جستجوی مقالات مرتبط با کلیدواژه « یادگیری ماشین » در نشریات گروه « علوم انسانی »

The intersection of artificial intelligence (AI), psychology and applied linguistics particularly in the realm of language learning, has opened up a fascinating avenue for exploring the intricate processes and mechanisms underlying human cognition. Machine learning algorithms have the potential to shed light on the fundamental principles of learning language specially on reading comprehension as the core language learning variable.

In this research, we used supervised machine learning techniques in order to discover the most important syntactic and lexical features affecting the reading comprehension of English language learners.

The design of present study is causal comparative type (ex post facto). the population includes all second secondary level students who learn English in language training institutions. To select the participants, language training institutes in Tehran were referred. The participants (n=360) answered BALA exam (Young, 2022) questions in written and spoken form.

260 features were extracted from the computer texts prepared from the speech and writing learners responses by natural language processing (NLP) algorithms. We used learning models of decision tree, nearest neighbor, support vector method, neural network and regularized linear method to predict reading comprehension using extracted linguistic features.

The results showed that the variance of language learners' reading comprehension can be well modeled using the extracted grammatical and lexical features, and in addition, twenty features that play the most important role in explaining the variance were identified. This study shows that ML methods can determine the detailed investigation of language processes related to reading comprehension.

The current research was conducted to find the optimal portfolio for investing in stock exchange stocks, and one of the methods that is currently very popular among analysts and researchers in this field is methods based on artificial intelligence, followed by methods aimed at reducing risk metrics. The aim of the current research is to form a portfolio using machine learning methods, risk measurement and its combination with fuzzy theory, which has a better return than the average return of the market. The output of each method is entered into the random forest algorithm and prediction is made by this algorithm, and in the last step, the prediction output is entered into the value-at-risk and value-at-risk optimization model with the fuzzy theory approach to form the capital portfolio. Shares information is daily and its time period is from the beginning of 2014 to the middle of 2018. At the end of each of these methods and steps, it was compared with the real return of the market. the CVAR risk measure has a better ability than the VAR risk measure, and the random forest algorithm among the used machine learning algorithms has achieved better results in choosing the investment portfolio.

In this research, based on the historical statistics of the deposits of a private bank (Ansar bank before merging) into four different categories (funds), we are looking for the results and prediction of bank deposits in each of the categories, using machine learning prediction methods for the first time in internal studies. In this forecasting method, the superiority of the machine learning method is confirmed due to the examination of all the time changes of the data in a large volume (from the beginning until now). Forecasting the above deposit statistics for bank decision makers in the future according to the economic conditions can be the basis of macro banking decisions. The results show that partly stable long-term deposit and stable deposit will take a downward trend in the future in the Ansar bank, and unstable and interest-sensitive deposit will also take a downward trend. Qarzol-ha-sa-ne deposit has an upward trend, although it has always fluctuations similar to sin-cos fluctuations. It should be noted that this machine learning prediction method is a stable method without sensitivity analysis and has high reliability. According to this research, paying attention to long-term and stable deposits as well as Qarzol-ha-sa-ne is of great importance in the bank’s budget.

One of the key priorities of the Ministry of Agriculture Jihad is the mapping of croplands to estimate crop acreage and annual yield. In recent decades, remote sensing technology has proven to be highly effective in estimating the extent of crop cultivation through the use of timely images and synchronized data with diverse spatial, temporal, and spectral resolutions, leveraging advanced machine-learning algorithms. This study presented a framework for crop mapping in Marvdasht, Fars Province, by utilizing time series of Landsat-8 satellite images and advanced machine-learning algorithms. The employed algorithms included Decision Tree (DT), Random Forest (RF), Rotation Forest (RoF), Support Vector Machine (SVM), and Dynamic Time Warping (DTW) analysis. The results indicated that the dynamic time warping and random forest methods outperformed others, achieving significantly higher accuracy (with an overall accuracy improvement of 10-12%) in generating the agricultural land-use map of the study area. Furthermore, this research demonstrated the effectiveness of Bands 2-5 of Landsat-8 satellite in confidently identifying all crops in this region using the mentioned methods.

Identifying and mapping crops provides important information to agricultural lands management and cultivation area estimation of crops. Optical and radar images are valuable resources for classifiying agricultural land. Features deriverd from optical images contain information about the reflectance signatures of various products, while radar images provides information about the structural characteristics and distribution mechanisms of products. The combination of these two sources can create a complementary dataset with a significant number of spectral, texture and polarized temporal features for the classification of agricultural land

This study aims to explore the significance of red edge bands for the segregation of crops such aswheat, barley, alfalfa, beans, broad beans, flax, corn, sugar beet and potatoes using the random forest method and support vector machine. To conduct the analysis, a time series of Sentinel-1 and 2 images 2019 in the northwest region of Ardabil was retrieved from the Google Earth Engine (GEE) platform. The study evaluates the effectiveness of spectral and temporal information, plant indices and backscatter information on the crop mapping by examining different combinations of bands. Through the random forest feature selection method, essential features are identified and utilized as inputs for both the random forest and support vector machine classifiers.

The random forest provided the most favorable outcomes across all scenarios. The results revealed that incorporating red edge wavelengths and red edge-based vegetation indices proved more beneficial than other bands and vegetation indices for differentiating between barley, beans, broad beans, and flax. The most optimal outcome among various feature combinations was associated with the time series of spectral features from Sentinel-2 images combined with the time series of Sentinel-1 images, resulting in an overall accuracy of 84.67% and a kappa coefficient of 82.31%.  Furthermore, the results demonstrated that red edge bands and red edge-based vegetation indices effectively distinguish between different types of crops

It is recommended to carefully consider the selection of specific spectral bands to achieve higher accuracy in separation of crops. It is important to highlight that combining radar and optical images consistently yields superior results compared to classification methods based on a single sensor, leading to increased classification information.

The aim of the current research is to provide a comprehensive model for providing a model for operational budgeting in fossil power plants based on machine learning and ground theory methods. In this research, the impact of the participatory approach in performance-based budgeting on accountability in the public sector was investigated. Budgeting on the basis of performance provides a basis for more accountability against the use of the organization's resources. In addition, issues such as environmental protection, high initial investment cost and optimal use of resources have a direct impact on the performance of the players in this market. Unfortunately, in the new competitive conditions, the traditional and classic models that assume the economic factors (producers or consumers) to be homogeneous and without interaction do not have the necessary efficiency and do not provide acceptable results. Operational investigation explains the behavior of heterogeneous production units and allows interaction and learning in a dynamic environment, but due to the breadth of the model and its complexities, an analytical solution cannot be used to obtain variables in equilibrium conditions. Due to the advancement of computing technology, the results of agent-based models can usually be evaluated with simulation methods in the context of different scenarios. Production units present their proposed production schedule for every hour to the operating entity. According to the consumption demand forecast for the next twenty-four hours and by implementing the auction mechanism, this institution determines the winning units and the market price, and then by implementing the settlement mechanism, sales and settlements are done. Considering the necessity of balance of consumption and production in the whole country as well as the geographical distribution of production and consumer units, in addition to the amount of consumption, economic restrictions and technical restrictions related to production, transmission and distribution are also considered in the auction mechanism. The findings of the research show that the existing infrastructure of performance-based budgeting does not affect financial accountability in the public sector, while it has a negative effect on operational accountability. The establishment of performance-based budgeting infrastructure and the use of a participatory approach in budgeting will increase the level of accountability of the government in the public sector.

The warming of the urban environment is one of the consequences of unsustainable growth. This research aims to investigate the possibility of modeling the effect of the structural parameters on the city’s surface temperature in the summer season in Tehran. For this purpose, the Landsat-8 image taken in 2018 was used to calculate the surface temperature. In order to determine the study units in this research, the segmentation method was used on the Sentinel-2 image of 2018, and the ratio of the vegetation cover and the separation of built-up areas from non-built-up ones were extracted using this image. The multi-layer perceptron neural network and the convolutional neural network methods were used to model the effect of urban structural parameters on the surface temperature during the summer. The results obtained from random forest feature selection  for the summer indicates that the presence of vegetation and urban uses that include residential and industrial areas, the presence of mixed residential/commercial/administrative areas, and the presence of vegetation affect changes in the urban surface temperature. Further, the information layers of road and population density in this season have an effect on the changing temperature of the earth's surface. Additionally, the results obtained through modeling and t-test of paired samples demonstrate the superiority of the convolutional neural network method, with a root mean square error of 0.61, determination coefficient of 0.62, and 17.75% estimation error, compared to the multi-layer perceptron model, which had 0.82 root mean square error, 0.26 determination coefficient, and 23.34% estimation error.

  As one of the most essential needs of living beings, clean air quality has been threatened by natural and human activities. In recent years, dust storms have been increasing spatially and temporally, causing numerous damages to social, economic, and environmental health for the residents of the southern and southwestern regions of Iran. In the present study, MODIS sensor data were used to investigate dust storms and detect horizontal optical depth.

The  advantages of MODIS sensor data include high spectral and temporal resolution. Additionally, meteorological station data were collected based on the study period. After preprocessing the data and preparing field observations, the necessary features for modeling were extracted using the differential method between selected bands of each MODIS sensor image, along with features extracted from ground-based meteorological station sensors. After further investigations and evaluations and using the viewpoints of meteorological experts, 36 differential features from various MODIS image bands and six features from ground-based meteorological station data, totaling 42 features, were extracted. Subsequently, using feature selection techniques, the best features were identified. A novel method named ML-Based GMDH, which improves the GMDH neural network by altering partial functions with machine learning models, was employed to detect dust concentration and horizontal optical depth. To achieve optimal accuracy, the hyper-parameters of this model were heuristically tuned using the TLBO optimization algorithm. Additionally, machine learning methods such as Basic GMDH, SVM, MLP, MLR, RF, and their ensemble models were implemented to compare with the main approach. According to the results, the TLBO-tuned ML-Based GMDH method provided superior accuracy in detecting dust concentration compared to the aforementioned machine-learning methods.

The SVM-PSO method was selected as the best method in the feature selection phase, the RF method was chosen as the best method among basic classification methods, and the Ensemble SVM and Ensemble RF methods were selected as the best methods in the ensemble and classification phase. It was also observed that using the ensemble approach led to a desirable improvement in horizontal optical depth classification. In the second approach, a method titled ML-Based GMDH, which improves the GMDH neural network by altering partial functions with machine learning algorithms, was used for estimating dust concentration. Additionally, to achieve suitable accuracy, the hyper-parameters of this model were finely tuned using the TLBO optimization algorithm. The results showed that this method provided appropriate accuracy in estimating dust concentration and horizontal optical depth, out performing the best-selected methods from the first approach

The digital world provides many opportunities for marketers to reach customers. However, in the fast-paced world, finding new and innovative ways to advertise and sell products and services is very important. Due to the advancement of artificial intelligence and its development in the field of advertising and sales, professionals now have the tools to completely redefine the current understanding of branding, marketing, advertising and sales. The growing popularity of the Internet and the increased use of mobile devices are generating massive amounts of consumer data that feed artificial intelligence-based systems. This research is a type of mixed research with a qualitative and quantitative approach, which is a survey descriptive study in terms of its purpose, application, and in terms of data collection. The statistical population of the research was managers and experts in the field of digital marketing and IT in the field of advertising and sales, who were selected using the snowball sampling method. In the qualitative part, the tools for collecting information were library and articles review, interviews, and in the quantitative part, questionnaires. In the qualitative part of the data analysis method, using the theme analysis that was compiled with MAXQDA software and using the coding method, and in the quantitative part, the analysis method was based on Kendall's correlation test. According to the results of the research, 7 main themes, 22 sub-themes and 44 codes were discovered, which included the consequences of using artificial intelligence and machine learning in advertising and sales

Machine learning is a subfield of artificial intelligence (AI). The goal of machine learning in general is to understand the structure of data and fit that data into models that people can understand and use. Today, every technology user has benefited from machine learning. Facial recognition technology allows social media platforms to help users tag and share photos of their friends. Optical Character Recognition (OCR) technology converts text images into animated type. Recommendation engines, using machine learning, suggest what movies or TV shows to watch next, based on user preferences. Self-driving cars that rely on machine learning to navigate may soon be available to consumers. Machine learning is a constantly developing field. For this reason, there are considerations to keep in mind when working with machine learning methods or analyzing the impact of machine learning processes. In this article, common machine learning methods of supervised and unsupervised learning and common algorithmic approaches in machine learning. including k-nearest neighbor algorithm, decision tree learning and deep learning. His findings showed. With the expansion of the scope and fields of study and the use of machine learning in different fields, depending on the changing needs and complexity of different problems, different types of machine learning algorithms have emerged, each of which has its own efficiency and applications. . So you can choose the algorithm that best suits your business needs. In some cases, experts use a combination of these algorithms because one algorithm alone may not be able to solve a particular problem.

 New technologies have profoundly changed the way people communicate and interact with their surroundings. These technologies affect every industry. Now, if according to the common definition, we define the supply chain as a set of interconnected activities that include coordination, planning and control of products and services between suppliers and customers. Looking at technological advances, we realize that these traditional structures are no longer self-sufficient, because digitalization has affected almost all aspects of human life, especially supply chain processes. The technologies of the fourth generation of industry represent the industrial revolution that has combined the Internet of Things with automatic systems such as artificial intelligence and its subset of machine learning, which are self-adjusting and self-learning. Such technological systems can change the supply chain from a centralized state to a distributed state, in fact, the deployment of these technologies provides distribution and decentralization for supply chains. The globalization of the economy and the increase in commercial competition have increased the importance of using innovative methods to achieve the goals of the supply chain. With the automation of processes, business activities have moved from manual operations to electronic transactions and all organizational processes have benefited from information and communication technologies. Considering that the design of most processes is at the disposal of centralized centers; There are always problems such as: poor efficiency, coordination at a low level and poor cooperation between the departments of a business unit, the emergence of distributed frameworks such as the blockchain platform and 4.0 generation technologies in addition to Organizations are helped in having complete transparency in transactions and cooperation with each other. They can share transactions on a peer-to-peer page. The purpose of research is to identify the components of the intelligent distributed supply chain and provide the structure of causal relationships for them, as well as the analysis of each of them in the framework of the presented structure. In the present research, codes and categories were first identified using the grounded method, and then the fuzzy cognitive mapping method was used to determine causal-effect relationships.In order to design the structure of the cause-effect relationships of the components of the intelligent distributed supply chain, it was extracted according to the fuzzy cognitive mapping method. Collective mapping was obtained by calculating the average of experts' opinions. According to its components, distributed decentralized supply chain can lead to improvement in information flows, promote healthcare and provide fair access to medical services. Also, assigning treatment priority to patients according to their physical condition for medical care, complying with the terms and conditions of the production line, reducing fraud, detecting authorized hazardous substances and removing drugs that have been licensed outside of the legal criteria can influence the company. High rents in the supply, prescription and treatment of the country to a great extent. The components of the correct implementation of the guidelines for hazardous drugs, the correct implementation of GMP rules, improvement in demand forecasting, correct and timely response of suppliers and suppliers, transparency and traceability, reduction in executive costs, Reducing the risk of implementing projects and carrying out contracts, behavioral data analysis algorithms, error and fraud detection algorithms, processes of identification, discovery, analysis, redesign, implementation and deployment, execution and monitoring, analysis of purchase plans and Procurement provides the possibility of tracking the information, political, monetary and back and forth flows of medicine, which represents its supply and demand among the manufacturer, government sector, patients, pharmacies, insurers, retailers of raw materials and importers. and reduces the possibility of fraud and corruption.Also, allocating treatment priority to patients according to their physical condition for medical care, complying with the terms and conditions of the production line, reducing fraud, detecting authorized hazardous substances and removing drugs that have been licensed outside of the legal criteria can influence the company. High rents in the supply, prescription and treatment of the country will be reduced to a great extent, which can be more effective for special and incurable patients, we know for certain that the required medicine for import and production, it is difficult and sometimes impossible to correctly identify and choose, with these created solutions, transparency is determined in whether the goal of the treatment program has been taken into account. Because we know that the distribution of special benefits is under the control of government officials and they define the priorities of subsidy allocation according to their decision-making power; This can be considered as one of the cases of corruption in the country's pharmaceutical industry. In the end, the improvement in the stock of raw materials and finished products, the non-issue of licenses for non-hazardous production lines in line with the production of hazardous drugs, the non-importation of domestic similar drugs, the elimination of middlemen and backdoors, the transparency of production costs. Transparency in contracts, transparency in the interests of the involved parties, transparency in payment to pharmaceutical companies, transparency in payment of pharmaceutical centers to drug dealers, transparency in pharmacy drug items, transparency in the risks after taking drugs, transparency in the way the budget is spent, The transparent and traceable payment system makes it clear whether the distribution and granting of licenses to natural or legal persons is done based on legal criteria and competently, while the government authority and decision-maker to avoid disclosure or The monopoly of the market cannot limit the transaction within the circle of friends and acquaintances, the benefit of political influence for the production of drugs and the distribution of imported drugs has been greatly reduced, the privileges have been removed from the circle of friends and acquaintances and in a competitive environment, they are given to companies. It will be real or legal that provide fair access to medical services.

The present study compared the predictive performance of machine-learning models and statistical models for forecasting profit and operational cash flow by using a combination of accrual and cash variables. The research method encompassed 3 main stages: data set and variable selection, modeling, and estimation. The study focused on companies listed on the Tehran Stock Exchange (TSE), analyzing data from 184 companies over the period of 2012-2021. The findings indicated that accrual variables exhibited greater explanatory power than cash variables in predicting net profit and future operating cash flow. Furthermore, the comparison of machine-learning and statistical models for forecasting net profit and future operating cash flow revealed that the artificial intelligence approach exhibited superior capability. Specifically, symbolic regression among the machine-learning models and the probit model among the statistical models demonstrated higher performance. Additionally, the results indicated that certain statistical models outperformed some machine-learning models while, on average, machine-learning models outperformed statistical models.

Predicting student performance has become an urgent demand in most educational and higher education institutions. This is essential to help at-risk students and ensure their retention, provide excellent learning resources and experiences, and improve the ranking and reputation of institutions. However, this may be difficult to achieve for start-up organizations with small records to analyze. The purpose of the current research was to provide approaches based on traditional machine learning and regression on predicting students' performance.

The current research was of the qualitative research type and applied in terms of purpose and experimental analytical research in terms of method. Linear regression, decision tree, random forest and support vector machine methods were used in this research. In this section, after introducing the implementation environment, simulation parameters were introduced. In the following, by introducing the efficiency evaluation criteria of the proposed method, based on the described evaluation criteria, the findings were compared with other similar methods. For these comparisons, deep learning approach based on deep convolutional network and other deep learning approaches are used. In this research, the data collection of Dr. Hasht Roudi boys' school, which is among the top 10 institutions in Tehran, was used. The data of this institution are publicly available and can be downloaded through the GitHub site. Further investigation has been done on these data. The figure below shows the frequency of features in the dataset. that these features are considered as and on models.

The correct storage and arrangement of products in the warehouse increase efficiency in responding to requests, accelerate the identification of products, increase accessibility of items in the warehouse, make more use of available space in the warehouse, reduce the possibility of product damage, and increase flexibility. The review of studies in the field of warehousing and arrangement of products in warehouses revealed that the use of machine learning algorithms in this field is one of the important research gaps. Therefore, in this article, using machine learning algorithms, we aim to present an innovative algorithm for allocating goods to different parts of a warehouse, for which a real case study is used. The goal of categorizing products based on their characteristics is to minimize the total cost of the system. Hence, spatial clustering algorithms based on the density of applications with noise (DBSCAN), self-organizing mapping neural network (SOM), and AGNES are used. The obtained results show that SOM has better performance than DBSCAN. Also, the DBSCAN algorithm performs better than AGNES.

The goal of any legal system is to develop social order and justice for all through solving the disputes of individual members of society. It seems that with the development of new technologies, reaching this goal can be pursued more quickly. The two leading legal systems in the world, the Roman-German legal system with the application of codified regulations and the common law system relying on precedents, are the source of inspiration for many countries' legal systems. In this research, with the look at the experiences of the Chinese judicial system in the use of artificial intelligence, which has been using the descriptive analytical research method and library tools, we are looking for an answer to the question of whether machine learning tool based on finding patterns in similar decisions is used, will the civil law system lose its traditional framework and in some way integrated into the common law system? It seems that the subordinates of the civil law system can achieve convergence with the common law system by maintaining their principles without losing the roots based on civil law.

The aim of this paper is to explore the ethical implications of artificial intelligence (AI) in the realm of art. Drawing on the philosophy and ethics of AI, this study examines the ethical challenges that may arise in the art domain. Given the increasing use and penetration of AI in the field of art, there is a need for more careful attention to the ethical dimensions of this phenomenon by scholars of art and philosophy. To achieve the objective of the paper, concepts such as AI, some of its techniques, and the ethical issues of AI in art are explained and analyzed using the analytical-descriptive method. The findings of the study reveal that ethical issues such as privacy and surveillance, manipulation of behavior, opacity and transparency, bias in decision systems, and automation and employment can be investigated in the field of art. These ethical issues are sometimes in conflict and sometimes in direct relation with each other. This study suggests that addressing the ethical questions of AI in the field of art requires addressing the ethical questions of AI in general.

Building Information Modeling (BIM) has revolutionized the construction industry by improving efficiency and simplifying building project methods. Integrating BIM with digital systems such as artificial intelligence (AI) removes barriers and makes the project life cycle more productive and beneficial. The benefits of BIM and AI go beyond 3D modeling and building plans. They manage and control the entire construction project life cycle from start to the end. The aim of the current research is to provide a comprehensive understanding of the process of AI-BIM integration, which has been carried out by various researchers around the world. To achieve this goal, 380 articles published in 2015-2021 have been systematically analyzed through Scopus reference database. This research presents a systematic review of qualitative research to identify the characteristics of BIM, AI, their integration and implementation in construction. It also provides future research trends and insights and emphasizes interoperability in BIM. On the other hand, it reinforces the need for future research to focus on the interoperability of artificial intelligence and other intelligent systems in BIM to foster integrated science based on digitalization and information and communication technology. Finally, it also highlights the extension of the findings during the life cycle of the building construction project. The research results show that the integration of artificial intelligence and BIM has the capacity to change the construction industry. Because it has the ability to significantly reduce errors, to save time and resources (human resources and construction materials), to increase productivity and to adapt the map based on the user's needs through controller modules, database and machine learning according to building regulations. This research also identifies some of the challenges hindering the integration of AI and BIM, such as the lack of interoperability standards, data privacy concerns, and insufficient training for professionals.

One of the most important security challenges in e-banking security centers is the inability of the internet to deal with attacks. These attacks are easily implemented and can be controlled locally or remotely. Most of these attacks are successful in reaching the main targets of the attack and bring the attacker to their desires. The reason for this is that there are many mechanisms for launching attacks based on the characteristics of the victim's server, which makes it impossible to provide a comprehensive defense solution against the attacks. Several strategies have been proposed to identify and deal with these attacks. In this paper, a combination of algorithm for selecting genetic features and machine learning methods, including decision tree algorithm, deep neural network and KNN, are presented. Provide guidelines for validation, the results obtained with other techniques such as machine learning techniques and combined with other optimization methods are compared and evaluated. In this research, 10% of KDD Cup 99 dataset for simulation has been used. First, in the preprocessing of data, the values of all attributes are converted to numbers, and the output characteristic values are changed to two values of zero and one. The results of the research indicate that the accuracy of the proposed strategy for detecting intruders compared to other recent methods is about 5%.

Recommender systems try to help users and customers in choosing options and products. This article is part of the results of a research project in which a recommender system has been developed that can offer suitable courses to the student by receiving his preferences and attributes. The required basic concepts, applicable methods and evaluation criteria are introduced and the efficiency of the proposed method is verified with the help of test and evaluation. The obtained results show that the course recommender system can guide trainees to suitable training courses with proper accuracy.

Ground surface ozone is one of the most dangerous pollutants that has significant harmful effects on the residents of urban areas. The purpose of this study is to identify the factors affecting ozone concentration and modeling its changes using satellite data and different machine learning methods in Tehran. For this purpose, pollutant concentration and meteorological data were used along with the satellite product of land surface temperature (LST) in the period from 2015 to 2021. After calculating the correlation between ozone concentration and independent parameters, ozone concentration modeling was done in five different modes in terms of input parameters and learning method and applying data refinement. In the first and second mode, modeling was done using pollutant concentration and meteorological data through multivariate linear regression method. The only difference between these two modes is the filtering of the input data using the WTEST method in the second mode. In the third mode, the LST product was added to the input data, and in the fourth and fifth mode, ozone modeling was done using multilayer neural network and recurrent neural network, respectively. The comparison of the five modes showed that the modeling of the first to fifth stages with adjusted coefficient of determination of 0.5, 0.64, 0.69, 0.74 and 0.8 were able to recover the ozone concentration, respectively. It was also found that among different pollutants, nitrogen monoxide, nitrogen dioxide and nitrox have the greatest impact on ozone concentration, just as temperature, humidity and wind speed are the most influential among meteorological data. Although the use of WTEST statistics led to the identification and elimination of inconsistencies and errors in the observations of pollution measurement stations, the neural network learning method showed better performance in modeling than multivariate regression due to its less sensitivity to noise. As a notable result, adding the LST product to the input data brought a 5% increase in accuracy in estimating ozone concentration.