19 August 2026
This study examined the applicability of forest therapy in alleviating psychological distress among socially isolated young adults, with a particular focus on their associations with the mindfulness. A total of 159 socially isolated young adults were...
Battery voltage is a pivotal parameter for evaluating battery health and safety. The precise prediction of battery voltage and the implementation of anomaly detection are imperative for ensuring the secure and dependable operation of battery systems....
Outlier detection is an important research direction in the field of data mining. Aiming at the problem of unstable detection results and low efficiency caused by randomly dividing features of the data set in the Isolation Forest algorithm in outlier...
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...
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...
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...
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...
With the continuous development of technologies such as the Internet of Things (IoT) and cloud computing, sensors collect and store large amounts of sensory data, realizing real-time recording and perception of the environment. Due to the open charac...
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...
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...
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...
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...
Geographically isolated wetland and surrounding landscape features affect the ecology and life history of amphibian species. We used multistate mark recapture methods and data from over 30,000 captures of adult Ambystoma opacum to explore how surviva...
Periodic manual oversight is difficult to scale for large portfolios of funded research projects. Progress delays, budget irregularities, and superficial reporting often go undetected until final acceptance. Many conventional detection methods also g...
This study applies the Isolation Forest (IF) algorithm to detect anomalies in a district metered area (DMA) in Emilia-Romagna, Italy. Multiple datasets are analyzed, including 15-min inflows, daily minima, and inflows excluding the consumption of a h...
Short-term load forecasting is a prerequisite for achieving intra-day energy management and optimal scheduling in integrated energy systems. Its prediction accuracy directly affects the stability and economy of the system during operation. To improve...
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...
This study investigates the impact of data distribution and bootstrap resampling on the anomaly detection performance of the Isolation Forest (iForest) algorithm in statistical process control. Although iForest has received attention for its multivar...
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...
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...
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...
Anomaly data detection is not only an important part of the condition monitoring process of rolling element bearings, but also the premise of data cleaning, compensation and mining. Aiming at the abnormal data segment detection of the vibration signa...
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...
To effectively extract fault characteristics from complex vibration signals and improve the diagnostic performance of deep learning networks, this paper introduces a wind turbine blade fault diagnosis method that combines Multi-scale Enhanced Hierarc...
Outliers of ship trajectory from the Automatic Identification System (AIS) onboard a ship will affect the accuracy of maritime situation awareness, especially for a regular ship trajectory mixed with a spoofing ship, which has an unauthorized Maritim...
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...
Traditional specific emitter identification (SEI) systems often suffer significant performance degradation when confronted with previously unseen signal sources, underscoring the critical need for accurate detection and rejection of novel-class insta...
Within the context of Industry 4.0, quality assessment procedures using data-driven techniques are becoming more critical due to the generation of massive amounts of production data. In this paper, we address the detection of abnormal screw tightenin...
This paper examines land management technologies to enhance environmental monitoring more efficiently. The study highlights the interactions between human activities and environmental systems with a data-driven environmental monitoring approach. Ther...
Taiwan’s rapid demographic shift toward a super-aged society has heightened demand for long-term care, yet limited staffing creates safety risks from fires; heating, ventilation, and air conditioning failures; and health incidents. To address t...
Due to the large amount of network traffic, manual network security management has become increasingly difficult. This paper proposes an open-source network anomaly detection system prototype that operates with the Security Onion 2.4 platform. The sy...
Landslide susceptibility prediction (LSP) is the first step to ease landslide disasters with the application of various machine learning methods. A complete landslide inventory, which is essential but difficult to obtain, should include high-quality...
With the rapid development of cybersecurity technologies, cybersecurity testing has played an increasingly critical role in scientific research, new technology validation, system performance evaluation, and talent development. A central challenge in...
Lithium-ion batteries in hybrid solar-grid systems experience complex electro-thermal dynamics and stochastic mode switching that threshold-based battery management systems fail to capture. This paper proposes a hybrid deviation detection framework t...
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 man...
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 increasing volume and dimensionality of electrical monitoring data in healthcare facilities create challenges for identifying unusual operating behavior without verified fault labels. This study presents a reproducible unsupervised framework for...
This paper proposes a hybrid machine learning framework for detecting electricity fraud within the broader context of Non-Technical Losses (NTLs) in power-distribution systems. The framework combines unsupervised anomaly detection using Isolation For...
We report the production and degradation of quorum sensing N-acyl-homoserine lactones by bacteria isolated from Malaysian montane forest soil. Phylogenetic analysis indicated that these isolates clustered closely to the genera of Arthrobacter, Bacill...
An aerobic, oxidase-positive, catalase-negative, non-motile, oval/rod-shaped, and Gram-negative strain RN2-1T was isolated from a Korean forest soil sample. Cells grew in a pH range of 6–9 (optimum range: 7–8), in a temperature range of 2...
Isoprene is a climate-active biogenic volatile organic compound (BVOC), emitted into the atmosphere in abundance, mainly from terrestrial plants. Soil is an important sink for isoprene due to its consumption by microbes. In this study, we report the...
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...
Actinomycetes, especially the genus Streptomyces, are one of the most promising sources of bioactive natural products. In this study, a novel Streptomyces strain, RCU-064T, was isolated from a soil sample collected from a peat swamp forest in Thailan...
The rapid deployment of Internet of Things (IoT) devices across smart cities, healthcare systems, industrial automation, transportation networks, smart grids, and cyber-physical infrastructures has expanded the modern cyberattack surface. IoT devices...
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...
Habitat loss, and subsequent fragmentation, can increase the amount of forest edge. Primate species vary in their responses to such changes in their habitat. We studied the movement ecology of a group of reddish-brown cuxiú monkeys (Chiropotes...
(1) Pholidoptera griseoaptera (De Geer, 1773) (Orthoptera, Tettigoniidae) is a common and widespread inhabitant of forest edges in Europe and may therefore serve as a suitable model species for understanding past and future changes in forest wildlife...
Visual perception-based methods are a promising means of capturing the surface damage state of wire ropes and hence provide a potential way to monitor the condition of wire ropes. Previous methods mainly concentrated on the handcrafted feature-based...
The Staphylococcus (S.) aureus complex, including methicillin-resistant S. aureus (MRSA) and methicillin-susceptible S. aureus (MSSA), and S. argenteus are bacterial pathogens that are responsible for both human and animal infection. However, insight...
The Australian Wet Tropics World Heritage Area (WTWHA) in northeast Queensland is home to approximately 18 percent of the nation’s total vascular plant species. Over the past century, human activity and industrial development have caused global...
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