Statistical Machine Learning Algorithms Research Articles

Comparative analysis of the predictive power of statistical models of macroeconomic indicators in conditions of permanent crises

Subject. The article discusses prediction of the state of the economy, the accuracy of forecasts of traditional models during crises, the need to find more effective model specifications to predict macro indicators. Objectives. The purpose is to carry out a comparative analysis of the predictive ability of ensemble methods in comparison with a set of models, including traditional statistical algorithms and machine learning algorithms. Methods. The comparative analysis of predictive ability of the models and interpretation of results obtained were performed using a dynamic factor model (DFM), a neural network with long-term short-term memory (LSTM), and integrated gradient methods (IG). Results. We performed the analysis of predictive ability of the ensemble model to forecast GDP, which combines DFM and LSTM to account for both linear and nonlinear dependencies in the data; the analysis of predictive power of various indicators, which showed that an increase in forecast error is observed for all models except DFM, the ensemble model with an error correction structure, and ARMAX. The obtained results can be used to build models of macroeconomic indicators in order to make strategic decisions by enterprises of various industries operating in a highly uncertain environment. Conclusions. The combination of DFM and LSTM in the ensemble provides higher accuracy forecasts than LSTM and competitor models, however, with less predictive power than DFM.

Будущее уголовного правосудия: роль искусственного интеллекта в предиктивной аналитике

Introduction. This article focuses on the importance and prospects for the use of artificial intelligence in predictive analytics in the criminal justice context. The research is motivated by the significant development of artificial intelligence and machine learning technologies, which are being used in a multitude of fields, including criminal justice. The authors detail the theoretical and practical aspects of predictive analytics, which makes it possible to predict future events based on statistical data and machine learning algorithms. Special attention is paid to the difference between artificial intelligence and predictive analytics. The effectiveness of the application of predictive analytics in criminal justice, including optimising preliminary investigations, improving criminal prosecution and predicting the outcome of criminal cases, is highlighted. Methods. The basis of the research methodology is dialectical materialism, applied general scientific (system-structural and formallogical, inductive and deductive, analysis and synthesis) and special (formal-legal, comparativelegal) methods. Results. The authors conclude that artificial intelligence spans a wider range of tasks requiring human intelligence, while predictive analytics concentrates on making predictions. Advanced technologies that are already in active use in various countries, improving and optimising the allocation of law enforcement and judicial resources, are described. The prospect of integrating virtual and augmented reality technologies into criminal justice is considered, which can radically change approaches to predictive analytics and criminal procedure in general, enriching visualisation and interactive cooperation between participants of legal relations.

Advanced Cybersecurity Enhancement through Mathematical Models: A Comprehensive Approach Using Statistical Methods, Cryptographic Techniques, and Machine Learning Algorithms (SVM, Random Forest, and Neural Networks) for Behavior-Based Malware Detection

Advanced Cybersecurity Enhancement through Mathematical Models: A Comprehensive Approach Using Statistical Methods, Cryptographic Techniques, and Machine Learning Algorithms (SVM, Random Forest, and Neural Networks) for Behavior-Based Malware Detection

Markers of early post-stroke cognitive impairment

Aim. To identify significant indicators of cognitive dysfunction based on discriminant analysis and to assess the influence of the course, nature and localization of ischemic stroke on the cognitive status of the patient.Materials and methods. We examined 290 patients diagnosed with ischemic stroke in the carotid artery area. Depending on presence of cognitive dysfunction according to the Montreal Cognitive Assessment Scale (MoSA) patients were divided into 2 groups: 240 patients with cognitive decline (≤25 point by MoCA) and 50 patients without it. In order to verify the markers, anamnestic characteristics were assessed, cognitive-functional indicators (according to the scales of the National Institutes of Health, MoCA, Bartel, Rankin, IQCODE questionnaire, additional scales to assess praxis, semantic aphasia, perception and executive function), data of neuroimaging studies. For statistical analysis machine learning algorithms and Python with its libraries (Pandas and SciPy) were implied.Results. The main neuropsychological indicators for patients with early post-stroke cognitive impairment were decline in the areas of perception, executive function, memory and semantic information processing, affective disturbances and physical fatigue. Relevant indicators identified during estimation of the instrumental and clinical examination results were severity of IS, left frontal and right parietal localisations of ischemia focus, presence of cortical atrophy and leukoaraiosis.Conclusion. Based on multi-factor analysis of clinical and paraclinical parameters using machine learning algorithms, the main markers of cognitive decline of early post-stroke impairments were identified. This will allow us to optimise the choice of neurocognitive rehabilitation strategies and to personalise the approach in the further management of the stroke patient.

Revolutionising waste management with the impact of Long Short-Term Memory networks on recycling rate predictions

This study explores the efficacy of Long Short-Term Memory (LSTM) networks in predicting recycling rates and enhancing resource allocation in waste management systems. It addresses the limitations of traditional statistical models and machine learning algorithms that struggle with sequential data and temporal dependencies. The methodology comprised collecting extensive datasets from public repositories, configuring the LSTM network architecture, training the model with historical data, and testing various activation functions and hyperparameters. The model’s performance was rigorously compared to traditional models and alternative machine learning algorithms using metrics such as Mean Absolute Error (MAE), Root Mean Square Error (RMSE), and R-squared (R2). The findings demonstrate that the LSTM model significantly outperformed traditional approaches, achieving an MAE of 3.5%, an RMSE of 2.8%, and an R2 of 0.92. These results underscore the superior capability of LSTM networks to capture complex temporal patterns in recycling data, offering substantial improvements in predictive accuracy and reliability. Consequently, the study highlights the potential of LSTM networks to revolutionize waste management strategies, contributing to more effective and sustainable practices.

An Investigation in to Sports Analytics and Related Tools

This study explores the application of sports analytics and the utilization of various tools in the sports industry. Sports analytics, also known as sports data analytics, is the application of data analysis and statistical approaches to extract important insights. Additionally, it aids in making well-informed judgments and enhancing performance in the realm of sports. Sports data analytics include a diverse array of methods and approaches, such as statistical analysis, machine learning, data visualization, predictive modeling, and optimization algorithms. The objective of sports business analytics is to get practical insights from a dataset in order to enhance decision-making and foster innovation in the sports industry

Simulation-Based Assessment of Classification Methods: Statistical Models vs. Machine Learning Algorithms

Simulation-Based Assessment of Classification Methods: Statistical Models vs. Machine Learning Algorithms

Rapid detection and quantification of melamine, urea, sucrose, water, and milk powder adulteration in pasteurized milk using Fourier transform infrared (FTIR) spectroscopy coupled with modern statistical machine learning algorithms

There is an evident requirement for a rapid, efficient, and simple method to screen the authenticity of milk products in the market. Fourier transform infrared (FTIR) spectroscopy stands out as a promising solution. This work employed FTIR spectroscopy and modern statistical machine learning algorithms for the identification and quantification of pasteurized milk adulteration. Comparative results demonstrate modern statistical machine learning algorithms will improve the ability of FTIR spectroscopy to predict milk adulteration compared to partial least square (PLS). To discern the types of substances utilized in milk adulteration, a top-performing multiclassification model was established using multi-layer perceptron (MLP) algorithm, delivering an impressive prediction accuracy of 97.4 %. For quantification purposes, bayesian regularized neural networks (BRNN) provided the best results for the determination of both melamine, urea and milk powder adulteration, while extreme gradient boosting (XGB) and projection pursuit regression (PPR) gave better results in predicting sucrose and water adulteration levels, respectively. The regression models provided suitable predictive accuracy with the ratio of performance to deviation (RPD) values higher than 3. The proposed methodology proved to be a cost-effective and fast tool for screening the authenticity of pasteurized milk in the market.

Plant Disease Detection

Abstract: This project addresses the critical challenge of plant disease detection in India, a nation where agriculture supports nearly 70% of the population. Traditional procedures for identifying plant diseases are both labor-intensive and time-consuming. To enhance efficiency and accuracy, this project introduces an innovative approach that integrates machine learning and image processing techniques. By analyzing images of leaves, the system identifies diseases based on key factors such as leaf color, damage extent, area, and texture parameters. Unlike conventional methods that depend on visual inspections or chemical processes, this automated system provides a cost-effective and efficient solution for monitoring extensive crop fields. By leveraging statistical machine learning and advanced image processing algorithms, the proposed solution ensures precise disease detection with minimized computational complexity and reduced prediction time

A strategy integrating GC–MS, UPLC profiling, and DNA metabarcoding for characterization and discrimination of the medicinal and culinary pieces from four Curcuma species

Curcumae pieces prepared in ready-to-use forms, consumed extensively for their significant medicinal and culinary benefits, present a challenge in discrimination due to their heterogeneous origins, analogous morphologies, and varied components. This study explored the metabolites of nine Curcumae pieces (NCPs) by Gas Chromatography-Mass Spectrometry (GC–MS) and Ultra-Performance Liquid Chromatography/Ultra High-Performance Liquid Chromatography–Quadrupole Time of Flight–Mass Spectrometry (UPLC/UHPLC–QTOF–MS) technologies, augmented by multivariate statistical analysis (Principal Component Analysis, PCA; Orthogonal Partial Least Squares Discriminant Analysis, OPLS–DA) and machine learning algorithms (Random Forest, RF). Twenty-six key differential markers in volatile and non-volatile metabolites each were found subsequently. Additionally, DNA metabarcoding was employed to develop specific Single Nucleotide Polymorphism (SNP) markers for the precise identification of Curcuma species in single and mixed samples, and for CRa even contained in Chinese patent prescriptions. The integration of these three methodologies facilitates a more reliable authentication of NCPs, enhancing the overall strategy for their global discrimination. This research contributes to the advancement of quality control and the development of functional foods or products derived from Curcumae pieces, tailored to meet specific health and dietary requirements.

Classifying PDO Kalamata Olive Oil from Geographic Origins of the Messenia Region based on Statistical Machine Learning

Kalamata is a smart city located in southeastern Greece in the Mediterranean basin and it is the capital of the Messenia regional unit. It is known for the famous Protected Designation of Origin (PDO) Kalamata olive oil produced mainly from the Koroneiki olive variety. The PDO Kalamata olive oil, established by Council regulation (EC) No 510/2006, owes its quality and special characteristics to the geographical environment, olive tree variety, and human factor. The PDO Kalamata olive oil is produced exclusively in the regional unit of Messenia, being the main profit of local farmers. However, soil chemical composition, microclimates, and agronomic factors are changed within the Messenia spatial area leading to differentiation of PDO Kalamata olive oil characteristic. In this paper, we use statistical machine learning algorithms to determine the geographical origin of Kalamata olive oil at PDO level based on synchronous excitation−emission fluorescence spectroscopy of olive oils. Evaluations of the statistical models are promising for differentiating the origin of PDO Kalamata olive oil with high values of prediction accuracy thus enabling companies that process and bottle kalamata olive oil to choose olive oil from a specific region of Messenia that fulfills certain characteristics. Concretely, the current research effort focuses on a specific olive oil variety within a limited geographic region. Intuitively, future research should also focus on validation of the proposed methodology to other olive oil varieties and production areas.

Analysis of Methods and Algorithms for Remote Photoplethysmography Signal Diagnostic and Filtering

Remote photoplethysmography is becoming increasingly common in telemedicine for non-invasive physiological monitoring of the cardiovascular system. However, signal reliability has been reduced due to noise and artifacts, which requires reliable diagnostic and filtering methods. The research aim is to evaluate existing methods and algorithms for diagnosing and filtering remote photoplethysmography signals to improve the accuracy of human cardiovascular monitoring. A systematic review has identified methodologies for improving remote photoplethysmography signals by analyzing their principles, implementation, and effectiveness. Various approaches have been analyzed, including the use of statistical computing, adaptive filters, and machine learning algorithms. Each approach offers unique advantages and limitations in terms of noise reduction and artifact removal.

Ad-Tl: Alzheimer’s Disease Prediction Using Transfer Learning

Although many statistical methods and machine learning algorithms have been explored in both clinical and research settings to extract these patterns from neuroimaging data, differentiating between Alzheimer's disease and healthy brain data in older adults (age > 75) has proven challenging due to highly similar patterns of brain atrophy and image intensities. Medical image analysis is just one field that has benefited from the widespread use of deep learning technologies in recent years. This research paper proposed AD prediction using transfer learning (AD-TL) methods. The MRI dataset has been normalized using the Multi-Layer Perception model (MLP) with the CNN algorithm. In order to improve images, the CLAHE (Contrast-Limited Adaptive Histogram Equalization) technique has been used. Image segmentation has been done with Watershed Image segmentation. The training has been done with the Residual network (ResNet 50) with Alex net. Finally, the classification has been done with the Deep Convolutional neural network (DCNN) algorithm.According to the experimental data, the classification accuracy of the technique provided in this study may reach 99%.

Detection of atypical attentional behaviors in young subjects

BackgroundVigilance ability refers to the accuracy and speed with which a person performs a cognitive-motor task, either voluntarily (endogenous mode) or following a warning stimulus (exogenous mode). In the context of a force production task, our study focuses on the impact of the states of vigilance by proposing an original approach that allows distinguishing between good (inlier) and poor (outlier) participants. We assume that the use of an external signal and duration of the temporal preparation (foreperiod) increase the speed and the precision of motor responses. Our objective is particularly challenging in the context of a limited dataset with a high level of noise. New methodOur original methodological approach consists of coupling the RANSAC (RANdom SAmple Consensus) algorithm with a statistical machine learning algorithm to handle noise. Comparison with existing methodsOur clustering approach, based on the coupling of RANSAC methodology with ensemble classifiers, overcomes the limitations of conventional supervised algorithms that are either not robust to outliers (such as K-Nearest Neighbors) and/or not adapted to few-shot learning (such as Support Vector Machines and Artificial Neural Networks). ResultsThe clustering results were validated in terms of reaction time distributions and force error distributions with respect to participant groups. We show that the use of an external signal and duration of the temporal preparation (foreperiod) increase the speed and the precision of motor responses. ConclusionOur study has allowed us to detect atypical attentional patterns and succeeds in separating the inliers from the outliers.

Analysis of short-term wind speed variation, trends and prediction: A case study of Tamil Nadu, India

Abstract Purpose The purpose of this research article is to analyze the short-term wind speed and develop a framework model to overcome the challenges in the wind power industry. Design/Methodology/Approach Real data with a case study of wind speed is presented to illustrate the advantages of this new wind speed analytical framework. Hourly measurements of wind speed are observed, and the experiments are conducted using tools such as ANOVA, control charts, trend analysis, and predictive models. The August month data for over 13 years from modern era retrospective-analysis for research and applications (MERRA) National aeronautics and space administration (NASA) for Coimbatore and Erode locations in Tamil Nadu, India, have been used. The results were considered for the study to understand the wind speed data and the implementation of new wind power projects in India. Findings The essence of the proposed wind speed analytical framework is its flexible approach, which enables the effective integration of wind firms’ individual requirements by developing tailor-made analytical evaluations. Originality/Value This article derives the wind speed analytical framework with the application of statistical tools and machine learning algorithms.

Proposed Integrated Customer Churn Predication Model using Several Statistical Feature Scaling and Machine Learning Algorithms

Proposed Integrated Customer Churn Predication Model using Several Statistical Feature Scaling and Machine Learning Algorithms

Estimating the natural gas compressibility factor using a statistical correlations and machine learning approaches

Gas compressibility factor plays an critical role in petroleum engineering applications such as gas metering, pipeline design, reserve estimation, gas flow rate, material balance calculations, and many other significant tasks. Therefore, it is crucial to accurately estimate the gas compressibility factor. There have been a lot of studies on calculating the gas compressibility factor from laboratory data, which can be summarized into two main approaches: statistical correlations and machine learning algorithms. In this study, on statistical correlations the authors implement explicit and implicit method while on machine learning algorithms, we use Artificial Neural Network (ANN) and Least-Squares Support Vector Machine (LS-SVM). The data was collected from open literature. Implementing the two approaches mentioned above and comparing statistical parameters such as Mean Squared Error (MSE), Root Mean Squared Error (RMSE), and Coefficient of Determination (R2 ) found that machine learning algorithms give much more accurate estimation results than statistical correlations, and besides, the ANN algorithm has the most accurate prediction results with the lowest MSE and RMSE (0.000002 and 0.0016) and the highest R2 (0.9999). The high-precision calculation results show that the ANN algorithm mentioned above can be applied to estimate other real gas compressibility factor data sets. On the other hand, this study can be extended to another subset of machine learning algorithms, such as deep learning and ensemble learning.

Machine learning analysis reveals tumor stiffness and hypoperfusion as biomarkers predictive of cancer treatment efficacy

In the pursuit of advancing cancer therapy, this study explores the predictive power of machine learning in analyzing tumor characteristics, specifically focusing on the effects of tumor stiffness and perfusion (i.e., blood flow) on treatment efficacy. Recent advancements in oncology have highlighted the significance of these physiological properties of the tumor microenvironment in determining treatment outcomes. We delve into the relationship between these tumor attributes and the effectiveness of cancer therapies in preclinical tumor models. Utilizing robust statistical methods and machine learning algorithms, our research analyzes data from 1365 cases of various cancer types, assessing how tumor stiffness and perfusion influence the efficacy of treatment protocols. We also investigate the synergistic potential of combining drugs that modulate tumor stiffness and perfusion with standard cytotoxic treatments. By incorporating these predictors into treatment planning, our study aims to enhance the precision of cancer therapy, tailoring treatment to individual tumor profiles. Our findings demonstrate a significant correlation between stiffness/perfusion and treatment efficacy, highlighting a new way for personalized cancer treatment strategies.

Are biomarkers expression and clinical-pathological factors predictive markers of the efficacy of neoadjuvant chemotherapy for locally advanced cervical cancer?

IntroductionTo predict the overall pathologic response to neoadjuvant chemotherapy (NACT) of patients with locally advanced cervical cancer (LACC) creating a prediction model based on clinical-pathological factors and biomarkers (p53, Bcl1 and Bcl2) and to evaluate the prognostic outcomes of NACT. Materials and methodsThis is a retrospective study of 88 consecutive patients with LACC who underwent NACT followed by nerve sparing surgery with retroperitoneal lymphadenectomy at National Cancer Institute of Milan, between January 2000 and June 2013. Clinical pathologic data were retrieved from the institutional database. Biomarkers (p53, Bcl1 and Bcl2) were evaluated before and after NACT in the specimen. To investigate their role as predictors of response, we tried several statistical machine learning algorithms. ResultsResponders to NACT showed a 5-years survival between 100%(CR) and 85.7%(PR). Clinical factors were the most important predictor of response. Age, BMI and grade represented the most important predictors of response at random forest analysis. Tree-based boosting revealed that after adjusting for other prognostic factors, age, grade, BMI and tumor size were independent predictors of response to NACT, while p53 was moderately related to response to NACT. Area under the curve (crude estimate): 0.871. Whereas Bcl1 and Bcl2, were not predictors for response to NACT. The final logistic regression reported that grade was the only significant predictor of response to NACT. ConclusionCombined model that included clinical pathologic variables plus p53 cannot predict response to NACT. Despite this, NACT remain a safe treatment in chemosensitive patients avoiding collateral sequelae related to chemo-radiotherapy.

Translational analysis of data science and causal learning in real-world clinical evaluation of traditional Chinese medicine

Abstract Real-world clinical evaluation of traditional Chinese medicine (RWCE-TCM) is a method for comprehensively evaluating the clinical effects of TCM, with the aim of delving into the causality between TCM intervention and clinical outcomes. The study explored data science and causal learning methods to transform RWD into reliable real-world evidence, aiming to provide an innovative approach for RWCE-TCM. This study proposes a 10-step data science methodology to address the challenges posed by diverse and complex data in RWCE-TCM. The methodology involves several key steps, including data integration and warehouse building, high-dimensional feature selection, the use of interpretable statistical machine learning algorithms, complex networks, and graph network analysis, knowledge mining techniques such as natural language processing and machine learning, observational study design, and the application of artificial intelligence tools to build an intelligent engine for translational analysis. The goal is to establish a method for clinical positioning, applicable population screening, and mining the structural association of TCM characteristic therapies. In addition, the study adopts the principle of real-world research and a causal learning method for TCM clinical data. We constructed a multidimensional clinical knowledge map of “disease-syndrome-symptom-prescription-medicine” to enhance our understanding of the diagnosis and treatment laws of TCM, clarify the unique therapies, and explore information conducive to individualized treatment. The causal inference process of observational data can address confounding bias and reduce individual heterogeneity, promoting the transformation of TCM RWD into reliable clinical evidence. Intelligent data science improves efficiency and accuracy for implementing RWCE-TCM. The proposed data science methodology for TCM can handle complex data, ensure high-quality RWD acquisition and analysis, and provide in-depth insights into clinical benefits of TCM. This method supports the intelligent translation and demonstration of RWD in TCM, leads the data-driven translational analysis of causal learning, and innovates the path of RWCE-TCM.