Results 91 to 100 of about 13,509 (257)

Edge-Federated Learning-Based Intelligent Intrusion Detection System for Heterogeneous Internet of Things

IEEE Access
Distributed denial of service (DDoS) is an awful cyber threat, becoming more prevalent with mature heterogeneous IoT (HetIoT) applications like intelligent agriculture, wearables, and self-driving cars. Developing intelligent intrusion detection systems (
Shalaka S. Mahadik, Pranav M. Pawar, Raja Muthalagu   +2 more
doaj   +1 more source

The SIR Model in a Moving Population: Propagation of Infection and Herd Immunity

Communications on Pure and Applied Mathematics, EarlyView.
ABSTRACT In a collection of particles performing independent random walks on Zd$\mathbb {Z}^d$ we study the spread of an infection with SIR dynamics. Susceptible particles become infected when they meet an infected particle. Infected particles heal and are removed at rate ν$\nu$.
Duncan Dauvergne, Allan Sly
wiley   +1 more source

Advanced Optimization Techniques for Federated Learning on Non-IID Data

Future Internet
Federated learning enables model training on multiple clients locally, without the need to transfer their data to a central server, thus ensuring data privacy.
Filippos Efthymiadis, Aristeidis Karras, Christos Karras, Spyros Sioutas   +3 more
doaj   +1 more source

Fintech Policy and the Rise of Green Technological Inventions

Corporate Social Responsibility and Environmental Management, EarlyView.
ABSTRACT Technological transformation can enhance enterprises' green invention by promoting knowledge spillovers, alleviating financial constraints, and supporting innovative business models. Within this framework, our study investigates the effect of technological infrastructure construction on green technology invention, using the ‘Fintech China ...
Tao Huang, Qurat ul Ain, Tahir Yousaf, Kaiyang Zhong   +3 more
wiley   +1 more source

2D Implementation of Kinetic‐Diffusion Monte Carlo in Eiron

Contributions to Plasma Physics, EarlyView.
ABSTRACT Particle‐based kinetic Monte Carlo simulations of neutral particles are one of the major computational bottlenecks in tokamak scrape‐off layer simulations. This computational cost comes from the need to resolve individual collision events in high‐collisional regimes.
Oskar Lappi, Emil Løvbak, Thijs Steel, Giovanni Samaey   +3 more
wiley   +1 more source

Global Convergence of Continual Learning on Non-IID Data

CoRR
Continual learning, which aims to learn multiple tasks sequentially, has gained extensive attention. However, most existing work focuses on empirical studies, and the theoretical aspect remains under-explored. Recently, a few investigations have considered the theory of continual learning only for linear regressions, establishes the results based on ...
Fei Zhu 0004, Yujing Liu, Wenzhuo Liu, Zhaoxiang Zhang 0001   +3 more
openaire   +2 more sources

Epileptic arousals: A neglected clinical entity

Epilepsia, EarlyView.
Abstract Objective Epileptic arousals (EAs) are seizures characterized solely by arousal from sleep. EAs are unrecognized in current seizure classifications. Their subtle semiology and inconsistently detectable ictal activity in scalp electroencephalography (EEG) complicate differentiation from physiological arousals (PAs). This study characterizes EAs
Lea Fisel, Nóra Pető, Sotirios Kalousios, Nicolas Zabler, Armin Brandt, Peter C. Reinacher, Dieter Riemann, Mohammad F. Khazali, Andreas Schulze‐Bonhage   +8 more
wiley   +1 more source

VINO_EffiFedAV: VINO with efficient federated learning through selective client updates for real-time autonomous vehicle object detection

Results in Engineering
The advancement of autonomous vehicle technology relies heavily on sophisticated machine-learning models that facilitate real-time object detection and classification.
K. Vinoth, P. Sasikumar
doaj   +1 more source

Complex Versus Parsimonious Site‐Based Stochastic Ground Motion Models: Which One Is Better?

Earthquake Engineering &Structural Dynamics, EarlyView.
ABSTRACT Stochastic ground motion models (GMMs) provide a probabilistic representation of seismic input and are increasingly important for uncertainty quantification (UQ) in earthquake engineering. This study focuses on site‐based stochastic GMMs, which learn the statistical features of selected datasets of seismic records and generate statistically ...
Maijia Su, Mayssa Dabaghi, Marco Broccardo   +2 more
wiley   +1 more source

Fairness amidst non‐IID graph data: A literature review

AI Magazine
AbstractThe growing importance of understanding and addressing algorithmic bias in artificial intelligence (AI) has led to a surge in research on AI fairness, which often assumes that the underlying data are independent and identically distributed (IID).
Wenbin Zhang 0002, Jeremy C. Weiss, Shuigeng Zhou, Toby Walsh   +3 more
openaire   +2 more sources