Arc Detection and Severity Analysis for Plasma Etching Processes

The efficacy of traditional batch fraud detection methods is diminishing due to the rise in digital financial transactions. To address contemporary banking fraud, this research explores real-time stream processing. It investigates memory-efficient and low-latency techniques such as One-Class SVM, Incremental Local Outlier Factor, and Isolation Forest. Additionally, the study analyzes real-time data processing methods including sampling, filtering, and approximation counting techniques like Bloom Filters and Count-Min Sketch. It discusses how existing frameworks utilizing Apache Kafka, Apache Flink, FastAPI, and Redis can be integrated to combine these various techniques as components. Models such as Random Forest and LightGBM, which effectively balance accuracy and performance during transaction processing, are found to be the most effective in detecting fraud, based on experiments conducted on a publicly available credit card dataset.

BACKGROUND Patient heterogeneity is a key challenge in immunotherapy for IDH-wildtype glioblastoma (GBM), where hypermutated tumors or highly immunosuppressive tumor microenvironments (TME) act as clinical outliers affecting treatment response and trial stratification. We developed Alert-GBM, a radiogenomic AI model designed to flag such outliers based on radiomic signatures from diagnostic imaging. MATERIAL AND METHODS We used multicenter open-access radiogenomic datasets (n = 176) to train unsupervised outlier detection models. Principal Component Analysis (PCA) served as a first-pass filter to harmonize data before applying Isolation Forest (IF) modeling. Three IF models were trained using inter-dataset leave-one-out cross-validation (LOOCV) to simulate real-world performance and were benchmarked against each other. The final model was selected using stratified 10-fold cross-validation (CV). Outliers were validated through visual inspection, statistical profiling, comparison with other outlier detection methods (Local Outlier Factor, One-Class SVM, Mahalanobis distance), and correlation with biological and immune profiles from imaging and omics data. RESULTS The PCA filter identified 3 initial outliers based on MRI metadata. The final IF model, following stratified 10-fold CV, detected 6 outliers across the datasets, with anomaly scores plotted. Flagged cases included large, highly enhancing tumors, minimally enhancing tumors, and post-operative scans. Visualizations using PCA, t-SNE, and UMAP were generated to illustrate separability. False positives and negatives were assessed using immune-based scores, and the distribution of anomaly scores was analyzed with KDE plots. Cross-validation against alternative outlier models confirmed consistency of the flagged cases. CONCLUSION Alert-GBM is a proof-of-concept AI architecture for outlier detection in GBM MRI radiogenomics, capable of flagging cases with atypical immune TME profiles. Integrating such models with radiogenomic immune predictors at the time of diagnosis may support multidisciplinary decision-making and improve patient stratification for immunotherapy trials. Incorporating a human-in-the-loop framework ensures that AI outputs remain clinically interpretable and actionable.

Fault detection and diagnosis in process data using one-class support vector machines

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.

Steep slope failures adjacent to residential areas are becoming an increasingly serious hazard. However, satellite-based monitoring is often limited by revisit time and spatial resolution, which can impede the timely identification of small, precursory deformations. To support dense in situ surveillance, embedded glass fiber-reinforced polymer (GFRP) sensor rods were installed in a susceptible slope, and ground-displacement data were recorded at 5 min intervals for five months. Based on these multivariate time series, we propose PRISM-TAD, a masked Transformer-based anomaly detection approach that integrates kinematic priors computed from displacement and velocity to model normal slope dynamics and detect departures from typical behavior. The proposed method was benchmarked against six baselines: robust velocity threshold screening, PCA-based reconstruction, Isolation Forest, one-class SVM, a 1D convolutional autoencoder, and a standard Transformer reconstructor. In a field test using a documented slope failure case in Seocheon, PRISM-TAD generated an alert approximately 22 h before collapse while yielding the lowest false alarm rate. Although some baseline methods showed longer nominal lead times, they produced substantially more false positives. Overall, the results suggest that coupling high-frequency IoT displacement sensing with domain-informed deep learning can enhance the operational reliability of early warning for slope failures.

This paper describes a technique for using time series data to monitor and diagnose faults in production systems. Predictive maintenance is the focus of research in order to assess equipment condition and anticipate potential failures. The method reduces the need for user intervention by combining Automatic Diagnostic Systems (ADS) with continuous monitoring systems to detect equipment failures early on. Three distinct techniques were tested on temperature sensor time series data: Isolation Forest (IF), Local Outlier Factor (LOF), and One-Class Support Vector Machine (OCSVM). Graphs showing how each method distinguished between normal and abnormal sensor behavior were used to evaluate each method’s efficacy in detecting anomalies. According to the results, IF is best used for ongoing monitoring, OCSVM for more thorough anomaly detection, and LOF for distinguished outlier identification. The findings are intended to improve problem detection and maintenance oversight in production systems.

Most working professionals as well as school and college students require very less physical activity and they are mostly seated during most part of the day. Maintaining a good posture while sitting is a step towards avoiding several lifestyle diseases that arise due to decreased physical activity. In this work, posture detection by placing multiple accelerometers on the back of user is presented. Novel use of one-class classification for detecting poor posture is proposed. One-class support vector machine and isolation forest are employed for posture detection. The classifiers are provided unlabeled data containing mostly samples of good posture. During testing, the poor postures, which we be different postures other than that considered as the good posture, are predicted as outliers. The performance of the models is compared quantitatively. The performance of one-class classifiers is also compared with commonly used unsupervised learning algorithm, k-mean clustering. Results are presented for tuning of the considered models and to study the effect of sensor placement. The isolation forest provides the highest accuracy of 99.3% and F1-score of 0.99.

The growth of cyber threats and network traffic has highlighted the need for effective methods for detecting anomalies. The purpose of the study was to develop a hybrid model for detecting anomalies in telecommunications networks with increased accuracy based on comparative analysis of uncontrolled algorithms. Four main algorithms were compared: Isolation Forest, Local Outlier Factor (LOF), One-Class Support Vector Machine (SVM), and Elliptic Envelope using simulated network traffic data. Analysis methods included data normalisation using Z-score and Min-Max Scaling to eliminate large-scale differences between traits. An ensemble of 100 decision trees was used to improve the accuracy of anomaly detection. As a result, it was found that Isolation Forest provided the highest accuracy of anomaly detection (F1-Score = 85.8%) and high processing speed (processing time per 10,000 records in 2.5 seconds), which is important for real-world conditions of telecommunications networks with large amounts of data. LOF showed high accuracy in detecting local anomalies, but with greater computational complexity. The one-class SVM algorithm detected global anomalies, but showed lower accuracy for local ones. Elliptic Envelope had limited performance due to the assumption of normal data distribution. Additionally, a comprehensive model was developed that combined the advantages of Isolation Forest, LOF, and Density-Based Spatial Clustering of applications with Noise, which allowed increasing the F1-Score to 88% and exceeding the results of individual models. The hybrid model showed adaptability to multimodal distributions and efficiency in detecting local anomalies. Practical significance lies in improving the accuracy and stability of network security systems and form the basis for adaptive real-time algorithms, which allowed cybersecurity and telecommunications specialists to more effectively monitor and protect networks from threats

Credit card fraud is a major problem in today’s financial world. It induces severe damage to financial institutions and individuals. There has been an exponential increase in the losses due to fraud in recent years. Hence, effectively detecting fraudulent behavior is of vital importance for either financial institutions or individuals. Since credit fraud events account for a small proportion of all transaction events in real life, the datasets about credit fraud are usually imbalanced. Some common classifiers, such as decision tree and naïve Bayes, are unable to detect fraud. Furthermore, in some cases, traditional strategies for dealing with an imbalanced problem, such as the synthetic minority oversampling technique (SMOTE), are not effective for the fraud detection datasets. To accurately detect fraud behavior, this study uses anomaly detection on imbalanced data, as well as Isolation Forest (IForest) with kernel principal component analysis. A one-class support vector machine (OCSVM) with AdaBoost is used as two models to detect outliers which significantly improves detection accuracy and efficiency. The model achieved 96% accuracy, 100% precision, 96% recall, and 98% F 1 score, respectively. The proposed model is expected to become a helpful tool for finding credit card fraud detection, and the analysis presented in this study will provides useful insights into credit card fraud detection mechanisms.

As semiconductor devices are highly integrated, etching control is much more difficult in semiconductor industry. Plasma etching has better performance than wet etching especially in small open area etching. In semiconductor manufacturing industry, the optical emission spectroscopy (OES) is widely used to collect data from plasma chamber. The enormous OES data need to be transformed for accutate endpoint detection. There are some research results regarding endpoint detection in plasma etching using various methods such as Principal Component Analysis (PCA), Partial Least Squares (PLS), etc. In this paper a new approach is proposed for endpoint detection of small open area wafers in plasma etching. The new approach utilizes a Fast Fourier Transform (FFT), which is an efficient algorithm to detect frequency changes in signal, and this property is applied to OES data to detect endpoint in etching process. By applying FFT, noise is reduced compared to PCA data. The data after FFT is used to detect endpoint using Support Vector Machine (SVM). The combination of FFT and SVM performs excellent in terms of endpoint detection, and the performance of proposed FFT/SVM algorithm is compared to the performance of PCA/SVM algorithm.

Artificial Intelligence in Financial Fraud Detection: A Comprehensive Approach to Enhancing Financial Security The rise of artificial intelligence (AI) offers an opportunity to significantly strengthen financial security by combating financial fraud, which has become increasingly complex and widespread. Traditional detection methods are often insufficient in identifying and preventing fraudulent activities, prompting a shift towards AI-based solutions. This study explores the application of AI, particularly machine learning algorithms, in improving the accuracy and efficiency of fraud detection. By analyzing large financial datasets, AI can detect anomalies that may indicate fraudulent behavior more effectively than traditional approaches. This research adopts a two-phase methodology. The first phase involves a thorough review of existing financial fraud detection methods, comparing traditional techniques with AI-based models to identify gaps. Various machine learning approaches, including supervised, unsupervised, and deep learning algorithms, are reviewed for their effectiveness in detecting fraud. The second phase involves developing and testing an AI model to identify fraudulent patterns within transactional data. The model uses machine learning algorithms to process vast datasets and detect deviations from typical financial behaviors, flagging potentially fraudulent activities. The expected results indicate that AI systems can outperform traditional fraud detection methods by significantly reducing false positives and improving the detection rate of genuine fraud. This reduction in false positives is vital for financial institutions, as it reduces unnecessary investigations and saves valuable resources. Additionally, enhanced fraud detection protects both institutions and consumers from financial losses.The findings of this study aim to provide financial institutions with practical insights into the implementation of AI-driven fraud detection systems. Furthermore, the research highlights the need for continuous refinement of AI models to adapt to the evolving nature of financial fraud. By leveraging AI technologies, financial institutions can revolutionize their approach to fraud detection, making financial systems more secure and responsive to emerging threats.

Plasma etching, which is expected to play an important role in the manufacture of semiconductor and other devices requiring fine–line lithography, involves using a glow discharge to generate chemically reactive species from relatively inert molecular gases.1 These reactive species combine chemically with certain solid materials to form volatile compounds which are removed by the vacuum pumping system. Although the general concept is relatively simple, the complexities of the glow discharge itself and of the associated parameter space have impeded the development of a detailed understanding of the chemical reactions involved in the process, which in turn has impeded the implementation of plasma etching in device manufacture.

Deploying unsupervised anomaly detection systems in heterogeneous smart home environments is hindered by the need for costly, per-site hyperparameter tuning. This paper addresses the critical challenge of hyperparameter transferability for creating zero-tune, plug-and-play security solutions. We systematically evaluate five unsupervised machine learning models [Elliptic Envelope (EE), Isolation Forest (IF), Local Outlier Factor (LoF), One-Class SVM (oSVM), and an Autoencoder (AE)] across five prominent IoT datasets. Using a rigorous dataset-specific hyperparameter tuning approach, we benchmark the performance of transferred configurations against both per-dataset optimization and default settings. Our findings establish a clear performance hierarchy: while dataset-specific tuning remains the gold standard, an intelligent transfer strategy significantly outperforms default configurations. Notably, we identify the IoTID20 dataset as the most effective source. Our quantitative topological analysis supports this, revealing that IoTID20’s high feature space complexity and cluster overlap (evidenced by low Silhouette scores) create a rigorous training environment that produces robust, portable hyperparameters. Furthermore, our analysis reveals a strategic trade-off: Autoencoders and LoF deliver the highest absolute performance, whereas IF offers the most substantial improvement over default settings. This work provides a quantitative framework for dataset-driven initialization, guiding the development of robust, low-maintenance intrusion detection systems.

In semiconductor manufacturing, black silicon (bSi) has traditionally been considered as a sign of unsuccessful etching. However, after more careful consideration, many of its properties have turned out to be so superior that its integration into devices has become increasingly attractive. In devices where bSi covers the whole wafer surface, such as solar cells, the integration is already rather mature and different bSi fabrication technologies have been studied extensively. Regarding the integration into devices where bSi should cover only small selected areas, existing research focuses on device properties with one specific bSi fabrication method. Here, we fabricate bSi patterns with varying dimensions ranging from millimeters to micrometers using three common bSi fabrication techniques, i.e., plasma etching, metal-assisted chemical etching (MACE) and femtosecond-laser etching, and study the corresponding fabrication characteristics and resulting material properties. Our results show that plasma etching is the most suitable method in the case of <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\mu \text{m}$ </tex-math></inline-formula> -scale devices, while MACE reaches surprisingly almost the same performance. Femtosecond-laser has potential due to its maskless nature and capability for hyperdoping, however, in this study its moderate accuracy, large silicon consumption and spreading of the etching damage outside the bSi region leave room for improvement.

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