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Short-term forecasting of emergency medical services demand exploring machine learning☆

COMPUTERS & INDUSTRIAL ENGINEERING 2025年 200卷

作者： Shahidian, Nika Abreu, Paulo Santos, Daniel Barbosa-Povoa, Ana Univ Lisbon CEGIST Inst Super Tecn Lisbon Portugal

This study addresses the challenge of forecasting short-term demand in Emergency Medical Services (EMS) using machine learning techniques, which is essential for improving resource allocation, optimizing response times, and enhancing overall system efficiency. In emergency situations, the swift allocation of medical personnel and vehicles is crucial for ensuring faster response times and increasing survival rates. To tackle this, the study explores nine machine learning algorithms, including Gaussian Mixture Models, Naive Bayes, KNearest-Neighbors, Support Vector Machine, Random Forest, Extremely Randomized Trees, Gradient boosting, Adaptive boosting, and Bagging, to provide accurate forecasts. Model performance is analyzed using two variable selection methods: filter and wrapper methods. The best-performing algorithms, Gradient boosting and AdaBoost, undergo further analysis after hyperparameter fine-tuning. Principal results show that Gradient boosting and AdaBoost outperformed other algorithms, with Random Search and Genetic Algorithm methods enhancing model performance. The study also demonstrates how the combination of different methods influences both performance and computational complexity. In this context, the developed models not only achieve high levels of accuracy in predicting short-term EMS demand but also serve as versatile tools. They distinguish priority calls and vehicle types when predicting call volume per hour from different districts and the expected number of dispatches at each base per shift, respectively. These models can be used as standalone tools or integrated with other optimization approaches, providing valuable inputs for optimizing staff scheduling, location, and relocation of emergency vehicles, thus contributing to the efficiency and performance improvement of EMS systems.

关键词： Machine learning Short-term forecasting Genetic algorithms Computational complexity analysis boosting algorithms

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XGBoost-enabled low-complexity approach for signal integrity assessment in coherent communication systems

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PHYSICA SCRIPTA 2025年第1期100卷

作者： Jha, Dhirendra Kumar Mishra, Jitendra K. Indian Inst Informat Technol Ranchi Dept Elect & Commun Engn Ranchi 835217 Jharkhand India

Error vector magnitude (EVM) provides critical information for assessing signal integrity and system performance in optical communication systems. In this research, a regression approach using boosting algorithms is developed to retrieve EVM information from complex signal constellation structures applicable to various modulation formats. Amplitude histograms are created and collected at different OSNR levels, launch powers, and transmission distances using an offline preprocessing approach. The impact of various evaluation techniques, including mean absolute error, coefficient of determination, root mean square error (RMSE), and mean absolute percentage error (MAPE) is discussed in detail. The results show that the proposed extreme gradient boosting framework considerably increases estimation precision compared to categorical boosting and light gradient boosting machine, especially under diverse transmission conditions, with MAPE staying below 1.7% and RMSE below 0.5, thereby enhancing overall performance monitoring in optical communication systems. This research provides a comprehensive and accurate representation of the proposed EVM estimation technique, making it a valuable resource for the development of advanced optical performance monitoring systems in the future.

关键词： error vector magnitude optical performance monitoring deep learning boosting algorithms extreme gradient boosting

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Convolution neural network based multi-class classification of rehabilitation exercises for diastasis recti abdominis using wearable EMG-IMU sensors

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ENGINEERING COMPUTATIONS 2024年第10期41卷 2381-2403页

作者： Radhakrishnan, Menaka Premkumar, Vinitha Joshy Prahaladhan, Viswanathan Balasubramanian Mukesh, Baskaran Nithish, Purushothaman Vellore Inst Technol Ctr Cyber Phys Syst Chennai India Vellore Inst Technol Sch Elect Engn Chennai India

PurposeGlobally, postnatal women endure a prominent issue caused by midline separation of abdominal recti muscles, characterized by a sagging and pouch-like appearance of the belly termed as Diastasis Recti Abdominis (DRA). The necessity of ensuring the efficacy of rehabilitative workouts for individuals with DRA cannot be overstated, as inaccurate exercises can exacerbate the condition and deteriorate the health of affected women. The purpose of these exercises is to specifically focus on the rectus abdominis muscles to facilitate the reapproximation of the linea alba. The primary aim of this research work is to assess the effectiveness of rehabilitation exercises for DRA women obtained from Inertial Measurement Unit (IMU) and Electromyography (EMG) ***/methodology/approachConvolutional neural networks (CNN) employs convolutional activation functions and pooling layers. Recently, 1D CNNs have emerged as a promising approach used in various applications, including personalized biomedical data classification and early diagnosis, structural health monitoring and anomaly detection. Yet another significant benefit is the feasibility of a real-time and cost-effective implementation of 1D CNN. The EMG and IMU signals serve as inputs for the 1D CNN. Features are then extracted from the fully connected layer of the CNN and fed into a boosting machine learning algorithm for *** findings demonstrate that a combination of sensors provides more details about the exercises, thereby contributing to the classification *** implicationsIn real time, collecting data from postnatal women was incredibly challenging. The process of examining these women was time-consuming, and they were often preoccupied with their newborns, leading to a reluctance to focus on their own health. Additionally, postnatal women might not be fully aware of the implications of DRA and the importance of rehabilitation exercises. Many might not realize that negle

关键词： Diastasis recti abdominis Electromyography Inertial measurement unit Convolutional neural network boosting algorithms

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Modelling biochemical oxygen demand in a large inland aquaculture zone of India: Implications and insights

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SCIENCE OF THE TOTAL ENVIRONMENT 2024年 906卷 167386页

作者： Nagaraju, T. Vamsi Bala, G. Sri Bonthu, Sridevi Mantena, Sireesha SRKR Engn Coll Dept Civil Engn Bhimavaram India SRKR Engn Coll Ctr Clean & Sustainable Environm Bhimavaram India Vishnu Inst Technol Dept Comp Sci & Engn Kovvada India Andhra Univ Coll Engn Dept Geoengn Visakhapatnam Andhra Pradesh India

Water quality surveillance is tough, and a specific timely management is necessary for the inland aquaculture ponds and ecology as well. Real time quality monitoring involves the study of numerous parameters includes physical (turbidity, temperature, and specific conductivity), chemical (pH, calcium, manganese, chlorides, iron, biochemical oxygen demand), and biological (bacteria and algae). It is also crucial to recognize the interdependence among the parameters. Alternatively, these relationships can be predicted with statistical and numerical modelling. Organic strength parameter 5-day biochemical oxygen demand (BOD) is a significant parameter to evaluate since its impact is very high on the quality of water, aquatic life, and other biological concerns. This study focuses on the prediction of BOD using six traditional and four boosting algorithms considering ten input physicochemical attributes. The attributes were fine-tuned for highly precise predictions by removing extreme values from the data set using data outlier treatment. The prediction results are compared using performance metrics such as coefficient of determination (R2), root mean square error (RMSE), mean square error (MSE), and mean absolute error (MAE). The findings revealed that boosting algorithms outperform the results of traditional models with the highest prediction accuracy. Among the boosting algorithms, eXtreme Gradient boosting algorithm (XGBM) is found highly appropriate for the inland aquaculture waters with R2 =

关键词： Ammonia Aquaculture Water quality BOD boosting algorithms

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boosting Algorithm Techniques for Detecting Credit Card Fraud in Heterogeneous Datasets 24th

Boosting Algorithm Techniques for Detecting Credit Card Frau...

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24th International Multidisciplinary Conference on Reliability and Statistics in Transportation and Communication, RelStat 2024

作者： Viduss, Justs Spiridovska, Nadezda Transport and Telecommunication Institute Lauvas iela 2 Riga1019 Latvia

ISBN: (纸本)9783031875311

Manual reviews and rule-based systems, along with data mining techniques such as clustering and classification algorithms, are crucial for identifying credit card fraud as they help identify fraudulent transactions. Despite challenges in gathering training data, more data has recently become available;however, a comprehensive comparison of current machine learning approaches has yet to be conducted. algorithms like XGBoost, AdaBoost, and Gradient boosting Machine frequently outperform older methods. This study compares boosting algorithms to traditional approaches using three different credit card transaction datasets: synthetic, balanced with 50% fraudulent transactions, and highly unbalanced with only 0.17% fraudulent transactions. The genuine transaction datasets contained 28 anonymized parameters, including time and location. Each method was evaluated using the F1 score, accuracy, precision, and recall. This study offers recommendations on which algorithms to use in real-world scenarios, providing valuable insights for future research and practical applications in credit card fraud detection. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： boosting algorithms Fraud Detection Machine Learning

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Data-driven machine learning approach for predicting dwell fatigue life in two classes of Titanium alloys

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ENGINEERING FRACTURE MECHANICS 2024年 306卷

作者： Rahman, Syed Abdur Chandraker, Abhinav Prakash, Om Chauhan, Ankur Indian Inst Sci IISc Dept Mat Engn Extreme Environm Mat Grp Bengaluru 560012 Karnataka India Boeing Res & Technol India Ctr Bangalore 560016 India

Predicting dwell fatigue life accurately in Titanium (Ti) alloys is vital for designing reliable components such as aero-engine discs and blades. This study proposes a data-driven machine learning (ML) approach for precisely predicting dwell fatigue life in two classes of Ti alloys: near alpha and alpha + beta alloys. To build a robust model, the data-driven approach tackles this multidimensional problem by training a comprehensive dataset of various input features, including alloy composition, monotonic properties, and fatigue testing parameters. Advanced ensemblebased algorithms, Gradient boosting (GBOOST) and Extreme Gradient boosting (XGBOOST) are employed. The split-insensitive best-performing XGBOOST model achieved a high accuracy (R2) score of 89.9 %, indicating impressive prediction capability. The model's robustness is further assessed using an unseen validation dataset to draw qualitative and quantitative conclusions. ML model's validation further involved comparing its predictions with actual experimental trends across relationships, such as dwell life versus dwell time and dwell-debit versus dwell time. Dwell-debit is also predicted as a function of varying stresses normalized to 0.2 % yield strength, highlighting the susceptibility of investigated Ti alloys to dwell fatigue. Additionally, the SHapley Additive exPlanations (SHAP) analysis was conducted to gain deeper insights into various input features affecting dwell fatigue life.

关键词： Titanium alloys Fatigue Dwell fatigue Machine learning boosting algorithms

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Prediction of Load Capacities of Closed-Ended Piles Using boosting Machine Learning Methods 5th

Prediction of Load Capacities of Closed-Ended Piles Using Bo...

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5th International Conference on New Developments in Soil Mechanics and Geotechnical Engineering (ICNDSMGE-ZM)

作者： Karakas, S. Ulker, M. B. C. Taskin, G. Istanbul Univ Cerrahpasa Dept Civil Engn TR-34320 Istanbul Turkiye Istanbul Tech Univ Inst Disaster Management Earthquake Engn Program Istanbul Turkiye

ISBN: (纸本)9783031201745;9783031201721;9783031201714

In this study, a novel data-driven model is developed using boosting-type machine learning algorithms with the aim of predicting the ultimate load-bearing capacities of closed-ended piles. A comprehensive database is gathered using the full-scale load test data with four features. Special boosting type machine learning methods are trained and tested with the database. Once predictions are made, a newly developed machine learning algorithm called Shapley method is utilized to decide the effectiveness of the selected features in predicting pile capacities. Results indicate that the pile cross-section area and length features are sufficient to achieve accurate predictions covering the parameters on the pile side and the CPT-based tip resistance is the only parameter needed on the soil side. While different boosting methods result in different levels of accuracy in predicting the load bearing capacities of closed-ended piles, it is generally possible to determine the minimum number of features necessary to satisfy a high goodness of fit. In the end, optimum number of features are determined in the prediction process using the Shapley method through the boosting algorithms giving us a valuable prediction tool for estimating the bearing capacity of closed-ended piles.

关键词： boosting algorithms Closed-ended piles CPT test Load-bearing capacity Machine learning Shapley method

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Imbalanced data preprocessing model for web service classification

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INTERNATIONAL JOURNAL OF SYSTEM ASSURANCE ENGINEERING AND MANAGEMENT 2024年第10期15卷 4825-4837页

作者： Rhmann, Wasiur Ishrat, Amaan Lovely Profess Univ Dept Comp Applicat Jalandhar Punjab India Shri Ramswaroop Mem Univ Dept Comp Sci & Engn Lucknow India

Web services are a novel method of web application development. They allow business to adapt to a new environment and change quickly according to customer needs. The client requires high-quality web services with minimal response time, more security, and high availability. With the increasing demand for web services, the introduction of web services rapidly in the business environment has influenced rapidly the web service quality. In the present work, a novel model for web service classification is proposed. Three metaheuristic techniques: Whale optimization algorithm, Simulated annealing algorithm, and Ant colony optimization are used to select the best subset of features. Web service-based imbalanced dataset is balanced using SMOTETomek (Synthetic minority oversampling + Tomek link). Ensemble Adaboost and Gradient boosting algorithms are used for the creation of a web service prediction model. The publicly available QWS dataset is used for experimental purposes. The results of the proposed models are compared with machine learning techniques. It was observed that the Ant colony algorithm performed best for relevant feature selection and the Ensemble Adaboost and Gradient boosting algorithm outperformed all other machine learning techniques for web service classification.

关键词： Web services Machine learning boosting algorithms Ensemble techniques

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Comparing Out-of-Sample Performance of Machine Learning Methods to Forecast US GDP Growth

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COMPUTATIONAL ECONOMICS 2023年第4期62卷 1567-1609页

作者： Chu, Ba Qureshi, Shafiullah Carleton Univ Dept Econ 1125 Colonel By Dr Ottawa ON Canada NUML Dept Econ Islamabad Pakistan

We run a 'horse race' among popular forecasting methods, including machine learning (ML) and deep learning (DL) methods, that are employed to forecast U.S. GDP growth. Given the unstable nature of GDP growth data, we implement a recursive forecasting strategy to calculate the out-of-sample performance metrics of forecasts for multiple subperiods. We use three sets of predictors: a large set of 224 predictors [of U.S. GDP growth] taken from a large quarterly macroeconomic database (namely, FRED-QD), a small set of nine strong predictors selected from the large set, and another small set including these nine strong predictors together with a high-frequency business condition index. We then obtain the following three main findings: (1) when forecasting with a large number of predictors with mixed predictive power, density-based ML methods (such as bagging, boosting, or neural networks) can somewhat outperform sparsity-based methods (such as Lasso) for short-horizon forecast, but it is not easy to distinguish the performance of these two types of methods for long-horizon forecast;(2) density-based ML methods tend to perform better with a large set of predictors than with a small subset of strong predictors, especially when it comes to shorter horizon forecast;and (3) parsimonious models using a strong high-frequency predictor can outperform other sophisticated ML and DL models using a large number of low-frequency predictors at least for long-horizon forecast, highlighting the important role of predictors in economic forecasting. We also find that ensemble ML methods (which are the special cases of density-based ML methods) can outperform popular DL methods.

关键词： Lasso Ridge regression Random forest boosting algorithms Artificial neural networks Dimensionality reduction methods MIDAS GDP growth

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Short-term wind power forecasting using integrated boosting approach

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FRONTIERS IN ENERGY RESEARCH 2024年 12卷

作者： Ahmed, Ubaid Muhammad, Rasheed Abbas, Syed Sami Aziz, Imran Mahmood, Anzar Mirpur Univ Sci & Technol Dept Elect Engn Mirpur Pakistan Uppsala Univ Dept Phys & Astron Uppsala Sweden

Rapidly increasing global energy demand and environmental concerns have shifted the attention of policymakers toward the large-scale integration of renewable energy resources (RERs). Wind energy is a type of RERs with vast energy potential and no environmental pollution is associated with it. The sustainable development goals: affordable and clean energy, climate action, and industry, innovation and infrastructure, can be achieved by integrating wind energy into the existing power systems. However, the integration of wind energy will bring instability challenges due to its intermittent nature. Mitigating these challenges necessitates the implementation of effective wind power forecasting models. Therefore, we have proposed a novel integrated approach, Boost-LR, for hour-ahead wind power forecasting. The Boost-LR is a multilevel technique consisting of non-parametric models, extreme gradient boosting (XgBoost), categorical boosting (CatBoost), and random forest (RF), and parametric approach, linear regression (LR). The first layer of the Boost-LR uses the boosting algorithms that process the data according to their tree development architectures and pass their intermediary forecast to LR which is deployed in layer two and processes the intermediary forecasts of layer one models to provide the final predicted wind power. To demonstrate the generalizability and robustness of the proposed study, the performance of Boost-LR is compared with the individual models of CatBoost, XgBoost, RF, deep learning networks: long short-term memory (LSTM) and gated recurrent unit (GRU), Transformer and Informer models using root mean square error (RMSE), mean square error (MSE), mean absolute error (MAE) and normalized root mean square error (NRMSE). Findings demonstrate the effectiveness of the Boost-LR as its forecasting performance is superior to the compared models. The improvement in MAE of Boost-LR is recorded as to be 31.42%, 32.14%, and 27.55% for the datasets of Bruska, Jelina

关键词： wind power forecasting hybrid model boosting algorithms deep learning network LSTM

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