NEURAL NETWORK MODELING AND FORECASTING OF IMBALANCES IN UKRAINE’S LABOR MARKET UNDER EXTREME CONDITIONS

In this study, the informative bounds of neural networks (NN) prediction with respect to the utilization of less fatigue data for fatigue life assessment of composite material covering a wide range of stress ratios R was examined and investigated. Fiberglass reinforced polyester of [90/0/±45/0] S lay-up with fatigue data of various stress ratios ( R = 0.1, 0.5, 0.7, 0.8, 0.9, -0.5, -1, -2 and 10) was examined in the present paper. Multi-layer Perceptrons (MLP) trained with Levenberg-Marquardt algorithm was utilized to result in fast and efficient NN model and Bayesian regularization technique was incorporated to deal with limited training data chosen for the model. The developed NN model was trained with fatigue data from only two stress ratios, where three sets of two stress ratio values were formed and used as the training sets, namely R = 0.1 and 0.5, R = 0.1 and -1, and R = 0.1 and 10, respectively. It was obtained that fatigue data from R = 10 produced the widest bounds of prediction, namely having the highest estimated standard deviation value from the fatigue lives predicted. Furthermore, it is revealed in the current study knowing the fact that fatigue data from R = 10 have the highest estimated standard deviation and subsequently including the fatigue data as one of the training data set, the NN model trained could produce the lowest mean squared error (MSE) value for the results of fatigue life prediction. This is justifying also the selection of training set of R = 0.1 and 10 as best training set in the previous study, which is based on the stress ratios’ better relative positions in the corresponding constant life diagram (CLD). Finally, taking the highest estimated standard deviation value from fatigue data of R = 10 as the conservative estimated bounds of NN prediction, it was shown that for the NN prediction of fatigue life whose noticeable discrepancies with the experimental data, the discrepancies were well confined within the conservative bounds of prediction.

Purpose: This study developed a predictive model based on employee characteristics, analyzes multidimensional data to identify predictive factors of promotion outcomes, and provides data-driven insights for organizations to improve talent management. Methods: Decision tree, random forest, and neural network models were built on the existing dataset (N=54,808). Then, the models predictive efficacies were evaluated to select the optimal model using several methods. Results: The accuracy of decision tree was 85.7%. The accuracy of artificial neural network and random forest model was 93%. For promoted class, random forest had higher precision, while neural network performed slightly better in recall and F1 score. For non-promoted class, both models perform almost identically in precision, recall, and F1 score. Conclusion: Random forest and neural network had good predictive efficacy for employee promotion. If avoiding false promotions is more important (i.e., precision matters more), random forest is the better model. If capturing more promotions is critical (i.e., recall matters more), even at the cost of some false positives, then the neural network is slightly better.

Labour Market Imbalances and Adjustments: Applied Forecast Model with RAS Component

Although precision classification is a vital issue for therapy, cancer diagnosis has been shown to have serious constraints. In this paper, we proposed a deep learning model based on gene expression data to perform a pan-cancer classification on 16 cancer types. We used principal component analysis (PCA) to decrease data dimensionality before building a neural network model for pan-cancer prediction. The performance of accuracy was monitored and optimized using the Adam algorithm. We compared the results of the model with a random forest classifier and XGBoost. The results show that the neural network model and random forest achieve high and similar classification performance (neural network mean accuracy: 0.84; random forest mean accuracy: 0.86; XGBoost mean accuracy: 0.90). Thus, we suggest future studies of neural network, random forest and XGBoost models for the detection of cancer in order to identify early treatment approaches to enhance cancer survival.

Due to the structure of recurrent neural networks, they are used for the prediction tasks, such as price prediction. The price prediction tasks are based on the historical data of price movements during the specified period. This data can be used for training the recurrent neural network for price prediction. Expected, that the neural network will recognize specific patterns in sequential data and will be able to predict the next trends etc. From the 2021 year, virtual assets such as Bitcoin increase their popularity all around the world. Virtual assets such as Bitcoin are classified as highly volatile assets. For this type of asset, the prediction task is so important, due to the ability to make a long and a short position several times per day, week, etc. Using the recurrent neural network, against the ARIMA methods, can help to include the other data except for the price history. For example, it can you the history of several operations for these assets per day, the price history of the other virtual assets, that can have some relations with, etc. In this article, as a first step, work was focused on properly formatting the price history data for achieving the lowest prediction error. Also, the idea is to create a framework for working with different virtual assets for prediction. The problem with using a neural network for prediction is that absolute values of virtual assets price are not stationary. And the neural network training process stuck between the minimum and maximum values of training data. It creates a problem, where the trained neural network cannot handle the data, that is bigger than the trained and it always tried to predict the value in the trained range. This work, investigated the neural network for single prediction only. Also, was compared the two possible ways to format the data to the stationary data. Key words : recurrent neural networks, prediction, virtual assets.

Numerical modeling and neural networks to identify constitutive parameters from in situ tests

'What I cannot create (and control), I do not understand' (Richard Feynman; modified Bertolero & Bassett, 20191) As anyone with an interest in the works of JK Rowling knows nifflers like shiny treasure and go to extreme lengths to find it. Cardiovascular disease (CVD) physicians are similar in their wish to find deposits of atherosclerosis but are far less accomplished at it. Atherosclerosis is a cryptic disease starting in the vascular wall and only later manifesting within the artery lumen with long-term consequences in the form of plaque rupture or erosion (type 1 lesions) but also vasospasm through secondary endothelial dysfunction (type 2 disease).2 Detecting atherosclerosis is possible using imaging either thorough the detection of early lesions on ultrasound or in the vessel wall (intima-media thickness) and late-stage calcified plaques (coronary artery calcium).3 The most sophisticated approach is to image atheroma in the wall either in large arteries by magnetic resonance imaging or in coronary arteries by intravascular ultrasound on angiography. Further developments now include three-dimensional imaging techniques applying computerised image reconstruction techniques. However, all of these direct approaches are limited in their application by the expense and size of the machinery required and the logistics of managing patient flows to central sites. Instead a cheaper and easier approach is pursued by all health systems. The availability of large epidemiological databases and cohort studies now extending in some cases to up to three generations (Framingham)4 means that high-risk individuals can be identified easily from common parameters. These studies maintain assay standardisation which may not apply to electronic health records (EHRs) linked to standard laboratory assays which evolve with time.5 Landmark analyses starting in 1987 identified certain key CVD risk factors and remarkably quickly these were standardised as age, gender, smoking, blood pressure, diabetes and cholesterol (later divided into total and high-density lipoprotein (HDL) cholesterol).4, 6, 7 A multitude of additional CVD risk factors have since been described but all of these added little to the basic predictive model which is mostly driven by age, gender and ethnicity.8 Risk factor counting and set intervention levels were the basis of defining high-risk patients for intervention. These still persist in modern guidelines, for example, stage 2 hypertension or total cholesterol > 7.5mmol/L and more usefully the concept of two CVD risk factors predicting lifetime risk from age 55.9 Yet these crude cut-offs had the significant limitation that they only identified a small fraction of patients at risk of CVD—that is, high specificity but limited sensitivity. The next development in the 1990s was the beginning of the use of mathematical models based on logistic regression analyses of epidemiological datasets once semiconductor-based scientific notation calculators became available. These could be simplified into paper-based systems or mechanical tools for routine clinical use.10, 11 Now that substantial computing capacity is available through cell phones or internet-based systems these are now universally recommended for assessment of patients with a risk of CVD. The desire to increase convenience has now led to the wish to simplify the process further by abolishing the most logistically difficult (and expensive) aspect which comprises the cholesterol blood tests. In fact, the Framingham risk engine can be easily reformatted by substituting body mass index for lipids but surprisingly this has not achieved great popularity for initial risk stratification despite its simplicity.12 The main quest in CVD risk estimation, however, has been to improve sensitivity and specificity. The main methods used have been to use larger more representative datasets based either on aggregating epidemiological cohort studies (eg US atherosclerotic CVD score- ASCVD13) or national EHRs (eg QRISK in the UK14). The best predictive performance of epidemiological datasets is an average area under curve (AUC) for receiver operator characteristic (ROC) curves (ie C-statistic) of approximately 0.70-0.75.11 Adding imaging data from coronary artery calcium increases this to 0.79 with less benefit from the far more convenient ultrasound techniques or biomarkers such as high-sensitivity troponin measurements.15, 16 The next great hope is to exploit the developments in electronic databases and advances in computing. Models to date have relied on deterministic processes guided by humans yet the suspicion has remained that information may have been lost by these decisions so other statistical data interpretation techniques are being explored. Machine learning and Bayesian analysis are the current trendy concepts but many others exist. Neural nets, the best known form of machine learning, were first described in 1943 but it has taken 70 years to make them practical as they require large scale computing to make them practical.17, 18 The techniques of neural net analysis rely on large scale data inputs, intermediate layer (or layers) of nodes linked back to the data and forward to the outputs—in this case CVD events (Figure 1). Nodes are set randomly and then iterate and adjust input weights to optimise the prediction of the outputs.17, 18 Finally, as in classic epidemiological models the outputs are validated in another dataset. In contrast to classic calculators, neural nets are multilayer of which many aspects are obscured but if collapsed down to a single layer these can be isolated and described in classic terms. Whether this concept represents an electronic obscurial more than just a black box is the subject of debate. Until now the commonest application of neural nets in medicine has been in the analysis of images as these were data rich and the most problematic for classical methods.19 The problem in CVD for risk prediction has been the availability of large EHR datasets. This is now changing with the rapid computerisation of health systems. In this issue of International Journal of Clinical Practice, Quesada and colleagues describe multi-model analyses of an EHR comprising 38.527 patients with a 5-year follow-up and a likely 5%-10% CVD event rate as is typical in cohorts of this type.20 In their analysis quadratic discriminant analysis and Naïve Bayes ranked above (area under curve [AUC] 0.70) neural nets and classical logistic regression model -derived calculators such as the European Systematic COronary Risk Evaluation (SCORE; CVD mortality alone21) or the US Framingham study-related REGICOR score for CVD events (AUC = 0.63).22 Ten of 15 computer models were better than the classical methods but not by much. This is common in studies which attempt to improve the standard AUC of 0.65-0.75 found for classical CVD risk calculators in populations that match their original derivation and validation cohorts. This study lacked comparisons with recalibrated Spanish cohorts as opposed to generic models so it is unclear how much extra predictive capacity was actually added. Other studies have compared computerised models including neural nets with logistic regression models. One study using 689 patients from India, but using a validation population of 5209 US patients from the Framingham study, pre-specified classical risk factors and a quantum neural net approach suggested that this model was superior to the classical FRS.23 This is not surprising as the CVD risk factor weighting is different in Indian Asians from US populations. A similar criticism would apply to the Korean National Health and Nutrition Evaluation study (KNHANES-6) using 4244 EHR records with complete pre-specified six CVD risk factor data and a deep belief network (DBN) analysis and a restricted Boltzman Hopfield network that optimised to six nodes in one layer.24 The statistical DBN gave an AUC of 0.79 compared with 0.72 for logistic regression. This study did not assess their performance against classical or modified (ie recalibrated) CVD risk calculators. These have been investigated in Korean populations where in a study of 200 010 patients the ASCVD equation has an AUC of 0.73-0.75 but calibration errors with an excess 57%-74% in men and a deficit of 28% in women but was useful in enabling a Korean-specific CVD score to be derived.25 The neural net analysis of the Multi-Ethnic Study of Atherosclerosis (MESA) cohort of 6814 patients followed up for 12 years and 735 variables derived from biochemistry, questionnaires and imaging was used by random survival forest analysis to derive top 20 predictors for individual CVD outcomes.15 In this study, nine models were tested including Cox and LASSO-Cox models, and Aikake information criterion applied to regression analysis as well as random survival forest analysis. Predictably age was the most important predictor of mortality. Coronary artery calcium was the best predictor of coronary heart disease or CVD with glucose and carotid ultrasound for stroke. In contrast to usual expectations, troponin was the strongest predictive of heart failure while NTproB-type natriuretic peptide was the best predictor of CVD. A UK study used data from 378 256 primary care patients in the Clinical Practice Research Database (CPRD) and 24 970 recorded CVD events (6.6%) to compare various computational methods of CVD risk prediction.26 This study compared the US ASCVD score (not interestingly UK QRISK) with machine learning models. The standard ASCVD model had an AUC of 0.73, with the random forest model 0.75, logistic regression, gradient boosting or neural networks 0.76. The neural network algorithm predicted 4998 of 7404 cases (sensitivity 68%, positive predictive value (PPV) 18%) and 53 458 of 75 585 non-cases (specificity 72%, negative predictive value (NPV) 96%), predicting 8% more patients who developed CVD compared with the established ASCVD baseline model which predicted 53 106 non-cases from 75 585 non-cases, resulting in a specificity of 70% and NPV of 95%. As is true of all CVD risk prediction models because of their structure of containing many unaffected patients the greatest power is to rule out disease (negative predictive value). The small addition to risk prediction, which is better described in the form of net (or total) reclassification indices (NRI), is not unusual in this type of analyses.27 More recently a comparative study was conducted in a cohort of 109 490 individual using aggregated and longitudinal features from EHR involving analysis of historical and prospective phases.28 The models tested included logistic regression, random forests, gradient boosting trees, convolutional neural networks (CNN) and recurrent neural networks with long short-term memory (LSTM) units. A further analysis of 10 612 patients used late-fusion approach to incorporate genetic risk score data. The ASCVD equation achieved a typical ROC AUC of 0.73, while machine learning models using only classical CVD risk factors doing no better. Incorporation of EHR features mostly relating to the length of the EHR and variances in biochemical analytes achieved an AUC of 0.77-0.78. By adding temporal features, logistic regression (LR), gradient boosting trees (GBT) and deep learning models improved the AUC to 0.78-0.79. Both GBT and convolutional neural networks (CNN) achieved an AUC of 0.79 (ie 7.9% improvement from baseline). Most of the studies reviewed in this article use ROC curves which present graphically the trade-off between the true positive rate (TP) (sensitivity) and false positive (FP) (1-specificity) rate for a predictive model using different probability thresholds. In contrast Precision-Recall curves (PRC) and their graphical outputs summarise the trade-off between the true positive (TP) rate and the positive predictive value (PPV; precision) for a predictive model using different probability thresholds.29 Mathematically ROC curves are appropriate when the observations are balanced between each class, whereas PRCs are appropriate for unbalanced datasets as is commonly the case for epidemiological cohort datasets being used to predict events as only a minority develop CVD. In this study Area under PRC (AUPRC) analysis showed that machine learning using temporal features improved predictions founded on baseline data (0.25-0.29 vs 0.19, a 33%-44% improvement) more clearly than that for ROC curves. The top features in all machine learning models include some conventional CVD risk factors such as age, blood pressure (BP) and total cholesterol, as well as several new features not included in standard CVD risk calculators such as body mass index (BMI),30 creatinine,31 glucose.32 However, all of these have been previously identified in the Framingham study or have been used other CVD scoring systems (eg QRISK).12, 14 Among drug therapies use of anti-platelet agents was also predictive. Distribution data for laboratory values (eg fasting lipid values) and physical measurements (eg BMI and blood pressure) contributed more than median values to the models. In the models incorporating longitudinal data such as logistic regression selected biochemical data distribution in two separate sampling periods while random forests selected BMI. The effect of variation in CVD risk factors such as blood pressure, cholesterol, glucose and body mass index (BMI) has previously been linked to risk of CVD in classic epidemiological studies.33 This has been validated for blood pressure and is included in one currently nationally approved CVD risk calculator (QRISK-3).14 It also exists for glucose and cholesterol but this data has not been included in any guideline approved CVD risk calculator to date. Gradient boosting tree (GBT) analysis preferred historic diagnostic codes such as heart valve disorders, lipid disorders and hypertension over other features. One problem of large scale EHRs is the quality of data recording so this historical data may reflect single anomalous values being entered as diagnostic codes (ie a proxy for variance) or the lack of original untreated values in the EHR. Similar considerations apply to anti-platelet therapies such aspirin-clopidogrel acting as proxies for unrecorded diagnoses of significant CVD (or peripheral arterial disease) or in the case of aspirin alone—clinical suspicion of high-risk status. Genetic risk scores (GRS) are easily derived given the increasing ease of obtaining large scale genome variation data. Many studies are now investigating the utility of adding GRS to classical CVD risk factors in risk prediction.34 A multiplicity of scores have been investigated using limited panels and whole genome data applied to cohort data sets of up to 300 000 patients but whether any of these are superior to imaging remains unclear.34 GBT using classical CVD risk factors gave similar results to standard methods. Adding longitudinal EHR features to GBT increased AUC to 0.71 vs 0.70; AUPRC of 0.43 vs 0.40 and the genetic risk score (GRS) improved the AUROC and AUPRC by 2% and 9%. The GRS data included known CVD risk factor genes such as melanoma inhibitory activity protein 3 (MIA3; 2 loci) also known as Transport and Golgi organisation protein 1 (TANGO1) involved in chylomicron and very low-density lipoprotein transport, and lipoprotein (a) (LPA;2 loci) as well as chemokine C-X-C motif chemokine 12 (CXCL12) (stromal cell derived factor-1) involved in inflammation and a check point gene cyclin-dependent kinase Inhibitor 2A (CDKN2A) involved in angiogenesis. As in the field of CVD risk scores standardisation of inputs and data transparency are becoming essential to allow comparison of different strategies for the purposes of quality appraisal for evidence-based guidelines. The variety and quality of data set reporting, analytical and statistical approaches, provision of absolute as opposed to relative effect sizes and lack of specificity and sensitivity data at set points remain common problems.35, 36 Such approaches are now standard for epidemiological cohorts (CONSORT statement) and diagnostic assays (STARD).35, 36 Reporting standards have been introduced for single-nucleotide polymorphism (SNP) association data and genome wide association studies to provide greater clarity for journal referees and editors assessing these studies and for readers to understand them and conduct validation studies. The increasing popularity and complexity of mathematical models applied to CVD and other endpoint data means that similar provisions need to be applied to these studies as well.37 The electronic nature of modern scientific literature means model derivation structures and data can easily be added as appendices or contributed to public scientific data repositories. A number of publications and review articles have begun to request certain details of mathematical models in addition to data and ideally model transparency and ideally availability. A suggested scheme based on data presented in studies reviewed in this field is presented in Table 1. 1. Formal presentation of research questions 2. Data selection Public databases vs electronic health record databases vs registry data 3. Hardware selection 4. Data preparation 5. Feature selection This should not be necessary. Multi-dimensional datasets may require strategies such as vector embedding to enable features to be passed to other directed learning models 6. Data splitting Design and justify the proportion of training, validation and testing in the dataset (ie 70/10/20 or 80/10/10 or 60/20/20) and ideally provide comparison data 7. Modelling selection 8. Technical details for model Specification of technical terms to communicate with data scientists or programmers and allow understanding of the process of model development (learning rate selection, tuning hyperparameter, batch dropout and normalisation, regularisation strategies, loss function selection and network optimisation). Methods used in model structure- logistic regression, Cox regression, random forest or gradient boosting models, neural networks 9. Evaluation of model discrimination and calibration Precision recall curve (PRC; unbalanced data) or receiver operator curve (ROC; balanced data) analysis of data with presentation of C-statistics, Brier scores from probabilistic outcomes Presentation of NPV, PPV, sensitivity and specificity at specific set points Comparison with standard statistical approaches (ie multi-variable regression), goodness-of-fit, calibration plots or the decision-curve analysis 10. Clinical Validation Comparison with expert opinion or published data of other current clinical strategies 11. Publication and transparency Sharing of codes with journal (ie online supplements) or public space (ie Github, bioRxiv). Directed learning methodologies should be clearly explained in data appendices. Consider strategies for computational anonymisation It will take consensus conferences between investigators, journal editors, computer modelling specialists, evidence assessment groups and ideally academic funding agencies to finalise and agree the final set of quality metrics. These aurors will then pronounce on the quality of the work submitted. After all you need to remove the magic is to identify the underlying nature of the work—or to truly see Grindelwald. Otherwise fantastic means mythical as opposed to wonderful. The authors thank Dr Scooter Morris of the Pharmaceutical Chemistry group in the School of Pharmacy at the University of California, San Francisco, USA for his helpful comments on this manuscript. None.

Machine learning (ML) methods have shown promise in a wide range of applications including the development of patient-specific predictive models before surgical interventions. The purpose of this study was to develop, test, and compare four distinct ML models to predict postoperative parameters after primary total hip arthroplasty. Data from the Nationwide Inpatient Sample were used to identify patients undergoing total hip arthroplasty from 2016 to 2017. Linear support vector machine (LSVM), random forest (RF), neural network (NN), and extreme gradient boost trees (XGBoost) predictive of mortality, length of stay, and discharge disposition were developed and validated using 15 predictive patient-specific and hospital-specific factors. Area under the curve of the receiver operating characteristic (AUCROC) curve and accuracy were used as validity metrics, and the strongest predictive variables under each model were assessed. A total of 177,442 patients were included in this analysis. For mortality, the XGBoost, NN, and LSVM models all had excellent responsiveness during validation while RF had fair responsiveness. LSVM had the highest responsiveness with an AUCROC of 0.973 during validation. For the length of stay, the LSVM and NN models had fair responsiveness while the XGBoost and random forest models had poor responsiveness. LSVM had the highest responsiveness with an AUCROC of 0.744 during validation. For the discharge disposition outcome, LSVM had good responsiveness while the XGBoost, NN, and RF models all had fair responsiveness. LSVM had the highest responsiveness with an AUCROC of 0.801. The ML methods tested demonstrated a range of poor-to-excellent responsiveness and accuracy in the prediction of the assessed metrics, with LSVM being the best performer. Such models should be further developed, with eventual integration into clinical practice to inform patient discussions and management decision making, with the potential for integration into tiered bundled payment models.

Optimization of a vertical axis wind turbine with a deflector under unsteady wind conditions via Taguchi and neural network applications

In this research, in order to address interferences of air traffic from complex factors like weather and local data abnormality of radar samples, fuzzy clustering and neural network ensemble were introduced into the short-term forecasting of air traffic flow. Firstly, with K-means cluster analysis, this research compared traffic volume at different time with that of each clustering center to identify the temporal clustering of traffic volume. Secondly, according to different data sets from clustering analysis, corresponding neural network models were established. On the basis of Bagging method, a neural network ensemble weight allocation algorithm of fuzzy subordinative degree was also built to identify weight of each neural network and to establish neural network ensembles model. Finally, according to 3 principle of normal distribution, abnormal data out of S -+ ection (μ 3σ, μ 3σ) was cleaned and short-term forecasting results were acquired. Our model showed superior results of short-term radar data forecasting for Shanghai Terminal Area, overmatching regression analysis and neural network forecasting. The experiment verified that the method is valid and feasible for short-term forecasting of air traffic flow.

Purpose This study aims to analyze the interrelationship between internal migration, socioeconomic variables and unemployment across 26 NUTS2 sub-regions of Türkiye over the period 2008–2021. The time frame was selected due to pronounced regional economic divergence and accelerated internal mobility patterns in Türkiye. Design/methodology/approach The empirical strategy is grounded in the extended Harris-Todaro Migration Model and employs the seemingly unrelated regression method for spatial panel data to account for cross-sectional dependence and spatial autocorrelation. Spatial weight matrices were constructed based on two criteria: border neighborhood and dynamic migration. Findings The results demonstrate that higher levels of civic association density and agricultural output are positively associated with net migration rates, whereas increases in per capita income and fixed capital investment exhibit a statistically significant negative effect. In the models where regional unemployment is specified as the dependent variable, net migration emerges as a significant positive determinant, indicating that migratory inflows exert upward pressure on unemployment levels in destination regions. These findings suggest a recursive feedback mechanism: structurally weaker regions with low agricultural productivity and income levels experience labor outflows, which, upon agglomeration in urban centers, exacerbate unemployment, reinforcing the so-called migration-unemployment spiral. This cyclical dynamic underlines the endogenous nature of regional disparities and labor market imbalances. Research limitations/implications These findings suggest a recursive feedback mechanism: structurally weaker regions with low agricultural productivity and income levels experience labor outflows, which, upon agglomeration in urban centers, exacerbate unemployment – reinforcing the so-called migration-unemployment spiral. This cyclical dynamic underlines the endogenous nature of regional disparities and labor market imbalances. Originality/value The study emphasizes the necessity of targeted regional development policies aimed at enhancing local economic resilience, particularly in agriculture and productive investment, to mitigate involuntary migration and its associated labor market distortions. These results offer actionable insights for policy formulation in the context of spatial labor market equilibrium and regional development strategy. Furthermore, this study introduces a novel variable – civic association density, representing local kinship and hometown networks–hometown associations – absent from previous empirical research. Incorporating this unique social capital indicator into the migration–unemployment framework enriches the literature and offers new insights into spatial labor market dynamics.

Comparison between neural network and mathematical modeling of supercritical CO 2 extraction of black pepper essential oil

Introduction. In the context of the crisis of the Ukrainian economy, which was significantly exacerbated by labour market imbalances, institutional structures and businesses need to promptly identify asymmetries of labour market factors and relevant results of trendwatching of the market situation. Simultaneously, trendwatching is significantly complicated by uncertainty and sharp changes in the main indicators of demand and supply in the Ukrainian labour market. This necessitates the development of a new mathematical model of trendwatching that is suitable for use in the specifics of the labour market and its application in forecasting the indicators of the specified market. Aim and tasks. This study aims to develop new mathematical tools for trendwatching, adapted to the conditions of Ukraine, and their use for assessing and forecasting labour market indicators. Results. Forecasting the impact of the main factors on the labour market of Ukraine, contributing to the increase in wages in the IT industry, using the developed mathematical model, indicated that the impact of uncertainty is significant, from 5% to 20%. The differences in the average monthly wage by industry tend to grow, and the forecast data for 2025 will be 1.53 times greater than the indicator for 2021. This indicates that not only the magnitude (by two or more times) but also the directions of the demand-supply vectors change per quarter, even in critical infrastructure sectors. Simultaneously, a significant loss of high-quality human resources is indicated due to the outflow of highly qualified workers abroad. Conclusions. The labour market trendwatching indicated that the difference in the rate of change in the number of resumes in the period 2023-2024 is 12.79 times greater than the rate of change in the number of vacancies, with a concomitant low level of convergence of the nonlinear trends of the specified indicators, which is evidence of a significant gap in the demand and supply of employee competencies. This is confirmed by a significant (1.5 times higher than the EU) unemployment rate among people with higher education levels. A significant slope ratio of the linear trend of the ratio of the standard deviation of the average monthly salary by industry to its average value in the economy (4.382) confirms the trend of increasing discrepancies in the personnel shortage by industry and the post-war period. Simultaneously, a trend towards an increase in job offers with increasing wages was detected, indicating a tendency in Ukraine to abandon the policy of cheap labour.

This research investigates the potential of artificial intelligence (AI) to enhance two of the most critical processes in the insurance industry: risk assessment and fraud detection. As insurers face increasing pressure to process complex data, minimize losses, and provide more personalized services, traditional statistical models such as logistic regression often prove insufficient in capturing the nonlinear relationships and subtle patterns embedded in client behavior and claim history. The central research question addressed in this study is: How can artificial intelligence improve risk assessment and fraud detection in insurance compared to traditional statistical approaches? To answer this question, a simulation-based experimental methodology was employed. A synthetic dataset containing 10,000 insurance client profiles was generated, incorporating realistic demographic, behavioral, and policy-related variables. Several machine learning models were implemented, including supervised algorithms (Logistic Regression, Random Forest, XGBoost, Neural Networks, SVM) and unsupervised methods (Isolation Forest, Autoencoder, K-Means), with performance evaluated using accuracy, F1 score, precision, and recall. Model training and validation were conducted using cross-validation techniques and hyperparameter optimization to ensure robustness and generalizability. The results demonstrate a clear performance advantage of AI-based models over traditional statistical methods. XGBoost emerged as the best-performing model, achieving the highest accuracy (87.3%), F1 score (0.86), and strong precision and recall, confirming its robustness in both classification and detection tasks. Random Forest and Neural Networks also performed well, while Logistic Regression lagged significantly behind. Unsupervised models such as Isolation Forest and Autoencoders proved particularly useful in fraud detection, offering high recall rates suitable for anomaly detection and early-stage screening. The integration of multiple models into a hybrid AI architecture is recommended to balance precision with sensitivity. In conclusion, the findings of this research affirm that artificial intelligence provides a powerful and scalable solution for modern insurance analytics. AI not only improves the precision and efficiency of risk assessment and fraud detection but also enables dynamic, data-driven decision-making that traditional models cannot replicate. As the insurance industry becomes increasingly digitized, AI adoption represents not merely an opportunity but a strategic necessity for organizations seeking to optimize operations and maintain a competitive edge.

The article analyzes the impact of the ongoing war in Ukraine on the labor market and economic situation. It discusses the main socioeconomic consequences of the wartime, including a decline in employment levels, a decrease in economic potential, a shortage of jobs, and mass emigration of the working-age population. The issues of structural asymmetry in the labor market are examined, particularly identifying the imbalance in the demand and supply of labor by professions and sectors, which significantly complicates employment processes in certain regions of the country. The asymmetry coefficient, which characterizes the imbalance in the demand and supply of labor by profession, is 0.49. This indicates a high level of imbalance in the labor market. The asymmetry coefficient by economic activity amounted to 2.39, which indicates a critical level of imbalance in the labor market that requires active intervention from the government to stabilize the situation. The necessity of researching labor market trends through both statistical analysis and representative surveys has been established. The sociological survey conducted by the Razumkov Centre’s sociological service enables a deeper analysis of the imbalance. The results of the study indicate significant challenges in Ukraine’s labor market that require comprehensive solutions, including retraining programs, attracting young specialists, supporting older workers, and encouraging the return of labor migrants. Another perspective on analyzing Ukraine’s labor market is identifying and interpreting the discrepancies between employees’ expectations and the actual conditions offered by employers. The need for new managerial approaches to regulate the labor market in wartime conditions is being analyzed. The article emphasizes the need to develop flexible forms of work, encourage businesses to create jobs in regions affected by the war, and support people with disabilities and older individuals. It is also emphasized the importance of the return of labor migrants to the country by creating favorable conditions for their employment. The authors note that a comprehensive State policy is needed to overcome the negative effects of the war on the labor market, which includes retraining of workers and the development of digital and flexible forms of employment.