One-class Classification-Based Machine Learning Model for Estimating the Probability of Wildfire Risk

Anomalies on Cyber-Physical System (CPS) can have a devastating effect on the entire system of complex CPS. Thus, it is important to detect anomalies quickly. Since CPS can collect sensor data in near real-time throughout the process, many attempts have been made to solve this problem from the perspective of data-driven security based on the collected data. However, since the CPS datasets are big data and most of the data are normal data, it has always been a great challenge to analyze the data and implement the anomaly detection model. In this paper, we propose and evaluate the Clustered Deep One-Class Classification (CD-OCC) model that combines the clustering algorithm and deep learning (DL) models using only a normal dataset for anomaly detection. We classify normal data into optimal cluster size using the K-means clustering algorithm. DL models train to classify each cluster based on clustered normal data, and we can obtain the softmax values in the process of predicting the cluster. We use the softmax values as a dataset with distilled knowledge of the DL model for anomaly detection. We transfer the softmax values to one-class classification (OCC) models to detect anomalies. As a result of the experiment, the F1-score of the proposed model shows performance close to 0.8 and performance improvement of about 0.5 compared to the encoded OCC model, which has reduced-dimensionality through auto-encoder as well as the basic OCC model.

Anomaly detection is a cornerstone of modern intelligent systems, with critical applications in healthcare, cybersecurity, smart grids, and IoT environments. While traditional machine learning and deep learning models have shown promise in identifying outliers, they often face challenges such as dependence on large labeled datasets, high computational costs, and limited scalability to edge devices or high-dimensional data streams. This study introduces D2H-AD, a novel anomaly detection framework built upon Hyperdimensional Computing (HDC), a brain-inspired paradigm that encodes information using high-dimensional distributed vectors. Unlike prior HDC-based approaches, D2H-AD fuses distance-based similarity and density-aware encoding in a hybrid design, significantly improving anomaly characterization and detection accuracy. Ablation experiments confirm that hyperdimensional encoding alone contributes up to 5.4% higher ROC-AUC compared to applying the same density-distance scoring in the original Euclidean feature space, and D2H-AD consistently outperforms all five published baselines (HDAD, ODHD, One-Class SVM, Isolation Forest, and Autoencoders) across every evaluated dataset. The method is designed to be lightweight, interpretable, and computationally efficient, suggesting strong suitability for deployment in resource-constrained and real-time environments. To validate its effectiveness, we evaluate D2H-AD on five diverse benchmark datasets, comparing its performance against five leading techniques: HDAD, ODHD, One-Class SVM, Isolation Forest, and Autoencoders. Our results show that D2H-AD consistently achieves superior F1 scores and ROC-AUC metrics, demonstrating robustness against class imbalance, noise, and data complexity. Beyond accuracy, D2H-AD offers practical advantages including scalability, minimal memory footprint, and an expected low-latency profile derived from its binary operations and lightweight design. These characteristics are crucial for TinyML and edge AI applications. This work highlights the untapped potential of HDC for high-performance anomaly detection and opens new avenues for secure, interpretable, and energy-efficient AI solutions in dynamic environments such as IoT, embedded systems, and beyond. Additionally, D2H-AD provides feature-level interpretability through hypervector decoding, enabling transparent explanations for safety-critical applications.

A comparative investigation of data-driven approaches based on one-class classifiers for condition monitoring of marine machinery system

COVID-19 is highly infectious and causes acute respiratory disease. Machine learning (ML) and deep learning (DL) models are vital in detecting disease from computerized chest tomography (CT) scans. The DL models outperformed the ML models. For COVID-19 detection from CT scan images, DL models are used as end-to-end models. Thus, the performance of the model is evaluated for the quality of the extracted feature and classification accuracy. There are four contributions included in this work. First, this research is motivated by studying the quality of the extracted feature from the DL by feeding these extracted to an ML model. In other words, we proposed comparing the end-to-end DL model performance against the approach of using DL for feature extraction and ML for the classification of COVID-19 CT scan images. Second, we proposed studying the effect of fusing extracted features from image descriptors, e.g., Scale-Invariant Feature Transform (SIFT), with extracted features from DL models. Third, we proposed a new Convolutional Neural Network (CNN) to be trained from scratch and then compared to the deep transfer learning on the same classification problem. Finally, we studied the performance gap between classic ML models against ensemble learning models. The proposed framework is evaluated using a CT dataset, where the obtained results are evaluated using five different metrics The obtained results revealed that using the proposed CNN model is better than using the well-known DL model for the purpose of feature extraction. Moreover, using a DL model for feature extraction and an ML model for the classification task achieved better results in comparison to using an end-to-end DL model for detecting COVID-19 CT scan images. Of note, the accuracy rate of the former method improved by using ensemble learning models instead of the classic ML models. The proposed method achieved the best accuracy rate of 99.39%.

Massive amounts of data are created and collected by data acquisition systems, including SCADA and embedded systems in industrial machines.The data may be analysed by AI algorithms to gain a better understanding of the process and identify any irregularities in the machines.This is a major benefit of Industry 4.0.This paper will examine predictive maintenance as a process in industry and demonstrate how it may make full use of technologies from Industry 4.0.Using a weighted average, our method integrates Three Machine Learning models: Auto encoder (AE), One-Class Support Vector Machine (OCSVM), and Local Outlier Factor (LOF).This allows us to discover anomalies in real-time.Anomaly detection will be enhanced as a result.Improve real-time anomaly detection using the suggested hybrid machine-learning ensemble pipeline by combining the outputs of three ML models: auto encoder (AE), one-class support vector machine (OCSVM), and local outlier factor (LOF).

An anomaly, defined as something that deviates from what is normal, expected, or usual. It signifies abnormality or an irregularity that stands out from typical behaviours or patterns. Detecting anomalies is significant among numerous sectors due to the reasons of signal potential difficulties or opportunities. For an instance, in retail, detecting anomalies in sales data might prompt for further analysis into operational issues or customer behaviour to reduce losses and capitalize on its trends. Hence, different techniques are used for Anomaly Detection. However, anomaly detection using manual method are measured for time consuming, prone to error and can be tedious process. Therefore, different approaches have been considered for anomaly detection as AI (Artificial Intelligence) methods are efficient, faster, provides high level accuracy by effectively detecting the abnormalities. Owing to these aspects, this paper focuses on compiling different techniques and emphasizes on reviewing all anomaly detection using numerous techniques like ML (Machine Learning) and DL (Deep Learning) classifiers, statistical methods, one-class classification, clustering and density-based models which helps with identifying and comprehending the diversity of detection techniques that are applied in various domains like finance, retail, healthcare and cyber security. Various existing researches on anomaly detection are reviewed in the study. In addition to an overview, certain studies also deals with applications of detection models and future trends are reviewed in precise. Finally, the challenges are identified through the analysis of existing researchers and future recommendations are provided for overcoming the gaps that are intended to create promising work in this area.

Social media platforms provide valuable insights into mental health trends by capturing user-generated discussions on conditions such as depression, anxiety, and suicidal ideation. Machine learning (ML) and deep learning (DL) models have been increasingly applied to classify mental health conditions from textual data, but selecting the most effective model involves trade-offs in accuracy, interpretability, and computational efficiency. This study evaluates multiple ML models, including logistic regression, random forest, and LightGBM, alongside DL architectures such as ALBERT and Gated Recurrent Units (GRUs), for both binary and multi-class classification of mental health conditions. Our findings indicate that ML and DL models achieve comparable classification performance on medium-sized datasets, with ML models offering greater interpretability through variable importance scores, while DL models are more robust to complex linguistic patterns. Additionally, ML models require explicit feature engineering, whereas DL models learn hierarchical representations directly from text. Logistic regression provides the advantage of capturing both positive and negative associations between features and mental health conditions, whereas tree-based models prioritize decision-making power through split-based feature selection. This study offers empirical insights into the advantages and limitations of different modeling approaches and provides recommendations for selecting appropriate methods based on dataset size, interpretability needs, and computational constraints.

The increasing usage of interconnected devices within the Internet of Things (IoT) and Industrial IoT (IIoT) has significantly enhanced efficiency and utility in both personal and industrial settings but also heightened cybersecurity vulnerabilities, particularly through IoT malware. This paper explores the use of one-class classification, a method of unsupervised learning, which is especially suitable for unlabeled data, dynamic environments, and malware detection, which is a form of anomaly detection. We introduce the TF-IDF method for transforming nominal features into numerical formats that avoid information loss and manage dimensionality effectively, which is crucial for enhancing pattern recognition when combined with n-grams. Furthermore, we compare the performance of multi-class vs. one-class classification models, including Isolation Forest and deep autoencoder, that are trained with both benign and malicious NetFlow samples vs. trained exclusively on benign NetFlow samples. We achieve 100% recall with precision rates above 80% and 90% across various test datasets using one-class classification. These models show the adaptability of unsupervised learning, especially one-class classification, to the evolving malware threats in the IoT domain, offering insights into enhancing IoT security frameworks and suggesting directions for future research in this critical area.

One-Class Support Vector Machines (OCSVMs) distinguish outliers by computing a hyper-plane in feature space. The choice of the origin as separation point is arbitrary, which affects the decision boundary, and the distribution of samples has impact on the performance. Expanding the algorithm into solving two-class classification problems overcomes the drawbacks to a certain degree. However, the class imbalance problem is serious and the labeled outliers are rare or even non-existing. In this paper, a new Outlier OCSVM is proposed and a framework is designed for unsupervised outlier detection. Respectively scored by distance from hyper-plane and probabilistic output value, two definitions of outlier degree are presented. After picking out some suspicious outliers via combining the two criterions of outlier degree, the adjusted Outlier OCSVM starts the training operations, two parts of the dataset are updated interactively through comparison of the outputs. Experiment results on benchmark datasets show that the method can effectively improve the detection rate and reduce false positive rate, easy and reliable.

Data is one of the most important limiting factors of deep machine learning (ML) model in drilling applications. Though a big size of historical data can be available, high-quality cleaned and labeled data is usually limited. In this case study, we show that with limited labeled data, physics-based data augmentation combined with unsupervised learning significantly improves both stability and accuracy in bit wear ML model. It provides a pathway to overcome labeled data shortage and field data quality limitations. Labeled bit wear data is usually limited because only the final bit dull state can be labeled using dull photos for the entire drilling distance. To overcome this difficulty, an encoder-decoder unsupervised ML framework based on Bi-directional LSTM architecture is first applied to the data of entire drilling distance to extract and maximize data learnings. Then, a physics-based wear estimator is implemented in the learned latent space to augment labeled wear dataset, guided by true wear labels and offset-well data. Weighting factor in loss function is applied to optimize augmented and true datasets in final supervised learning step. The proposed real-time bit wear model is built on a combination of physics-based data augmentation and unsupervised data learning method. The model is applied on multiple field bit runs. Results show that by implementing the unsupervised data learnings only, the prediction accuracy is improved by 30% compared to the baseline ML model. By combining physics-based augmentation, the accuracy is further improved by 10%. More importantly, adding physics-based data augmentation significantly reduces prediction variance and unphysical wear outputs, therefore improving prediction stability by more than 30%. It should be noted that the prediction stability of AI/ML model is crucial in real-time application and decision making. The results show physics-based data augmentation not only increases the size of label dataset and prevents model overfitting, but also applies physics-based guidance to the ML model effectively. It is learned weighting factor plays a crucial role in balancing loss contributions from true wear labels and physics-based labels. While low weighting factor of physics-based labels diminishes the augmented data, high weighting factor disrespects the true wear labels leading to a high prediction bias. Overall, the bit wear model study shows physic-based data augmentation combined with unsupervised data learning can effectively improve model accuracy, stability, and overcome labeled data shortage difficulty. The proposed paper shows a case study for the bit wear ML model using a combination of physics-based data augmentation and unsupervised data learning. While labeled data is one of the major challenges of ML models in many drilling applications, this study provides a pathway to improve both accuracy and stability of deep ML models with limited labeled data.

In finance domain, the acquisition of in-time and comprehensive intra-industry information is important for decision-makers and stock investors to maximize their investment profits. But there are following problems in the retrieval and recommendation of financial industry information. (l)Unlike the concrete conceptions, industry could not be perfectly delineated with keywords. (2)It's difficult to calculate the relevance between document and industry. (3)The massive search results confused the user as a result of the information overload. In this paper, this problem is treated as a classification of relevance. The one-class classification model is adopted to calculate the relevance between document and industry since the lack of well sampled non-relevant documents. Based on selected industry-specific description terms, three different one-class classifiers k-means, one-class SVM and language model algorithm are trained with only relevant (positive) documents to help making recommendation decisions. The experimental results show that the proposed methods perform well with high micro-average F1 and macro-average F1 both up to the 80%. We also perform experiments to verify the relationship between parameters and performance.

Anomaly detection can be seen as an unsupervised learning task in which a predictive model created on historical data is used to detect outlying instances in new data. This work addresses possibly promising but relatively uncommon application of anomaly detection to text data. A Polish Internet discussion forum devoted to psychoactive substances received from home-grown plants, such as hashish or marijuana, serves as a text source that is both realistic and possibly interesting on its own, due to potential associations with drug-related crime. Forum posts are preprocessed by stopword removal, spelling correction, stemming, and frequency-based term filtering. The Global Vectors (GloVe) text representation, which is an example of the increasingly popular word embedding approach, is combined with two unsupervised anomaly detection algorithms, based on one-class SVM classification and based on dissimilarity to k-medoids clusters. The cluster dissimilarity approach combined with the GloVe representation outperforms one-class SVM with respect to detection quality and appears a more promising approach to anomaly detection in text data.

This paper compares the classification performance of machine learning classifiers vs. deep learning-based handcrafted models and various pretrained deep networks. The proposed study performs a comprehensive analysis of object classification techniques implemented on low-altitude UAV datasets using various machine and deep learning models. Multiple UAV object classification is performed through widely deployed machine learning-based classifiers such as K nearest neighbor, decision trees, naïve Bayes, random forest, a deep handcrafted model based on convolutional layers, and pretrained deep models. The best result obtained using random forest classifiers on the UAV dataset is 90%. The handcrafted deep model's accuracy score suggests the efficacy of deep models over machine learning-based classifiers in low-altitude aerial images. This model attains 92.48% accuracy, which is a significant improvement over machine learning-based classifiers. Thereafter, we analyze several pretrained deep learning models, such as VGG-D, InceptionV3, DenseNet, Inception-ResNetV4, and Xception. The experimental assessment demonstrates nearly 100% accuracy values using pretrained VGG16- and VGG19-based deep networks. This paper provides a compilation of machine learning-based classifiers and pretrained deep learning models and a comprehensive classification report for the respective performance measures.

SoftId: An autoencoder-based one-class classification model for software authorship identification

Accurate prediction of urban residential rents is of great importance for landlords, tenants, and investors. However, existing rents prediction models face challenges in meeting practical demands due to their limited perspectives and inadequate prediction performance. The existing individual prediction models often lack satisfactory accuracy, while ensemble learning models that combine multiple individual models to improve prediction results often overlook the impact of spatial heterogeneity on residential rents. To address these issues, this paper proposes a novel prediction model called GERPM, which stands for Geographically Weighted Stacking Ensemble Learning-Based Urban Residential Rents Prediction Model. GERPM comprehensively analyzes the influencing factors of residential rents from multiple perspectives and leverages a geographically weighted stacking ensemble learning approach. The model combines multiple machine learning and deep learning models, optimizes parameters to achieve optimal predictions, and incorporates the geographically weighted regression (GWR) model to consider spatial heterogeneity. By combining the strengths of deep learning and machine learning models and taking into account geographical factors, GERPM aims to improve prediction accuracy and provide robust predictions for urban residential rents. The model is evaluated using housing data from Nanjing, a major city in China, and compared with representative individual prediction models, the equal weight combination model, and the ensemble learning model. The experimental results demonstrate that GERPM outperforms other models in terms of prediction performance. Furthermore, the model’s effectiveness and robustness are validated by applying it to other major cities in China, such as Shanghai and Hangzhou. Overall, GERPM shows promising potential in accurately predicting urban residential rents and contributing to the advancement of the rental market.