Results 21 to 30 of about 171,040 (210)

Deep Learning in Forest Structural Parameter Estimation Using Airborne LiDAR Data

IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2021
Accurately estimating and mapping forest structural parameters are essential for monitoring forest resources and understanding ecological processes. The novel deep learning algorithm has the potential to be a promising approach to improve the estimation ...
Hao Liu, Xin Shen, Lin Cao, Ting Yun, Zhengnan Zhang, Xiaoyao Fu, Xinxin Chen, Fangzhou Liu   +7 more
doaj   +1 more source

Hyperparameter Importance Across Datasets

, 2018
With the advent of automated machine learning, automated hyperparameter optimization methods are by now routinely used in data mining. However, this progress is not yet matched by equal progress on automatic analyses that yield information beyond ...
Bergstra J., Bonilla E. V., Brazdil P., Demvsar J., Feurer M., Jamieson K., Joachims T., Klein A., Li L., Loshchilov I., Sobol I. M., van Rijn J. N., van Rijn J. N., Wistuba M.   +13 more
core   +1 more source

Novel GA-Based DNN Architecture for Identifying the Failure Mode with High Accuracy and Analyzing Its Effects on the System

Applied Sciences
Symmetric data play an effective role in the risk assessment process, and, therefore, integrating symmetrical information using Failure Mode and Effects Analysis (FMEA) is essential in implementing projects with big data. This proactive approach helps to
Naeim Rezaeian, Regina Gurina, Olga A. Saltykova, Lokmane Hezla, Mammetnazar Nohurov, Kazem Reza Kashyzadeh   +5 more
doaj   +1 more source

Brain Tumor Detection and Classification Using an Optimized Convolutional Neural Network

Diagnostics
Brain tumors are a leading cause of death globally, with numerous types varying in malignancy, and only 12% of adults diagnosed with brain cancer survive beyond five years.
Muhammad Aamir, Abdallah Namoun, Sehrish Munir, Nasser Aljohani, Meshari Huwaytim Alanazi, Yaser Alsahafi, Faris Alotibi   +6 more
doaj   +1 more source

Is One Hyperparameter Optimizer Enough?

, 2018
Hyperparameter tuning is the black art of automatically finding a good combination of control parameters for a data miner. While widely applied in empirical Software Engineering, there has not been much discussion on which hyperparameter tuner is best ...
Bergstra J., Bergstra J., Lewis C., Menzies T.   +3 more
core   +1 more source

Applicability of mitotic figure counting by deep learning: a development and pan‐cancer validation study

FEBS Open Bio, EarlyView.
In this study, we developed a deep learning method for mitotic figure counting in H&E‐stained whole‐slide images and evaluated its prognostic impact in 13 external validation cohorts from seven different cancer types. Patients with more mitotic figures per mm2 had significantly worse patient outcome in all the studied cancer types except colorectal ...
Joakim Kalsnes, Maria X. Isaksen, Frida Julbø, Manohar Pradhan, Andreas Kleppe, Sepp De Raedt, Ole‐Johan Skrede, Turid Torheim, John Arne Nesheim, Hans Martin Mohn, Hanne A. Askautrud, Karolina Cyll, Wanja Kildal, Emma Rewcastle, Melinda Lillesand, Vebjørn Kvikstad, Emiel Janssen, Robert Jones, Odd Terje Brustugun, Bjørn Brennhovd, Erik Skaaheim Haug, Lill‐Tove Rasmussen Busund, Elin Richardsen, Sigve Andersen, Tom Dønnem, Kristina Lindemann, Gunnar Kristensen, Neil A. Shepherd, Marco Novelli, Knut Liestøl, David Kerr, Håvard E. Danielsen, Tarjei S. Hveem   +32 more
wiley   +1 more source

Deep Learning–Assisted Differentiation of Four Peripheral Neuropathies Using Corneal Confocal Microscopy

Annals of Clinical and Translational Neurology, EarlyView.
ABSTRACT Objective Peripheral neuropathies contribute to patient disability but may be diagnosed late or missed altogether due to late referral, limitation of current diagnostic methods and lack of specialized testing facilities. To address this clinical gap, we developed NeuropathAI, an interpretable deep learning–based multiclass classification ...
Chaima Ben Rabah, Ioannis N. Petropoulos, Mark Stettner, Maryam Ferdousi, Uazman Alam, Nathan Efron, Ahmed Serag, Rayaz A. Malik   +7 more
wiley   +1 more source

Predicting Epileptogenic Tubers in Patients With Tuberous Sclerosis Complex Using a Fusion Model Integrating Lesion Network Mapping and Machine Learning

Annals of Clinical and Translational Neurology, EarlyView.
ABSTRACT Objective Accurate localization of epileptogenic tubers (ETs) in patients with tuberous sclerosis complex (TSC) is essential but challenging, as these tubers lack distinct pathological or genetic markers to differentiate them from other cortical tubers.
Tinghong Liu, Qi Wang, Suhui Kuang, Dezhi Cao, Ping Ding, Shaohui Zhang, Haihua Wei, Zhirong Wei, Jinshan Xu, Xinyu Huang, Bing Liu, Shuli Liang   +11 more
wiley   +1 more source

Forecasting day-ahead electricity prices in Europe: the importance of considering market integration

, 2017
Motivated by the increasing integration among electricity markets, in this paper we propose two different methods to incorporate market integration in electricity price forecasting and to improve the predictive performance.
De Ridder, Fjo, De Schutter, Bart, Lago, Jesus, Vrancx, Peter   +3 more
core   +2 more sources

Characterization of Defect Distribution in an Additively Manufactured AlSi10Mg as a Function of Processing Parameters and Correlations with Extreme Value Statistics

Advanced Engineering Materials, EarlyView.
Predicting extreme defects in additive manufacturing remains a key challenge limiting its structural reliability. This study proposes a statistical framework that integrates Extreme Value Theory with advanced process indicators to explore defect–process relationships and improve the estimation of critical defect sizes. The approach provides a basis for
Muhammad Muteeb Butt, Varma Kvvssn, Hossein Laieghi, Zia Uddin, Peyman Ansari, E. Kıvanç Sadak, Can Barış Toprak, Hüseyin Kızıl, Enrico Salvati   +8 more
wiley   +1 more source