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Amachine learning technique embedded reference-dependent choice model for explanatory power improvement: Shifting of reference point as a key factor in vehicle purchase decision-making

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TRANSPORTATION RESEARCH PART B-METHODOLOGICAL 2025年 191卷

作者： Kim, Kyungah Kim, Jinseok Park, Subin Lee, Jongsu Kim, Junghun Seoul Natl Univ Technol Management Econ & Policy Program 1 Gwanak Ro Seoul 08826 South Korea Korea Univ Grad Sch Management Technol 145 Anam Ro Seoul 02841 South Korea Stanford Univ Walter H Shorenstein Asia Pacific Res Ctr 616 Jane Stanford Way Stanford CA 94305 USA

machine learning is a powerful tool with the potential to improve a choice model's ability to explain consumer behavior. Although the reference-dependent choice model, developed with an emphasis on real decision-making processes, has an advantage over general discrete choice models in terms of explanatory power and interpretability, there is still a lack of consensus on how the reference point should be set. Currently, the common practice is to design a reference point-based utility equation to make an arbitrary decision between past experience, the status quo, and future expectations as the reference point. However, as individual consumers may differ from researchers in how they set their reference points, the current method is rather limited for understanding consumer choice behavior. Therefore, this study proposes a new approach to more accurately understand consumer choice behavior by shifting individual reference points using ANNs (Artificial Neural Networks). The analysis results show that the model proposed in this study has better explanatory power than both the discrete choice model and the existing reference-dependent choice model. This implies that the reference point typically set by researchers approximates each individual's actual reference point through artificial neural networks. This study is significant in that it confirms the possibility that the current status, which showed the highest model fit among several reference price proxy candidates in vehicle choice, may not function as the actual reference price, while also proposing a foundational framework for identifying each consumer's true reference price. Furthermore, it can contribute to corporate strategies and government policy recommendations based on consumer preference analysis, where high explanatory power is required.

关键词： Discrete choice experiment Reference-dependent preferences Purchasing price Reference point heterogeneity Reference point shifting machine learning algorithms

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A novel multiple analysis method for assessing seismic deformations of GRS walls: combinations of FEM, machine learning and performance-based fragility curves

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STRUCTURE AND INFRASTRUCTURE ENGINEERING 2025年

作者： Rafsanjani, Ali Akbar Heshmati Razeghi, Hamid Reza Alavi, Seyed Meisam Iran Univ Sci & Technol Sch Civil Engn Tehran Iran

Assessment of seismic deformations of geosynthetic reinforced soil (GRS) walls in literature has dealt with unsolved challenges, encompassing time-consuming analyses, lack of probabilistic-based analyses, ignored inherent uncertainties of seismic loadings and limited investigated scenarios of these structures, especially for tall walls. Hence, a novel multiple analysis method has been proposed, founded on over 257,400 machine learning simulations (trained with 1582 finite element method analyses) and numerous performance-based fragility curves, to promptly evaluate the seismic vulnerability. The conducted probabilistic parametric study revealed that simultaneously considering several intensity measures for fragility curves is inevitable, preventing engineering judgement bias (up to 52% discrepancies in damage possibilities). Up to 75% contrasts between failure possibilities of 8 and 20 m walls, especially under earthquakes with common intensities (e.g. PGA <= 0.3g), raised serious concerns in the application of height-independent designing methods of GRS walls (e.g. AASHTO Simplified Method). Decreases in deformation possibilities were nearly the same due to increasing reinforcement stiffness (J) (1000 to 2000 kN/m) and reinforcement length to wall height ratio (L/H) (0.8 to 1.5);a decisive superiority of J variations over increasing L/H, as a remedial plan. The proposed methodology privileges engineers to swiftly assess the seismic deformations of multiple GRS walls at the design stage.

关键词： Finite element method geosynthetic reinforced soil walls machine learning algorithms performance-based fragility curves probabilistic parametric study seismic analyses

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Multi-parameter optimization and design of self-triggered low voltage hybrid DC circuit breaker based on machine learning

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ELECTRIC POWER SYSTEMS RESEARCH 2025年 244卷

作者： Zhang, Zhaozi Gao, Caizhi Cao, Weidong Wang, Qian Chen, Silei Li, Xingwen Huang, Yinfang Gong, Zuoyong Xi An Jiao Tong Univ State Key Lab Elect Insulat & Power Equipment Xian 710049 Peoples R China Xian Univ Technol Sch Sci Xian 710048 Peoples R China Xian Univ Technol Sch Elect Engn Xian 710048 Peoples R China Shanghai Liangxin Elect Co Ltd Nader Shanghai 201206 Peoples R China

The low-voltage direct current (LVDC) hybrid circuit breaker (HCB), with advantages of low conduction loss and high breaking performance is better to meet the application needs of photovoltaic system. Among these, selftriggered hybrid circuit breakers (STHCBs) based on natural commutation have become an important development direction for LVDC HCBs due to their simple structure and selective protection. However, existing design methods do not adequately consider the influence of multiple parameters, resulting in unreliable current commutation during interruption. To address these issues, this paper proposes a machine learning-based multiparameter optimization method. The method employed Long Short-Term Memory (LSTM) algorithm to predict the interruption waveforms of mechanical switches under different short-circuit fault conditions as an input to the optimization. In addition, an interruption model of HCBs was developed, which comprehensively considered power electronic device characteristics, arc behavior and drive circuit. Based on the theoretical analysis and predicted waveforms, we used Genetic Algorithm (GA) to determine the optimal design parameters for the corresponding interruption waveforms. Experiment results confirm that this method can effectively enhance the interruption reliability of STHCBs while also demonstrates adaptability. The proposed method provides technical reference for the design of LVDC HCBs.

关键词： LVDC Hybrid circuit breaker Arc prediction Multi-parameter optimization Short-circuit interruption machine learning algorithms

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Statistical analysis and predictive modeling of cutting parameters in EN-GJL-250 cast iron turning: application of machine learning and MOALO optimization

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INTERNATIONAL JOURNAL OF ADVANCED MANUFACTURING TECHNOLOGY 2025年第3-4期137卷 1991-2009页

作者： Reffas, Omar Boumediri, Haithem Karmi, Yacine Kahaleras, Mohamed Said Bousba, Issam Aissa, Laouissi Univ Constantine 1 Inst Appl Sci & Tech Electromech Dept Constantine Algeria Univ Constantine 1 Inst Appl Sci & Tech Mech Dept Constantine Algeria Univ Badji Mokhtar Annaba Lab LRTAPM BP 12 Annaba 23000 Algeria Natl Higher Sch Technol & Engn Dept Proc & Energy Engn Annaba 23005 Algeria Univ Jijel Fac Sci & Technol Jijel 18000 Algeria Univ Bordj Bou Arreridj Fac Sci & Technol Dept Mech Engn Bordj Bou Arreridj Algeria 8 Mai 1945 Univ LMS Lab Guelma 24000 Algeria

The objective of this study is to investigate the impact of cutting parameters (depth of cut, feed rate, cutting speed, and cutting tool material) on the resultant cutting force (Fr), surface roughness (Rz), and cutting pressure (Kc) during the turning of EN-GJL-250 cast iron. An L54 experimental design was adopted with the following input factors and levels: type of tools (coated Si3N4, uncoated Si3N4), depth of cut (0.25, 0.5, 0.75 mm), feed rate (0.08, 0.14, 0.2 mm/rev), and cutting speed (260, 370, 530 m/min). To determine the contribution of each cutting parameter to the studied factors, an ANOVA analysis was conducted. machine learning algorithms employed include the Levenberg-Marquardt backpropagation algorithm (LM), decision tree algorithm (DT), support vector machines algorithm (SVM), and Dragonfly Algorithm-optimized deep neural network (Da-DNN). These algorithms generated predictive models of the technological parameters, and their performance was compared and discussed. To optimize the cutting parameters, the desirability function method (DF) and the Multi-Objective Ant Lion Optimizer (MOALO) algorithm were used. Statistical analysis demonstrated the significant role of insert coating in improving surface roughness and reducing cutting forces and pressures. The findings from MOALO are promising for predicting and optimizing the turning process.

关键词： Cutting parameters machine learning algorithms Optimization Surface roughness Experimental design

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Concept Neural Network Based on Time-Delay Regret for Dynamic Stream learning

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IEEE TRANSACTIONS ON PATTERN ANALYSIS AND machine INTELLIGENCE 2025年第5期47卷 3796-3814页

作者： Mi, Yun-Long Cent South Univ Changsha 410083 Peoples R China Chinese Acad Sci Res Ctr Fictitious Econ & Data Sci Beijing 100190 Peoples R China

Dynamic stream learning, which emphasizes high-velocity, single-pass, real-time responses to arriving data, is revealing new challenges to the standard machine learning paradigm. In particular, existing (deep) neural networks perform poorly when learning on data streams, as they often require having access to a large amount of training data. Therefore, to address the limitations of existing neural networks in high-speed data streams with a stationary environment, we propose a novel dynamic neural network, called Concept Neural Network (ConceptNN), by combining concepts and two different online updating strategies. First, we construct a new concept space, where each concept consists of two components: the feature vector (regarded as a concept's intent) and its weight information (derived from a concept's extent), for training an initial neural network. During training, the sample weight information directly works on the loss function of ConceptNN. Second, we propose a time-delay regret theory (namely, real-time prediction, then delayed update) based on online optimization theory for data stream learning. Finally, based on time-delay regret theory, we employ two online updating paradigms (i.e., the one-by-one updating strategy and chunk-by-chunk updating strategy) to update our model in the face of new data arriving continuously in a stream, and subsequently present their upper and lower bounds. Experimental results on various datasets demonstrate that the proposed ConceptNN makes it possible to learn fast-evolving data streams with better learning performance (simultaneously considering time-cost and accuracy) than the state-of-the-art dynamic learning algorithms.

关键词： Neural networks Heuristic algorithms machine learning Incremental learning Data models Training Real-time systems learning systems Standards machine learning algorithms Concept neural network concept learning online learning data stream mining time-delay regret

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Raman spectroscopy combined with machine learning and chemometrics analyses as a tool for identification atherosclerotic carotid stenosis from serum

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SPECTROCHIMICA ACTA PART A-MOLECULAR AND BIOMOLECULAR SPECTROSCOPY 2025年 326卷 125198页

作者： Kesik, Jan Jakub Paja, Wieslaw Terlecki, Piotr Ilzecki, Marek Klebowski, Bartosz Depciuch, Joanna Med Univ Lublin Dept Vasc Surg & Angiol Lublin Poland Univ Rzeszow Inst Comp Sci Coll Nat Sci Rzeszow Poland Polish Acad Sci Inst Nucl Phys Krakow Poland Med Univ Lublin Dept Biochem & Mol Biol Lublin Poland

Atherosclerosis carotid stenosis (ACS) is one of the main causes of stroke. Unfortunately, the highest number of people go to the doctor with an advanced disease or as a result of a stroke, because carotid atherosclerosis does not cause obvious symptoms. Therefore, it is important to find a diagnostic method to detect the disease during routine tests (using blood or serum). Consequently, in this article, Raman spectroscopy was tested as a potential diagnostic method. Indeed, Raman spectra of serum collected from ACS and control patients showed decrease of Raman peak around 1520 cm(-1) and increase of peak around 3050 cm(-1) in people with ACS. Moreover in people with ACS shift of peaks originating from amides II, I and lipids vibrations were noticed in comparison with control group. Interestingly, decision tree algorithm showed that peaks at 1656 cm(-1) and 2957 cm(-1) could be a spectroscopy markers of atherosclerotic carotid stenosis. Continuing, Principal Component Analysis (PCA) clearly showed distinguishing between serum collected from ACS and control patients, while machine learning algorithms showed high value of accuracy, sensitivity and selectivity (more than 90 %). Finally, value of area under the curve of Receiver Operating Characteristic (AUC-ROC) showed value of 0.81 for Raman range between 800 cm(-1) and 1800 cm(-1) and 0.86 for 2800 cm(-1)-3000 cm(-1) range. Obtained results clearly showed possibility of Raman spectroscopy in detection of ACS from serum.

关键词： Atherosclerotic carotid stenosis Raman spectroscopy Chemometrics Marker machine learning algorithms

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Maximal Information Coefficient-Based Undersampling Method for Highly-Imbalanced learning

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IEEE ACCESS 2025年 13卷 4126-4135页

作者： Qin, Haiou Nanchang Inst Technol Sch Informat Engn Nanchang 330099 Peoples R China Jiangxi Prov Key Lab Smart Water Conservancy Nanchang 330099 Peoples R China

learning from highly-imbalanced datasets is still a big challenge in the field of machine learning because models created by general learning algorithms are weak in recognizing the samples from the minority class correctly. Undersampling is an alternative kind of methods to deal with imbalanced learning. In this paper, we propose a new undersampling method based on maximal information coefficient (including two algorithms MICU-1 and MICU-2) to rebalance the datasets. In order to evaluate the effectiveness of the method, 20 highly- imbalanced datasets are used for the benchmarks. Results show that compared with other undersampling methods, maximal information coefficient-based undersampling method are competitive in terms of G-mean and F-measure.

关键词： Microwave integrated circuits Generative adversarial networks Noise measurement machine learning algorithms Classification algorithms Training Software packages Shape Sensitivity Sampling methods Imbalanced classification imbalanced learning maximal information coefficient maximal information coefficient-based undersampling undersampling

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Enhancing Mobile App Recommendations With Crowdsourced Educational Data Using machine learning and Deep learning

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IEEE ACCESS 2025年 13卷 38778-38792页

作者： Mirbahar, Naadiya Kumar, Kamlesh Laghari, Asif Ali Sindh Madressatul Islam Univ Dept Comp Sci Karachi 74000 Pakistan Sindh Madressatul Islam Univ Dept Software Engn Karachi 74000 Pakistan

In the rapidly evolving digital landscape, personalized recommendations have become essential for enhancing user experience. machine learning models analyze user behavior patterns to suggest relevant entertainment, education, or e-commerce content. Mobile devices make it easier to gather educational data through crowdsourcing, which opens new possibilities for improving app recommendation algorithms. This paper provides valuable methodologies for scalable student recommendation and educational systems, highlighting DL's advantages over CF in handling sparse, time-sensitive datasets. The objective of this study is to recommend apps to university students by category based on app usage patterns. Data was used to evaluate these 806 university students to train the Collaborative Filtering (CF) and Contemporary Deep learning (DL) models. The results demonstrate that Gated Recurrent Units (GRU) are the best option for real-time, customized suggestions because of their capacity to simulate successive interactions and adjust to changing user behavior. The GRU yields the lowest mean errors MAE of 0.2246, RMSE=0.2516, and superior short-term predictions k=4 MAE of 0.1319 and RMSE of 0.1319. Other techniques, i.e., Stacked Auto-Encoder, exhibit the sign of overfitting with an MAE of 0.0001, whereas the LSTM and Graph Auto-Encoder perform below GRU with an MAE of 0.3453 and 0.8992. Although the CF techniques suffer from temporal dynamics and data sparsity, even the KNNBasic stands out among all CF algorithms with the lowest MAE of 0.548 and RMSE of 0.754, demonstrating the highest predictive accuracy.

关键词： Prediction algorithms Autoencoders Accuracy machine learning algorithms Training Predictive models Mobile applications History machine learning deep learning recommender system deep learning recommender system

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Analysis of machine learning approaches for predictive modeling in heart disease detection systems

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BIOMEDICAL SIGNAL PROCESSING AND CONTROL 2025年 106卷

作者： Mandal, Akshaya Kumar Dehuri, Satchidananda Sarma, Pankaj Kumar Deva Assam Univ Cent Univ India Dept Comp Sci Silchar 788011 Assam India Fakir Mohan Univ Dept Comp Sci Balasore 756019 Odisha India

Heart disease is a leading global cause of death, making early prediction crucial for saving lives. Although the exact causes of heart disease are not fully known, its strong links to high mortality, severe morbidity, and disability highlight the need for advanced predictive technologies. Artificial Intelligence (AI) plays a crucial role in developing these technologies, with machine learning (ML) being a key tool for analyzing data from AI devices. However, creating effective ML-based approaches for predicting heart disease is highly challenging. This study aims to conduct an empirical analysis of twelve (12) promising machine learning approaches with their detailed mathematical analysis for predicting heart disease with optimal accuracy for diagnostic purposes. It details the application development stages, including dataset collection and dataset attribute evaluation. In data analysis, metrics such as accuracy, ROC curves, confusion matrices, and feature importance provide graphical significance, while summary plots of shapely values highlight the significance of features in the applied models, which are used to evaluate the approximate accuracy in detecting heart disease. In conclusion, among the twelve machine learning models evaluated, support vector machines (SVM) achieved the highest performance, with an accuracy of 89%, a receiver operating characteristic (ROC) score of 92%, and a precision-recall curve (PRC) score of 93%.

关键词： machine learning algorithms Heart disease detection system Computational method Feature selection Disease prediction

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XAI-Enhanced machine learning for Obesity Risk Classification: A Stacking Approach With LIME Explanations

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IEEE ACCESS 2025年 13卷 13847-13865页

作者： Azad, Mohammad Khan, Md Faraz Kabir Abd El-Ghany, Sameh Jouf Univ Coll Comp & Informat Sci Dept Comp Sci Sakaka 72341 Saudi Arabia Univ Western Australia Dept Comp Sci & Software Engn Perth WA 6009 Australia Jouf Univ Coll Comp & Informat Sci Dept Informat Syst Sakaka 72341 Saudi Arabia

Obesity remains a critical global health challenge, necessitating early risk assessment to guide preventive measures and mitigate potential complications. While various research endeavors have explored obesity classification, many existing approaches lack reliability due to limited integration with explainable artificial intelligence (XAI) methodologies. In this study, we propose a robust machine learning framework that incorporates Explainable AI (XAI) principles to accurately estimate obesity levels and provide insights into the factors influencing the predictions. We utilize the publicly available dataset from Palechor and Manotas available in the UCI ML repository which contains relevant information on individuals' physical characteristics and behaviors. Our proposed model employs an ensemble approach, specifically a stacking algorithm, where the base estimators include the Light Gradient Boosting machine (LGBM) classifier, the Logistic Regression (LR) classifier, and the Random Forest (RF) Classifier, and the Stochastic Gradient Descent (SGD) classifier is selected as the final estimator. To enhance model interpretability and reliability, we integrate a widely accepted XAI method, Local Interpretable Model-agnostic Explanations (LIME). Our proposed framework achieves a peak accuracy of 98.82%, surpassing most existing techniques. By incorporating LIME, we not only enhance model trustworthiness but also provide deeper insights into the factors driving obesity risk. Overall, our approach contributes to advancing personalized interventions and bridging the gap between model complexity and human understanding.

关键词： Obesity Data models Random forests Boosting Accuracy Numerical models machine learning algorithms Correlation machine learning stacking explainable AI stacking explainable AI

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