Results 101 to 110 of about 151,967 (341)

Full‐Field Damage Monitoring in Architected Lattices Using In situ Electrical Impedance Tomography

Advanced Functional Materials, EarlyView.
In situ electrical impedance tomography (EIT) turns 3D‐printed, CNT‐infused architected lattices into full‐field damage‐imaging systems. Tunable Voronoi‐based geometries act as active sensing architectures, enabling conductivity maps to detect early‐stage damage and localise sequential ligament fracture before catastrophic failure.
Akash Deep, Andrea Samore, Alistair McEwan, Andrew McBride, Shanmugam Kumar   +4 more
wiley   +1 more source

Evaluation of Spatial Variability of Soil Nutrients in Saline–Alkali Farmland Using Automatic Machine Learning Model and Hyperspectral Data

ISPRS International Journal of Geo-Information
Saline–alkali soils represent a significant reserve of arable land, playing a vital role in ensuring national food security. Given that saline–alkali soil has low soil organic matter (SOM) and soil nutrient contents, and that soil quality degradation ...
Meiyan Xiang, Qianlong Rao, Xiaohang Yang, Xiaoqian Wu, Dexi Zhan, Jin Zhang, Miao Lu, Yingqiang Song   +7 more
doaj   +1 more source

Enhancing brain tumor diagnosis: an optimized CNN hyperparameter model for improved accuracy and reliability

PeerJ Computer Science
Hyperparameter tuning plays a pivotal role in the accuracy and reliability of convolutional neural network (CNN) models used in brain tumor diagnosis. These hyperparameters exert control over various aspects of the neural network, encompassing feature ...
Abdullah A. Asiri, Ahmad Shaf, Tariq Ali, M. Aamir, Muhammad Irfan, Saeed Alqahtani   +5 more
semanticscholar   +1 more source

List of hyperparameter values.

, 2019
List of hyperparameter values.
Jae Joseph Russell Rodriguez (8082014), Joverlyn Gaudillo (8082011), Rommel Bulalacao (8082026), Lei Rigi Baltazar (8082020), Julianne Vilela (8082023), Allen Nazareno (8082017), Mario Domingo (8082029), Jason Albia (8082032)   +7 more
core   +1 more source

HDS-LEE Course on Hyperparameter Optimization [PDF]

, 2020
Part I: Theory - Basics of Hyperparameter Optimization - Exhausive Searches - Surrogate-based Optimization, Sequential Model-based Optimization and Bayesian Inference - Evolutionary Strategies Part II: Hands-on programming session "Hyperparameter
Rüttgers, Alexander, Debus, Charlotte
core  

LEAD: Literature Enhanced Ab Initio Discovery of Nitride Dusting Layers for Enhanced Tunnel Magnetoresistance and Lower Resistance Magnetic Tunnel Junctions

Advanced Materials, EarlyView.
Magnetic tunnel junctions (MTJs) using MgO tunnel barriers face challenges of high resistance‐area product and low tunnel magnetoresistance (TMR). To discover alternative materials, Literature Enhanced Ab initio Discovery (LEAD) is developed. The LEAD‐predicted materials are theoretically evaluated, showing that MTJs with dusting of ScN or TiN on ...
Sabiq Islam, William S. Rogers, Chen‐Yu Hu, Ming‐Yuan Song, Xinyu Bao, Shehrin Sayed, Jean Anne C. Incorvia   +6 more
wiley   +1 more source

Perbandingan Algoritma Machine Learning Menggunakan Pemilihan Fitur Chi-square dalam Pengklasifikasian Penyakit Jantung

Jurnal Saintekom
Heart disease is one of the deadliest diseases worldwide. This condition often presents symptoms that do not immediately cause severe effects on the sufferer, making early anticipation crucial.
Hirmayanti Hirmayanti, Ema Utami
doaj   +1 more source

Empirical Enhancement of Intrusion Detection Systems: A Comprehensive Approach with Genetic Algorithm-based Hyperparameter Tuning and Hybrid Feature Selection

The Arabian journal for science and engineering
Machine learning-based IDSs have demonstrated promising outcomes in identifying and mitigating security threats within IoT networks. However, the efficacy of such systems is contingent on various hyperparameters, necessitating optimization to elevate ...
Halit Bakır, Özlem Ceviz
semanticscholar   +1 more source

Hyperparameter optimization results.

, 2023
This file stores the hyperparameter search ranges and their optimized values of each prediction model. (XLSX)
Takuma Shibahara (5437208), Atsushi Okamoto (198481), Chisa Wada (10825570), Masamichi Sato (455506), Junichi Kuwata (15810664), Yasuho Yamashita (10825573), Kazuhiro Fujita (740922), Yoshimasa Ono (10825576)   +7 more
core   +1 more source

Organic Materials of Tomorrow: Horizons of Artificial Intelligence

Advanced Materials, EarlyView.
This review examines machine learning techniques accelerating the discovery of organic semiconductors by linking molecular structure to properties. Key methods include graph neural networks, generative models, and active learning. Applications to organic photovoltaics demonstrate practical impact.
Harold Mena, J. Terence Blaskovits, Kun‐Han Lin, Denis Andrienko   +3 more
wiley   +1 more source