Yıldırım Özüpak

A Hybrid ARIMA–LGBM Framework with Optuna Optimization for Long-Term Dissolved Oxygen Prediction in Aquatic Systems

Valorizing Residue Biomass into Bioenergy: An Explainable Hybrid Machine Learning Model for Predicting Higher Heating Value (HHV) from Elemental Composition

Transforming waste and agricultural-residue biomass into bioenergy is central to the circular bioeconomy, yet routing such heterogeneous residues to the right thermochemical pathway depends on the higher heating value (HHV), which is conventionally measured by slow, resource-intensive bomb calorimetry. Here, we present...

Machine Learning Based Predictive Maintenance: Comparative Evaluation of Fault Prediction Models

This study investigates the effectiveness of machine learning techniques in predictive maintenance for reducing unexpected machine faults and optimizing maintenance strategies. A synthetic dataset was utilized to model Faults based on key operational parameters such as temperature, torque, rotational speed, and product...

Explainable and statistically validated machine learning for earthquake alert-level classification using seismic intensity features

Explainable CNN–GRU learning on Mel-spectrogram acoustic signals for bearing fault diagnosis under small-sample experimental conditions

The results demonstrate that acoustic sensing combined with explainable CNN–GRU learning can provide a non-contact and interpretable alternative for bearing fault diagnosis, and the limited dataset size remains an important constraint, and future studies should validate the framework on larger record-level and cross-do...

Optimizing Electricity Demand Forecasting Using ARIMA, SARIMA, and GRU with Weather and Calendar Variables

The results of the experiment demonstrate that the GRU model performs better than ARIMA and SARIMA models especially for longer forecasting horizons due to its capability to learn nonlinear relationships and long-term temporal dependencies.

Comparing Explainable Artificial Intelligence and Deep Learning Models for MRI-Based Brain Tumor Diagnosis

This study investigates the application of deep learning architectures, including Convolutional Neural Network, VGG16, VGG19, ResNet50, and MobileNet, for brain tumor detection and classification and confirms that advanced deep learning architectures not only achieve high classification accuracy but also improve interp...

Robust fault diagnosis of electric vehicle induction motors via Gramian angular field encoding and metaheuristic-optimized deep transfer learning

An advanced diagnostic pipeline is proposed that transforms one-dimensional time-series current and voltage signals into informative two-dimensional spatial representations using Gramian angular field encoding and Coati optimization algorithm-optimized transfer learning framework provides an accurate, interpretable, an...