Results 81 to 90 of about 1,984 (174)

Explainable Artificial Intelligence (XAI) for Flood Susceptibility Assessment in Seoul: Leveraging Evolutionary and Bayesian AutoML Optimization

Remote Sensing
This study aims to enhance the accuracy and interpretability of flood susceptibility mapping (FSM) in Seoul, South Korea, by integrating automated machine learning (AutoML) with explainable artificial intelligence (XAI) techniques.
Kounghoon Nam, Youngkyu Lee, Sungsu Lee, Sungyoon Kim, Shuai Zhang   +4 more
doaj   +1 more source

Analysis of the Assistance Model for LKPM Through the Zoom Application at the DPMPTSP of South Lampung Regency using XGBoost

Journal of Applied Informatics and Computing
This study aims to analyze the success of LKPM mentoring through the Zoom application at DPMPTSP South Lampung Regency using the XGBoost model. The research identifies key features influencing the success of mentoring, including session duration, number ...
Firdaus Firdaus, Anuar Sanusi
doaj   +1 more source

Improved prediction of childhood anemia using hybrid ensemble learning and dual-level explainability

Journal of Public Health Research
Purpose Millions of children under five, particularly in low- and middle-income countries, suffer from preventable anemia, making early detection critical for improving public health outcomes.
Ashima Kukkar, Julius Moinget Loibor, Mehmet Akif Cifci, Manash Pratim Barman, Sadiq Hussain, Silvia Gaftandzhieva, Rositsa Doneva   +6 more
doaj   +1 more source

Employing machine learning for enhanced abdominal fat prediction in cavitation post-treatment

Scientific Reports
This study investigates the application of cavitation in non-invasive abdominal fat reduction and body contouring, a topic of considerable interest in the medical and aesthetic fields.
Doaa A. Abdel Hady, Omar M. Mabrouk, Tarek Abd El-Hafeez   +2 more
doaj   +1 more source

Optuna-Optimized Ensemble and Neural Network Models for Static Characteristics Prediction of Active Bearings with Geometric Adjustments

Modelling
Active vibration control designs for journal bearings have improved rotordynamic stability and led to advancements in adjustable bearing types that enable precise control of bearing geometry. In this study, optimized machine learning (ML) algorithms were
Girish Hariharan, Ravindra Mallya, Nitesh Kumar, Deepak Doreswamy, Gowrishankar Mandya Chennegowda, Subraya Krishna Bhat   +5 more
doaj   +1 more source

TABULAR TRANSFORMER ARCHITECTURE WITH OPTUNA OPTIMIZATION FOR EARLY DIAGNOSIS OF ALZHEIMER'S DISEASE

Kahramanmaraş Sütçü İmam Üniversitesi Mühendislik Bilimleri Dergisi
Alzheimer's disease (AD) represents the leading cause of dementia globally and is characterized by progressive neurodegeneration. In this study, a Tabular Transformer architecture optimized with the Optuna algorithm is proposed for the early diagnosis of Alzheimer's disease.
openaire   +1 more source

Fine-Tuning CNN-BiGRU for Intrusion Detection with SMOTE Optimization Using Optuna

Salud, Ciencia y Tecnología - Serie de Conferencias
Network security faces a significant challenge in developing effective models for intrusion detection within network systems. Network Intrusion Detection Systems (NIDS) are vital for protecting network traffic and preempting potential attacks by identifying signatures and rule violations.
Asmaa BENCHAMA, Khalid ZEBBARA
openaire   +1 more source

Long-term prediction modeling of shallow rockburst with small dataset based on machine learning

Scientific Reports
Rockburst present substantial hazards in both deep underground construction and shallow depths, underscoring the critical need for accurate prediction methods.
Guozhu Rao, Yunzhang Rao, Jiazheng Wan, Qiang Huang, Yangjun Xie, Qiande Lai, Zhihua Yang, Run Xiang, Laiye Zhang   +8 more
doaj   +1 more source

Soft ground micro TBM jack speed and torque prediction using machine learning models through operator data and micro TBM-log data synchronization

Scientific Reports
Tunnel Boring Machines (TBMs) are pivotal in underground projects like subways, highways, and water supply tunnels. Predicting and monitoring jack speed and torque is crucial for optimizing TBM excavation efficiency.
Kursat Kilic, Owada Narihiro, Hajime Ikeda, Tsuyoshi Adachi, Youhei Kawamura   +4 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
doaj   +1 more source