Results 81 to 90 of about 96,348 (272)

An Optimized Hyperparameter Tuning for Improved Hate Speech Detection with Multilayer Perceptron

Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi)
Hate speech classification is a critical task in the domain of natural language processing, aiming to mitigate the negative impacts of harmful content on digital platforms.
Muhamad Ridwan, Ema Utami
doaj   +1 more source

MGM as a Large‐Scale Pretrained Foundation Model for Microbiome Analyses in Diverse Contexts

Advanced Science, EarlyView.
We present the Microbial General Model (MGM), a transformer‐based foundation model pretrained on over 260,000 microbiome samples. MGM learns contextualized microbial representations via self‐supervised language modeling, enabling robust transfer learning, cross‐regional generalization, keystone taxa discovery, and prompt‐guided generation of realistic,
Haohong Zhang, Yuli Zhang, Zixin Kang, Jiayun Xiong, Ronghua Yang, Kang Ning   +5 more
wiley   +1 more source

Hybrid photovoltaic/thermal performance prediction based on machine learning algorithms with hyper-parameter tuning

International Journal of Sustainable Energy
A hybrid Photovoltaic/Thermal(PV/T) approach is proposed in this study based on extensive research and a comparative analysis of several hyperparameter tuning methods. The models analyzed are Linear Regression (LR), Random Forest (RF), XGBoost Regression,
Karthikeyan Ganesan, Satheeshkumar Palanisamy, Valarmathi Krishnasamy, Ayodeji Olalekan Salau, Vinoth Rathinam, Sankar Ganesh Seeni Nayakkar   +5 more
doaj   +1 more source

Evaluating the Utilities of Foundation Models in Single‐Cell Data Analysis

Advanced Science, EarlyView.
This study delivers the first systematic, task‐level evaluation of single‐cell foundation models across eight core analytical tasks. By benchmarking 10 leading models with the scEval framework, it reveals where foundation models truly add value, where task‐specific methods still dominate, and provides concrete, reproducible guidelines to steer the next
Tianyu Liu, Kexing Li, Yuge Wang, Hongyu Li, Hongyu Zhao   +4 more
wiley   +1 more source

High‐Fidelity Synthetic Data Replicates Clinical Prediction Performance in a Million‐Patient Diabetes Cohort

Advanced Science, EarlyView.
This study generates high‐fidelity synthetic longitudinal records for a million‐patient diabetes cohort, successfully replicating clinical predictive performance. However, deeper analysis reveals algorithmic biases and trajectory inconsistencies that escape standard quality metrics. These findings challenge current validation norms, demonstrating why a
Francisco Ortuño, Víctor M. de la Oliva Roque, Javier‐Ignacio Ramirez‐Lopez, David P. Kreil, Joaquín Dopazo, Carlos Loucera   +5 more
wiley   +1 more source

Machine-Learning Crop-Type Mapping Sensitivity to Feature Selection and Hyperparameter Tuning

Remote Sensing
To improve crop yields and incomes, farmers consistently adapt their practices to climate and market fluctuations, resulting in highly variable crop field distribution and coverage in space and time.
Mayra Perez-Flores, Frédéric Satgé, Jorge Molina-Carpio, Renaud Hostache, Ramiro Pillco-Zolá, Diego Tola, Elvis Uscamayta-Ferrano, Lautaro Bustillos, Marie-Paule Bonnet, Celine Duwig   +9 more
doaj   +1 more source

Tuning the Tuner: Introducing Hyperparameter Optimization for Auto-Tuning

2025 IEEE International Conference on eScience (eScience)
Automatic performance tuning (auto-tuning) is widely used to optimize performance-critical applications across many scientific domains by finding the best program variant among many choices. Efficient optimization algorithms are crucial for navigating the vast and complex search spaces in auto-tuning. As is well known in the context of machine learning
Willemsen, Floris-Jan, van Nieuwpoort, Rob V., van Werkhoven, Ben   +2 more
openaire   +2 more sources

Deep Learning‐Powered Scalable Cancer Organ Chip for Cancer Precision Medicine

Advanced Science, EarlyView.
This scalable, low‐cost Organ Chip platform, made via injection molding, uses capillary pinning for hydrogel confinement and supports versatile tissue coculture and robust imaging. Deep learning enables label‐free, sensitive phenotypic analysis.
Yu‐Chieh Yuan, Beibei Xu, Jenna McCormack, XuHai Huang, Jingzhe Ma, Thomas Marshall, Yacong Sun, Hardeep Singh, Alyssa Fanelli, Gauri Kulkarni, Ji Hye Seo, Paige Gilbride, Bing Wei, Bo Wang, Yanyan Liu, Fei Ma, Lin Zhou, Shuyang Wang, Xiaohua Qian, Zhiyong Xie, Polina Golland, Longlong Si, Yu Shrike Zhang, Xin Xie, Haiqing Bai   +24 more
wiley   +1 more source

Efficient Q-learning hyperparameter tuning using FOX optimization algorithm

Results in Engineering
Reinforcement learning is a branch of artificial intelligence in which agents learn optimal actions through interactions with their environment. Hyperparameter tuning is crucial for optimizing reinforcement learning algorithms and involves the selection ...
Mahmood A. Jumaah, Yossra H. Ali, Tarik A. Rashid   +2 more
doaj   +1 more source

Structural Eigenmodes of the Brain to Improve the Source Localization of EEG: Application to Epileptiform Activity

Advanced Science, EarlyView.
Geometry and connectivity are complementary structures, which have demonstrated their ability to represent the brain's functional activity. This study evaluates geometric and connectome eigenmodes as biologically informed constraints for EEG source localization.
Pok Him Siu, Philippa J. Karoly, Sina Mansour L., Artemio Soto‐Breceda, Levin Kuhlmann, Mark J. Cook, David B. Grayden   +6 more
wiley   +1 more source