Results 1 to 10 of about 98,022 (269)

Hyperparameter Tuning with High Performance Computing Machine Learning for Imbalanced Alzheimer’s Disease Data [PDF]

Applied Sciences (Switzerland), 2022
Accurate detection is still a challenge in machine learning (ML) for Alzheimer’s disease (AD). Class imbalance in imbalanced AD data is another big challenge for machine-learning algorithms working under the assumption that the data are evenly ...
Fan Zhang, Leigh A Johnson
exaly   +4 more sources

Optimizing lung cancer classification through hyperparameter tuning [PDF]

Digital Health
Artificial intelligence is steadily permeating various sectors, including healthcare. This research specifically addresses lung cancer, the world's deadliest disease with the highest mortality rate.
Syed Muhammad Nabeel, Sibghat Ullah Bazai, Nada Alasbali, Yifan Liu, Muhammad Imran Ghafoor, Rozi Khan, Chin Soon Ku, Jing Yang, Sana Shahab, Lip Yee Por   +9 more
doaj   +4 more sources

Hyperparameter Tuning for Machine Learning Algorithms Used for Arabic Sentiment Analysis

Informatics, 2021
Machine learning models are used today to solve problems within a broad span of disciplines. If the proper hyperparameter tuning of a machine learning classifier is performed, significantly higher accuracy can be obtained.
Enas Elgeldawi, Alaa M Zaki
exaly   +3 more sources

Efficient Hyperparameter Tuning with Grid Search for Text Categorization using kNN Approach with BM25 Similarity

Open Computer Science, 2019
In machine learning, hyperparameter tuning is the problem of choosing a set of optimal hyperparameters for a learning algorithm. Several approaches have been widely adopted for hyperparameter tuning, which is typically a time consuming process.
Raji Ghawi, Jürgen Pfeffer
exaly   +2 more sources

Accelerating Hyperparameter Tuning in Machine Learning for Alzheimer’s Disease With High Performance Computing [PDF]

Frontiers in Artificial Intelligence, 2021
Driven by massive datasets that comprise biomarkers from both blood and magnetic resonance imaging (MRI), the need for advanced learning algorithms and accelerator architectures, such as GPUs and FPGAs has increased.
Fan Zhang, Fan Zhang, Melissa Petersen, Melissa Petersen, Leigh Johnson, Leigh Johnson, James Hall, James Hall, Sid E. O’Bryant, Sid E. O’Bryant   +9 more
doaj   +2 more sources

Heuristic based federated learning with adaptive hyperparameter tuning for households energy prediction [PDF]

Scientific Reports
Federated Learning is transforming electrical load forecasting by enabling Artificial Intelligence (AI) models to be trained directly on household edge devices.
Liana Toderean, Mihai Daian, Tudor Cioara, Ionut Anghel, Vasilis Michalakopoulos, Efstathios Sarantinopoulos, Elissaios Sarmas   +6 more
doaj   +2 more sources

Klasifikasi COVID-19 menggunakan Filter Gabor dan CNN dengan Hyperparameter Tuning

Jurnal Elkomika, 2021
ABSTRAK Penyakit COVID-19 dapat timbul karena berbagai faktor sebab dan akibat, sehingga penyakit ini memiliki efek buruk bagi penderita. Pencitraan CT-Scan memiliki keunggulan dalam memproyeksikan kondisi paru-paru pasien penderita, sehingga dapat ...
AGUS EKO MINARNO, MOCHAMMAD HAZMI COKRO MANDIRI, MUHAMMAD RIFAL ALFARIZY   +2 more
doaj   +1 more source

Seleksi Fitur dengan Particle Swarm Optimization pada Klasifikasi Penyakit Parkinson Menggunakan XGBoost

Jurnal Teknologi Informasi dan Ilmu Komputer, 2023
Penyakit Parkinson merupakan gangguan pada sistem saraf pusat yang mempengaruhi sistem motorik. Diagnosis penyakit ini cukup sulit dilakukan karena gejalanya yang serupa dengan penyakit lain.
Deni Kurnia, Muhammad Itqan Mazdadi, Dwi Kartini, Radityo Adi Nugroho, Friska Abadi   +4 more
doaj   +3 more sources

Learning Multiple Defaults for Machine Learning Algorithms [PDF]

, 2021
The performance of modern machine learning methods highly depends on their hyperparameter configurations. One simple way of selecting a configuration is to use default settings, often proposed along with the publication and implementation of a new ...
Bischl, Bernd, Müller, Andreas, Pfisterer, Florian, Probst, Philipp, van Rijn, Jan N.   +4 more
core   +3 more sources

Impact of Hyperparameter Tuning on Machine Learning Models in Stock Price Forecasting

IEEE Access, 2021
Stock price forecasting has been reported as a challenging task in the scientific and financial communities due to stock prices’ nonlinear and dynamic nature.
Kazi Ekramul Hoque, Hamoud Aljamaan
doaj   +1 more source