A Stacked Generalization Model to Enhance Prediction of Earthquake-Induced Soil Liquefaction

Preethaa, K. R. Sri; Natarajan, Yuvaraj; Rathinakumar, Arun Pandian; Lee, Dong‐Eun; Choi, Y.; Park, Young-Jun; Yi, Chang-Yong

doi:10.3390/s22197292

Cited by 6 publications

(3 citation statements)

References 32 publications

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“…A combination of IG and PCA is applied to reduce the dataset and a combination of IBK, SVM, and MLP is formulated to classify the attacks based on the aggregation of the classifiers [31]. Stacked deeplearning models have shown improved performance [32]. Continued research and development in intrusion detection are vital to navigate the constantly evolving cyber threat landscape.…”

Section: Related Workmentioning

confidence: 99%

A Novel Hybrid Feature Selection with Cascaded LSTM: Enhancing Security in IoT Networks

Sundaram,

Natarajan,

Perumalsamy

et al. 2024

Wireless Communications and Mobile Computing

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The rapid growth of the Internet of Things (IoT) has created a situation where a huge amount of sensitive data is constantly being created and sent through many devices, making data security a top priority. In the complex network of IoT, detecting intrusions becomes a key part of strengthening security. Since IoT environments can be easily affected by a wide range of cyber threats, intrusion detection systems (IDS) are crucial for quickly finding and dealing with potential intrusions as they happen. IDS datasets can have a wide range of features, from just a few to several hundreds or even thousands. Managing such large datasets is a big challenge, requiring a lot of computer power and leading to long processing times. To build an efficient IDS, this article introduces a combined feature selection strategy using recursive feature elimination and information gain. Then, a cascaded long–short-term memory is used to improve attack classifications. This method achieved an accuracy of 98.96% and 99.30% on the NSL-KDD and UNSW-NB15 datasets, respectively, for performing binary classification. This research provides a practical strategy for improving the effectiveness and accuracy of intrusion detection in IoT networks.

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Section: Related Workmentioning

confidence: 99%

A Novel Hybrid Feature Selection with Cascaded LSTM: Enhancing Security in IoT Networks

Sundaram,

Natarajan,

Perumalsamy

et al. 2024

Wireless Communications and Mobile Computing

Self Cite

View full text Add to dashboard Cite

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“…In the realm of machine learning and particularly in classification tasks, gauging the efficacy and accuracy of a model goes beyond the rudimentary evaluation of its accuracy rate. A more nuanced approach encompasses metrics like precision, recall, the F-score, and the Receiver Operating Characteristic (ROC) curve [41][42][43]. Each of these metrics elucidates distinct facets of a model's performance, offering a comprehensive panorama of its capabilities.…”

Section: B Evaluation Metricsmentioning

confidence: 99%

Deep Convolutional Neural Network for Accurate Prediction of Seismic Events

Turarbek,

Bektemesov,

Ongarbayeva

et al. 2023

IJACSA

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In recent years, the realm of seismology has witnessed an increased integration of advanced computational techniques, seeking to enhance the precision and timeliness of earthquake predictions. The paper titled "Deep Convolutional Neural Network and Machine Learning Enabled Framework for Analysis and Prediction of Seismic Events" embarks on an ambitious exploration of this interstice, marrying the formidable prowess of Deep Convolutional Neural Networks (CNNs) with an array of machine learning algorithms. At the forefront of our investigation is the Deep CNN, known for its unparalleled capability to process spatial hierarchies and multi-dimensional seismic data. Accompanying this neural behemoth is LightGBM, a gradient boosting framework that offers superior speed and performance, especially with voluminous datasets. Additionally, conventional neural networks, noted for their adeptness in pattern recognition, offer a robust method to gauge the intricacies of seismic data. Our exploration doesn't halt here; the research delves deeper with Random Forest and Support Vector Machines (SVM), both renowned for their resilient performance in classification tasks. By amalgamating these diverse methodologies, this research crafts a multifaceted and synergistic framework. The culmination is a sophisticated tool poised to not only discern the minutiae of seismic activities with heightened accuracy but to predict forthcoming events with a degree of certainty previously deemed elusive. In this era of escalating seismic activities, our research offers a timely beacon, heralding a future where communities are better equipped to respond to the Earth's capricious tremors.

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“…et al, a detailed nonlinear relocation-based strategy is a complex computation for improving a structure’s seismic presentation. Machine learning strategies are computationally requested to evaluate enormous topographical regions [ 3 ]. Tianyu Ci et al stated that, after an earthquake, it is crucial to assess the level of endangerment sustained by buildings so that people can avoid being in unsafe structures.…”

Section: Introductionmentioning

confidence: 99%

Novel ANOVA-Statistic-Reduced Deep Fully Connected Neural Network for the Damage Grade Prediction of Post-Earthquake Buildings

Preethaa

Devi

Rajendran

et al. 2023

Sensors

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Earthquakes are cataclysmic events that can harm structures and human existence. The estimation of seismic damage to buildings remains a challenging task due to several environmental uncertainties. The damage grade categorization of a building takes a significant amount of time and work. The early analysis of the damage rate of concrete building structures is essential for addressing the need to repair and avoid accidents. With this motivation, an ANOVA-Statistic-Reduced Deep Fully Connected Neural Network (ASR-DFCNN) model is proposed that can grade damages accurately by considering significant damage features. A dataset containing 26 attributes from 762,106 damaged buildings was used for the model building. This work focused on analyzing the importance of feature selection and enhancing the accuracy of damage grade categorization. Initially, a dataset without primary feature selection was utilized for damage grade categorization using various machine learning (ML) classifiers, and the performance was recorded. Secondly, ANOVA was applied to the original dataset to eliminate the insignificant attributes for determining the damage grade. The selected features were subjected to 10-component principal component analysis (PCA) to scrutinize the top-ten-ranked significant features that contributed to grading the building damage. The 10-component ANOVA PCA-reduced (ASR) dataset was applied to the classifiers for damage grade prediction. The results showed that the Bagging classifier with the reduced dataset produced the greatest accuracy of 83% among all the classifiers considering an 80:20 ratio of data for the training and testing phases. To enhance the performance of prediction, a deep fully connected convolutional neural network (DFCNN) was implemented with a reduced dataset (ASR). The proposed ASR-DFCNN model was designed with the sequential keras model with four dense layers, with the first three dense layers fitted with the ReLU activation function and the final dense layer fitted with a tanh activation function with a dropout of 0.2. The ASR-DFCNN model was compiled with a NADAM optimizer with the weight decay of L2 regularization. The damage grade categorization performance of the ASR-DFCNN model was compared with that of other ML classifiers using precision, recall, F-Scores, and accuracy values. From the results, it is evident that the ASR-DFCNN model performance was better, with 98% accuracy.

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A Stacked Generalization Model to Enhance Prediction of Earthquake-Induced Soil Liquefaction

Cited by 6 publications

References 32 publications

A Novel Hybrid Feature Selection with Cascaded LSTM: Enhancing Security in IoT Networks

A Novel Hybrid Feature Selection with Cascaded LSTM: Enhancing Security in IoT Networks

Deep Convolutional Neural Network for Accurate Prediction of Seismic Events

Novel ANOVA-Statistic-Reduced Deep Fully Connected Neural Network for the Damage Grade Prediction of Post-Earthquake Buildings

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