An unsupervised learning algorithm for computer vision-based blind modal parameters identification of output-only structures from video measurements

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%.

Customer relationship management (CRM) is the tool to enhance customer relationship in any business. Due to the exponential growth of data volume, in any field, it is significant to develop new techniques to discover the customer knowledge, automation of the system and moreover customer satisfaction to win customer lifetime value. CRM with machine learning could bring a catalytic change in business. Several supervised and unsupervised machine learning techniques are utilized to improve the customer experience and profitability of business. This paper reviews the available literature on the CRM with machine learning techniques for customer identification, customer attraction, and customer retention and customer development. This study reveals that supervised learning techniques are 48.48% utilized, unsupervised learning techniques are utilized 15.15%, and 9.09% utilized other techniques in CRM. Paradigm is also shifted toward the deep learning from machine learning as 28.28% text has been reported to deep learning. Decision tree-based algorithm and support vector machine algorithms are most utilized algorithm of supervised learning. E-commerce and telecommunication sectors are the most important areas identified with the exponential growth of the users and hence need a suitable machine learning techniques for customer satisfaction and business profitability.

In rarefied gas flows, discontinuity phenomena such as velocity slip and temperature jump commonly appear in the gas layer adjacent to a solid boundary. Due to the physical complexity of the interactions at the gas-solid interface, particularly in the case of systems with local nonequilibrium state, boundary models with limited number of parameters cannot completely describe the reflection of gas molecules at the boundary. In this work, the Gaussian mixture (GM) model, which is an unsupervised machine learning technique, is employed to construct a statistical gas-solid surface scattering model based on the collisional data obtained from molecular dynamics (MD) simulations. The GM model is applied to study Couette flow for different inert gases (Ar and He) confined between two parallel infinite gold walls at different temperatures. A direct comparison between the results obtained from the GM model and the Cercignani-Lampis-Lord (CLL) scattering kernel against the MD collisional data in terms of the distribution of the predicted postcollisional velocities, and accommodation coefficients has shown that the results from the GM model are an excellent match with the MD results outperforming the CLL scattering kernel. As an example, for He gas, while the predicted energy accommodation coefficient by the CLL model is more than two times higher than the MD predictions, the value computed by the GM model is in excellent agreement with the MD results. This superior performance of the GM model confirms its high potential to derive a generalized boundary condition in systems encountered with highly nonequilibrium and complex gas flow conditions.

Technological developments have affected the decision-making phase in football matches. The viewing pleasure of fans and the result of the match is a matter of debate determined by the referees. Throughout time, the objectiveness of referees was questioned by the technological tools. Video Assistant Referee (VAR) is an example approach to ensure whether the referees react to similar positions of different matches in the same manner or not. One of the problems of this topic is the controversial decisions of some referees leading to unexpected results. In this research, as a different approach to the referees’ objectivity problem, referees are tried to be classified based on the statistical outcome of the matches using unsupervised machine learning techniques. Meaningful clusters should not be found to be able to state the referees are objective. This study is conducted on 10-years English Premier League between 2009–2018 data. Principal Component Analysis is going to be used for grouping the variables to perform exploratory data analysis. K-Means, hierarchical clustering, and Fuzzy C-Means are going to be used for dividing the referees into various subgroups. R programming language is used for examining data. In conclusion of the analysis, four different referee groups are defined.KeywordsFootballEnglish Premier LeagueRefereeClusteringMachine learningUnsupervised learning

ML-Diabetes is a machine learning-based predictive model for the early detection of diabetes. Diabetes is a chronic metabolic disorder that affects millions of people worldwide. Early detection of diabetes can help prevent its complications and improve patient outcomes. ML-Diabetes is designed to use demographic and clinical data to predict the likelihood of a patient developing diabetes. The model uses a combination of supervised and unsupervised machine learning techniques to analyse and classify data. ML-Diabetes uses a dataset containing demographic and clinical information of patients, including age, sex, BMI, blood pressure, and glucose levels. The dataset is preprocessed and cleaned to remove missing values and outliers. The processed data is then split into training and testing sets, and the model is trained on the training set. The model uses a combination of supervised and unsupervised machine learning techniques, including logistic regression, decision trees, and k-means clustering, to predict the likelihood of a patient developing diabetes. The model is evaluated on the testing set using various performance metrics, including accuracy, precision, recall, and F1-score. The results show that ML-Diabetes is a reliable and accurate predictive model for the early detection of diabetes. The model achieves an accuracy of 85%, precision of 90%, recall of 80%, and F1-score of 85%. The model can be used by healthcare professionals to screen patients for diabetes and provide early interventions to prevent complications.

Who cares about coal? Analyzing 70 years of German parliamentary debates on coal with dynamic topic modeling

Mangroves are indispensable in the coastal ecosystem and are depleting due to man-made and natural causes. Attempts are being made to mitigate the impact on mangroves, and transplantation is considered a strategy for mangrove regeneration. However, it is essential to identify suitable regions and select mangrove species compatible with these regions to achieve maximum benefit from transplantation. Mangrove transplantation is increasingly adopted as a restoration strategy, yet its success hinges on selecting species adapted to site-specific environmental conditions. This study utilized an unsupervised machine learning (ML) approach, a Self-Organizing Map (SOM) model, to predict mangrove species distribution using readily available data, requiring minimal labor, time, and cost. The data used in this study were gathered from the literature and included climatic, hydrological, and solar insolation variables. An SOM model with an 11 × 8 neuron grid was developed to cluster species based on their ecological preferences. Model accuracy was assessed through Quantization error (0.0003) and Topographic error (0.0548), confirming strong predictive performance and topological preservation. The SOM model effectively predicts mangrove species across climatic zones, capturing their zonal preferences based on input variables. This approach offers a practical decision-support tool for restoration practitioners, improving site–species matching and thereby increasing transplantation success rates. Future refinement could be achieved by integrating additional environmental parameters such as soil salinity, tidal dynamics, and seasonal microclimatic variations to better capture temporal shifts in species distribution. Hence, findings highlight the potential of ML to guide cost-effective, evidence-based mangrove conservation and ecological restoration strategies.

In this paper, an unsupervised machine learning technique is proposed to localize the Subthalamic Nucleus (STN) during deep brain stimulation (DBS) Surgery. DBS is one of most common treatments for advanced Parkinson’s disease (PD). The purpose of this surgery is to permanently implant stimulation electrodes inside the STN to deliver electrical currents. It is clinically shown that DBS surgery can significantly reduce motor symptoms of PD (such as tremor). However, the outcome of this surgery is highly dependent on the location of the stimulating electrode. Since STN is a very small region inside the basal ganglia, accurate placement of the electrode is a challenging task for the surgical team. During DBS surgery, the team uses Micro-Electrode Recording (MER) of electrophysiological neural activities to intraoperatively track the location of electrodes and estimate the borders of the STN. In this work, we propose a composite unsupervised machine learning clustering approach that is capable of detecting the dorsal borders of the STN during DBS operation. For this, MER signals from 50 PD patients were recorded and used to validate the performance of the proposed method. Results show that the approach is capable of detecting the dorsal border of the STN in an online manner with an accuracy of 80% without using any supervised training.

Assessment of damage evolution of concrete beams strengthened with BFRP sheets with acoustic emission and unsupervised machine learning

Ocean data classification using unsupervised machine learning: Planning for hybrid wave-wind offshore energy devices

The COVID-19 virus exhibits pneumonia-like symptoms, including fever, cough, and shortness of breath, and may be fatal. Many COVID-19 contraction experiments require comprehensive clinical procedures at medical facilities. Clinical studies help to make a correct diagnosis of COVID-19, where the disease has already spread to the organs in most cases. Prompt and early diagnosis is indispensable for providing patients with the possibility of early clinical diagnosis and slowing down the disease spread. Therefore, clinical investigations in patients with COVID-19 have revealed distinct patterns of breathing relative to other diseases such as flu and cold, which are worth investigating. Current supervised Machine Learning (ML) based techniques mostly investigate clinical reports such as X-Rays and Computerized Tomography (CT) for disease detection. This strategy relies on a larger clinical dataset and does not focus on early symptom identification. Towards this end, an innovative hybrid unsupervised ML technique is introduced to uncover the probability of COVID-19 occurrence based on the breathing patterns and commonly reported symptoms, fever, and cough. Specifically, various metrics, including body temperature, breathing and cough patterns, and physical activity, were considered in this study. Finally, a lightweight ML algorithm based on the K-Means and Isolation Forest technique was implemented on relatively small data including 40 individuals. The proposed technique shows an outlier detection with an accuracy of 89%, on average.

Dealing with weighting scheme in composite indicators: An unsupervised distance-machine learning proposal for quantitative data

Nowadays, the enormous quantity of consented healthcare data is quickly accessible. But, it is quite large and complicated; therefore, it is very challenging to quickly analyse the data for generating an essential judgement related to patient health. So, there is a requirement for a mechanism that supports health researchers in producing effective healthcare policies, building drug recommendation systems, and developing health profiles of personage. In this situation, data mining techniques can also support to identify and recognize unknown patterns in a data set.Let us take an example, where a model is seeking to recognize that an individual is suffering from diabetes or not. If inside your data set the output is labelled, then one can use a supervised classification model. But if there are no labelled data sets or they are unreliable, then one can employ unsupervised learning methods such as clustering to determine a structure in a collection of data by considering that a reliable label output will be provided. Clustering is an unsupervised machine learning technique, with several valuable applications in healthcare.For example, in the diabetes prediction system, the data are usually collected and distributed for model training based on ICD-10 codes. However, it may be possible that in the data set, several ICD-10 codes for diabetes can be missing. In that case, when the ICD-10 code cannot be applied for model training, then diabetic/non-diabetic group structure needs to be discovered based on input values alone.But to select the appropriate clustering technique and the optimal estimation of clusters is still a challenging task. It is tough to identify a suitable clustering algorithm due to little knowledge of data mining. In this chapter, different clustering techniques have been discussed that can be applied to the healthcare data set to discover the most suitable algorithms that can detect the optimized group of clusters. For experimental analysis, different healthcare data sets such as Diabetes data set and Breast cancer dataset have been taken into consideration. On these data sets, different clustering algorithms have been applied to discover the optimal clusters and to judge the accuracy of algorithms.KeywordsClusteringK-means clusteringHierarchical clusteringCancer data setTraining set

Hollow-dependent wildlife has been declining globally due to the removal of hollow-bearing trees, yet these trees are often unaccounted for in habitat mapping. As on-ground field surveys are costly and time-consuming, we aimed to develop a simple, accessible and transferrable geospatial approach using freely accessible LiDAR to refine habitat mapping by identifying high densities of potential hollow-bearing trees. We assessed if LiDAR from 2009 could be accurately used to detect tree heights, which would correlate to tree diameter at breast height (DBH), which in turn would identify trees that are more likely to be hollow-bearing. Here, we use habitat mapping of greater gliders (Petauroides spp.) in the Fraser Coast region of Australia as a case study. Across four sites, field surveys were conducted in 2023 to assess the tree height and density of large trees (>50 cm DBH per 1 km2) at 19 transects (n = 91). This was compared to outputs from individual tree detection derived from unsupervised classification using a local maximal filter and variable window size to identify treetops in freely available LiDAR. Tree height was measured with an accuracy of RMSE 5.75 m, and we were able to identify transects with large trees (>50 cm DBH), which were more likely hollow bearing. However, there was no statistical evidence to suggest that transects with a high density of large trees could be accurately identified based on LiDAR alone (>50 cm DBH p 0.2298). Despite this, we have demonstrated that freely accessible LiDAR and unsupervised machine learning techniques can be utilised to identify large, potentially hollow-bearing trees on a broad scale to refine habitat mapping for hollow-dependent species. It is important to develop geospatial analysis methods that are more accessible to land managers, as deep machine learning methods and current LiDAR can be computationally intensive and expensive. We propose a workflow using free and accessible geospatial analysis methods to identify large, potentially hollow-bearing trees and determine how to address some limitations in this geospatial approach.

Text mining in manufacturing process using unsupervised techniques of Machine learning