Data Classification Methods Research Articles

Binary imbalanced data classification based on diversity oversampling by generative models

In many practical applications, the data are class imbalanced. Accordingly, it is very meaningful and valuable to investigate the classification of imbalanced data. In the framework of binary imbalanced data classification, the synthetic minority oversampling technique (SMOTE) is the best-known oversampling method. However, for each positive sample, SMOTE generates only k synthetic samples on the lines between the positive sample and its k-nearest neighbors, resulting in three drawbacks: (1) SMOTE cannot effectively extend the training field of positive samples; (2) the generated positive samples lack diversity; (3) SMOTE does not accurately approximate the probability distribution of the positive samples. Therefore, two binary imbalanced data classification methods named BIDC1 and BIDC2 based on diversity oversampling by generative models are proposed. The BIDC1 and BIDC2 conduct diversity oversampling using extreme learning machine autoencoder and generative adversarial network, respectively. Extensive experiments on 26 data sets are conducted to compare the two methods with 14 state-of-the-art methods using five metrics: F-measure, G-means, AUC-area, MMD-score, and Silhouette-score. The experimental results demonstrate that the two methods outperform the other 14 methods.

An Ensemble Classification Method for High-Dimensional Data Using Neighborhood Rough Set

Mining useful knowledge from high-dimensional data is a hot research topic. Efficient and effective sample classification and feature selection are challenging tasks due to high dimensionality and small sample size of microarray data. Feature selection is necessary in the process of constructing the model to reduce time and space consumption. Therefore, a feature selection model based on prior knowledge and rough set is proposed. Pathway knowledge is used to select feature subsets, and rough set based on intersection neighborhood is then used to select important feature in each subset, since it can select features without redundancy and deals with numerical features directly. In order to improve the diversity among base classifiers and the efficiency of classification, it is necessary to select part of base classifiers. Classifiers are grouped into several clusters by k-means clustering using the proposed combination distance of Kappa-based diversity and accuracy. The base classifier with the best classification performance in each cluster will be selected to generate the final ensemble model. Experimental results on three Arabidopsis thaliana stress response datasets showed that the proposed method achieved better classification performance than existing ensemble models.

Medical and Health Data Classification Method Based on Machine Learning.

The information defined in medical health data is researched based on machine learning-related algorithms. Also, this paper used random forest and other related algorithms to perform health data training and fitting. Research shows that the algorithm proposed in the paper can improve the progress of health data classification. The algorithm can provide technical support for the improvement of medical data classification.

Research on data classification and feature fusion method of cancer nuclei image based on deep learning

AbstractThere are many different types of nuclei in a tumor tissue. We can identify the specific nuclei and their distribution in the tissue to reflect the current cancer state of histopathological images. However, due to the existence of cellular heterogeneity, the recognition of nuclei in histopathological images has always been a problem of computer vision. In the paper, we use the transfer learning of Deep Convolutional Neural Network to classify nuclei, and found that adjusting the size of the nuclear image to a certain size can improve the accuracy of the nuclei classification model, while not significantly reducing the nuclei classification efficiency. Through further research, it was confirmed that the environment around the nuclei can bring great help to the model classification. Based on the principle, we design a feature fusion model. We extract features from nuclei image different sizes by CNN, fuse the features, and then use fully connected layer to classify the features. Experiments have proved that the feature fusion model has a considerable improvement in accuracy compared to the normal classification model.

Classification of pulse flours using near-infrared hyperspectral imaging

With increasing consumer interest in healthy food formulations made from pulse flours, the research gap in characterizing them based on their pulse type and milling technique has come to limelight. To this end, the feasibility of using hyperspectral imaging in the visible near infrared (Vis-NIR) (400–1000 nm) and short wave infrared (SWIR) (1000–2500 nm) regions to classify pulse flours (viz. chickpea, yellow pea, navy bean and green lentil) based on the pulse type and milling methods was investigated. Unsupervised and supervised classification models were developed using unsupervised principal component analysis (PCA) and supervised partial least squares discriminant analysis (PLS-DA). Supervised classification for pulse flour type demonstrated 100% accuracy in the Vis-NIR wavelength range of 530–700 nm, which primarily exploited the color attributes of the flour samples. For milling method based classification, PLS-DA models developed using SWIR regions of 1370–1500 nm and 1700–2000 nm played a significant role in discriminating flour samples yielding 95% classification accuracy. These regions are associated with the O–H and N–H overtones of the proteins found in flour samples. Conclusively, hyperspectral imaging in the range of 400–2500 nm combined with multivariate data classification methods can reliably be used by the food industry for characterization of pulse flours.

Ten simple rules for reporting machine learning methods implementation and evaluation on biomedical data

AbstractThere is a huge discrepancy in how researchers implement, evaluate, and report the performance of a machine learning method for classification or segmentation of biomedical data. Poor reporting and inadequate inferences are, however, not unusual to see in current literature. More specifically, vague aims and scope, missing details about the data, ambiguous preprocessing procedures, lack of clarity regarding the method's implementation, poor validation and testing, invalid comparisons between methods, and the absence of a clear rationale for performance metric choices are making it difficult to draw the right conclusions from many studies in the field. This report suggests 10 guidelines and principles that should be followed when reporting the implementation of a method and the evaluation of its performance in order to make the study transparent, interpretable, replicable, and useful. All stages of data processing and method's performance evaluation should be clearly described, and parameters and metric choices must be justified in order to aid readers in appreciating the performance of the method or in comparing it with other relevant methods. We feel that these guidelines are important for clear scientific communication in the field of biomedical data processing.

Data organization issues in civil engineering

In today's organization of data in civil engineering there remains ambiguity, diversity of terminology and consequent lack of clarity. The daily reality is to work with hybrid data sources in a variety of software programs. In CAD programs it is possible to import and convert elements from GIS, but they will be classified only into layers. Or, attach external references from CAD files in BIM, but there still remains the problem of finding your way around the confusing list of layers, and their abbreviations. Also broadly used filesystems as place for store and organise data on disk into files (entities) and hierarchical structure of folders (categories) is limited by reductionist categorization approach. Another problem shows with absence of multilingual approach. The limits are in the methods of data organization, and so only partially portable. Therefore, if there is a common denominator to all problems, it is the absence of a unifying organizational environment. In current computer applications used in civil engineering, methods of data classification are implemented that do not allow multiplicative or faceted categorization and thus inevitably create an antagonistic environment with any previous or future classification system. Unfortunately, even if a software tool implements a modern multiplicative categorization or domain ontology, it will not significantly solve the overall situation, because it will still not be possible to use such a method of categorization in other applications. Therefore, if there should exist a solution that would significantly facilitate cooperation and orientation in the data, it must inevitably work independently, across diverse software applications, with the ability to link the functions of the application with such a separate categorization system.

Decision Support System for Hyperspectral Remote-Sensing Data of Yellow River Estuary, China

There are many wetland resources in the area where the Yellow River enters the sea. This area has good ecological and economic value. Therefore, wetlands are precious resources. The accuracy of traditional wetland classification methods is low (for example, based on the support machine method). In order to explore ways to improve the accuracy of wetland classification, this paper selected the wetland at the mouth of the Yellow River as the study area. And, we used the hyperspectral data of “Zhuhai No. 1” as the research data. Then, we used the logarithmic transformation method to enhance the spectral characteristics of remote-sensing images. Finally, we used Markov random field method (MRF) and support vector machine method (SVM) to finely classify the wetlands in the Yellow River estuary area. We used these experiments to explore wetland classification methods for hyperspectral data. The results showed that the settings of the coupling coefficient and the initial value in the Markov model had a greater impact on the classification results. We found that the best result was when the initial classification number is 50 and the coupling coefficient is 0.5. Compared with the SVM classification method, the overall classification accuracy of our proposed method was improved by 3.9672%, and the Kappa coefficient was improved by 0.042.

Multi-Dimensional Encoding Image Based Time Series Feature Extraction for Improved Colorimetric Gas Concentration Estimation

Industrial risk can be reduced through the early detection and intervention of gas leaks. Numerous early detection methods have been developed and applied in industrial areas, but they require expert analysis skills, a lengthy analysis time, or relatively expensive equipment. The development of novel, efficient, and reliable devices is continuously required as a strategy to reduce industrial gas risks.In the past decades, various types of gas sensors have been developed based on semiconductor, optical fiber, acoustic wave, and other technologies. A significant amount of work has been undertaken in recent years to make sensors smaller, more sensitive, more visually responsive, easier to handle, more personal, and more wearable. However, these functions require expensive equipment, complex skills, and large amounts of power. In this respect, colorimetric sensors are of great interest owing to their simplicity, easy fabrication, low cost, visual response, and ease of interpretation without power consumption. However, currently, colorimetric sensors have several limitations that need to be overcome, such as sensitivity, high cost, early detection, and accuracy for use in industrial safety systems.Existing colorimetric sensor studies use an algorithm that compares discoloration using the naked eye, estimates gas concentration with a simple statistical method, or uses a machine learning algorithm to estimate time series data by signal processing simple discoloration information. The algorithm learned the measurement image or the time series data extracted from the image. Signal processing was focused on noise removal, which was performed by scaling the time series data. These studies using machine learning and deep learning-based analysis for colorimetric sensor research have not sufficiently considered data coding. Machine learning of time series data is carried out using the time series decomposition method that separates time dependencies. Attempts are being made to perform time series data analysis through image encoding. To the best of our knowledge, the present study is the first to use image encoding for time series colorimetric sensor data.This study applied a time series data analysis technique using a convolutional neural network (CNN) with multi-dimensional image encoding to improve gas concentration estimation accuracy. The estimation accuracy obtained in this study was more than that obtained by applying existing statistical methods, such as auto-regressive integrated moving average (ARIMA) and exponential smoothing (ETS), and machine learning techniques such as multi-layer perceptron (MLP). We focused on gas concentration estimation using a time series-based colorimetric analysis method, which was developed based on colorimetric fabric detection data. The time series data obtained through the gas experiment were subjected to image encoding to classify the trend in the color value change of the gas concentration. Image encoding improves the efficiency with which the gas exposure section is specified, as well as the gas concentration estimation, within the entire section. The concentration estimation accuracy of the existing data classification method was 74.8%, while that of MLP was more than 80.5%. Furthermore, the accuracy was improved by applying a CNN that learned the multi-dimensional processing of sensor data through image encoding. From the results, an estimation accuracy of 90.1% was confirmed. The proposed technique can be applied to next-generation smart industrial safety systems. Figure 1

First-in-human pilot study of snapshot multispectral endoscopy for early detection of Barrett's-related neoplasia.

.Significance: The early detection of dysplasia in patients with Barrett’s esophagus could improve outcomes by enabling curative intervention; however, dysplasia is often inconspicuous using conventional white-light endoscopy.Aim: We sought to determine whether multispectral imaging (MSI) could be applied in endoscopy to improve detection of dysplasia in the upper gastrointestinal (GI) tract.Approach: We used a commercial fiberscope to relay imaging data from within the upper GI tract to a snapshot MSI camera capable of collecting data from nine spectral bands. The system was deployed in a pilot clinical study of 20 patients (ClinicalTrials.gov NCT03388047) to capture 727 in vivo image cubes matched with gold-standard diagnosis from histopathology. We compared the performance of seven learning-based methods for data classification, including linear discriminant analysis, -nearest neighbor classification, and a neural network.Results: Validation of our approach using a Macbeth color chart achieved an image-based classification accuracy of 96.5%. Although our patient cohort showed significant intra- and interpatient variance, we were able to resolve disease-specific contributions to the recorded MSI data. In classification, a combined principal component analysis and -nearest-neighbor approach performed best, achieving accuracies of 95.8%, 90.7%, and 76.1%, respectively, for squamous, non-dysplastic Barrett’s esophagus and neoplasia based on majority decisions per-image.Conclusions: MSI shows promise for disease classification in Barrett’s esophagus and merits further investigation as a tool in high-definition “chip-on-tip” endoscopes.

Framework for classification of cancer gene expression data using Bayesian hyper-parameter optimization.

Computational classification of cancers is an important research problem. Gene expression data has 1000s of features, very few samples, and a class imbalance problem. In this paper, we have proposed a framework for the classification of cancer gene expression profiles. The framework consists of a pipeline of methods for data pre-processing, feature selection, and classification. Data pre-processing is done by standard scaling and normalization of the features. The feature selection is performed in two steps. First, recursive feature elimination (RFE) is used; then, a genetic algorithm is applied only in case RFE results in a feature subset of size more than a specific threshold. Next, is a meta-pool of diverse, individual as well as ensemble classifiers. Hyper-parameters of each member in the meta-pool are optimized using Bayesian Optimization. An algorithm is developed to select the best classifier from the meta-pool based on classification accuracy and computation time taken. We evaluated the framework on 6 publicly available microarray datasets and the PAN-Cancer RNA Sequencing dataset. We found that the classifier selected by the proposed framework produced significant improvement in classification accuracy and computation time required to predict labels for test datasets. A detailed comparison with the state-of-the-art methods shows that the proposed framework outperforms all of them.

A Study on Cloud Storage Security Method Using Data Classification

Cloud computing is a new model for providing diverse services of software and hardware. This paradigm refers to a model for enabling on-demand network access to a shared pool of configurable computing resources, that can be rapidly provisioned and released with minimal service provider interaction .It helps the organizations and individuals deploy IT resources at a reduced total cost. However, the new approaches introduced by the clouds, related to computation outsourcing, distributed resources and multi-tenancy concept, increase the security and privacy concerns and challenges. It allows users to store their data remotely and then access to them at any time from any place .Cloud storage services are used to store data in ways that are considered cost saving and easy to use. In cloud storage, data are stored on remote servers that are not physically known by the consumer. Thus, users fear from uploading their private and confidential files to cloud storage due to security concerns. The usual solution to secure data is data encryption, which makes cloud users more satisfied when using cloud storage to store their data. Motivated by the above facts; we have proposed a solution to undertake the problem of cloud storage security. In cloud storage, there are public data that do not need any security measures, and there are sensitive data that need applying security mechanisms to keep them safe. In that context, data classification appears as the solution to this problem. The classification of data into classes, with different security requirements for each class is the best way to avoid under security and over security situation. The existing cloud storage systems use the same Journal of University of Shanghai for Science and Technology ISSN: 1007-6735 Volume 23, Issue 9, September – 2021 Page-1105 key size to encrypt all data without taking into consideration its confidentiality level. Treating the low and high confidential data with the same way and at the same security level will add unnecessary overhead and increase the processing time. In our proposal, we have combined the K-NN (K Nearest Neighbors) machine learning method and the goal programming decision-making method, to provide an efficient method for data classification. This method allows data classification according to the data owner security needs. Then, we introduce the user data to the suitable security mechanisms for each class. The use of our solution in cloud storage systems makes the data security process more flexible, besides; it increases the cloud storage system performance and decreases the needed resources, which are used to store the data.

Сучасні препарати в лікуванні цукрового діабету 2 типу

The purpose of the study is to analyze the impact of type 2 diabetes on the skin, to establish the pathogenesis and analysis of modern treatments. Materials and methods. The materials and methods of research were theoretical analysis of scientific literature; statistical methods of literature data analysis, comparative analysis and classification according to the protocol of the international prospective study International Prevalence and Treatment of Diabetes and Depression (INTERPRET-DD). The emphasis is placed on the relevance of research in ways to treat diabetes as a serious disease with high mortality. The statistical indicators of the disease are given and the percentages of disability are indicated. The pathogenesis of type 2 diabetes mellitus is considered and risk factors for the disease are listed. Some diabetic-associated skin symptoms are said to be a direct result of metabolic changes such as hyperglycemia and hyperlipidemia. Progressive damage to the vascular, nervous or immune systems also greatly contributes to the development of skin manifestations. Early medication of insulin should be considered in case of continuous weight loss, symptoms of hyperglycemia or HbA1c in blood > 10% (86 mmole/mole) or plasma glucose ≥300 mg/dL (≥16.7 mmole/l). In patients with type 2 diabetes mellitus with cardiovascular diseases of atherosclerotic etiology or high risk, diabetic kidney disease or heart failure, it is recommended to use inhibitors of the sodium-dependent glucose cotransporter of the type 2 or agonists of the glucagon-1 peptide receptor, which are effective with cardiovascular diseases, regardless of the level of HbA1c in the blood, taking into account individual factors. Conclusion. It was found that the initial treatment, according to the World Health Organization in 2020, should begin with the appointment of metformin, which is a first-line drug for the treatment of patients, that does not cause weight gain, does not provoke hypoglycemia and is recommended for the treatment of people who do not reach the desired level of glycemia through diet and physical activity. It is emphasized that diabetes should be treated comprehensively: diet, physical activity, metformin. Metformin drugs are the basic therapy and should be prescribed in any situation in the presence of disorders of carbohydrate metabolism and if there are no contraindications to their use. It is important to increase the dosage of metformin gradually according to the diabetes management protocol: all hypoglycemic drugs should be titrated from the lowest dosage to that necessary to ensure good compensation of carbohydrate indices. Intensification of treatment of patients with type 2 diabetes mellitus in case of failure to achieve the main goals of therapy should be timely

Influence of topographic features and stream network structure on the spatial distribution of hydrological response

Topographic data and geomorphological characteristics are recognized as significant factors that affect the hydrological response of a river basin. There have been a variety of lumped or semi-distributed hydrological models developed based on the use of geomorphological parameters. However, the possibility of applying geomorphological factors to describe the temporal and spatial distribution of hydraulic variables in a watershed is still questioned. This study focuses on thoroughly exploring the relationship between watershed topography and two variables, the water depth and discharge. Machine learning (ML) techniques are utilized to predict the spatial distribution of the two hydraulic variables based on geomorphological factors. To enhance the overall predicted accuracy, the structure and unique distribution of the stream network exiting a river basin are investigated for use in modifying the training procedure of the ML model. A novel method of data classification according to the stream network structure and cluster analysis is established to reinforce the capability of predicting hydraulic variables. Application results of two river basins in Taiwan island show that the performance of water depth and discharge predictions are satisfactory as long as the data classification is conducted for model training. Therefore, the ML-based model developed in this study is a promising way to replace the fully distributed numerical model in order to improve executing efficiency and bypass any problem of instability.

Ordinal classification of 3D brain structures by functional data analysis

We introduce several ordinal classification methods for functional data, specifically multiargument and multivariate functional data. Their performance is analyzed in four real data sets that belong to a neuroeducational problem and a neuropathological problem.

Predicting Drug-Induced Liver Injury Using Machine Learning on a Diverse Set of Predictors.

A major challenge in drug development is safety and toxicity concerns due to drug side effects. One such side effect, drug-induced liver injury (DILI), is considered a primary factor in regulatory clearance. The Critical Assessment of Massive Data Analysis (CAMDA) 2020 CMap Drug Safety Challenge goal was to develop prediction models based on gene perturbation of six preselected cell-lines (CMap L1000), extended structural information (MOLD2), toxicity data (TOX21), and FDA reporting of adverse events (FAERS). Four types of DILI classes were targeted, including two clinically relevant scores and two control classifications, designed by the CAMDA organizers. The L1000 gene expression data had variable drug coverage across cell lines with only 247 out of 617 drugs in the study measured in all six cell types. We addressed this coverage issue by using Kru-Bor ranked merging to generate a singular drug expression signature across all six cell lines. These merged signatures were then narrowed down to the top and bottom 100, 250, 500, or 1,000 genes most perturbed by drug treatment. These signatures were subject to feature selection using Fisher’s exact test to identify genes predictive of DILI status. Models based solely on expression signatures had varying results for clinical DILI subtypes with an accuracy ranging from 0.49 to 0.67 and Matthews Correlation Coefficient (MCC) values ranging from -0.03 to 0.1. Models built using FAERS, MOLD2, and TOX21 also had similar results in predicting clinical DILI scores with accuracy ranging from 0.56 to 0.67 with MCC scores ranging from 0.12 to 0.36. To incorporate these various data types with expression-based models, we utilized soft, hard, and weighted ensemble voting methods using the top three performing models for each DILI classification. These voting models achieved a balanced accuracy up to 0.54 and 0.60 for the clinically relevant DILI subtypes. Overall, from our experiment, traditional machine learning approaches may not be optimal as a classification method for the current data.

A Transfer Classification Method for Heterogeneous Data Based on Evidence Theory

It remains a challenging problem for data classification without training patterns. In many applications, there may exist some labeled data in other related domains (called source domain), and such labeled data can be helpful to solve the classification problem in the target domain. It is considered that the source domain and target domain are heterogeneous here and they represent the distinct feature spaces. A new transfer classification method for heterogeneous data is proposed based on the evidence theory. Some pattern pairs in the source domain and target domain are given to predict the link of these two domains. For each pattern in the target domain, we estimate its possible mapping value in the source domain using these pattern pairs with a self-organizing map (SOM) technique, and then the mapping value is classified using the labeled data in the source domain. However, the patterns with close values in the target domain may have more or less different values in the source domain due to the distinct characteristics of these two domains. So the mapping value can be very uncertain sometimes. In such a case, the target pattern is allowed to have multiple mapping values with different weights/reliabilities in the source domain. These mapping values can produce different classification results. The evidence theory is good at characterizing and combining uncertain information. In order to improve the classification accuracy, a new evidence-based weighted fusion method is developed for combining these classification results, which are discounted by the corresponding weights under the belief functions framework, and the final class decision is made according to the combination result. In experimental applications, some heterogeneous remote sensing data and UCI data are used to test the performance of new method with respect to several other methods, and it shows that the new method can efficiently improve the classification accuracy.

Topological Machine Learning for Mixed Numeric and Categorical Data

Topological data analysis is a relatively new branch of machine learning that excels in studying high-dimensional data, and is theoretically known to be robust against noise. Meanwhile, data objects with mixed numeric and categorical attributes are ubiquitous in real-world applications. However, topological methods are usually applied to point cloud data, and to the best of our knowledge there is no available framework for the classification of mixed data using topological methods. In this paper, we propose a novel topological machine learning method for mixed data classification. In the proposed method, we use theory from topological data analysis such as persistent homology, persistence diagrams and Wasserstein distance to study mixed data. The performance of the proposed method is demonstrated by experiments on a real-world heart disease dataset. Experimental results show that our topological method outperforms several state-of-the-art algorithms in the prediction of heart disease.

Single-particle diffusional fingerprinting: A machine-learning framework for quantitative analysis of heterogeneous diffusion

Single-particle tracking (SPT) is a key tool for quantitative analysis of dynamic biological processes and has provided unprecedented insights into a wide range of systems such as receptor localization, enzyme propulsion, bacteria motility, and drug nanocarrier delivery. The inherently complex diffusion in such biological systems can vary drastically both in time and across systems, consequently imposing considerable analytical challenges, and currently requires an a priori knowledge of the system. Here we introduce a method for SPT data analysis, processing, and classification, which we term "diffusional fingerprinting." This method allows for dissecting the features that underlie diffusional behavior and establishing molecular identity, regardless of the underlying diffusion type. The method operates by isolating 17 descriptive features for each observed motion trajectory and generating a diffusional map of all features for each type of particle. Precise classification of the diffusing particle identity is then obtained by training a simple logistic regression model. A linear discriminant analysis generates a feature ranking that outputs the main differences among diffusional features, providing key mechanistic insights. Fingerprinting operates by both training on and predicting experimental data, without the need for pretraining on simulated data. We found this approach to work across a wide range of simulated and experimentally diverse systems, such as tracked lipases on fat substrates, transcription factors diffusing in cells, and nanoparticles diffusing in mucus. This flexibility ultimately supports diffusional fingerprinting's utility as a universal paradigm for SPT diffusional analysis and prediction.

Psychological Resources of the Modification in Self-Fulfillment of Personality

The aim of the article is an empirical definition of the psychological resources of modification in the self-fulfillment of the person. The empirical study used methods of psychological questioning, as well as such methods of mathematical and statistical data analysis, in particular, regression, correlation, classification, discriminant, and multifactor analysis. The empirical study was implemented according to Nelson’s model, which made it possible to characterize the required psychological resources by discriminating against them. Research results. It was found that the reflected psychological resources in the configurations of self-fulfillment – existential, personal, and professional self-fulfillment – are: creativity, work on yourself, the ability to update their own resources, coherence, charity, meaningful life, expression of emotions. Interpretive resources «creativity» and «ability to update own resources» are predictors of all three configurations of self-realization. Psychological resources that moderate the relationship of existential motivation as a manifestation of the inner world of man and life tasks are identified as creativity, self-work, coherence, charity, expression of emotions. Psychological resources that are factors of self-realization, self-actualization, self-fulfillment of the person are creativity, the ability to update their own resources, coherence. Coherence is a factor for all three configurations of self-realization. Conclusions. The sequence of the resourcefulness of changes in self-fulfillment is as follows: first, due to the resource of work on oneself, self-actualization is carried out, the effect of which is existential self-fulfillment as a sense of real and authorial life lived by the person; further, the ability to update their own resources leads to the transformation of forms in personal and professional self-fulfillment; at the end of the change, the resource of creativity affects the possibility of self-fulfillment of the person. The whole process of reflected changes is patronized by coherence, as a factor in the significance of a life lived by the person, as well as its optimal and healthy functioning.