Kết quả tìm kiếm - "hyperparameters optimization"

Hyperparameter optimization (HPO), which is also called hyperparameter tuning, is a vital component of developing machine learning models. These parameters, which regulate the behavior of the machine learning algorithm and cannot be directly learned from the given training data, can significantly af...

Mô tả đầy đủ

Nowadays, anomaly detection in streaming data has gained considerable attention due to the exponential growth in the data gathered by Internet of Things applications. Analyzing and processing vast data volumes requires a system capable of working in real-time. Moreover, obtaining labeled data for su...

Mô tả đầy đủ

Background/Objectives: The manual tuning of exoskeleton control parameters is tedious and often ineffective for adapting to individual users. Human-in-the-loop (HIL) optimization offers an automated approach, but existing methods typically rely on metabolic cost, which requires prolonged data collec...

Mô tả đầy đủ

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 on the model’s estimation results. The model’s...

Mô tả đầy đủ

Additive manufacturing (AM) presents significant opportunities for advancing sustainability through optimized process control and material utilization. This research investigates the application of machine learning (ML) models to directly associate AM process parameters with sustainability metrics,...

Mô tả đầy đủ

The complex geological environment in western Sichuan, China, leads to frequent debris flow disasters, posing significant threats to the lives and property of local residents. In this study, debris flow susceptibility models were developed using three machine learning algorithms: Support Vector Mach...

Mô tả đầy đủ

This paper applies machine learning models to predict travel time reliability in transportation networks, using XGBoost, LightGBM, and CatBoost optimized with seven metaheuristic algorithms. The models were fine-tuned with a four-year dataset (2014–2017) covering 59 interstate sections in Virginia....

Mô tả đầy đủ

The issue of insufficient multi-scale feature extraction and difficulty in accurately classifying fault features in rolling bearing fault diagnosis is addressed by proposing a novel diagnostic method that integrates stochastic convolutional neural networks (SCNNs) and a hybrid kernel extreme learnin...

Mô tả đầy đủ

Tool wear in machining operations compromises tool lifespan and performance. Machine learning models, particularly eXtreme Gradient Boosting (XGBoost), demonstrate pattern recognition capabilities for such predictions. However, their effectiveness is highly dependent on hyperparameters, and empirica...

Mô tả đầy đủ

Existing anomaly detectors in AMIs suffer from shallow architectures, which impede their ability to capture temporal correlations and complex patterns in electricity consumption data, thus impact detection performance adversely. A deep (stacked) autoencoder structure based on Long Short-Term Memory...

Mô tả đầy đủ

Decision trees in machine learning achieved satisfactory performance in classification. Decision trees offer the advantage of handling high-dimensional and complexly correlated data through feature combination and selection. Extreme Gradient Boosting (XGBoost) overcomes the issue of overfitting in d...

Mô tả đầy đủ

Face recognition is one of the main challenges in the development of computer vision technology. This study aims to develop a face recognition system using a Faster R-CNN architecture, optimized through hyperparameter tuning. This research utilizes the "Face Recognition Dataset" from Kaggl...

Mô tả đầy đủ

This study investigates the use of a Gated Recurrent Unit (GRU) model with a four-layer architecture for daily gold price closing prediction, motivated by the model's ability to effectively capture temporal dependencies in time series data. Gold price forecasting is highly challenging due to it...

Mô tả đầy đủ

This paper investigates the significance of hyperparameter optimization in meta-learning for image classification tasks. Despite advancements in deep learning, real-time image classification applications often suffer from data inadequacy. Few-shot learning addresses this challenge by enabling learni...

Mô tả đầy đủ

Polyvinyl alcohol (PVA) hydrogels have emerged as versatile materials due to their exceptional biocompatibility and tunable mechanical properties, showing great promise for flexible sensors, smart wound dressings, and tissue engineering applications. However, rational design remains challenging due...

Mô tả đầy đủ

Managing citrus diseases is important for lowering crop losses and raising the economic value of citrus output. To provide a novel approach for the identification and classification of three significant citrus diseases—Citrus Canker, Citrus Greening, and Citrus Black Spot—this study uses a Deep Con...

Mô tả đầy đủ

Cardiovascular disease (CVD) is the leading cause of death worldwide, emphasizing the importance of accurate early detection. This study examines the efficacy of tree-based ensemble machine learning models that have been improved using Feature Expansion and Selection (FES-EM). We considered the Mend...

Mô tả đầy đủ

Abstract A novel deep learning framework for recognition of distracted driving behavior is proposed in this paper. The proposed framework consists of hybrid convolutional neural network and bidirectional long short term memory network to extract multi-scale spatiotemporal features of high-dimensiona...

Mô tả đầy đủ

AutoML (Automated Machine Learning) is an emerging field that aims to automate the process of building machine learning models. AutoML emerged to increase productivity and efficiency by automating as much as possible the inefficient work that occurs while repeating this process whenever machine lear...

Mô tả đầy đủ

This paper describes the application of particle swarm optimization (PSO) for the hyperparameter optimization problem of multi-layered perceptron (MLP) model. Several PSO algorithms are presented by many researchers; basic PSO, PSO with inertia weight (PSO-w), PSO with constriction factor (PSO-cf),...

Mô tả đầy đủ