Statistical Machine Learning Algorithms Research Articles

Experimental study and model prediction of the influence of different factors on the mechanical properties of saline clay

Water conveyance channels in cold and arid regions pass through several saline-alkali soil areas. Canal water leakage exacerbates the salt expansion traits of such soil, damaging canal slope lining structures. To investigate the mechanical properties of saline clay, this study conducted indoor tests, including direct shear, compression, and permeation tests, and scanning electron microscopy (SEM) analysis of soil samples from typical sites. This study aims to elucidate the impact of various factors on the mechanical properties of saline clay from a macro–micro perspective and reveal its physical mechanisms. A prediction model is formulated and validated. The findings indicate the following: (1) Cohesion in direct shear tests has a linear negative correlation with water content and a positive correlation with dry density and initially decreases with increasing salt content until 2%, after which it increases. The internal friction angle initially increases and then decreases with increasing water content, reaching a peak at the optimal water content, and then gradually increases with dry density while initially decreasing, followed by an increase in salt content, stabilizing thereafter. Water content, dry density, or salt content chiefly affect cohesion by influencing electrostatic attraction, van der Waals forces, particle cementation, and valence bonds at particle contact points. (2) Compression tests reveal a linear positive correlation between the compression coefficient and water content, a negative correlation with dry density, and a stepwise linear correlation with salt content, peaking at 2%. The compression index decreases with increasing water content and dry density, following a trend similar to that of the compression coefficient with increasing salt content. The rebound index shows a linear negative correlation with water content and dry density, transitioning from a negative to a positive correlation at 2% salt content. Scanning electron microscopy analysis revealed particle flattening and increased aggregation with increasing consolidation pressure, reducing compressibility. Large pores and three-dimensional porosity have the greatest influence on soil compressibility. (3) Permeability tests reveal an exponential negative correlation between the permeability coefficient and dry density. As the dry density increases, the particle arrangement becomes denser, decreasing the pore quantity, with micropores disproportionately impacting the permeability coefficient. An increase in salinity initially increases the permeability coefficient before it decreases. The boundary point of the 2% salt content divides the effect of salt ions from promoting free water flow to blocking seepage channels, with the proportion of micropores being the primary influencing factor. (4) Employing statistical theory and machine learning algorithms, dry density, water content, and salinity are used to predict mechanical index values. The improved particle swarm optimization-support vector regression (PSO-SVR) model has high accuracy and general applicability. These findings offer insights for the construction and upkeep of open channel projects in arid regions.

Sexual dimorphism of the humerus bones in a French sample: comparison of several statistical models including machine learning models.

Sex estimation is an important part of skeletal analysis and forensic identification. Traditionally pelvic traits are utilized for accurate sex estimation. However, the long bones, especially humerus, have been proved to be as effective for determine the sex of the individual.The aim of this study was to compare the predictive accuracy of seven statistical modelling techniques including classical statistical methods and machine learning algorithms, to assess the sexual dimorphism of humerus on a French sample based on a metric analysis of 26 measurements. A total of 98 humeral bones (divided in two samples) were measured. Seven statistical models were compared: Linear Discriminant Analysis (LDA), Regularized Discriminant Analysis (RDA), Penalized Logistic Regression (PLR), Flexible Discriminant Analysis (FDA), Support Vector Machine (SVM), and Artificial Neural Network (ANN) and Random Forest (RF).With cross validation, classification accuracy was greater than 90% (ranges between 92% and 98%) for all models without variable selection methods. The simplification of the models has improved the accuracy between 98% and 100% and also a reduction of the number of variables to 6 or less. Penalized logistic regression (PLR), Random Forest (RF) and Linear discriminant analysis (LDA) were the best accuracy models.The measurements made at the proximal part of the humerus (WTT, CSD), at distal part (BEW, WT, MAW, THT) and of the entire bone (PLCT) stand out among the various models.The present study suggests that the humerus is an interesting alternative for sex estimation and that non-classical statistical models can provide a new approach.

BharatSim: An agent-based modelling framework for India.

BharatSim is an open-source agent-based modelling framework for the Indian population. It can simulate populations at multiple scales, from small communities to states. BharatSim uses a synthetic population created by applying statistical methods and machine learning algorithms to survey data from multiple sources, including the Census of India, the India Human Development Survey, the National Sample Survey, and the Gridded Population of the World. This synthetic population defines individual agents with multiple attributes, among them age, gender, home and work locations, pre-existing health conditions, and socio-economic and employment status. BharatSim's domain-specific language provides a framework for the simulation of diverse models. Its computational core, coded in Scala, supports simulations of a large number of individual agents, up to 50 million. Here, we describe the design and implementation of BharatSim, using it to address three questions motivated by the COVID-19 pandemic in India: (i) When can schools be safely reopened given specified levels of hybrid immunity?, (ii) How do new variants alter disease dynamics in the background of prior infections and vaccinations? and (iii) How can the effects of varied non-pharmaceutical interventions (NPIs) be quantified for a model Indian city? Through its India-specific synthetic population, BharatSim allows disease modellers to address questions unique to this country. It should also find use in the computational social sciences, potentially providing new insights into emergent patterns in social behaviour.

Radiomics of the Paranasal Sinuses: A Systematic Review of Computer-Assisted Techniques to Assess Computed Tomography Radiological Data.

Radiomics is a quantitative approach to medical imaging, aimed to extract features into large datasets. By using artificial intelligence (AI) methodologies, large radiomic data can be analysed and translated into meaningful clinical applications. In rhinology, there is heavy reliance on computed tomography (CT) imaging of the paranasal sinus for diagnostics and assessment of treatment outcomes. Currently, there is an emergence of literature detailing radiomics use in rhinology. This systematic review aims to assess the current techniques used to analyze radiomic data from paranasal sinus CT imaging. A systematic search was performed using Ovid MEDLINE and EMBASE databases from January 1, 2019 until March 16, 2024 using the Preferred Reporting Items for Systematic Reviews and Meta-analyses (PRISMA) checklist and Cochrane Library Systematic Reviews for Diagnostic and Prognostic Studies. The QUADAS-2 and PROBAST tools were utilized to assess risk of bias. Our search generated 1456 articles with 10 articles meeting eligibility criteria. Articles were divided into 2 categories, diagnostic (n = 7) and prognostic studies (n = 3). The number of radiomic features extracted ranged 4 to 1409, with analysis including non-AI-based statistical analyses (n = 3) or machine learning algorithms (n = 7). The diagnostic or prognostic utility of radiomics analyses were rated as excellent (n = 3), very good (n = 2), good (n = 2), or not reported (n = 3) based upon area under the curve receiver operating characteristic (AUC-ROC) or accuracy. The average radiomics quality score was 36.95%. Radiomics is an evolving field which can augment our understanding of rhinology diseases, however there are currently only minimal quality studies with limited clinical utility.

Modeling the impacts of governmental and human responses on COVID-19 spread using statistical machine learning

ABSTRACT Understanding the impacts of governmental and human responses on the pandemic control is imperative for forecasting pandemic spread under various responsive scenarios and guiding localized interventions before pharmaceutical interventions are available. This study analyzed multiple data sets, including social media, mobility, policy evaluations, and COVID-19 infection reports, to delineate the interactions between governmental and human responses and COVID-19 spread in the United States in 2020 when vaccinations were unavailable. The contributions are (1) uncovering the spatiotemporal variations in governmental and human responses during COVID-19; (2) developing a statistical machine learning algorithm that incorporates spatiotemporal dependencies and temporal lag effects to model the relationships between governmental and human responses and the pandemic spread; (3) dissecting the impacts of human responses on the pandemic across space and time. Results reveal that the determinants of COVID-19 health impacts transitioned from human mobility during the initial outbreak phase to both human mobility and stay-at-home policies during the rapid spread phase, and ultimately to the compound of human mobility, stay-at-home policies and the public awareness in the full-blown phase. These findings furnish guidance for policymakers in implementing adaptive and phased strategies.

Demand Forecasting for Aqua Tech Technical Textiles

Demand forecasting plays a significant role in the Aquatech industry, where innovation integration with aquacultural practices drives efficiency and effectiveness. This abstract outline the importance, challenges, techniques, and implications of demand forecasting in the sector. Demand forecasting is essential for businesses to anticipate trends, optimize resource allocation, and enhance operational efficiency. By accurately predicting future demand for aquacultural products, services, and technological outcomes, companies can make informed decisions regarding production, operations, and marketing strategies. However, demand forecasting in this industry faces several challenges, including seasonal variations, data availability, technological complexity, and uncertainties. Aquaculture demand is influenced by factors such as precipitation patterns, regulatory policies, and consumer preferences, making it difficult to develop precise forecasting models. Various techniques are used for demand forecasting, including statistical methods, data analytics, and machine learning algorithms. Time series analysis, regression analysis, and hybrid forecasting approaches are commonly employed to analyze historical data and predict future demand patterns. Accurate demand forecasting has significant implications for businesses, including improved resource planning, supply chain management, and customer satisfaction. By aligning production capacities with anticipated demand and optimizing supply levels, companies can minimize costs, reduce waste, and enhance competitiveness in the market. Demand forecasting is a critical aspect of strategic planning and decision-making in the specialized industry. By utilizing advanced techniques and effectively addressing challenges, businesses can enhance their ability to anticipate trends, meet customer demands, and drive sustainable growth in an increasingly dynamic and competitive environment

Predicting Immune Flares in Untreated Chronic Hepatitis B Patients Using Novel Risk Factors and the FLARE-B Score.

Risk factors of chronic hepatitis B (CHB) immune flares are poorly understood. The primary aim of this study was to discover predictors of the CHB flare in non-cirrhotic, untreated CHB patients and develop a simple risk-stratifying score to predict the CHB flare. The secondary aim was to compare different machine learning methods for prediction. A retrospective cohort of untreated, non-cirrhotic CHB patients with normal baseline ALT was followed up over time until an immune flare as defined by ALT twice the upper limit of normal. Statistical learning and machine learning algorithms were used to develop predictive models using baseline variables. Bootstrap validation was used to internally validate the models. Of 405 patients (median age 44y; 41% male, 10% HBeAg positive), 67 (17%) experienced an immune flare by 5 years (annual incidence 4.0%). Predictors of flare included raised serum globulin, younger age, HBeAg positive status, higher viral load and raised liver stiffness. A simple predictive model "FLARE-B" had optimism-adjusted 1, 3 and 5-year AUCs of 0.813, 0.728 and 0.702, respectively. The random survival forest algorithm had the highest optimism-adjusted AUCs of 0.861, 0.766 and 0.725, respectively. New, novel predictors of the CHB flare include a raised serum globulin and possibly raised liver stiffness and the absence of liver steatosis. FLARE-B can be used to risk-stratify individuals and potentially guide personalized management strategies such as monitoring schedules and proactive antiviral treatment in high-risk patients.

Time Series Analysis of Housing Demand: A Forecasting Model for Ankara, Turkey

The property boom in Ankara surrounded by urban arrangements, calls for complicated forecasting approaches so that stakeholders can benefit from logical decision-making. The researchers apply up-to-date time series analysis methodology to forecast the housing demand in the area. It implicates the historical sales of housing and economic indicators combined with demographic factors are the sources that develop a comprehensive model of forecasting which allows to explore and track the intrinsic dynamics of the housing market. The methodology, in turn, is the application of cutting-edge statistical models and machine learning algorithms in the process of capturing the complex trend that is explicit in the time series data. In terms of our approach, we will include seasonality as well as trend components as well as those external factors, which affect the level of houses' demands. The study also analyzes the outcomes caused by economic shocks, public policies, and urban planning on housing market equilibrium. The study carried out demand forecasting concerning the sale of houses in Turkey which is supported by the data. The study is based on TURKSTAT numbers on the number of houses sold within the year 2021 (S.O.D) by Turkish provinces that cover Ankara province where the data is retrieved from. Considering the sales of houses in Ankara from 2014-2018 as a basis, this study intends to find a numerical forecasting model that is most suited to the observed dataset and thus, determine the number of houses sold in Ankara in the year 2019 using this particular method. Output from time series analysis provides the developers and investors with significant information by the way of anticipating market fluctuations, improving their investment strategies, and choosing the right policies according to the markets' needs. Moreover, an accurate model needs to be analyzed through serious validation techniques to identify its authenticity in its real-life examples. This research is, at the same time, an attempt to make progress in the field of demand forecasting in the real estate market as well as an attempt to provide stakeholders working in Ankara Province with a comprehensive guide while moving through a changing housing market. The utilization of technology and a careful investigation of relevant factors lends this study credibility as well as makes it a necessary literary component for those pursuing a deeper comprehension of housing demand in the region.

Predicting Chronic Liver Disease and Jaundice Detection an Integrated Approach Using Statistical Feature Extraction and Machine Learning Algorithms

An important health concern with chronic liver disease is early identification, which is essential for management and therapy. This work suggests an integrated method to identify tattoo-induced jaundice and forecast chronic liver disease by combining statistical feature extraction with machine learning techniques. First, we use statistical techniques based on projections to extract pertinent aspects from patient data, such as test results and clinical markers. We then use Artificial Neural Networks (ANN), KNN, and Naive Bayes to categorize individuals according to the probability that they have chronic liver illness and to pinpoint jaundice episodes associated with tattooing. The KNN technique offers interpretability, managing categorical data is made simple by Naive Bayes, and complicated patterns are captured by ANN using layers of neural networks. These models' performance is assessed using the following metrics: F1 score, recall, accuracy, and precision. Our research aims to improve prediction accuracy and offer practical advice for early diagnosis and individualized treatment plans. By utilizing advanced data analysis and machine learning instruments, the integrated strategy shows promise in enhancing healthcare outcomes.

Machine learning for improved drought forecasting in Chhattisgarh India: a statistical evaluation

Meteorological drought is one of the major natural hazards that affects the ecosystem of the Central Indian state of Chhattisgarh. This study delves into the analysis, comparison, and prediction of drought trends spanning the period from 1993 to 2023 in the study area. Employing a comprehensive methodology, utilization of the Modified Mann–Kendall test to analyze drought trends, while assessing drought severity through the Standardized Precipitation Index (SPI) and the Standardized Precipitation Evapotranspiration Index (SPEI) has been done. Further research entails assessing the link between SPI and SPEI utilizing the Pearson correlation coefficient and simple linear regression techniques. Additionally, the Support Vector Machine (SVM) and Random Forest (RF) methods were used for predictive modelling feasibility. The findings helped to deepen our understanding of drought dynamics in the region, providing important insights for drought mitigation and adaptation efforts. This study emphasizes the importance of using a variety of statistical techniques and machine learning algorithms to thoroughly analyze, compare, and forecast drought patterns, thereby informing evidence-based decision-making for sustainable water resource management and agricultural planning in Chhattisgarh, India.

Genome-wide association studies of ischemic stroke based on interpretable machine learning.

Despite the identification of several dozen genetic loci associated with ischemic stroke (IS), the genetic bases of this disease remain largely unexplored. In this research we present the results of genome-wide association studies (GWAS) based on classical statistical testing and machine learning algorithms (logistic regression, gradient boosting on decision trees, and tabular deep learning model TabNet). To build a consensus on the results obtained by different techniques, the Pareto-Optimal solution was proposed and applied. These methods were applied to real genotypic data of sick and healthy individuals of European ancestry obtained from the Database of Genotypes and Phenotypes (5,581 individuals, 883,749 single nucleotide polymorphisms). Finally, 131 genes were identified as candidates for association with the onset of IS. UBQLN1, TRPS1, and MUSK were previously described as associated with the course of IS in model animals. ACOT11 taking part in metabolism of fatty acids was shown for the first time to be associated with IS. The identified genes were compared with genes from the Illuminating Druggable Genome project. The product of GPR26 representing the G-coupled protein receptor can be considered as a therapeutic target for stroke prevention. The approaches presented in this research can be used to reprocess GWAS datasets from other diseases.

Palawan Peacock Pheasant (Polyplectron Napoleonis) Species Distribution Modeling: A Review of the Impact of Typhoon Rai/Odette on the Diversity and Distribution of an Endemic Bird Species

This study quantified the habitat requirements and, consequently, described the remaining suitable habitats through species distribution modeling of Polyplectron napoleonis within Cleopatra’s Needle Forest Reserve and Puerto Princesa Subterranean River National Park after the onslaught of Typhoon Odette in the Palawan region. This was accomplished using MaxEnt software, a statistical machine-learning algorithm that creates niche models with presence-only (PO) data and surrounding environmental variables (Dudik et al., 2007; Merow et al., 2013). Previous studies have identified precipitation, elevation, and land cover as the top environmental variables influencing tandikan habitat suitability, as revealed by their percent contributions. There were earlier claims of the noticeable movement of habitat-suitable areas to the fringes and beyond Cleopatra's Needle Forest Reserve. The migration seems to be going towards highland forests. Two factors could be behind this migration: the tandikan are responding to climate change or avoiding human disturbance. As this species is known to have a high tolerance for temperature change, the latter could be the more plausible factor. For 2022, post-Odette, MaxEnt analysis revealed a shift of percent contributions with elevation and land cover classification outranking aspect and slope. Environmental layers were limited to four, as these were the only currently available data. This shift in percent contributions could be linked to the direct damage to forests brought by the Typhoon in December of 2021. The land cover map generated for this year has shown large areas of forests that were converted into non-forests. Conservation efforts, therefore, for the tandikan, a forest species, should be recalibrated in response to this current development. Further, measures to mitigate the impact of solid typhoons on forests should be reviewed and evaluated.

Toward a realistic theoretical electronic spectra of metal aqua ions in solution: The case of Ce(H2O)n3+ using statistical methods and quantum chemistry calculations.

Accurately predicting spectra for heavy elements, often open-shell systems, is a significant challenge typically addressed using a single cluster approach with a fixed coordination number. Developing a realistic model that accounts for temperature effects, variable coordination numbers, and interprets experimental data is even more demanding due to the strong solute-solvent interactions present in solutions of heavy metal cations. This study addresses these challenges by combining multiple methodologies to accurately predict realistic spectra for highly charged metal cations in aqueous media, with a focus on the electronic absorption spectrum of Ce3+ in water. Utilizing highly correlated relativistic quantum mechanical (QM) wavefunctions and structures from molecular dynamics (MD) simulations, we show that the convolution of individual vertical transitions yields excellent agreement with experimental results without the introduction of empirical broadening. Good results are obtained for both the normalized spectrum and that of absolute intensity. The study incorporates a statistical machine learning algorithm, Gaussian Mixture Models-Nuclear Ensemble Approach (GMM-NEA), to convolute individual spectra. The microscopic distribution provided by MD simulations allows us to examine the contributions of the octa- and ennea-hydrate of Ce3+ in water to the final spectrum. In addition, the temperature dependence of the spectrum is theoretically captured by observing the changing population of these hydrate forms with temperature. We also explore an alternative method for obtaining statistically representative structures in a less demanding manner than MD simulations, derived from QM Wigner distributions. The combination of Wigner-sampling and GMM-NEA broadening shows promise for wide application in spectroscopic analysis and predictions, offering a computationally efficient alternative to traditional methods.

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.

High-performance virtual sensors array based on a single-chip FBAR for volatile organic compounds (VOCs) detection and classification

The detection and classification of volatile organic compounds (VOCs) are of great significance in atmospheric environment monitoring and have garnered extensive attention in recent decades. This study proposes a virtual sensor array (VSA) for VOC detection and classification fabricating a single-chip high-Q-factor film bulk acoustic resonator (FBAR) functionalized with MIL-101(Cr) thin film, known for its porous crystal structure and high specific surface area. The sensing performance of five different VOCs was evaluated to characterize the VOCs, achieving high detection accuracy calibrated with the Langmuir adsorption model. The FBAR’s frequency response was utilized to determine the sensitivity and limit of detection (LOD), yielding sensitivity and LOD of 85.45 Hz/ppm and 51 ppm, respectively, for isopropanol. The identification and classification capabilities of the proposed VSA for VOCs and their mixtures were assessed using statistical methods and machine learning algorithms, realizing classification accuracies of 100 % for pure VOCs and 94.6 % for mixtures. The developed VSA demonstrates superior performances in VOC detection and classification, with potential applications across various domains.

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.

How to Improve Accessories Sales Forecasting of a Medium-Sized Swiss Enterprise? A Comparison Between Statistical Methods and Machine Learning Algorithms

How to Improve Accessories Sales Forecasting of a Medium-Sized Swiss Enterprise? A Comparison Between Statistical Methods and Machine Learning Algorithms

ANALIZA STATYSTYK SIECI KOMPUTEROWYCH W CELU IDENTYFIKACJI PRZEPŁYWÓW INFORMACJI ZAKŁÓCAJĄCYCH STABILNOŚĆ W WOJSKOWYCH SIECIACH LOKALNYCH

The increasing complexity and scope of military computer networks necessitate robust methods to ensure network stability and security. This study presents a comprehensive analysis of computer network statistics in military local networks to develop a method for detecting information flows that disrupt stability. By leveraging advanced statistical techniques and machine learning algorithms, this research aims to enhance the cybersecurity posture of military local networks globally. Military networks are vital for communication, data exchange, and operational coordination. However, the dynamic nature of network traffic and the persistent threat of cyberattacks pose significant challenges to maintaining network stability. Traditional monitoring techniques often fail to meet the unique requirements of military networks, which demand high levels of security and rapid response capabilities. This study employs a multi-faceted approach to detect anomalies in network traffic, utilizing statistical methods such as Z-score analysis, Principal Component Analysis (PCA), and Autoregressive Integrated Moving Average (ARIMA) models. Machine learning techniques, including Support Vector Machines (SVM), Random Forests, Neural Networks, K-means clustering, and Reinforcement Learning, are also applied to identify patterns indicative of stability-disrupting information flows. The integration of statistical and machine learning methods forms a hybrid model that enhances anomaly detection, providing a robust framework for network security. The research problem is formulated as follows: does data collection include comprehensive network traffic data from various segments of military local area networks, including packet flows, transmission rates, and error rates over a specified period? Statistical analysis identifies patterns in the network traffic, which are then used to train machine learning models to classify normal and abnormal traffic. The research hypothesis states that machine learning models achieve high accuracy in detecting stability-disrupting information flows, with a precision rate exceeding 90%. The models identified several instances of stability-disrupting events, correlating these with known security incidents to validate the effectiveness of the detection method. This study underscores the importance of continuous monitoring and analysis of network statistics to ensure stability and security. The proposed method can be integrated with existing network monitoring and intrusion detection systems, providing a comprehensive approach to network security. Future research can build on these findings to develop more sophisticated models and explore additional factors influencing network stability, including the incorporation of advanced machine learning techniques, such as deep learning, and the exploration of other network metrics, like latency and packet loss. This comprehensive approach aims to enhance the security and operational reliability of military local networks.

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.