Results 181 to 190 of about 436,028 (265)

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

Datasets for distributed denial-of-service detection in healthcare internet of things environments. [PDF]

Data Brief
Akhi M, Eising C, Dhirani LL.
europepmc   +1 more source

Device‐Level Implementation of Reservoir Computing With Memristors

Advanced Intelligent Systems, EarlyView.
Reservoir computing (RC) is an emerging computing scheme that employs a reservoir and a single readout layer, which can be actualized in the nanoscale with memristors. As a comprehensive overview, the principles of RC and the switching mechanisms of memristors are discussed, followed by actual demonstrations of memristor‐based RC and the remaining ...
Sunbeom Park, Hyojung Kim, Ho Won Jang
wiley   +1 more source

Federated reinforcement learning-driven multi-task optimization for robust and ethical edge internet of things security. [PDF]

Sci Rep
Li Y, Wang H, Xu G.
europepmc   +1 more source

Artificial Intelligence (AI) and Agribusiness: From Automation to Augmentation in a Global Context

Agribusiness, EarlyView.
Alexis H. Villacis
wiley   +1 more source

Machine Learning‐Driven Digital Twin of a Field‐Effect Transistor‐Based Hormone Biosensor for Real‐Time Estradiol Monitoring

Advanced Intelligent Systems, EarlyView.
A machine learning‐driven digital twin simulates an aptamer‐functionalized BioFET measuring 17β‐estradiol. Real‐time Isd signals are processed, features are extracted, and trained models estimate hormone concentration. In parallel, a one‐step‐ahead forward model learns biosensor dynamics and generates realistic synthetic signals, enabling in silico ...
Anastasiia Gorelova, Alexandra Parichenko, Shirong Huang, Santiago Melia, Gianaurelio Cuniberti   +4 more
wiley   +1 more source

Lightweight Federated Learning Approach for Resource-Constrained Internet of Things. [PDF]

Sensors (Basel)
Baqer M.
europepmc   +1 more source

Ultralow‐Power Real‐Time On‐Chip Thermal Prediction via Finite Element Method–Machine Learning Codesign and Field‐Programmable Gate Array Deployment

Advanced Intelligent Systems, EarlyView.
A lightweight machine learning (ML)‐based thermal prediction framework is demonstrated and implemented on a field‐programmable gate array (FPGA). Using measured temperature data from a real chiplet, the approach enables real‐time, die‐level heat‐map inference with low power consumption, validating practical on‐chip thermal monitoring for advanced ...
Jun Ho Lee, Jae Gyu Kim, Sung Jin Kim, Jae Yong Song, Byoung Don Kong   +4 more
wiley   +1 more source

Optimizing internet of things security through blockchain enabled software defined networking. [PDF]

Sci Rep
Tumula S, Ramadevi Y, Rudra Kumar M, Chithaluru P, Palamakula RB, Goli S, Narsimhulu P, Jamjoom M, Abd Elminaam DS.   +8 more
europepmc   +1 more source

Deep Reinforcement Learning Approaches for Sensor Data Collection by a Swarm of UAVs

Advanced Intelligent Systems, EarlyView.
This article presents four decentralized reinforcement learning algorithms for autonomous data harvesting and investigates how collaboration improves collection efficiency. It also presents strategies to minimize training times by improving model flexibility, enabling algorithms to operate with varying number of agents and sensors.
Thiago de Souza Lamenza, Bruno José Olivieri de Souza, Markus Endler   +2 more
wiley   +1 more source