Results 1 to 10 of about 1,953 (143)

Machine learning model optimization with optuna for accurate prediction of strength and crack behavior in prestressed concrete beams [PDF]

Scientific Reports
Prestressed concrete beams are widely used in bridge and building structures, and their performance is directly related to the overall safety and durability.
Yuanyong Wen, Rongxin Guo, Zhihong Duan, Yue Tong, Xiayu Tang, Tinghong Pan, Chaoshu Fu   +6 more
doaj   +3 more sources

An Approach to Truck Driving Risk Identification: A Machine Learning Method Based on Optuna Optimization

IEEE Access
In order to provide safe development of road freight traffic, this paper proposes a truck driving risk identification method based on Optuna optimization of machine learning model.
Zhaofei Wang, Hao Li, Qiuping Wang
doaj   +4 more sources

Optimizing Depression Classification Using Combined Datasets and Hyperparameter Tuning with Optuna

Sensors
This research focuses on the depression states classification of EEG signals using the EEGNet model optimized with Optuna. The purpose was to increase model performance by combining data from healthy and depressed subjects, which ensured model robustness
Ștefana Duță, Alina Elena Sultana
doaj   +5 more sources

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.
Rafi Jusar Wishnuwardana, Madiah Binti Omar, Haslinda Binti Zabiri, Mochammad Faqih, Kishore Bingi, Rosdiazli Ibrahim   +5 more
doaj   +4 more sources

PhishNet 1.0: optuna-optimized stacking ensemble with Boruta-based feature selection for phishing URL detection [PDF]

Scientific Reports
The objective of this research is to enhance phishing detection through ensemble learning integrated with well-structured metaheuristic algorithms. Various classifiers, including Logistic Regression, Nearest Neighbors, Support Vector Machine, Decision ...
Achin Jain, Shakir Khan, Kashish Koli, Harsh Taneja, Arvind Panwar, Thamer Alshammari, Essam Mohamed Obaid, Seid Mehammed Abdu   +7 more
doaj   +2 more sources

Optimizing Multilayer Perceptron for Car Purchase Prediction with GridSearch and Optuna

Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi)
Multilayer Perceptron (MLP) is a powerful machine learning algorithm capable of modeling complex, non-linear relationships, making it suitable for predicting car purchasing power. However, its performance depends on hyperparameter tuning and data quality.
Ginanti Riski, Dedy Hartama, Solikhun
doaj   +2 more sources

Optimizing electric vehicle energy consumption prediction through machine learning and ensemble approaches [PDF]

Scientific Reports
Accurately predicting energy consumption in electric vehicles (EVs) is essential for enhancing energy efficiency and improving infrastructure planning.
Izhar Hussain, Kok Boon Ching, Chessda Uttraphan, Kim Gaik Tay, Adeeb Noor, Sufyan Ali Memon   +5 more
doaj   +2 more sources

Optimized Breast Cancer Classification Using PCA-LASSO Feature Selection and Ensemble Learning Strategies With Optuna Optimization

IEEE Access
Breast cancer is one of the most prevalent and life-threatening diseases among women worldwide, making early and accurate detection crucial for effective treatment.
Prabhat Kumar Sahu, Taiyaba Fatma
doaj   +2 more sources

Improving Tomato Ripeness Classification Using Knowledge Distillation and Hyperparameter Optimization with Optuna

Journal of Electrical Engineering and Computer
Automatic classification of tomato ripeness plays a crucial role in ensuring post-harvest quality and efficiency in the horticultural industry. This study proposes a combined strategy of Knowledge Distillation (KD) and hyperparameter optimization using ...
Iasya Sholihin, Andi Sunyoto
doaj   +2 more sources

Research on the Prediction of Coal Workers’ Pneumoconiosis Based on Easily Detectable Clinical Data: Machine Learning Model Development and Validation Study [PDF]

JMIR Medical Informatics
BackgroundCoal workers’ pneumoconiosis (CWP) is the most prevalent occupational disease that causes irreversible lung damage. Early prediction of CWP is the key to blocking the irreversible process of pulmonary fibrosis.
Haiquan Li, Jiaqi Jia, Xu Shi, Yudie Dong, Songquan Wang, Yuming Cui, Wenlu Hang, Dekun Zhang   +7 more
doaj   +2 more sources