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Performance degradation assessment methods for rolling bearings under vibration signal monitoring typically involve extracting signal degradation features and inputting them directly into unoptimized assessment models. However, this method often fails to characterize degradation trends and detect early faults in bearings effectively. Moreover, it is susceptible to interference from outliers and false signal fluctuations, posing challenges for accurate performance degradation assessment. To solve the above problems, a novel performance degradation assessment method of rolling bearings based on optimized variational mode decomposition to construct the maximum information degradation feature set and improved hippopotamus optimization algorithm optimized fuzzy support vector data description is proposed. This method effectively suppresses the signal degradation feature pollution caused by the redundancy of irrelevant frequency components, realizes the screening of effective degradation feature sets, and further improves the accuracy of bearing performance degradation assessment. Through experimental verification, this research method uses early healthy rolling bearing samples to establish an assessment model, which can adaptively determine the starting point and degradation trend of bearing degradation. In comparison with other methods for performance degradation assessment under vibration monitoring, it offers distinct advantages. [ABSTRACT FROM AUTHOR]

The high heritability of amyotrophic lateral sclerosis (ALS) contrasts with its low molecular diagnosis rate post-genetic testing, pointing to potential undiscovered genetic factors. To aid the exploration of these factors, we introduced EpiOut, an algorithm to identify chromatin accessibility outliers that are regions exhibiting divergent accessibility from the population baseline in a single or few samples. Annotation of accessible regions with histone chromatin immunoprecipitation sequencing and Hi-C indicates that outliers are concentrated in functional loci, especially among promoters interacting with active enhancers. Across different omics levels, outliers are robustly replicated, and chromatin accessibility outliers are reliable predictors of gene expression outliers and aberrant protein levels. When promoter accessibility does not align with gene expression, our results indicate that molecular aberrations are more likely to be linked to post-transcriptional regulation rather than transcriptional regulation. Our findings demonstrate that the outlier detection paradigm can uncover dysregulated regions in rare diseases. EpiOut is available at github.com/uci-cbcl/EpiOut.

ABSTRACT Demographic processes can substantially affect a species' response to changing ecological conditions, necessitating the combined consideration of genetic responses to environmental variables and neutral genetic variation. Using a seascape genomics approach combined with population demographic modelling, we explored the interplay of demographic and environmental factors that shaped the current population structure in Indo‐Pacific bottlenose dolphins (Tursiops aduncus) along the Western Australian coastline. We combined large‐scale environmental data gathered via remote sensing with RADseq genomic data from 133 individuals at 19 sampling sites. Using population genetic and outlier detection analyses, we identified three distinct genetic clusters, coinciding with tropical, subtropical and temperate provincial bioregions. In contrast to previous studies, our demographic models indicated that populations occupying the paleo‐shoreline split into two demographically independent lineages before the last glacial maximum (LGM). A subsequent split after the LGM 12—15 kya gave rise to the Shark Bay population, thereby creating the three currently observed clusters. Although multi‐locus heterozygosity declined from north to south, dolphins from the southernmost cluster inhabiting temperate waters had higher heterozygosity in potentially adaptive loci compared to dolphins from subtropical and tropical waters. These findings suggest ongoing adaptation to cold‐temperate waters in the southernmost cluster, possibly linked to distinct selective pressures between the different bioregions. Our study demonstrated that in the marine realm, without apparent physical boundaries, only a combined approach can fully elucidate the intricate environmental and genetic interactions shaping the evolutionary trajectory of marine mammals. [ABSTRACT FROM AUTHOR]

The outliers in dam safety monitoring data can have an impact on the correctness and timeliness of dam safety analysis and decision-making. In order to accurately and efficiently detect outliers in dam safety monitoring data, an anomaly data detection algorithm based on IF Encoder was proposed. The target sequence was rebuilt based on the correlation between time series, and the residual size between the re- built sequence and the target sequence was compared to identify outliers. In addition, according to regulatory requirements, a correlation based outlier identification method was proposed, which divided the detected outliers into true and false anomalies, and removed false anomalies while retaining the true outliers. The results show that compared with the quartile method, Rayda criterion, KNN nearest neighbor method, and DBSCAN clustering method, the IF-Encoder algorithm has improved recall, precision, and accuracy in detecting outliers, and its recognition of outliers is more reliable and effective. The correlation based outlier identification method has an accuracy rate of 92% for identifying true anomalies and 100% for identifying false anomalies, which can effectively classify outliers. [ABSTRACT FROM AUTHOR]

Excessive carbon dioxide ( CO 2 ) emissions pose a formidable challenge, driving global climate change and necessitating urgent attention. Striking a balance between curbing CO 2 emissions and fostering economic growth hinges upon the ability to reliably forecast CO 2 emissions. Such forecasts are indispensable for policymakers as they endeavor to make informed decisions and proactively implement mitigation measures. In this research, we introduce an innovative deep ensemble prediction model for CO 2 emissions. This model is constructed around four parallel Long Short-Term Memory (LSTM) neural networks, complemented by a novel Multi-Layer Perception (MLP)-based ensemble framework, equipped with an outlier detection mechanism and an order-invariant ranking module. To enhance prediction accuracy and stability, a k-nearest neighbor (KNN)-based outlier detection module is employed to identify non-outliers and reasonable predictions for the ensemble models. Additionally, a novel feature ranking module is proposed to mitigate prediction fluctuations. The performance evaluation of our model is conducted using historical CO 2 emission data spanning from 1971 to 2021, encompassing six representative countries. Our findings demonstrate that the proposed methodology outperforms existing approaches across various evaluation metrics, offering considerably reduced prediction variances and greater stability. Moreover, long-term CO 2 emission predictions for the corresponding six countries have been provided, which might offer policymakers some basis for making decisions. [ABSTRACT FROM AUTHOR]

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Outlier detection has garnered considerable attention in recent years due to its wide-ranging applications across various research domains. This surge in interest has led to the development of numerous detection techniques, predominantly based on distance or density metrics. A notable limitation of these existing methods is their reliance on parameter adjustments, significantly affecting the outcome. Additionally, these methods exhibit intrinsic flaws: distance-based approaches struggle with clusters with varying local densities, while density-based methods fail to identify patterns within low-density areas. Moreover, most prior techniques are adept at identifying only one kind of outlier—local, global, or group of outliers. Addressing these challenges, we introduce the Adaptive Radius Density-Based Outlier Detection (ARDOD) method, which departs from the traditional parameter-dependent approach. ARDOD is a novel parameter-free algorithm that dynamically determines the necessary parameters based on the data distribution within the feature space. This innovative method demonstrates robust performance in detecting all three categories of outliers. The efficacy and superior performance of ARDOD are validated through an extensive experimental analysis involving various synthetic and real-world datasets. This analysis showcases ARDOD's advantages over seven established methods: Local Outlier Factor (LOF), Angle-Based Outlier Detection (ABOD), Robust Distance-Based Outlier Score (RDOS), Directed density ratio Changing Rate-based outlier detection (DCROD), Empirical-Cumulative-distribution-based Outlier Detection(ECOD), mean-shift outlier detector(MOD +),and Local–Global Outlier Detection (LGOD), underscoring its potential as a versatile tool in outlier detection research. [ABSTRACT FROM AUTHOR]

Explainable AI refers to techniques by which the reasons underlying decisions taken by intelligent artifacts are single out and provided to users. Outlier detection is the task of individuating anomalous objects within a given data population they belong to. In this paper we propose a new technique to explain why a given data object has been singled out as anomalous. The explanation our technique returns also includes counterfactuals, each of which denotes a possible way to "repair" the outlier to make it an inlier. Thus, given in input a reference data population and an object deemed to be anomalous, the aim is to provide possible explanations for the anomaly of the input object, where an explanation consists of a subset of the features, called choice, and an associated set of changes to be applied, called mask, in order to make the object "behave normally". The paper presents a deep learning architecture exploiting a features choice module and mask generation module in order to learn both components of explanations. The learning procedure is guided by an ad-hoc loss function that simultaneously maximizes (minimizes, resp.) the isolation of the input outlier before applying the mask (resp., after the application of the mask returned by the mask generation module) within the subspace singled out by the features choice module, all that while also minimizing the number of features involved in the selected choice. We consider also the case in which a common explanation is required for a group of outliers provided together in input. We present experiments on both artificial and real data sets and a comparison with competitors validating the effectiveness of the proposed approach. [ABSTRACT FROM AUTHOR]

Unsupervised learning methods are well established in the area of anomaly detection and achieve state of the art performances on outlier datasets. Outliers play a significant role, since they bear the potential to distort the predictions of a machine learning algorithm on a given dataset. Especially among PCA-based methods, outliers have an additional destructive potential regarding the result: they may not only distort the orientation and translation of the principal components, they also make it more complicated to detect outliers. To address this problem, we propose the robust outlier detection algorithm CoMadOut, which satisfies two required properties: (1) being robust towards outliers and (2) detecting them. Our CoMadOut outlier detection variants using comedian PCA define, dependent on its variant, an inlier region with a robust noise margin by measures of in-distribution (variant CMO) and optimized scores by measures of out-of-distribution (variants CMO*), e.g. kurtosis-weighting by CMO+k. These measures allow distribution based outlier scoring for each principal component, and thus, an appropriate alignment of the degree of outlierness between normal and abnormal instances. Experiments comparing CoMadOut with traditional, deep and other comparable robust outlier detection methods showed that the performance of the introduced CoMadOut approach is competitive to well established methods related to average precision (AP), area under the precision recall curve (AUPRC) and area under the receiver operating characteristic (AUROC) curve. In summary our approach can be seen as a robust alternative for outlier detection tasks. [ABSTRACT FROM AUTHOR]

Global Navigation Satellite System fast precise positioning can be achieved with accurate ionospheric corrections computed from an adequate number of GNSS stations in a local region. In low-latitude regions, the presence of electron density gradients over short distances can lead to outliers in the map of ionospheric corrections and decrease its accuracy. In this study, we explored outlier detection in ionospheric correction mapping through statistical residuals during a four-month test in 2021. Our findings indicate that the residuals of the local ionospheric model conform to the Laplace distribution. To determine outliers, we use an empirical rule for the Laplace distribution, setting thresholds at μ ± 3b, μ ± 3.5b, and μ ± 5.8b for data retention rates of 95%, 97%, and 99.7%, respectively. Here, μ represents the location parameter, which corresponds to the median of data, and b is the scale parameter, calculated as the medium absolute deviation. We found that while removing outliers can improve model accuracy, it may result in unavailable prediction due to a lack of data in a spare network. For example, applying a μ ± 3.5b threshold for outlier removal led to approximately 2.5% of recording time having no ionospheric corrections map in low-latitude regions, however, the local model has the potential to improve its mean accuracy by up to 50% for both low and mid-latitudes. Therefore, choosing the appropriate percentile threshold depends on the network configuration and the desired accuracy. Removing erroneous satellite data to improve ionospheric accuracy brings positive impacts on precise positioning. [ABSTRACT FROM AUTHOR]

Climate change is having an impact on locally adapted species, including the cork oak (Quercus suber L.), a key tree species in the Mediterranean region especially due to its role in carbon sequestration. Notably, the Maâmora forest in Morocco stands as the largest forest in the Mediterranean basin with the highest diversity compared to similar forests across Morocco. In this study, we genotyped 240 samples from natural populations of cork oak in the Maâmora forest using seven microsatellite markers, to investigate signatures of local adaptation. Using a genomic scan approach, we identified outliers loci experiencing divergent natural selection pressures, which were later correlated with climatic, edaphic, and geographic conditions of the Maâmora forest. Statistical analysis of the data shows a high allelic variation. The detection of outliers, carried out using BayeScan v2.1 software, revealed the presence of two significant outliers loci ZAG20-171 (qvalue = 0.0004) and MSQ4-211 (qvalue = 0.04). The result of the environmental association analysis revealed a significant correlation between locus ZAG20-171 with mean diurnal range, isothermality, annual precipitation, precipitation of the driest month, precipitation of the wettest quarter and altitude. This work aims to help understanding the genetic basis of adaptation in Q. suber L., and thus the selection of suitable provenances for further breeding efforts. [ABSTRACT FROM AUTHOR]

This paper provides a review and comparative analysis of trajectory outlier detection methods. It presents the definition of outliers in trajectory data and the existing types to further examine the advanced approaches. Basic steps for detecting an outlier, which include data preprocessing, feature extraction, modeling, and similar, have been presented. Moreover, advanced methods such as autoencoders and the use of deep learning for outlier detection have been explored. In the end, this paper evaluates the techniques and compares them using common metrics, mainly focusing on the techniques based on autoencoders or deep learning. It covers applications in real life and practice along with any limitations, challenges, and perspective ideas for the future. Ultimately, it can be a useful resource for expanding the understanding of domain researchers and practitioners. [ABSTRACT FROM AUTHOR]

As lithium-ion batteries are widely used in electric vehicles, safety accidents caused by battery failures emerge one after another. Nevertheless, failures caused by changes in the internal structure or characteristics of the battery, such as sudden and progressive failures, are still a serious problem for electric vehicles, challenging existing fault diagnosis methods. This paper first performs wavelet packet decomposition on the battery's raw voltage signal to obtain high-quality low-frequency and high-frequency characteristic signal components. Then performs singular value decomposition on the characteristic signal components to extract the corresponding singular value characteristic parameters, and introduces the Manhattan average distance algorithm to battery faults. Diagnosing and locating faulty battery units using the Laida criterion (3-σ criterion) outlier detection method. Finally, actual vehicle data were used to verify the reliability, stability, accuracy of the method, and compared with the traditional Manhattan distance, correlation coefficient, information entropy methods. The method in this paper has good fault detection effects on vehicles with sudden and progressive faults vehicles. [ABSTRACT FROM AUTHOR]

Exploring the data quality problems in the context of federated cooperation and adopting corresponding governance countermeasures can facilitate the smooth progress of federated cooperation and obtain high-performance models. However, previous studies have rarely focused on quality issues in federated cooperation. To this end, this paper analyzes the quality problems in the federated cooperation scenario and innovatively proposes a "Two-stage" data quality governance framework for the federated collaboration scenarios. The first stage is mainly local data quality assessment and optimization, and the evaluation is performed by constructing a metrics scoring formula, and corresponding optimization measures are taken at the same time. In the second stage, the outlier processing mechanism is introduced, and the Data Quality Federated Averaging (Abbreviation DQ-FedAvg) aggregation method for model quality problems is proposed, so as to train high-quality global models and their own excellent local models. Finally, experiments are conducted in real datasets to compare the model performance changes before and after quality governance, and to validate the advantages of the data quality governance framework in a federated learning scenario, so that it can be widely applied to various domains. The governance framework is used to check and govern the quality problems in the federated learning process, and the accuracy of the model is improved. [ABSTRACT FROM AUTHOR]

Hit screening, which involves the identification of compounds or targets capable of modulating disease-relevant processes, is an important step in drug discovery. Some assays, such as image-based high-content screenings, produce complex multivariate readouts. To fully exploit the richness of such data, advanced analytical methods that go beyond the conventional univariate approaches should be employed. In this work, we tackle the problem of hit identification in multivariate assays. As with univariate assays, a hit from a multivariate assay can be defined as a candidate that yields an assay value sufficiently far away in distance from the mean or central value of inactives. Viewed another way, a hit is an outlier from the distribution of inactives. A method was developed for identifying multivariate hit in high-dimensional data sets based on principal components and robust Mahalanobis distance (the multivariate analogue to the Z- or T-statistic). The proposed method, termed mROUT (multivariate robust outlier detection), demonstrates superior performance over other techniques in the literature in terms of maintaining Type I error, false discovery rate and true discovery rate in simulation studies. The performance of mROUT is also illustrated on a CRISPR knockout data set from in-house phenotypic screening programme. [ABSTRACT FROM AUTHOR]

Coronavirus disease (COVID-19) is a respiratory condition that has quickly expanded to pandemic levels, affecting people in over 192 countries around the world. Estimating the number of new COVID-19 cases is never an easy undertaking. This study explores the application of Auto-Regressive Integrated Moving Average (ARIMA) modelling combined with Discrete Wavelet Transform (DWT) for analyzing and predicting time series data of Covid-19 patients. It aims to comprehend the disease progression and forecast future trends. The methodology makes use of the ARIMA model, a statistical tool that captures temporal correlations, K-means clustering for outlier detection and removal, and the discrete wavelet transform (DWT) for feature selection. This combination allows for a more thorough evaluation of the patterns and deviations in the COVID-19 patient data. The Enhanced ARIMA (EARIMA) model aims to enhance the current ARIMA-based time series prediction methods. By employing this hybrid approach, the study aims to provide a robust framework for understanding the temporal dynamics of Covid-19 patient data, offering more accurate and interpretable predictions for future trends in the progression of the disease. It improves on the current ARIMA based time-series prediction algorithms. The EARIMA was evaluated using COVID-19 data set gotten from the WHO official website and was compared with various state-of-the-art algorithms of today. The EARIMA received an RSS score of 0.326 and an accuracy of 98.7%, which is better than the existing ARIMA model, which has an RSS score of 0.659 and an accuracy of 93%. The findings contribute to enhancing forecasting accuracy and aiding in decision-making processes for healthcare resource allocation and pandemic management strategies. [ABSTRACT FROM AUTHOR]

Discriminant analysis (DA) is one of the most popular methods for classification due to its conceptual simplicity, low computational cost, and often solid performance. In its standard form, DA uses the arithmetic mean and sample covariance matrix to estimate the center and scatter of each class. We discuss and illustrate how this makes standard DA very sensitive to suspicious data points, such as outliers and mislabeled cases. We then present an overview of techniques for robust DA, which are more reliable in the presence of deviating cases. In particular, we review DA based on robust estimates of location and scatter, along with graphical diagnostic tools for visualizing the results of DA. This article is categorized under:Statistical and Graphical Methods of Data Analysis > Robust MethodsStatistical Learning and Exploratory Methods of the Data Sciences > Clustering and Classification [ABSTRACT FROM AUTHOR]

Froth flotation, a widely used mineral beneficiation technique, generates substantial volumes of data, offering the opportunity to extract valuable insights from these data for production line analysis. The quality of flotation data is critical to designing accurate prediction models and process optimisation. Unfortunately, industrial flotation data are often compromised by quality issues such as outliers that can produce misleading or erroneous analytical results. A general approach is to preprocess the data by replacing or imputing outliers with data values that have no connection with the real state of the process. However, this does not resolve the effect of outliers, especially those that deviate from normal trends. Outliers often occur across multiple variables, and their values may occur in normal observation ranges, making their detection challenging. An unresolved challenge in outlier detection is determining how far an observation must be to be considered an outlier. Existing methods rely on domain experts' knowledge, which is difficult to apply when experts encounter large volumes of data with complex relationships. In this paper, we propose an approach to conduct outlier analysis on a flotation dataset and examine the efficacy of multiple machine learning (ML) algorithms—including k-Nearest Neighbour (kNN), Local Outlier Factor (LOF), and Isolation Forest (ISF)—in relation to the statistical 2 σ rule for identifying outliers. We introduce the concept of "quasi-outliers" determined by the 2 σ threshold as a benchmark for assessing the ML algorithms' performance. The study also analyses the mutual coverage between quasi-outliers and outliers from the ML algorithms to identify the most effective outlier detection algorithm. We found that the outliers by kNN cover outliers of other methods. We use the experimental results to show that outliers affect model prediction accuracy, and excluding outliers from training data can reduce the average prediction errors. [ABSTRACT FROM AUTHOR]

The problem of Power Quality analysis is becoming crucial to ensuring the proper functioning of complex systems and big plants. In this regard, it is essential to rapidly detect anomalies in voltage and current signals to ensure a prompt response and maximize the system's availability with the minimum maintenance cost. In this paper, anomaly detection algorithms based on machine learning, such as One Class Support Vector Machine (OCSVM), Isolation Forest (IF), and Angle-Based Outlier Detection (ABOD), are used as a first tool for rapid and effective clustering of the measured voltage and current signals directly on-line on the sensing unit. If the proposed anomaly detection algorithm detects an anomaly, further investigations using suitable classification algorithms are required. The main advantage of the proposed solution is the ability to rapidly and efficiently detect different types of anomalies with low computational complexity, allowing the implementation of the algorithm directly on the sensor node used for signal acquisition. A suitable experimental platform has been established to evaluate the advantages of the proposed method. All the different models were tested using a consistent set of hyperparameters and an output dataset generated from the principal component analysis technique. The best results achieved included models reaching 100% recall and a 92% F1 score. [ABSTRACT FROM AUTHOR]

BACKGROUND Outpatient visits for nonmelanoma skin cancer (NMSC) and actinic keratoses (AK) have risen steadily in the United States, notably among Medicare beneficiaries. Individuals may delay seeking care for minimally symptomatic conditions until they qualify for Medicare coverage, indicating potential delay of nonurgent screening interventions for uninsured or underinsured patients younger than 65 years. OBJECTIVE This study investigates whether an atypical increase in outpatient visits for NMSC, AK, or actinic cheilitis (AC) occurs at the age of Medicare transition by utilizing the National Ambulatory Care Survey from 1993 to 2019. MATERIALS AND METHODS The National Ambulatory Care Survey data were analyzed for patients aged within 5 years of 65 years. Diagnoses were identified using International Classification of Diseases codes. Linear regression and outlier detection were used to identify a relationship between Medicare eligibility and outpatient visits for NMSC and AK/AC. RESULTS Predicted visits for AK/AC and NMSC increased with age. However, there was no evidence of a disproportionate increase in outpatient visits for NMSC and AK/AC at the age of Medicare eligibility. CONCLUSION Outside evidence indicates health care utilization increases after Medicare transition. This study's data do not support a corresponding rise in outpatient visits for NMSC and AK/AC at the age of Medicare eligibility. [ABSTRACT FROM AUTHOR]

Plant pathogens are constantly under selection pressure for host resistance adaptation. Soybean cyst nematode (SCN, Heterodera glycines) is a major pest of soybean primarily managed through resistant cultivars; however, SCN populations have evolved virulence in response to selection pressures driven by repeated monoculture of the same genetic resistance. Resistance to SCN is mediated by multiple epistatic interactions between Rhg (for resistance to H. glycines) genes. However, the identity of SCN virulence genes that confer the ability to overcome resistance remains unknown. To identify candidate genomic regions showing signatures of selection for increased virulence, we conducted whole genome resequencing of pooled individuals (Pool‐Seq) from two pairs of SCN populations adapted on soybeans with Peking‐type (rhg1‐a, rhg2, and Rhg4) resistance. Population differentiation and principal component analysis‐based approaches identified approximately 0.72–0.79 million SNPs, the frequency of which showed potential selection signatures across multiple genomic regions. Chromosomes 3 and 6 between population pairs showed the greatest density of outlier SNPs with high population differentiation. Conducting multiple outlier detection tests to identify overlapping SNPs resulted in a total of 966 significantly differentiated SNPs, of which 285 exon SNPs were mapped to 97 genes. Of these, six genes encoded members of known stylet‐secreted effector protein families potentially involved in host defence modulation including venom‐allergen‐like, annexin, glutathione synthetase, SPRYSEC, chitinase, and CLE effector proteins. Further functional analysis of identified candidate genes will provide new insights into the genetic mechanisms by which SCN overcomes soybean resistance and inform the development of molecular markers for rapidly screening the virulence profile of an SCN‐infested field. [ABSTRACT FROM AUTHOR]

This study introduces an enhanced recursive least-squares algorithm that applies the adaptive accelerated proximal gradient method to identify Auto-Regressive Exogenous models with output outliers. First, the outlier problem was converted into a robust principal component analysis problem. The adaptive accelerated proximal gradient method is then introduced to recover the information matrix, and the recursive least squares algorithm is applied to estimate the model parameters. Furthermore, we demonstrated that the parameter estimation is unbiased and that the parameter estimation error converges to zero under persistent excitation. A series of bench tests consistently highlighted the practicality and effectiveness of the proposed algorithm. • The accelerated proximal gradient algorithm is introduced for effective outlier recovery. • The proposed adaptive accelerated proximal gradient algorithm can adaptively adjust the step-size. • A comprehensive framework is established by integrating accelerated proximal gradient and recursive least squares algorithms. • The proposed algorithm is validated through a state of charge estimation problem, demonstrating its effectiveness. [ABSTRACT FROM AUTHOR]

This paper proposes a method for assessing differential item functioning (DIF) in item response theory (IRT) models. The method does not require pre-specification of anchor items, which is its main virtue. It is developed in two main steps: first by showing how DIF can be re-formulated as a problem of outlier detection in IRT-based scaling and then tackling the latter using methods from robust statistics. The proposal is a redescending M-estimator of IRT scaling parameters that is tuned to flag items with DIF at the desired asymptotic type I error rate. Theoretical results describe the efficiency of the estimator in the absence of DIF and its robustness in the presence of DIF. Simulation studies show that the proposed method compares favorably to currently available approaches for DIF detection, and a real data example illustrates its application in a research context where pre-specification of anchor items is infeasible. The focus of the paper is the two-parameter logistic model in two independent groups, with extensions to other settings considered in the conclusion. [ABSTRACT FROM AUTHOR]

Crowdsourcing holds broad applications in information acquisition and dissemination, yet encounters challenges pertaining to data quality assessment and user reputation management. Reputation mechanisms stand as crucial solutions for appraising and updating participant reputation scores, thereby elevating the quality and dependability of crowdsourced data. However, these mechanisms face several challenges in traditional crowdsourcing systems: 1) platform security lacks robust guarantees and may be susceptible to attacks; 2) there exists a potential for large-scale privacy breaches; and 3) incentive mechanisms relying on reputation scores may encounter issues as reputation updates hinge on task demander evaluations, occasionally lacking a dedicated reputation update module. This paper introduces a reputation update scheme tailored for crowdsourcing, with a focus on proficiently overseeing participant reputations and alleviating the impact of malicious activities on the sensing system. Here, the reputation update scheme is determined by an Empirical Cumulative distribution-based Outlier Detection method (ECOD). Our scheme embraces a blockchain-based crowdsourcing framework utilizing a homomorphic encryption method to ensure data transparency and tamper-resistance. Computation of user reputation scores relies on their behavioral history, actively discouraging undesirable conduct. Additionally, we introduce a dynamic weight incentive mechanism that mirrors alterations in participant reputation, enabling the system to allocate incentives based on user behavior and reputation. Our scheme undergoes evaluation on 11 datasets, revealing substantial enhancements in data credibility for crowdsourcing systems and a reduction in the influence of malicious behavior. This research not only presents a practical solution for crowdsourcing reputation management but also offers valuable insights for future research and applications, holding promise for fostering more reliable and high-quality data collection in crowdsourcing across diverse domains. [ABSTRACT FROM AUTHOR]

Intrusion detection systems (IDS) are an important tool for securing computer networks from various types of cyberattacks. The increasing complexity of network attacks demands more sophisticated approaches to intrusion detection. This paper presents an innovative method for IDS that involves combining Z-Score outlier detection, KMeans clustering, and Random Forest classification techniques. We tested our methodology using the CICIDS2017 dataset, which is a standardization dataset for intrusion detection that is frequently utilized. Our proposed approach first uses Z-Score outlier detection to identify abnormal traffic flows in the network. Next, KMeans clustering is used to group the traffic flows into different clusters based on their similarity. Finally, Random Forest classification is used to classify each traffic flow into normal or abnormal categories. Based on our experimental results, our approach for intrusion detection shows superior performance compared to several other state-of-the-art methods in terms of accuracy and precision. Our proposed method achieved an accuracy rate of 95.75% and a precision of 95.76%, surpassing the performance of KNN, SVM, and decision trees approaches. In conclusion, the proposed Z-K-R approach offers a promising solution for IDS by leveraging the strengths of Z-Score outlier detection, KMeans clustering, and Random Forest classification techniques. This strategy has the potential to increase the efficiency of IDS and boost network security in applications that take place in the real world. [ABSTRACT FROM AUTHOR]

Recently, the commercialization of autonomous vehicles has increased the importance of verifying vehicle safety through autonomous trials. Autonomous driving trials are conducted in limited areas within artificially constructed test roads and pilot districts and directly explore road sections and areas with similar environments to ensure the safety of AVs driving on real roads. Many previous studies have evaluated the complex response potential of AVs by deriving edge scenarios to ensure their safety. However, difficulties arise in exploring real roads with traffic accident factors and configurations similar to those in edge scenarios, making validation on real roads uncertain. This paper proposes a novel method for exploring pilot zones using traffic accident data to verify the safety of autonomous vehicles (AVs). The method employs a CNN + BiGRU model trained on DMV dataset data to classify traffic accidents as AV- or human-caused. The model's classification accuracy was evaluated using recall, precision, F1 score, and accuracy, achieving 100.0%, 97.8%, 98.9, and 99.5%, respectively. The trained model was applied to the KNPA dataset, identifying 562 out of 798 cases as AV-like, indicating potential areas of high accident density due to AV operation. Outlier detection and DBSCAN clustering were utilized to identify compact pilot zones, effectively reducing the area size compared to raw data clusters. This approach significantly lowers the cost and time associated with selecting test roads and provides a viable alternative for countries lacking real AV accident data. The proposed method's effectiveness in identifying pilot zones demonstrates its potential for advancing AV safety validation. [ABSTRACT FROM AUTHOR]

Diabetes mellitus (DM) is a global health challenge that requires advanced strategies for its early detection and prevention. This study evaluates the South Korean population using the Korea National Health and Nutrition Examination Survey (KNHANES) dataset from 2015 to 2021, provided by the Korea Disease Control and Prevention Agency (KDCA), focusing on improving diabetes prediction models. Outlier removal was implemented using Mahalanobis distance (MAH), and feature selection was based on multicollinearity (MC) and reliability analysis (RA). The proposed Extreme Gradient Boosting (XGBoost) model demonstrated exceptional performance, achieving an accuracy of 98.04% (95% CI: 97.89~98.59), an F1-score of 98.24%, and an Area Under the Curve (AUC) of 98.71%, outperforming other state-of-the-art models. The study highlights the significance of rigorous outlier detection and feature selection in enhancing the predictive power of diabetes risk models. Notably, a significant increase in diabetes cases was observed during the COVID-19 pandemic, particularly linked to male sex, older age, rural location, hypertension, and obesity, underscoring the need for enhanced public health strategies for early intervention and targeted prevention. [ABSTRACT FROM AUTHOR]

The imminent depletion of oil resources and increasing environmental pollution have driven the use of clean energy, particularly wind energy. However, wind turbines (WTs) face significant challenges, such as critical component failures, which can cause unexpected shutdowns and affect energy production. To address this challenge, we analyzed the Supervisory Control and Data Acquisition (SCADA) data to identify significant differences between the relationship of variables based on data reconstruction errors between actual and predicted values. This study proposes a hybrid short- and long-term memory autoencoder model with multihead self-attention (LSTM-MA-AE) for WT converter fault detection. The proposed model identifies anomalies in the data by comparing the reconstruction errors of the variables involved. However, more is needed. To address this model limitation, we developed a fault prediction system that employs an adaptive threshold with an Exponentially Weighted Moving Average (EWMA) and a fixed threshold. This system analyzes the anomalies of several variables and generates fault warnings in advance time. Thus, we propose an outlier detection method through data preprocessing and unsupervised learning, using SCADA data collected from a wind farm located in complex terrain, including real faults in the converter. The LSTM-MA-AE is shown to be able to predict the converter failure 3.3 months in advance, and with an F1 greater than 90% in the tests performed. The results provide evidence of the potential of the proposed model to improve converter fault diagnosis with SCADA data in complex environments, highlighting its ability to increase the reliability and efficiency of WTs. [ABSTRACT FROM AUTHOR]

Statistical depths provide a fundamental generalization of quantiles and medians to data in higher dimensions. This paper proposes a new type of globally defined statistical depth, based upon control theory and eikonal equations, which measures the smallest amount of probability density that has to be passed through in a path to locations on the boundary of the support of the distribution or spatial infinity for distributions with unbounded support. This depth is easy to interpret and compute, expressively captures multi-modal behavior, and extends naturally to data that is non-Euclidean. We prove various properties of this depth, and provide discussion of computational considerations. In particular, we demonstrate that this notion of depth is robust under an approximate isometrically constrained adversarial model, a property which is not enjoyed by the Tukey depth. Finally we give some illustrative examples in the context of two-dimensional mixture models and MNIST. [ABSTRACT FROM AUTHOR]

Being the parent distribution in Romania, the Pearson III distribution (PE3) is one of the statistical distributions that is most frequently used in flood frequency analysis (FFA). All the components required for the simple implementation of PE3 distribution in FFA are presented in this manuscript. The estimated methods and exact and approximate relationships for estimating the parameters and frequency factors particular to the distribution are described. All of these factors are used to identify maximum flows with various annual exceedance probabilities, using data collected at the 6 rivers with different morphometric characteristics and different lengths of data series. Five parameter estimation techniques are used in this comparative analysis, i.e. the method of ordinary moments (MOM), the method of linear moments (L-moments), the high-order linear moments method (LH-moments), the method of maximum likelihood (MLE) and the method of least squares (LSM). Given the results, it can be assumed that the L-moments approach is more reliable, stable, robust and less sensitive to variations in recorded data lengths, as well as to the presence of outliers. [ABSTRACT FROM AUTHOR]

While field research in indoor air quality (IAQ) increasingly uses large numbers of low-cost sensors, detailed validation of each sensor is becoming infeasible, ignored, or undeclared. Questions remain about whether these sensors meet manufacturer or third-party specifications and whether their utility improves with some validation, even under imperfect conditions. This study details the outcomes of a co-location experiment with 87 low-cost IAQ monitors, conducted with the objective of verifying their performance in measuring total volatile organic compounds (tVOCs), particulate matter 2.5 (PM2.5), carbon dioxide (CO2), temperature, and relative humidity. The monitors were installed next to professional-grade reference IAQ instruments in an office with controlled ventilation. A mixed validation methodology was used, involving outlier detection, uncertainty quantification, and performance metric calculations. Results showed that CO2, temperature, and humidity sensors reliably met manufacturer specifications, while tVOC sensors had significant accuracy issues, deviating by up to 79% from reference measurements—substantially more than the stated uncertainty of ±20%. PM2.5 sensors were more consistent but had an error margin of ±27%, compared to the stated ±15%. A total of 5 of the 87 sensors (5.7%) were outliers for at least one IAQ indicator. Despite the need for further long-term validation, this study highlights the importance of performing an experimental evaluation of low-cost IAQ monitors before field deployment. [ABSTRACT FROM AUTHOR]

Safety monitoring of hydraulic structures is a critical task in the field of hydraulic engineering construction. This study developed a method for preprocessing and classifying monitoring data for the identification of gross errors in hydraulic structures. By utilizing linear regression and wavelet analysis techniques, it effectively differentiated various waveform characteristics in data sets, such as Sinusoidal Wave Cyclical, Triangular Wave Cyclical, Seasonal Cyclical, and Weakly Cyclical growth types. In the experiments for gross error identification, the 3σ algorithm, K-medoids algorithm, and Isolation Forest algorithm were applied to test the data. The results showed that the K-medoids algorithm excelled in processing Sinusoidal Wave Cyclical Data Sets; the 3σ algorithm adapted better to Triangular Wave Cyclical Data Sets; the Isolation Forest algorithm performed well in handling data sets with significant anomalies or atypical fluctuations and excelled in scenarios with strong seasonality and large data fluctuations; and for complex Weakly Cyclical Growth Data Sets, all three algorithms were less effective, indicating the potential need for more advanced analysis methods or a combination of multiple techniques. Testing on actual engineering data further confirmed the importance of using specific gross error identification techniques for special data types after data set pre-classification, providing a more effective technical solution for the safety monitoring of hydraulic structures. [ABSTRACT FROM AUTHOR]

The Internet of Underwater Things (IoUT) represents an emerging and innovative field with the potential to revolutionize underwater exploration and monitoring. Despite its promise, IoUT faces significant challenges related to reliability and security, which hinder its development and deployment. A particularly critical issue is the establishment of trustworthy communication networks, necessitating the adaptation and enhancement of existing models from terrestrial and marine systems to address the specific requirements of IoUT. This work explores the problem of dishonest recommendations within trust modelling systems, a critical issue that undermines the integrity of trust modelling in IoUT networks. The unique environmental and operational constraints of IoUT exacerbate the severity of this issue, making current detection methods insufficient. To address this issue, a recommendation evaluation method that leverages both filtering and weighting strategies is proposed to enhance the detection of dishonest recommendations. The model introduces a filtering technique that combines outlier detection with deviation analysis to make initial decisions based on both majority outcomes and personal experiences. Additionally, a belief function is developed to weight received recommendations based on multiple criteria, including freshness, similarity, trustworthiness, and the decay of trust over time. This multifaceted weighting strategy ensures that recommendations are evaluated from different perspectives to capture deceptive acts that exploit the complex nature of IoUT to the advantage of dishonest recommenders. To validate the proposed model, extensive comparative analyses with existing trust evaluation methods are conducted. Through a series of simulations, the efficacy of the model in capturing dishonest recommendation attacks and improving the accuracy rate of detecting more sophisticated attack scenarios is demonstrated. These results highlight the potential of the model to significantly enhance the trustworthiness of IoUT establishments. [ABSTRACT FROM AUTHOR]

In order to improve the detection accuracy of CO2 and other gases in the flue gas emitted from thermal power plants, a CO2 concentration detection model based on tunable semiconductor laser absorption spectroscopy was proposed. First, a variational mode decomposition model was used to filter the harmonic signal after removing the outliers to reduce the influence of noise on the detection results. Suitable absorption lines and concentration characteristics were then selected according to the gas absorption properties and correlation theory. Finally, the CO2 concentration inversion was completed using long short-term memory networks, and a Bayesian optimization algorithm was introduced to optimize the hyperparameters of the network. The experimental results showed that the R 2 and RMSE of the test set were 0.998 84 and 0.116 08, respectively, in the concentration range of 1%–12%. In addition, the Allan analysis of variance revealed that the maximum measurement error of CO2 was only 0.005 619% when the integration time was 38 s. Compared to the traditional CO2 detection schemes, the detection accuracy and stability are significantly improved, which provides a feasible scheme for flue gas detection in thermal power plants. [ABSTRACT FROM AUTHOR]

Renewable energy accommodation in power grids leads to frequent load changes in power plants. Sensitive turbine fault monitoring technology is critical to ensure the stable operation of the power system. Existing techniques do not use information sufficiently and are not sensitive to early fault signs. To solve this problem, an unsupervised fault warning method based on hybrid information gain and a convolutional autoencoder (CAE) for turbine intermediate flux is proposed. A high-precision intermediate-stage flux prediction model is established using the CAE. The hybrid information gain calculation method is proposed to filter the features of multi-dimensional sensors. The Hampel filter for time series outlier detection is introduced to deal with factors such as sensor faults and noise. The proposed method achieves the highest fault diagnosis accuracy through experiments on real data compared to traditional methods. Real data experiments show that the proposed method relatively improves the diagnostic accuracy by an average of 2.12% compared to the gate recurrent unit networks, long short-term memory networks, and other traditional models. Meanwhile, the proposed hybrid information gain can effectively improve the detection accuracy of the traditional models, with a maximum of 1.89% relative accuracy improvement. The proposed method is noteworthy for its superiority and applicability. [ABSTRACT FROM AUTHOR]

As the number of European Union (EU) visitors grows, implementing novel border control solutions, such as mobile devices for passenger identification for land and sea border control, becomes paramount to ensure the convenience and safety of passengers and officers. However, these devices, handling sensitive personal data, become attractive targets for malicious actors seeking to misuse or steal such data. Therefore, to increase the level of security of such devices without interrupting border control activities, robust user authentication mechanisms are essential. Toward this direction, we propose a risk-based adaptive user authentication mechanism for mobile passenger identification devices for land and sea border control, aiming to enhance device security without hindering usability. In this work, we present a comprehensive assessment of novelty and outlier detection algorithms and discern OneClassSVM, Local Outlier Factor (LOF), and Bayesian_GaussianMixtureModel (B_GMM) novelty detection algorithms as the most effective ones for risk estimation in the proposed mechanism. Furthermore, in this work, we develop the proposed risk-based adaptive user authentication mechanism as an application on a Raspberry Pi 4 Model B device (i.e., playing the role of the mobile device for passenger identification), where we evaluate the detection performance of the three best performing novelty detection algorithms (i.e., OneClassSVM, LOF, and B_GMM), with B_GMM surpassing the others in performance when deployed on the Raspberry Pi 4 device. Finally, we evaluate the risk estimation overhead of the proposed mechanism when the best performing B_GMM novelty detection algorithm is used for risk estimation, indicating efficient operation with minimal additional latency. [ABSTRACT FROM AUTHOR]

Streaming data are present all around us. From traditional radio systems streaming audio to today's connected end-user devices constantly sending information or accessing services, data are flowing constantly between nodes across various networks. The demand for appropriate outlier detection (OD) methods in the fields of fault detection, special events detection, and malicious activities detection and prevention is not only persistent over time but increasing, especially with the recent developments in Telecommunication systems such as Fifth Generation (5G) networks facilitating the expansion of the Internet of Things (IoT). The process of selecting a computationally efficient OD method, adapted for a specific field and accounting for the existence of empirical data, or lack thereof, is non-trivial. This paper presents a thorough survey of OD methods, categorized by the applications they are implemented in, the basic assumptions that they use according to the characteristics of the streaming data, and a summary of the emerging challenges, such as the evolving structure and nature of the data and their dimensionality and temporality. A categorization of commonly used datasets in the context of streaming data is produced to aid data source identification for researchers in this field. Based on this, guidelines for OD method selection are defined, which consider flexibility and sample size requirements and facilitate the design of such algorithms in Telecommunications and other industries. [ABSTRACT FROM AUTHOR]

Summary Peter J. Rousseeuw is a statistician known mainly for his work on robust statistics and cluster analysis. Among his creations are least trimmed squares regression, the minimum covariance determinant estimator, the partitioning around medoids clustering method and the silhouettes graphical display. Peter obtained his PhD in 1981 following research carried out at the ETH in Zürich, Switzerland, which led to a book on influence functions. Later, he was a professor at Delft University of Technology, The Netherlands, and at the University of Antwerp, Belgium. Next, he was a researcher at Renaissance Technologies in New York for over a decade. He then returned to Belgium as a full professor at KU Leuven, until becoming emeritus in 2022. He is an elected member of the International Statistical Institute and a fellow of the Institute of Mathematical Statistics and the American Statistical Association. In the course of his career, Peter published three books and over 200 papers, together receiving over 100 000 citations. He was awarded the George Box Medal for Business and Industrial Statistics, the Research Medal of the International Federation of Classification Societies, the Frank Wilcoxon Prize, and twice the Jack Youden Prize. Recently, Peter received the 2024 ASA Noether Distinguished Scholar Award for nonparametric statistics. His former PhD students include Annick Leroy, Rik Lopuhaä, Geert Molenberghs, Christophe Croux, Mia Hubert, Stefan Van Aelst, Tim Verdonck and Jakob Raymaekers. He is the creator and sole sponsor of the Rousseeuw Prize for Statistics, which was first handed out by the King of Belgium in 2022. [ABSTRACT FROM AUTHOR]

In this contribution L1-norm target function is minimized by Whale algorithm for the first time. It is a meta-heuristic optimization method which mimics the social behavior of humpback whales. The Whale algorithm is simple and flexible. It takes advantage of a derivation-free mechanism. L1-norm is an efficient tool for outlier detection, nevertheless, its implementation is complex since after formulation of L1-norm minimization for a certain problem, one must solve a linear programming problem by a cumbersome search method while here we only need to set the corresponding L1-norm cost function. During this contribution we also investigate other advantages of the proposed method over traditional methods numerically. Furthermore, the proposed algorithm still has better performance in convergence rate compared to some well-known metaheuristic methods. As the Whale algorithm cannot deal with rank deficient problems, it must be improved. Thus the second algorithm of this contribution is an improved Whale algorithm which is developed here. Three geodetic applications approve the robustness of the proposed approach. [ABSTRACT FROM AUTHOR]

Objective: Our objective is to create an effective ensemble tool that can accurately predict MEFV gene variants and determine the threshold value for pathogenicity based on the optimal distribution. Methods: First, we extracted a dataset from the Infevers database [https://infevers.umai-montpellier.fr/web/search.php?n=1]. Second, we merged the variant classification into 2 categories: likely benign and likely pathogenic. Third, we implemented our high-sensitivity model to obtain disease-causing variants. In the 4 steps, we implemented curve estimation analysis to determine which curve was fitting our variant distribution. We implemented the receiver operating curve after the curve estimation analysis to find suitable in silico tool models for logistic regression. Repeated outlier detection analysis was performed in the fifth step until no outliers were detected. Ensemble tree-based machine-learning models were used to test a statistical model in the final step. Results: When outliers were taken out, the Revel and BayesDel algorithms both had much higher ROCAUC scores (0.982 [0.967-0.998], P < .001 for the combined model; 0.982 [0.967-0.998], P < .001 for Revel; and 0.933 [0.889-0.977], P < .001 for BayesDel). AdaBoost was the most accurate machine learning model, with 0.982 ROACUAC scores. Conclusion: Our study revealed that the implementation of outlier and anomaly detection techniques can enhance the accuracy of statistical models and yield more precise outcomes in machine learning datasets. [ABSTRACT FROM AUTHOR]

Imbalanced data poses a significant challenge in machine learning, as conventional classification algorithms often prioritize majority class samples, while accurately classifying minority class samples is more crucial. The synthetic minority oversampling technique (SMOTE) represents one of the most renowned methods for handling imbalanced data. However, both SMOTE and its variants have limitations due to their insufficient consideration of data distribution, leading to the generation of incorrect and unnecessary samples. This paper, therefore, introduces a novel oversampling algorithm called data distribution and spectral clustering-based SMOTE (DDSC-SMOTE). This algorithm addresses the shortcomings of SMOTE by introducing three innovative data distribution-based improvement strategies: adaptive allocation of synthetic sample quantities strategy, seed sample adaptive selection strategy, and synthetic sample improvement strategy. First, we use the k-nearest neighbor sample labels and the local outlier factor algorithm to remove noisy and outlier samples. Next, we leverage spectral clustering to identify clusters within the minority class and propose a dual-weight factor that considers inter-cluster and intra-cluster distances to allocate the number of synthetic samples effectively, addressing interclass and intraclass imbalances. Furthermore, we introduce a relative position weight coefficient to determine the probability of selecting seed samples within the subcluster, ensuring that important minority samples have higher chances of being sampled. Finally, we improve the SMOTE sample synthesis formula for safer generation. Extensive comparisons on real datasets from the UCI repository demonstrate that DDSC-SMOTE outperforms seven state-of-the-art oversampling algorithms significantly in terms of G-mean and F1-score, presenting a data distribution-focused solution for addressing imbalanced data challenges. [ABSTRACT FROM AUTHOR]

Prior model construction is a fundamental component in geophysical inversion, especially Bayesian inversion. The prior model, usually derived from available geological information, can reduce the uncertainty of model characteristics during the inversion. However, the prior geological data for inferring a prior distribution model are often limited in real cases. Our work presents a novel framework to create 3D geophysical prior models using soil geochemistry and borehole rock sample measurements. We focus on the Bayesian inversion, which enables encoding of knowledge and multiple non-geophysical data into the prior. The new framework developed in our research comprises three main parts, namely correlation analysis, prior model reconstruction, and Bayesian inversion. We investigate the correlations between surface and subsurface geochemical features, as well as the correlation between geochemistry and geophysics, using canonical correlation analysis for the surface and borehole geochemistry. Based on the resulting correlations, we construct the prior susceptibility model. The informed prior model is then tested using geophysical forward modeling and outlier detection methods. In this test, we aim to falsify the prior model, which happens when the model cannot predict the field geophysical observation. To obtain the posterior models, the reliable prior models are incorporated into a Bayesian inversion framework. Using a real case of exploration in the Central African Copperbelt, we illustrate the workflow of constructing the high-resolution 3D stratigraphic model conditioned on soil geochemistry, borehole data, and airborne geophysics. [ABSTRACT FROM AUTHOR]

Classical estimators fail to be efficient in practical scenarios when data is riddled with extreme values known as outliers. Robust estimation strategies are insensitive to outliers and may be used in such cases. The current work is focused on developing a novel robust estimation strategy using Huber M-estimation. A new chain-product type estimator for population mean has been suggested utilizing data on two auxiliary variables. A numerical comparison has been carried out between the proposed robust estimator and the corresponding classical estimator using real and simulated data containing outliers. Recommendations have been made for its practical use based on the encouraging results. [ABSTRACT FROM AUTHOR]

This study evaluated the impact and clinical utility of an auto-contouring system for radiation therapy treatments. The auto-contouring system was implemented in 2019. We evaluated data from 2428 patients who underwent adjuvant breast radiation therapy before and after the system's introduction. We collected the treatment's finalized contours, which were reviewed and revised by a multidisciplinary team. After implementation, the treatment contours underwent a finalization process that involved manual review and adjustment of the initial auto-contours. For the preimplementation group (n = 369), auto-contours were generated retrospectively. We compared the auto-contours and final contours using the Dice similarity coefficient (DSC) and the 95% Hausdorff distance (HD95). We analyzed 22,215 structures from final and corresponding auto-contours. The final contours were generally larger, encompassing more slices in the superior or inferior directions. Among organs at risk (OAR), the heart, esophagus, spinal cord, and contralateral breast demonstrated significantly increased DSC and decreased HD95 postimplementation (all P <.05), except for the lungs, which presented inaccurate segmentation. Among target volumes, CTVn_L2, L3, L4, and the internal mammary node showed increased DSC and decreased HD95 postimplementation (all P <.05), although the increase was less pronounced than the OAR outcomes. The analysis also covered factors contributing to significant differences, pattern identification, and outlier detection. In our study, the adoption of an auto-contouring system was associated with an increased reliance on automated settings, underscoring its utility and the potential risk of automation bias. Given these findings, we underscore the importance of considering the integration of stringent risk assessments and quality management strategies as a precautionary measure for the optimal use of such systems. [ABSTRACT FROM AUTHOR]

The development of smart cities will require the construction of smart buildings. Smart buildings will demand the incorporation of elements for efficient monitoring and control of electrical consumption. The development of efficient AI algorithms is needed to generate more accurate electricity consumption predictions; therefore; anomaly detection in electricity consumption predictions has become an important research topic. This work focuses on the study of the detection of anomalies in domestic electrical consumption in Mexico. A predictive machine learning model of future electricity consumption was generated to evaluate various anomaly-detection techniques. Their effectiveness in identifying outliers was determined, and their performance was documented. A 30-day forecast of electrical consumption and an anomaly-detection model have been developed using isolation forest. Isolation forest successfully captured up to 75% of the anomalies. Finally, the Shapley values have been used to generate an explanation of the results of a model capable of detecting anomalous data for the Mexican context. [ABSTRACT FROM AUTHOR]

Currently, the decision boundary of the multi-class anomaly detection algorithm based on deep learning does not sufficiently capture the positive class region, posing a risk of abnormal sample features falling into the domain of normal sample features and potentially leading to misleading outcomes in practical applications. In response to the above problems, this paper proposes a new method called multi-class hypersphere anomaly detection (MMHAD) based on the edge outlier exposure set and margin. The method aims to utilize convolutional neural networks for joint training of all normal object classes, identifying a shared set of outlier exposures, learning compact identification features, and setting appropriate edge parameters to guide the model in mapping outliers outside the hypersphere. This approach enables more comprehensive detection of various types of exceptions. The experiments demonstrate that the algorithm is superior to the most advanced baseline method, with an improvement of 26.0%, 8.2%, and 20.1% on CIFAR-10 and 14.8%, 12.0%, and 20.1% on FMNIST in the cases of (2/8), (5/5), and (9,1), respectively. Furthermore, we investigate the challenging (2/18) case on CIFAR-100, where our method achieves approximately 17.4% AUROC gain. Lastly, for a recycling waste dataset with the (4/1) case, our MMHAD yields a notable 22% enhancement in performance. Experimental results show the effectiveness of the proposed model in multi-classification anomaly detection. [ABSTRACT FROM AUTHOR]

Some of the collected data by wireless sensor networks (WSNs) often digress from the normal pattern of the set and are called the 'outliers' or 'anomalies'. These outlier data are usually derived from an error that can be due to the constraints of sensor nodes such as problems in the processing unit, power supply, and component failure or an event occurring at the development site of sensor nodes, including the earthquake or flood, affecting the quality of the collected data and their reliability for decision making. In this work, a method is proposed for detecting outlier data in networks based on the hidden Markov model and the copula theory. Copula was used because of its ability to deal with multivariate sets and its unlimitedness in such a way that it required no assumption about data distribution, and Markov has been applied due to its good performance in forecasting. The results of evaluations on the actual data from the Intel laboratory represent the efficiency of the proposed method. Bases on the performance evaluations of the proposed method, in comparison with a relatively new work leading to good results. [ABSTRACT FROM AUTHOR]

Traditional feature detection and description methods, such as scale-invariant feature transform, are susceptible to nonlinear radiation distortions (NRDs) and geometric distortions (GDs), which in turn generate a large number of outliers or incorrect correspondences. To address this issue, this paper proposes a simple yet effective heterogeneous model fitting (MIMF) for multi-source image correspondences. First, a multi-orientation phase consistency model is constructed, which fuses phase consistency, image amplitude and orientation to detect the correct correspondences of feature points. This model effectively reduces the influence of NRDs. Second, sub-region grids and orientation histograms are exploited to construct the log-polar descriptors with variable-size bins, which are robust to GDs. Finally, a heterogeneous model fitting method is proposed, which can effectively estimate the parameters of the transformation model for alleviating the influence of outliers. Experiments are performed on six public datasets and one constructed dataset containing ten types of multi-source images, and the experimental results show that the proposed MIMF method outperforms several state-of-the-art competing methods in terms of matching performance. [ABSTRACT FROM AUTHOR]