Relationship between structure and dynamics of an icosahedral quasicrystal using unsupervised machine learning.

Assessing the detection of lateral movement through unsupervised learning techniques

Background Anomaly detection is vital in industrial settings for identifying abnormal behaviors that suggest faults or malfunctions. Artificial intelligence (AI) offers significant potential to assist humans in addressing these challenges. Methods This study compares the performance of supervised and unsupervised machine learning (ML) techniques for anomaly detection. Additionally, model-specific explainability methods were employed to interpret the outputs. A novel explainability approach, MLW-XAttentIon, based on causal reasoning in attention networks, was proposed to visualize the inference process of transformer models. Results Experimental results revealed that unsupervised models perform well without requiring labeled data, offering significant promise. In contrast, supervised models demonstrated greater robustness and reliability. Conclusions Unsupervised ML techniques present a feasible, resource-efficient option for anomaly detection, while supervised methods remain more reliable for critical applications. The MLW-XAttentIon approach enhances interpretability of transformer-based models, contributing to trust and transparency in AI-driven anomaly detection systems.

The study aimed to assess the performance of several unsupervised machine learning (ML) techniques in online anomaly (The term “anomaly” is used here to indicate a departure from expected process behavior which may indicate a quality issue which requires further investigation. The term “defect detection” has often been used previously but the specific imperfection is often indirectly inferred.) detection during surface tension transfer (STT)-based wire arc additive manufacturing. Recent advancements in quality monitoring for wire arc manufacturing were reviewed, followed by a comparison of unsupervised ML techniques using welding current and welding voltage data collected during a defect-free deposition process. Both time domain and frequency domain feature extraction techniques were applied and compared. Three analysis methodologies were adopted: ML algorithms such as isolation forest, local outlier factor, and one-class support vector machine. The results highlight that incorporating frequency analysis, such as fast Fourier transform (FFT) and discrete wavelet transform (DWT), for feature extraction based on general frequency response and defined bandwidth frequency response, significantly improves performance, reflected in a 14% increase in F2 score, compared with time-domain features extraction. Additionally, a deep learning approach employing a convolutional autoencoder (CAE) demonstrated superior performance by processing time-frequency domain data stored as spectrograms obtained through short-time Fourier transform (STFT) analysis. The CAE method outperformed frequency domain analysis and traditional ML approaches, achieving an additional 5% improvement in F2-score. Notably, the F2-score (The F2 score is the weighted harmonic mean of the precision and recall (given a threshold value). Unlike the F1 score, which gives equal weight to precision and recall, the F2 score gives more weight to recall than to precision.) increased significantly from 0.78 in time domain analysis to 0.895 in time-frequency analysis. The study emphasizes the potential of utilizing low-cost sensors to develop anomaly detection modules with enhanced accuracy. These findings underscore the importance of incorporating advanced data processing techniques in wire arc additive manufacturing for improved quality control and process optimization.

Machine learning techniques will contribution towards making Internet of Things (IoT) symmetric applications among the most significant sources of new data in the future. In this context, network systems are endowed with the capacity to access varieties of experimental symmetric data across a plethora of network devices, study the data information, obtain knowledge, and make informed decisions based on the dataset at its disposal. This study is limited to supervised and unsupervised machine learning (ML) techniques, regarded as the bedrock of the IoT smart data analysis. This study includes reviews and discussions of substantial issues related to supervised and unsupervised machine learning techniques, highlighting the advantages and limitations of each algorithm, and discusses the research trends and recommendations for further study.

The demand for wearable devices that can detect anxiety and stress when driving is increasing. Recent studies have attempted to use multiple biosignals to detect driving stress. However, collecting multiple biosignals can be complex and is associated with numerous challenges. Determining the optimal biosignal for assessing driving stress can save lives. To the best of our knowledge, no study has investigated both longitudinal and transitional stress assessment using supervised and unsupervised ML techniques. Thus, this study hypothesizes that the optimal signal for assessing driving stress will consistently detect stress using supervised and unsupervised machine learning (ML) techniques. Two different approaches were used to assess driving stress: longitudinal (a combined repeated measurement of the same biosignals over three driving states) and transitional (switching from state to state such as city to highway driving). The longitudinal analysis did not involve a feature extraction phase while the transitional analysis involved a feature extraction phase. The longitudinal analysis consists of a novel interaction ensemble (INTENSE) that aggregates three unsupervised ML approaches: interaction principal component analysis, connectivity-based clustering, and K-means clustering. INTENSE was developed to uncover new knowledge by revealing the strongest correlation between the biosignal and driving stress marker. These three MLs each have their well-known and distinctive geometrical basis. Thus, the aggregation of their result would provide a more robust examination of the simultaneous non-causal associations between six biosignals: electrocardiogram (ECG), electromyogram, hand galvanic skin resistance, foot galvanic skin resistance, heart rate, respiration, and the driving stress marker. INTENSE indicates that ECG is highly correlated with the driving stress marker. The supervised ML algorithms confirmed that ECG is the most informative biosignal for detecting driving stress, with an overall accuracy of 75.02%.

Many modern day applications require the ability to identify those observations or data that deviate from the ones that are considered to be normal by domain expert. Anomaly detection helps to identify these anomalies and once identified, then the system can take the necessary changes. In data mining, this problem is tackled using supervised and unsupervised machine learning techniques. Since in many practical applications, data used will have no labels, unsupervised learning techniques are well suited. This work was aimed at comparing various unsupervised anomaly detection techniques using performance metrics like precision, recall, F-score and area under the curve. The unsupervised learning techniques used in this work are One Class Support Vector Machine(OneClassSVM), Local Outlier Factor(LOF), Isolation Forest(IF) and Elliptic Envelope(EE). Shuttle and satellite datasets were used for experimentation. Performance of these unsupervised learning techniques were compared with supervised learning techniques such as SVM and k-NN. Results show that unsupervised learning techniques are on par or better for anomaly detection compared to supervised learning techniques for the shuttle and satellite datasets.

Atrial Fibrillation (AF) is the most common cardiac arrhythmia, and it is associated with an increased risk of embolic stroke. It is known that AF-related thrombus formation occurs predominantly in the left atrial appendage (LAA). However, it is still unknown the structural and functional characteristics of the left atria (LA) that promote low velocities and stagnated blood flow, thus a high risk of thrombogenesis. In this work, we investigated morphological and in-silico haemodynamic indices of the LA and LAA with unsupervised machine learning (ML) techniques, to identify the most relevant features that could subsequently be used to generate thrombus prediction models. A fully automatic pipeline was implemented to extract multiple morphological parameters from a 3D mesh of a LA. Morphological parameters were then combined with particle flow parameters from in-silico fluid simulations. Unsupervised multiple kernel learning (MKL) was used for dimensionality reduction, resulting in a latent space positioning patients based on feature similarity. Clustering applied to the MKL output space estimated clusters with different proportion of thrombus cases. The cluster with the highest risk of thrombus formation was characterised by high values of LAA height, tortuosity and ostium perimeter, as well as total number of flow particles in the LAA and low angle between the LAA and the left superior pulmonary vein, proving the usefulness of unsupervised ML techniques to extract knowledge from the data, and early identify AF patients at higher risk of thrombus formation.KeywordsAtrial fibrillationLeft atrial appendageUnsupervised machine-learningThrombus

Bookkeeping data free of fraud and errors are a cornerstone of legitimate business operations. The highly complex and laborious work of financial auditors calls for finding new solutions and algorithms to ensure the correctness of financial statements. Both supervised and unsupervised machine learning (ML) techniques nowadays are being successfully applied to detect fraud and anomalies in data. In accounting, it is a long-established problem to detect financial misstatements deemed anomalous in general ledger (GL) data. Currently, widely used techniques such as random sampling and manual assessment of bookkeeping rules become challenging and unreliable due to increasing data volumes and unknown fraudulent patterns. To address the sampling risk and financial audit inefficiency, we applied seven supervised ML techniques inclusive of deep learning and two unsupervised ML techniques such as isolation forest and autoencoders. We trained and evaluated our models on a real-life GL dataset and used data vectorization to resolve journal entry size variability. The evaluation results showed that the best trained supervised and unsupervised models have high potential in detecting predefined anomaly types as well as in efficiently sampling data to discern higher-risk journal entries. Based on our findings, we discussed possible practical implications of the resulting solutions in the accounting and auditing contexts.

ABSTRACT: The correlation of rock mechanical properties from one well to another across an area of interest poses a classical and ongoing problem in rock mechanics. This work illustrates identification of the mechanical layers/zones in a geothermal reservoir using unsupervised machine learning (ML) techniques. Mechanical stratigraphy was defined using well logs obtained from three wells located at the Utah FORGE geothermal site: 58-32, 16A(78)-32 and 16B(78)-32. The widely accepted unsupervised ML techniques including K-means clustering, Gaussian mixture models, and DBSCAN (density-based spatial clustering of applications with noise) were utilized to generate the rock classes based on similarities/differences in mechanical attributes. The rock mechanical classifications were performed using a combination of parameters including measured log data (compressional and shear wave interval transit times) and augmented features such as Poisson's ratio, and Young's modulus. The performance of ML clustering models were evaluated using Silhouette index (SI) and Davies-Bouldin index (DBI) criteria. The evaluation measures of predicted classification reflected the effectiveness and applicability of the proposed ML approaches to generate mechanical stratigraphy. Evaluation measures SS and DBI represent the good quality and reliability of proposed classification with higher SI, CHI, and lower DBI scores. The best performance for the proposed clustering model was exhibited by K-means algorithm with SI, DBI and CHI scores of 0.86, 0.4, and 79, respectively. The proposed mechanical units cluster models were observed to be consistent with the lithological stratigraphy of the studied wells. This approach is therefore shown to provide efficient and reliable identification of mechanical stratigraphy for FORGE with the capability for application across a wide range of subsurface reservoirs. 1. INTRODUCTION Rocks are formed in different lithostratigraphic units that have a wide range of mechanical characteristics (Boersma et al., 2020). According to Ferrill et al. (2017) and Smart et al. (2014). The mechanical characteristics are often described in terms of stiffness and strength properties, including elastic parameters, tensile strength, and compressive strength (Roche et al., 2013).

The study examines language dimensions of mathematical word problems and the classification of mathematical word problems according to these dimensions with unsupervised machine learning (ML) techniques. Previous research suggests that the language dimensions are important for mathematical word problems because it has an influence on the linguistic complexity of word problems. Depending on the linguistic complexity students can have language obstacles to solve mathematical word problems. A lot of research in mathematics education research focus on the analysis on the linguistic complexity based on theoretical build language dimensions. To date, however it has been unclear what empirical relationship between the linguistic features exist for mathematical word problems. To address this issue, we used unsupervised ML techniques to reveal latent linguistic structures of 17 linguistic features for 342 mathematical word problems and classify them. The models showed that three- and five-dimensional linguistic structures have the highest explanatory power. Additionally, the authors consider a four-dimensional solution. Mathematical word problem from the three-dimensional solution can be classify in two groups, three- and five-dimensional solutions in three groups. The findings revealed latent linguistic structures and groups that could have an implication of the linguistic complexity of mathematical word problems and differ from language dimensions, which are considered theoretically. Therefore, the results indicate for new design principles for interventions and materials for language education in mathematics learning and teaching.

Evaluating genotoxicity of metal oxide nanoparticles: Application of advanced supervised and unsupervised machine learning techniques

Operational modal analysis (OMA) is required to maintain large-scale and necessary civil infrastructures. The non-contact method of OMA using digital image correlation and point tracking algorithms requires a speckle pattern placed on the structure. Alternatively, advanced computer vision methods like optical flow and phase-based video motion magnification (PBVMM) techniques are used to measure modal parameters. Despite the importance of PBVMM, the users should know the range of frequencies in which the natural structure frequency lies. A methodology based on an unsupervised machine learning technique is developed to extract the modal parameters blindly from its recorded digital video. The proposed methodology uses complex steerable pyramids and an unsupervised machine learning technique, also known as principal component analysis, and analytical mode decomposition with a random decrement technique to blindly extract the modal parameters of a structure. This study validated the proposed methodology using a multi-degree of freedom (DOF) numerical model. The results are compared with theoretical and estimated values and are in good agreement. Furthermore, it is implemented on a laboratory benchmark SDOF, MDOF, and real-time videos of the London Millennium and Tacoma Narrows bridges for blindly extracting the modal frequencies and damping ratios.

Sedentary lifestyle, poor diet and work pressure lead the diabetes disease which may cause several fatal health issues like heart attack, strokes, kidney failure, nerve damage etc. Diabetes can be effectively managed when caught early with high accuracy. Machine Learning (ML) approaches are very effective to early detection and prediction of diabetes. The goal of this paper is to offer the inclusive examination of the diagnosis of diabetes by supervised and unsupervised ML algorithms. This survey includes papers on the diagnosis of diabetes from 2018-2020. Decision tree based algorithm such as C4.5, AdaBoost, XGBoost, etc., have predicted the diabetes with high accuracy. Unsupervised learning techniques such as PCA and K-Mean are also useful in the attribute selection and outlier detection from the large dataset. This study reveals that K-Mean and SVM have also diagnosed and evaluated diabetes by high accuracy as an amalgamation of supervised and unsupervised machine learning techniques.

RansomDroid: Forensic analysis and detection of Android Ransomware using unsupervised machine learning technique

In recent decades, the use of technological resources such as the eye tracking methodology is providing cognitive researchers with important tools to better understand the learning process. However, the interpretation of the metrics requires the use of supervised and unsupervised learning techniques. The main goal of this study was to analyse the results obtained with the eye tracking methodology by applying statistical tests and supervised and unsupervised machine learning techniques, and to contrast the effectiveness of each one. The parameters of fixations, saccades, blinks and scan path, and the results in a puzzle task were found. The statistical study concluded that no significant differences were found between participants in solving the crossword puzzle task; significant differences were only detected in the parameters saccade amplitude minimum and saccade velocity minimum. On the other hand, this study, with supervised machine learning techniques, provided possible features for analysis, some of them different from those used in the statistical study. Regarding the clustering techniques, a good fit was found between the algorithms used (k-means ++, fuzzy k-means and DBSCAN). These algorithms provided the learning profile of the participants in three types (students over 50 years old; and students and teachers under 50 years of age). Therefore, the use of both types of data analysis is considered complementary.