GraphFedAI framework for DDoS attack detection in IoT systems using federated learning and graph based artificial intelligence

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Now is an exciting time for the domain of Earth observation (EO), with a multitude of diverse sensors looking at the planet from satellites, airplanes or drones. The volume of imagery acquired is massive, and hold great potential for a variety of applicati ...

As one of the major threats to women's health worldwide, breast cancer requires early diagnosis and accurate classification, since they are key to optimizing therapeutic interventions and ensuring precise prognosis. Recently, deep learning has demonstrated ...

Deploying energy-efficient deep neural networks on energy-constrained edge devices is an important research topic in both machine learning and circuit design communities. Both artificial neural networks (ANNs) and spiking neural networks (SNNs) have been p ...

Facial paralysis is a highly burdening condition, resulting in a patient's inability to move his musculature on one or both sides of the mouth. The restoration of facial movement is crucial for enhancing the overall patients' quality of life. The use of DE ...

Dynamic situations and applications are supported by the diverse devices and communication technologies that constitute the Internet of Things concept. Despite this, communication backlogs are common due to rising network demand and insufficient resource a ...

Contemporary AI faces important limitations that remain unresolved despite significant successes. Chief among these are the inability to acquire new knowledge without destroying old, the incomprehensible and nonengineerable nature of internal representatio ...

Deep neural networks (DNNs) are receiving increasing attention in wind power forecasting due to their ability to effectively capture complex patterns in wind data. However, their forecast errors are severely limited by the local optimal weight issue in opt ...

Many empirical studies have provided evidence for the emergence of algorithmic mechanisms (abilities) in the learning of language models, that lead to qualitative improvements in the model capabilities. Yet, a theoretical characterization of how such mecha ...

We present a reinforcement learning framework for constructing assemblies composed of rigid parts, which are commonly seen in many historical masonry buildings and bridges. Traditional construction methods for such structures often depend on dense scaffold ...

Differences in acquisition protocols or hardware result in measurable changes in image characteristics. These differences affect distributional properties and can also affect the spatial and temporal characteristics of the images. Likewise, the distributio ...

This paper investigates the fundamental regression task of learning k neurons (a.k.a. teachers) from Gaussian input, using two-layer ReLU neural networks with width m (a.k.a. students) and m, k = O(1), trained via gradient descent under proper initializati ...

High-dimensional data must be highly structured to be learnable. Although the compositional and hierarchical nature of data is often put forward to explain learnability, quantitative measurements establishing these properties are scarce. Likewise, accessin ...

Sign Language conveys information through multiple channels composed of manual (handshape, hand movement) and non-manual (facial expression, mouthing, body posture) components. Sign language assessment involves giving granular feedback to a learner, in ter ...

Purpose: To investigate the efficacy of Fluid and White Matter Suppression (FLAWS) MRI sequence in improving Deep Learning (DL)-based detection and segmentation of cortical lesions in Multiple Sclerosis (MS) patients even, and to develop models that can ge ...

Real-time bioaerosol monitoring is improving the quality of life for people affected by allergies, but it often relies on deep learning models which pose challenges for widespread adoption. These models are typically trained in a supervised fashion and req ...

Deploying compute-intensive deep neural networks (DNNs) on resource-constrained end devices has become a prominent trend, enabling localized intelligence. However, efficiently deploying these DNNs at scale poses challenges. To address this, extensive resea ...

Federated learning (FL) has recently emerged as a compelling machine learning paradigm, prioritizing the protection of privacy for training data. The increasing demand to address issues such as "the right to be forgotten" and combat data poisoning attacks ...

Buildings significantly impact urban energy consumption. Mobile passive solutions, such as manual solar protections, can mitigate heat gains, but their effectiveness depends on occupants’ decisions. Analyzing occupant adjustments of manual systems is chall ...

Earth scientists study a variety of problems with remote sensing data, but they most often consider them in isolation from each other, which limits information flows across disciplines. In this work, we present METEOR, a meta-learning methodology for Earth ...

A range of behavioral and contextual factors, including eating and drinking behavior, mood, social context, and other daily activities, can significantly impact an individual's quality of life and overall well-being. Therefore, inferring everyday life aspe ...

GraphFedAI framework for DDoS attack detection in IoT systems using federated learning and graph based artificial intelligence

Chattez avec Graph Search

Interact with Earth Observation Images using AI: Transparent Methods and Evaluation for Visual Question Answering

Mspo: a Machine Learning Hyperparameter Optimization Method for Enhanced Breast Cancer Image Classification

Eflop: a Sparsity-aware Metric for Evaluating Computational Cost in Spiking and Non-spiking Neural Networks

DEMO: Dielectric elastomer actuators (DEAs) for the restoration of facial movements

Enhancing dynamic resource management in decentralized federated learning for collaborative edge Internet of Things

Foundations of a New Learning Paradigm in AI Grounded in the Principles of Evolutionary Developmental Biology

Improving the accuracy and interpretability of neural networks for wind power forecasting

A phase transition between positional and semantic learning in a solvable model of dot-product attention

Learning to Assemble with Alternative Plans

Domain shift, domain adaptation, and generalization: A focus on MRI

How Gradient Descent Balances Features: A Dynamical Analysis For Two-Layer Neural Networks

Probing the latent hierarchical structure of data via diffusion models

Posterior-Based Analysis of Spatio-Temporal Features for Sign Language Assessment

Fluid and White Matter Suppression contrasts MRI improves Deep Learning detection of Multiple Sclerosis Cortical Lesions

Self-supervised and few-shot learning for robust bioaerosol monitoring

RAPNet: Resolution-Adaptive and Predictive Early Exit Network for Efficient Image Recognition

Fast-FedUL: A Training-Free Federated Unlearning with Provable Skew Resilience

Machine-learning to analyze human-building interactions: How do people use mobile solar protections?

Meta-learning to address diverse Earth observation problems across resolutions

Generalization and Personalization of Machine Learning for Multimodal Mobile Sensing in Everyday Life