Results 111 to 120 of about 983,800 (290)

Effect of Hyperparameter Tuning on the Performance of YOLOv8 for Multi Crop Classification on UAV images

Applied Sciences
This study investigates the performance of YOLOv8, a Convolutional Neural Network (CNN) architecture, for multi-crop classification in a mixed farm with Unmanned Aerial Vehicle (UAV) imageries.
O. Ajayi, P. Ibrahim, Oluwadamilare Samuel Adegboyega   +2 more
semanticscholar   +1 more source

ML Workflows for Screening Degradation‐Relevant Properties of Forever Chemicals

Advanced Science, EarlyView.
The environmental persistence of per‐ and polyfluoroalkyl substances (PFAS) necessitates efficient remediation strategies. This study presents physics‐informed machine learning workflows that accurately predict critical degradation properties, including bond dissociation energies and polarizability.
Pranoy Ray, Andrew R. Castillo, Manoj Kolel‐Veetil, Surya R. Kalidindi   +3 more
wiley   +1 more source

Performance–Complexity Trade‐Offs in Battery Lifetime Prediction with Task‐Aware Transformers

Advanced Science, EarlyView.
FAST‐BatPro integrates convolutional feature extraction, flash Attention, and sparse attention for efficient battery lifetime prediction. Using limited early‐cycle data across multiple chemistries and operating conditions, it achieves robust accuracy while reducing inference latency, computational cost, and energy consumption.
Jingyuan Zhao, Misheng Cai, Zhenghong Wang, Yan Wang, Yuqi Li, Bo Dong, Hewu Wang, Andrew F. Burke, Simona Onori, Stephen J. Harris   +9 more
wiley   +1 more source

Metaheuristics Approach for Hyperparameter Tuning of Convolutional Neural Network

Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi)
Deep learning is an artificial intelligence technique that has been used for various tasks. Deep learning performance is determined by its hyperparameter, architecture, and training (connection weight and bias).
H. Purnomo, Tad Gonsalves, Evangs Mailoa, Fian Julio Santoso, Muhammad Rizky Pribadi   +4 more
semanticscholar   +1 more source

Overcoming Artificial Structures in Resolution‐Enhanced Hi‐C Data by Signal Decomposition and Multi‐Scale Attention

Advanced Science, EarlyView.
Deep‐learning‐based signal enhancement is an effective way to recover high‐resolution details from a low‐resolution chromatin contact map. However, due to computational challenges, existing methods commonly divide up the contact map into small patches and create artificial discontinuities at patch boundaries.
Qinyao Li, Kelly Yichen Li, Chiara Nicoletti, Stephen Kwok‐Wing Tsui, Pier Lorenzo Puri, Qin Cao, Kevin Y. Yip   +6 more
wiley   +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, F-.J., Nieuwpoort, R.V. van, Werkhoven, B.J.C. van   +2 more
openaire   +5 more sources

Deciphering Short‐Range Order in 2D Transition Metal Dichalcogenides: From Origin to Multi‐Scale Property Modulation

Advanced Science, EarlyView.
Short‐range order in 2D transition metal dichalcogenides is revealed as a new design paradigm. Driven by chemical affinity and atomic size, it governs properties across scales. Weak ordering tunes site‐resolved magnetism and d‐band centers, while strong ordering eliminates gap states to open band gaps.
Hanyu Liu, Linggang Zhu, Jian Zhou, Zhimei Sun   +3 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

High‐Throughput Screening and Interpretable Machine Learning for Rational Design of Bimetallic Catalysts for Methane Activation

Advanced Science, EarlyView.
ABSTRACT Methane's efficient catalytic removal is vital for sustainable development. Bimetallic catalysts, though promising for methane activation, pose a design challenge due to their complex compositional space. This work introduces an integrated framework that combines high‐throughput density functional theory (DFT) and interpretable machine ...
Mingzhang Pan, Tian Zhang, Jiawei Dong, Yubao Xie, Kaifeng Zhong, Wei Guan, Haiqiao Wei, Changcheng Fu, Yaqiong Su   +8 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