hight, Heiko A. Kaiser   +3 more
doaj   +1 more source

The Effect of Sustainability Information on Consumers' Preferences for Fungus‐Resistant Grape Wine

Agribusiness, EarlyView.
ABSTRACT The demand for sustainable food production is intensifying the need to reduce the environmental and social impacts of pesticide use in agriculture. In viticulture, where dependence on pesticides is high because grapevines are susceptible to fungal diseases, fungus‐resistant grape varieties (PIWI) offer a promising strategy to reduce chemical ...
Daniel Vecchiato, Andrea Dominici, Fabio Boncinelli   +2 more
wiley   +1 more source

Explainable Federated Learning for Brain Tumor Classification Using Multi-Source MRI Data

Iraqi Journal for Computers and Informatics
Early diagnosis and clinical decision-making depend on accurate brain tumor classification using magnetic resonance imaging (MRI). However, traditional deep learning methods usually rely on centralized medical data, which raises privacy concerns and ...
Suhad Muhy Helal, Belal Al-Khateeb
doaj   +1 more source

DCFL: Non-IID awareness Data Condensation aided Federated Learning [PDF]

, 2023
Federated learning is a decentralized learning paradigm wherein a central server trains a global model iteratively by utilizing clients who possess a certain amount of private datasets. The challenge lies in the fact that the client side private data may
Sun, YaFeng, Sha, Shaohan
core  

Federated PAC-Bayesian Learning on Non-IID Data

ICASSP 2024 - 2024 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
Existing research has either adapted the Probably Approximately Correct (PAC) Bayesian framework for federated learning (FL) or used information-theoretic PAC-Bayesian bounds while introducing their theorems, but few considering the non-IID challenges in FL. Our work presents the first non-vacuous federated PAC-Bayesian bound tailored for non-IID local
Zihao Zhao 0001, Yang Liu 0165, Wenbo Ding 0001, Xiao-Ping Zhang 0003   +3 more
openaire   +4 more sources