Results 101 to 110 of about 4,344 (219)

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

Based on the WSP-Optuna-LightGBM model for wind power prediction

Journal of Physics: Conference Series
Abstract In order to optimize energy dispatch and enhance the predictive performance of wind power forecast, this study proposes a WSP-Optuna-LightGBM mixed regression prediction model based on wind speed-power curve (WSP), Optuna parameter optimization, and Light Gradient Boosting Machine (LightGBM).
Bo Xiang, Zhenqing Liu, Linhong Huang, Ming Qin   +3 more
openaire   +1 more source

Accelerating Primary Screening of USP8 Inhibitors from Drug Repurposing Databases with Tree‐Based Machine Learning

Advanced Intelligent Discovery, Volume 2, Issue 5, October 2026.
This study introduces a tree‐based machine learning approach to accelerate USP8 inhibitor discovery. The best‐performing model identified 100 high‐confidence repurposable compounds, half already approved or in clinical trials, and uncovered novel scaffolds not previously studied. These findings offer a solid foundation for rapid experimental follow‐up,
Yik Kwong Ng, Sharon Ai Er Lam, Wei Chun Lim, Hannah Hui Min Toh, Shi Jun Ang   +4 more
wiley   +1 more source

ZeroTune 2.0:Enhanced Meta-parameter Selection and Optuna Integration for Zero-Shot Hyperparameter Optimisation

Hyperparameter optimisation is crucial for maximising machine learning model performance. Still, it is computationally intensive due to the iterative nature of hyperparameter optimisation methods like SMAC, SMBOX, and frameworks like Optuna. We introduce
Salhi, Tarek, Woodward, John; id_orcid
core   +1 more source

Multimodal Video Summarization Using Vision‐Language Embeddings and Hierarchical Temporal Modeling

Applied AI Letters, Volume 7, Issue 3, October 2026.
Combining BLIP‐2 image captions with CLIP vision–language embeddings gives video frames semantic meaning that pixels alone cannot convey. Processed by a multi‐scale temporal U‐Net and hierarchical shot‐aware transformer, these multimodal features achieve a state‐of‐the‐art 59.27% F1‐score on SumMe using only 28.27M parameters.
Saadman Sakib, Kaushik Deb
wiley   +1 more source

A Multimodal Deep Learning Based Framework for Early and Accurate Diagnosis of Depression Using Electroencephalography and Event‐Related Potential Signals

Applied AI Letters, Volume 7, Issue 3, October 2026.
Cross Wavelet Transform (XWT) is combined with a pre‐trained AlexNet to extract rich time‐frequency features from non‐stationary EEG signals. Also, the proposed framework simultaneously extracts and integrates spatial (Node2vec), time‐frequency (XWT + AlexNet), and spectral (PSD + TSCN) features from EEG and ERP signals within a parallel architecture ...
Atefeh Abedzadeh Attar, Mohammad Hossein Moattar, Yahya Forghani   +2 more
wiley   +1 more source

The Use of Hyperparameter Tuning in Model Classification: A Scientific Work Area Identification

JOIV: International Journal on Informatics Visualization
This research aims to investigate the effectiveness of hyperparameter tuning, particularly using Optuna, in enhancing the classification performance of machine learning models on scientific work reviews. The study focuses on automating the classification
Nadya Alinda Rahmi, Sarjon Defit, - Okfalisa   +2 more
doaj   +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

Optimization of diabetes prediction methods based on combinatorial balancing algorithm

Nutrition & Diabetes
Background Diabetes, as a significant disease affecting public health, requires early detection for effective management and intervention. However, imbalanced datasets pose a challenge to accurate diabetes prediction.
HuiZhi Shao, Xiang Liu, DaShuai Zong, QingJun Song   +3 more
doaj   +1 more source

Development of the Optuna-NGBoost-SHAP model for estimating ground settlement during tunnel excavation

Underground Space
This study aims to develop and evaluate a natural gradient boosting (NGBoost) model optimized with Optuna for estimating ground settlement during tunnel excavation, incorporating Shapley additive explanations (SHAP) to perform interpretability analysis ...
Yuxin Chen, Mohammad Hossein Kadkhodaei, Jian Zhou   +2 more
doaj   +1 more source