Results 41 to 50 of about 4,344 (219)

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

Optimization of Scene and Material Parameters for the Generation of Synthetic Training Datasets for Machine Learning-Based Object Segmentation

Computers
Synthetic training data is often essential for neural-network-based segmentation when real datasets are difficult or impossible to obtain. Conventional synthetic data generation relies on manually selecting scene and material parameters. This can lead to
Malte Nagel, Kolja Hedrich, Nils Melchert, Lennart Hinz, Eduard Reithmeier   +4 more
doaj   +1 more source

OOG- Optuna Optimized GAN Sampling Technique for Tabular Imbalanced Malware Data

2022 IEEE International Conference on Big Data (Big Data), 2022
Cyberspace occupies a large portion of people's life in the age of modern technology, and while there are those who utilize it for good, there are also those who do not. Malware is an application whose construction was not motivated by a benign goal and it can harm, steal, or even alter personal information and secure applications and software.
S. M. Towhidul Islam Tonmoy, S. M. Mehedi Zaman   +1 more
openaire   +2 more sources

Optuna-Optimized Machine Learning for Traffic Accident Severity Prediction in Jordan under Class Imbalance Conditions

Communications
A machine learning framework for predicting the traffic accident severity under class imbalance conditions is predented in Amman, Jordan. The methodology began with a preprocessing pipeline consisting of IQR-based (Interquartile range) outlier removal ...
Maen Qaseem Ghadi
doaj   +1 more source

Continual Learning for Multimodal Data Fusion of a Soft Gripper

Advanced Robotics Research, EarlyView.
Models trained on a single data modality often struggle to generalize when exposed to a different modality. This work introduces a continual learning algorithm capable of incrementally learning different data modalities by leveraging both class‐incremental and domain‐incremental learning scenarios in an artificial environment where labeled data is ...
Nilay Kushawaha, Egidio Falotico
wiley   +1 more source

Hybrid Machine Learning Approach for Nutrient Deficiency Detection in Lettuce

TIERS Information Technology Journal
Early detection of nutrient deficiencies in lettuce is essential for precision agriculture. However, this task remains challenging due to limited data availability and class imbalance, which reduce model sensitivity toward minority classes and hinder ...
Zuriati Zuriati, Dewi Kania Widyawati, Oki Arifin, Kurniawan Saputra, Sriyanto Sriyanto, Asmala Ahmad   +5 more
doaj   +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

Optuna-LightGBM : An Optuna hyperparameter optimization framework for the determination of solvent components in acid gas removal unit using LightGBM

Cleaner Engineering and Technology
Acid gas removal unit (AGRU) serves as an essential process in gas treatment, specifically designed to eliminate acid gases like hydrogen sulfide (H2S) and carbon dioxide (CO2) from natural gas. Absorption-based AGRU are extensively employing chemical solvents because of their strong performance and effectiveness.
Rafi Jusar Wishnuwardana, Madiah Binti Omar, Haslinda Binti Zabiri, Mochammad Faqih, Kishore Bingi, Rosdiazli Ibrahim   +5 more
openaire   +2 more sources

High‐Throughput Data Generation and Transfer Learning Enabled Microstructure‐Property Integrated Design of Nickel‐Based Powder Metallurgy Superalloy

Advanced Science, EarlyView.
An integrated transfer learning framework integrates CALPHAD simulations, diffusion‐multiple experiments, and literature data to predict long‐term microstructural stability and short‐term mechanical properties of Ni‐based powder metallurgy superalloys. Based on these model predictions, a high‐performance, low‐density alloy, USTB‐PM750, is designed from
Zixin Li, Hongtao Zhang, Zichao Peng, Yuheng Zhang, Qian Lv, Yihuan Cao, Xuqing Wang, Huadong Fu, Jianxin Xie   +8 more
wiley   +1 more source

NICE: A Two‐Step Non‐Invasive Framework for Embryo cfDNA Read Enrichment and Quality Assessment

Advanced Science, EarlyView.
The non‐invasive NICE framework, built on an ensemble stacking machine learning model, prioritizes embryos by analyzing cell‐free DNA from spent culture medium. By integrating multimodal signals, including genomic and epigenetic profiles, this automated approach standardizes morphological assessment without human bias, paving the way for more precise ...
Xueya Zhou, Shu Ding, Zhenyi Zhang, Qiaoling Shangguan, Jie Qiao, Peijie Zhou, Yidong Chen   +6 more
wiley   +1 more source