Cluster Of Machines Research Articles

Application of machine learning for mass spectrometry-based multi-omics in thyroid diseases

Thyroid diseases, including functional and neoplastic diseases, bring a huge burden to people’s health. Therefore, a timely and accurate diagnosis is necessary. Mass spectrometry (MS) based multi-omics has become an effective strategy to reveal the complex biological mechanisms of thyroid diseases. The exponential growth of biomedical data has promoted the applications of machine learning (ML) techniques to address new challenges in biology and clinical research. In this review, we presented the detailed review of applications of ML for MS-based multi-omics in thyroid disease. It is primarily divided into two sections. In the first section, MS-based multi-omics, primarily proteomics and metabolomics, and their applications in clinical diseases are briefly discussed. In the second section, several commonly used unsupervised learning and supervised algorithms, such as principal component analysis, hierarchical clustering, random forest, and support vector machines are addressed, and the integration of ML techniques with MS-based multi-omics data and its application in thyroid disease diagnosis is explored.

Cybersecurity of smart grids: Comparison of machine learning approaches training for anomaly detection

Objectives. The transformation of modern electric grids into decentralized smart grids presents new challenges in the field of cybersecurity. The purpose of this work is to conduct research and analysis into the effectiveness of different machine-learning methods for identifying anomalies in decentralized smart networks, including cyberattacks and emergency modes, as well as to develop recommendations on the optimal combination of these methods for ensuring effective cybersecurity under conditions of changing electrical loads.Methods. We consider several machine learning methods for identifying anomalies in power systems that simulate network behavior under conditions of cyberattacks and emergency modes. The relative effectiveness of such methods as multifractal analysis using wavelets, the Isolation Forest model, local outlier factor (LOF), k-means clustering, and one-class support vector machine (One-Class SVM), is analyzed.Results. The comparison of machine learning methods reveals the varying effectiveness of anomaly detection methods used to detect cyber threats and deviations in electrical systems. Isolation Forest is best at detecting abrupt changes related to cyberattacks with high accuracy and a minimum of false positives. While LOF can also be effective in detecting cyberattacks, its increased sensitivity to minor deviations increases the number of false positives. K-means and One-Class SVMs are less effective in detecting abrupt anomalies but are useful for general clustering of data and detecting both abrupt and smooth changes, respectively.Conclusions. The obtained research results indicate the advantages of using a combination of machine learning algorithms to ensure the reliable protection of smart networks from cyberattacks taking into account the nature of the electrical load.

Customer Segmentation using Machine Learning

In the competitive landscape of financial services, understanding customer behavior is paramount for effective marketing strategies and personalized services. This study explores customer segmentation techniques applied to credit card holders utilizing a rich dataset comprising demographic, transactional, and behavioral information. Through advanced data analytics, including clustering algorithms and machine learning models, we unveil distinct customer segments based on spending habits, payment behavior, credit utilization, and other relevant features. Our findings reveal several distinct segments within the credit card holder population, each exhibiting unique characteristics and preferences. By delineating these segments, financial institutions can tailor their marketing campaigns, product offerings, and customer service initiatives to better meet the diverse needs of their clientele. Moreover, the segmentation analysis provides insights into risk assessment, fraud detection, and customer retention strategies, thus enhancing overall business performance and customer satisfaction.

A Hybrid Model to Enhance The Performance of Classifier in Financial Distress Prediction

Accurately predicting financial distress is a critical issue in financial decision-making. Financial distress must be detected as early as possible as an important determining factor in decision-making for internal companies and financial institutions related to financing or loan decisions. Various studies on financial distress prediction in Indonesia have been carried out, ranging from traditional statistical approaches to machine learning. However, the performance of the two methods is still not optimal. Therefore, this study tries to develop machine learning techniques by combining cluster analysis and classification in a hybrid model to improve the prediction model’s performance. The case study adopted in this study is the prediction of financial distress in non-financial companies listed on the IDX from 2018-2021 by combining k-means clustering and Support Vector Machine. The analysis results show that the hybrid classifier has an accuracy value of 92.7%, which is higher than the accuracy of the single classifier, which is 88.6%.

Forecasting Upwelling Phenomena in Lake Laut Tawar: A Semi-Supervised Learning Approach

The current climate change is causing the upwelling phenomenon to occur frequently in lakes and reservoirs. As a result of this phenomenon, thousands of fish die, causing floating net cage fish farmers to suffer losses. From existing studies, temperature sensors are used to determine the current condition of a body of water experiencing upwelling or not. Therefore, this study applies clustering to historical climate data from 2017-2023 using a semi-supervised learning approach that produces two labels: "potential for upwelling" and "no potential for upwelling." In the clustering process, the data is divided into two clusters using K-Means Clustering, and Support Vector Machine (SVM) is chosen to classify them. The performance of the proposed algorithm is expressed with accuracy, precision, recall, and F1-score values of 0.99, 0.995, 0.970, and 0.985, respectively. The analysis results show that this model has excellent performance in identifying upwelling potential. By using this method, information about upwelling potential can be obtained more quickly and accurately, allowing fish farmers to take appropriate preventive measures. This study also shows that the combination of K-Means Clustering and Support Vector Machine (SVM) can be effectively used to analyze historical climate data and generate useful predictions.

Clustering Portuguese municipalities based on population health indicators

Abstract Background The large number of publicly available health-related indicators poses challenges in health planning to policymakers. Identifying patterns among municipalities may assist these processes, by grouping similar contexts and pointing out relevant differences among neighbouring locations. The aim of this work was to define clusters of municipalities based on health-related indicators. Methods All health-related indicators available at municipality level for the year 2021 were obtained from the Portuguese National Statistics Office website. Missing values were imputed with the closest available year or excluded. Indicators were standardized and K-means clustering was applied. Elbow criterion was used to select the optimal number of clusters. Sensitivity analysis was performed with a priori exclusion of indicators less directly associated with health outcomes. Results The clustering analysis revealed five distinct clusters of municipalities. Clusters were associated with specific socio-demographic patterns, providing insights into common health challenges and strengths within each group. Interestingly, geographic profiles emerged (e.g. coastal vs inland), despite no geographic information being specifically provided. One outlier municipality was identified (Lisbon), highlighting a need for special consideration. Conclusions Clustering techniques provide a data-driven approach to inform public health planning. Our findings show previously unrecognized patterns and relationships between municipalities. Using these clusters, policymakers may implement interventions based on what has been proven to work in similar contexts, enhance collaboration opportunities and promote a tailored policy development, contributing to an overall improvement of public health outcomes. Key messages • Clustering machine learning techniques provide a data-driven approach to inform public health planning. • Identifying comparable municipalities based on health-related indicators allows targeted public health interventions.

Maintenance plan adaptation based on health ratings of servitised machines through a fleet-wide machine clustering method

The increased requests for value-added services to integrate product performance push manufacturing companies to extend their service offerings to meet customers’ needs. In this context, maintenance planning can leverage new possibilities offered by digital technologies for data analytics services. The present research then proposes an approach for maintenance plan adaptation based on a data-driven method applied over a fleet of machines installed in different production sites. The method relies on collaborative prognostics to develop a clustering of machines’ behaviour aimed at providing the health ratings of the machines and the subsequent maintenance plan adaptation due to the deviation from the expected behaviour. The method is adopted from the perspective of an Original Equipment Manufacturer, as part of a transformation path towards an advanced provision of digitalization for maintenance service offerings. The method is validated in the context of two lines at selected customer’s premises. This demonstrates the viability and effectiveness of adapting the maintenance plans thanks to the data analytics in light of the current behaviour of the machines within the lines.

Predicting the Recurrence of Ovarian Cancer Based on Machine Learning.

Recurrence is the main factor for poor prognosis in ovarian cancer, but few prognostic biomarkers were reported. In this study, we used machine learning methods based on multiple biomarkers to develop a specific prediction model for the recurrence of ovarian cancer. A total of 277 ovarian cancer patients were enrolled in this study and randomly classified into training and testing cohorts. The prediction information was obtained through 47 clinical parameters using six supervised clustering machine learning algorithms, including K-Nearest Neighbor (K-NN), Decision Tree (DT), Random Forest (RF), Adaptive Boosting (AdaBoost), Gradient Boosting Machine (GBM), and Extreme Gradient Boosting (XGBoost). In predicting the recurrence of ovarian cancer, machine learning algorithm was superior to conventional logistic regression analysis. In this study, XGBoost showed the best performance in predicting the recurrence of ovarian cancer, with an accuracy of 0.95. In addition, neoadjuvant chemotherapy, Monocyte ratio (MONO%), Hematocrit (HCT), Prealbumin (PAB), Aspartate aminotransferase (AST), and carbohydrate antigen 125 (CA125) are the most important biomarkers to predict the recurrence of ovarian cancer. The machine learning techniques can achieve a more accurate assessment of the recurrence of ovarian cancer, which can help clinicians make decisions, and develop personalized treatment strategies.

K-Medoids and Support Vector Machine in Predicting the Level og Building Damage in Earthquake Insurance Modeling

Yogyakarta, an Indonesian province prone to earthquakes, frequently suffers extensive damage to buildings, necessitating insurance coverage to mitigate potential losses. This study aims to forecast earthquake insurance premiums by predicting building damage levels resulting from earthquakes. Utilizing data from buildings affected by the June 30, 2023, earthquake in Yogyakarta, we employ K-Medoids Clustering and Support Vector Machine (SVM) to predict two categories of building damage: minor (labelled as 1) and heavy (labelled as 2). The total premiums for minor damage range from approximately USD 86.55 to USD 288.50, while for heavy damage, they range from USD 120.05 to USD 400.18 using the K-Medoids algorithm. Meanwhile, premiums for minor damage range from USD 83.14 to USD 277.13, and for heavy damage, they range from USD 223.67 to USD 745.55 using the SVM algorithm.

The data recovery strategy on machine learning against false data injection attacks in power cyber physical systems

During the transmission of power measurement data through communication networks from remote terminal unit (RTU) to the state estimator in Supervisory Control and Data Acquisition (SCADA), power cyber-physical systems (PCPSs) are more susceptible to cyber-attacks. To mitigate that threat, this paper is concerned with a new data recovery strategy on machine learning against false data injection attacks (FDIAs) in PCPSs. Firstly, in view of the limited resources (such as limited energy) of adversaries and system protections, a sparse target false data injection attack (FDIA) is constructed. Then, the FDIA detection problem is transformed into a tripartite separation problem, and the alternating direction method of multipliers on proximal exchange (ADMM-PE) is adopted to complete the intrusion detection of FDIAs. In addition, with the help of reliable mask information and real incomplete measurement data provided by the FDIA detection, a similar supervised generative adversarial imputation networks (GAIN) is proposed to complete the measurement data recovery after FDIAs. Specifically, the pseudo labels generated by data analysis methods such as k-means clustering and support vector machine (SVM) to improve the accuracy of measurement data recovery. Finally, the experimental results of PCPSs show the effectiveness and superiority of the proposed data recovery strategy against FDIAs.

A missing data processing method for dam deformation monitoring data using spatiotemporal clustering and support vector machine model

Deformation monitoring is a critical measure for intuitively reflecting the operational behavior of a dam. However, the deformation monitoring data are often incomplete due to environmental changes, monitoring instrument faults, and human operational errors, thereby often hindering the accurate assessment of actual deformation patterns. This study proposed a method for quantifying deformation similarity between measurement points by recognizing the spatiotemporal characteristics of concrete dam deformation monitoring data. It introduces a spatiotemporal clustering analysis of the concrete dam deformation behavior and employs the support vector machine model to address the missing data in concrete dam deformation monitoring. The proposed method was validated in a concrete dam project, with the model error maintaining within 5%, demonstrating its effectiveness in processing missing deformation data. This approach enhances the capability of early-warning systems and contributes to enhanced dam safety management.

Integrated Supervised and Unsupervised Machine Learning Approach to Map the Electrochemical Windows Over 4500 Solvents for Battery Applications.

The compatibility between solvent electrolytes and high-voltage electrode materials represents a significant impediment to the progress of rechargeable metal-ion batteries. Rapidly identifying suitable solvent electrolytes with optimized electrochemical windows (ECWs) within an extensive search space poses a formidable challenge. In this study, we introduce a combined supervised and unsupervised (clustering) machine learning (ML) approach to discern distinct clusters of solvent electrolytes exhibiting varying ECW ranges. Through supervised machine learning, we have accurately predicted optimal solvent electrolytes with desired ECWs from a vast pool of 4882 solvents. Our ML model boasts superior accuracy compared to previously reported data from density functional theory (DFT). Besides, the exploration of the vast solvent space through K-means clustering (unsupervised approach) yields 11 optimal clusters, each encompassing different solvents characterized by diverse ECW ranges and frequencies. The expedited reduction of solvent space achieved through clustering occurs within a very short time frame and with minimal resource expenditure. Consequently, this method is highly capable of streamlining the subsequent experimental investigations for battery applications, avoiding the need for a trial-and-error approach.

Fuzzy Cluster Pitch Synthesis System for the Violin Sound with Machine Learning

Pitch synthesis with violin sound involves the generation of musical pitches using technology to mimic the distinctive tonal characteristics of a violin. This process typically employs digital signal processing techniques to recreate the timbre, articulation, and nuances of a real violin. Advanced algorithms analyze and model the acoustic properties of a violin sound, allowing for the synthesis of realistic pitch variations and expressive qualities. Whether utilized in electronic music production, virtual instruments, or sound design, pitch synthesis with violin sound aims to emulate the rich and complex sonic palette of the violin, offering musicians and composers versatile tools for creative expression and sonic exploration. In this paper proposed Fuzzy Pitch Clustering Machine Learning (FPC-ML) for the violin Music Pitch Synthesis using Machine Learning. The proposed FPC-ML model uses the Fuzzy Clustering model for the estimation of pitches in the violin music signal. Based on the Fuzzy clustering model membership degree is computed for the proposed FPC-ML for the estimation of the pitch in the violin music. With the estimation of linguistic variables, clustering is performed in the Music signal for the computation of pitches. With the estimated pitches in the violin music, the features are trained in the machine learning model for the classification and estimation of features in the Violin Music. Simulation analysis demonstrated that the proposed FPC-ML model computes the features of the Violin Music Pitch values based on the estimated clustering values synthesis performed for the classification of the Violin Music signal. The proposed FPC-ML technique achieves an accuracy value of 0.98 for the violin signal with an iteration of 20. With the increase in several iterations and epoch, the accuracy of the FPC-ML model is further increased for the synthesis of the Violin Music.

A combined technique for power transformer fault diagnosis based on k‐means clustering and support vector machine

AbstractThis contribution presents a two‐step hybrid diagnostic approach, combining k‐means clustering for subset formation, followed by subset analysis conducted by human experts. As the feature input vector has a significant influence on the performance of unsupervised machine learning algorithms, seven feature input vectors derived from traditional methods, including Duval pentagon method, Rogers ratio method, three ratios technique, Denkyoken method, ensemble gas characteristics method, Duval triangle method, and Gouda triangle method were explored for the subset formation stage. The seven proposed individual methods, corresponding to the seven feature input vectors, were implemented using a dataset of 595 DGA samples and tested on an additional 254 DGA samples. Furthermore, a combined technique based on a support vector machine was introduced, utilising the diagnostic results of the individual methods as input features. From training and testing, with diagnostic outcomes of 91.09% and 90.94%, the combined technique demonstrated the highest overall diagnostic accuracies. Using the IEC TC10 database, the diagnosis accuracies of the proposed diagnostic methods were compared to existing methods of literature. From the results obtained, the combined technique outperformed the proposed individual methods and existing methods used for comparison.

A genetic algorithm‐based virtual machine scheduling algorithm for energy‐efficient resource management in cloud computing

SummaryTo address the unbalanced resource load of a virtual machine cluster, the author proposes an energy‐saving virtual machine scheduling algorithm based on resource management cloud computing technology. This article analyzes the current cloud computing and virtual machine scheduling research in the cloud computing environment. It discusses the concept, characteristics, classification, application scenarios, and key cloud computing technologies. A genetic algorithm is used to solve the problem of high energy consumption in the data center. The test results show that in the same original configuration scheme, the migration times based on the greedy algorithm adopted by GA2ND are about 1000, and the migration times of GA1ST are between 200 and 500. The GA2ND migration scheme requires fewer virtual machines. In the result analysis, the experiments compare the proposed algorithms—DVFS, IMC, GA1ST, and GA2ND—with a focus on energy consumption and virtual machine migration. Notably, DVFS serves as a reference for energy efficiency, IMC represents the proposed algorithm without genetic optimization, GA1ST denotes the genetic algorithm under a heterogeneous model, and GA2ND signifies the enhanced genetic algorithm introduced in this article. The comparison aims to assess the energy efficiency and virtual machine migration performance of each algorithm in the context of a simulated cloud computing environment. Therefore, the algorithm proposed in this article can effectively reduce energy consumption and avoid frequent migration of virtual machines.

Study of anisotropy in polydispersed 2D micro and nano-composites by Elbow and K-Means clustering methods

A new two-dimensional polydisperse model for testing multiphase composites is developed. The verification of the model is carried out based on the microstructure of two composites obtained in the ex-situ and in-situ techniques produced in the Friction Stir Processing (Al–Si–Cu/SiCp) and the Self-propagating High temperature Synthesis (Al/nano-TiCp). The examined microstructure is considered on macro-, meso- and micro- levels. Besides the reinforcement phase distribution concentration, an anisotropy coefficient κ is calculated for every meso-cell. It is the cumulative vector parameter that characterizes the spatial two-point correlation of inclusions. In particular, κ determines the principal axes of the effective conductivity tensor over a meso-cell. The anisotropy spatial distribution ϰ(x) is introduced as the discrete function of values κ calculated over meso-cells centered at the point x. This new distribution ϰ(x) accumulates the geometrical information of the considered microstructure. A computational experiment is designed and carried out in order to study the influence of the reinforcement phase redistribution on the anisotropy. Therefore, it adequately describes the anisotropy of fields in composites modeling thermal and electric conductivity, diffusion, and elastic antiplane deformation. The results of the computational experiments are analyzed using the Elbow and K-Means clustering machine learning algorithm. The detailed analysis is carried out to divide the data set into up to 5 clusters. A research hypothesis concerning the criterion of isotropy of the distribution of reinforcing phase particles in the composite structure is formulated

Minimum-energy virtual machine placement using embedded sensors and machine learning

Cloud data centers (DCs) consume large amounts of energy and contribute significantly to environmental concerns. Furthermore, with the advent of 5G and B5G networks, increasingly software-oriented and becoming highly dependent on cloud computing, it becomes imperative to optimize their energy consumption. Thus, in this study, we present a virtual machine placement algorithm that minimizes the energy consumption of a cluster of server machines. Our solution is embodied through the use of sensors embedded inside physical server machines, enabling the introduction of new features for sensitive thermal awareness and proactive hot spot avoidance. Leveraging this significantly enhanced feature space, we implement data-driven predictive machine learning models along with a heuristic placement algorithm (CPP), enabling proactive VM placements that are both energy-aware and thermal-aware. Indeed, experiments carried out on a cluster of physical server machines demonstrate high performance by both the ML models and the placement algorithm (CPP). Compared with the best baseline algorithm, our solution reduced power consumption and temperature by 7% and 2%, respectively, while avoiding hot spots and maintaining efficient load distribution, thereby reducing the overhead of physical machines by 28%.

Socket performance influence on data processing intensity in a virtual machine cluster with heterogeneous conditions.

Socket performance influence on data processing intensity in a virtual machine cluster with heterogeneous conditions.

A clustering machine learning approach for improving concrete compressive strength prediction

AbstractThis study investigates the application of clustering techniques to enhance the accuracy of hierarchical classification and regression (HCR) models for predicting concrete compressive strength (CCS). Following the hypothesis that integrating clustering at the initial levels of model hierarchy reduces classification errors and prevents their propagation to subsequent levels, HCR models were developed utilizing both the unweighted pair group method with arithmetic mean (UPGMA) and hard clustering (HC) methods. Findings demonstrate that models using UPGMA significantly outperform those based on HC. Furthermore, it was demonstrated that further hierarchical clustering allows for multilayered HCR models that improve predictive performance by further leveraging parent–child relationships within data clusters. Overall, this study demonstrates that the proposed methodology can be introduced in the model development pipeline to enhance the prediction accuracy of CCS models.

InfusedHeart: A Novel Knowledge-Infused Learning Framework for Diagnosis of Cardiovascular Events

In the undertaken study, we have used a customized dataset termed ``Cardiac-200'' and the benchmark dataset ``PhysioNet.'' which contains 1500 heartbeat acoustic event samples (without augmentation) and 1950 samples (with augmentation) heartbeat acoustic events such as normal, murmur, extrasystole, artifact, and other unlabeled heartbeat acoustic events. The primary reason for designing a customized dataset, ``cardiac-200,'' is to balance the total number of samples into categories such as normal and abnormal heartbeat acoustic events. The average duration of the recorded heartbeat acoustic events is 10-12 s. In the undertaken study, we have analyzed and evaluated various heartbeat acoustic events using audio processing libraries such as Chromagram, Chroma-cq, Chroma-short-time Fourier transform (STFT), Chroma-cqt, and Chroma-cens to extract more information from the recorded heartbeat sound signals. The noise removal process has been carried out using local binary pattern (LBP) methodology. The noise-robust heartbeat acoustic images are classified using long short-term memory (LSTM)-convolutional neural network (CNN), recurrent neural network (RNN), LSTM, Bi-LSTM, CNN, K-means Clustering, and support vector machine (SVM) methods. The obtained results have shown that the proposed InfusedHeart Framework had outclassed all the other customized machine learning and deep learning approaches such as RNN, LSTM, Bi-LSTM, CNN, K-means Clustering, and SVM-based classification methodologies. The proposed Knowledge-infused Learning Framework has achieved an accuracy of 89.36% (without augmentation), 93.38% (with augmentation), and a standard deviation of 10.64 (without augmentation), and 6.62 (with augmentation). Furthermore, the proposed framework has been tested for various signal-to-noise ratio conditions such as SignaltoNoiseRatio0, SignaltoNoiseRatio3, SignaltoNoiseRatio6, SignaltoNoiseRatio9, SignaltoNoiseRatio12, SignaltoNoiseRatio15, and SignaltoNoiseRatio18. In the end, we have shown a detailed comparison of texture and without texture approaches and have discussed future enhancements and prospective ways for future directions.