Results 41 to 50 of about 151,967 (341)

Transformer-Based Semantic Segmentation for Extraction of Building Footprints from Very-High-Resolution Images

Sensors, 2023
Semantic segmentation with deep learning networks has become an important approach to the extraction of objects from very high-resolution remote sensing images.
Jia Song, A-Xing Zhu, Yunqiang Zhu
doaj   +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

A Joint-Parameter Estimation and Bayesian Reconstruction Approach to Low-Dose CT

Sensors, 2023
Most penalized maximum likelihood methods for tomographic image reconstruction based on Bayes’ law include a freely adjustable hyperparameter to balance the data fidelity term and the prior/penalty term for a specific noise–resolution tradeoff.
Yongfeng Gao, Siming Lu, Yongyi Shi, Shaojie Chang, Hao Zhang, Wei Hou, Lihong Li, Zhengrong Liang   +7 more
doaj   +1 more source

Hyperparameter Optimization: A Spectral Approach

CoRR, 2017
We give a simple, fast algorithm for hyperparameter optimization inspired by techniques from the analysis of Boolean functions. We focus on the high-dimensional regime where the canonical example is training a neural network with a large number of hyperparameters.
Elad Hazan, Adam R. Klivans, Yang Yuan 0010   +2 more
openaire   +4 more sources

Optimizing microservices with hyperparameter optimization

2021 17th International Conference on Mobility, Sensing and Networking (MSN), 2021
In the last few years, the cloudification of applications requires new concepts and techniques to fully reap the benefits of the new computing paradigm. Among them, the microservices architectural style, which is inspired by service-oriented architectures, has gained attention from both industry and academia.
Hai Dinh-Tuan, Katerina Katsarou, Patrick Herbke   +2 more
openaire   +4 more sources

Hyperparameter Tuning in Machine Learning: A Comprehensive Review

Journal of Engineering Research and Reports
Hyperparameter tuning is essential for optimizing the performance and generalization of machine learning (ML) models. This review explores the critical role of hyperparameter tuning in ML, detailing its importance, applications, and various optimization ...
Justus A Ilemobayo, O. Durodola, Oreoluwa Alade, Opeyemi J Awotunde, Adewumi T Olanrewaju, Olumide Babatope Falana, Adedolapo Ogungbire, A. Osinuga, Dabira Ogunbiyi, Ark O. Ifeanyi, Ikenna E Odezuligbo, O. E. Edu   +11 more
semanticscholar   +1 more source

Be aware of overfitting by hyperparameter optimization! [PDF]

Journal of Cheminformatics
Hyperparameter optimization is very frequently employed in machine learning. However, an optimization of a large space of parameters could result in overfitting of models.
I. Tetko, R. van Deursen, Guillaume Godin   +2 more
semanticscholar   +1 more source

Variation in predictive performance across hyperparameter combinations.

, 2022
In Analysis 4, we used nested cross validation to evaluate multiple hyperparameter combinations for each classification algorithm. We assessed the extent to which the area under the receiver operating characteristic curve (AUROC) varied across the ...
Dustin B. Miller (7064549), Stephen R. Piccolo (8367780), Nathan P. Golightly (12231716), Jérémie L. Johnson (12231719), Avery Mecham (12231713)   +4 more
core   +1 more source

Spatial and single‐nuclei transcriptomics reveals idiosyncratic and generic patterns in papillary and anaplastic thyroid cancers

Molecular Oncology, EarlyView.
Matched spatial transcriptomics and single‐nuclei RNA‐seq were generated for anaplastic and BRAFV600E papillary thyroid cancers revealing generic and tumor‐specific states occurring in cancer cells and in the tumor microenvironment. In this context, cancer dedifferentiation mirrored organoid maturation through ordered thyroid marker gain/loss ...
Adrien Tourneur, Joel Rodrigues Vitória, Manuel Saiselet, Ligia Craciun, Denis Larsimont, Anne Lefort, Frédérick Libert, Carine Maenhaut, Sabine Costagliola, Maxime Tarabichi, Mirian Romitti, Vincent Detours   +11 more
wiley   +1 more source