Search

Though economic growths of most of the nations have seen exponential rise due to industrialization, it has also caused proportional increase in their carbon emissions. This paper exploits this proportionate relationship of carbon emission with GDP to predict the per-capita GDP of those nations whose GDP values are missing in the world bank database. The reason behind the same was, those countries were either war-torn or politically isolated/unstable. To achieve the objective of predicting the missing GDP values of those countries from their carbon emissions, this paper exploits the non-linear relationship among the carbon emissions from solid fuels, liquid fuels, and gaseous fuels. It is so because even the differential utilization of these fuels impact economy differently. Use of traditional solid fuel for cooking points toward energy poverty, and access to clean cooking gas indicates higher living standard. However, the available data from the war-torn or isolated countries are very little, and hence insufficient for building a robust predictive machine learning model. So, this paper employs multi-source unsupervised transfer learning to precisely estimate the missing per-capita GDP of those nations. It suitably enlarges the training domains for the prediction models to be more robust. We empirically evaluate the proposed methodology for different regression techniques to estimate the missing GDP values of eleven different nations belonging to diverse strata of economies viz. developed economies, developing, and/or least developing economies. Proposed methodology profoundly improves the prediction preciseness of these regression techniques in estimating the missing per-capita GDP of the considered nations. [ABSTRACT FROM AUTHOR]

There is a recent proliferation of research on the integration of machine learning and optimization. One expansive area within this research stream is optimization over pre-trained predictive models, which proposes the use of pre-trained predictive models as surrogates for uncertain or highly complex objective functions. In this setting, features of the predictive models become decision variables in the optimization problem. Despite a recent surge in publications in this area, only a few papers note the importance of incorporating trust-region considerations in this decision-making pipeline, that is, enforcing solutions to be similar to the data used to train the predictive models. Without such constraints, the evaluation of the predictive model at solutions obtained from optimization cannot be trusted and the practicality of the solutions may be unreasonable. In this paper, we provide an overview of the approaches appearing in the literature to construct a trust region and propose three alternative approaches. Our numerical evaluation highlights that trust-region constraints learned through our newly proposed approaches compare favorably with previously suggested approaches, both in terms of solution quality and computational time. History: Accepted by Pascal Van Hentenryck, Area Editor for Computational Modeling: Methods & Analysis. Supplemental Material: The software that supports the findings of this study is available within the paper and its Supplemental Information (https://pubsonline.informs.org/doi/suppl/10.1287/ijoc.2022.0312) as well as from the IJOC GitHub software repository (https://github.com/INFORMSJoC/2022.0312). The complete IJOC Software and Data Repository is available at https://informsjoc.github.io/. [ABSTRACT FROM AUTHOR]

With the increasing adoption of Internet of Things (IoT) networks, ensuring their security has become a critical concern due to resource limitations and the growing complexity of malicious attacks. Intrusion Detection and Prevention Systems play a pivotal role in safeguarding network performance, but traditional methods often struggle with attack severity and classifying unknown packets. In this research, we introduce the Attention-IDS model, a comprehensive solution comprising five stages: two-fold authentication, local density-based clustering, flow-based feature extraction, intrusion detection system (IDS), and intrusion severity detection. Leveraging IoT devices and user-based authentication, our model effectively detects and prevents unauthorized access attempts, while ensuring enhanced security through the utilization of the Combine Counter Mode algorithm on the blockchain. The IDS stage, powered by the Isolation Forest algorithm, accurately classifies features as normal, malicious, or unknown. Leveraging the proposed Attention-based ResNet model, our approach intelligently classifies unknown packets into normal and malicious categories, employing feature extraction, selection, and classification. Additionally, the Extended Kalman Filter determines intrusion severity, enabling network-wide notification alarms for frequent intrusions and targeted responses for rare intrusions. Extensive simulations using the NS3.26 network simulator demonstrate the superior performance of Attention-IDS compared to existing methods. [ABSTRACT FROM AUTHOR]

During recent decades, using credit cards represents a pivotal part of the financial lifeline. Credit cards and online payment gateways are vital elements in the world of world-wide-web. Given the fact that credit cards play an essential role in today's society, the misuse of these cards will lead to significant damages. One of the common ways to deal with these possible damages is using anomaly detection systems. These systems aim to take account of changes in customer and fraudsters' behavior to detect anomaly patterns. In the current study, we present a model namely IF-GWO to learn fraudulent patterns through analyzing past transactions. The method employs a novel ensemble learning method using isolation forest (IF) and Grey Wolf Optimizer (GWO). The experimental results indicate the priority of our presented fraud-detection system based on a noticeable number of credit card account transactions. Compared to the conventional model used for anomaly detection, the proposed model can detect more fraud accounts with fewer false positives over comparative procedures. Based on a comparison with other models using the dataset contains 284,807 transactions that are made by European cardholders, the proposed model outperformed the other approaches and achieved the highest performance in terms of F-Measure (93.52%), Area under receiver operating characteristic curve (AUC) (94.17%), and G-means (94.10%). [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]

Outliers can occur in application areas, adversely affecting the prediction method's performance. Outliers can be removed by using robust statistical algorithms. However, statistical methods have limitations in capturing the outlier for high-dimensional data. Approaches using Machine Learning (ML) are offered as they develop rapidly due to their excellent interpretability and strong generalization capabilities. So, ML is popular in detecting or eliminating outliers to increase the accuracy of forecasting methods, such as Isolation Forest (IF), an unsupervised outlier detection strategy using a collective approach to calculate the isolation score for every data point. This research objective is to improve the prediction accuracy of the Decision Tree Regression (DTR) method by proposing an IF as an ML-based outlier removal method. The proposed method was tested by two Air Quality Index (AQI) dataset that contained outliers with Mean Absolute Error (MAE), RSquare, and Root Mean Square Error (RMSE) as the accuracy measurements. The results showed that the proposed method outperforms previous studies. [ABSTRACT FROM AUTHOR]

X bots pose a significant issue in the social media landscape, with many shared links originating from bot-like accounts. This study introduces the application of the Isolation Forest algorithm, aimed explicitly at identifying anomalies such as bots by analyzing X account details. This study utilizes a dataset that merges data from Botometer with supplementary metrics like 'average tweets per day' and 'account age in days', contributed by David Martín Gutiérrez. This approach was adopted due to the increasing difficulties accessing the X API. The dataset comprises 37,438 instances, with 25,013 labeled human accounts and 12,425 labeled bot accounts. Preprocessing is performed to remove irrelevant features, and the dataset is split into Training, Validation, and Test sets in a 70:15:15 ratio. The training set undergoes hyperparameter and threshold tuning to identify the best configuration for this specific dataset (n_estimators: 50, contamination: 0.5, bootstrap: True), achieving a training set F1-score of 0.211001. Despite these optimization efforts, the Isolation Forest model's performance remains relatively low. The Test set evaluation yields modest precision, recall, and F1-score values (0.1801, 0.2795, and 0.2190, respectively), with a ROC AUC score of 0.3272. While the Isolation Forest algorithm shows promise in detecting X bots, its performance on this specific dataset is limited. Isolation Forest may not be the most suitable algorithm for this particular bot detection task on this dataset. Future work will explore techniques to enhance the performance of bot detection for a more comprehensive analysis. [ABSTRACT FROM AUTHOR]

This study presents a comprehensive evaluation of five prominent unsupervised machine learning anomaly detection algorithms: One-Class Support Vector Machine (One-Class SVM), One-Class SVM with Stochastic Gradient Descent (SGD), Isolation Forest (iForest), Local Outlier Factor (LOF), and Robust Covariance (Elliptic Envelope). Through systematic analysis on a synthetically simulated dataset, the study assessed each algorithm’s predictive performance using accuracy, precision, recall, and F1 score specifically for outlier detection. The evaluation reveals that One-Class SVM, Isolation Forest, and Robust Covariance are more effective in identifying outliers in the synthetic simulated dataset, with Isolation Forest slightly outperforming the other algorithms in terms of balancing precision and recall. One-Class SVM with SGD shows promise in precision but needs adjustment to improve recall. Local Outlier Factor may require parameter tuning or may not be as suitable for this particular dataset’s characteristics. The findings reveal significant variations in performance, highlighting the strengths and limitations of each method in identifying anomalies. This research contributes to the field of machine learning by demonstrating that the selection of an anomaly detection algorithm should be a considered decision, taking into account the specific characteristics of the data and the operational context of its application. Future work should explore parameter optimization, the impact of dataset characteristics on model performance, and the application of these models to real-world datasets to validate their efficacy in practical anomaly detection scenarios.

Abstract Type II diabetes mellitus (T2DM) is a rising global health burden due to its rapidly increasing prevalence worldwide, and can result in serious complications. Therefore, it is of utmost importance to identify individuals at risk as early as possible to avoid long-term T2DM complications. In this study, we developed an interpretable machine learning model leveraging baseline levels of biomarkers of oxidative stress (OS), inflammation, and mitochondrial dysfunction (MD) for identifying individuals at risk of developing T2DM. In particular, Isolation Forest (iForest) was applied as an anomaly detection algorithm to address class imbalance. iForest was trained on the control group data to detect cases of high risk for T2DM development as outliers. Two iForest models were trained and evaluated through ten-fold cross-validation, the first on traditional biomarkers (BMI, blood glucose levels (BGL) and triglycerides) alone and the second including the additional aforementioned biomarkers. The second model outperformed the first across all evaluation metrics, particularly for F1 score and recall, which were increased from 0.61 ± 0.05 to 0.81 ± 0.05 and 0.57 ± 0.06 to 0.81 ± 0.08, respectively. The feature importance scores identified a novel combination of biomarkers, including interleukin-10 (IL-10), 8-isoprostane, humanin (HN), and oxidized glutathione (GSSG), which were revealed to be more influential than the traditional biomarkers in the outcome prediction. These results reveal a promising method for simultaneously predicting and understanding the risk of T2DM development and suggest possible pharmacological intervention to address inflammation and OS early in disease progression.

Objective. The study is devoted to assessing the applicability of the Isolation Forest method in the task of detecting anomalies in network traffic data characterized by insufficient markup. The main purpose of the work is to evaluate the effectiveness of Isolation Forest with limited data markup and its potential in critical areas such as cybersecurity and financial analytics.Method. The study includes data preprocessing, training the model on the training set, and evaluating the model's performance on the test set using accuracy metrics, error matrix, and classification report. To implement this research, the Python programming language and the scikit-learn library were chosen to implement the Isolation Forest, as well as Pandas for working with data.Result. Evaluating the applicability of the Isolation Forest method on unstructured data revealed its potential for identifying anomalous patterns without the need for extensive labeling. This confirms the effectiveness of Isolation Forest in environments where access to labeled data is limited or absent.Conclusion. The results demonstrate high anomaly detection recall despite relatively low overall accuracy, indicating the importance of contextual interpretation of metrics in the task of detecting rare events in data.

Anomaly detection is used to explore the link between data-driven anomalous storms and their socio-economic impact on countries within the North-West Pacific. Three anomaly detection models are trialled using three distinct algorithms on the storm tracks and temperature profiles of storms. A feature-based comparison of the top 5% of anomalous storms from each model is used to reveal variations in anomalous storm activity. Further to this, the socio-economic impact of the anomalous storms is assessed, revealing a link between the anomalous behaviour of storms and the impact experienced by countries on their path. A final cross-comparison shows that the k-Nearest Neighbour and Isolation Forest algorithms succeeded at identifying high-impacting storms. However, the agglomerative clustering model found many unique storms that had low impact. This highlights the importance of considering both trajectory and temperature in determining the severity and impact of erroneous storms. HIGHLIGHTS Anomaly detection models found a link between anomalous nature and high socio-economic impact.; K-Nearest Neighbour and Isolation Forest algorithms identified high-impact storms.; Contradicting trajectory and temperature patterns were highlighted by the algorithms and were linked to high impact.;

Type II diabetes mellitus (T2DM) is a rising global health burden due to its rapidly increasing prevalence worldwide, and can result in serious complications. Therefore, it is of utmost importance to identify individuals at risk as early as possible to avoid long-term T2DM complications. In this study, we developed an interpretable machine learning model leveraging baseline levels of biomarkers of oxidative stress (OS), inflammation, and mitochondrial dysfunction (MD) for identifying individuals at risk of developing T2DM. In particular, Isolation Forest (iForest) was applied as an anomaly detection algorithm to address class imbalance. iForest was trained on the control group data to detect cases of high risk for T2DM development as outliers. Two iForest models were trained and evaluated through ten-fold cross-validation, the first on traditional biomarkers (BMI, blood glucose levels (BGL) and triglycerides) alone and the second including the additional aforementioned biomarkers. The second model outperformed the first across all evaluation metrics, particularly for F1 score and recall, which were increased from 0.61 ± 0.05 to 0.81 ± 0.05 and 0.57 ± 0.06 to 0.81 ± 0.08, respectively. The feature importance scores identified a novel combination of biomarkers, including interleukin-10 (IL-10), 8-isoprostane, humanin (HN), and oxidized glutathione (GSSG), which were revealed to be more influential than the traditional biomarkers in the outcome prediction. These results reveal a promising method for simultaneously predicting and understanding the risk of T2DM development and suggest possible pharmacological intervention to address inflammation and OS early in disease progression. [ABSTRACT FROM AUTHOR]

Unsupervised defect detection methods are applied to an unlabeled dataset by producing a ranked list based on defect scores. Unfortunately, many of the top-ranked instances by unsupervised algorithms are not defects, which leads to high false-positive rates. Active Defect Discovery (ADD) is proposed to overcome this deficiency, which sequentially selects instances to get the labeling information (defects or not). However, labeling is often costly. Therefore, balancing detection accuracy and labeling cost is essential. Along this line, this article proposes a novel ADD method to achieve the goal. Our approach is based on the state-of-the-art unsupervised defect detection method, namely, Isolation Forest, as the baseline defect detector to extract features. Thereafter, the sparsity of the extracted features is utilized to adjust the defect detector so that it can focus on more important features for defect detection. To enforce the sparsity of the features and subsequent improvement of the detection accuracy, a new algorithm based on online gradient descent, namely, Sparse Approximated Linear Defect Discovery (SALDD), is proposed with its theoretical Regret analysis. Extensive experiments are conducted on real-world datasets including healthcare, manufacturing, security, etc. The performance demonstrates that the proposed algorithm significantly outperforms the state-of-the-art algorithms for defect detection. [ABSTRACT FROM AUTHOR]

Today, Permanent Magnet Synchronous Motors (PMSMs) are a dominant choice in industry applications. During operation, different possible faults in the system can occur, so early and automated fault detection and severity estimation are required to ensure smooth operation and optimal maintenance planning. In this direction, outlier detection methods are employed in this paper. The motor's current signals are used to extract useful indicators of the fault, along with d-q transform. Statistical indicators in both time and frequency domains are selected to describe fault-related patterns. Based on the extracted features, three outlier detection methods are investigated: the Isolation Forest, the One Class Support Vector Machine, and the Robust Covariance Ellipse. Each method is investigated through different model parameters to evaluate fault detection and severity estimation capabilities. Finally, an ensemble approach is proposed based on decisions and outlier score ensemble. The proposed methodology is verified through different operating conditions in a PMSM test bench. [ABSTRACT FROM AUTHOR]

Stock market manipulation, defined as any attempt to artificially influence stock prices, poses significant challenges by causing financial losses and eroding investor trust. The prevalent reliance on supervised learning models for detecting such manipulations, while showing promise, faces notable hurdles due to the dearth of labeled data and the inability to recognize novel manipulation tactics beyond those explicitly labeled. This study ventures into addressing these gaps by proposing a novel detection framework aimed at identifying suspicious hourly manipulation blocks through an unsupervised learning approach, thereby circumventing the limitations of data labeling and enhancing the adaptability to emerging manipulation strategies. Our methodology involves the innovative creation of features reflecting the behavior of stocks across various time windows followed by the segmentation of the dataset into k subsets. This setup facilitates the identification of potential manipulation instances via a voting ensemble composed of k isolation forest models, which have been chosen for their efficiency in pinpointing anomalies and their linear computational complexity—attributes that are critical for analyzing vast datasets. Evaluated against eight real stocks known to have undergone manipulation, our approach demonstrated a remarkable capability to identify up to 89% of manipulated blocks, thus significantly outperforming previous methods that do not utilize a voting ensemble. This finding not only surpasses the detection rates reported in prior studies but also underscores the enhanced robustness and adaptability of our unsupervised model in uncovering varied manipulation schemes. Through this research, we contribute to the field by offering a scalable and efficient unsupervised learning strategy for stock manipulation detection, thereby marking a substantial advancement over traditional supervised methods and paving the way for more resilient financial markets. [ABSTRACT FROM AUTHOR]

This study presented an unsupervised anomaly detection-based framework for distributed damage detection in concrete using ultrasonic response signals. A deep fully connected auto-encoder was employed to reconstruct the ultrasonic response signals. This model was trained on the intact specimen's responses. The auto-encoder demonstrated a relatively high prediction error encountering the damaged specimen's responses. Two time-domain features (mean squared error and reconstructed-to-original signal ratio) and one frequency-domain feature (fundamental amplitude ratio) were defined to measure the reconstruction error of the auto-encoder (the damage-sensitive features). Finally, the Isolation Forest algorithm was implemented for anomaly (damage) detection. The beauty of this framework is that it requires a few numbers of data only from the intact specimen for training the auto-encoder and collecting the binary decision trees of the Isolation Forest. The framework was successfully implemented for damage detection in five geopolymer concrete specimens with different mix proportions. Using all three introduced damage-sensitive features, the framework demonstrated an average prediction accuracy of 95.0% and 93.0% for damaged and intact stages, respectively. [ABSTRACT FROM AUTHOR]

Migrant remittances have become significant in poverty alleviation and microeconomic development in low-income countries. However, the ease of conducting global migrant remittance transfers has also introduced the risk of misuse by terrorist organizations to quickly move and conceal operational funds, facilitating terrorism financing. This study aims to develop an unsupervised machine learning algorithm capable of detecting suspicious financial transactions associated with terrorist financing in migrant remittances. To achieve this goal, a structural equation model (SEM) and an outlier detection algorithm were developed to analyze and identify suspicious transactions among the financial activities of migrants residing in Belgium. The results show that the SEM model classifies a significantly high number of transactions as suspicious, making it prone to detecting false positives. Finally, the study developed an ensemble outlier detection algorithm that comprises an isolation forest (IF) and a local outlier factor (LOF) to detect suspicious transactions in the same dataset. The model performed exceptionally well, being able to detect over 90% of suspicious transactions. [ABSTRACT FROM AUTHOR]

Soft sensors have been extensively utilized to approximate real-time power prediction in wind power generation, which is challenging to measure instantaneously. The short-term forecast of wind power aims at providing a reference for the dispatch of the intraday power grid. This study proposes a soft sensor model based on the Long Short-Term Memory (LSTM) network by combining data preprocessing with Variational Modal Decomposition (VMD) to improve wind power prediction accuracy. It does so by adopting the isolation forest algorithm for anomaly detection of the original wind power series and processing the missing data by multiple imputation. Based on the process data samples, VMD technology is used to achieve power data decomposition and noise reduction. The LSTM network is introduced to predict each modal component separately, and further sum reconstructs the prediction results of each component to complete the wind power prediction. From the experimental results, it can be seen that the LSTM network which uses an Adam optimizing algorithm has better convergence accuracy. The VMD method exhibited superior decomposition outcomes due to its inherent Wiener filter capabilities, which effectively mitigate noise and forestall modal aliasing. The Mean Absolute Percentage Error (MAPE) was reduced by 9.3508%, which indicates that the LSTM network combined with the VMD method has better prediction accuracy. [ABSTRACT FROM AUTHOR]

Anomaly detection algorithms are widely used across various domains, but they often suffer from high false positive rates and lack of interpretability. This paper introduces xAAD, a novel approach that combines Active Anomaly Discovery (AAD) with the Assist-Based Weighting Scheme (AWS) explainability metric for Isolation Forest-based anomaly detection. Our method enhances model interpretability and reduces false positives by incorporating expert feedback and providing post-feedback feature importance values. We evaluate xAAD on both synthetic and real-world datasets, demonstrating improved performance in terms of binary classification accuracy and root mean square error (RMSE) compared to traditional AWS. The results show that xAAD consistently outperforms the baseline in both simulated and real-world scenarios, suggesting a positive impact on the interpretability of anomaly detection systems across multiple industries.

Checking receipt data is essential for tax authorities to ensure compliance with financial reporting regulations, identify potential anomalies, and prevent tax violations. This study explores the application of genetic algorithms (GA) for hyperparameter optimization (HPO) in Isolation Forest (IF) and Autoencoder (AE) models with objective to enhance anomaly detection (AD) with focus on identifying cash registers (CRs) that display patterns closely resembling known anomalies, thereby improving AD capabilities. The dataset, comprising 2.7 million receipt records, encompassing transactional details such as payment amounts, dates, and the ratio of cash to cashless transactions. As anomalies were identified on CR level, the receipt dataset was underwent feature engineering aggregated by cash and obtained included 40,715 CRs, with 200 known anomalies. The primary goal was to accurately identify existing anomalies and those closely resembling them, which are referred to as false positives (FP) obtained by the best optimized IF and AE models, as comprehensively as possible. The best-performing IF model on a test set of 20,375 CR records with 100 known anomalies correctly identified 25 anomalies and flagged 999 similar (FP) patterns. The AE model, tested on 8,154 records with 39 known anomalies, correctly identified 15 and flagged an additional 437 CRs (FP). Subsequent clustering analysis of FP and existing anomalies - true positives (TP) reveals nuanced restaurant transactional patterns suggestive of potential tax evasion, aiding in strategic decision-making regarding tax authority inspections.

This study analyzes the mechanism of Internal Short Circuits (ISCs) in Lithium-ion batteries (LIBs) and identifies the factors contributing to their development. A simulation environment has been used to design a custom battery pack with a nominal capacity of $3kWh$ (total pack capacity $62.5~Ah$ ) and conduct various fault simulations to analyze the battery pack’s thermal behaviour under different conditions. The discharge rate is variable, with a value of $0.25C$ for all 18650 LIBs, and the temperature ranges from -20°C to +60°C. The collected temperature data has been preprocessed and analyzed using various anomaly detection algorithms, including the proposed LevelshiftAD method, Isolation Forest, and Elliptical Envelope. The study demonstrates that the proposed LevelshiftAD method performs better in accurately detecting temperature faults at the threshold limit faster than the other anomaly detection methods. These findings highlight the potential of the proposed approach for enhancing the accuracy with 97% and efficacy of fault diagnosis in LIBs.

Recognizing anomalies is an extremely important process in data analysis, aimed at identifying patterns in data that deviate from known norms or typical standards. These anomalies are often indicative of significant, and sometimes critical, issues such as fraud, network intrusions, and system failures. Traditional anomaly detection algorithms primarily focus on the attributes of individual observations within a dataset, typically establishing a ‘normal’ profile and flagging deviations from this profile as anomalies. This paper introduces an innovative enhancement to the Isolation Forest algorithm, a renowned method for anomaly detection known for its effectiveness and efficiency, especially in large datasets. The Isolation Forest algorithm operates by randomly partitioning the data space and constructing a binary tree, where the oddity score of a data point is ascertained based on its separation from the extremity to the base of the structure, enabling the autonomous detection of outliers in a completely unsupervised manner. The methodology presented in the paper is based on repeatedly building Isolation Forest models on datasets from which individual attributes are excluded. In our research, we used the SHAP (SHapley Additive exPlanations) method which comes from game theory and is used to determine the impact of individual features on the result of the model. When training the Isolation Forest on the full dataset, the SHAP method is used to obtain the coefficients of influence of model attributes on the prediction result. Both negative and positive influences are considered significant when counting the anomaly score. On the foundations of the results from all sub-models, a weighted average is calculated, to which weights are calculated based on the SHAP model. The comparative analysis of evaluation metrics revealed a substantial enhancement attributed to the implemented methodology. The metrics used for evaluation have shown improvement in most cases from 3.5 to 6 percent point. One of the metrics have shown an improvement of 12 percent. Obtained results demonstrate that this integrated approach not only enhances the prediction accuracy of the Isolation Forest algorithm but also offers a more interpretable understanding of the data. This advancement in anomaly detection methodology promises significant implications for various fields where quick and accurate detection of outliers is paramount.

In the field of health management, predicting the remaining useful life (RUL) of a device becomes critical. However, the RUL prediction process is often affected by a various of confounding factors, resulting in reduced prediction accuracy. To improve the accuracy of RUL prediction, this study first extracts the root mean square, skewness, and Kurtosis from the bearing characteristics, and adopts the multidimensional scale change features to construct a health indicator that fully reflects the bearing degradation trend. Then, a combination of the Isolation forest algorithm and the $3\sigma $ criterion was used to adaptively determine the first prediction time (FPT) of the bearings. Subsequently, the time series model SCINet is introduced for the first time into the field of bearing life prediction and used to predict the RUL of bearings. Finally, a series of multi-step prediction experiments are conducted on two publicly available datasets, PHM2012 and XJTU-SY, and compared with LSTM, GRU, Informer, and TCN models. The results show that the improved IF-SCINET has a stronger prediction capability compared to the traditional model, which significantly improves the accuracy and stability of bearing RUL prediction.

Proteomic analysis of extracellular vesicles presents several challenges due to the unique nature of these small membrane-bound structures. Alternative analyses could reveal outcomes hidden from standard statistics to explore and develop potential new biological hypotheses that may have been overlooked during the initial evaluation of the data. An analysis sequence focusing on deviating protein expressions from donors' primary cells was performed, leveraging machine-learning techniques to analyze small datasets, and it has been applied to evaluate extracellular vesicles' protein content gathered from mesenchymal stem cells cultured on bioactive glass discs doped or not with metal ions. The goal was to provide additional opportunities for detecting details between experimental conditions that are not entirely revealed with classic statistical inference, offering further insights regarding the experimental design and assisting the researchers in interpreting the outcomes. The methodology extracted a set of EV-related proteins whose differences between conditions could be partially explainable with statistics, suggesting the presence of other factors involved in the bioactive glasses' interactions with tissues. Outlier identification of extracellular vesicles' protein expression levels related to biomaterial preparation was instrumental in improving the interpretation of the experimental outcomes. [ABSTRACT FROM AUTHOR]

Hyperspectral anomaly detection is used to recognize unusual patterns or anomalies in hyperspectral data. Currently, many spectral–spatial detection methods have been proposed with a cascaded manner; however, they often neglect the complementary characteristics between the spectral and spatial dimensions, which easily leads to yield high false alarm rate. To alleviate this issue, a spectral–spatial information fusion (SSIF) method is designed for hyperspectral anomaly detection. First, an isolation forest is exploited to obtain spectral anomaly map, in which the object-level feature is constructed with an entropy rate segmentation algorithm. Then, a local spatial saliency detection scheme is proposed to produce the spatial anomaly result. Finally, the spectral and spatial anomaly scores are integrated together followed by a domain transform recursive filtering to generate the final detection result. Experiments on five hyperspectral datasets covering ocean and airport scenes prove that the proposed SSIF produces superior detection results over other state-of-the-art detection techniques. [ABSTRACT FROM AUTHOR]

In this study, we present a novel machine learning framework for web server anomaly detection that uniquely combines the Isolation Forest algorithm with expert evaluation, focusing on individual user activities within NGINX server logs. Our approach addresses the limitations of traditional methods by effectively isolating and analyzing subtle anomalies in vast datasets. Initially, the Isolation Forest algorithm was applied to extensive NGINX server logs, successfully identifying outlier user behaviors that conventional methods often overlook. We then employed DBSCAN for detailed clustering of these anomalies, categorizing them based on user request times and types. A key innovation of our methodology is the incorporation of post-clustering expert analysis. Cybersecurity professionals evaluated the identified clusters, adding a crucial layer of qualitative assessment. This enabled the accurate distinction between benign and potentially harmful activities, leading to targeted responses such as access restrictions or web server configuration adjustments. Our approach demonstrates a significant advancement in network security, offering a more refined understanding of user behavior. By integrating algorithmic precision with expert insights, we provide a comprehensive and nuanced strategy for enhancing cybersecurity measures. This study not only advances anomaly detection techniques but also emphasizes the critical need for a multifaceted approach in protecting web server infrastructures. [ABSTRACT FROM AUTHOR]

Long-term electroencephalogram (Long-Term EEG) has the capacity to monitor over a long period, making it a valuable tool in medical institutions. However, due to the large volume of patient data, selecting clean data segments from raw Long-Term EEG for further analysis is an extremely time-consuming and labor-intensive task. Furthermore, the various actions of patients during recording make it difficult to use algorithms to denoise part of the EEG data, and thus lead to the rejection of these data. Therefore, tools for the quick rejection of heavily corrupted epochs in Long-Term EEG records are highly beneficial. In this paper, a new reliable and fast automatic artifact rejection method for Long-Term EEG based on Isolation Forest (IF) is proposed. Specifically, the IF algorithm is repetitively applied to detect outliers in the EEG data, and the boundary of inliers is promptly adjusted by using a statistical indicator to make the algorithm proceed in an iterative manner. The iteration is terminated when the distance metric between clean epochs and artifact-corrupted epochs remains unchanged. Six statistical indicators (i.e., min, max, median, mean, kurtosis, and skewness) are evaluated by setting them as centroid to adjust the boundary during iteration, and the proposed method is compared with several state-of-the-art methods on a retrospectively collected dataset. The experimental results indicate that utilizing the min value of data as the centroid yields the most optimal performance, and the proposed method is highly efficacious and reliable in the automatic artifact rejection of Long-Term EEG, as it significantly improves the overall data quality. Furthermore, the proposed method surpasses compared methods on most data segments with poor data quality, demonstrating its superior capacity to enhance the data quality of the heavily corrupted data. Besides, owing to the linear time complexity of IF, the proposed method is much faster than other methods, thus providing an advantage when dealing with extensive datasets. [ABSTRACT FROM AUTHOR]

Reliable monitoring of mineral process systems is key to more efficient plant operation. Multivariate statistical process control based on principal component analysis is well-established in industry but may not be effective when dealing with dynamic nonlinear or transient processes, where process behavior may change rapidly from time to time. Although a large variety of nonlinear models have been proposed to address these problems, the monitoring of complex dynamic process systems remains challenging. Isolation forests are unsupervised machine learning models that provide an interesting approach to process monitoring that has not been explored extensively yet. In this investigation, this approach is compared with traditional multivariate statistical process monitoring based on principal component models. Three real-world case studies are considered. The first case study is based on coal flotation, the second is based on features extracted from a platinum group metal flotation froth; and the third is based on data from an industrial semi-autogenous grinding circuit. In each case, the models were trained on data representing normal operating conditions and then tested on new process data that were generally different from the training data to test their ability to identify these data as out-of-control. The isolation forest models performed better than the principal component models when the data were nonlinear, but not when the data associated with normal operation and faulty conditions were linearly separable, as was the case with the flotation data. [ABSTRACT FROM AUTHOR]

Aiming at the problem that the isolation forest algorithm cannot detect local anomalies parallel to the axes and the tree structure is unable to be dynamically updated, this paper proposed a generalized isolation forest anomaly detection algorithm based on expert feedback. Firstly, it projected the data to the sampled normal unit vector, and selected a split point from the mapping area to divide the data space, then repeated these operations until constructed a generalized isolation tree. Secondly, it introduced the weights of the leaf nodes of each tree in the generalized isolation forest, which comprehensively considered the influence of the number of subspace partitions and the sample size in the subspace on anomaly scores. Finally, it calculated the weighted anomaly scores of each data, and submitted data with high anomaly scores to expert for batch labeling, then the algorithm updated the weights of the leaf nodes according to the labeling results, so as to dynamically adjust the structure of the generalized isolation tree. The experimental results show that the numbers of real abnormal data are marked by expert in 7 datasets are better than that of the other tree-based anomaly detection algorithms, and the average precision in 12 datasets are 38.952%, 49.144% and 49.144% higher than isolation forest, extended isolation forest, generalized isolation forest, respectively. [ABSTRACT FROM AUTHOR]

Copyright of WISTA Wirtschaft und Statistik is the property of Statistisches Bundesamt and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Understanding the key variables that characterise fire propagation is important for a better estimation of fire events and their impacts. This study uses machine learning combined with satellite remote sensing and atmospheric modelled data to enhance estimations of burned areas. It focuses on the intense early summer weather patterns in South Asia during April and May 2022 and explores the relationship between environmental factors and fire spread. The study employs various algorithms, including random forest, extra trees, extreme gradient boosting (XGBoost), gradient boosting regressor, support vector regressor and neural networks. XGBoost proves to be the most accurate approach. An isolation forest algorithm is used to adjust for outliers in burned area estimations. The comprehensive analysis conducted includes the identification of key variables and sensitivity tests incorporating changes of up to 25 % in natural environmental conditions to assess the model’s consistency. The results indicate that integrating vegetation, atmospheric, and human-related variables with the XGBoost algorithm, and incorporating outlier adjustments leads to the most effective performance (R2 ≥ 0.7), with jet stream variables enhancing the accuracy by approximately 11.5 %. The study highlights the notable impact on fire propagation of increases in the value of 300-hPa meridional circulation index flow (MCI300) and a high 500-hPa geopotential height anomaly (ΔZ500), indicating the development of strong atmospheric blocking (upper tropospheric ridge). As compared to other factors, e.g. land surface temperature, vapour pressure deficit, soil moisture and vegetation optical depth, the impact of changes in jet stream metrics (MCI300 and ΔZ500) was more pronounced, indicating greater sensitivity. These insights emphasise the complexity of fire spread, and the importance of using atmospheric factors to estimate burned areas, particularly during severe heatwaves.

Detecting fraud in financial control processes poses significant challenges due to the complex nature of financial transactions and the evolving tactics employed by fraudsters. This paper investigates the integration of data mining techniques, specifically the combination of Benford's Law and machine learning algorithms, to create an enhanced framework for fraud detection. The paper highlights the importance of combating fraudulent activities and the potential of data mining techniques to bolster detection efforts. The literature review explores existing methodologies and their limitations, emphasizing the suitability of Benford's Law for fraud detection. However, shortcomings in practical implementation necessitate improvements for its effective utilization in financial control. Consequently, the article proposes a methodology that combines informative statistical features revealed by Benford’s law tests and subsequent clustering to overcome its limitations. The results present findings from a financial audit conducted on a road-construction company, showcasing representations of primary, advanced, and associated Benford’s law tests. Additionally, by applying clustering techniques, a distinct class of suspicious transactions is successfully identified, highlighting the efficacy of the integrated approach. This class represents only a small proportion of the entire sample, thereby significantly reducing the labor costs of specialists for manual audit of transactions. In conclusion, this paper underscores the comprehensive understanding that can be achieved through the integration of Benford's Law and other data mining techniques in fraud detection, emphasizing their potential to automate and scale fraud detection efforts in financial control processes.

Container-based virtualization has gradually become a main solution in today's cloud computing environments. Detecting and analyzing anomaly in containers present a major challenge for cloud vendors and users. This paper proposes an online container anomaly detection system by monitoring and analyzing multidimensional resource metrics of the containers based on the optimized isolation forest algorithm. To improve the detection accuracy, it assigns each resource metric a weight and changes the random feature selection in the isolation forest algorithm to the weighted feature selection according to the resource bias of the container. In addition, it can identify abnormal resource metrics and automatically adjust the monitoring period to reduce the monitoring delay and system overhead. Moreover, it can locate the cause of the anomalies via analyzing and exploring the container log. The experimental results demonstrate the performance and efficiency of the system on detecting the typical anomalies in containers in both simulated and real cloud environments.