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The Airy line ensemble is a positive-integer indexed system of random continuous curves whose finite dimensional distributions are given by the multi-line Airy process. It is a natural object in the KPZ universality class: for example, its highest curve, the Airy In this paper, we employ the Brownian Gibbs property to make a close comparison between the Airy line ensemble’s curves after affine shift and Brownian bridge, proving the finiteness of a superpolynomially growing moment bound on Radon-Nikodym derivatives. We also determine the value of a natural exponent describing in Brownian last passage percolation the decay in probability for the existence of several near geodesics that are disjoint except for their common endpoints, where the notion of ‘near’ refers to a small deficit in scaled geodesic energy, with the parameter specifying this nearness tending to zero. To prove both results, we introduce a technique that may be useful elsewhere for finding upper bounds on probabilities of events concerning random systems of curves enjoying the Brownian Gibbs property. Several results in this article play a fundamental role in a further study of Brownian last passage percolation in three companion papers (Hammond 2017a,b,c), in which geodesic coalescence and geodesic energy profiles are investigated in scaled coordinates.

The existing strategy for evaluating the damage condition of structures mostly focuses on feedback supplied by traditional visual methods, which may result in an unreliable damage characterization due to inspector subjectivity or insufficient level of expertise. As a result, a robust, reliable, and repeatable method of damage identification is required. Ensemble learning algorithms for identifying structural damage are evaluated in this article, which use deep convolutional neural networks, including simple averaging, integrated stacking, separate stacking, and hybrid weighted averaging ensemble and differential evolution (WAE-DE) ensemble models. Damage identification is carried out on three types of damage. The proposed algorithms are used to analyze the damage of 4585 structural images. The effectiveness of the ensemble learning techniques is evaluated using the confusion matrix. For the testing dataset, the confusion matrix achieved an accuracy of 94 percent and a minimum recall of 92 percent for the best model (WAE-DE) in distinguishing damage types as flexural, shear, combined, or undamaged.

This work presents a detailed analysis of building damage recognition, employing multi-source data fusion and ensemble learning algorithms for rapid damage mapping tasks. A damage classification framework is introduced and tested to categorize the building damage following the recent 2018 Sulawesi earthquake and tsunami. Three robust ensemble learning classifiers were investigated for recognizing building damage from Synthetic Aperture Radar (SAR) and optical remote sensing datasets and their derived features. The contribution of each feature dataset was also explored, considering different combinations of sensors as well as their temporal information. SAR scenes acquired by the ALOS-2 PALSAR-2 and Sentinel-1 sensors were used. The optical Sentinel-2 and PlanetScope sensors were also included in this study. A non-local filter in the preprocessing phase was used to enhance the SAR features. Our results demonstrated that the canonical correlation forests classifier performs better in comparison to the other classifiers. In the data fusion analysis, Digital Elevation Model (DEM)- and SAR-derived features contributed the most in the overall damage classification. Our proposed mapping framework successfully classifies four levels of building damage (with overall accuracy >90%, average accuracy >67%). The proposed framework learned the damage patterns from a limited available human-interpreted building damage annotation and expands this information to map a larger affected area. This process including pre- and post-processing phases were completed in about 3 h after acquiring all raw datasets.

To improve the accuracy of change detection in urban areas using bi-temporal high-resolution remote sensing images, a novel object-based change detection scheme combining multiple features and ensemble learning is proposed in this paper. Image segmentation is conducted to determine the objects in bi-temporal images separately. Subsequently, three kinds of object features, i.e., spectral, shape and texture, are extracted. Using the image differencing process, a difference image is generated and used as the input for nonlinear supervised classifiers, including k-nearest neighbor, support vector machine, extreme learning machine and random forest. Finally, the results of multiple classifiers are integrated using an ensemble rule called weighted voting to generate the final change detection result. Experimental results of two pairs of real high-resolution remote sensing datasets demonstrate that the proposed approach outperforms the traditional methods in terms of overall accuracy and generates change detection maps with a higher number of homogeneous regions in urban areas. Moreover, the influences of segmentation scale and the feature selection strategy on the change detection performance are also analyzed and discussed.

Today, most of the people are affected by lung cancer, mainly because of the genetic changes of the tissues in the lungs. Other factors such as smoking, alcohol, and exposure to dangerous gases can also be considered the contributory causes of lung cancer. Due to the serious consequences of lung cancer, the medical associations have been striving to diagnose cancer in its early stage of growth by applying the computer-aided diagnosis process. Although the CAD system at healthcare centers is able to diagnose lung cancer during its early stage of growth, the accuracy of cancer detection is difficult to achieve, mainly because of the overfitting of lung cancer features and the dimensionality of the feature set. Thus, this paper introduces the effective and optimized neural computing and soft computing techniques to minimize the difficulties and issues in the feature set. Initially, lung biomedical data were collected from the ELVIRA Biomedical Data Set Repository. The noise present in the data was eliminated by applying the bin smoothing normalization process. The minimum repetition and Wolf heuristic features were subsequently selected to minimize the dimensionality and complexity of the features. The selected lung features were analyzed using discrete AdaBoost optimized ensemble learning generalized neural networks, which successfully analyzed the biomedical lung data and classified the normal and abnormal features with great effectiveness. The efficiency of the system was then evaluated using MATLAB experimental setup in terms of error rate, precision, recall, G-mean, F-measure, and prediction rate.

The implementation of tree‐ensemble models has become increasingly essential in solving classification and prediction problems. Boosting ensemble techniques have been widely used as individual machine learning algorithms in predicting house prices. One of the techniques is LGBM algorithm that employs leaf wise growth strategy, reduces loss and improves accuracy during training which results in overfitting. However, XGBoost algorithm uses level wise growth strategy which takes time to compute resulting in higher computation time. Nevertheless, XGBoost has a regularization parameter, implements column sampling and weight reduction on new trees which combats overfitting. This study focuses on developing a hybrid LGBM and XGBoost model in order to prevent overfitting through minimizing variance whilst improving accuracy. Bayesian hyperparameter optimization technique is implemented on the base learners in order to find the best combination of hyperparameters. This resulted in reduced variance (overfitting) in the hybrid model since the regularization parameter values were optimized. The hybrid model is compared to LGBM, XGBoost, Adaboost and GBM algorithms to evaluate its performance in giving accurate house price predictions using MSE, MAE and MAPE evaluation metrics. The hybrid LGBM and XGBoost model outperformed the other models with MSE, MAE and MAPE of 0.193, 0.285, and 0.156 respectively. The article proposes an integration of advanced ML algorithms, LGBM and XGBoost techniques in predicting house prices. The proposed model is compared to individual boosting ensemble learning algorithms to evaluate its performance. The hybrid LGBM and XGBoost model has better performance accuracy results in predicting house prices compared to the individual models.

In this work, an Incremental Learning Algorithm via Dynamically Weighting Ensemble Learning (DWE-IL) is proposed to solve the problem of Non-Stationary Time Series Prediction (NS-TSP). The basic principle of DWE-IL is to track real-time data changes by dynamically establishing and maintaining a knowledge base composed of multiple basic models. It trains the base model for each non-stationary time series subset, and finally combine each base model with dynamically weighting rules. The emphasis of the DWE-IL algorithm lies in the update of data weights and base model weights and the training of the base model. Finally, the experimental results of the DWE-IL algorithm on six non-stationary time series datasets are presented and compared with those of several other excellent algorithms. It can be concluded from the experimental results that the DWE-IL algorithm provides a good solution to the challenges of the NS-TSP tasks and has significantly superior performance over other comparative algorithms.

Excessive and untimely maintenance prompts economic losses and unnecessary workload. Therefore, predictive maintenance models are developed to estimate the right time for maintenance. In this study, predictive models that estimate the remaining useful life of turbofan engines have been developed using deep learning algorithms on NASA’s turbofan engine degradation simulation dataset. Before equipment failure, the proposed model presents an estimated timeline for maintenance. The experimental studies demonstrated that the stacking ensemble learning and the convolutional neural network (CNN) methods are superior to the other investigated methods. While the convolution neural network (CNN) method was superior to the other investigated methods with an accuracy of 93.93%, the stacking ensemble learning method provided the best result with an accuracy of 95.72%.

Traditional approaches to safety event analysis in autonomous systems have relied on complex machine and deep learning models and extensive datasets for high accuracy and reliability. However, the emerge of multimodal large language models (MLLMs) offers a novel approach by integrating textual, visual, and audio modalities. Our framework leverages the logical and visual reasoning power of MLLMs, directing their output through object-level question–answer (QA) prompts to ensure accurate, reliable, and actionable insights for investigating safety-critical event detection and analysis. By incorporating models like Gemini-Pro-Vision 1.5, we aim to automate safety-critical event detection and analysis along with mitigating common issues such as hallucinations in MLLM outputs. The results demonstrate the framework’s potential in different in-context learning (ICT) settings such as zero-shot and few-shot learning methods. Furthermore, we investigate other settings such as self-ensemble learning and a varying number of frames. The results show that a few-shot learning model consistently outperformed other learning models, achieving the highest overall accuracy of about 79%. The comparative analysis with previous studies on visual reasoning revealed that previous models showed moderate performance in driving safety tasks, while our proposed model significantly outperformed them. To the best of our knowledge, our proposed MLLM model stands out as the first of its kind, capable of handling multiple tasks for each safety-critical event. It can identify risky scenarios, classify diverse scenes, determine car directions, categorize agents, and recommend the appropriate actions, setting a new standard in safety-critical event management. This study shows the significance of MLLMs in advancing the analysis of naturalistic driving videos to improve safety-critical event detection and understanding the interactions in complex environments.

Skin cancer is one of the widespread diseases among existing cancer types. More importantly, the detection of lesions in early diagnosis has tremendously attracted researchers’ attention. Thus, artificial intelligence (AI)-based techniques have supported the early diagnosis of skin cancer by investigating deep-learning-based convolutional neural networks (CNN). However, the current methods remain challenging in detecting melanoma in dermoscopic images. Therefore, in this paper, we propose an ensemble model that uses the vision of both EfficientNetV2S and Swin-Transformer models to detect the early focal zone of skin cancer. Hence, we considerthat the former architecture leads to greater accuracy, while the latter model has the advantage of recognizing dark parts in an image. We have modified the fifth block of the EfficientNetV2S model and have included the Swin-Transformer model. Our experiments demonstrate that the constructed ensemble model has attained a higher level of accuracy over the individual models and has also decreased the losses as compared to traditional strategies. The proposed model achieved an accuracy score of 99.10%, a sensitivity of 99.27%, and a specificity score of 99.80%.