Results 91 to 100 of about 93,163 (295)
Can edge AI mitigate environmental effects on camera trap performance?
Wildlife Society BulletinAbiotic and biotic conditions can affect camera trap performance, and failure to account for environmental factors can bias wildlife research and management inferences modeled from camera trap data. We investigated whether a camera trap enabled with edge
Taylor L. Kaltenbach +3 more
doaj +1 more source
A review of progresses in low-power artificial intelligence computing systems
Gongneng cailiao yu qijian xuebaoRecently, with the rapid growth of big data and hardware capabilities, artificial intelligence (AI) has achieved significant development. Artificial neural networks (ANNs) have been successfully applied to solve numerous problems in academia and industry.
Hua CHEN, Yiming QU, Wenhao WU, Yi ZHAO
doaj +1 more source
Advanced Engineering Materials, EarlyView.A unified research data management framework for heterogeneous materials data is presented. The system integrates multimodal datasets using ontologies and knowledge graphs, enabling interoperability and FAIR (findable, accessible, interoperable, reusable) data principles. By linking data across scales and workflows, it supports reproducible, Artifitial
Doaa Mohamed +6 more
wiley +1 more source
AI-Based Model for Home Waste Separation Using Raspberry Pi 5 AI Kit
Фізика і хімія твердого тілаEffective domestic waste separation is also very critical to enhancing recycling and minimizing environmental pollution. Manual sorting, however, is labor-intensive, prone to errors and not practical to be widely adopted.
H. A. Mutar +4 more
doaj +1 more source
AI‐EDGE: An NSF AI institute for future edge networks and distributed intelligence
AI MagazineAbstractThis paper highlights the overall endeavors of the NSF AI Institute for Future Edge Networks and Distributed Intelligence (AI‐EDGE) to create a research, education, knowledge transfer, and workforce development environment for developing technological leadership in next‐generation edge networks (6G and beyond) and artificial intelligence (AI ...
Peizhong Ju +3 more
openaire +1 more source
A Knowledge‐Based Approach for Understanding and Managing Additive Manufacturing Data
Advanced Engineering Materials, EarlyView.Additive manufacturing processes generate a large amount of data. Effectively managing, understanding, and retrieving information from this data remains a major challenge. Therefore, we propose an ontology‐based approach to integrate heterogeneous data, enable semantic queries, and support decision‐making.
Mina Abd Nikooie Pour +5 more
wiley +1 more source
Scientific ReportsIn the rapidly growing Internet of Things (IoT) landscape, federated learning (FL) plays a crucial role in enhancing the performance of heterogeneous edge computing environments due to its scalability, robustness, and low energy consumption. However, one
Fahad Razaque Mughal +6 more
doaj +1 more source
Journal of Informatics and Web EngineeringOver the last ten years, telemedicine has undergone significant developments, from simple communication-based healthcare to smart and data-driven solutions.
Do Duc Thinh +3 more
doaj +1 more source
An AI-based Virtual Assistant for Mental Training [PDF]
, 2023 reservedThis thesis embarks on an exploration of Generative Artificial Intelligence within the context of Mental Economy Training, an innovative cognitive enhancement approach. The primary objective is the development of a sophisticated Virtual Assistant
DEL FIUME, GABRIELE
core
Current Status and Challenges in Data Collection for Aerospace Coatings Deposited by Plasma Spraying
Advanced Engineering Materials, EarlyView.An innovative approach has been integrated into the GRENAT project to optimize plasma spraying and coating performance. Raw materials are accelerated and melted in the plasma generated by torches, creating coatings. Monitoring sensors collect process data which are combined with ex situ characterization data.
Lila Randriamananjara +8 more
wiley +1 more source