An XGBoost-based casualty prediction method for terrorist attacks

Machine learning-based sales forecasting during crises: Evidence from a Turkish women's clothing retailer

BACKGROUND

Retail involves directly delivering goods and services to end consumers. Natural disasters and epidemics/pandemics have significant potential to disrupt supply chains, leading to shortages, forecasting errors, price increases, and substantial financial strains on retailers. The COVID-19 pandemic highlighted the need for retail sectors to prepare for crisis impacts on sales forecasts by regularly assessing and adjusting sales volumes, consumer behavior, and forecasting models to adapt to changing conditions.

METHODS

This study explores strategies for adapting sales forecasts and retail approaches in response to such crises. By employing different machine learning (ML) methods, we analyze consumer behavior changes and sales impacts across various product categories, including bottom wear, top wear, one piece, accessories, outwear, and shoes during the COVID-19 pandemic.

RESULTS

The gradient boosting and CatBoost algorithms excelled in product groups with significant sales changes during the pandemic. The Multi-Layer Perceptron (MLP) algorithm performed well in low-volume categories like accessories and footwear. Meanwhile, MLP, LightGBM, and XGBoost were effective in medium-volume categories such as outerwear and underwear.

CONCLUSION

The findings highlight the efficacy of these models in adapting sales forecasts to crisis conditions, offering a practical approach to enhancing retail resilience against future disruptions. This study offers an effective approach for adapting sales forecasting to shifting consumer behaviors during crises.

Numerical and machine learning modeling of GFRP confined concrete-steel hollow elliptical columns

This article investigates the behavior of hybrid FRP Concrete-Steel columns with an elliptical cross section. The investigation was carried out by gathering information through literature and conducting a parametric study, which resulted in 116 data points. Moreover, multiple machine learning predictive models were developed to accurately estimate the confined ultimate strain and the ultimate load of confined concrete at the rupture of FRP tube. Decision Tree (DT), Random Forest (RF), Adaptive Boosting (ADAB), Categorical Boosting (CATB), and eXtreme Gradient Boosting (XGB) machine learning techniques were utilized for the proposed models. Finally, these models were visually and quantitatively verified and evaluated. It was concluded that the CATB and XGB are standout models, offering high accuracy and strong generalization capabilities. The CATB model is slightly superior due to its consistently lower error rates during testing, indicating it is the best model for this dataset when considering both accuracy and robustness against overfitting.

Emergence angle: Comprehensive analysis and machine learning prediction for clinical application

PURPOSE

To analyze and compare the emergence angle (EA) using two measurement methods, conventional and modified (EA-GPT and EA-R), the EAs of all-natural teeth were evaluated and classified to derive a suitable and predictable clinically applicable measurement method.

METHODS

Natural human teeth (n=600) were classified, cleaned, and thoroughly inspected. Teeth were scanned using an intraoral scanner. The scanned data were analyzed using three-dimensional analysis software for both methods with several points per surface. A Bland-Altman analysis was used for statistical analysis and a heat map and a nonparametric density plot to assess the repetition and distribution. An XGBoost regression model was used for prediction.

RESULTS

The EA-R method showed significantly different values compared to the EA-GPT method, representing an increase of 17.5-20.7% for the proximal surfaces. An insignificant difference between the two methods was observed for other surfaces. Different teeth classes showed variation in the normal range, thereby resulting in a new classification of the EA for all-natural teeth based on the interquartile range. The machine learning gradient boosting model predicted conventional data with an average mean absolute error of 0.9.

CONCLUSIONS

Variations in the natural teeth EA and measurement methods, suggest a new classification for EA. The established artificial intelligence method demonstrated robust performance, which could aid in implementing EA measurement in prosthetic designs.

Selective quantum ensemble learning inspired by improved AdaBoost based on local sample information

In ensemble learning, random subspace technology not only easily loses some important features but also easily produces some redundant subspaces, inevitably leading to the decline of ensemble learning performance. In order to overcome the shortcomings, we propose a new selective quantum ensemble learning model inspired by improved AdaBoost based on local sample information (SELA). Firstly, SELA combines information entropy and random subspace to ensure that the important features of the classification task in each subspace are preserved. Then, we select the base classifier that can balance accuracy and diversity among a group of base classifiers generated based on local AdaBoost in each iteration. Finally, we utilize the quantum genetic algorithm to search optimal weights for base learners in the label prediction process. We use UCI datasets to analyze the impact of important parameters in SELA on classification performance and verify that SELA is usually superior to other competitive algorithms.

Optimization of Tree-Based Machine Learning Models to Predict the Length of Hospital Stay Using Genetic Algorithm

The length of hospital stay (LOS) is a significant indicator of the quality of patient care, hospital efficiency, and operational resilience. Considering the importance of LOS in hospital resource management, this research aims to improve the accuracy of LOS prediction using hyperparameter optimization (HPO). Expert physicians and related studies were reviewed to determine the variables affecting LOS. The electronic medical records of 200 patients in the department of internal medicine of a hospital in Iran were collected randomly. As the performance of machine learning (ML) models can vary based on the characteristics of the features, several models were applied and evaluated in this study. In particular, k-nearest neighbors (KNN), multivariate regression, decision tree (DT), random forest (RF), artificial neural network (ANN), and XGBoost have been evaluated and improved. The genetic algorithm (GA) was applied to optimize the tree-based models. In addition, the dummy coding technique, sometimes called the One-Hot encoding, was used to encode categorical features to increase prediction accuracy. Compared with other algorithms, the XGBoost model optimized by GA (XGB_GA) achieved higher accuracy and better prediction performance. The mean and median of absolute errors in the test dataset for this model were 1.54 and 1.14 days, respectively. In other words, the XGB_GA model reduced the mean absolute error by 37%, which is beneficial in the reliable design of a clinical decision support system.

An Application of Natural Language Processing to Classify What Terrorists Say They Want

Knowing what perpetrators want can inform strategies to achieve safe, secure, and sustainable societies. To help advance the body of knowledge in counterterrorism, this research applied natural language processing and machine learning techniques to a comprehensive database of terrorism events. A specially designed empirical topic modeling technique provided a machine-aided human decision process to glean six categories of perpetrator aims from the motive text narrative. Subsequently, six different machine learning models validated the aim categories based on the accuracy of their association with a different narrative field, the event summary. The ROC-AUC scores of the classification ranged from 86% to 93%. The Extreme Gradient Boosting model provided the best predictive performance. The intelligence community can use the identified aim categories to help understand the incentive structure of terrorist groups and customize strategies for dealing with them.

An enhanced group teaching optimization algorithm for multi-product disassembly line balancing problems

Big data have been widely studied by numerous scholars and enterprises due to its great power in making highly reliable decisions for various complex systems. Remanufacturing systems have recently received much attention, because they play significant roles in end-of-life product recovery, environment protection and resource conservation. Disassembly is treated as a critical step in remanufacturing systems. In practice, it is difficult to know the accurate data of end-of-life products such as disassembly time because of their various usage processes, leading to the great difficulty of making effective and reliable decisions. Thus, it is necessary to model the disassembly process with stochastic programming method where the past collected data are fitted into stochastic distributions of parameters by applying big data technology. Additionally, designing and applying highly efficient intelligent optimization algorithms to handle a variety of complex problems in the disassembly process are urgently needed. To achieve the global optimization of disassembling multiple products simultaneously, this work studies a stochastic multi-product disassembly line balancing problem with maximal disassembly profit while meeting disassembly time requirements. Moreover, a chance-constrained programming model is correspondingly formulated, and then, an enhanced group teaching optimization algorithm incorporating a stochastic simulation method is developed by considering this model’s features. Via performing simulation experiments on real-life cases and comparing it with five popularly known approaches, we verify the excellent performance of the designed method in solving the studied problem.

An effective matching algorithm with adaptive tie-breaking strategy for online food delivery problem

With the prosperity of e-commerce, ordering food online has become increasingly prevalent nowadays. Derived from the dispatching problem in Meituan, a real online food delivery (OFD) platform in China, this paper addresses an OFD problem (OFDP). To solve the OFDP efficiently, an effective matching algorithm with adaptive tie-breaking strategy (MAATS) is proposed by collaboratively fusing the optimization methods with machine learning (ML) techniques. First, to efficiently generate a partial solution with a certain quality, a best-matching heuristic is proposed. Second, to break the ties occurring in the best-matching heuristic and obtain a complete solution with high quality, multiple tie-breaking operators are designed. Third, to adapt to different scenarios, the tie-breaking operators are utilized in a dynamic way which is achieved by using ML methods including decision trees and a specially-designed deep neural network. Fourth, problem-specific features are extracted as decision information to assist the ML models to predict the best tie-breaking operator for use in the current scenario. Preliminary offline simulations are carried out on real historical data sets to validate the effectiveness of the proposed algorithm. Moreover, rigorous online A/B tests are conducted to evaluate the performance of MAATS in practical applications. The results of offline and online tests demonstrate both the effectiveness of MAATS to solve the OFDP and the application value to improve customer satisfaction and delivery efficiency on Meituan platform.

Towards secure deep learning architecture for smart farming-based applications

The immense growth of the cloud infrastructure leads to the deployment of several machine learning as a service (MLaaS) in which the training and the development of machine learning models are ultimately performed in the cloud providers’ environment. However, this could also cause potential security threats and privacy risk as the deep learning algorithms need to access generated data collection, which lacks security in nature. This paper predominately focuses on developing a secure deep learning system design with the threat analysis involved within the smart farming technologies as they are acquiring more attention towards the global food supply needs with their intensifying demands. Smart farming is known to be a combination of data-driven technology and agricultural applications that helps in yielding quality food products with the enhancing crop yield. Nowadays, many use cases had been developed by executing smart farming paradigm and promote high impacts on the agricultural lands.