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Optimization seismic resilience: a machine learning approach for vertical irregular buildings

Asian Journal of Civil Engineering 2024年第8期25卷 6233-6248页

作者： SALAMA, Ahmed Hamed El-Sayed Civil Engineering Department Faculty of Engineering Ajloun National University Ajloun 26810 Jordan Civil Engineering Department Al-Azhar University Nasr City Cairo 11884 Egypt

The paper is a landmark in earthquake and structural engineering, with modern machine-learning techniques applied to introduce innovative investigations into forecasting seismic behavior for vertically uneven structures using sophisticated machine-learning methodologies. The research constructs a very accurate model for making predictions using the xgboost algorithm with the Owl Search algorithm (OSA) for hyperparameter tuning, which explicitly considers complex behavior in the structures under seismic stresses. The variety within the dataset is broad and covers all kinds of irregularities in the structures, such as stiffness and mass irregularities;thus, it has been used to accurately represent the complex characteristics of actual buildings. The results indicate a strong dependence of base shear capacity and seismic performance on the irregularity of stiffness and mass. The test accuracy of the optimized xgboost model was 98.8%. The result was better than that of conventional models, thus proving the effectiveness of integrating the Owl Search algorithm in further fine-tuning the parameters. These results give new variables as insight into affecting earthquake resilience and represent practical applications that enhance building design and retrofitting processes. This is further underlined by the proposal of future research directions that would extend the model’s applicability to other structural anomalies and include additional machine-learning methodologies. Through AI-driven approaches, this study captured complicated structural dynamics with the utmost precision, thus opening new insights that could be brought into practice to improve building design and retrofitting strategies in a way that would diminish the impact of seismic events. Graphical abstract: (Figure presented.) © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2024.

关键词： Machine learning Optimization Owl search algorithm (OSA) Seismic resilience Vertical Irregular buildings xgboost algorithm

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The Power of Words: Predicting Stock Market Returns with Fine-Grained Sentiment Analysis and xgboost

The Power of Words: Predicting Stock Market Returns with Fin...

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Intelligent Systems Conference (IntelliSys)

作者： Balaneji, Farshid Maringer, Dietmar Spasic, Irena Univ Basel Fac Business & Econ Peter Merian Weg 6 CH-4002 Basel Switzerland Cardiff Univ Sch Comp Sci & Informat Abacws Senghennydd Rd Cardiff Wales

ISBN: (纸本)9783031477201;9783031477218

This study investigates the relationship between news sentiment and the stock market's return. The sentiment was automatically analyzed using four methods, including lexicon-based and deep learning-based approaches, at three levels of granularity, i.e., sentence, paragraph, and full text. The sentiment was combined with features from the calendar year, lagged returns, and news publishers, which were fed into the xgboost algorithm trained to classify the direction of market return for the following business day. The performance was maximized using Bayesian hyperparameter optimization and evaluated using nested cross-validation. The proof of concept was demonstrated using ten companies in the Dow Jones Index, which were grouped into five sectors. The findings indicate an asymmetric power of sentiment measures in different sectors, with the petroleum industry being the most responsive to the sentiment expressed in the news. The study highlights the significance of targeted sentiment measures in making informed decisions about the market direction, particularly for the petroleum industry.

关键词： Sentiment Analysis xgboost algorithm Hyperparameter Optimization Nested Cross-validation Stock Markets

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Method for predicting the remaining mileage of electric vehicles based on dimension expansion and model fusion

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IET INTELLIGENT TRANSPORT SYSTEMS 2022年第8期16卷 1074-1091页

作者： Tian, Sheng Li, Chengwei Lv, Qing Li, Jia South China Univ Technol Sch Civil Engn & Transportat Guangzhou Peoples R China

Accurately predicting the remaining mileage of electric vehicles (EVs) can effectively alleviate user's mileage anxiety and develop refinement of energy management strategy. However, traditional prediction methods not only consume time and resources, but also accumulate errors and lack interpretability. In this paper, we proposed a model based on dimension expansion and model fusion strategy, which uses the extreme gradient boosting (xgboost) algorithm to directly predict the remaining mileage of EVs. After pre-processing the real running data of EVs, we constructed the field of remaining driving range and analyzed the relationship between features and remaining driving range, and then directly predicted the remaining driving mileage. Compared with other machine learning methods, xgboost model has the highest accuracy. Then dimensional extended data set was obtained based on prior knowledge and symbol conversion, which improved the model performance. Finally, the model fusion strategy was adopted to further improve the generalization ability and stability of the model. The experimental results show that the Bootstrap aggregating (Bagging) fusion model has the highest predictive performance on the test set and outperformed other methods. The maximum RAE is not more than 3.5%, RMSE is less than 3km and MAE is about 2 km.

关键词： energy management RMSE electric vehicles RAE model fusion dimension expansion supervised learning data handling dimensional extended data set neural nets generalisation (artificial intelligence) machine learning predictive performance remaining driving range symbol conversion remaining driving mileage bagging user mileage anxiety bootstrap aggregating fusion model extreme gradient boosting prior knowledge remaining mileage prediction xgboost algorithm generalization ability energy management systems traffic engineering computing mean square error methods

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A Novel Approach for Accurate Stock Market Forecasting by Integrating ARIMA and xgboost

A Novel Approach for Accurate Stock Market Forecasting by In...

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2024 IEEE International Students' Conference on Electrical, Electronics and Computer Science, SCEECS 2024

作者： Somkunwar, Rachna K. Pimpalkar, Amit Srivastava, Vaibhav Dr. D. Y. Patil Institute of Technology Department of Computer Engineering Pune Pimpri India Nagpur India

ISBN: (纸本)9798350348460

In today's economy, the prediction and analysis of stock market data play a crucial role. Over the last decade, researchers have employed the neural network, an intelligent data mining technique, across various domains. Forecasting algorithms can be broadly classified into linear models and non-linear models. This paper employs the Autoregressive Integrated Moving Average (ARIMA) linear model to develop a robust predictive framework for forecasting stock market trends. This study introduces an innovative method by combining the ARIMA algorithm with xgboost machine learning to create an advanced predictive model for stock market trends. By integrating temporal dependency capture from ARIMA with the improved predictive capabilities of the machine-learning model, this approach aims to enhance the accuracy and effectiveness of stock market trend predictions. Leveraging the strengths of both methodologies, the hybrid model exhibits superior accuracy, surpassing standalone ARIMA and xgboost models. Training the model involves utilizing historical stock prices, trading volumes, and pertinent financial indicators. Evaluation will encompass the Google stock dataset to estimate the model's performance across various market conditions. By analyzing diverse historical stock market data, including prices, trading volumes, and financial indicators, the integrated model achieves a remarkable prediction accuracy of over 96.7%. The results underscore the efficacy of combining traditional time series analysis with advanced machine learning, positioning the ARIMA-xgboost hybrid model as a potent tool for precise and robust stock market trend forecasting. © 2024 IEEE.

关键词： ARIMA algorithm Machine Learning Stock Market Time Series Analysis xgboost algorithm

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Interpretable Machine Learning Models for Credit Risk Assessment 18

Interpretable Machine Learning Models for Credit Risk Assess...

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18th INDIAcom;11th International Conference on Computing for Sustainable Global Development, INDIACom 2024

作者： Hassan, Md Al Mahedi Rakshit Govind, T. Mansur, Usman Muhammad Jha, Roshan Fahim, Md Forkan Hossain Mahesh, T.R. Department of Computer Science and Engineering Bengaluru India Bmsit&m Department of Ece Bengaluru India

ISBN: (纸本)9789380544519

For lending organizations, determining a borrower's creditworthiness is essential to determining their capacity to repay loans. The prediction of credit scores using feature engineering and machine learning techniques is the main emphasis of this study. Using the Kaggle Family Credit Default Risk dataset, the AUC scores of several machine learning models are compared. Modern machine learning techniques, including well-established methods like Random Forest and Linear Support Vector Machines, can be effectively applied to credit scoring. Ensemble models, such as LightGBM, offer advantages like improved predictions and increased stability, making them well-suited for this specific use case. Combining predictions from multiple models often results in less noisy outcomes compared to using a single model, outperforming other techniques like xgboost, SVMs, and logistic regression. © 2024 Bharati Vidyapeeth, New Delhi.

关键词： credit risk prediction LightGBM Machine Learning SVM xgboost algorithm

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Machine Learning Approach Accelerates Search for Solid State Electrolytes

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Chinese Journal of Chemical Physics 2024年第4期37卷 505-512,I0039-I0041,I0094页

作者： Le Tang Guozhen Zhang Jun Jiang Department of Chemical Physics University of Science and Technology of ChinaHefei 230026China Hefei National Research Center for Physical Sciences at the Microscale University of Science and Technology of ChinaHefei 230026China

In the current aera of rapid development in the field of electric vehicles and electrochemical energy storage,solid-state battery technology is attracting much research and ***-state electrolytes,as the key component of next-generation battery technology,are favored for their high safety,high energy density,and long ***,finding high-performance solid-state electrolytes is the primary challenge for solid-state battery *** on inorganic solid-state electrolytes,this work highlights the need for ideal solid-state electrolytes to have low electronic conductivity,good thermal stability,and structural and phase *** experimental and theoretical computational methods suffer from inefficiency,thus machine learning methods become a novel path to intelligently predict material properties by analyzing a large number of inorganic structural properties and *** the gradient descent-based xgboost algorithm,we successfully predicted the energy band structure and stability of the materials,and screened out only 194 ideal solid-state electrolyte structures from more than 6000 structures that satisfy the requirements of low electronic conductivity and stability simultaneously,which greatly accelerated the development of solid-state batteries.

关键词： Solid-state battery Solid-state electrolyte xgboost algorithm Low electronic conductivity Thermal stability

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Cocoon morphological Features Based Silk Quality Prediction Using XG Boost algorithm

Cocoon morphological Features Based Silk Quality Prediction ...

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2024 IEEE International Students' Conference on Electrical, Electronics and Computer Science, SCEECS 2024

作者： Nivetha, M. Sudha, I. Saveetha University Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Department of Artificial Intelligence and Data Science Tamil Nadu Chennai India Saveetha University Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Department of Computer Science and Engineering Tamil Nadu Chennai India

ISBN: (纸本)9798350348460

Silk quality is a critical determinant in the silk industry, significantly influencing the market value of silk products. This study proposes an innovative method for forecasting silk quality by leveraging cocoon morphological features through the application of the XG Boost algorithm. The morphological characteristics of the cocoon, including dimensions, shape, and colour, are identified as key indicators of silk quality. The XG Boost algorithm, known for its effectiveness in handling complex datasets, is employed for building a predictive model. A comprehensive dataset is assembled, encompassing a diverse set of cocoon morphological features obtained from a representative sample of silkworms. These features comprise cocoon size, length, width, shape parameters, and colour attributes. The dataset is then utilized to train and validate the XG Boost model, fine-tuning its parameters to ensure accurate prediction of silk quality. The outcomes of this study showcase the effectiveness of the proposed approach, demonstrating high predictive accuracy in determining silk quality based on cocoon morphological features. Moreover, the XG Boost algorithm facilitates the identification of crucial features that significantly contribute to the prediction, offering valuable insights into the factors influencing silk quality. In conclusion, the integration of cocoon morphological features and the XG Boost algorithm presents a promising avenue for accurate silk quality prediction. This research contributes to the advancement of silk production methodologies, introducing innovative technologies to the silk industry and fostering a pathway for the adoption of cutting-edge practices. © 2024 IEEE.

关键词： Data Security Deep Learning Machine Learning Quality Prediction Silk Quality xgboost algorithm

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Electric theft detection in advanced metering infrastructure using Jaya optimized combined Kernel-Tree boosting classifier-A novel sequentially executed supervised machine learning approach

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IET GENERATION TRANSMISSION & DISTRIBUTION 2022年第6期16卷 1257-1275页

作者： Hussain, Saddam Mustafa, Mohd Wazir Al-Shqeerat, Khalil Hamdi Ateyeh Al-rimy, Bander Ali Saleh Saeed, Faisal Univ Teknol Malaysia Sch Elect Engn Johor Baharu 81310 Malaysia Qassim Univ Dept Comp Sci Coll Comp Buraydah Saudi Arabia Univ Teknol Malaysia Sch Comp Fac Engn Johor Baharu 81310 Johor Malaysia Birmingham City Univ Sch Comp & Digital Technol Birmingham W Midlands England

This paper presents a novel, sequentially executed supervised machine learning-based electric theft detection framework using a Jaya-optimized combined Kernel and Tree Boosting (KTBoost) classifier. It utilizes the intelligence of the xgboost algorithm to estimate the missing values in the acquired dataset during the data pre-processing phase. An oversampling algorithm based on the Robust-SMOTE technique is utilized to avoid the unbalanced data class distribution issue. Afterward, with the aid of few very significant statistical, temporal, and spectral features extracted from the acquired kWh dataset, the complex underlying data patterns are comprehended to enhance the accuracy and detection rate of the classifier. For effectively classifying the consumers into "Honest" and "Fraudster," the ensemble machine learning-based classifier KTBoost, with Jaya algorithm optimized hyperparameters, is utilized. Finally, the developed model is re-trained using a reduced set of highly important features to minimize the computational resources without compromising the performance of the developed model. The outcome of this study reveals that the proposed theft detection method achieves the highest accuracy (93.38%), precision (95%), and recall (93.18%) among all the studied methods, thus signifying its importance in the studied area of research.

关键词： trees (mathematics) machine learning-based electric theft detection framework sampling methods regression analysis Kernel-Tree boosting classifier data pre-processing phase complex underlying data patterns detection rate pattern classification unbalanced data class distribution issue xgboost algorithm ensemble machine learning-based classifier KTBoost oversampling algorithm significant statistical features feature extraction acquired dataset theft detection method Jaya algorithm spectral features advanced metering infrastructure temporal, features Robust-SMOTE technique power engineering computing learning (artificial intelligence)

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A hybrid machine learning approach for early mortality prediction of ICU patients

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PROGRESS IN ARTIFICIAL INTELLIGENCE 2022年第4期11卷 333-347页

作者： Mansouri, Ardeshir Noei, Mohammadreza Abadeh, Mohammad Saniee Tarbiat Modares Univ Dept Elect & Comp Engn Tehran Iran George Mason Univ Dept Comp Sci Fairfax VA 22030 USA

Hospitals face many pressures, including limited budgets and resources. The intensive care unit (ICU), has attracted much attention from the medical community. Patients in the ICU are monitored continuously and their physiological data is consistently measured. This provides a valuable opportunity to analyze significant clinical data. Predicting the mortality of patients in the ICU is crucial. By using this prediction, intensivists identify patients who will not benefit from receiving treatment in the ICU and focus more on the care of patients who will benefit from this treatment. To date, various scoring systems and machine learning models have been developed to predict mortality in intensive care units. In this paper, our goal is to provide a model that can predict mortality for up to 24 h after ICU admission. This research has been conducted using Medical Information Mart for Intensive Care III (MIMIC-III) database. Relevant data have been extracted, preprocessed, and prepared for data mining analysis. The proposed framework has two algorithmic stages. In the first step, time-series data within 24 h of admission is fed to a convolutional neural network with optimized hyperparameters. In the second step, by using the output of the previous stage and a filtering strategy for temporal features, a new data set is created and is fed to an xgboost algorithm, for final classification. The proposed framework outperforms severity of illness scores and machine learning models within 24 h of admission to the ICU and attains a ROC AUC of 0.863 (+/- 0.004).

关键词： Mortality prediction ICU MIMIC III Convolutional neural network xgboost algorithm Hybrid approach

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Experimental Study and Machine Learning Aided Modelling of the Mechanical Behaviour of Rammed Earth

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GEOTECHNICAL AND GEOLOGICAL ENGINEERING 2022年第10期40卷 5007-5027页

作者： Kardani, Navid Zhou, Annan Lin, Xiaoshan Nazem, Majidreza Royal Melbourne Inst Technol RMIT Sch Engn Civil & Infrastruct Engn Discipline Melbourne Vic 3001 Australia

Rammed earth is a sustainable building technique for constructing foundations, floors, and walls using natural raw materials such as earth, chalk, lime, with stabilizers like cements. As the proportion of various materials changes, the mechanical properties of rammed earth materials are also varying correspondingly. A series of experimental studies are first conducted to evaluate the effects of different proportions of raw materials including clay, sand, cement, and water under various loading rates on the strength/deformation properties (peak strength, q(f);residual strength, q(res);initial modulus, E-max;secant modulus at 50% peak strength, E-50) and stress-strain relationships (sigma(1) similar to epsilon(1)) of rammed earth. A soft computing method (extreme gradient boosting machine, xgboost) is then developed to model peak strength, residual strength, initial modulus, secant modulus and entire stress-strain relationships obtained from the experimental studies. Three performance metrics including the root mean squared error, variance accounted for and R-squared value (R-2) are used to measure the performance of the applied model. Comparisons between simulations and experiments show that the developed xgboost algorithm is a promising alternative in modelling key mechanical properties and entire stress-strain relationships for rammed earth. For stress-strain relationships calculated R-squared value for the training set is 0.978 and that for the testing dataset is 0.908. The key factor that most significantly affects the peak strength, residual strength, initial modulus, secant modulus and entire stress-strain relationships for rammed earth can be identified by using the developed soft computing method.

关键词： Rammed earth Key mechanical properties Peak strength Initial modulus xgboost algorithm Experimental analysis

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