Machine Learning Methods Research Articles

Modified Kernel Global-Local Marginal Fisher Analysis for Rolling Bearing Feature Extraction

Abstract The vibration signals of rolling bearings usually exhibit high-dimensional, nonlinear, and non-Gaussian distribution characteristics due to long-term operation under complex working conditions. Therefore, we proposed a novel algorithm named Modified Kernel Global-Local Marginal Fisher Analysis (MKGLMFA) for bearing feature extraction and dimensionality reduction. The proposed MKGLMFA algorithm introduces the kernel function to map data into a high-dimensional space to represent data nonlinearly first. It enhances the within-class compactness and between-class dispersibility by considering spatial relationships and label information when constructing adjacency graphs and simultaneously exploits the local and global geometry of data. Furthermore, a bearing fault diagnosis approach is presented based on MKGLMFA. It first processes the original vibration signals through MKGLMFA to obtain low-dimensional manifold features. Then these characteristics were input into the K-nearest neighbor (KNN) classifier to achieve fault pattern recognition. The superiority of the proposed MKGLMFA algorithm in feature extraction is verified in comparison with some existing state-of-the-art machine learning methods on three rolling bearings datasets. And the subsequent classification diagnosis experiments indicate the effectiveness and high efficiency of the newly raised MKGLMFA algorithm. In comparison with the representative diagnosis methods, the proposed method can extract more sensitive discriminant features, and the classification accuracy of diagnosis is significantly improved in consequence.

Generative Interpretable Visual Design: Using Disentanglement for Visual Conjoint Analysis

This article develops a method to automatically discover and quantify human-interpretable visual characteristics directly from product image data. The method is generative and can create new visual designs spanning the space of visual characteristics. It builds on disentanglement methods in deep learning using variational autoencoders, which aim to discover underlying statistically independent and interpretable visual characteristics of an object. The impossibility theorem in the deep learning literature indicates that supervision with ground truth characteristics would be required to obtain unique disentangled representations. However, these are typically unknown in real-world applications, and are in fact exactly the characteristics that need to be discovered. Extant machine learning methods are unsuitable since they require ground truth labels for each visual characteristic. In contrast, this method postulates the use of readily available product characteristics (such as brand and price) as proxy supervisory signals to enable disentanglement. This method discovers and quantifies human-interpretable and statistically independent characteristics without any specific domain knowledge on the product category. It is applied to a dataset of watches to automatically discover interpretable visual product characteristics, obtain consumer preferences over visual designs, and generate new ideal point designs targeted to specific consumer segments.

Aerial Images Segmentation with Graph Neural Network

Abstract. The development of remote sensing platforms and sensors, as well as the improvement of remote data processing tools and methods, create new opportunities for automatic updating of maps. Currently, aerial photographs serve as the main source for automatic map updates due to their accessibility and significant informational value. One of the core elements for image to maps transition is accurate image segmentation. Nowadays, machine learning methods demonstrate the best results in task of image segmentation. At its core, maps represent information about a certain area in a vector form, that not only contains visual information about area, but also reflects some relations between objects in the map. This quality makes a map more convenient for human perception than an aerial photograph (raster image). This study addresses the problem of accurate aerial image segmentation with taking the advantages of using graph neural network as the more adequate model of map structure. We use graph neural network for retrieving semantic and vector information about a captured area from its aerial image. The developed framework at first phase utilizes visual transformer for retrieving deep features from the input aerial image. The graph neural network then performs clustering of the extracted deep features to obtain semantic segmentation of the image. To train and evaluate the developed framework, a special dataset is collected and annotated. It contains more than 10k aerial photographs representing various types of objects taken in different years and seasons. The evaluation results on the created dataset proved the state-of-the-art performance of the developed framework.

A Review of Studies Using Machine Learning to Detect Voice Biomarkers for Depression

AbstractVoice biomarkers developed using machine learning are a promising potential biomarker for mental disorders, including depression. This paper presents a narrative review with a systematic search of the evidence surrounding the efficacy of voice biomarkers as indicators of depression. The review considers two research questions: (i) What is the efficacy of voice biomarkers as potential biomarkers for depression? (ii) What are the variations in the samples and design methodologies employed? Nineteen papers were identified as examining voice biomarkers for depression using machine learning methods between January 2019 and February 2022. A subset of guidelines recommended in a previous systematic review was selected and adapted to investigate aspects of the field since that review. Seventeen studies used classification methods, and two used regression methods. Within the papers that examined classification, sensitivity (recall) was used by 76% of papers, accuracy by 65%, AUC by 59%, and F1 score by 59%. From these papers, the average performance achieved for the following metrics was 0.78 for sensitivity (recall), 0.76 for F1 score, and 0.78 for AUC. This review found that the efficacy of vocal biomarkers as indicators for depression is below that of the PHQ-9 form, a tool commonly used in psychology. The PHQ-9 can serve as a benchmark against which to compare these models. Difficulties were observed in comparing these models due to the variety of performance metrics used. Recommendations are presented as to how the generalisability of these models may be strengthened, e.g., testing on unseen data after models are developed.

Melanoma Skin Cancer Prediction Using Machine Learning Algorithm

The academic paper titled "Melanoma Skin Cancer Prediction Using Machine Learning Algorithms" provides a detailed analysis of the application of machine learning techniques in the timely detection and categorization of melanoma skin cancer. The research aims to enhance the accuracy and reliability of predicting the distinction between malignant melanomas and benign lesions by utilizing a diverse set of skin lesion images. The study examines various machine learning models, including Logistic Regression, Support Vector Machines (SVM), K-Nearest Neighbor (KNN), Decision Trees, Gaussian Naïve Bayes, and Ensemble Techniques, as well as advanced deep learning approaches like Convolutional Neural Networks (CNNs). Emphasizing the potential, the paper underscores the benefits of integrating advanced machine learning methods in clinical settings to improve the timely detection of melanoma, thus ultimately boosting patient outcomes and treatment efficacy.

Deep learning-based intraoperative guidance system for anatomical identification in laparoscopic surgery: A review

The power of Artificial Intelligence (AI) combined with the surgeons’ expertise leads to breakthroughs in surgical care, bringing new hope to patients. Utilizing deep learning-based computer vision techniques in surgical procedures will enhance the healthcare industry. Laparoscopic surgery holds excellent potential for computer vision due to the abundance of real-time laparoscopic recordings captured by digital cameras containing significant unexplored information. Furthermore, with computing power resources becoming increasingly accessible and Machine Learning methods expanding across various industries, the potential for AI in healthcare is vast. There are several objectives of AI’s contribution to laparoscopic surgery; one is an image guidance system to identify anatomical structures in real-time. However, few studies are concerned with intraoperative anatomy recognition in laparoscopic surgery. This study provides a comprehensive review of the current state-of-the-art semantic segmentation techniques, which can guide surgeons during laparoscopic procedures by identifying specific anatomical structures for dissection or avoiding hazardous areas. This review aims to enhance research in AI for surgery to guide innovations towards more successful experiments that can be applied in real-world clinical settings. This AI contribution could revolutionize the field of laparoscopic surgery and improve patient outcomes.

Antimicrobial Peptides as Broad-Spectrum Therapeutics: Computational Analysis to Identify Universal Physical-Chemical Features Responsible for Multitarget Activity.

Antimicrobial peptides (AMPs) hold significant potential as broad-spectrum therapeutics due to their ability to target a variety of different pathogens, including bacteria, fungi, and viruses. However, the rational design of these peptides requires the molecular understanding of properties that enable such broad-spectrum activity. In this study, we present a computational analysis that utilizes machine-learning methods to distinguish peptides with single-target activity from those with activity against multiple pathogens. By optimizing a feature-selection procedure, the most relevant physical-chemical properties, such as dipeptide compositions, solvent accessibility, charge distributions, and optimal hydrophobicity, that differentiate between narrow-spectrum and broad-spectrum peptides are identified. Possible molecular scenarios responsible for the universality of these features are discussed. These findings provide valuable insights into the molecular mechanisms and rational design of multitarget AMPs.

An efficient unsupervised classification model for galaxy morphology: Voting clustering based on coding from ConvNeXt large model

By combining unsupervised and supervised machine learning methods, we have proposed a framework, called USmorph to carry out automatic classifications of galaxy morphologies. In this work, we update the unsupervised machine learning (UML) step by proposing an algorithm based on ConvNeXt large model coding to improve the efficiency of unlabeled galaxy morphology classifications. The method can be summarized into three key aspects as follows: (1) a convolutional autoencoder is used for image denoising and reconstruction and the rotational invariance of the model is improved by polar coordinate extension; (2) utilizing a pre-trained convolutional neural network (CNN) named ConvNeXt for encoding the image data. The features were further compressed via a principal component analysis (PCA) dimensionality reduction; (3) adopting a bagging-based multi-model voting classification algorithm to enhance robustness. We applied this model to I-band images of a galaxy sample with $I_ mag < 25$ in the COSMOS field. Compared to the original unsupervised method, the number of clustering groups required by the new method is reduced from 100 to 20. Finally, we managed to classify about 53<!PCT!> galaxies, significantly improving the classification efficiency. To verify the validity of the morphological classification, we selected massive galaxies with $M_ M_ sun $ for morphological parameter tests. The corresponding rules between the classification results and the physical properties of galaxies on multiple parameter surfaces are consistent with the existing evolution model. Our method has demonstrated the feasibility of using large model encoding to classify galaxy morphology, which not only improves the efficiency of galaxy morphology classification, but also saves time and manpower. Furthermore, in comparison to the original UML model, the enhanced classification performance is more evident in qualitative analysis and has successfully surpassed a greater number of parameter tests. The enhanced UML method will support the Chinese space station telescope in the future.

Machine Learning Reveals Demographic Disparities in Palliative Care Timing Among Patients With Traumatic Brain Injury Receiving Neurosurgical Consultation.

Timely palliative care (PC) consultations offer demonstrable benefits for patients with traumatic brain injury (TBI), yet their implementation remains inconsistent. This study employs machine learning methods to identify distinct patient phenotypes and elucidate the primary drivers of PC consultation timing variability in TBI management, aiming to uncover disparities and inform more equitable care strategies. Data on admission, hospital course, and outcomes were collected for a cohort of 232 patients with TBI who received both PC consultations and neurosurgical consultations during the same hospitalization. Patient phenotypes were uncovered using principal component analysis and K-means clustering; time-to-PC consultation for each phenotype was subsequently compared by Kaplan-Meier analysis. An extreme gradient boosting model with Shapley Additive Explanations identified key factors influencing PC consultation timing. Three distinct patient clusters emerged: cluster A (n = 86), comprising older adult White women (median 87years) with mild TBI, received the earliest PC consultations (median 2.5days); cluster B (n = 108), older adult White men (median 81years) with mild TBI, experienced delayed PC consultations (median 5.0days); and cluster C (n = 38), middle-aged (median: 46.5years), severely injured, non-White patients, had the latest PC consultations (median 9.0days). The clusters did not differ by discharge disposition (p = 0.4) or inpatient mortality (p > 0.9); however, Kaplan-Meier analysis revealed a significant difference in time-to-PC consultation (p < 0.001), despite no differences in time-to-mortality (p = 0.18). Shapley Additive Explanations analysis of the extreme gradient boosting model identified age, sex, and race as the most influential drivers of PC consultation timing. This study unveils crucial disparities in PC consultation timing for patients with TBI, primarily driven by demographic factors rather than clinical presentation or injury characteristics. The identification of distinct patient phenotypes and quantification of factors influencing PC consultation timing provide a foundation for developing for standardized protocols and decision support tools to ensure timely and equitable palliative care access for patients with TBI.

Feature mining for thermoelectric materials based on interpretable machine learning.

In previous laboratory preparation processes, the selection and proportioning of specific experimental parameters often stemmed from the empirical experience of predecessors, necessitating the derivation of the optimal scheme for the target experiment through extensive trial and error. This process typically required the consumption of substantial resources and time. Thanks to the advancement of machine learning technologies, these have gradually become a powerful tool for addressing complex functional problems in material optimization. This study is based on a collected database of thermoelectric materials, aiming to clarify the correspondence between physical characteristics and the thermoelectric figure of merit, zT. The key features that can directly affect the experimental results are identified, and the features that indirectly affect the experimental results are also analyzed. By employing interpretable machine learning methods, this research analyzes critical molecular features in the characterized data, utilizing feature engineering techniques to construct and optimize machine learning models for the selected key features. Furthermore, in the subsequent model fitting and analysis phase, by comparing the efficiency of different feature combinations, the optimal feature descriptors for the current experimental system were determined. The adoption of the aforementioned improved methods endowed the related models with the potential for high-throughput screening, thereby further enhancing the efficiency of experimental optimization.

A Machine Learning-Based Parameterized Tropical Cyclone Precipitation Model

AbstractCurrent simulation models considerably underestimate local-scale, short-duration extreme precipitation induced by tropical cyclones (TCs). This problem needs to be addressed to establish active response policies for TC-induced disasters. Taking Shanghai, a coastal megacity, as a study area and based on the observations from 192 meteorological stations in the city during 2005–2018, this study optimized the parameterized Tropical Cyclone Precipitation Model (TCPM) initially designed for TCs at the national scale (China) to the local or regional scales by using machine learning (ML) methods, including the random forest (RF), extreme gradient boosting (XGBoost), and ensemble learning (EL) algorithms. The TCPM-ML was applied for multiple temporal scale hazard assessment. The results show that: (1) The TCPM-ML not only improved TCPM performance for simulating hourly extreme precipitations, but also preserved the physical meaning of the results, contrary to ML methods; (2) Machine learning algorithms enhanced the TCPM ability to reproduce observations, although the hourly extreme precipitations remained slightly underestimated; (3) Best performance was obtained with the XGBoost or EL algorithms. Combining the strengths of both XGBoost and RF, the EL algorithm yielded the best overall performance. This study provides essential model support for TC disaster risk assessment and response at the local and regional scales in China.

UDP-glucuronosyltransferases 2A3 as a biomarker for ulcerative colitis and colon cancer

BackgroundUlcerative colitis has a serious impact on the quality of life of patients and is more likely to progress to colon cancer. Therefore, early diagnosis and timely intervention are of considerable importance.MethodsGene expression data of active ulcerative colitis were downloaded from the Gene Expression Omnibus (GEO) database, and genes with significant differential expression were identified. Biochemical markers with diagnostic significance were selected through machine learning methods. The expression differences of the selected markers between colon adenocarcinoma (COAD) and healthy control groups in The Cancer Genome Atlas (TCGA) database were analyzed to evaluate their diagnostic value. In addition, the correlation between the selected markers and clinical indicators, as well as their predictive efficacy for the survival of COAD patients, was explored.ResultsThrough machine learning and LASSO regression analysis, UGT2A3 was finally determined as a diagnostic marker for ulcerative colitis. It demonstrated high diagnostic accuracy in both the training set and the external validation set. Furthermore, UGT2A3 was significantly downregulated in COAD tissues compared to normal control tissues. The ROC curve suggested that UGT2A3 could serve as a diagnostic marker for COAD with excellent performance, achieving an AUC of 0.969. Immune infiltration analysis indicated a significant negative correlation between the expression of UGT2A3 and neutrophils. Correlation analysis suggested a link between UGT2A3 and the pathological classification of colon cancer. Survival analysis showed that UGT2A3 is negatively correlated with OS, PPS, and RFS in colon cancer.ConclusionThe author identified UGT2A3 as a diagnostic marker for ulcerative colitis through bioinformatics methods, and verified its significant downregulation in colon cancer, as well as its predictive role in the survival of COAD patients. These findings suggest that UGT2A3 may serve not only as a diagnostic marker for ulcerative colitis and colon cancer but also as a potential prognostic indicator for colon cancer.

Inertial measurement unit signal-based machine learning methods for frailty assessment in geriatric health

Frailty is a geriatric syndrome that may result in poor health outcomes such as hospitalization, disability, psychological distress, and reduced life satisfaction, and it is also associated with higher healthcare costs. The aim of this study is to classify frailty in elders at an early stage (pre-frail) to lower the risk of frailty and, hence, improve the quality of life. The other two classes in the classification task are frail and robust (non-frail). To achieve this, a dataset based on gait was utilized, which was recorded by an Inertial Measurement Unit (IMU) sensor, including gyroscope and accelerometer data. In this study, two approaches were assessed: the first used advanced Deep Learning (DL) algorithms to analyze raw IMU signals, and the second used conventional Machine Learning (ML) methods with hand-crafted features. The DL model, i.e., InceptionTime, beat the other algorithms in the DL approach with a remarkable test accuracy of 98%. On the ML side, Random Forest reported the most successful ML method, which achieved a test accuracy of 63.3%. For a careful assessment of the models, other evaluation metrics like Precision, Recall, and F1-score were also evaluated. The evaluation of both approaches produces research benefits for the classification of frailty in older people and allows for the investigation of new areas, promoting deeper comprehension and well-informed decision-making, particularly in healthcare systems.

Exploring the forensic effectiveness and population genetic differentiation in Guizhou Miao and Bouyei group by the self-constructed panel of X chromosomal multi-insertion/deletions

In this research, a self-developed panel comprising 22 X chromosomal multi-InDels and one X-STR was used to explore the genetic polymorphisms and forensic characteristics of these loci in Guizhou Miao and Guizhou Bouyei populations. Besides, genetic affiliations among Guizhou Miao, Guizhou Bouyei and Guizhou Han populations were investigated using principal component analysis, STRUCTURE and machine learning methods. The findings indicated that these loci in the male and female samples had comprehensive discrimination powers greater than 0.999999999. Meanwhile, the cumulative mean exclusion chance of these 23 loci for trio and duo cases were also greater than 0.9999 in Guizhou Miao and Guizhou Bouyei populations. Population genetic analyses of three Guizhou populations revealed that there were relatively low genetic divergences among these populations based on the self-constructed panel. In conclusion, this system could be utilized as the valuable tool for forensic personal identification and parentage testing in Guizhou Miao and Guizhou Bouyei populations.

How the US Macroeconomic Factors Affect the Gold Price?

Nowadays, investing in gold as a hedge against macroeconomic factors risk has been brought into the spotlight. It is essential for investors to predict gold prices before making their hedging decisions. After a thorough study of past precursors, the machine learning method, especially the gradient boosting technique, has been proven to be a powerful method for predicting gold prices. This study tests macroeconomic factors as well as oil prices and uses them as independent variables to predict gold prices. By using advanced machine learning technique: gradient boosting, this study achieves finding the best four factors model. The research shows that oil prices, the U.S. dollar index, interest rate, and GDP growth are significant factors in predicting gold prices. Among all the significant factors, oil price is the most important one. The explicit reasons of this result is because that transportation cost and operation cost of gold mining is largely influenced by oil price. This study lays the foundations for investors to make insightful decisions.

Evaluating Binary Classifiers for Cardiovascular Disease Prediction: Enhancing Early Diagnostic Capabilities

Cardiovascular disease (CVD) is a significant global health concern and the leading cause of death in many countries. Early detection and diagnosis of CVD can significantly reduce the risk of complications and mortality. Machine learning methods, particularly classification algorithms, have demonstrated their potential to accurately predict the risk of cardiovascular disease (CVD) by analyzing patient data. This study evaluates seven binary classification algorithms, including Random Forests, Logistic Regression, Naive Bayes, K-Nearest Neighbors (kNN), Support Vector Machines, Gradient Boosting, and Artificial Neural Networks, to understand their effectiveness in predicting CVD. Advanced preprocessing techniques, such as SMOTE–ENN for addressing class imbalance and hyperparameter optimization through Grid Search Cross-Validation, were applied to enhance the reliability and performance of these models. Standard evaluation metrics, including accuracy, precision, recall, F1-score, and Area Under the Receiver Operating Characteristic Curve (ROC-AUC), were used to assess predictive capabilities. The results show that kNN achieved the highest accuracy (99%) and AUC (0.99), surpassing traditional models like Logistic Regression and Gradient Boosting. The study examines the challenges encountered when working with datasets related to cardiovascular diseases, such as class imbalance and feature selection. It demonstrates how addressing these issues enhances the reliability and applicability of predictive models. These findings emphasize the potential of kNN as a reliable tool for early CVD prediction, offering significant improvements over previous studies. This research highlights the value of advanced machine learning techniques in healthcare, addressing key challenges and laying a foundation for future studies aimed at improving predictive models for CVD prevention.

Genomic predictors of drug sensitivity in cancer: Integrating genomic data for personalized medicine in the USA

Despite applying conventional predictive methodologies to obtain genomic insights, predicting drug sensitivity for healthcare organizations in the USA remains a daunting challenge. Cancer is a highly dynamic, adaptive disease tumor cells have repeatedly shown the capability to evolve mechanisms whereby therapeutic interventions can be evaded. Besides, one genomic alteration seldom predicts drug sensitivity. This research project aimed to address the challenges of predicting drug sensitivity by leveraging the GDSC dataset, an extensive resource connecting genomic profiles of cancer cell lines with their sensitivity to a wide range of anti-cancer drugs. This research's key focus was identifying robust genomic markers, including any specific mutations, gene expression patterns, or epigenetic modifications associated with drug sensitivity or resistance. Advanced machine learning and statistical methods were utilized by the predictive models to analyze complex relations that may exist between different genomic alterations and their drug sensitivity. The dataset used for this research project was derived from the Kaggle website. This dataset was compiled by the research project Genomics of Drug Sensitivity in Cancer collaboration between the Sanger Institute in the United Kingdom and the Massachusetts General Hospital Cancer Center in the United States. In their investigation, there was a massive screening of human cancer cell lines with a wide range of anti-cancer drugs. Data collection was performed by large-scale screening of diverse anti-cancer drugs against human cancer cell lines of various types. Cell viability was measured using the Cell-Titer-Glo assay following 72 hours of drug treatment. Several machine learning models were deployed, namely, Random Forest, Linear Regression, and XG-Boost, which exhibited specific strengths. Specific performance metrics used included MSE, RMSE, MAE, and R². As the statistics indicate, among the three models, Random Forest stands out and performs the best on this dataset across all metrics. A smaller value of MAE, MSE, and RMSE signifies that it provided the best forecast for the target variable. It also gave the highest R-squared value. Application of drug sensitivity prediction analysis in cancer can provide an overview of the mechanisms that underlie both tumor response and resistance by investigating the model predictions. The proposed predictive models have the potential to make significant impacts on clinical decision-making in cancer therapy. Predictive models derive informed decisions regarding a patient's risk of recurrence of disease, their response to certain therapies, and their prognosis based on complex clinical and genomic data. Keywords: Genomic Predictors; Drug Sensitivity; Genomic Markers; Personalized medicine; Machine Learning; Random Forest Algorithm.

Development of the new machine-learning approach in pipeline condition assessment prediction and optimizing rehabilitation strategies

Purpose This paper aims to outlines a model for water main rehabilitation in Kitchener, Ontario, using a machine-learning approach. Water main networks are vital infrastructure, requiring regular condition assessments to ensure consistent service. Budgets are often allocated for nondestructive testing methods, but using machine learning to predict network conditions offers cost benefits. Design/methodology/approach The study focuses on a prediction approach that includes the rehabilitation requirement model. The Decision Tree machine learning method was applied to predict water main pipe breaks in 2024. Based on the predictions, 24 pipes were identified for rehabilitation, and the appropriate Trenchless Rehabilitation Method was selected accordingly. Findings The model, applied to data from Kitchener, successfully predicted 24 water main pipe breaks for 2024. The largest pipe diameter was 1200 mm, and the longest length was 6977 m. A cost comparison, factoring in Environmental and Social (E&amp;S) costs, showed that open-cut methods were 25% more expensive than Cured-in-Place Pipe (CIPP). When E&amp;S costs were included, the total cost of the open-cut method increased by approximately 300% compared to sliplining. Originality/value Based on the pipe characteristics, CIPP lining and sliplining are recommended for rehabilitation by the City of Kitchener. This study presents a novel approach using Decision Tree machine learning techniques to predict pipe breaks, with a 97% prediction accuracy, making it a promising alternative to traditional models.

Information Technology for Sound Analysis and Recognition in the Metropolis based on Machine Learning Methods

The goal of designing and implementing an intelligent information system for the recognition and classification of sound signals is to create an effective solution at the software level, which would allow analysis, recognition, classification and forecasting of sound signals in megacities and smart cities using machine learning methods. This system can help people in various fields to simplify their lives, for example, it can help farmers protect their crops from animals, in the military it can help with the identification of weapons and the search for flying objects, such as drones or missiles, in the future there is a possibility for recognizing the distance to sound, also, in cities can help with security, so a preventive response system can be built, which can check if everything is in order based on sounds. Also, it can make life easier for people with impaired hearing to detect danger in everyday life. In the part of the comparison of analogues of the developed product, 4 analogues were found: Shazam, sound recognition from Apple, Vocapia, and SoundHound. A table of comparisons was made for these analogues and the product under development. Also, after comparing analogues, a table for evaluating the effects of the development was built. During the system analysis section, a variety of audio research materials were developed to indicate the characteristics that can be used for this design: period, amplitude, and frequency, and, as an example, an article on real-world audio applications is shown. A precedent scenario is described using the RUP methodology and UML diagrams are constructed: Diagram of use cases; Class diagram; Activity chart; Sequence diagram; Diagram of components; and Deployment diagram. Also, sound data analysis was performed, sound data was visualized as spectrograms and sound waves, which clearly show that the data are different, so it is possible to classify them using machine learning methods. An experimental selection of the machine learning method as staandart clasificers for building a sound recognition model was made. The best method turned out to be SVC, the accuracy of which reflects more than 30 per cent. A neural network was also implemented to improve the obtained results. The result of training a model based on a neural network during 100 epochs achieved a result of 97.7% accuracy for training data and 47.8% accuracy when checking performance on test data. This result should be higher, so it is necessary to consider improving recognition algorithms, increasing the amount of data, and changing the recognition method. Testing of the project was carried out, showing its operation and pointing out shortcomings that need to be corrected in the future.

Unlocking the Potential of Remanufacturing Through Machine Learning and Data-Driven Models—A Survey

As a key strategy for achieving a circular economy, remanufacturing involves bringing end-of-use (EoU) products or cores back to a ‘like new’ condition, providing more affordable and sustainable alternatives to new products. Despite the potential for substantial resources and energy savings, the industry faces operational challenges. These challenges arise from uncertainties surrounding core quality and functionality, return times, process variation required to meet product specifications, and the end-of-use (EoU) product values, as well as their new life expectancy after extended use as a ‘market product’. While remanufacturing holds immense promise, its full potential can only be realized through concerted efforts towards resolving the inherent complexities and obstacles that impede its operations. Machine learning (ML) and data-driven models emerge as transformative tools to mitigate numerous challenges encountered by manufacturing industry. Recently, the integration of cutting-edge technologies, such as sensor-based product data acquisition and storage, data analytics, machine health management, artificial intelligence (AI)-driven scheduling, and human–robot collaboration (HRC), in remanufacturing procedures has received significant attention from remanufacturers and the circular economy community. These advanced computational technologies help remanufacturers to implement flexible operation scheduling, enhance quality control, and streamline workflows for EoU products. This study embarks on a comprehensive review and in-depth analysis of state-of-the-art algorithms across various facets of remanufacturing processes and operations. Additionally, it identifies key challenges to advancing remanufacturing practices through data-driven and ML methods and uncovers research opportunities in synergy with smart manufacturing techniques. The study aims to offer guidelines for stakeholders and to reinforce the industry’s pivotal role in circular economy initiatives.