Results 81 to 90 of about 4,087 (203)

Prediksi Laju Inflasi di Jawa Timur Menggunakan Model N-BEATS dan Optimasi Optuna: Prediction of Inflation Rate in East Java Using the N-BEATS Model and Optuna Optimization

Inflasi merupakan indikator penting yang memengaruhi kestabilan dan pertumbuhan ekonomi suatu wilayah. Prediksi inflasi yang akurat sangat dibutuhkan guna mendukung perumusan kebijakan ekonomi yang tepat.
Trimono, Trimono, Riswanda, Mohammad Nizar, Saputra, Wahyu Syaifullah Jauharis   +2 more
core   +1 more source

Interpreting Non‐Linear Compaction Mechanics of Expansive Soils Using Reliability Calibrated Framework

Applied Research, Volume 5, Issue 5, October 2026.
This study introduces a reliability‐calibrated machine learning framework to predict the maximum dry density and optimum moisture content of heterogeneous soils. By integrating 15 algorithms with conformal prediction, the models deliver accurate compaction estimates alongside rigorous, distribution‐free 90% confidence intervals.
Farjad Aziz, Muhammad Waseem, Zeeshan Asghar, Megersa Kebede Leta   +3 more
wiley   +1 more source

Interpretable Machine Learning for Forecasting Renewable Energy Production: A Cross‐Country Analysis

Engineering Reports, Volume 8, Issue 10, October 2026.
Our study introduces a hybrid machine learning framework that integrates real‐time analytics and explainable AI to forecast energy crises and renewable adoption with superior accuracy. By outperforming traditional statistical models, the research identifies key policy and infrastructure drivers, offering actionable pathways for resilient global energy ...
Sejuti Najneen, Niloy Ahmed Showrav, Azmaain Noor Miazi, Saikat Barua, Mahdy Rahman Chowdhury   +4 more
wiley   +1 more source

An integrated machine learning and hyperparameter optimization framework for noninvasive creatinine estimation using photoplethysmography signals

Healthcare Analytics
Frequent measurement of creatinine levels is vital for patients with chronic kidney disease. Traditional creatinine level measurement requires invasive blood test which has several disadvantages like discomfort, anxiety, panic, pain, risk of infection ...
Parama Sridevi, Zawad Arefin, Sheikh Iqbal Ahamed   +2 more
doaj   +1 more source

ESpma: A method for assessing biological/non‐biological interfaces using point‐clouds‐based structural features and protein language models

Protein Science, Volume 35, Issue 10, October 2026.
Abstract Distinguishing biological protein–protein interfaces from non‐biological contacts remains an important task in structural biology, particularly as protein complex structures continue to accumulate. Recent advances in protein language models (pLMs) have expanded their use across diverse protein prediction tasks, including sequence‐based protein–
Sarah Nozawa, Kentaro Tomii, Yoshinori Fukasawa   +2 more
wiley   +1 more source

Neural Surrogate HMC: On Using Neural Likelihoods for Hamiltonian Monte Carlo in Simulation‐Based Inference

Journal of Geophysical Research: Machine Learning and Computation, Volume 3, Issue 5, October 2026.
Abstract Bayesian inference methods such as Markov Chain Monte Carlo (MCMC) typically require repeated computations of the likelihood function, but in some scenarios, this is infeasible and alternative methods are needed. Simulation‐based inference methods address this problem by using machine learning to amortize computations.
L. M. Wolniewicz, P. Sadowski, C. Corti
wiley   +1 more source

Classification of Alzheimer's Disease Using a Hybrid Technique Integration Between CNN and Optuna Optimization

NTU Journal of Engineering and Technology
Alzheimer's Disease (AD) is considered one of the most prevalent neurological disorders, primarily affecting elderly people and adversely impacting their brain functions. This disease is characterized by the gradual deterioration of cognitive functions,
Nawzt Sadiq Jaafar Al-Bayati, Raid Rafi Omar Al-Nima   +1 more
doaj   +1 more source

On the Adversarial Robustness of Hydrological Models

Water Resources Research, Volume 62, Issue 10, October 2026.
Abstract The evaluation of hydrological models is essential for both model selection and reliability assessment. However, comparing predictions with observations alone is insufficient to fully characterize model behavior. This limitation is particularly relevant to deep learning (DL) models because of their complex internal structures.
Yang Yang, Joseph Janssen, Hoshin Gupta, Ting Fong May Chui   +3 more
wiley   +1 more source

A Self‐Supervised Framework for Space Object Behaviour Characterisation

Expert Systems, Volume 43, Issue 10, October 2026.
ABSTRACT Foundation Models, which leverage large neural networks pre‐trained on unlabelled data before fine‐tuning for specific tasks, are increasingly being applied to specialised domains. Recent examples include ClimaX for climate and Clay for satellite Earth observation, but a Foundation Model for Space Object Behavioural Analysis (SOBA) has not yet
Ian Groves, Andrew Campbell, James Fernandes, Diego Ramírez Rodríguez, Paul Murray, Massimiliano Vasile, Victoria Nockles   +6 more
wiley   +1 more source

Predicting Nepheline Formation in Nuclear Waste Glasses Using Machine Learning With Uncertainty Quantification

International Journal of Applied Glass Science, Volume 17, Issue 4, October 2026.
ABSTRACT The vitrification of high‐level waste (HLW) into borosilicate glass is a cornerstone of nuclear waste management, yet one of the key challenges in glass formulation for high‐aluminum‐containing waste streams is the precipitation of nepheline during the canister cooling stage.
Mayra Diaz‐Acevedo, Chloe E. Curry, Xiaonan Lu, Saehwa Chong, Sarah I. Allec, John D. Vienna   +5 more
wiley   +1 more source