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Facial expression recognition (FER) plays a crucial role in understanding human emotions and is becoming increasingly relevant in educational contexts, where personalized and empathetic interactions are essential. The problems with existing approaches are typically solved using a single deep learning method, which is not robust with complex datasets, such as FER data, which have a characteristic imbalance and multi-class labels. In this research paper, an innovative approach to FER using a homogeneous ensemble convolutional neural network, called HoE-CNN, is presented for future online learning education. This paper aims to transfer the knowledge of models and FER classification using ensembled homogeneous conventional neural network architectures. FER is challenging to research because there are many real-world applications to consider, such as adaptive user interfaces, games, education, and robot integration. HoE-CNN is used to improve the classification performance on an FER dataset, encompassing seven main multi-classes (Angry, Disgust, Fear, Happy, Sad, Surprise, Neutral). The experiment shows that the proposed framework, which uses an ensemble of deep learning models, performs better than a single deep learning model. In summary, the proposed model will increase the efficiency of FER classification results and solve FER2013 at a accuracy of 75.51%, addressing both imbalanced datasets and multi-class classification to transfer the application of the model to online learning applications. [ABSTRACT FROM AUTHOR]

As the deployment of Autonomous Vehicles (AVs) gains momentum, ensuring both security and accountability becomes paramount. This paper proposes a comprehensive approach to address these concerns. With the increasing importance of speaker identification, our first contribution lies in implementing a robust mechanism for identifying authorized users within AVs, enhancing security. To counter the threat of voice spoofing, an ensemble-based approach leveraging speaker verification techniques is presented, ensuring the authenticity of user commands. Furthermore, in scenarios of accidents involving AVs, the need for accurate accountability and liability allocation arises. To address this, we introduce a novel application of blockchain technology, enabling an event recording system that ensures transparent and tamper-proof records. The proposed system enhances AV security and establishes a framework for reliable accident investigation using speakers' records. In addition, this paper presents an innovative concept where vehicles act as impartial judges during accidents, utilizing location-based identification. Results show the viability of the proposed solution for accident investigation and analysis. [ABSTRACT FROM AUTHOR]

This paper explores the potential of using the SAM (Segment-Anything Model) segmentator to enhance the segmentation capability of known methods. SAM is a promptable segmentation system that offers zero-shot generalization to unfamiliar objects and images, eliminating the need for additional training. The open-source nature of SAM allows for easy access and implementation. In our experiments, we aim to improve the segmentation performance by providing SAM with checkpoints extracted from the masks produced by mainstream segmentators, and then merging the segmentation masks provided by these two networks. We examine the "oracle" method (as upper bound baseline performance), where segmentation masks are inferred only by SAM with checkpoints extracted from the ground truth. One of the main contributions of this work is the combination (fusion) of the logit segmentation masks produced by the SAM model with the ones provided by specialized segmentation models such as DeepLabv3+ and PVTv2. This combination allows for a consistent improvement in segmentation performance in most of the tested datasets. We exhaustively tested our approach on seven heterogeneous public datasets, obtaining state-of-the-art results in two of them (CAMO and Butterfly) with respect to the current best-performing method with a combination of an ensemble of mainstream segmentator transformers and the SAM segmentator. The results of our study provide valuable insights into the potential of incorporating the SAM segmentator into existing segmentation techniques. We release with this paper the open-source implementation of our method. [ABSTRACT FROM AUTHOR]

Recognizing aircraft automatically by using satellite images has different applications in both the civil and military sectors. However, due to the complexity and variety of the foreground and background of the analyzed images, it remains challenging to obtain a suitable representation of aircraft for identification. Many studies and solutions have been presented in the literature, but only a few studies have suggested handling the issue using semantic image segmentation techniques due to the lack of publicly labeled datasets. With the advancement of CNNs, researchers have presented some CNN architectures, such as U-Net, which has the ability to obtain very good performance using a small training dataset. The U-Net architecture has received much attention for segmenting 2D and 3D biomedical images and has been recognized to be highly successful for pixel-wise satellite image classification. In this paper, we propose a binary image segmentation model to recognize aircraft by exploiting and adopting the U-Net architecture for remote sensing satellite images. The proposed model does not require a significant amount of labeled data and alleviates the need for manual aircraft feature extraction. The public dense labeling remote sensing dataset is used to perform the experiments and measure the robustness and performance of the proposed model. The mean IoU and pixel accuracy are adopted as metrics to assess the obtained results. The results in the testing dataset indicate that the proposed model can achieve a 95.08% mean IoU and a pixel accuracy of 98.24%. [ABSTRACT FROM AUTHOR]

Emergency events arise when a serious, unexpected, and often dangerous threat affects normal life. Hence, knowing what is occurring during and after emergency events is critical to mitigate the effect of the incident on humans' life, on the environment and our infrastructures, as well as the inherent financial consequences. Social network utilization in emergency event detection models can play an important role as information is shared and users' status is updated once an emergency event occurs. Besides, big data proved its significance as a tool to assist and alleviate emergency events by processing an enormous amount of data over a short time interval. This paper shows that it is necessary to have an appropriate emergency event detection ensemble model (EEDEM) to respond quickly once such unfortunate events occur. Furthermore, it integrates Snapchat maps to propose a novel method to pinpoint the exact location of an emergency event. Moreover, merging social networks and big data can accelerate the emergency event detection system: social network data, such as those from Twitter and Snapchat, allow us to manage, monitor, analyze and detect emergency events. The main objective of this paper is to propose a novel and efficient big data-based EEDEM to pinpoint the exact location of emergency events by employing the collected data from social networks, such as "Twitter" and "Snapchat", while integrating big data (BD) and machine learning (ML). Furthermore, this paper evaluates the performance of five ML base models and the proposed ensemble approach to detect emergency events. Results show that the proposed ensemble approach achieved a very high accuracy of 99.87% which outperform the other base models. Moreover, the proposed base models yields a high level of accuracy: 99.72%, 99.70% for LSTM and decision tree, respectively, with an acceptable training time. [ABSTRACT FROM AUTHOR]

The uncertainty in the initial condition seriously affects the forecasting skill of numerical models. Targeted observations play an important role in reducing uncertainty in numerical prediction. The conditional nonlinear optimal perturbation (CNOP) method is a useful tool for studying adaptive observation. However, the traditional CNOP method highly relies on the adjoint model, and it is difficult to find the global optimal solution. In this paper, a pre-screening and ensemble CNOP hybrid method called PECNOP is proposed to identify optimal sensitive areas in targeted observations. PECNOP is an adjoint-free method that captures global CNOP with high probability, which can effectively solve the two major problems faced by traditional CNOP methods. We evaluated the performance of PECNOP by building an observation simulation system consisting of an ocean model and data assimilation. One of the assimilation experiments was dedicated to evaluating the stability and effectiveness of PECNOP in extreme events. The results show that, compared with traditional methods, PECNOP can stably capture the global CNOP. Extra observations and assimilation in the optimal sensitive areas identified by PECNOP can effectively improve forecasting by about 20% within 30 days. Therefore, PECNOP has potential to reduce the initial error of numerical models, which is important for improving forecasting. [ABSTRACT FROM AUTHOR]

Training-anomaly-based, machine-learning-based, intrusion detection systems (AMiDS) for use in critical Internet of Things (CioT) systems and military Internet of Things (MioT) environments may involve synthetic data or publicly simulated data due to data restrictions, data scarcity, or both. However, synthetic data can be unrealistic and potentially biased, and simulated data are invariably static, unrealistic, and prone to obsolescence. Building an AMiDS logical model to predict the deviation from normal behavior in MioT and CioT devices operating at the sensing or perception layer due to adversarial attacks often requires the model to be trained using current and realistic data. Unfortunately, while real-time data are realistic and relevant, they are largely imbalanced. Imbalanced data have a skewed class distribution and low-similarity index, thus hindering the model's ability to recognize important features in the dataset and make accurate predictions. Data-driven learning using data sampling, resampling, and generative methods can lessen the adverse impact of a data imbalance on the AMiDS model's performance and prediction accuracy. Generative methods enable passive adversarial learning. This paper investigates several data sampling, resampling, and generative methods. It examines their impacts on the performance and prediction accuracy of AMiDS models trained using imbalanced data drawn from the UNSW_2018_IoT_Botnet dataset, a publicly available IoT dataset from the IEEEDataPort. Furthermore, it evaluates the performance and predictability of these models when trained using data transformation methods, such as normalization and one-hot encoding, to cover a skewed distribution, data sampling and resampling methods to address data imbalances, and generative methods to train the models to increase the model's robustness to recognize new but similar attacks. In this initial study, we focus on CioT systems and train PCA-based and oSVM-based AMiDS models constructed using low-complexity PCA and one-class SVM (oSVM) ML algorithms to fit an imbalanced ground truth IoT dataset. Overall, we consider the rare event prediction case where the minority class distribution is disproportionately low compared to the majority class distribution. We plan to use transfer learning in future studies to generalize our initial findings to the MioT environment. We focus on CioT systems and MioT environments instead of traditional or non-critical IoT environments due to the stringent low energy, the minimal response time constraints, and the variety of low-power, situational-aware (or both) things operating at the sensing or perception layer in a highly complex and open environment. [ABSTRACT FROM AUTHOR]

Analyzing the condition and function of the heart is very important because cardiovascular diseases (CVDs) are responsible for high mortality rates worldwide and can lead to strokes and heart attacks; thus, early diagnosis and treatment are important. Phonocardiogram (PCG) signals can be used to analyze heart rate characteristics to detect heart health and detect heart-related diseases. In this paper, we propose a method for designing using wavelet analysis techniques and an ensemble of deep learning models from phonocardiogram (PCG) for heart sound classification. For this purpose, we use wavelet scattering transform (WST) and continuous wavelet transform (CWT) as the wavelet analysis approaches for 1D-convolutional neural network (CNN) and 2D-CNN modeling, respectively. These features are insensitive to translations of the input on an invariance scale and are continuous with respect to deformations. Furthermore, the ensemble model is combined with 1D-CNN and 2D-CNN. The proposed method consists of four stages: a preprocessing stage for dividing signals at regular intervals, a feature extraction stage through wavelet scattering transform (WST) and continuous wavelet transform (CWT), a design stage of individual 1D-CNN and 2D-CNN, and a classification stage of heart sound by the ensemble model. The datasets used for the experiment were the PhysioNet/CinC 2016 challenge dataset and the PASCAL classifying heart sounds challenge dataset. The performance evaluation is performed by precision, recall, F1-score, sensitivity, and specificity. The experimental results revealed that the proposed method showed good performance on two datasets in comparison to the previous methods. The ensemble method of the proposed deep learning model surpasses the performance of recent studies and is suitable for predicting and diagnosing heart-related diseases by classifying heart sounds through phonocardiogram (PCG) signals. [ABSTRACT FROM AUTHOR]

Current deep-learning-based cervical cell classification methods suffer from parameter redundancy and poor model generalization performance, which creates challenges for the intelligent classification of cervical cytology smear images. In this paper, we establish a method for such classification that combines transfer learning and knowledge distillation. This new method not only transfers common features between different source domain data, but also realizes model-to-model knowledge transfer using the unnormalized probability output between models as knowledge. A multi-exit classification network is then introduced as the student network, where a global context module is embedded in each exit branch. A self-distillation method is then proposed to fuse contextual information; deep classifiers in the student network guide shallow classifiers to learn, and multiple classifier outputs are fused using an average integration strategy to form a classifier with strong generalization performance. The experimental results show that the developed method achieves good results using the SIPaKMeD dataset. The accuracy, sensitivity, specificity, and F-measure of the five classifications are 98.52%, 98.53%, 98.68%, 98.59%, respectively. The effectiveness of the method is further verified on a natural image dataset. [ABSTRACT FROM AUTHOR]

For robust classification, selecting a proper classifier is of primary importance. However, selecting the best classifiers depends on the problem, as some classifiers work better at some tasks than on others. Despite the many results collected in the literature, the support vector machine (SVM) remains the leading adopted solution in many domains, thanks to its ease of use. In this paper, we propose a new method based on convolutional neural networks (CNNs) as an alternative to SVM. CNNs are specialized in processing data in a grid-like topology that usually represents images. To enable CNNs to work on different data types, we investigate reshaping one-dimensional vector representations into two-dimensional matrices and compared different approaches for feeding standard CNNs using two-dimensional feature vector representations. We evaluate the different techniques proposing a heterogeneous ensemble based on three classifiers: an SVM, a model based on random subspace of rotation boosting (RB), and a CNN. The robustness of our approach is tested across a set of benchmark datasets that represent a wide range of medical classification tasks. The proposed ensembles provide promising performance on all datasets. [ABSTRACT FROM AUTHOR]

Feature selection is a crucial step in machine learning, aiming to identify the most relevant features in high-dimensional data in order to reduce the computational complexity of model development and improve generalization performance. Ensemble feature-ranking methods combine the results of several feature-selection techniques to identify a subset of the most relevant features for a given task. In many cases, they produce a more comprehensive ranking of features than the individual methods used alone. This paper presents a novel approach to ensemble feature ranking, which uses a weighted average of the individual ranking scores calculated using these individual methods. The optimal weights are determined using a Taguchi-type design of experiments. The proposed methodology significantly improves classification performance on the CSE-CIC-IDS2018 dataset, particularly for attack types where traditional average-based feature-ranking score combinations result in low classification metrics. [ABSTRACT FROM AUTHOR]

Outlier detection is an important task in the field of data mining and a highly active area of research in machine learning. In industrial automation, datasets are often high-dimensional, meaning an effort to study all dimensions directly leads to data sparsity, thus causing outliers to be masked by noise effects in high-dimensional spaces. The "curse of dimensionality" phenomenon renders many conventional outlier detection methods ineffective. This paper proposes a new outlier detection algorithm called EOEH (Ensemble Outlier Detection Method Based on Information Entropy-Weighted Subspaces for High-Dimensional Data). First, random secondary subsampling is performed on the data, and detectors are run on various small-scale sub-samples to provide diverse detection results. Results are then aggregated to reduce the global variance and enhance the robustness of the algorithm. Subsequently, information entropy is utilized to construct a dimension-space weighting method that can discern the influential factors within different dimensional spaces. This method generates weighted subspaces and dimensions for data objects, reducing the impact of noise created by high-dimensional data and improving high-dimensional data detection performance. Finally, this study offers a design for a new high-precision local outlier factor (HPLOF) detector that amplifies the differentiation between normal and outlier data, thereby improving the detection performance of the algorithm. The feasibility of this algorithm is validated through experiments that used both simulated and UCI datasets. In comparison to popular outlier detection algorithms, our algorithm demonstrates a superior detection performance and runtime efficiency. Compared with the current popular, common algorithms, the EOEH algorithm improves the detection performance by 6% on average. In terms of running time for high-dimensional data, EOEH is 20% faster than the current popular algorithms. [ABSTRACT FROM AUTHOR]

Ecoacoustics is arguably the best method for monitoring marine environments, but analyzing and interpreting acoustic data has traditionally demanded substantial human supervision and resources. These bottlenecks can be addressed by harnessing contemporary methods for automated audio signal analysis. This paper focuses on the problem of assessing dolphin whistles using state-of-the-art deep learning methods. Our system utilizes a fusion of various resnet50 networks integrated with data augmentation (DA) techniques applied not to the training data but to the test set. We also present training speeds and classification results using DA to the training set. Through extensive experiments conducted on a publicly available benchmark, our findings demonstrate that our ensemble yields significant performance enhancements across several commonly used metrics. For example, our approach obtained an accuracy of 0.949 compared to 0.923, the best reported in the literature. We also provide training and testing sets that other researchers can use for comparison purposes, as well as all the MATLAB/PyTorch source code used in this study. [ABSTRACT FROM AUTHOR]

Gait recognition, the task of identifying an individual based on their unique walking style, can be difficult because walking styles can be influenced by external factors such as clothing, viewing angle, and carrying conditions. To address these challenges, this paper proposes a multi-model gait recognition system that integrates Convolutional Neural Networks (CNNs) and Vision Transformer. The first step in the process is to obtain a gait energy image, which is achieved by applying an averaging technique to a gait cycle. The gait energy image is then fed into three different models, DenseNet-201, VGG-16, and a Vision Transformer. These models are pre-trained and fine-tuned to encode the salient gait features that are specific to an individual's walking style. Each model provides prediction scores for the classes based on the encoded features, and these scores are then summed and averaged to produce the final class label. The performance of this multi-model gait recognition system was evaluated on three datasets, CASIA-B, OU-ISIR dataset D, and OU-ISIR Large Population dataset. The experimental results showed substantial improvement compared to existing methods on all three datasets. The integration of CNNs and ViT allows the system to learn both the pre-defined and distinct features, providing a robust solution for gait recognition even under the influence of covariates. [ABSTRACT FROM AUTHOR]

The World Health Organization and Korea National Health Insurance assert that the number of alopecia patients is increasing every year, and approximately 70 percent of adults suffer from scalp problems. Although alopecia is a genetic problem, it is difficult to diagnose at an early stage. Although deep-learning-based approaches have been effective for medical image analyses, it is challenging to generate deep learning models for alopecia detection and analysis because creating an alopecia image dataset is challenging. In this paper, we present an approach for generating a model specialized for alopecia analysis that achieves high accuracy by applying data preprocessing, data augmentation, and an ensemble of deep learning models that have been effective for medical image analyses. We use an alopecia image dataset containing 526 good, 13,156 mild, 3742 moderate, and 825 severe alopecia images. The dataset was further augmented by applying normalization, geometry-based augmentation (rotate, vertical flip, horizontal flip, crop, and affine transformation), and PCA augmentation. We compare the performance of a single deep learning model using ResNet, ResNeXt, DenseNet, XceptionNet, and ensembles of these models. The best result was achieved when DenseNet, XceptionNet, and ResNet were combined to achieve an accuracy of 95.75 and an F1 score of 87.05. [ABSTRACT FROM AUTHOR]

The tremendous growth in online activity and the Internet of Things (IoT) led to an increase in cyberattacks. Malware infiltrated at least one device in almost every household. Various malware detection methods that use shallow or deep IoT techniques were discovered in recent years. Deep learning models with a visualization method are the most commonly and popularly used strategy in most works. This method has the benefit of automatically extracting features, requiring less technical expertise, and using fewer resources during data processing. Training deep learning models that generalize effectively without overfitting is not feasible or appropriate with large datasets and complex architectures. In this paper, a novel ensemble model, Stacked Ensemble—autoencoder, GRU, and MLP or SE-AGM, composed of three light-weight neural network models—autoencoder, GRU, and MLP—that is trained on the 25 essential and encoded extracted features of the benchmark MalImg dataset for classification was proposed. The GRU model was tested for its suitability in malware detection due to its lesser usage in this domain. The proposed model used a concise set of malware features for training and classifying the malware classes, which reduced the time and resource consumption in comparison to other existing models. The novelty lies in the stacked ensemble method where the output of one intermediate model works as input for the next model, thereby refining the features as compared to the general notion of an ensemble approach. Inspiration was drawn from earlier image-based malware detection works and transfer learning ideas. To extract features from the MalImg dataset, a CNN-based transfer learning model that was trained from scratch on domain data was used. Data augmentation was an important step in the image processing stage to investigate its effect on classifying grayscale malware images in the MalImg dataset. SE-AGM outperformed existing approaches on the benchmark MalImg dataset with an average accuracy of 99.43%, demonstrating that our method was on par with or even surpassed them. [ABSTRACT FROM AUTHOR]

In digital farming, the use of technology to increase agricultural production through automated tasks has recently integrated the development of AgBots for more reliable data collection using autonomous navigation. These AgBots are equipped with various sensors such as GNSS, cameras, and LiDAR, but these sensors can be prone to limitations such as low accuracy for under-canopy navigation with GNSS, sensitivity to outdoor lighting and platform vibration with cameras, and LiDAR occlusion issues. In order to address these limitations and ensure robust autonomous navigation, this paper presents a sensor selection methodology based on the identification of environmental conditions using sensor data. Through the extraction of features from GNSS, images, and point clouds, we are able to determine the feasibility of using each sensor and create a selection vector indicating its viability. Our results demonstrate that the proposed methodology effectively selects between the use of cameras or LiDAR within crops and GNSS outside of crops, at least 87% of the time. The main problem found is that, in the transition from inside to outside and from outside to inside the crop, GNSS features take 20 s to adapt. We compare a variety of classification algorithms in terms of performance and computational cost and the results show that our method has higher performance and lower computational cost. Overall, this methodology allows for the low-cost selection of the most suitable sensor for a given agricultural environment. [ABSTRACT FROM AUTHOR]

In this paper, we propose a framework that constructs two types of image aesthetic assessment (IAA) models with different CNN architectures and improves the performance of image aesthetic score (AS) prediction by the ensemble. Moreover, the attention regions of the models to the images are extracted to analyze the consistency with the subjects in the images. The experimental results verify that the proposed method is effective for improving the AS prediction. The average F1 of the ensemble improves 5.4% over the model of type A, and 33.1% over the model of type B. Moreover, it is found that the AS classification models trained on the XiheAA dataset seem to learn the latent photography principles, although it cannot be said that they learn the aesthetic sense. [ABSTRACT FROM AUTHOR]

For cases in which a machine learning model needs to be adapted to a new task, various approaches have been developed, including model-agnostic meta-learning (MAML) and transfer learning. In this paper, we investigate how the differences in the data distributions between the old tasks and the new target task impact performance in regression problems. By performing experiments, we discover that these differences greatly affect the relative performance of different adaptation methods. Based on this observation, we develop ensemble schemes combining multiple adaptation methods that can handle a wide range of data distribution differences between the old and new tasks, thus offering more stable performance for a wide range of tasks. For evaluation, we consider three regression problems of sinusoidal fitting, virtual reality motion prediction, and temperature forecasting. The evaluation results demonstrate that the proposed ensemble schemes achieve the best performance among the considered methods in most cases. [ABSTRACT FROM AUTHOR]

Social media platforms such as Twitter are a vital source of information during major events, such as natural disasters. Studies attempting to automatically detect textual communications have mostly focused on machine learning and deep learning algorithms. Recent evidence shows improvement in disaster detection models with the use of contextual word embedding techniques (i.e., transformers) that take the context of a word into consideration, unlike the traditional context-free techniques; however, studies regarding this model are scant. To this end, this paper investigates a selection of ensemble learning models by merging transformers with deep neural network algorithms to assess their performance in detecting informative and non-informative disaster-related Twitter communications. A total of 7613 tweets were used to train and test the models. Results indicate that the ensemble models consistently yield good performance results, with F-score values ranging between 76% and 80%. Simpler transformer variants, such as ELECTRA and Talking-Heads Attention, yielded comparable and superior results compared to the computationally expensive BERT, with F-scores ranging from 80% to 84%, especially when merged with Bi-LSTM. Our findings show that the newer and simpler transformers can be used effectively, with less computational costs, in detecting disaster-related Twitter communications. [ABSTRACT FROM AUTHOR]

Various machine-learning methods have been applied to anomaly intrusion detection. However, the Intrusion Detection System still faces challenges in improving Detection Rate and reducing False Positive Rate. In this paper, a Class-Level Soft-Voting Ensemble (CLSVE) scheme based on the Chaos Bat Algorithm (CBA), called CBA-CLSVE, is proposed for intrusion detection. The Support Vector Machine (SVM), K-Nearest Neighbor (KNN) and Decision Tree (DT) are selected as the base learners of the ensemble. The Chaos Bat Algorithm is used to generate class-level weights to create the weighted voting ensemble. A weighted fitness function considering the tradeoff between maximizing Detection Rate and minimizing False Positive Rate is proposed. In the experiments, the NSL-KDD, UNSW-NB15 and CICIDS2017 datasets are used to verify the scheme. The experimental results show that the class-level weights generated by CBA can be used to improve the combinative performance. They also show that the same ensemble performance can be achieved using about half the total number of features or fewer. [ABSTRACT FROM AUTHOR]

Caries prevention is essential for oral hygiene. A fully automated procedure that reduces human labor and human error is needed. This paper presents a fully automated method that segments tooth regions of interest from a panoramic radiograph to diagnose caries. A patient's panoramic oral radiograph, which can be taken at any dental facility, is first segmented into several segments of individual teeth. Then, informative features are extracted from the teeth using a pre-trained deep learning network such as VGG, Resnet, or Xception. Each extracted feature is learned by a classification model such as random forest, k-nearest neighbor, or support vector machine. The prediction of each classifier model is considered as an individual opinion that contributes to the final diagnosis, which is decided by a majority voting method. The proposed method achieved an accuracy of 93.58%, a sensitivity of 93.91%, and a specificity of 93.33%, making it promising for widespread implementation. The proposed method, which outperforms existing methods in terms of reliability, and can facilitate dental diagnosis and reduce the need for tedious procedures. [ABSTRACT FROM AUTHOR]

Water levels in rivers are measured by various devices installed mostly in remote locations along the rivers, and the collected data are then transmitted via telemetry systems to a data centre for further analysis and utilisation, including producing early warnings for risk situations. So, the data quality is essential. However, the devices in the telemetry station may malfunction and cause errors in the data, which can result in false alarms or missed true alarms. Finding these errors requires experienced humans with specialised knowledge, which is very time-consuming and also inconsistent. Thus, there is a need to develop an automated approach. In this paper, we firstly investigated the applicability of Deep Reinforcement Learning (DRL). The testing results show that whilst they are more accurate than some other machine learning models, particularly in identifying unknown anomalies, they lacked consistency. Therefore, we proposed an ensemble approach that combines DRL models to improve consistency and also accuracy. Compared with other models, including Multilayer Perceptrons (MLP) and Long Short-Term Memory (LSTM), our ensemble models are not only more accurate in most cases, but more importantly, more reliable. [ABSTRACT FROM AUTHOR]

Learning label noise is gaining increasing attention from a variety of disciplines, particularly in supervised machine learning for classification tasks. The k nearest neighbors (kNN) classifier is often used as a natural way to edit the training sets due to its sensitivity to label noise. However, the kNN-based editor may remove too many instances if not designed to take care of the label noise. In addition, the one-sided nearest neighbor (NN) rule is unconvincing, as it just considers the nearest neighbors from the perspective of the query sample. In this paper, we propose an ensemble and iterative recovery strategy-based kGNN method (EIRS-kGNN) to edit data with label noise. EIRS-kGNN first uses the general nearest neighbors (GNN) to expand the one-sided NN rule to a binary-sided NN rule, taking the neighborhood of the queried samples into account. Then, it ensembles the prediction results of a finite set of ks in the kGNN to prudently judge the noise levels for each sample. Finally, two loops, i.e., the inner loop and the outer loop, are leveraged to iteratively detect label noise. A frequency indicator is derived from the iterative processes to guide the mixture approaches, including relabeling and removing, to deal with the detected label noise. The goal of EIRS-kGNN is to recover the distribution of the data set as if it were not corrupted. Experimental results on both synthetic data sets and UCI benchmarks, including binary data sets and multi-class data sets, demonstrate the effectiveness of the proposed EIRS-kGNN method. [ABSTRACT FROM AUTHOR]

In this paper, we dive into sign language recognition, focusing on the recognition of isolated signs. The task is defined as a classification problem, where a sequence of frames (i.e., images) is recognized as one of the given sign language glosses. We analyze two appearance-based approaches, I3D and TimeSformer, and one pose-based approach, SPOTER. The appearance-based approaches are trained on a few different data modalities, whereas the performance of SPOTER is evaluated on different types of preprocessing. All the methods are tested on two publicly available datasets: AUTSL and WLASL300. We experiment with ensemble techniques to achieve new state-of-the-art results of 73.84% accuracy on the WLASL300 dataset by using the CMA-ES optimization method to find the best ensemble weight parameters. Furthermore, we present an ensembling technique based on the Transformer model, which we call Neural Ensembler. [ABSTRACT FROM AUTHOR]

Landslide is a devastating natural disaster, causing loss of life and property. It is likely to occur more frequently due to increasing urbanization, deforestation, and climate change. Landslide susceptibility mapping is vital to safeguard life and property. This article surveys machine learning (ML) models used for landslide susceptibility mapping to understand the current trend by analyzing published articles based on the ML models, landslide causative factors (LCFs), study location, datasets, evaluation methods, and model performance. Existing literature considered in this comprehensive survey is systematically selected using the ROSES protocol. The trend indicates a growing interest in the field. The choice of LCFs depends on data availability and case study location; China is the most studied location, and area under the receiver operating characteristic curve (AUC) is considered the best evaluation metric. Many ML models have achieved an AUC value > 0.90, indicating high reliability of the susceptibility map generated. This paper also discusses the recently developed hybrid, ensemble, and deep learning (DL) models in landslide susceptibility mapping. Generally, hybrid, ensemble, and DL models outperform conventional ML models. Based on the survey, a few recommendations and future works which may help the new researchers in the field are also presented. [ABSTRACT FROM AUTHOR]

The modern technology universities have the necessary resource and material base for developing and transferring R&D products. However, the cost estimation process is not formalized. There are many methods of estimating the cost of R&D products' commercialization processes. However, in some cases, we cannot consider any single technique to be the best one as each of them has advantages and disadvantages. In such conditions, all efforts should be made to use a combination of the estimation techniques to arrive at a better cost and quality estimate. The effectiveness of the valuation of R&D products is of particular importance in today's economy and due to the need to analyze large data sets prepared for transfer from universities to the business environment. This paper presents the model, two methods, and general information technology for R&D products' readiness level assessment and R&D products' cost estimation. The article presents the complex method for determining the cost of R&D products, which will allow: increasing the efficiency of the transfer, commercialization, and market launch of R&D products, and promoting the interaction of all components of the national innovation infrastructure, innovations, etc. The need to consider many different indicators when evaluating R&D products has determined the need to use machine learning algorithms. We have designed a new machine learning-based model for the readiness assessment of R&D products, which is based on the principle of "crowd wisdom" and uses a stacking strategy to integrate machine learning methods. It is experimentally established that the new stacking model based on machine learning algorithms that use random forest as a meta-algorithm provides a minimum of a 1.03 times higher RMSE compared to other ensemble strategies. [ABSTRACT FROM AUTHOR]

Smart devices are used in the era of the Internet of Things (IoT) to provide efficient and reliable access to services. IoT technology can recognize comprehensive information, reliably deliver information, and intelligently process that information. Modern industrial systems have become increasingly dependent on data networks, control systems, and sensors. The number of IoT devices and the protocols they use has increased, which has led to an increase in attacks. Global operations can be disrupted, and substantial economic losses can be incurred due to these attacks. Cyberattacks have been detected using various techniques, such as deep learning and machine learning. In this paper, we propose an ensemble staking method to effectively reveal cyberattacks in the IoT with high performance. Experiments were conducted on three different datasets: credit card, NSL-KDD, and UNSW datasets. The proposed stacked ensemble classifier outperformed the individual base model classifiers. [ABSTRACT FROM AUTHOR]

An accurate vessel fuel consumption prediction is essential for constructing a ship route network and vessel management, leading to efficient sailings. Besides, ship data from monitoring and sensing systems accelerate fuel consumption prediction research. However, the ship data consist of three properties: sequential, irregular time interval, and feature importance, making the predicting problem challenging. In this paper, we propose Time-aware Attention (TA) and Feature-similarity Attention (FA) applied to bi-directional Long Short-Term Memory (LSTM). TA acquires time importance by nonlinear function from irregular time intervals in each sequence and emphasizes data depending on the importance. FA emphasizes data based on similarities of features in the sequence by estimating feature importance with learnable parameters. Finally, we propose the ensemble model of TA and FA-based BiLSTM. The ensemble model, which consists of fully connected layers, is capable of simultaneously capturing different properties of ship data. The experimental results on ship data showed that the proposed model improves the performance in predicting fuel consumption. In addition to model performance, visualization results of attention maps and feature importance help to understand data properties and model characteristics. [ABSTRACT FROM AUTHOR]

Machine learning is used to develop predictive models to diagnose different diseases, particularly kidney transplant survival prediction. The paper used the collected dataset of patients' individual parameters to predict the critical risk factors associated with early graft rejection. Our study shows the high pairwise correlation between a massive subset of the parameters listed in the dataset. Hence the proper feature selection is needed to increase the quality of a prediction model. Several methods are used for feature selection, and results are summarized using hard voting. Modeling the onset of critical events for the elements of a particular set is made based on the Kapplan-Meier method. Four novel ensembles of machine learning models are built on selected features for the classification task. Proposed stacking allows obtaining an accuracy, sensitivity, and specifity of more than 0.9. Further research will include the development of a two-stage predictor. [ABSTRACT FROM AUTHOR]

Traffic accidents are emerging as a serious social problem in modern society but if the severity of an accident is quickly grasped, countermeasures can be organized efficiently. To solve this problem, the method proposed in this paper derives the MDG (Mean Decrease Gini) coefficient between variables to assess the severity of traffic accidents. Single models are designed to use coefficient, independent variables to determine and predict accident severity. The generated single models are fused using a weighted-voting-based bagging method ensemble to consider various characteristics and avoid overfitting. The variables used for predicting accidents are classified as dependent or independent and the variables that affect the severity of traffic accidents are predicted using the characteristics of causal relationships. Independent variables are classified as categorical and numerical variables. For this reason, a problem arises when the variation among dependent variables is imbalanced. Therefore, a harmonic average is applied to the weights to maintain the variables' balance and determine the average rate of change. Through this, it is possible to establish objective criteria for determining the severity of traffic accidents, thereby improving reliability. [ABSTRACT FROM AUTHOR]

Offline changepoint detection (CPD) algorithms are used for signal segmentation in an optimal way. Generally, these algorithms are based on the assumption that signal's changed statistical properties are known, and the appropriate models (metrics, cost functions) for changepoint detection are used. Otherwise, the process of proper model selection can become laborious and time-consuming with uncertain results. Although an ensemble approach is well known for increasing the robustness of the individual algorithms and dealing with mentioned challenges, it is weakly formalized and much less highlighted for CPD problems than for outlier detection or classification problems. This paper proposes an unsupervised CPD ensemble (CPDE) procedure with the pseudocode of the particular proposed ensemble algorithms and the link to their Python realization. The approach's novelty is in aggregating several cost functions before the changepoint search procedure running during the offline analysis. The numerical experiment showed that the proposed CPDE outperforms non-ensemble CPD procedures. Additionally, we focused on analyzing common CPD algorithms, scaling, and aggregation functions, comparing them during the numerical experiment. The results were obtained on the two anomaly benchmarks that contain industrial faults and failures—Tennessee Eastman Process (TEP) and Skoltech Anomaly Benchmark (SKAB). One of the possible applications of our research is the estimation of the failure time for fault identification and isolation problems of the technical diagnostics. [ABSTRACT FROM AUTHOR]

In the construction industry, it is difficult to predict occupational accidents because various accident characteristics arise simultaneously and organically in different types of work. Furthermore, even when analyzing occupational accident data, it is difficult to deduce meaningful results because the data recorded by the incident investigator are qualitative and include a wide variety of data types and categories. Recently, numerous studies have used machine learning to analyze the correlations in such complex construction accident data; however, heretofore the focus has been on predicting severity with various variables, and several limitations remain when deriving the correlations between features from various variables. Thus, this paper proposes a data processing procedure that can efficiently manipulate accident data using optimal machine learning techniques and derive and systematize meaningful variables to rationally approach such complex problems. In particular, among the various variables, the most influential variables are derived through methods such as clustering, chi-square, Cramer's V, and predictor importance; then, the analysis is simplified by optimally grouping the variables. For accident data with optimal variables and elements, a predictive model is constructed between variables, using a support vector machine and decision-tree-based ensemble; then, the correlation between the dependent and independent variables is analyzed through an alluvial flow diagram for several cases. Therefore, a new processing procedure has been introduced in data preprocessing and accident prediction modelling to overcome difficulties from complex and diverse construction occupational accident data, and effective accident prevention is possible by deriving correlations of construction accidents using this process. [ABSTRACT FROM AUTHOR]

In recent years, with the development of artificial intelligence and human–computer interaction, more attention has been paid to the recognition and analysis of facial expressions. Despite much great success, there are a lot of unsatisfying problems, because facial expressions are subtle and complex. Hence, facial expression recognition is still a challenging problem. In most papers, the entire face image is often chosen as the input information. In our daily life, people can perceive other's current emotions only by several facial components (such as eye, mouth and nose), and other areas of the face (such as hair, skin tone, ears, etc.) play a smaller role in determining one's emotion. If the entire face image is used as the only input information, the system will produce some unnecessary information and miss some important information in the process of feature extraction. To solve the above problem, this paper proposes a method that combines multiple sub-regions and the entire face image by weighting, which can capture more important feature information that is conducive to improving the recognition accuracy. Our proposed method was evaluated based on four well-known publicly available facial expression databases: JAFFE, CK+, FER2013 and SFEW. The new method showed better performance than most state-of-the-art methods. [ABSTRACT FROM AUTHOR]

Automated car damage detection using computer vision techniques has been studied using several datasets, but real cases for insurance companies are usually dependent on private methods and datasets. Furthermore, there are no metrics or standardized processes that describe the situation in which the company analyzes the customer's images, the models used for the inference, and the results. We perform extensive experiments to show that our proposal, an ensemble of 10 deep learning detectors based on YOLOv5, improves the state-of-the-art not only in terms of typical metrics but also in terms of inference speed, allowing scalability to thousands of instances per minute. A comparison with YOLOv8 is carried out, showing the differences between both ensembles. Furthermore, a dataset called TartesiaDS, labeled under the supervision of professional appraisers from insurance companies, is available to the community for evaluation of future proposals. [ABSTRACT FROM AUTHOR]

The continuous evolution of the IoT paradigm has been extensively applied across various application domains, including air traffic control, education, healthcare, agriculture, transportation, smart home appliances, and others. Our primary focus revolves around exploring the applications of IoT, particularly within healthcare, where it assumes a pivotal role in facilitating secure and real-time remote patient-monitoring systems. This innovation aims to enhance the quality of service and ultimately improve people's lives. A key component in this ecosystem is the Healthcare Monitoring System (HMS), a technology-based framework designed to continuously monitor and manage patient and healthcare provider data in real time. This system integrates various components, such as software, medical devices, and processes, aimed at improvi1g patient care and supporting healthcare providers in making well-informed decisions. This fosters proactive healthcare management and enables timely interventions when needed. However, data transmission in these systems poses significant security threats during the transfer process, as malicious actors may attempt to breach security protocols.This jeopardizes the integrity of the Internet of Medical Things (IoMT) and ultimately endangers patient safety. Two feature sets—biometric and network flow metric—have been incorporated to enhance detection in healthcare systems. Another major challenge lies in the scarcity of publicly available balanced datasets for analyzing diverse IoMT attack patterns. To address this, the Auxiliary Classifier Generative Adversarial Network (ACGAN) was employed to generate synthetic samples that resemble minority class samples. ACGAN operates with two objectives: the discriminator differentiates between real and synthetic samples while also predicting the correct class labels. This dual functionality ensures that the discriminator learns detailed features for both tasks. Meanwhile, the generator produces high-quality samples that are classified as real by the discriminator and correctly labeled by the auxiliary classifier. The performance of this approach, evaluated using the IoMT dataset, consistently outperforms the existing baseline model across key metrics, including accuracy, precision, recall, F1-score, area under curve (AUC), and confusion matrix results. [ABSTRACT FROM AUTHOR]

The present study explores the field of text classification in the Greek language. A novel ensemble classification scheme based on generated embeddings from Greek text made by the multilingual capabilities of the E5 model is presented. Our approach incorporates partial transfer learning by using pre-trained models to extract embeddings, enabling the evaluation of classical classifiers on Greek data. Additionally, we enhance the predictive capability while maintaining the costs low by employing a soft voting combination scheme that exploits the strengths of XGBoost, K-nearest neighbors, and logistic regression. This method significantly improves all classification metrics, demonstrating the superiority of ensemble techniques in handling the complexity of Greek textual data. Our study contributes to the field of natural language processing by proposing an effective ensemble framework for the categorization of Greek texts, leveraging the advantages of both traditional and modern machine learning techniques. This framework has the potential to be applied to other less-resourced languages, thereby broadening the impact of our research beyond Greek language processing. [ABSTRACT FROM AUTHOR]

One of the most critical aspects in the seismic behavior or reinforced concrete (RC) structures pertains to beam–column joints. Modern seismic design codes dictate that, if failure is to occur, then this should be the ductile yielding of the beam and not brittle shear failure of the joint, which can lead to sudden collapse and loss of human lives. To this end, it is imperative to be able to predict the failure mode of RC joints for a large number of structures in a building stock. In this research effort, various ensemble machine learning algorithms were employed to develop novel, robust classification models. A dataset comprising 486 measurements from real experiments was utilized. The performance of the employed classifiers was assessed using Precision, Recall, F1-Score, and overall Accuracy indices. N-fold cross-validation was employed to enhance generalization. Moreover, the obtained models were compared to the available engineering ones currently adopted by many international organizations and researchers. The novel ensemble models introduced in this research were proven to perform much better by improving the obtained accuracy by 12–18%. The obtained metrics also presented small variability among the examined failure modes, indicating unbiased models. Overall, the results indicate that the proposed methodologies can be confidently employed for the prediction of the failure mode of RC joints. [ABSTRACT FROM AUTHOR]

Can bi-encoders, without additional fine-tuning, achieve a performance comparable to fine-tuned BERT models in classification tasks? To answer this question, we present a simple yet effective approach to text classification using bi-encoders without the need for fine-tuning. Our main observation is that state-of-the-art bi-encoders exhibit varying performance across different datasets. Therefore, our proposed approaches involve preparing multiple bi-encoders and, when a new dataset is provided, selecting and ensembling the most appropriate ones based on the dataset. Experimental results show that, for text classification tasks on subsets of the AG News, SMS Spam Collection, Stanford Sentiment Treebank v2, and TREC Question Classification datasets, the proposed approaches achieve performance comparable to fine-tuned BERT-Base, DistilBERT-Base, ALBERT-Base, and RoBERTa-Base. For instance, using the well-known bi-encoder model all-MiniLM-L12-v2 without additional optimization resulted in an average accuracy of 77.84%. This improved to 89.49% through the application of the proposed adaptive selection and ensemble techniques, and further increased to 91.96% when combined with the RoBERTa-Base model. We believe that this approach will be particularly useful in fields such as K-12 AI programming education, where pre-trained models are applied to small datasets without fine-tuning. [ABSTRACT FROM AUTHOR]

Various factors such as natural disasters, vandalism, weather, and environmental conditions can affect the physical state of traffic signs. The proposed model aims to improve detection of traffic signs affected by partial occlusion as a result of overgrown vegetation, displaced signs (those knocked down, bent), perforated signs (those damaged with holes), faded signs (color degradation), rusted signs (corroded surface), and de-faced signs (placing graffiti, etc., by vandals). This research aims to improve the detection of bad traffic signs using three approaches. In the first approach, Spiral Pooling Pyramid-Fast (SPPF) and C3TR modules are introduced to the architecture of Yolov5 models. SPPF helps provide a multi-scale representation of the input feature map by pooling at different scales, which is useful in improving the quality of feature maps and detecting bad traffic signs of various sizes and perspectives. The C3TR module uses convolutional layers to enhance local feature extraction and transformers to boost understanding of the global context. Secondly, we use predictions of Yolov5 as base models to implement a mean ensemble to improve performance. Thirdly, test time augmentation (TTA) is applied at test time by using scaling and flipping to improve accuracy. Some signs are generated using stable diffusion techniques to augment certain classes. We test the proposed models on the CCTSDB2021, TT100K, GTSDB, and GTSRD datasets to ensure generalization and use k-fold cross-validation to further evaluate the performance of the models. The proposed models outperform other state-of-the-art models in comparison. [ABSTRACT FROM AUTHOR]

The pervasive spread of fake news in online social media has emerged as a critical threat to societal integrity and democratic processes. To address this pressing issue, this research harnesses the power of supervised AI algorithms aimed at classifying fake news with selected algorithms. Algorithms such as Passive Aggressive Classifier, perceptron, and decision stump undergo meticulous refinement for text classification tasks, leveraging 29 models trained on diverse social media datasets. Sensors can be utilized for data collection. Data preprocessing involves rigorous cleansing and feature vector generation using TF-IDF and Count Vectorizers. The models' efficacy in classifying genuine news from falsified or exaggerated content is evaluated using metrics like accuracy, precision, recall, and more. In order to obtain the best-performing algorithm from each of the datasets, a predictive model was developed, through which SG with 0.681190 performs best in Dataset 1, BernoulliRBM has 0.933789 in Dataset 2, LinearSVC has 0.689180 in Dataset 3, and BernoulliRBM has 0.026346 in Dataset 4. This research illuminates strategies for classifying fake news, offering potential solutions to ensure information integrity and democratic discourse, thus carrying profound implications for academia and real-world applications. This work also suggests the strength of sensors for data collection in IoT environments, big data analytics for smart cities, and sensor applications which contribute to maintaining the integrity of information within urban environments. [ABSTRACT FROM AUTHOR]

Over recent decades, natural and artificial colloids, as well as nanoparticles, have been increasingly used in various applications. Consequently, with this rising consumption, surface and subsurface environments are more exposed to these particles. The presence of these particles and the colloid-facilitated transport of microorganisms, the interactions between dissolved contaminants and mobile colloids in porous media, and the fate and transport of colloids through groundwater—one of the primary sources of water supply for human societies—have attracted extensive research. This study investigates the performance of several image processing methods in the field of colloid detection, which is a prerequisite for the subsequent steps in porous media research. We employed four different categories of image processing approaches on microscopy images—segmentation-based methods, background-detection-based methods, filter-based methods, and morphology-based methods—to conduct the detection process of colloids. Eight methods were applied and subsequently analyzed in terms of their drawbacks and advantages to determine the best ones in this domain. Finally, we proposed an ensemble approach that leverages the strengths of the three best methods using a majority vote to detect colloids more accurately. In experiments, Precision, Recall, F-measure, and TCR criteria were considered as evaluation tools. Experimental results demonstrate the high accuracy of image processing methods in recognizing colloids. Among all these methods, morphology-based methods were the most successful, achieving the best detection performance and improving the limited distinguishing features of small colloids. Moreover, our ensemble approach, achieving perfect scores across all evaluation criteria, highlights its superiority compared with other detection methods. [ABSTRACT FROM AUTHOR]

A Bayesian additive regression tree (BART) is a recent statistical method that blends ensemble learning with nonparametric regression. BART is constructed using a Bayesian approach, which provides the benefit of model-based prediction uncertainty, enhancing the reliability of predictions. This study proposes the development of a BART model with a binomial likelihood to predict the percentage of students retained in tutorial classes using attendance data sourced from a South African university database. The data consist of tutorial dates and encoded (anonymized) student numbers, which play a crucial role in deriving retention variables such as cohort age, active students, and retention rates. The proposed model is evaluated and benchmarked against the random forest regressor (RFR). The proposed BART model reported an average of 20% higher predictive performance compared to RFR across six error metrics, achieving an R-squared score of 0.9414. Furthermore, the study demonstrates the utility of the highest density interval (HDI) provided by the BART model, which can help in determining the best- and worst-case scenarios for student retention rate estimates. The significance of this study extends to multiple stakeholders within the educational sector. Educational institutions, administrators, and policymakers can benefit from this study by gaining insights into how future tutorship programme student retention rates can be predicted using predictive models. Furthermore, the foresight provided by the predicted student retention rates can aid in strategic resource allocation, facilitating more informed planning and budgeting for tutorship programmes. [ABSTRACT FROM AUTHOR]

Complex systems pose significant challenges to traditional scientific and statistical methods due to their inherent unpredictability and resistance to simplification. Accurately detecting complex behavior and the uncertainty which comes with it is therefore essential. Using the context of previous studies, we introduce a new information-theoretic measure, termed "incoherence". By using an adapted Jensen-Shannon Divergence across an ensemble of outcomes, we quantify the aleatoric uncertainty of the system. First we compared this measure to established statistical tests using both continuous and discrete data. Before demonstrating how incoherence can be applied to identify key characteristics of complex systems, including sensitivity to initial conditions, criticality, and response to perturbations. [ABSTRACT FROM AUTHOR]

Featured Application: Domestic cats are ancient human pet animal that communicate through generating sounds. Automatic animal sound classification creates a better human-animal communication environment that will obviously be helpful to know the pet animal requirements more clearly. Sometime pet also protect human being in case of natural disaster or in criminal activity by alerting messages. In this work, our main contribution is domestic cat sound classification using limited number of available data. We try to overcome the lack of data problem using different well-known techniques such as transfer learning, ensemble, cross validation, etc. We propose frequency division average pooling (FDAP) technique instead of global average pooling (GAP) to make a robust prediction using various frequency band features. In order to better understand our results, we visualize networks learned features, confusion matrix, and receiver operating characteristic (ROC) curve. The proposed techniques to deal with lack of data problem for domestic cat sound classification can be useful to other researchers working in similar domain. The domestic cat (Feliscatus) is one of the most attractive pets in the world, and it generates mysterious kinds of sound according to its mood and situation. In this paper, we deal with the automatic classification of cat sounds using machine learning. Machine learning approach for the classification requires class labeled data, so our work starts with building a small dataset named CatSound across 10 categories. Along with the original dataset, we increase the amount of data with various audio data augmentation methods to help our classification task. In this study, we use two types of learned features from deep neural networks; one from a pre-trained convolutional neural net (CNN) on music data by transfer learning and the other from unsupervised convolutional deep belief network that is (CDBN) solely trained on a collected set of cat sounds. In addition to conventional GAP, we propose an effective pooling method called FDAP to explore a number of meaningful features. In FDAP, the frequency dimension is roughly divided and then the average pooling is applied in each division. For the classification, we exploited five different machine learning algorithms and an ensemble of them. We compare the classification performances with respect following factors: the amount of data increased by augmentation, the learned features from pre-trained CNN or unsupervised CDBN, conventional GAP or FDAP, and the machine learning algorithms used for the classification. As expected, the proposed FDAP features with larger amount of data increased by augmentation combined with the ensemble approach have produced the best accuracy. Moreover, both learned features from pre-trained CNN and unsupervised CDBN produce good results in the experiment. Therefore, with the combination of all those positive factors, we obtained the best result of 91.13% in accuracy, 0.91 in f1-score, and 0.995 in area under the curve (AUC) score. [ABSTRACT FROM AUTHOR]

Despite achieving numerous successes with surface defect inspection based on deep learning, the industry still faces challenges in conducting packaging defect inspections that include critical information such as ingredient lists. In particular, while previous achievements primarily focus on defect inspection in high-quality images, they do not consider defect inspection in low-quality images such as those containing image blur. To address this issue, we proposed a noble inference technique named temporal-quality ensemble (TQE), which combines temporal and quality weights. Temporal weighting assigns weights to input images by considering the timing in relation to the observed image. Quality weight prioritizes high-quality images to ensure the inference process emphasizes clear and reliable input images. These two weights improve both the accuracy and reliability of the inference process of low-quality images. In addition, to experimentally evaluate the general applicability of TQE, we adopt widely used convolutional neural networks (CNNs) such as ResNet-34, EfficientNet, ECAEfficientNet, GoogLeNet, and ShuffleNetV2 as the backbone network. In conclusion, considering cases where at least one low-quality image is included, TQE has an F1-score approximately 17.64% to 22.41% higher than using single CNN models and about 1.86% to 2.06% higher than an average voting ensemble. [ABSTRACT FROM AUTHOR]

In the realm of disaster-related tweet classification, this study presents a comprehensive analysis of various machine learning algorithms, shedding light on crucial factors influencing algorithm performance. The exceptional efficacy of simpler models is attributed to the quality and size of the dataset, enabling them to discern meaningful patterns. While powerful, complex models are time-consuming and prone to overfitting, particularly with smaller or noisier datasets. Hyperparameter tuning, notably through Bayesian optimization, emerges as a pivotal tool for enhancing the performance of simpler models. A practical guideline for algorithm selection based on dataset size is proposed, consisting of Bernoulli Naive Bayes for datasets below 5000 tweets and Logistic Regression for larger datasets exceeding 5000 tweets. Notably, Logistic Regression shines with 20,000 tweets, delivering an impressive combination of performance, speed, and interpretability. A further improvement of 0.5% is achieved by applying ensemble and stacking methods. [ABSTRACT FROM AUTHOR]

Fire models predict fire behavior and effects. However, there is a need to know how confident users can be in forecasts. This work developed a probabilistic methodology based on ensemble simulations that incorporated uncertainty in weather, fuel loading, and model physics parameters. It provided information on the most likely forecast scenario, confidence levels, and potential outliers. It also introduced novel ways to communicate uncertainty in calculation and graphical representation and applied this to diverse wildfires using ensemble simulations of the CAWFE coupled weather–fire model ranging from 12 to 26 members. The ensembles captured many features but spread was narrower than expected, especially with varying weather and fuel inputs, suggesting errors may not be easily mitigated by improving input data. Varying physics parameters created a wider spread, including identifying an outlier, underscoring modeling knowledge gaps. Uncertainty was communicated using burn probability, spread rate, and heat flux, a fire intensity metric related to burn severity. Despite limited ensemble spread, maps of mean and standard deviation exposed event times and locations where fire behavior was more uncertain, requiring more management or observations. Interpretability was enhanced by replacing traditional hot–cold color palettes with ones that accommodate the vision-impaired and adhere to web accessibility standards. [ABSTRACT FROM AUTHOR]

Chest X-rays are vital in healthcare for diagnosing various conditions due to their low Radiation exposure, widespread availability, and rapid interpretation. However, their interpretation requires specialized expertise, which can limit scalability and delay diagnoses. This study addresses the multi-label classification challenge of chest X-ray images using the Chest X-ray14 dataset. We propose a novel online ensemble technique that differs from previous penalty-based methods by focusing on combining individual model losses with the overall ensemble loss. This approach enhances interaction and feedback among models during training. Our method integrates multiple pre-trained CNNs using strategies like combining CNNs through an additional fully connected layer and employing a label-weighted average for outputs. This multi-layered approach leverages the strengths of each model component, improving classification accuracy and generalization. By focusing solely on image data, our ensemble model addresses the challenges posed by null vectors and diverse pathologies, advancing computer-aided radiology. [ABSTRACT FROM AUTHOR]

We propose a method for selecting the optimal set of weather features for wind energy prediction. This problem is tackled by developing a wrapper approach that employs binary differential evolution to search for the best feature subset, and an ensemble of artificial neural networks to predict the energy production from a wind plant. The main novelties of the approach are the use of features provided by different weather forecast providers and the use of an ensemble composed of a reduced number of models for the wrapper search. Its effectiveness is verified using weather and energy production data collected from a 34 MW real wind plant. The model is built using the selected optimal subset of weather features and allows for (i) a 1% reduction in the mean absolute error compared with a model that considers all available features and a 4.4% reduction compared with the model currently employed by the plant owners, and (ii) a reduction in the number of selected features by 85% and 50%, respectively. Reducing the number of features boosts the prediction accuracy. The implication of this finding is significant as it allows plant owners to create profitable offers in the energy market and efficiently manage their power unit commitment, maintenance scheduling, and energy storage optimization. [ABSTRACT FROM AUTHOR]