Results 101 to 110 of about 7,279,238 (253)

Cell Segmentation Beyond 2D—A Review of the State‐of‐the‐Art

open access: yesAdvanced Intelligent Discovery, EarlyView.
Cell segmentation underpins many biological image analysis tasks, yet most deep learning methods remain limited to 2D despite the inherently 3D nature of cellular processes. This review surveys segmentation approaches beyond 2D, comparing 2.5D and fully 3D methods, analyzing 31 models and 32 volumetric datasets, and introducing a unified reference ...
Fabian Schmeisser   +6 more
wiley   +1 more source

An Autonomous Large Language Model‐Agent Framework for Transparent and Local Time Series Forecasting

open access: yesAdvanced Intelligent Discovery, EarlyView.
Architecture of the proposed large language model (LLM)‐based agent framework for autonomous time series forecasting in thermal power generation systems. The framework operates through a vertical pipeline initiated by natural language queries from users, which are processed by the LLM Agent Core powered by Llama.cpp and a ReAct loop with persistent ...
William Gouvêa Buratto   +5 more
wiley   +1 more source

Making differentiable neural-architecture searches applicable and hardware-aware

open access: yes
Neuronale Netze sind ein weit verbreiteter Computeralgorithmus mit vielseitigen Anwendungsgebieten, von Bilderkennung bis hin zu Chatbots. Während die Ergebnisse in diesen Anwendungen exzellent sind, führen die großen Mengen an Berechnungen auch dazu, dass neuronale Netze viel Energie und Latenz benötigen, um ausgeführt zu werden. Um Latenz und Energie
openaire   +2 more sources

Hardware-Aware Neural Architecture Search for Encrypted Traffic Classification on Resource-Constrained Devices

open access: yesIEEE Transactions on Network and Service Management
This paper presents a hardware-efficient deep neural network (DNN), optimized through hardware-aware neural architecture search (HW-NAS); the DNN supports the classification of session-level encrypted traffic on resource-constrained Internet of Things (IoT) and edge devices.
Adel Chehade   +3 more
openaire   +2 more sources

Hardware aware neural architecture search for optimizing EEG seizure detection in closed loop neurostimulator

open access: yes
One of the challenges of developing an implanted closed-loop neurostimulator for epilepsy treatment is to optimize the seizure detection algorithms for very low energy use while keeping the accuracy at an acceptable threshold.
Woias, Peter   +2 more
core  

Autonomous AI‐Driven Design for Skin Product Formulations

open access: yesAdvanced Intelligent Discovery, EarlyView.
This review presents a comprehensive closed‐loop framework for autonomous skin product formulation design. By integrating artificial intelligence‐driven experiment selection with automated multi‐tiered assays, the approach shifts development from trial‐and‐error to intelligent optimisation.
Yu Zhang   +5 more
wiley   +1 more source

Enhancing Neuromorphic Photonic Hardware Performance through Neural Architecture Search

open access: yes
Current neuromorphic photonic neural networks cannot achieve the performance of electronic neural networks due to the presence of physical constraints, such as noise and distortions, affecting the implementations based on analog photonic hardware.
Nicola Andriolli   +2 more
core  

Accelerating Discovery of Organic Molecular Crystals via Materials Informatics and Autonomous Experiments

open access: yesAdvanced Intelligent Discovery, EarlyView.
Materials informatics and autonomous experimentation are transforming the discovery of organic molecular crystals. This review presents an integrated molecule–crystal–function–optimization workflow combining machine learning, crystal structure prediction, and Bayesian optimization with robotic platforms.
Takuya Taniguchi   +2 more
wiley   +1 more source

Exploiting Ferroelectric and Spintronic Dynamics for Neural Network Computation

open access: yesAdvanced Intelligent Systems, EarlyView.
Ferroelectric and spintronic devices, relying on the control of polarization and magnetization, offer intrinsically fast, durable, energy‐efficient, and low‐latency building blocks for analog in‐memory computing. The hysteretic dynamics of an order parameter are leveraged to provide nonvolatile, multistate memory and nonlinear switching. Brain‐inspired
Dashiell Harrison   +4 more
wiley   +1 more source

Ecoefficiency Analysis and Regression in Data Conversion for Spiking Neural Network Training

open access: yesAdvanced Intelligent Systems, EarlyView.
The environmental footprint of spiking neural networks is quantified during dataset encoding and training for autonomous driving regression across three benchmarks. Temporal depth emerges as the dominant driver of energy consumption and CO2 emissions, while the accuracy–energy trade‐off proves dataset‐dependent. On conventional hardware, spiking models
Fernando S. Martínez   +3 more
wiley   +1 more source

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