Results 101 to 110 of about 3,964 (194)

Deep Learning Framework With Optuna-Based Hyperparameter Tuning for Predicting Dry Turning Process Performance of 42CrMo4 Steel

IEEE Access
This study presents an integrated experimental and data-driven modeling framework for predicting key machining responses such as tool wear (TW), material removal rate (MRR), and surface roughness (Ra) during dry turning of 42CrMo4 alloy steel ...
Vasanth Siva Kumar, Mogana Priya Chinnasamy, Swetha R Kumar   +2 more
doaj   +1 more source

Low-Resource Speech Recognition by Fine-Tuning Whisper with Optuna-LoRA

Applied Sciences
In low-resource speech recognition, the performance of the Whisper model is often limited by the size of the available training data. To address this challenge, this paper proposes a training optimization method for the Whisper model that integrates Low ...
Huan Wang, Jie Bin, Chunyan Gou, Lian Yang, Baolin Hou, Mingwei Qin   +5 more
semanticscholar   +1 more source

CO₂ Emission Prediction for Coal-Fired Power Plants by Random Forest-Recursive Feature Elimination-Deep Forest-Optuna Framework

Energies
As the greenhouse effect intensifies, China faces pressure to manage CO2 emissions. Coal-fired power plants are a major source of CO2 in China. Traditional CO2 emission accounting methods of power plants are deficient in computational efficiency and ...
Kezhi Tu, Yanfeng Wang, Xian Li, Xiangxi Wang, Zhenzhong Hu, Bo Luo, Liu Shi, Minghan Li, Guangqian Luo, Hong Yao   +9 more
doaj   +1 more source

Forecasting grocery item sales using gradient boosting models: A study of GridSearchCV, RandomizedSearchCV, and optuna optimization approaches

Ain Shams Engineering Journal
This study introduces predictive modeling based on Extreme Gradient Boosting (XGBoost), which utilizes Optuna for hyperparameter optimization and evaluates performance against GridSearchCV and RandomizedSearchCV using a 500-day dataset.
Puja Supakar, Mitali Sarkar, Biswajit Sarkar   +2 more
doaj   +1 more source

Optuna-Optimized Pythagorean Fuzzy Deep Neural Network: A Novel Framework for Uncertainty-Aware Image Classification

Applied Sciences
By using Geographic Information Systems, satellite imagery from remote sensing techniques provides quantitative and qualitative data about Earth’s natural and human elements.
Asli Kaya Karakutuk, Ozer Ozdemir, S. Senturk   +2 more
semanticscholar   +1 more source

Modified Prophet+Optuna Prediction Method for Sales Estimations

International Journal of Advanced Computer Science and Applications, 2022
Kohei Arai, Ikuya Fujikawa, Yusuke Nakagawa, Tatsuya Momozaki, Sayuri Ogawa   +4 more
openaire   +1 more source

Enhancing Hepatitis C Diagnosis: The Impact of SMOTE, Optuna, and SHAP on Detection Methods [PDF]

Iranian Journal of Electrical and Electronic Engineering
Hepatitis C virus (HCV) detection is a critical aspect of early intervention and effective management of the disease. This paper presents a comprehensive study focused on enhancing the detection accuracy of HCV through the integration of advanced ...
S.M Mehzabeen, R Gayathri, Pattunnarajam Paramasaivam, Ramya A   +3 more
doaj  

Interpretable machine learning for predicting EUR of shale gas wells in the Weiyuan block

Scientific Reports
For shale gas development, it is essential to clarify the main controlling factors of EUR and realize its accurate prediction. Based on data from 123 wells in the Weiyuan block, this study combines Pearson correlation analysis, RF-RFE algorithm, and ...
Sijie He, Feifei Fang, Jin Chang, Xizhe Li, Ruilan Luo, Nijun Qi   +5 more
doaj   +1 more source

Using a hybrid attention mechanism as a method to improve the efficiency of network intrusion detection systems

Радіоелектронні і комп'ютерні системи
The subject matter of this article is a HybridAttention mechanism integrated into a deep neural architecture for Network Intrusion Detection Systems (NIDS). This study aims to develop and study a HybridAttention mechanism based on a combination of global
Andrii Nikitenko, Yevhen Bashkov
doaj   +1 more source

Advanced stacked ensembles for coal-fired thermal plants gross load prediction

Discover Mechanical Engineering
This study presents a novel approach employing stacked ensemble of different machine learning and deep learning models for hourly plant gross load (PGL) prediction in a coal-fired thermal power plant considering python-spyder environment.
Ashwani Kharola, Nitin Kumar, Vivek John, Ajay Kumar, Harvinder Singh, Jeewan Singh, Purnendu Shekar Pandey, Yohannes Mengist   +7 more
doaj   +1 more source