RPig: A scalable framework for machine learning and advanced statistical functionalities

Food flavor analysis 4.0: A cross-domain application of machine learning

Deciphering the complex links between inflammatory bowel diseases and NAFLD through advanced statistical and machine learning analysis

An increasing number of power transformers operating in the developed countries do not have OEM technical support. There is an urgent need to obtain transformer design information such as winding types through interpretation of measured FRA data. Winding types may be manifested as different features in FRA traces within specific frequency ranges. The objective of this paper is to identify the winding type via FRA measurement data in order to help manage transformer assets. Support Vector Machines (SVM) are supervised learning models for classification analysis in machine learning. Given a set of training FRA examples with known winding categories, an SVM algorithm is built to assign new FRA traces to a most possible winding category. A group of 400/275 kV transformers are tested in this study with excellent classification results of winding types, indicating a promising “digital twin” technological approach to transformer asset management.

In conventional distributed machine learning methods, distributed support vector machines (SVM) algorithms are trained over pre-configured intranet/internet environments to find out an optimal classifier. These methods are very complicated and costly for large datasets. Hence, we propose a method that is referred as the Cloud SVM training mechanism (CloudSVM) in a cloud computing environment with MapReduce technique for distributed machine learning applications. Accordingly, (i) SVM algorithm is trained in distributed cloud storage servers that work concurrently; (ii) merge all support vectors in every trained cloud node; and (iii) iterate these two steps until the SVM converges to the optimal classifier function. Single computer is incapable to train SVM algorithm with large scale data sets. The results of this study are important for training of large scale data sets for machine learning applications. We provided that iterative training of splitted data set in cloud computing environment using SVM will converge to a global optimal classifier in finite iteration size.

<h3>Abstract</h3> <h3>Background</h3> With the global increase of Cesarean section delivery rates, the long-term effects of Cesarean delivery have started to become more clear. One of the most prominent complications of...

India is ranked 120th among 122 countries globally in WaterAid&amp;#8217;s water quality index. Regular water quality monitoring is essential to determine which inland water bodies are experiencing depreciating water quality. Long-term trends have been obtained using satellite remote sensing, necessitating multiple image analysis. The computational burden of processing numerous satellite images can be reduced using Google Earth Engine&amp;#8217;s (GEE) cloud computing capabilities. Thorough research was conducted to determine the global spatio-temporal and biochemical factors impacting surface water quality (WQ). The public availability of geospatial datasets and free access to cloud-based geo-computing platforms such as GEE are widely used for spatio-temporal mapping, global surface water monitoring, water quality parametric variation, and real-time forecasting. Many researchers have recently focused on improving data mining and machine learning algorithms to accurately deal with image classification and predictive problems for crop identification and monitoring. In the present study, we propose to define band spectral ratios, spectral band equations, and empirical models for water quality parameters. A few neural network models will be used to (i) query and pre-process satellite earth observations that coincide with the study area, (ii) extract the spectra, and (iii) use spectral band wavelength charts, time-series charts, spatial-distribution maps, and the development of an online dashboard application to visualize the results graphically upon using integrated Landsat (8,9), Sentinel-2A/B and PlanetScope satellite data for the pre-monsoon and post-monsoon seasons in 2023 in the pan-India region. MODIS-TERRA provides LST spatial-temporal monitoring. In this, we have assessed and compared the performance of CART, SVM, and RF ML algorithms. We found that RF outperforms CART and SVM algorithms in the GEE platform with PlanetScope data (80.71% overall accuracy (OA) with Kappa 0.89) and also with the integration of PlanetScope and Sentinel-2A/B data (OA = 85.53%, Kappa 0.91). But CART outperforms RF and SVM algorithms with Sentiel-2A/B data (OA = 81.59%, Kappa 0.85). The SAM technique, spectral feature fitting, continuum band removal, and other band-spectral ratio techniques are employed for quantitative hyperspectral data analysis. Specifically, the performance metrics of XGBoost and SGD for both Chl-a (R^2 = 0.818) and Turbidity (R^2 = 0.815) models exhibited robust accuracy. We have also developed a Google API-based JavaScript code that can be tested under complex coastal shores, challenging inundation and variable climatic conditions. Our method provides the end-to-end cloud computing workflow shown in this research, considering cost and computational efficiency for timely information delivery.Keywords: Classification and Regression Trees (CART), Chlorophyll-a (conc), Color dissolved organic matter (CDOM), Composite water management index (CWMI), Environmental Mapping and Analysis Program (EnMAP), Land Surface Temperature (LST), Machine Learning (ML), Random Forest (RF), Spectral Angle Mapper (SAM), Stochastic Gradient Descent (SGD), Support Vector Machine (SVM), Total Suspended Solids (TSS), Water Quality (WQ), XGBoost (Extreme Gradient Boosting).

Objective: The present study aimed to predict adolescent risk behavior by examining the combined effects of family monitoring, sensation seeking, and peer deviance using machine learning analytical approaches. Methods and Materials: This cross-sectional predictive study was conducted among 512 adolescents aged 14 to 18 years recruited from secondary schools in Tunisia using multistage cluster random sampling. Data were collected using standardized self-report instruments, including the Youth Risk Behavior Surveillance Questionnaire, the Parental Monitoring Scale, the Brief Sensation Seeking Scale, and the Peer Delinquency Scale, all of which demonstrated acceptable reliability and validity. Statistical analyses were performed using SPSS-27 to compute descriptive indices and Pearson correlations. Machine learning models, including random forest, support vector machine, gradient boosting, and multilayer perceptron neural networks, were implemented in Python. Data preprocessing included normalization and feature scaling, and model performance was evaluated using k-fold cross-validation and metrics such as accuracy, precision, recall, F1-score, and area under the ROC curve. Findings: The results revealed that adolescent risk behavior was significantly negatively associated with family monitoring and positively associated with sensation seeking and peer deviance. Among the predictive models, the neural network demonstrated the highest performance, achieving the greatest accuracy and discriminative power. Feature importance analyses consistently identified peer deviance as the strongest predictor of risk behavior, followed by sensation seeking, while family monitoring showed a weaker but still significant contribution. The models indicated that nonlinear interactions among predictors significantly improved prediction accuracy compared to linear approaches. Conclusion: The findings highlight the critical role of peer deviance and sensation seeking in shaping adolescent risk behavior, while confirming the protective function of family monitoring. The superior performance of machine learning models underscores their utility in capturing complex behavioral patterns and enhancing predictive precision.

SUMMARYNeuronal oscillations are suggested to play an important role in auditory working memory (WM), but their contribution to content-specific representations has remained unclear. Here, we measure magnetoencephalography during a retro-cueing task with parametric ripple-sound stimuli, which are spectrotemporally similar to speech but resist non-auditory memory strategies. Using machine learning analyses, with rigorous between-subject cross-validation and non-parametric permutation testing, we show that memorized sound content is strongly represented in phase-synchronization patterns between subregions of auditory and frontoparietal cortices. These phase-synchronization patterns predict the memorized sound content steadily across the studied maintenance period. In addition to connectivity-based representations, there are indices of more local, “activity silent” representations in auditory cortices, where the decoding accuracy of WM content significantly increases after task-irrelevant “impulse stimuli.” Our results demonstrate that synchronization patterns across auditory sensory and association areas orchestrate neuronal coding of auditory WM content. This connectivity-based coding scheme could also extend beyond the auditory domain.

Machine learning (ML) has emerged as a transformative force in cardiovascular disease research, offering innovative approaches to prevention, diagnosis, and treatment. This report provides an overview of the current landscape of ML applications in cardiology, highlighting its potential to revolutionize the field. Key ML algorithms such as Support Vector Machine (SVM), Random Forests, Adaboost, XGBoost, and Convolutional Neural Networks (CNN) are discussed for their unique capabilities in handling complex cardiovascular data and improving disease prediction and risk stratification. Notably, the integration of feature selection techniques with ML algorithms enables the identification of biomarkers and clinical indicators, facilitating personalized care and targeted interventions. The potential of ML extends to early disease diagnosis, patient stratification, and the design of new therapeutic strategies, thereby transforming cardiology into a domain of precision medicine. While the advancements are promising, there is a pressing need for further research to enhance the interpretability, generalizability, and integration of ML models into real-world healthcare settings. Collaborative efforts between ML experts, healthcare providers, and policymakers are essential to address regulatory, ethical, and privacy concerns and to fully harness the capabilities of ML in cardiovascular medicine.

BackgroundWith global aging, cognitive impairment, including Alzheimer's disease and related dementias, has become a critical public health challenge, driving the need for convenient screening tools to facilitate early intervention.ObjectiveThis study aimed to develop an efficient and noninvasive risk assessment model for identifying potential cognitive impairment in the elderly using machine learning algorithms based on comprehensive geriatric assessment (CGA).MethodsWe included 1410 participants aged 50 and older from geriatric clinics and community. Feature selection was performed on the CGA indicators using a combination of expert knowledge and machine learning. Logistic regression (LR), naive Bayes, support vector machines, neural networks, and random forests were comprehensively evaluated based on common classification performance metrics. The optimal machine learning algorithms and feature subset are used to construct the final prediction model. Shapley Additive exPlanations (SHAP) was used to explain the model.ResultsThirteen noninvasive predictors were identified, including ‘Bathing’, ‘Age’, ‘Caregiver’, ‘Sleep duration’, ‘Homekeeping’, ‘Right Ear’, ‘Stand BWEO’, ‘Hobbies’, ‘Focusing Difficulty’, ‘UI effect’, and ‘Housework’. The LR model performed best on the test set, with an AUC of 0.877 and high accuracy (0.815), sensitivity (0.767), and specificity (0.827). The SHAP results illustrated the role of these key features in cognitive impairment, which is highly consistent with clinical knowledge.ConclusionsThis study identifies convenient, noninvasive predictors for screening-oriented prediction of cognitive impairment, develops an efficient machine learning model, and employs SHAP analysis for interpretation. This facilitates widespread screening, providing guidance for early detection and intervention in high-risk populations.

ObjectiveEnhanced technology in computer and internet has driven scale and quality of data to be improved in various areas including healthcare sectors. Machine Learning (ML) has played a pivotal role in efficiently analyzing those big data, but a general misunderstanding of ML algorithms still exists in applying them (e.g., ML techniques can settle a problem of small sample size, or deep learning is the ML algorithm). This paper reviewed the research of diagnosing mental illness using ML algorithm and suggests how ML techniques can be employed and worked in practice.MethodsResearches about mental illness diagnostic using ML techniques were carefully reviewed. Five traditional ML algorithms-Support Vector Machines (SVM), Gradient Boosting Machine (GBM), Random Forest, Naïve Bayes, and K-Nearest Neighborhood (KNN)-frequently used for mental health area researches were systematically organized and summarized.ResultsBased on literature review, it turned out that Support Vector Machines (SVM), Gradient Boosting Machine (GBM), Random Forest, Naïve Bayes, and K-Nearest Neighborhood (KNN) were frequently employed in mental health area, but many researchers did not clarify the reason for using their ML algorithm though every ML algorithm has its own advantages. In addition, there were several studies to apply ML algorithms without fully understanding the data characteristics.ConclusionResearchers using ML algorithms should be aware of the properties of their ML algorithms and the limitation of the results they obtained under restricted data conditions. This paper provides useful information of the properties and limitation of each ML algorithm in the practice of mental health.

Alterations in microbiome composition have been linked to many human diseases, including colorectal cancer or precancerous liver diseases. Findings from microbiome-association studies are increasingly explored as promising avenues for clinical applications due to their diagnostic or therapeutic potential. However, microbiome data complexity and the lack of integrated computational tools make it difficult to arrive at robust associations and predictive disease models. Here, we present the SIAMCAT R package as a modular and user-friendly toolbox for machine learning workflows, statistical analysis, and confounder detection for case-control microbiome datasets. Previously, we conducted a machine learning meta-analysis of colorectal cancer metagenomics studies (Wirbel, Pyl et al., Nat Med 2019) using the functionalities of SIAMCAT, which is now available to the wider community via Bioconductor. We showcase how SIAMCAT is a versatile tool by applying it to a large set of metagenomic datasets that have been processed with a wide variety of taxonomic and functional profiling tools. We furthermore demonstrate how SIAMCAT can help to detect confounding factors in microbiome association studies. By making stringent machine learning workflows and analysis pipelines available through a user-friendly and flexible interface, SIAMCAT will facilitate microbiome data analyses, biomarker discovery, and thus the translation of microbiome research to clinical applications, while simultaneously improving statistical rigor and safeguarding against common machine learning pitfalls. Citation Format: Jakob Wirbel, Konrad Zych, Morgan Essex, Nicolai Karcher, Ece Kartal, Guillem Salazar, Peer Bork, Shinichi Sunagawa, Georg Zeller. The SIAMCAT R package enables statistical and machine learning analyses for case-control microbiome datasets [abstract]. In: Proceedings of the AACR Special Conference on the Microbiome, Viruses, and Cancer; 2020 Feb 21-24; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2020;80(8 Suppl):Abstract nr A40.

Machine learning for classifying chronic ankle instability based on ankle strength, range of motion, postural control and anatomical deformities in delivery service workers with a history of lateral ankle sprains.

According to statistics from the Global Burden of Cancer Study (Globocon) of the World Health Organization (WHO), cancer, particularly breast cancer, is a severe health issue in Indonesia with 68,858 new cases and 22,000 deaths recorded in 2020. Ultrasonography (USG) technology is acknowledged as one of the potentials to support early detection, which is vital in reducing mortality from breast cancer. This study focuses on classifying ultrasound images using the Support Vector Machine (SVM) algorithm, GLCM feature extraction, Min-Max normalization, and Mutual Information with SelectKBest Feature Selection. From several experiments using the SVM algorithm with various combinations of parameter values that have been set and different Tests, namely using a Train/Test Split with a proportion of 80/20 and K-Fold Cross Validation, it shows that the SVM algorithm is capable of classifying ultrasound images of breast cancer. into two categories (Benign Tumor and Malignant Tumor) with the same maximum accuracy of 79% after applying the SMOTE Balancing Data technique or without using the Balancing Data technique. As a result, the Support Vector Machine (SVM) algorithm has the potential to be an effective model for identifying breast cancer ultrasound images, both on data from the original set that has not been balanced and data from the set that has been balanced.

Campus data is increasingly gaining the attention of college student management staff; they hope that through the use of machine learning and big data technology theory, analysis of students during the period of school data finds students growth law, timely find hidden dangers and properly handle, for students to develop more scientific and humanized management plan, truly in accordance with their individual aptitude. For Grade 13 students, the course management optimization success rates were 62.90% for Algorithm 1 and 69.35% for Algorithm 2. For grade 15 students, the success rate of course management optimization was 64.41% for algorithm 1 and 89.83% for algorithm 2. By studying the relevant techniques of student group behavior pattern analysis, we explore how to classify the behavior patterns of college students by combining machine learning K-means algorithm and NMF algorithm. Using the emotion polarity classification algorithm based on dependence, we paper how to classify the emotion polarity of student network comments and classify some real student comments in a university forum. The paper also completed the analysis of student group behavior patterns based on machine learning K-means algorithm and NMF algorithm, and obtained the classification results and characteristics of group behavior patterns, classified the real comments of some students based on the forum based on dependency, and determined the related theory based on support vector machine (SVM) algorithm. The characteristics of students’ behavior in school were analyzed from three aspects, and the K-means algorithm in the cluster analysis was used to mine the data of these three aspects, which obtained the distribution of students’ behavior characteristics of five types of consumption habits, three types of living habits and four types of learning habits. Students’ long Internet time, low number of books borrowing, and provide targeted management for teachers and schools according to the characteristics of these problems propose.