Traditional Statistical Methods Research Articles

Efficient characterization of blinking quantum emitters from scarce data sets via machine learning

Single photon emitters are core building blocks of quantum technologies, with established and emerging applications ranging from quantum computing and communication to metrology and sensing. Regardless of their nature, quantum emitters universally display fluorescence intermittency or photoblinking: interaction with the environment can cause the emitters to undergo quantum jumps between on and off states that correlate with higher and lower photoemission events, respectively. Understanding and quantifying the mechanism and dynamics of photoblinking is important for both fundamental and practical reasons. However, the analysis of blinking time traces is often afflicted by data scarcity. Blinking emitters can photo-bleach and cease to fluoresce over time scales that are too short for their photodynamics to be captured by traditional statistical methods. Here, we demonstrate two approaches based on machine learning that directly address this problem. We present a multi-feature regression algorithm and a genetic algorithm that allow for the extraction of blinking on/off switching rates with ⩾85% accuracy, and with ⩾10× less data and ⩾20× higher precision than traditional methods based on statistical inference. Our algorithms effectively extend the range of surveyable blinking systems and trapping dynamics to those that would otherwise be considered too short-lived to be investigated. They are therefore a powerful tool to help gain a better understanding of the physical mechanism of photoblinking, with practical benefits for applications based on quantum emitters that rely on either mitigating or harnessing the phenomenon.

The Impact of Overall Intervention Model on Price of Wheat

The analysis of observed data at different time points is prominent to single problems when the scientist studied random sampling data over time restricts the applicability of many traditional statistical methods that require random sampling. The analysis of this data is usually mentioned to the stochastic process, in a special case, if it relates to time, then it is called time series [1]. The aim of our study is to focus on the case of observations that are made at certain times of the year, specifically in the autumn season, in equal periods of time, and we estimate the effect of interference on wheat prices n order to obtain the best predictive model and the best order of this model for the overall effect on the price of wheat for the coming years.

A fusion of machine learning algorithms and traditional statistical forecasting models for analyzing American healthcare expenditure

The American healthcare system allocates considerable resources compared to peer-developed nations. However, outcomes significantly trail behind, particularly in life expectancy. This study addresses questions about the enduring trends in healthcare spending as a percentage of Gross Domestic Product (GDP), notable factors contributing to this concerning trend, and the timing to apply an emergency brake to curb this accelerating trajectory. Advanced machine learning algorithms, such as Random Forest and Support Vector Regression (SVR), in conjunction with traditional statistical forecasting methods, are used to forecast future patterns. The research underscores the importance of healthcare analytics in unraveling the intricacies of the healthcare system. The findings highlight the pressing need for effective policies to confront this mounting challenge.

GADNN: a revolutionary hybrid deep learning neural network for age and sex determination utilizing cone beam computed tomography images of maxillary and frontal sinuses

IntroductionThe determination of identity factors such as age and sex has gained significance in both criminal and civil cases. Paranasal sinuses like frontal and maxillary sinuses, are resistant to trauma and can aid profiling. We developed a deep learning (DL) model optimized by an evolutionary algorithm (genetic algorithm/GA) to determine sex and age using paranasal sinus parameters based on cone-beam computed tomography (CBCT).MethodsTwo hundred and forty CBCT images (including 129 females and 111 males, aged 18–52) were included in this study. CBCT images were captured using the Newtom3G device with specific exposure parameters. These images were then analyzed in ITK-SNAP 3.6.0 beta software to extract four paranasal sinus parameters: height, width, length, and volume for both the frontal and maxillary sinuses. A hybrid model, Genetic Algorithm-Deep Neural Network (GADNN), was proposed for feature selection and classification. Traditional statistical methods and machine learning models, including logistic regression (LR), random forest (RF), multilayer perceptron neural network (MLP), and deep learning (DL) were evaluated for their performance. The synthetic minority oversampling technique was used to deal with the unbalanced data.ResultsGADNN showed superior accuracy in both sex determination (accuracy of 86%) and age determination (accuracy of 68%), outperforming other models. Also, DL and RF were the second and third superior methods in sex determination (accuracy of 78% and 71% respectively) and age determination (accuracy of 92% and 57%).ConclusionsThe study introduces a novel approach combining DL and GA to enhance sex determination and age determination accuracy. The potential of DL in forensic dentistry is highlighted, demonstrating its efficiency in improving accuracy for sex determination and age determination. The study contributes to the burgeoning field of DL in dentistry and forensic sciences.

ADVANCING WATER QUALITY PREDICTION: THE ROLE OF MACHINE LEARNING IN ENVIRONMENTAL SCIENCE

This article delves into the burgeoning domain of machine learning (ML) applications within environmental science, with a specific focus on water quality prediction. Amidst escalating environmental challenges, the precision and efficiency of ML models have emerged as pivotal tools for analyzing complex datasets, offering nuanced insights and forecasts about water quality trends. We explore the integration of ML in environmental monitoring, highlighting its comparative advantage over traditional statistical methods in handling vast, multifaceted data streams. This exploration encompasses a critical evaluation of various ML algorithms tailored for predictive accuracy in water quality assessment, including supervised and unsupervised learning models. The article also addresses the challenges inherent in ML applications, such as data quality and model interpretability, and anticipates future trajectories in this rapidly evolving field. The potential for ML to revolutionize environmental policy-making and resource management through enhanced predictive capabilities is a central theme, underscoring the transformative impact of these technologies in environmental science.

Machine learning-driven identification of air toxic combinations associated with asthma symptoms among elementary school children in Spokane, Washington, USA

Air toxics are atmospheric pollutants with hazardous effects on health and the environment. Although methodological constraints have limited the number of air toxics assessed for associations with health and disease, advances in machine learning (ML) enable the assessment of a much larger set of environmental exposures. We used ML methods to conduct a retrospective study to identify combinations of 109 air toxics associated with asthma symptoms among 269 elementary school students in Spokane, Washington. Data on the frequency of asthma symptoms for these children were obtained from Spokane Public Schools. Their exposure to air toxics was estimated by using the Environmental Protection Agency's Air Toxics Screening Assessment and National Air Toxics Assessment. We defined three exposure periods: the most recent year (2019), the last three years (2017–2019), and the last five years (2014–2019). We analyzed the data using the ML-based Data-driven ExposurE Profile (DEEP) extraction method. DEEP identified 25 air toxic combinations associated with asthma symptoms in at least one exposure period. Three combinations (1,1,1-trichloroethane, 2-nitropropane, and 2,4,6-trichlorophenol) were significantly associated with asthma symptoms in all three exposure periods. Four air toxics (1,1,1-trichloroethane, 1,1,2,2-tetrachloroethane, BIS (2-ethylhexyl) phthalate (DEHP), and 2,4-dinitrophenol) were associated only in combination with other toxics, and would not have been identified by traditional statistical methods. The application of DEEP also identified a vulnerable subpopulation of children who were exposed to 13 of the 25 significant combinations in at least one exposure period. On average, these children experienced the largest number of asthma symptoms in our sample. By providing evidence on air toxic combinations associated with childhood asthma, our findings may contribute to the regulation of these toxics to improve children's respiratory health.

Deep Learning Model Effectiveness in Forecasting Limited-Size Aboveground Vegetation Biomass Time Series: Kenyan Grasslands Case Study

Timely forecasting of aboveground vegetation biomass is crucial for effective management and ensuring food security. However, research on predicting aboveground biomass remains scarce. Artificial intelligence (AI) methods could bridge this research gap and provide early warning to planners and stakeholders. This study evaluates the effectiveness of deep learning (DL) algorithms in predicting aboveground vegetation biomass with limited-size data. It employs an iterative forecasting procedure for four target horizons, comparing the performance of DL models—multi-layer perceptron (MLP), long short-term memory (LSTM), gated recurrent unit (GRU), convolutional neural network (CNN), and CNN-LSTM—against the traditional seasonal autoregressive integrated moving average (SARIMA) model, serving as a benchmark. Five limited-size vegetation biomass time series from Kenyan grasslands with values at 15-day intervals over a 20-year period were chosen for this purpose. Comparing the outcomes of these models revealed significant differences (p < 0.05); however, none of the models proved superior among the five time series and the four horizons evaluated. The SARIMA, CNN, and CNN-LSTM models performed best, with the statistical model slightly outperforming the other two. Additionally, the accuracy of all five models varied significantly according to the prediction horizon (p < 0.05). As expected, the accuracy of the models decreased as the prediction horizon increased, although this relationship was not strictly monotonic. Finally, this study indicated that, in limited-size aboveground vegetation biomass time series, there is no guarantee that deep learning methods will outperform traditional statistical methods.

Using multivariate statistics to link major ion chemistry changes at karst springs to agriculture

Agricultural contamination is a pervasive problem in karst landscapes that negatively impacts groundwater quality, and subsequently harms hydraulically connected springs and associated ecosystems. While most research on agricultural contamination in karst aquifers has focused on nitrate, agricultural operations also alter major ion chemistry in groundwater, which can have significant implications on water rock interactions, geochemical interpretability, and enrichment of harmful contamination. Additionally, recent evidence has questioned the singular role of nutrient enrichment on aquatic ecosystem degradation, thus prompting further investigation into the broader extent of agricultural contamination for improved water quality restoration. In karst aquifers, quantifying the disruption of groundwater quality from agricultural contamination at springs can be challenging as major ions associated with agricultural contamination may be conflated with natural processes and interpretation further compounded by complex flow dynamics. These factors can render traditional statistical methods inadequate for identifying more comprehensive groundwater quality disruptions. To overcome these challenges, we use principal component analysis to classify the major ion covariance signature of agriculturally impaired and unimpaired groundwater and develop a new impairment metric to characterize the relative major ion impairment from agriculture to karst spring water quality. We apply our method to a primarily rural region in northern Florida whereby major ion chemistry has been changing over the last several decades. Our results identify a significant difference in major ion covariance structure between agriculturally impaired and unimpaired groundwater which is exploited to directly link major ion changes at karst springs to agriculture via relating a derived impairment metric to measured nitrate. Our newly developed method is easy to apply and can be used to identify more comprehensive contamination from pollutant sources and prioritize relative water quality restoration.

Prediction of Cervical Cancer Patients' Survival Period with Machine Learning Techniques.

The objective of this research is to apply machine learning (ML) algorithms to predict the survival of cervical cancer patients. The aim was to address the limitations of traditional statistical methods, which often fail to provide accurate answers due to the complexity of the problem. This research employed visualization techniques for initial data understanding. Subsequently, ML algorithms were used to develop both classification and regression models for survival prediction. In the classification models, we trained the algorithms to predict the time interval between the initial diagnosis and the patient's death. The intervals were categorized as "<6 months," "6 months to 3 years," "3 years to 5 years," and ">5 years." The regression model aimed to predict survival time (in months). We used attribute weights to gain insights into the model, highlighting features with a significant impact on predictions and offering valuable insights into the model's behavior and decision-making process. The gradient boosting trees algorithm achieved an 81.55% accuracy in the classification model, while the random forest algorithm excelled in the regression model, with a root mean square error of 22.432. Notably, radiation doses around the affected areas significantly influenced survival duration. Machine learning demonstrated the ability to provide high-accuracy predictions of survival periods in both classification and regression problems. This suggests its potential use as a decision-support tool in the process of treatment planning and resource allocation for each patient.

P205 Histologic image-based ensemble model to identify myenteric plexitis and predict endoscopic postoperative recurrence in Crohn’s disease: a multicentre, retrospective study

Abstract Background Myenteric plexitis is correlated with postoperative recurrence of Crohn’s disease (CD) when relying on traditional statistical methods. However, comprehensive assessment of the myenteric plexus remains challenging. This study aimed to develop and validate a deep learning system to predict postoperative recurrence through automatic screening and identification of features of the muscular layer and myenteric plexus. Methods In this study, we retrospectively reviewed 205 CD patients who underwent bowel resection surgery from 2 hospitals. Patients were divided into a training cohort (n=108), an internal validation cohort (n=47) and an external validation cohort (n=50). A total of 190960 patches from 278 whole-slide images of surgical specimens were analysed using ResNet50, and 6144 features were extracted after transfer learning. Spearman correlation analysis, LASSO logistic regression and the interclass correlation coefficient test were utilized for feature selection. We used five robust algorithms to construct classification models. The performances of the models were evaluated by the area under the receiver operating characteristic curve (AUC) in three cohorts. Results The stacking model achieved satisfactory accuracy in predicting postoperative recurrence of CD in the training cohort (AUC: 0.980; 95% CI 0.960-0.999), internal validation cohort (AUC: 0.908; 95% CI 0.823-0.992), and external validation cohort (AUC: 0.868; 95% CI 0.761-0.975). The accuracy for identifying the severity of myenteric plexitis was 0.833, 0.745, and 0.694 in the training cohort, internal validation cohort and external validation cohort, respectively. Conclusion Our work initially established an interpretable stacking model based on muscular layer and myenteric plexus features extracted from histologic images to identify the severity of myenteric plexitis and predict postoperative recurrence of CD.

Identifying key soil characteristics for Francisella tularensis classification with optimized Machine learning models

Francisella tularensis (Ft) poses a significant threat to both animal and human populations, given its potential as a bioweapon. Current research on the classification of this pathogen and its relationship with soil physical–chemical characteristics often relies on traditional statistical methods. In this study, we leverage advanced machine learning models to enhance the prediction of epidemiological models for soil-based microbes. Our model employs a two-stage feature ranking process to identify crucial soil attributes and hyperparameter optimization for accurate pathogen classification using a unique soil attribute dataset. Optimization involves various classification algorithms, including Support Vector Machines (SVM), Ensemble Models (EM), and Neural Networks (NN), utilizing Bayesian and Random search techniques. Results indicate the significance of soil features such as clay, nitrogen, soluble salts, silt, organic matter, and zinc , while identifying the least significant ones as potassium, calcium, copper, sodium, iron, and phosphorus. Bayesian optimization yields the best results, achieving an accuracy of 86.5% for SVM, 81.8% for EM, and 83.8% for NN. Notably, SVM emerges as the top-performing classifier, with an accuracy of 86.5% for both Bayesian and Random Search optimizations. The insights gained from employing machine learning techniques enhance our understanding of the environmental factors influencing Ft’s persistence in soil. This, in turn, reduces the risk of false classifications, contributing to better pandemic control and mitigating socio-economic impacts on communities.

AF-OS-ELM-MVE: A new online sequential extreme learning machine of dam safety monitoring model for structure deformation estimation

Dam deformation monitoring is an important task in hydraulic engineering projects. Traditional statistical methods and machine learning algorithms have been widely applied in structural safety prediction due to the application’s convenience. However, most machine learning algorithms are offline models and are not suitable for simulating the complex dynamic process of dam displacement. In addition, dam safety monitoring should quantify the uncertainty of the time-dependent displacement and establish reliable prediction intervals. As a result, an online learning model that can qualify prediction intervals is necessary. In this paper, an online learning algorithm named AF-OS-ELM-MVE is proposed to solve the problems. It is based on the OS-ELM algorithm and optimized using the adaptive forgetting factor mechanism. Through selective forgetting for parameter tuning, it achieves self-adaptation to the underlying physical mechanism between the factors and the displacement data. Mean Variance Estimation (MVE) is also adapted to establish the new hybrid model. It can be used to estimate the variance of the prediction process, and the model can generate prediction intervals based on the minimum probability theory. The AF-OS-ELM-MVE model is verified using long-term monitoring data and the performance comparisons were made with a number of state-of-the-art models. The correlation coefficients of the proposed model in point prediction are above 0.99 for measuring points of different elevations, and at a 95% confidence level, it achieves 100% coverage of real displacements. The results demonstrate the effectiveness of the proposed model in predicting dam deformations and provide a more solid basis for dam safety analysis.

Assessing the virtual reality perspectives and self-directed learning skills of nursing students: A machine learning-enhanced approach

AimThis study aims to investigate nursing students’ perspectives on virtual reality technologies and their self-directed learning skills, specifically focusing on how these variables interact and influence each other in the context of nursing education. We also discern potential disparities in these skills based on descriptive characteristics, using both traditional statistical and advanced machine learning approaches for a comprehensive analysis. BackgroundRapid developments in technology, particularly during the Covid-19 pandemic, have brought virtual reality technologies to the forefront of nursing education. However, there is a gap in understanding how nursing students’ perceptions of these technological relate to their development of self-directed learning skills. DesignA descriptive and cross-sectional study design is employed to both quantify nursing students’ perspectives on virtual reality in their education and assess their self-directed learning skills. This approach integrates traditional statistical methods with advanced machine learning techniques, with the intention of offering a comprehensive and nuanced analysis to inform future teaching strategies in nursing. MethodsThe study used a blend of survey scales and a tree-based machine learning model to measure and analyze nursing students’ views, attitudes and self-directed learning levels. This dual approach allows for a more detailed assessment of the factors influencing self-directed learning abilities. Traditional statistical techniques were also applied to assess the reliability of the machine learning findings. ResultsFindings reveal that nursing students generally held positive views towards virtual reality technologies and exhibited a high level of self-directed learning skills. Notable differences in self-directed learning skills were influenced by gender on the overall scale (p <0.001), with male students scoring higher than their female counterparts in both specific sub-dimensions and on the overall scale, but not by academic year. The machine learning analysis provided deeper insights into these variations, highlighting subtle distinctions in student demographics that traditional statistical methods did not fully capture. ConclusionsThe study offers valuable insights into interconnected nature of nursing students’ views on virtual reality technologies and their self-directed learning skills. The results support the integration of virtual reality in nursing curriculum programs and underscore the importance of customizing teaching strategies based on insights gained from machine learning analyses. This approach has the potential to substantially improve both the learning experience and the overall quality of nursing education.

Parametric and Nonparametric Machine Learning Techniques for Increasing Power System Reliability: A Review

Due to aging infrastructure, technical issues, increased demand, and environmental developments, the reliability of power systems is of paramount importance. Utility companies aim to provide uninterrupted and efficient power supply to their customers. To achieve this, they focus on implementing techniques and methods to minimize downtime in power networks and reduce maintenance costs. In addition to traditional statistical methods, modern technologies such as machine learning have become increasingly common for enhancing system reliability and customer satisfaction. The primary objective of this study is to review parametric and nonparametric machine learning techniques and their applications in relation to maintenance-related aspects of power distribution system assets, including (1) distribution lines, (2) transformers, and (3) insulators. Compared to other reviews, this study offers a unique perspective on machine learning algorithms and their predictive capabilities in relation to the critical components of power distribution systems.

The Problem of Institutional Fit: Uncovering Patterns with Boosted Decision Trees

Complex social-ecological contexts play an important role in shaping the types of institutions that groups use to manage resources, and the effectiveness of those institutions in achieving social and environmental objectives. However, despite widespread acknowledgment that “context matters”, progress in generalising how complex contexts shape institutions and outcomes has been slow. This is partly because large numbers of potentially influential variables and non-linearities confound traditional statistical methods. Here we use boosted decision trees – one of a growing portfolio of machine learning tools – to examine relationships between contexts, institutions, and their performance. More specifically we draw upon data from the International Forest Resources and Institutions (IFRI) program to analyze (i) the contexts in which groups successfully self-organize to develop rules for the use of forest resources (local rulemaking), and (ii) the contexts in which local rulemaking is associated with successful ecological outcomes. The results reveal an unfortunate divergence between the contexts in which local rulemaking tends to be found and the contexts in which it contributes to successful outcomes. These findings and our overall approach present a potentially fruitful opportunity to further advance theories of institutional fit and inform the development of policies and practices tailored to different contexts and desired outcomes.

Predicting low-occurrence wind speed at the pedestrian levels of simplified arrays: Artificial neural network models versus statistical methods

Low-occurrence wind speed in urban areas has a profound impact on the daily activities of people. Although several statistical methods were developed for estimating the low-occurrence wind speeds, their accuracy and flexibility still need improvements. Based on the inference from the favorable outcomes of statistical models in which low-occurrence wind speeds can be estimated by the statistics including high-order moments, in this study, the artificial neural network (ANN) models were designed to estimate the low-occurrence wind speeds based on the moments. After the sensitivity tests for the dimensions, hidden layers, epochs and training data ratios of the ANN models, the optimal setting was applied to predict the low-occurrence wind speeds of two building array cases including square and staggered layouts. The applicability of the ANN models was also evaluated for the extrapolation method between two building array cases and compared with the traditional statistical methods. It was found that the ANN models with the higher-order statistic input showed better performance than those with the second-order statistic input. The results obtained from the interpolation method are more accurate than those from the extrapolation method. Compared with traditional statistical methods, the ANN models show greater superiority in the prediction accuracy. According to the comprehensive evaluation of the ANN models, although they require ample training datasets connecting the statistics and low-occurrence wind speeds, it is proved to be a promising approach for estimating the low-occurrence wind speeds in urban wind environment.

Spatial Distribution of Soil Carbon and Nitrogen Content in the Danjiangkou Reservoir Area and Their Responses to Land-Use Types

Understanding the spatial distribution of soil properties is essential for comprehending soil fertility, predicting ecosystem productivity, enhancing environmental quality, promoting sustainable agriculture, and addressing global climate change. This study focuses on investigating the spatial distribution and influencing factors of soil carbon (C) and nitrogen (N) in the Danjiangkou Reservoir area, a vital water source for the South-to-North Water Transfer Project. Utilizing both geostatistical and traditional statistical methods, this research explores the impact of various land-use types—such as orchards, drylands, paddy fields, and Hydro-Fluctuation Belts (HF belts)—on soil C and N content. The findings reveal predominantly low levels of soil organic carbon (SOC) (ranging from 2.95 to 21.50 g·kg−1), total nitrogen (TN) (ranging from 0.27 to 2.44 g·kg−1), and available nitrogen (AN) (ranging from 18.20 to 170.45 mg·kg−1), mostly falling into deficient categories. Notably, spatial variability is observed, especially in agriculturally developed regions, leading to areas of enrichment. Paddy fields and HF belts are identified as influential contributors to increased SOC and nitrogen content compared to orchards and drylands. Correlation and stepwise regression analyses unveil intricate interactions among SOC, TN, AN, and environmental factors, underscoring the necessity for a holistic approach to soil management. This study emphasizes the critical role of adopting rational land-use types and sustainable agricultural practices for effective soil management in the Danjiangkou Reservoir area.

An Optimal Random Forest Model for Enhancing Decision-Making in Improving Students’ Performance via Educational Data Mining

The need for measures to enhance students’ performance, especially in Science and Math as a panacea for development in this 4th industrial revolution has become a global call. The large stream and heterogeneous nature of educational data make it difficult to use traditional statistical methods for data analysis to support decisions. However, Educational data mining has been effective and efficient in addressing this issue but much focus of researchers has been on only students' academic records to determine performance. This study sought to propose a Random Forest model to predict and enhance students’ performance. The study adopted a 5-stage mining process to mine psycho-socio-economic demographics educational data with Mutual information, Chi-squared test, Featurewiz, RandomizedSearch CV, and GridSearch CV to optimize the model’s performance. The study’s outcome revealed the key factors affecting students’ performance and that the model was enhanced by a 10.4% increment in precision and f1-score and 9.1% recall value, 7.1secs (62%) improvement in execution time and 78.7% improvement in Root Mean Square Error. This outcome remains a contribution to guiding decision-making in the educational setting and a basis for further studies on model optimization.

An ensemble learning-based feature selection algorithm for identification of biomarkers of renal cell carcinoma.

Feature selection plays a crucial role in classification tasks as part of the data preprocessing process. Effective feature selection can improve the robustness and interpretability of learning algorithms, and accelerate model learning. However, traditional statistical methods for feature selection are no longer practical in the context of high-dimensional data due to the computationally complex. Ensemble learning, a prominent learning method in machine learning, has demonstrated exceptional performance, particularly in classification problems. To address the issue, we propose a three-stage feature selection algorithm framework for high-dimensional data based on ensemble learning (EFS-GINI). Firstly, highly linearly correlated features are eliminated using the Spearman coefficient. Then, a feature selector based on the F-test is employed for the first stage selection. For the second stage, four feature subsets are formed using mutual information (MI), ReliefF, SURF, and SURF* filters in parallel. The third stage involves feature selection using a combinator based on GINI coefficient. Finally, a soft voting approach is proposed to employ for classification, including decision tree, naive Bayes, support vector machine (SVM), k-nearest neighbors (KNN) and random forest classifiers. To demonstrate the effectiveness and efficiency of the proposed algorithm, eight high-dimensional datasets are used and five feature selection methods are employed to compare with our proposed algorithm. Experimental results show that our method effectively enhances the accuracy and speed of feature selection. Moreover, to explore the biological significance of the proposed algorithm, we apply it on the renal cell carcinoma dataset GSE40435 from the Gene Expression Omnibus database. Two feature genes, NOP2 and NSUN5, are selected by our proposed algorithm. They are directly involved in regulating m5c RNA modification, which reveals the biological importance of EFS-GINI. Through bioinformatics analysis, we shows that m5C-related genes play an important role in the occurrence and progression of renal cell carcinoma, and are expected to become an important marker to predict the prognosis of patients.

Machine learning based method for analyzing vibration and noise in large cruise ships.

Cruise ships are distinguished as special passenger ships, transporting passengers to various ports and giving importance to comfort. High comfort can attract lots of passengers and generate substantial profits. Vibration and noise are the most important indicators for assessing the comfort of cruise ships. Existing methods for analyzing vibration and noise data have shown limitations in uncovering essential information and discerning critical disparities in vibration and noise levels across different ship districts. Conversely, the rapid development in machine learning present an opportunity to leverage sophisticated algorithms for a more insightful examination of vibration and noise aboard cruise ships. This study designed a machine learning-driven approach to analyze the vibration and noise data. Drawing data from China's first large-scale cruise ship, encompassing 127 noise samples, this study sets up a classification task, where decks were assigned as labels and frequencies served as features. Essential information was extracted by investigating this problem. Several machine learning algorithms, including feature ranking, selection, and classification algorithms, were adopted in this method. One or two essential noise frequencies related to each of the decks, except the 10th deck, were obtained, which were partly validated by the traditional statistical methods. Such findings were helpful in reducing and controlling the vibration and noise in cruise ships. Furthermore, the study develops a classifier to distinguish noise samples, which utilizes random forest as the classification algorithm with eight optimal frequency features identified by LightGBM. This classifier yielded a Matthews correlation coefficient of 0.3415. This study gives a new direction for investigating vibration and noise in ships.