Results 1 to 10 of about 983,651 (147)

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   +3 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, Awny Sayed, Ahmed R. Galal, Alaa M. Zaki   +3 more
doaj   +4 more sources

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

Applied Sciences, 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, Melissa Petersen, Leigh Johnson, James Hall, Sid E. O’Bryant   +4 more
doaj   +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   +3 more sources

Semi-supervised GAN with hybrid regularization and evolutionary hyperparameter tuning for accurate melanoma detection. [PDF]

Sci Rep
Melanoma, influenced by changes in deoxyribonucleic acid (DNA), requires early detection for effective treatment. Traditional melanoma research often employs supervised learning methods, which necessitate large, labeled datasets and are sensitive to ...
Golkarieh A, Razmara P, Lagzian A, Dolatabadi A, Mousavirad SJ.   +4 more
europepmc   +2 more sources

A novel method of bayesian genetic optimization on automated hyperparameter tuning. [PDF]

Sci Rep
This paper presents a novel approach that integrates Symbolic Genetic Programming (SGP) with Bayesian techniques within a Deep Neural Network (DNN) framework.
Li Q, Kamaruddin N, Zhang J, Peng C, Sui Ki Khoo A.   +4 more
europepmc   +2 more sources

Ensemble genetic and CNN model-based image classification by enhancing hyperparameter tuning. [PDF]

Sci Rep
Model optimization is a problem of great concern and challenge for developing an image classification model. In image classification, selecting the appropriate hyperparameters can substantially boost the model’s ability to learn intricate patterns and ...
Hussain W, Mushtaq MF, Shahroz M, Akram U, Ghith ES, Tlija M, Kim TH, Ashraf I.   +7 more
europepmc   +2 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.
Ghawi Raji, Pfeffer Jürgen
doaj   +2 more sources

Research and Analysis of IndoBERT Hyperparameter Tuning in Fake News Detection

Jurnal Nasional Teknik Elektro dan Teknologi Informasi
The rapid advancement of communication technology has transformed how information is shared, but it has also brought concerns about the proliferation of false information.
Anugerah Simanjuntak, Rosni Lumbantoruan, Kartika Sianipar, Rut Gultom, Mario Simaremare, Samuel Situmeang, Erwin Panggabean   +6 more
doaj   +2 more sources

Optimizing Machine Learning Models for Urban Sciences: A Comparative Analysis of Hyperparameter Tuning Methods

Urban Science
Advancing urban scholarship and addressing pressing challenges such as gentrification, housing affordability, and urban sprawl require robust predictive models.
Tris Kee, Winky K.O. Ho
doaj   +2 more sources