Intelligent Data Analysis Methods Research Articles

Methodology for forming a digital farming management system

The paper presents methodological approaches for the creation of a digital farming management system (DFMS). A convergent approach, based on cognitive (conceptual) analysis methods, is employed in the research and applied to the formation of adaptive landscape farming systems. The fundamental principles of organizing DFMS include crop and environmental monitoring (in situ, remote sensing); the formation of farming system archetypes based on the analysis of long-term field experiments; spatial object modeling and land typology using GIS; planning and support for agrotechnologies to adapt to natural and economic conditions; modeling ecosystem services and biodiversity; assessing the impact on the sustainability and economics of crop production. The system is implemented using geoinformation models in a specific geographic coordinate. DFMS involves conducting a "inventory" of natural and production resources, as well as identifying limits of climatic, soil, and agrolandscape parameters at different levels of land use intensity. At each stage of organizing system blocks, methods of intelligent data analysis and machine learning are used, with the core of the system relying on the use of knowledge bases and logical rules of the subject area. A key element of the system is the scaling of the results of long-term field experiments and accumulated knowledge in different management areas based on the parameterization of the multi-level variability of farming systems and the formation of their archetypes. The practical implementation of the main provisions of DFMS allows approaching the solution of key issues related to reducing the level of uncertainty and associated risks in agriculture. This is achieved through scientifically justified organization of rational land use, increasing the resilience of crop production in different land use conditions, and providing information support to rural producers.

DATA MINING AND BUSINESS INTELLIGENCE IN MANAGING THE BUSINESS PROCESSES OF THE PROJECT OFFICE OF AN OUTSOURCING COMPANY

The article carries out a comprehensive study of the possibilities of applying modern business analytics and intelligent data analysis methods to optimize project management processes in the conditions of an outsourcing IT company. The work justifies the relevance of the problem associated with the avalanche-like growth of data volumes during the implementation of IT projects and the possibilities of innovative technologies for their intellectual analysis to identify hidden patterns. Scientific works of leading experts in project management and informatization have been critically analyzed, but a lack of comprehensive studies regarding data analysis specifically for project offices was noted. To achieve the research goal, a thorough analysis of the essence and possibilities of business analytics and intelligent data analysis was carried out, their role in managing modern organizations was examined in detail, and the scientific advantages of implementation in project offices of outsourcing companies were justified. The study of the practical use of business analytics and intelligent data analysis tools to solve the specific task of searching for new projects in the marketing activities of the IT outsourcing company was conducted. Effective methods of data collection, processing, and analysis concerning clients, market, competitors, communication channels, etc., were thoroughly analyzed. Thus, the article carries out a comprehensive study of the theoretical foundations and applied aspects of using advanced business analytics and intelligent data analysis tools with the aim of improving and optimizing the marketing activities of the modern project office of an outsourcing IT company by making timely well-founded managerial decisions based on deep analysis of available data sets.

Improvement of cereal harvest programming methods using computer simulation information technology

The paper is dedicated to a topical scientific and applied problem – the development of information technology of computer modelling intended for programming the yield of agricultural crops. The paper describes information technology of computer modelling of the yield of agricultural crops (on the example of Avena sativa subsp. nudisativa), which is based on the application of Bayesian methods to modelling and prediction in conditions of statistical, parametric and structural uncertainty. The study was based on the materials of laboratory experiments carried out in conditions close to natural, on the prediction of physiological processes occurring in plants under the influence of regulated and unregulated factors. Proposed approach described the change in the productivity of grain crops, in particular Avena sativa subsp. nudisativa, depending on the parameters of plant growth and development, photosynthetic apparatus and duration of its functioning. Scientific novelty of the work was application of probabilistic and statistical models in the form of Bayesian networks in the system of programming the yield of agricultural crops. The paper considered several scenarios of the combined effect of growth regulators and herbicides on the productivity of Avena sativa subsp. nudisativa. Net productivity of photosynthesis was chosen as the target variable of the studied process. Mathematical models in the form of Bayesian network turned out to be adequate for the process chosen for modelling. Achieved error of model classification was about 20%. The model structure was built in Genie 2.0 modelling system. It was found that by researching and simulating potential opportunities of ecological features of plants, it was possible to achieve an increase in yield by reducing the doses of herbicides and growth regulators by their combined use, which significantly increased the crop quality. Proposed information technology uses methods of intelligent data analysis, has a modular structure and can be used separately and as part of other information and analytical systems.

Тренды использования данных телеметрии систем водоснабжения

The article discusses the analysis of water consumption data obtained based on automated resource consumption accounting. The prerequisites for obtaining large arrays of telemetry data based on the introduction of "smart" housing and communal services technology have been identified. Typical purposes of water consumption data analysis are listed. It is noted that the use of artificial intelligence methods for analyzing housing and communal services data has a point character. The research is based on the principle of expediency of data analysis. The factors influencing the mode of water consumption in apartment buildings are considered. Methods of analyzing water consumption telemetry data are considered. The data of hourly automatic accounting of hot and cold water consumption in an apartment building is discussed as factual material for analysis. The data is a time series of water consumption accounting. Possible methods of statistical and intelligent analysis of telemetry data are discussed. It is concluded that the analysis of water consumption data should be carried out not only for the purpose of accrual and payment of payments, but also for the purpose of clarifying engineering calculations, adjusting regulatory indicators and making recommendations on resource conservation. Modern trends in the use of telemetry data are formulated: wider use of telemetry systems in apartment buildings, expansion of the purposes of using telemetry data analysis, the use of intelligent data analysis methods, the creation of a methodology for analyzing and evaluating telemetry data of water supply systems.

Partial Contribution of Socioeconomic Factors to the Mortality Rate of the Working-Age Population in Russia.

This study's relevance lies in the need to assess the role of socioeconomic, medical, and demographic factors on working-age population mortality in Russia. The purpose of this study is to substantiate the methodological tools for the assessment of the partial contribution of the most important factors that determine the dynamics of the mortality of the working-age population. Our hypothesis is that the factors determining the socioeconomic situation in the country affect the level and dynamics of mortality of the working-age population, but to a different extent in each separate period. To analyse the impact of the factors, we used official Rosstat data for the period from 2005 to 2021. We used the data that reflect the dynamics of socioeconomic and demographic indicators, including the dynamics of mortality of the working-age population in Russia as a whole and in its 85 regions. First, we selected 52 indicators of socioeconomic development and then grouped them into four factor blocks (working conditions, health care, life security, living standards). To reduce the level of statistical noise, we carried out a correlation analysis, which allowed us to narrow down the list to 15 key indicators with the strongest association with the mortality rate of the working-age population. The total period of 2005-2021 was divided into five segments of 3-4 years each, characterising the picture of the socioeconomic state of the country during the period under consideration. The socioeconomic approach used in the study made it possible to assess the extent to which the mortality rate was influenced by the indicators adopted for analysis. The results of this study show that over the whole period, life security (48%) and working conditions (29%) contributed most to the level and dynamics of mortality in the working-age population, while factors determining living standards and the state of the healthcare system accounted for much smaller shares (14% and 9%, respectively). The methodological apparatus of this study is based on the application of methods of machine learning and intelligent data analysis, which allowed us to identify the main factors and their share in the total influence on the mortality rate of the working-age population. The results of this study show the need to monitor the impact of socioeconomic factors on the dynamics and mortality rate of the working-age population in order to improve the effectiveness of social programme. When developing and adjusting government programmes to reduce mortality in the working-age population, the degree of influence of these factors should be taken into account.

МОДЕЛЮВАННЯ ЕКОНОМІЧНОГО РОСТУ МЕТОДАМИ ІНТЕЛЕКТУАЛЬНОГО АНАЛІЗУ ДАНИХ

The article substantiates the expediency of using intelligent data analysis in the study and forecasting of economic growth. Intelligent data analysis is a powerful tool that can help researchers and politicians understanding economic growth more deeply and making reasonable decisions by forming state policy. In addition, intelligent data analysis can be used to determine the potential areas of economic growth and investment opportunities. Attention is focused on using CART (predictive modeling tool, which has proved its high efficiency in understanding the non-obvious relationships between several variable statistical importance) and associative rules (intelligent data analysis, which can work with big data arrays and generate clear and effective conclusions, supported by statistical significance indicators). It is concluded that the methods of intelligent data analysis, such as CART and associative rules, are crucial for understanding the complex patterns that exist in large arrays of economic data. The advantages of these methods make them appropriate and effective tools for modeling and understanding of levers and tools of economic growth. Based on the analysis of data for 119 countries of the world for 2021 (the period of post-pandemic recovery), it was established that in order to promote economic growth, countries with low levels of export and import need to focus on the development of national industry. In addition, developing an economic policy aimed at promoting the recovery of the economy, it is necessary to increase the democratic status of the country. In general, countries that are less dependent on exports and imports tend to have more stable and self-sufficient economies. This can be explained by a number of factors, such as a strong domestic market, the development of national industry and less vulnerability to global economic shocks. In addition, a low level of imports may indicate that a country is able to produce and consume a significant portion of its own goods, which may contribute to further economic growth and development. It is important to note that these relationships are based on highlevel statistical correlations.

Review of Intelligent Data Analysis to Web-Development Applications

The essence of intelligent data analysis (data mining), methods of intelligent data analysis are considered. The field of application of intelligent data analysis and existing systems are analyzed. Conclusions were made regarding the prospects of using methods of intelligent data analysis in web development.

COMPREHENSIVE METHOD OF ENERGY-EFFICIENT WORKLOAD PROCESSING IN THE INFORMATION AND COMMUNICATION NETWORK

Background. Peculiarities of the workload in a modern information and communication network (ICN) determine specific requirements for energy efficiency, performance and availability of its processing system. Existing approaches to increase energy efficiency and performance of workload processing do not take into account the possibility of dynamic changes in ICN workload arrival rate and individual energy consumption characteristics of computing nodes of the system.
 Objective. The purpose of the paper is to increase the energy efficiency and performance of ICN workload processing while meeting the requirements for the availability of the processing system, taking into account dynamic changes in the input workload arrival rate and the individual characteristics of the computing nodes’ energy consumption.
 Methods. A mathematical model of the workload processing system was built using the queueing theory methods, and an ontological model of this system was built using intelligent data analysis methods, which made it possible to quantitatively and qualitatively describe the complex relationships between system parameters and workload processing efficiency indicators. On the basis of the built models, a comprehensive method of energy-efficient workload processing was proposed, which differs from the known ones by the use of individual energy consumption models of computing nodes, combining the advantages of horizontal scaling and energy-efficient scheduling taking into account dynamic changes in workload arrival rate.
 Results. The efficiency of ICN workload processing is increased by 15.722% according to the efficiency criterion, which includes energy efficiency and performance indicators, compared to the known energy-efficient Backfill approach while meeting the requirements for the availability of the processing system.
 Conclusions. The energy efficiency, performance and availability of the ICN workload processing system can be improved by combining horizontal scaling and energy-efficient scheduling approaches using individual energy consumption models of computing nodes and taking into account dynamic changes in the input workload arrival rate.

Scenario Modeling of Energy Policies for Sustainable Development

The article deals with the impacts of economic, ecological, and social development scenarios in ensuring sustainable energy development. EU countries were the statistical bases of the study; the assessment period was from 2000 to 2019. The information bases of the research were the World Bank, the Organization for Economic Cooperation and Development, and the European Commission data. Based on the generalized method of moments, the authors investigated the dependence of energy consumption on economic, environmental, and social development factors. The results confirm the positive relationship between renewable energy consumption and GDP per capita, foreign direct investment, and energy depletion. A negative relationship between the consumption of renewable energy, CO2 emissions, and domestic gas emissions was proved. Based on intelligent data analysis methods (methods involving one-dimensional branching CART and agglomeration), countries were clustered depending on the nature of the energy development policy; portraits of these clusters were formalized. The study results can be useful to authorized bodies when determining the most effective mechanisms for forming and implementing sustainable energy development policies.

Developing an Intelligent Data Analysis Approach for Marine Sediments.

(1) Background: As the chemical and physicochemical properties of marine sediments are closely related to natural and anthropogenic events, it is a real challenge to use their specific assessment as an indicator of environmental pollution discharges. (2) Methods: It is addressed in this study that collection with intelligent data analysis methods, such as cluster analysis, principal component analysis, and source apportionment modeling, are applied for the assessment of the quality of marine sediment and for the identification of the contribution of pollution sources to the formation of the total concentration of polluting species. A study of sediment samples was carried out on 174 samples from three different areas along the coast of the Varna Gulf, Bulgaria. This was performed to determine the effects of pollution. As chemical descriptors, 34 indicators (toxic metals, polyaromatic hydrocarbons, polychlorinated biphenyls, nutrient components, humidity, and ignition loss) were used. The major goal of the present study was to assess the sediment quality in three different areas along the Gulf of Varna, Bulgaria by the source apportionment method. (3) Results: There is a general pattern for identifying three types of pollution sources in each area of the coastline with varying degrees of variation between zone A (industrially impacted zones), zone B (recreational areas), and zone C (anthropogenic and industrial wastes). (4) Conclusions: The quantitative apportionment procedure made it possible to determine the contribution of each identified pollution source for each zone in forming the total pollutant concentrations.

Research on B2C Online Marketing Mode based on Multimodel Fusion and Intelligent Big Data Analysis Method.

B2C online marketing mode is the development trend of future marketing. The key to improve the efficiency of such a mode is to make efficient use of the current large-scale data and mining the corresponding potential value. To control the cost of B2C online marketing mode, this paper analyzes the UJRP model and proposes a hybrid bat difference algorithm (BADE). Experimental results are verified the effectiveness of the proposed method in diversity and cost control. Furthermore, we utilize multimodel fusion strategy (linear weighted fusion) to achieve better performances in B2C online marketing on cost than BADE method. Finally, in the random and diverse online marketing environment, such an improved method can provide the decision-makers of B2C online marketing mode with more flexible choices.

Identifying latent variables in Dynamic Bayesian Networks with bootstrapping applied to Type 2 Diabetes complication prediction

Predicting complications associated with complex disease is a challenging task given imbalanced and highly correlated disease complications along with unmeasured or latent factors. To analyse the complications associated with complex disease, this article attempts to deal with complex imbalanced clinical data, whilst determining the influence of latent variables within causal networks generated from the observation. This work proposes appropriate Intelligent Data Analysis methods for building Dynamic Bayesian networks with latent variables, applied to small-sized clinical data (a case of Type 2 Diabetes complications). First, it adopts a Time Series Bootstrapping approach to re-sample the rare complication class with a replacement with respect to the dynamics of disease progression. Then, a combination of the Induction Causation algorithm and Link Strength metric (which is called IC*LS approach) is applied on the bootstrapped data for incrementally identifying latent variables. The most highlighted contribution of this paper gained insight into the disease progression by interpreting the latent states (with respect to the associated distributions of complications). An exploration of inference methods along with confidence interval assessed the influences of these latent variables. The obtained results demonstrated an improvement in the prediction performance.

Decision Support System to Classify and Optimize the Energy Efficiency in Smart Buildings: A Data Analytics Approach.

In this paper, an intelligent data analysis method for modeling and optimizing energy efficiency in smart buildings through Data Analytics (DA) is proposed. The objective of this proposal is to provide a Decision Support System (DSS) able to support experts in quantifying and optimizing energy efficiency in smart buildings, as well as reveal insights that support the detection of anomalous behaviors in early stages. Firstly, historical data and Energy Efficiency Indicators (EEIs) of the building are analyzed to extract the knowledge from behavioral patterns of historical data of the building. Then, using this knowledge, a classification method to compare days with different features, seasons and other characteristics is proposed. The resulting clusters are further analyzed, inferring key features to predict and quantify energy efficiency on days with similar features but with potentially different behaviors. Finally, the results reveal some insights able to highlight inefficiencies and correlate anomalous behaviors with EE in the smart building. The approach proposed in this work was tested on the BlueNet building and also integrated with Eugene, a commercial EE tool for optimizing energy consumption in smart buildings.

A new data mining method for time series in visual analysis of regional economy

In order to improve the effect of visual analysis of regional economy, this paper uses machine learning algorithms to analyze time series data, uses various models and methods of intelligent data analysis to mine data laws from huge data, statistical data reports, and find problems in economic development. Moreover, this paper combines the time series algorithm to design and plan the functional structure of the system, and design a separate module structure from the actual situation of regional economic analysis, and build a model system from the overall structure. After constructing the system, this paper tests the system. From the results of the experimental research, we can see that the regional economic visualization system based on time series constructed in this paper has perfect system functions and can meet the needs of regional economic analysis.

ENSEMBLE MODELS FOR INTELLIGENT ANALYSIS OF SOCIOLOGICAL DATA

The paper investigates an approach to sociological information processing based on the use of intelligent data analysis methods applied to the task of processing the results of a questionnaire survey. The advantages of intelligent analysis of sociological data in comparison with traditional statistical processing are discussed, as well as the implementation features and applicability limits of various intelligent data analysis methods in solving problems of association, clustering and classification. Structure and features of representation of respondents’ survey data are considered, the appropriateness is substantiated and the advantages of their processing based on the combination of various methods of intelligent analysis within an ensemble of models are discussed. A structure of an ensemble of models is proposed based on the combination and joint use of association rules, clustering algorithms and decision trees, which makes it possible to jointly process numerical and categorical data contained in the respondent’s answers to the questionnaire and also to interpret the results of data clustering. The paper describes the results of using the constructed ensemble of models for processing and analyzing the data of a sociological survey conducted as part of the annual project for monitoring the drug abuse situation in the Bryansk region in 2013 – 2018. The use of an ensemble of intelligent data analysis models for processing the results of a sociological survey not only makes it possible to detect patterns in them that cannot be otherwise detected by traditional methods of statistical processing, but also contributes to an increase in the reliability, completeness and coherence of the analysis results, due to which the analyst creates a holistic systemic picture of the studied social phenomenon or process.

Construction and Simulation of Financial Audit Model Based on Convolutional Neural Network.

Big data has brought a new round of information revolution. Faced with the goal of full coverage of audit and supervision, making full use of big data is the main method to promote the realization of the goal of full coverage of audit and supervision. Data analysis and utilization is an indispensable task of auditing. Actively exploring multidimensional and intelligent data analysis methods and developing big data audit cases are the new development direction of auditing. The convolutional neural network's excellent ability to extract data features well meets the relevant requirements of financial auditing. However, in practical applications, convolutional neural networks often encounter various problems such as disappearance of gradients and difficulty in convergence, which reduces its expected performance in financial audit applications. In order to make the performance of the financial audit model based on convolutional neural network more excellent, after summarizing the characteristics of genetic algorithm, this article applies genetic algorithm to the optimization of the convolutional neural network model. We applied genetic algorithm to optimize the initial weights of the convolutional neural network. The error sensitivity and learning rate changes of different hidden layers are discussed, the influence of different learning rates on the convergence speed of convolutional neural networks is analyzed, and the recognition performance of other algorithms on financial audit data sets is simulated and compared. We conducted experiments on the network structure and parameter optimization on the financial audit database. The results show that the recognition error rate of the convolutional neural network model with improved learning rate algorithm in the financial audit data set is lower than that of the multilayer feedforward network, so it has better performance.

INTELLIGENT DATA ANALYSIS IN BIOMEDICAL RESEARCH: ARTIFICIAL NEURAL NETWORKS

Substantial amounts of biomedical data are being accumulated every year. Large datasets are accumulated in specialized repositories, electronic document management systems, medical information systems, and other repositories. Classical statistical analysis does not always provide opportunities for analysis of these large datasets; therefore, intelligent data analysis (IDA) is becoming more popular in biomedical research. This paper is an introduction to artificial neural networks - one of the most popular methods of IDA. An artificial neural network is an attempt to build a mathematical analog of the brain and mathematically simulate the transmission of a nerve impulse between neurons. We present an example of the application of artificial neural networks in medical research using SPSS and Statistica software packages. The article describes a medical research question, an example of a dataset and a guide on construction and training of an artificial neural network as well as interpretation of the results.

Kaplan-Meier plotter data analysis model in early prognosis of pancreatic cancer

Intelligent data analysis methods provide helpful tools for cancer researchers to detect the prognosis of patients with specific diseases. Yet, very little information is known about the features of these models used in data analysis methods. In this study, we presented a new Kaplan-Meier plotter model with a better-combination of input features for early prognosis tasks of pancreatic cancer. Our new model integrates gender, race, and follow up the threshold to get better verification of genes of interest as prognostic markers for predicting cancer at early stages. Assessment is made for the developed model to examine the important role of the oncogene RablA in early prediction of pancreatic cancer on the standard clinical datasets from The Human Protein Atlas. Our results showed that overexpression of the oncogene Rab1A in pancreatic cancer plays a vital role in its early prognosis (p<0.05). The proposed model results were also verified using an independent dataset deposited in The Human Protein Atlas. Altogether, the experimental results highlight Rab1A potential role in cancer prognosis.

Deep Learning in the Industrial Internet of Things: Potentials, Challenges, and Emerging Applications

Recent advances in the Internet of Things (IoT) are giving rise to a proliferation of interconnected devices, allowing the use of various smart applications. The enormous number of IoT devices generates a large volume of data that requires further intelligent data analysis and processing methods such as deep learning (DL). Notably, DL algorithms, when applied to the Industrial IoT (IIoT), can provide various new applications, such as smart assembling, smart manufacturing, efficient networking, and accident detection and prevention. Motivated by these numerous applications, in this article, we present the key potentials of DL in IIoT. First, we review various DL techniques, including convolutional neural networks, autoencoders, and recurrent neural networks, as well as their use in different industries. We then outline a variety of DL use cases for IIoT systems, including smart manufacturing, smart metering, and smart agriculture. We delineate several research challenges with the effective design and appropriate implementation of DL-IIoT. Finally, we present several future research directions to inspire and motivate further research in this area.

Modeling the volatility of financial processes

Tasks of risk management meet in all branches of practical activity of the person. Especially topical problems of mathematical modeling, estimation and forecasting of risks (which are characterized by the level of possible losses and their probability) is for the banking sector, insurance, investment companies. manufacturing enterprises operating in conditions of fierce competition and changing conditions, and for other types of activity. For the mathematical description of possible losses today there is a set of ideologically different approaches, which are based on classical statistical methods and methods of intelligent data analysis. Thus, to evaluate market and some other types of risks, various variants of the Value-at-Risk (VaR) technique are used, which makes it possible to obtain acceptable quality results for practical use.In the assessment of credit risks found the use of nonlinear models of classification type on the basis of logistical regression, linear regression, method of reference vectors (MOV), discriminatory analysis, fuzzy logic, neuro-fuzzy models, methods of Bayesian data analysis and decision tree, as well as combinations of these methods. To assess financial risks in insurance, the above-mentioned methods are used, as well as the theory of distributions of random variables, generalized linear models, regression analysis (linear and nonlinear models), Bayesian networks and other models and methods. The created computer system makes it possible to estimate the value of possible losses of VaR according to the methods of historical modeling and Monte Carlo. To solve this problem, you must first obtain estimates of the volatility forecasts of the relevant financial heteroscedatic processes. To calculate estimates of volatility forecasts, a model of generalized auto-aggression with conditional heteroscedasticity (UARUG or GARCH) is often used.