Anomalous behavior detection in business processes inspects abnormal situations, such as errors and missing values in system execution records, to facilitate safe system operation. Since anomaly information hinders the insightful investigation of eve...
Hyperspectral image (HSI) anomaly detection (HSI-AD) has become a hot issue in hyperspectral information processing as a method for detecting undesired targets without a priori information against unknown background and target information, which can...
This paper presents a comprehensive model for detecting and addressing concept drift in network security data using the Isolation Forest algorithm. The approach leverages Isolation Forest’s inherent ability to efficiently isolate anomalies in h...
A new modification of the isolation forest called the attention-based isolation forest (ABIForest) is proposed for solving the anomaly detection problem. It incorporates an attention mechanism in the form of Nadaraya–Watson regression into the...
In the prognosis of radar transmitter degradation malfunction, there are some restrictions, such as the fact that it is difficult to obtain fault samples and the monitoring data cannot reach the fault threshold. For these restrictions, a novel data-d...
Soil total nitrogen is one of the most important basic indicators for fertiliser decision making, but tens of millions of soil total nitrogen sampling data have been accumulated, forming a huge database. In this large database, there is a large amoun...
The success of distributed power generation by plants based on solid oxide fuel cells (SOFCs) is hindered by reliability problems that can be mitigated through an effective fault detection and isolation (FDI) system. However, the numerous operating c...
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 organiza...
Three-dimensional assets have evolved into a pivotal element within the domains of electronic entertainment, medicine, and engineering. Unfortunately, 3D models comprise voluminous data, which is not readily amenable to analysis or to the application...
Distributed measurement networks, from semiconductor testing arrays to environmental sensor grids, medical diagnostic systems, and agricultural monitoring stations, face a fundamental challenge: undetected site-to-site variations that silently corrup...
Mineral prospectivity mapping (MPM) is crucial for efficient mineral exploration, where prospective zones are identified in a cost-effective manner. This study focuses on generating prospectivity maps for hydrothermal polymetallic mineralization in t...
In the manufacturing process, digital twin technology can provide real-time mapping, prediction, and optimization of the physical manufacturing process in the information world. In order to realize the complete expression and accurate identification...
In view of the problem that traditional dam outlier identification methods mostly rely on single-monitoring-point models and do not fully consider the spatio-temporal correlation characteristics of deformation between monitoring points, which can eas...
As companies increasingly undergo digital transformation, the value of their data assets also rises, making them even more attractive targets for hackers. The large volume of weblogs warrants the use of advanced classification methodologies in order...
Connectivity is a landscape property that promotes gene flow between organisms located in different patches of habitat and provides a way to reduce habitat loss by maintaining flux of organisms through the landscape; it is an important factor for con...
Windstorms may have negative consequences on forest ecosystems, industries, and societies. Extreme events related to extra-tropical cyclonic systems remind us that better recognition and understanding of the factors driving forest damage are needed f...
Supervised land cover classification from remote sensing imagery is based on gathering a set of training areas to characterise each of the classes and to train a predictive model that is then used to predict land cover in the rest of the image. This...
The 0-day attack is a cyber-attack based on vulnerabilities that have not yet been published. The detection of anomalous traffic generated by such attacks is vital, as it can represent a critical problem, both in a technical and economic sense, for a...
Dam displacement monitoring is crucial for assessing structural safety; however, conventional models often prioritize single-task prediction, leading to an inherent difficulty in balancing monitoring data quality with model performance. To bridge thi...
In this paper we consider the D2D (Device-to-Device) communication taking place between Wireless Sensor Networks (WSN) elements operating in vegetation environments in order to achieve the radio channel characterization at 2.4 GHz, focusing on the ra...
Regeneration of Picea abies in high-elevation mountain forests often depends on the presence of coarse woody debris (CWD), as logs provide sites with more favorable conditions for spruce regeneration compared to the forest floor. However, there is li...
Wireless Sensor Networks (WSNs) play a critical role in environmental monitoring and early forest fire detection. However, they are susceptible to sensor malfunctions and network intrusions, which can compromise data integrity and lead to false alarm...
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...
This study compares and analyzes statistical, machine learning, and deep learning outlier-detection methods on real power-usage data from the metal, food, and chemical industries to propose the optimal model for improving energy-consumption efficienc...
Using terrestrial laser scanning (TLS) technology, forests can be digitized at the centimeter-level to enable fine-scale forest management. However, there are technical barriers to converting point clouds into individual-tree features or objects alig...
This work identifies the most effective machine learning techniques and supervised learning models to estimate power output from photovoltaic (PV) plants precisely. The performance of various regression models is analyzed by harnessing experimental d...
The assessment of a forest’s structure is pivotal in guiding effective forest management, conservation efforts, and ensuring sustainable development. However, traditional evaluation methods often focus on isolated forest parameters and incur su...
The Eastern Arc Mountains of Tanzania and Kenya, a montane archipelago of 13 uplifted fault blocks (sky islands) isolated by lowland arid savanna, are a center of exceptional biological endemism. Under the influence of humid winds from the Indian Oce...
Pollination mapping and modeling have opened new avenues for comprehending the intricate interactions between pollinators, their habitats, and the plants they pollinate. While the Lonsdorf model has been extensively employed in pollination mapping wi...
(1) Background: Social isolation is a major risk factor for suicidal ideation. In this study, we investigated whether the evaluation of both depression and social isolation in combination could effectively predict suicidal ideation; (2) Methods: A to...
The future integration of drones in 6G networks will significantly enhance their capabilities, enabling a wide range of new applications based on autonomous operation. However, drone networks are particularly vulnerable to jamming attacks, a type of...
Given the wide application of container technology, the accurate prediction of container CPU usage has become a core aspect of optimizing resource allocation and improving system performance. The high volatility of container CPU utilization, especial...
The integration of Advanced Metering Infrastructure (AMI) provides high-resolution electrical data, essential for enhancing industrial efficiency and monitoring equipment health. However, the utility of this data is frequently compromised by anomalie...
14 August 2025
The accelerated development of e-commerce has given rise to sophisticated systems defined by significant user interaction, a variety of product offerings, and considerable quantities of structured and unstructured data. Upholding trust and operationa...
We propose a machine learning-based methodology which makes use of ensemble methods with the aims (i) of treating missing data in time series with irregular observation times and detecting anomalies in the observed time behavior; (ii) of defining sui...
The novelty of this paper is in determining and using hyperparameters to improve the Extended Isolation Forest (EIF) algorithm, a relatively new algorithm, to detect malicious activities in network traffic. The EIF algorithm is a variation of the Iso...
Uncertainty in wind power is often unacceptably large and can easily affect the proper operation, quality of generation, and economics of the power system. In order to mitigate the potential negative impact of wind power uncertainty on the power syst...
Hydropower is important in the modern power system due to its ability to quickly adjust production. More frequent use of this ability may lead to increased maintenance needs, highlighting the importance of research in condition monitoring for hydropo...
Wind turbine pitch system condition monitoring is an active area of research, and this paper investigates the use of the Isolation Forest Machine Learning model and Supervisory Control and Data Acquisition system data for this task. This paper examin...
While climate is known to regulate forest productivity, the mechanistic contribution of soil microbial communities—and whether it differs between natural and plantation forests—remains poorly quantified at broad scales. Here, we provide a...
During the coronavirus disease (COVID-19) pandemic, we admitted suspected or confirmed COVID-19 patients to our isolation wards between 2 March 2020 and 4 May 2020, following a well-designed and efficient assessment protocol. We included 217 patients...
Objectives: This study aimed to develop and validate a machine learning (ML) algorithm to predict 30-day mortality following isolated coronary artery bypass grafting (CABG) and to compare its performance against the widely used European System for Ca...
Predicting forest cover change (FCC) and screening development scenarios are crucial for ecological resilience. However, quantitative evaluations of prioritizing forest change scenarios are limited. Here, we took five shared socio-economic pathways (...
With the proliferation of the Internet of Things, a large amount of data is generated constantly by industrial systems, corresponding in many cases to critical tasks. It is particularly important to detect abnormal data to ensure the accuracy of data...
Due to the accelerated growth of the PV plant industry, multiple PV plants are being constructed in various locations. It is difficult to operate and maintain multiple PV plants in diverse locations. Consequently, a method for monitoring multiple PV...
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...
This paper addresses the problem of the unsupervised approach of credit card fraud detection in unbalanced datasets using the ARIMA model. The ARIMA model is fitted to the regular spending behaviour of the customer and is used to detect fraud if some...
Lidar SLAM (simultaneous localization and mapping) systems provide vehicles with high-precision maps and localization for environmental perception. However, sensor noise and dynamic changes can lead to the localization drift or localization failure o...
Unsupervised anomaly detection algorithms have gained significant attention in the field of mineral prospectivity mapping (MPM) due to their ability to reveal hidden mineralization zones by effectively modeling complex, nonlinear relationships betwee...
Background: Industrial power time-series exhibit strong daily/weekly periodicities and nonstationary behaviors that challenge generic deep autoencoders. Methods: We take first differences of the signal, compute the FFT spectrum, and map top spectral...
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