Results 41 to 50 of about 212,881 (293)

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

Hyperparameter Optimization and Combined Data Sampling Techniques in Machine Learning for Customer Churn Prediction: A Comparative Analysis

Technologies, 2023
This paper explores the application of various machine learning techniques for predicting customer churn in the telecommunications sector. We utilized a publicly accessible dataset and implemented several models, including Artificial Neural Networks ...
Mehdi Imani, H. Arabnia
semanticscholar   +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

Hyperparameter Tuning Approaches

, 2023
AbstractThis chapter provides a broad overview over the different hyperparameter tunings. It details the process of HPT, and discusses popular HPT approaches and difficulties. It focuses on surrogate optimization, because this is the most powerful approach.
Thomas Bartz-Beielstein, Martin Zaefferer   +1 more
openaire   +1 more source

Learning Individualized Hyperparameter Settings

Algorithms, 2023
The performance of optimization algorithms, and consequently of AI/machine learning solutions, is strongly influenced by the setting of their hyperparameters. Over the last decades, a rich literature has developed proposing methods to automatically determine the parameter setting for a problem of interest, aiming at either robust or instance-specific ...
Vittorio Maniezzo, Tingting Zhou
openaire   +3 more sources

Predicting the Future Burden of Renal Replacement Therapy in Türkiye Using National Registry Data and Comparative Modeling Approaches

Therapeutic Apheresis and Dialysis, EarlyView.
ABSTRACT Background Chronic kidney disease is a growing public health problem worldwide, and the number of patients requiring renal replacement therapy is steadily increasing. Türkiye has experienced a similar rise in both the incidence and prevalence of renal replacement therapy over the past decades; however, national‐level projections of future ...
Arzu Akgül, Neriman Sıla Koç, Berrak Itır Aylı   +2 more
wiley   +1 more source

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, Abraham Osinuga, Dabira Ogunbiyi, Ark O. Ifeanyi, Ikenna E Odezuligbo, Oluwagbotemi E Edu   +11 more
semanticscholar   +1 more source

Accelerating Hyperparameter Optimisation with PyCOMPSs [PDF]

Workshop Proceedings of the 48th International Conference on Parallel Processing, 2019
Machine Learning applications now span across multiple domains due to the increase in computational power of modern systems. There has been a recent surge in Machine Learning applications in High Performance Computing (HPC) in an attempt to speed up training. However, besides training, hyperparameters optimisation(HPO) is one of the most time consuming
Njoroge Kahira, Albert, Bautista Gomez, Leonardo, Conejero, Javier, Badia Sala, Rosa Maria   +3 more
openaire   +2 more sources

Tumor mutational burden as a determinant of metastatic dissemination patterns

Molecular Oncology, EarlyView.
This study performed a comprehensive analysis of genomic data to elucidate whether metastasis in certain organs share genetic characteristics regardless of cancer type. No robust mutational patterns were identified across different metastatic locations and cancer types.
Eduardo Candeal, Andrea Moreno‐Manuel, Miguel Salvadó‐Pertierra, Cristina Santos‐Vivas, Rebeca Sanz‐Pamplona   +4 more
wiley   +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