Results 51 to 60 of about 4,684 (203)

When stakeholders are imposed: Non‐chosen stakeholder orientation and venture performance

Strategic Entrepreneurship Journal, EarlyView.
Abstract Research Summary Extending stakeholder theory into the entrepreneurship context, we examine the performance implications of externally imposed stakeholder orientation, which we conceptualize as non‐chosen stakeholder orientation. Given the resource constraints, short time horizons, and high levels of uncertainty that characterize ventures, we ...
Myeongho David Park, Per L. Bylund, Eunkwang Seo   +2 more
wiley   +1 more source

Federated transfer learning for distributed drought stage prediction

Discover Artificial Intelligence
Due to the uncertain nature of drought, it is one of the most menacing natural disasters. Drought modeling (Prediction, Detection, Forecasting, and Stage Prediction) is very essential for efficient policy making.
Muhammad Owais Raza, Aqsa Umar, Jawad Rasheed, Tunc Asuroglu, Shtwai Alsubai   +4 more
doaj   +1 more source

Decreasing the Environmental Impact of the Electric Steelmaking Route Through Advanced Modelling Techniques

steel research international, EarlyView.
Ensemble models are adopted to estimate the sterile content of scraps arriving to the scrap yard. Feed‐forward neural networks are exploited to estimate steel composition and temperature after Ladle furnace. The models are validated on data from two steelworks very satisfactory results and are inherently transferable to other steelworks, as they are ...
Valentina Colla, Antonella Zaccara, Stefano Dettori, Laura Laid, Ismael Matino, Silvia Cateni, Teresa Annunziata Branca, Lorenzo Vannini   +7 more
wiley   +1 more source

SemFedXAI: A Semantic Framework for Explainable Federated Learning in Healthcare

Information
Federated Learning (FL) is emerging as an encouraging paradigm for AI model training in healthcare that enables collaboration among institutions without revealing sensitive information.
Alba Amato, Dario Branco
doaj   +1 more source

Hierarchical Aggregation for Federated Learning in Heterogeneous IoT Scenarios: Enhancing Privacy and Communication Efficiency

Future Internet
Federated Learning (FL) is a distributed machine-learning paradigm that enables models to be trained across multiple decentralized devices or servers holding local data without transferring the raw data to a central location.
Chen Qiu, Ziang Wu, Haoda Wang, Qinglin Yang, Yu Wang, Chunhua Su   +5 more
doaj   +1 more source

Data privacy model using blockchain reinforcement federated learning approach for scalable internet of medical things

CAAI Transactions on Intelligence Technology, EarlyView.
Abstract Internet of Medical Things (IoMT) has typical advancements in the healthcare sector with rapid potential proof for decentralised communication systems that have been applied for collecting and monitoring COVID‐19 patient data. Machine Learning algorithms typically use the risk score of each patient based on risk factors, which could help ...
Chandramohan Dhasaratha, Mohammad Kamrul Hasan, Shayla Islam, Shailesh Khapre, Salwani Abdullah, Taher M. Ghazal, Ahmed Ibrahim Alzahrani, Nasser Alalwan, Nguyen Vo, Md Akhtaruzzaman   +9 more
wiley   +1 more source

Functional Independence Related to Oral Hygiene and Periodontal Status in Patients With Down Syndrome

International Journal of Dental Hygiene, EarlyView.
ABSTRACT Objectives This study aimed to investigate the relationship between functional independence, oral hygiene habits and periodontal status in patients with Down syndrome (DS). Methods A cross‐sectional observational study was conducted with 49 patients with Down Syndrome. Sociodemographic data, oral hygiene habits and functional independence were
Joana Albuquerque Bastos de Sousa, Kátia Maria Martins Veloso, Monique Maria Melo Mouchrek, Mayra Moura Franco, Cecilia Claudia Costa Ribeiro, Vandilson Pinheiro Rodrigues, Bruno Braga Benatti   +6 more
wiley   +1 more source

SMART-FL: Single-Shot Merged Adaptive Resource-Aware Tensor-Fusion for Efficient Federated Learning in Heterogeneous Cross-Silo Environments

IEEE Access
Federated Learning (FL) is an evolutionary approach for privacy-preserving distributed machine learning and is particularly significant in cross-silo settings where direct data sharing is restricted.
Vineetha Pais, Santhosha Rao, Balachandra Muniyal   +2 more
doaj   +1 more source

Gender Justice in the Triple Planetary Crisis: A Scoping Review

Journal of Advanced Nursing, EarlyView.
ABSTRACT Aim To identify and report how gender justice is conceptualised and discussed in contemporary health literature in relation to the Triple Planetary Crisis of climate change, pollution and biodiversity loss, with a particular focus on the experiences of women and gender‐diverse populations, and the representation of nurses and other healthcare ...
Catelyn Richards, Micah Peters, Julia Charalambous, Lila Singh‐Peterson, Lucy Osborn, Aletha Ward, Jessica LeClair, Tracy Levett‐Jones   +7 more
wiley   +1 more source

A Systematic Review of Federated Learning: Emerging Techniques, Challenges, and Research Directions

Iraqi Journal for Computers and Informatics
Federated Learning FL is a rapidly evolving machine learning paradigm that enables collaborative model training across decentralized data sources while preserving data privacy.
Mohammed Abdul Ameer Jabbar, Ahmed Sami Jaddoa, Usama Samir Mahmoud   +2 more
doaj   +1 more source