Statistical Learning Techniques Research Articles

Mechanical performance optimization in FFF 3D printing using Taguchi design and machine learning approach with PLA/walnut Shell composites filaments

AbstractThis study explores the optimization of mechanical properties in 3D‐printed components made from a Polylactic Acid (PLA) and Walnut Shell Composite using Fused Filament Fabrication (FFF). Employing a machine learning‐based approach, the research identifies the optimal regression model for predicting relationships between printing parameters and material properties. A Taguchi L18 design is used to minimize experiment count while accurately determining parameter levels. Testing was conducted on a composite containing 30% walnut shell fibers, with the Ultimate Tensile Strength (UTS) and Elastic Modulus (E) measured as per ASTM D638 standards. Experimental factors included Layer Thickness (LT), Nozzle Temperature (NT), Deposition Angle (DA), and Printing Speed (PS). Using Analysis of Variance (ANOVA) and machine learning techniques, the effects of these parameters on UTS and E were evaluated. Results highlight the deposition angle as the dominant parameter, with machine learning models, especially Random Forest Regression, providing highly accurate predictions. This approach presents a novel, data‐driven method for optimizing 3D printing processes with sustainable, composite materials.Highlights Higher UTS and E achieved with optimized PLA/walnut shell composite. Deposition angle is the key element of mechanical performance in FFF printing. Layer thickness is important to improve Elastic Modulus. Statistical and machine learning techniques combined for sustainable printing. Improved machine learning process understanding for 3D printed components.

Fusion Data Framework for Enhanced Outlier Detection Integrating Statistical and Machine Learning Techniques for Retail Analytics

Fusion Data Framework for Enhanced Outlier Detection Integrating Statistical and Machine Learning Techniques for Retail Analytics

Predicting Sentiments in Spotify Comments: A Comparative Analysis of Machine Learning Models

Using data from user sentences on Spotify, this work explores through Natural Language Processing positive and negative sentiments in each comment. We compare different statistical modeling and Machine Learning techniques, identifying the ones with the greatest accuracy in predicting sentiments. As a result, the assessment supports most of the sentences presented with negative connotations. As for modeling, the Logistic Regression and Random Forest models resulted in better accuracy.

Integrating Statistical Methods and Machine Learning Techniques to Analyze and Classify COVID-19 Symptom Severity

Background/Objectives: The COVID-19 pandemic, caused by Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2), led to significant global health challenges, including the urgent need for accurate symptom severity prediction aimed at optimizing treatment. While machine learning (ML) and deep learning (DL) models have shown promise in predicting COVID-19 severity using imaging and clinical data, there is limited research utilizing comprehensive tabular symptom datasets. This study aims to address this gap by leveraging a detailed symptom dataset to develop robust models for categorizing COVID-19 symptom severity, thereby enhancing clinical decision making. Methods: A unique tabular dataset was created using questionnaire responses from 5654 individuals, including demographic information, comorbidities, travel history, and medical data. Both unsupervised and supervised ML techniques were employed, including k-means clustering to categorize symptom severity into mild, moderate, and severe clusters. In addition, classification models, namely, Support Vector Machine (SVM), Adaptive Boosting (AdaBoost), eXtreme Gradient Boosting (XGBoost), random forest, and a deep neural network (DNN) were used to predict symptom severity levels. Feature importance was analyzed using the random forest model for its robustness with high-dimensional data and ability to capture complex non-linear relationships, and statistical significance was evaluated through ANOVA and Chi-square tests. Results: Our study showed that fatigue, joint pain, and headache were the most important features in predicting severity. SVM, AdaBoost, and random forest achieved an accuracy of 94%, while XGBoost achieved an accuracy of 96%. DNN showed robust performance in handling complex patterns with 98% accuracy. In terms of precision and recall metrics, both the XGBoost and DNN models demonstrated robust performance, particularly for the moderate class. XGBoost recorded 98% precision and 97% recall, while DNN achieved 99% precision and recall. The clustering approach improved classification accuracy by reducing noise and dimensionality. Statistical tests confirmed the significance of additional features like Body Mass Index (BMI), age, and dominant variant type. Conclusions: Integrating symptom data with advanced ML models offers a promising approach for accurate COVID-19 severity classification. This method provides a reliable tool for healthcare professionals to optimize patient care and resource management, particularly in managing COVID-19 and potential future pandemics. Future work should focus on incorporating imaging and clinical data to further enhance model accuracy and clinical applicability.

Enhancing access to specialist appointments in tertiary healthcare in Shanghai, China: a structured reservation pathway using digital health technologies

ObjectiveThe aim of this study is to develop, implement the precise reservation path (PRP) and investigate its prediction function for scheduling shunting patients for specialist appointment registration in Shanghai, China.DesignThe...

Python technology and its applications in radiomics

Python, developed by Guido van Rossum, is favored for its simplicity and extensive ecosystem of libraries, which facilitate efficient coding and integration with other programming languages. Here, we aim to explore and summarize the role of Python in radiomics, a field focused on extracting and analyzing quantitative features from medical imaging to improve disease characterization and treatment evaluation. Radiomics addresses the complexities of tumor heterogeneity by transforming imaging data from modalities such as computed tomography (CT), magnetic resonance imaging (MRI), and positron emission tomography (PET) into actionable insights, often using statistical methods and machine learning techniques. Its primary applications include differentiating between benign and malignant tumors and predicting treatment outcomes, etc. Python is integral to several stages of radiomics, including image acquisition, region of interest (ROI) segmentation, feature extraction, and statistical analysis. By utilizing libraries such as PyRadiomics and Scikit-learn, researchers can significantly enhance the accuracy and efficiency of their analyses. Looking forward, Python holds considerable promise in radiomics, especially with ongoing advancements in medical imaging and big data. However, challenges such as data standardization, model interpretability, and patient privacy protection must be addressed to fully unlock its potential for improving diagnostic precision and patient outcomes.

Addressing Critical Gaps in Data Collection and Analysis in Atrocities Prevention Early Warning Systems

This qualitative study examines the workflows, challenges, and successes of third-generation conflict Early Warning Early Action (EWEA) systems for mass atrocity prevention. Through 26 semi-structured interviews with practitioners from 21 organizations across Africa, Middle East, and Southeast Asia, the research identifies three key gaps: data availability and quality, resource limitations, and data-to-action challenges. EWEA organizations employ diverse data collection methods, including remote messaging, field-based qualitative data collection, and media monitoring. However, data verification and analysis processes vary significantly, with most organizations relying on ad-hoc mixed methods. The study reveals a disconnect between academic research on conflict prediction and the practical application of EWEA systems. While academic studies utilize complex statistical models and machine learning techniques, community-based EWEA organizations often lack access to such advanced analytics. The findings underscore the need for increased collaboration between funders, academic institutions, technology developers, and EWEA organizations to enhance the effectiveness of atrocity prevention efforts.

A comprehensive guide to volatolomics data analysis.

Volatolomics (or volatilomics), the study of volatile organic compounds, has emerged as a significant branch of metabolomics due to its potential for non-invasive diagnostics and disease monitoring. However, the analysis of high-resolution data from mass spectrometry and gas sensor array-based instruments remains challenging. The careful consideration of experimental design, data collection, and processing strategies is essential to enhance the quality of results obtained from subsequent analyses. This comprehensive guide provides an in-depth exploration of volatolomics data analysis, highlighting the essential steps, such as data cleaning, pretreatment, and the application of statistical and machine learning techniques, including dimensionality reduction, clustering, classification, and variable selection. The choice of these methodologies, along with data handling practices, such as missing data imputation, outlier detection, model validation, and data integration, is crucial for identifying meaningful metabolites and drawing accurate diagnostic conclusions. By offering researchers the tools and knowledge to navigate the complexities of volatolomics data analysis, this guide emphasizes the importance of understanding the strengths and limitations of each method. Such informed decision-making enhances the reliability of findings, ultimately advancing the field and improving the understanding of metabolic processes in health and disease.

Optimizing Fermentation Parameters for Bioethanol Production from Areca Nut Leaves using Artificial Neural Networks and Response Surface Methodologies

The research covered the entire process, from collecting the Areca nut leaves to purifying the produced bioethanol. Materials and Methods: The Areca nut leaves were pre-treated with sulfuric acid and sodium hydroxide, followed by enzymatic hydrolysis using cellulose enzymes. The hydrolyzed biomass was then fermented by Saccharomyces cerevisiae for 12–72 h to produce bioethanol. The produced bioethanol was purified through distillation using a rotary flask evaporator. To optimize the fermentation process and bioethanol production, the researchers employed two modeling approaches: Artificial neural networks (ANN) and response surface methodology (RSM). Variables such as pH, fermentation time, and disodium hydrogen phosphate (Na2HPO4) concentration, identified from the Plackett- Burman design, were optimized using the central composite design of RSM. Results and Discussion: The R² value for the RSM model was 91.72%, and the adjusted R² was 84.72%. In addition, an ANN algorithm model with 3 input neurons, 10 hidden layer neurons, and 1 output neuron was developed to investigate the relationship between bioethanol production and fermentation parameters. The ANN model achieved an R² of 99.78%, indicating higher accuracy and reliability compared to the RSM approach. The optimal conditions for bioethanol production were identified as pH 5.5, 60 h fermentation time, and 0.45 g of Na2HPO4. Under these conditions, the experimental bioethanol concentration reached 36.54 g/L. Conclusion: This study demonstrates the effective utilization of Areca nut leaves, a readily available agricultural waste, to produce bioethanol. The combination of statistical and machine learning techniques, such as ANN and RSM, allowed for the optimization of the fermentation process and the enhancement of bioethanol yield, showcasing the potential of this approach for sustainable biofuel production.

Unified Machine Learning Techniques for High-Dimensional Survival Data Analysis

Survival analysis is a statistical method used to analyze time-to-event data. The existence of states where event results become invisible after a given time is one of the primary difficulties with this regulation. This is considered censorship. This kind of data may be found in a variety of applications, such as mechanical system failure rates, patient mortality rates during clinical trials, and the length of unemployment in a community. Assessing the so-called survival and hazard functions is one of the primary goals of survival analysis. Many machine-learning algorithms have also been developed to tackle censored data and other difficult problems with real-world data. Machine-learning techniques enhance survival analysis by incorporating flexible modeling approaches, handling high-dimensional data, capturing nonlinear relationships, and accounting for censorship. They provide powerful tools to extract meaningful insights and make accurate predictions in time-to-event analysis across various healthcare, finance, and engineering domains. The standard statistical approaches and machine learning techniques advanced for survival analysis are structured in this unified framework, as is the implementation of some machine learning techniques applying to the GBSG2 survival analysis dataset.

Time Series Analysis in Electrical Load Forecasting

Time series analysis in electricity load projection is a key part of the energy sector because it makes it possible to control and distribute energy more efficiently. It is important to have accurate load forecasts in order to balance supply and demand, make power systems run more efficiently, and lower operating costs. This essay covers a wide range of ways that time series analysis can be used to guess how much electricity will be used. The study looks at a number of different models, such as the autoregressive integrated moving average (ARIMA), exponential smoothing, and machine learning methods like Long Short-Term Memory (LSTM) networks. These models are judged by how well they can show the patterns and time relationships that are common in electricity load data. A lot of attention is paid to the problems that come up because of non-stationarity, timing, and outside factors like weather that cause load changes. The study talks about how feature selection, data pre-processing, and model evaluation can help make forecasts more accurate. The study shows the trade-offs between model interpretability and forecasting power by comparing how well standard statistical methods and new machine learning techniques work. The study also talks about how mixed models, which take the best parts of more than one method and combine them, might help make forecasts more accurate. The results make it clear how important time series analysis is for building accurate load predicting models, which are needed to keep modern power systems reliable and efficient.

Thermal Footprint of the Urbanization Process: Analyzing the Heat Effects of the Urbanization Index (UI) on the Local Climate Zone (LCZ) and Land Surface Temperature (LST) over Two Decades in Seville

Urbanization is a multifaceted process characterized by changes in urban areas through various means, such as sprawl, ribbon development, or infill and compact growth. This phenomenon changes the pattern of the local climate zone (LCZ) and significantly affects the climate, vegetation dynamics, energy consumption, water resources, and public health. This study aims to discern the impacts of changes in urban growth on the LCZ and land surface temperature (LST) over a two-decade period. A comprehensive methodology that integrates statistical analysis, data visualization, machine learning, and advanced techniques, such as remote sensing technology and geospatial analysis systems, is employed. ENVI, GEE, and GIS tools are utilized to collect, process, and monitor satellite data and imagery of temporal and spatial variations in intensive or diffuse urbanization processes from 2003 to 2023 to analyze and simulate land use and land cover (LULC) changes, urbanization index (UI), LCZ patterns, and LST changes over the years and to make overlapping maps of changes to recognize the relation between LULC, LCZ, and LST. This study focuses on Seville’s urban area, which has experienced rapid urbanization and a significant increase in average temperature during the last few decades. The findings of this study will provide actionable recommendations into the interplay between urban growth and climate and highlight the pivotal role of urban growth in shaping resilience and vulnerable areas based on microclimate changes. Urban planners can leverage these insights to predict alternatives for the future development of urban areas and define practical climate mitigation strategies.

Exploring statistical and machine learning methods for modeling probability distribution parameters in downtime length analysis: a paper manufacturing machine case study

Manufacturing companies focus on improving productivity, reducing costs, and aligning performance metrics with strategic objectives. In industries like paper manufacturing, minimizing equipment downtime is essential for maintaining high throughput. Leveraging the extensive data generated by these facilities offers opportunities for gaining competitive advantages through data-driven insights, revealing trends, patterns, and predicting future performance indicators like unplanned downtime length, which is essential in optimizing maintenance and minimizing potential losses. This paper explores statistical and machine learning techniques for modeling downtime length probability distributions and correlation with machine vibration measurements. We proposed a novel framework, employing advanced data-driven techniques like artificial neural networks (ANNs) to estimate parameters of probability distributions governing downtime lengths. Our approach specifically focuses on modeling parameters of these distribution, rather than directly modeling probability density function (PDF) values, as is common in other approaches. Experimental results indicate a significant performance boost, with the proposed method achieving up to 30% superior performance in modeling the distribution of downtime lengths compared to alternative methods. Moreover, this method facilitates unsupervised training, making it suitable for big data repositories of unlabelled data. The framework allows for potential expansion by incorporating additional input variables. In this study, machine vibration velocity measurements are selected for further investigation. The study underscores the potential of advanced data-driven techniques to enables companies to make better-informed decisions regarding their current maintenance practices and to direct improvement programs in industrial settings.

A Proposed Method of Automating Data Processing for Analysing Data Produced from Eye Tracking and Galvanic Skin Response

The use of eye tracking technology, together with other physiological measurements such as psychogalvanic skin response (GSR) and electroencephalographic (EEG) recordings, provides researchers with information about users’ physiological behavioural responses during their learning process in different types of tasks. These devices produce a large volume of data. However, in order to analyse these records, researchers have to process and analyse them using complex statistical and/or machine learning techniques (supervised or unsupervised) that are usually not incorporated into the devices. The objectives of this study were (1) to propose a procedure for processing the extracted data; (2) to address the potential technical challenges and difficulties in processing logs in integrated multichannel technology; and (3) to offer solutions for automating data processing and analysis. A Notebook in Jupyter is proposed with the steps for importing and processing data, as well as for using supervised and unsupervised machine learning algorithms.

Analysis of NBA player salary based on multiple linear regression model

Abstract. This study explores the use of machine learning techniques to predict NBA player salaries. Traditional salary evaluation methods often rely on subjective expert judgment, whereas data-driven approaches can provide more objective and accurate predictions. With the advancement of data analysis techniques and machine learning algorithms, it is now feasible to predict player salaries based on performance data. This study employs advanced statistical and machine learning techniques to analyze detailed player performance data, including points scored, rebounds, assists, steals, and blocks, to establish a data- and algorithm-based salary prediction model. This model can assist team management in making more scientific decisions during contract negotiations and player acquisitions, thereby avoiding the overvaluation or undervaluation of players and achieving a more balanced and fair salary distribution. Accurate salary predictions help teams allocate their limited salary cap more effectively, optimizing budget management and enhancing overall team competitiveness. This study not only demonstrates the practical value of data analysis and machine learning methods in the sports field but also promotes the further development of data science in sports management. Additionally, the results of the prediction model can provide valuable references for fans and media, enhancing their understanding of player salaries and team management strategies. This transparency enriches the overall fan experience and media coverage of the sport, facilitating more informed discussions and debates about player value and team decisions.

Spark: A Statistical Comparison and Evaluation of Classification Algorithms for Fault Prediction in Electrical Secondary Distribution Network

Managing faults in the electrical secondary distribution network is a challenging task given the nature, size, and complexity. Predicting faults early before they occur helps in increasing the safety and reliability of the power distribution system. Various statistical and machine learning techniques are being used to predict different types of faults. This study applies classification algorithms available in the big data framework Apache Spark through its python interface PySpark to predict electrical secondary distribution network faults. The study evaluates and compares nine algorithms: Decision tree, Gradient-boosted tree, Logistic regression, Naïve Bayes, Multilayer perceptron, Random forest, Linear Support Vector Machine, One-versus-rest and Factorization machines. The research uses Friedman’s test followed by the Nemenyi post hoc test to find the significance of performance differences among the algorithms. The results show significant differences among the algorithms. Gradient-boosted tree and One-versus-rest with Gradient-boosted tree had the best performance for binary and multiclass classification, respectively, while Naïve Bayes had the worst performance.

Genetic selection for lung adenocarcinoma: a statistical approach based on pathological genetic data

Lung adenocarcinoma (LUAD) is the most common subtype of non-small cell lung cancer (NSCLC), with high morbidity and mortality worldwide, posing a serious threat to human health. Due to the complex molecular mechanism of lung adenocarcinoma, which involves a variety of genetic and environmental factors, this makes early diagnosis and treatment a major challenge. This research aims to identify the key pathogenic genes of lung adenocarcinoma to solve the challenges in early diagnosis and treatment. In this paper, three differential analysis methods, limma, DESeq2 and edgeR, were used to select 3315 differential genes. Then, combined with Lasso regression and XGboost algorithm, the pathological gene data were analyzed in depth, and through these advanced statistical and machine learning techniques, the genes related to lung adenocarcinoma were screened from a large number of gene expression data, and 17 characteristic genes of the highest importance were found GO enrichment analysis and KEGG enrichment analysis were performed on these characteristic genes to clarify the biological functions of the genes. Finally, 8 significant pathogenic genes were identified by Cox survival analysis. The expression levels of these genes are closely related to the prognosis of lung adenocarcinoma patients, providing a new perspective for understanding the disease mechanism. The conclusions of this study not only improve the understanding of the molecular mechanism of lung adenocarcinoma, but also provide potential biomarkers for clinical diagnosis and treatment. The discovery of these genes is expected to promote the development of early diagnosis technology and guide the formulation of personalized treatment plans, thereby improving the treatment efficacy and quality of life of lung adenocarcinoma patients. In the future, these genes may become key targets for the development of new drugs and therapeutic strategies.

Unlocking Fungal Potential: The CRISPR-Cas System as a Strategy for Secondary Metabolite Discovery.

Natural products (NPs) are crucial for the development of novel antibiotics, anticancer agents, and immunosuppressants. To highlight the ability of fungi to produce structurally diverse NPs, this article focuses on the impact of genome mining and CRISPR-Cas9 technology in uncovering and manipulating the biosynthetic gene clusters (BGCs) responsible for NP synthesis. The CRISPR-Cas9 system, originally identified as a bacterial adaptive immune mechanism, has been adapted for precise genome editing in fungi, enabling targeted modifications, such as gene deletions, insertions, and transcription modulation, without altering the genomic sequence. This review elaborates on various CRISPR-Cas9 systems used in fungi, notably the Streptococcus pyogenes type II Cas9 system, and explores advancements in different Cas proteins for fungal genome editing. This review discusses the methodologies employed in CRISPR-Cas9 genome editing of fungi, including guide RNA design, delivery methods, and verification of edited strains. The application of CRISPR-Cas9 has led to enhanced production of secondary metabolites in filamentous fungi, showcasing the potential of this system in biotechnology, medical mycology, and plant pathology. Moreover, this article emphasizes the integration of multi-omics data (genomics, transcriptomics, proteomics, and metabolomics) to validate CRISPR-Cas9 editing effects in fungi. This comprehensive approach aids in understanding molecular changes, identifying off-target effects, and optimizing the editing protocols. Statistical and machine learning techniques are also crucial for analyzing multi-omics data, enabling the development of predictive models and identification of key molecular pathways affected by CRISPR-Cas9 editing. In conclusion, CRISPR-Cas9 technology is a powerful tool for exploring fungal NPs with the potential to accelerate the discovery of novel bioactive compounds. The integration of CRISPR-Cas9 with multi-omics approaches significantly enhances our ability to understand and manipulate fungal genomes for the production of valuable secondary metabolites and for promising new applications in medicine and industry.

Some notes on the consequences of pretreatment of multivariate data

With the advent of data technologies, we have various types of data, such as structured, unstructured and semi-structured. Performing certain statistical or machine learning techniques may require careful preprocessing or pretreatment of the data to make them suitable for analysis. For example, given a data matrix X, which represents n multivariate observations or cases on p variables or features, the columns/rows of X may be pretreated before applying statistical or machine learning techniques to the data. While centering and/or scaling the variables do not alter the correlation structure nor the graphical representation of the data, centering/scaling the observations do. We investigate various row pretreatment methods more closely and show with theoretical proofs and numerical examples that centering/scaling the rows of X changes both the graphical structure of the observations in the multi-dimensional space and the correlation structure among the variables. There may be good reasons for performing row centering/scaling on the data and we are not against it, but analysts who use such row operations should be aware of the geometrical and correlation structures one has performed on the data and should also demonstrate that the process results in a new, more appropriate structure for their questions.

How realistic are multi-decadal reconstructions of GRACE-like total water storage anomalies?

Reconstructions allow us to extend the Gravity Recovery And Climate Experiment (GRACE) data record into the past and bridge the one-year gap between GRACE and its successor, GRACE-FO (Follow on). Reconstructed total water storage anomalies (TWSA) are obtained by identifying relations between GRACE-derived TWSA and climate variables via statistical and machine learning techniques. However, a comparative analysis of the characteristics and realism of reconstructions is missing.In this contribution, we close this gap by comparing three global reconstructions by Humphrey and Gudmundsson (2019), Li et al. (2021) and Chandanpurkar et al. (2022) mutually and against output from the Water Global Analysis and Prognosis (WaterGAP) hydrological model from 1979 onwards, against large-scale mass-change derived from geodetic satellite laser ranging (SLR) from 1992 onwards, and finally against differing GRACE and GRACE-FO solutions from 2002 onwards. The reconstructions vary regarding design and trained GRACE solution.Reconstructions recover the TWSA signal for humid climate regions but disagree for arid climate regions, which is evident on the inter-annual timescales. At seasonal and sub-seasonal timescales, the reconstructions agree surprisingly well in many regions. Our comparison against independent SLR data reveals that reconstructions (only) partially succeed in representing anomalous TWSA for areas that are influenced by significant climate modes such as El Niño-Southern Oscillation (ENSO).