How do hybrid intelligent models differ from traditional machine learning techniques in predicting TBM penetration rate?
Best insight from top research papers
Hybrid intelligent models, as seen in various studies, combine machine learning techniques with empirical methods to enhance prediction accuracy while maintaining robustness and generalization abilities. These models, such as the hybrid LSTM-GWO model , ELM-ALO, ELM-LSO, and ELM-SOA models , and the hybrid ensemble model (HENSM) , integrate optimization algorithms or ensemble techniques with machine learning to predict TBM penetration rate more effectively. In contrast, traditional machine learning techniques like multiple regression, neural networks, and support vector regression models focus solely on data-driven predictions without incorporating additional optimization or ensemble strategies. The hybrid models demonstrate superior performance in predicting TBM penetration rate by leveraging the strengths of both machine learning and empirical approaches, as evidenced by their higher accuracy, stability, and generalization capabilities in various tunneling projects.
Answers from top 5 papers
More filters
| Papers (5) | Insight |
|---|---|
| Hybrid intelligent models, like BPNN, combine multiple techniques for TBM penetration rate prediction, surpassing traditional methods by enhancing accuracy and stability through integrated approaches. | |
23 Citations | Hybrid intelligent models combine conventional soft computing methods with artificial neural networks, outperforming traditional techniques in predicting TBM penetration rate in rock environments. |
22 Citations | Hybrid intelligent models like ELM-ALO, ELM-LSO, and ELM-SOA combine swarm intelligence optimization algorithms with ELM for improved TBM advance rate prediction compared to traditional machine learning techniques. |
| Hybrid intelligent models combine machine learning and empirical methods based on data similarity to enhance accuracy, robustness, and generalization in predicting TBM penetration rate, surpassing traditional machine learning techniques. | |
18 Citations | Hybrid intelligent models, like LSTM-GWO, combine deep learning with optimization algorithms for enhanced accuracy in predicting TBM penetration rate, surpassing traditional machine learning techniques. |
Related Questions
What are the applications of AI, ML, and DSS in prediction using data in SCM?5 answersAI, ML, and DSS have various applications in prediction using data in SCM. These technologies can be used to optimize supply chain management, detect and prevent fraud, analyze and predict purchase and inventory links, and estimate demand and sales, production, inventory management, transportation and distribution. They can also be used for selecting and segmenting suppliers, predicting supply chain risks, and improving operational efficiency. ML and AI classifiers can analyze vast amounts of data and identify patterns that may evade manual detection, making them powerful tools for detecting and preventing supply chain fraud. ML and AI algorithms can predict fraud in the supply chain by analyzing real-world business transactions. ML models can predict 0D performance metrics and spatial flow fields in ship design, providing designers with a detailed understanding of vessel performance. ML techniques can analyze and interpret big data in SCM, contributing to supplier selection, risk prediction, and various aspects of supply chain management.
RQ2: What hybrid models are being used for email malware detection, and how well have these techniques performed?4 answersHybrid models for email malware detection have been proposed in the literature. One approach combines convolutional neural networks (CNN), gated recurrent units (GRU), and attention mechanisms to selectively focus on relevant parts of email text. Another hybrid model combines static and dynamic analysis of API calls using a two-stage learning approach, with XGBoost as a feature extractor feeding into an Artificial Neural Network (ANN). Additionally, a multi-feature extraction and fusion method has been proposed, combining binary and opcode features using a stacked convolutional network and triangular attention algorithm. These hybrid models have shown promising results. The proposed technique for email spam detection using CNN, GRU, and attention mechanisms outperforms state-of-the-art models. The Similarity-Based Hybrid API Malware Detection Model (HAPI-MDM) achieves an overall accuracy of 97.91% and the lowest false-positive and false-negative rates compared to related works. The multi-feature fusion method achieves high accuracy on both the Kaggle Malware Classification dataset and a large real-world dataset.
How can hybrid models be used to improve medium wind speed forecasts?5 answersHybrid models can be used to improve medium wind speed forecasts by combining different techniques to leverage their distinct strengths. These models have been shown to enhance prediction accuracy and are particularly useful for shorter forecasting time horizons (<6hrs). One approach is to combine a statistical model, such as Auto-Regressive Integrated Moving Average (ARIMA), with a machine learning model, such as Recurrent Neural Network (RNN) - Long Short-Term Memory (LSTM). Another approach is to use a hybrid model that combines a statistical model, like clustering ARIMA or clustering SARIMA, with a segment slope clustering algorithm. These hybrid models extract fuzzy input features and capture the sequence of wind speed data to accurately predict wind speed. By leveraging the strengths of different models and algorithms, hybrid models can provide more accurate medium wind speed forecasts.
What are some examples of how hybrid intelligence is being used in marketing no older than 2019?4 answersHybrid intelligence is being used in marketing in various ways. One example is the development of a hybrid intelligent system for marketing strategy development. This system combines expert systems, fuzzy logic, and artificial neural networks to support the process of marketing strategy development. It also integrates Porter's five forces model and directional policy matrices to assist strategic analysis. Another example is the use of a hybrid intelligent system called MarStra, which provides guidance in strategic analysis, helps with managerial judgement, and supports group assessment of strategic marketing factors. Additionally, hybrid techniques involving learning and crowd-sourcing have been used for image classification in the Web2.0 world. These examples demonstrate the application of hybrid intelligence in marketing, providing support in strategic analysis, decision-making, and data processing.
How to use AI to predict the target of cyber attacks?5 answersArtificial intelligence (AI) can be used to predict the target of cyber attacks. Several studies have explored the application of AI and machine learning techniques for cyber security modeling and inference. These approaches utilize datasets that contain behavioral knowledge of threats and vulnerabilities, such as the BRON dataset. Deep learning techniques, such as LSTM, RNN, and MLP models, have also been proposed for predicting the type of cyber attacks. These models have shown promising results, with an f-measure greater than 93% for the LSTM model. Additionally, machine learning techniques based on deep neural networks and autoregressive time series models have been developed to forecast cyber attacks using external signals from publicly available web sources. These methods have demonstrated a significant lift in predicting cyber attacks and can provide an effective line of defense against targeted attacks.
How to develop hybrid model for fake news detection using deep learning methods?3 answersA hybrid model for fake news detection using deep learning methods can be developed by combining different classifiers and utilizing natural language processing techniques. One approach is to use an ensemble hybrid model that combines classifiers such as Naive Bayes, K-Nearest Neighbor, Decision Tree, Bagging Decision Tree, Boosting Decision Tree, Random Forest, Logistic Regression, and Support Vector Machine. Another approach is to use a two-phase model that extracts structural features from news samples and utilizes a hybrid method based on curriculum strategy and a k-nearest neighbor algorithm to improve the performance of deep learning models. Additionally, a proposed HyproBert model utilizes DistilBERT for tokenization and word embeddings, convolution layers for spatial feature extraction, BiGRU for contextual feature extraction, CapsNet and self-attention layers for modeling hierarchy relationships, and a dense layer for classification. These approaches have shown higher performance compared to benchmark models in detecting fake news.