Results 111 to 120 of about 98,022 (269)

Cardiovascular disease detection from cardiac arrhythmia ECG signals using artificial intelligence models with hyperparameters tuning methodologies

Heliyon
Cardiovascular disease (CVD) is connected with irregular cardiac electrical activity, which can be seen in ECG alterations. Due to its convenience and non-invasive aspect, the ECG is routinely exploited to identify different arrhythmias and automatic ECG
Gowri Shankar Manivannan, Harikumar Rajaguru, Rajanna S, Satish V. Talawar   +3 more
doaj   +1 more source

Decoding Spatial Heterogeneity and Multi‐Omics Regulation with Hierarchical Graph Learning

Advanced Science, EarlyView.
ABSTRACT Recent advances in spatial multi‐omics technologies have enabled the simultaneous profiling of multiple molecular layers within the same tissue slice, providing unprecedented opportunities to investigate tissue spatial organization. However, most existing computational methods identify spatial domains in a purely data‐driven manner, rarely ...
Jiazhou Chen, Jiahui Xie, Yi Liao, Junyu Li, Longqi Liu, Xin Gao, Hongmin Cai   +6 more
wiley   +1 more source

Optimizing Machine Learning Models for Graduation on Time Prediction: A Comparative Study with Resampling and Hyperparameter Tuning

JOIN: Jurnal Online Informatika
Timely graduation prediction is a crucial issue in higher education, especially when academic, demographic, and behavioral factors interact in complex ways.
Rizal Bakri, Syamsu Alam, Niken Probondani Astuti, Muhammad Ilham Bakhtiar   +3 more
doaj   +1 more source

Fundamental Challenges, Physical Implementations, and Integration Strategies for Ising Machines in Large‐Scale Optimization Tasks

Advanced Electronic Materials, EarlyView.
Ising machines are emerging as specialized hardware solvers for computationally hard optimization problems. This review examines five major platforms—digital CMOS, analog CMOS, emerging devices, coherent optics, and quantum systems—highlighting physics‐rooted advantages and shared bottlenecks in scalability and connectivity.
Hyunjun Lee, Joon Pyo Kim, Sanghyeon Kim
wiley   +1 more source

Online Hyperparameter Tuning in Bayesian Optimization for Material Parameter Identification: An Application in Strain-Hardening Plasticity for Automotive Structural Steel

AppliedMath
Effective identification of strain-hardening parameters is essential for predictive plasticity models used in automotive applications. However, the performance of Bayesian optimization depends strongly on kernel hyperparameters in the Gaussian-process ...
Teng Long, Leyu Wang, Cing-Dao Kan, James D. Lee   +3 more
doaj   +1 more source

Analog Weight Update Rule in Ferroelectric Hafnia, Using picoJoule Programming Pulses

Advanced Electronic Materials, EarlyView.
Resistive, ferroelectric synaptic weights based on BEOL‐compatible hafnia/zirconia nanolaminates are fabricated. Lateral downscaling the devices below 10 µm2 enables 20 ns programming with electrical pulses, dissipating ≤ 3 pJ. Experimental results show that final conductance state is set by pulse amplitude, and is largely independent of the initial ...
Alexandre Baigol, Nikhil Garg, Matteo Mazza, Yanming Zhang, Elisa Zaccaria, Wooseok Choi, Bert Jan Offrein, Laura Bégon‐Lours   +7 more
wiley   +1 more source

Optimizing a Hybrid Deep Learning Model for DDoS Detection Using DBSCAN and PSO

Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi)
This study proposes a hybrid deep learning approach that combines Gated Recurrent Units (GRUs) and Convolutional Neural Networks (CNNs) for Distributed Denial of Service (DDoS) cyberattack detection.
Indrastanti Ratna Widiasari, Rissal Efendi   +1 more
doaj   +1 more source

Integrating Automated Electrochemistry and High‐Throughput Characterization with Machine Learning to Explore Si─Ge─Sn Thin‐Film Lithium Battery Anodes

Advanced Energy Materials, Volume 15, Issue 11, March 18, 2025.
A closed‐loop, data‐driven approach facilitates the exploration of high‐performance Si─Ge─Sn alloys as promising fast‐charging battery anodes. Autonomous electrochemical experimentation using a scanning droplet cell is combined with real‐time optimization to efficiently navigate composition space.
Alexey Sanin, Jackson K. Flowers, Tobias H. Piotrowiak, Frederic Felsen, Leon Merker, Alfred Ludwig, Dominic Bresser, Helge Sören Stein   +7 more
wiley   +1 more source

The Use of Hyperparameter Tuning in Model Classification: A Scientific Work Area Identification

JOIV: International Journal on Informatics Visualization
This research aims to investigate the effectiveness of hyperparameter tuning, particularly using Optuna, in enhancing the classification performance of machine learning models on scientific work reviews. The study focuses on automating the classification
Nadya Alinda Rahmi, Sarjon Defit, - Okfalisa   +2 more
doaj   +1 more source

Hyperparameter Tuning Through Pessimistic Bilevel Optimization

Automated hyperparameter search in machine learning, especially for deep learning models, is typically formulated as a bilevel optimization problem, with hyperparameter values determined by the upper level and the model learning achieved by the lower-level problem.
Ustun, Meltem Apaydin, Xu, Liang, Zeng, Bo, Qian, Xiaoning   +3 more
openaire   +2 more sources