Results 111 to 120 of about 151,967 (341)

AI-Driven Methane Emission Prediction in Rice Paddies: A Machine Learning and Explainability Framework

Methane
Rice cultivation accounts for roughly 10% of worldwide anthropogenic greenhouse gas emissions, making it a significant source of methane (CH4) Despite modest observational constraints, estimates of worldwide CH4 emissions from rice agriculture range from
Abira Sengupta, Fathima Nuzla Ismail, Shanika Amarasoma   +2 more
doaj   +1 more source

Classification algorithm hyperparameter combinations.

, 2022
This file indicates all hyperparameter combinations that we evaluated via nested cross-validation in Analysis 4. (XLSX)
Dustin B. Miller (7064549), Stephen R. Piccolo (8367780), Nathan P. Golightly (12231716), Jérémie L. Johnson (12231719), Avery Mecham (12231713)   +4 more
core   +1 more source

Data‐Driven Materials Science for Energy‐Sustainable Applications

Advanced Materials, EarlyView.
Data‐driven approaches powered by artificial intelligence are transforming materials discovery for energy sustainability. This review examines how auto‐generated high‐quality materials databases and domain‐specific language models accelerate research in photovoltaics, thermoelectrics, batteries and magnetic materials. Applications involve extraction of
Jacqueline M. Cole
wiley   +1 more source

Enhancing Photovoltaic Energy Output Predictions Using ANN and DNN: A Hyperparameter Optimization Approach

Energies
This study investigates the use of artificial neural networks (ANNs) and deep neural networks (DNNs) for estimating photovoltaic (PV) energy output, with a particular focus on hyperparameter tuning.
Atıl Emre Cosgun
doaj   +1 more source

Description of hyperparameter values.

, 2019
Description of hyperparameter values.
Tae San Kim (7387940), So Young Sohn (3233574), Won Kyung Lee (3233577)   +2 more
core   +1 more source

Atomically Precise Ag11 and Ag12 Nanocluster‐Assembled 2D Materials for Memristive and Neuromorphic Functionality

Advanced Materials, EarlyView.
Two‐dimensional Ag11 and Ag12 cluster‐assembled materials (CAMs) are synthesized, offering atomically precise platforms with tunable electronic properties. The resulting materials exhibit robust memristive switching and neuromorphic response, demonstrating their promise for advanced nanoelectronic and memory applications.
Noohul Alam, Towhidur Rahaman, Céline Besnard, Arnulf Rosspeintner, Latevi Max Lawson Daku, Saood Khan, Soumya Jyoti Ray, Thomas Bürgi   +7 more
wiley   +1 more source

Hyperparameter optimization for classification models.

, 2022
Hyperparameter optimization for classification models.
Maria Mahbub (11914235), Sudarshan Srinivasan (11914238), Suzanne Tamang (11914247), Gregory D. Peterson (8093489), Alina Peluso (9638408), Ioana Danciu (11914241), Edmon Begoli (11914244)   +6 more
core   +1 more source

Descriptors to Dynamics: A Materials and Device Perspective on in‐Materio Physical Reservoir Computing for Neuromorphic Edge Intelligence

Advanced Materials, EarlyView.
Intrinsic material dynamics are harnessed as computational resources for neuromorphic in‐materio physical reservoir computing. Defects, ionic motion, interfaces, percolation, geometry, and biasing shape transient states that provide fading memory, nonlinearity, and high‐dimensional projection for simple readout. A descriptor‐to‐dynamics framework links
Kshitij RB Singh, Alif Syafiq Kamarol Zaman, Muzhen Xu, Yuki Usami, Saptarshi Das, Hirofumi Tanaka   +5 more
wiley   +1 more source

Framework Flexibility on Water Sorption and Transport in ZIF‐90: Insights from Neural‐Network Molecular Dynamics

Advanced Materials Interfaces, EarlyView.
A DeepMD‐based molecular dynamics framework is developed to investigate water adsorption in ZIF‐90, explicitly accounting for framework flexibility. Results show that atomistic flexibility significantly influences adsorption energetics, diffusion, and structural correlations, yielding nearly constant heat of adsorption and enhanced mobility.
Nick Mackus, Ionut Tranca, Veerapandian Ponnuchamy, Silvia Gaastra‐Nedea   +3 more
wiley   +1 more source

Prospective quantitative analysis of hyperparameter and input optimization in GPT-5: comparative contribution to radiologist performance in abdominal radiology

Diagnostic and Interventional Radiology
PURPOSE: This study aims to evaluate the effect of input format and hyperparameter settings on GPT-5 and explore the contribution of GPT-5 assistance to radiologists’ performance in abdominal cases. METHODS: In this prospective study, 86 abdominal cases
Eren Çamur, Turay Cesur, Yasin Celal Güneş, Muhammed Batuhan Gökhan, Rıza Sarper Ökten   +4 more
doaj   +1 more source