Big Data - Science topic

Hey everyone,

I'm writing my master thesis on the impact of artificial intelligence on business productivity.

This study is mainly aimed at those of you who develop AI or use these technologies in your professional environment.

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Advanced analytical systems, including complex forecasting models that enable multi-criteria, highly sophisticated, big data and information processing-based forecasts of the development of multi-faceted climatic, natural, social, economic and other processes are increasingly based on new Industry 4.0/5.0 technologies, including Big Data Analytics and machine learning, deep learning and generative artificial intelligence. The use of generative artificial intelligence technologies enables the application of complex data processing algorithms according to precisely defined assumptions and human-defined factors. The use of computerized, integrated business intelligence information systems allows real-time analysis on the basis of continuously updated data provided and the generation of reports, reports, expert opinions in accordance with the defined formulas for such studies. The use of digital twin technology allows computers to build simulations of complex, multi-faceted, prognosticated processes in accordance with defined scenarios of the potential possibility of these processes occurring in the future. In this regard, it is also important to determine the probability of occurrence in the future of several different defined and characterized scenarios of developments, specific processes, phenomena, etc. In this regard, Business Intelligence analytics should also make it possible to precisely determine the level of probability of the occurrence of a certain phenomenon, the operation of a process, the appearance of described effects, including those classified as opportunities and threats to the future development of the situation. Besides, Business Intelligence analytics should enable precise quantitative estimation of the scale of influence of positive and negative effects of the operation of certain processes, as well as factors acting on these processes and determinants conditioning the realization of certain scenarios of situation development. Cloud computing makes it possible, on the one hand, to update the database with new data and information from various institutions, think tanks, research institutes, companies and enterprises operating within a selected sector or industry of the economy, and, on the other hand, to enable simultaneous use of a database updated in this way by many beneficiaries, many business entities and/or, for example, also by many Internet users in a situation where the said database would be made available on the Internet. In a situation where Internet of Things technology is applied, it would be possible to access the said database from the level of various types of devices equipped with Internet access. The application of Blockchain technology makes it possible to increase the scale of cybersecurity of the transfer of data sent to the database and Big Data information as part of the updating of the collected data and as part of the use of the analytical system thus built by external entities. The use of machine learning and/or deep learning technologies in conjunction with artificial neural networks makes it possible to train an AI-based system to perform multi-criteria analysis, build multi-criteria simulation models, etc. in the way a human would. In order for such complex analytical systems that process large amounts of data and information to work efficiently it is a good solution to use state-of-the-art super quantum computers characterized by high computing power to process huge amounts of data in a short time. A center for multi-criteria analysis of large data sets built in this way can occupy quite a large floor space equipped with many servers. Due to the necessary cooling and ventilation system and security considerations, this kind of server room can be built underground. while due to the large amounts of electricity absorbed by this kind of big data analytics center, it is a good solution to build a power plant nearby to supply power to the said data center. If this kind of data analytics center is to be described as sustainable, in line with the trends of sustainable development and green transformation of the economy, so the power plant powering the data analytics center should generate electricity from renewable energy sources, e.g. from photovoltaic panels, windmills and/or other renewable and emission-free energy sources of such a situation, i.e., when a data analytics center that processes multi-criteria Big Data and Big Data Analytics information is powered by renewable and emission-free energy sources then it can be described as sustainable, pro-climate, pro-environment, green, etc. Besides, when the Big Data Analytics analytics center is equipped with advanced generative artificial intelligence technology and is powered by renewable and emission-free energy sources then the AI technology used can also be described as sustainable, pro-climate, pro-environment, green, etc. On the other hand, the Big Data Analytics center can be used to conduct multi-criteria analysis and build multi-faceted simulations of complex climatic, natural, economic, social processes, etc. with the aim of, for example. to develop scenarios of future development of processes observed up to now, to create simulations of continuation in the future of diagnosed historical trends, to develop different variants of scenarios of situation development according to the occurrence of certain determinants, to determine the probability of occurrence of said determinants, to estimate the scale of influence of external factors, the scale of potential materialization of certain categories of risk, the possibility of the occurrence of certain opportunities and threats, estimation of the level of probability of materialization of the various variants of scenarios, in which the potential continuation of the diagnosed trends was characterized for the processes under study, including the processes of sustainable development, green transformation of the economy, implementation of sustainable development goals, etc. Accordingly, the data analytical center built in this way can, on the one hand, be described as sustainable, since it is powered by renewable and emission-free energy sources. In addition to this, the data analytical center can also be helpful in building simulations of complex multi-criteria processes, including the continuation of certain trends of determinants influencing the said processes and the factors co-creating them, which concern the potential development of sustainable processes, e.g. economic, i.e. concerning sustainable economic development. Therefore, the data analytical center built in this way can be helpful, for example, in developing a complex, multifactor simulation of the progressive global warming process in subsequent years, the occurrence in the future of the negative effects of the deepening scale of climate change, the negative impact of these processes on the economy, but also to forecast and develop simulations of the future process of carrying out a pro-environmental and pro-climate transformation of the classic growth, brown, linear economy of excess to a sustainable, green, zero-carbon zero-growth and closed-loop economy. So, the sustainable data analytical center built in this way will be able to be defined as sustainable due to the supply of renewable and zero-carbon energy sources, but will also be helpful in developing simulations of future processes of green transformation of the economy carried out according to certain assumptions, defined determinants, estimated probability of occurrence of certain impact factors and conditions, etc. orz estimating costs, gains and losses, opportunities and threats, identifying risk factors, particular categories of risks and estimating the feasibility of the defined scenarios of the green transformation of the economy planned to be implemented. In this way, a sustainable data analytical center can also be of great help in the smooth and rapid implementation of the green transformation of the economy.

Kluczowe kwestie dotyczące problematyki zielonej transformacji gospodarki opisałem w poniższym artykule:

IMPLEMENTATION OF THE PRINCIPLES OF SUSTAINABLE ECONOMY DEVELOPMENT AS A KEY ELEMENT OF THE PRO-ECOLOGICAL TRANSFORMATION OF THE ECONOMY TOWARDS GREEN ECONOMY AND CIRCULAR ECONOMY

Zastosowania technologii Big Data w analizie sentymentu, analityce biznesowej i zarządzaniu ryzykiem opisałem w artykule mego współautorstwa:

APPLICATION OF DATA BASE SYSTEMS BIG DATA AND BUSINESS INTELLIGENCE SOFTWARE IN INTEGRATED RISK MANAGEMENT IN ORGANIZATION

I have described the key issues of opportunities and threats to the development of artificial intelligence technology in my article below:

OPPORTUNITIES AND THREATS TO THE DEVELOPMENT OF ARTIFICIAL INTELLIGENCE APPLICATIONS AND THE NEED FOR NORMATIVE REGULATION OF THIS DEVELOPMENT

In view of the above, I address the following question to the esteemed community of scientists and researchers:

What do you think about this topic?

What is your opinion on this issue?

Please answer,

I invite everyone to join the discussion,

Thank you very much,

Best wishes,

Dariusz Prokopowicz

Production logistics management in a manufacturing company is currently one of the key areas of business management that significantly affects the level of technical and organizational efficiency of business operations. The change in the level of technical and organizational efficiency of business operations also usually has a significant impact and correlates with the issue of business efficiency and affects the financial results generated in the business entity. Among the key segments of logistics in the enterprise are also internal production logistics, on the way of organization of which the efficiency of the operation of production processes and the efficiency of the enterprise also largely depends. In recent years, more and more companies and enterprises have been optimizing production logistics through the implementation of information systems and automation of individual operations in the process. Production logistics is mainly concerned with ensuring the optimal flow of materials and information in the process of producing all types of goods. Production logistics does not deal with the technology of production processes, but only with the organization of the production system together with the storage and transport environment. Production logistics is mainly concerned with the optimization of all operations related to the production process, such as: supplying the plant with raw materials, semi-finished products and components necessary for production; transporting items between successive stages of production; and transferring the finished product to disposal warehouses. Precisely defining optimal production logistics is a lengthy process, requiring analysis and modification of almost every process taking place in a company. One of the key factors in the optimization of production logistics is the reduction of inventory levels and their adjustment to the ongoing production process. This translates directly into a decrease in storage costs. Effective management of production logistics should ensure timely delivery, while maintaining high product quality. Effective production logistics management can be supported by the implementation of new Industry 4.0/5.0 technologies, including Big Data and generative artificial intelligence.

The key issues of opportunities and threats to the development of artificial intelligence technology are described in my article below:

In view of the above, I address the following question to the esteemed community of scientists and researchers:

What do you think about this topic?

What is your opinion on this issue?

Please answer,

I invite everyone to join the discussion,

Thank you very much,

Best wishes,

Dariusz Prokopowicz

In recent years, there have been an increasing number of examples of situations of deliberate practices in which companies operating social media portals consciously shape the general social awareness of citizens, Internet users through specific information policies applied. The Senate Committees of Inquiry at the U.S. Capitol, which have been taking place for several years, address, among other things, the issue of verifying the use of, for example, algorithms on Facebook platforms that promote certain content, including not only socially positive content, but also socially negative content. The aforementioned algorithms are then changed so that the scale of social negativity is reduced. However, recently there have been an increasing number of similar socially negative cases of algorithms promoting specific political content, e.g. promoting content typical of right-wing political options and limiting the spread of certain social media sites typical of left-wing political content. Thus, these are situations of intentional discrimination against a part of the community of citizens holding certain political views, which the owners of certain companies operating social media portals have deemed to be contrary to the information policy applied in their social media and/or the specific ideology promoted in these media. This type of activity does not correlate with the issue of freedom of speech, unrestricted development of the information society, democracy.

Recently, companies running social media sites have been improving the aforementioned media through the implementation of new Industry 4.0/5.0 technologies, including Big Data Analytics and generative artificial intelligence. The aforementioned technologies can also be used to technically improve the algorithms that control and promote selected content typed and passed on by Internet users, users of the aforementioned online media, which is an important part of shaping information policy in these media.

I have described the issues of the role of information, information security, including business information transferred through social media, and the application of Industry 4.0/5.0 technologies to improve data and information transfer and processing systems in social media in the following articles:

In view of the above, I address the following question to the esteemed community of scientists and researchers:

What do you think about this topic?

What is your opinion on this issue?

Please answer,

I invite everyone to join the discussion,

Thank you very much,

Best regards,

Dariusz Prokopowicz

In recent years, the scale of application of ICT and Industry 4.0/5.0, including Big Data Analytics and Artificial Intelligence technologies in financial institutions, including commercial banks, has been increasing. The banking sector is among those sectors of the economy where the implementation of new information technologies used to build banking information systems is progressing rapidly. This process in highly developed countries has been taking place since the 1960s. Subsequently, the development of computer science, personal computer technology in the 1970s and 1980s, the development of the Internet and business applications of Internet technology since the 1990s and then the development of technologies typical of Industry 4.0/5.0 set the trends of technological progress, the effects of which in the form of new technological solutions quickly found applications in financial institutions. Commercial banks operating in the model of classic deposit-credit banking usually generate the largest part of their revenues from the sale of bank loans and credits. Large universal banks also develop selected elements of investment banking, in which they finance the construction of housing estates through their own development companies, make financial transactions with securities, financial transactions in foreign exchange markets and other capital markets. In all these areas of activity, the key categories of banking risk that banks manage include credit and interest rate risk and other financial risks, i.e. liquidity risk, debt risk. In addition, the key categories of risk that the bank manages in its banking operations include asset-liability mismatch risk in the balance sheet and various categories of operational risks related to the performance of certain activities at the bank, including personnel operational risk related to the staff employed, technical operational risk related to the technical equipment used, system operational risk related to the IT systems used, etc. On the other hand, risks operating in the bank's environment and affecting the bank's operations and indirectly also the bank's financial performance include market risk of changes in the prices of specific assortments relating to specific markets in which banks operate; foreign exchange risk associated with transactions made using different currencies; investment risk within investment banking; systemic risk associated with the functioning of the financial system; political risk associated with the government's economic policy; risks of high volatility of macroeconomic development of the economy associated with changes in the economy's economic situation in the context of business cycles realized on a multi-year scale, etc. However, in a situation where lending activities are the main types of sources of income for a commercial bank then a particularly important category of banking risk that the bank manages is credit risk. On the other hand, due to the rapid development of electronic, Internet and mobile banking, cyber risk management is also growing in importance. New ICT information technologies and Industry 4.0/5.0, including Big Data Analytics and Artificial Intelligence technologies, can be increasingly helpful in managing each of the aforementioned risk categories. The aforementioned new technologies prove to be particularly helpful in the situation of their effective implementation into banking activities in order to improve the processes of managing, among other things, credit risk. An important element of individual credit risk management, i.e. with regard to individual credit transactions, are the methodologies, procedures, processes, etc. concerning the analysis of a potential borrower's creditworthiness and credit risk arising from a bank loan carried out in commercial banks. In view of the above, the implementation of new technologies to support the implementation of the processes of examining the creditworthiness of potential borrowers and improving, among other things, credit scoring analytics, are particularly important aspects of credit risk management, which may translate into increased profitability of commercial banks' bank lending activities.

I described selected issues of improving credit risk management processes, including the issue of screening the creditworthiness of potential borrowers and credit scoring analytics, in an article of my co-authorship:

Determinants of credit risk management in the context of the development of the derivatives market and the cyclical conjuncture economic processes

IMPROVING MANAGING THE CREDIT RISK IN CONDITIONS SLOWING ECONOMIC GROWTH

THE IMPLEMENTATION OF AN INTEGRATED CREDIT RISK MANAGEMENT IN OPERATING IN POLAND COMMERCIAL BANKS

Importance and implementation of improvement process of prudential instruments in commercial banks on the background of anti-crisis socio-economic policy in Poland

GLOBALIZATIONAL AND NORMATIVE DETERMINANTS OF THE IMPROVEMENT OF THE BANKING CREDIT RISK MANAGEMENT IN POLAND

In view of the above, I address the following question to the esteemed community of scientists and researchers:

What do you think about this topic?

What is your opinion on this issue?

Please answer,

I invite everyone to join the discussion,

Thank you very much,

Best regards,

Dariusz Prokopowicz

In my opinion, I could say:

An important attribute of a modern agglomeration developed in accordance with the green smart city model is also the consideration of low greenhouse gas emissions, minimization of energy and resource consumption, minimization of the agglomeration's negative impact on the environment, on the surrounding biosphere and climate. Accordingly, agglomerations developed according to the green smart city should also become increasingly low-emission, energy-intensive and more neutral to the planet's biosphere and climate.

Urban development taking place according to the green smart city concept should also take into account the sustainable economic development of the urban agglomeration, which takes into account the principles of green economic transformation, the circular economy, reducing greenhouse gas emissions and maximizing the issue of self-sufficiency in energy and food production. In terms of achieving energy self-sufficiency, the development of renewable and emission-free energy sources can be helpful. Reducing greenhouse gas emissions can be achieved not only through the development of renewable and zero-emission energy sources, but also by insulating the facades of existing buildings to reduce energy intensity, and through the development of means of transportation within the framework of public urban transportation, taking into account electromobility and hydrogen-powered transportation. On the other hand, achieving self-sufficiency in food production can be helped by vertical vegetable and fruit crops created in abandoned office buildings and factory halls, as well as in new multi-story buildings created specifically for this purpose.

In addition to this, an important issue in achieving zero-carbon cities is to take into account the carbon footprint of not only the built housing blocks, office buildings, market halls, shopping centers, factory halls, public administration and public service buildings, etc., but also the greenhouse gas emissions emitted during the construction of the said housing developments, buildings, office buildings, halls, etc. As a result, there will probably be more than once issues to be resolved regarding the choice of less-emitting options, in which one will have to choose either to revitalize, insulate the exterior façade, install photovoltaic panels, etc. on existing buildings, or rather to demolish them and build new ones in their place, in which more modern construction, energy and material technologies, etc. will be used, ensuring greater durability, lower emissions and energy efficiency or even zero-energy efficiency of the new buildings.

In order to reduce the negative impact of urban agglomeration on the biosphere, it is essential to build wastewater treatment plants for rivers leaving the cities, and to establish water purification systems for ponds, lakes and other water bodies located in the city area or its suburbs. In order to reduce emissions, improve air quality, increase humidity and reduce air temperature, it is essential to create additional urban parks and other green areas. In the situation of limited space that can be allocated for the creation of additional green areas, urban parks, flower meadows, etc., an optional solution is the creation of many so-called pocket parks, i.e. parks and other green areas occupying small areas, which were created after removing parts of previously too extensive created concrete sidewalks, asphalt roadways, paved with concrete cubes squares, etc.

In view of the above, sustainable, modern urban agglomerations developed according to the green smart city model with the application of Industry 4.0 technologies, including Big Data and artificial intelligence technologies, should also be developed in accordance with the concept of sustainable economic development, the principles of green economy transformation, circular economy, reduction of greenhouse gas emissions, development of urban parks and other green areas, self-sufficiency in energy and food production, environmental and climate social responsibility, etc.

Key aspects of the negative effects of the progressive process of global warming and the associated necessary acceleration of the processes of green transformation of the economy in order to decarbonize the economy, slow down the process of global warming, protect the climate, biosphere and biodiversity of the planet's natural ecosystems I described in the article:

I described the applications of Big Data technologies in sentiment analysis, business analytics and risk management in my co-authored article:

I described the key issues of opportunities and threats to the development of artificial intelligence technology in my article below:

In view of the above, I address the following question to the esteemed community of scientists and researchers:

What do you think about this topic?

What is your opinion on this issue?

Please answer,

I invite everyone to join the discussion,

Thank you very much,

Best wishes,

Dariusz Prokopowicz

Due to the negative impact of human civilization on the planet's biosphere and climate, the sixth great extinction of many species of flora and fauna is currently taking place on planet Earth. The result is a rapidly progressive decline in the biodiversity of the planet's natural ecosystems. The aim is to halt or at least significantly slow down the progressive climate change, i.e. caused by increasing greenhouse gas emissions of the increasingly rapid global warming process. Many species of flora and fauna have already become extinct, many are on the verge of extinction due to a sharp decline in the number of individuals of the species. Some animal species are already found exclusively or almost exclusively in zoos. Few animal species have managed to be reconstructed on the basis of deliberate cross-breeding of species and varieties of closely related species. In order to increase the scale of the possibility of reproducing extinct species in the future, gene banks are being created, in which the genomes of specific species of flora and fauna are stored, including, above all, species threatened with extinction in the near future. Over the past few decades, insect populations, including pollinators, have declined by tens of percent in many parts of the world. The continuation of these processes, in addition to the rapidly advancing process of decline in biodiversity, the impoverishment of the planet's biosphere, will cause a rapidly advancing process of decline in the production of agricultural crops under the intensive, unsustainable and productive model of human farming. In countries occurring in the tropics, natural tropical rainforests continue to be cut down only to create new areas of arable land for the creation of arable fields where crops are grown to feed livestock generating high methane emissions. In addition, the new arable land created in this way is quickly eroded and the restoration of forest ecosystems back in these areas is also hindered for this reason. Coral reefs in the seas and oceans are also dying out, and the populations of marine fish and other animal species living in the seas and oceans are rapidly declining. Key factors in the still large-scale great sixth extinction of species of flora and fauna include continued deforestation, the cutting down of natural forest ecosystems to increase cultivated areas, the degradation of natural ecosystems caused by industrial development, continued high levels of environmental pollution, continued rising greenhouse gas emissions generating a progressive global warming process, etc. In view of the above, unsustainable intensive economy is causing a rapidly progressive decline in the biodiversity of the planet's natural ecosystems, and attempts are being made to counteract this. In order to slow down the progressive process of global warming, reduce greenhouse gas emissions, reduce the level of environmental pollution and devastation of natural ecosystems, reduce the scale of extinction of species of flora and fauna, slow down the process of decline in the biodiversity of natural ecosystems, it is necessary to urgently accelerate the processes of carrying out pro-environmental transformation of the economy in order to efficiently build a sustainable, green, zero-carbon zero-growth and closed-cycle economy. In the process of building a sustainable, green, zero-carbon zero-growth economy and a closed loop economy, new ICT information technologies and Industry 4.0/5.0 can help, including generative artificial intelligence technologies and multi-criteria processing of large data sets as part of ongoing research and analytical processes using Big Data Analytics. New technologies can help increase the efficiency and accelerate the process of a sustainable, green, zero-carbon, zero-growth and closed-loop economy and thus help protect the climate, biosphere and biodiversity of the planet's natural ecosystems. As part of the application of new Industry 4.0/5.0 technologies, including generative artificial intelligence and Big Data Analytics, it is possible to increase the efficiency of research conducted with the goal of reconstructing the genomes of extinct species of flora and/or fauna. In this regard, perhaps artificial intelligence combined with Big Data Analytics can help reconstruct the genomes of various species of flora and/or fauna that have recently become extinct due to humans and thus restore the former biodiversity of the planet's natural ecosystems.

I am conducting research in the issue of key determinants of the legitimacy of urgently carrying out a green transformation of the economy in order to build a sustainable, green circular economy. I have included the conclusions of my research in the following article:

IMPLEMENTATION OF THE PRINCIPLES OF SUSTAINABLE ECONOMY DEVELOPMENT AS A KEY ELEMENT OF THE PRO-ECOLOGICAL TRANSFORMATION OF THE ECONOMY TOWARDS GREEN ECONOMY AND CIRCULAR ECONOMY

I described the key issues of opportunities and threats to the development of artificial intelligence technology in my article below:

OPPORTUNITIES AND THREATS TO THE DEVELOPMENT OF ARTIFICIAL INTELLIGENCE APPLICATIONS AND THE NEED FOR NORMATIVE REGULATION OF THIS DEVELOPMENT

I described the applications of Big Data technologies in sentiment analysis, business analytics and risk management in my co-authored article:

APPLICATION OF DATA BASE SYSTEMS BIG DATA AND BUSINESS INTELLIGENCE SOFTWARE IN INTEGRATED RISK MANAGEMENT IN ORGANIZATION

In view of the above, I address the following question to the esteemed community of scientists and researchers:

What do you think about this topic?

What is your opinion on this issue?

Please answer,

I invite everyone to join the discussion,

Thank you very much,

Best wishes,

Dariusz Prokopowicz

Disinformation is currently the most frequently cited problem occurring in social media from which children and young people gain knowledge. Companies engage advertising companies that specialize in running online advertising campaigns, in which advertising spots, videos and banners informing people about promotional offers for products and services sold are posted on social media. The aforementioned online social media are also viewed by children and teenagers. For some of these social media, the primary audiences for profiled information and marketing messages are mainly school-aged youth. Children and adolescents are particularly susceptible to the influence of information transferred through the aforementioned online media. Advertisements are thematically profiled to correlate with issues that are in the field of the main interests of children and adolescents. Unfortunately, many offers of various products and services promoted through online advertising campaigns are not suitable for children and adolescents and/or generate a lot of negative effects. Nowadays, applications based on generative artificial intelligence technology, intelligent chatbots, are increasingly used to generate banners, graphics, photos, videos, animations, advertising spots. With the help of these tools, which are available on the Internet, it is possible to create a photo, graphic or video on the basis of a written command, i.e. a kind of digitally generated works of such high graphic quality that it is very difficult to determine whether they are, for example, authentic photos taken with a camera or smartphone or are supposedly photos generated by an intelligent chatbot. It is especially difficult to resolve this kind of issue for children and young people who view these kinds of artificial intelligence technology-generated "works" used in banners or advertising videos. It is necessary, therefore, that education should develop in children the ability to think critically, to ask questions, to question the veracity of the content of advertisements, not to accept uncritically everything found in online social media. It is essential to add the issue of learning critical thinking to the process of educating children and young people. The goal of such education should be, among other things, to develop in children and young people the ability to identify disinformation, including the increasingly common factoids, deepfakes, etc. in online social media. In connection with the fact that in the creation of disinformation occurring mainly in the aforementioned social media are involved applications based on artificial intelligence, so children and adolescents should, within the framework of education, learn about the applications available on the Internet based on generative artificial intelligence technology, through which it is possible to generate texts, graphics, photos, drawings, animations and videos in a partially automated manner according to a given verbal command. This is how the applications available on the Internet based on the new technologies of Industry 4.0/5.0, including generative artificial intelligence and Big Data technologies, should be used to help develop critical thinking and a kind of resistance to misinformation in young people. During school lessons, students should learn about the capabilities of AI-based applications available on the Internet and use them creatively to develop critical thinking skills. In this way, it is possible to reduce disinformation directed through online social media towards children and young people.

I described the key issues of opportunities and threats to the development of artificial intelligence technology in my article below:

OPPORTUNITIES AND THREATS TO THE DEVELOPMENT OF ARTIFICIAL INTELLIGENCE APPLICATIONS AND THE NEED FOR NORMATIVE REGULATION OF THIS DEVELOPMENT

I described the applications of Big Data technologies in sentiment analysis, business analytics and risk management in my co-authored article:

APPLICATION OF DATA BASE SYSTEMS BIG DATA AND BUSINESS INTELLIGENCE SOFTWARE IN INTEGRATED RISK MANAGEMENT IN ORGANIZATION

In view of the above, I address the following question to the esteemed community of scientists and researchers:

And what is your opinion about it?

What is your opinion on this issue?

Please answer,

I invite everyone to join the discussion,

Thank you very much,

Best regards,

Dariusz Prokopowicz

In order to reduce the growing scale of disinformation, including disinformation generated in social media through in the increasing scale of emerging fakenews, deepfakes, disinformation generated through the use of applications available on the Internet based on generative artificial intelligence technology, the just mentioned GAI technology can be used. Constantly improved, taught to carry out new types of activities, tasks and commands, intelligent chatbots and other applications based on generative artificial intelligence technology can be applied to identify instances of disinformation spread primarily in online social media. The aforementioned disinformation is particularly dangerous for children and adolescents, it can significantly affect the world view of the general public's awareness of certain issues, it can affect the formation of development trends of certain social processes, it can affect the results of parliamentary and presidential elections, it can also affect the level of sales of certain types of products and services, and so on. In the absence of a developed institutional system of media control institutions, including the new online media; lack of a developed system of control of the level of objectivity of content directed to citizens in advertising campaigns; lack of consideration of the issue of disinformation analysis by competition and consumer protection institutions; lack of or poorly functioning democracy protection institutions; lack of institutions that reliably take care of a high level of journalistic ethics and media independence, the scale of disinformation of citizens by various groups of influence, including public institutions and commercially operating business entities may be high and may generate high social costs. Accordingly, new technologies of Industry 4.0/5.0, including generative artificial intelligence (GAI) technologies, should be involved in order to reduce the scale of growing disinformation, including the generation of factoids, deepfakes, etc. in social media. The aforementioned GAI technologies can help identify fakenews pseudo-journalistic content, identify photos containing deepfakes, identify factually incorrect content contained in banners, spots and advertising videos published in various media as part of ongoing advertising and promotional campaigns aimed at activating sales of various products and services.

I described the applications of Big Data technologies in sentiment analysis, business analytics and risk management in an article of my co-authorship:

I described the key issues of opportunities and threats to the development of artificial intelligence technology in my article below:

In view of the above, I address the following question to the esteemed community of scientists and researchers:

What do you think about this topic?

What is your opinion on this issue?

Please answer,

I invite everyone to join the discussion,

Thank you very much,

Best regards,

Dariusz Prokopowicz

The growing volume of data that is processed in companies and enterprises determines the need to involve specialized software and information systems, thanks to which both the analysis of data will be carried out effectively and the results of the analytics carried out will enable the use of the resulting knowledge to support the management processes of the business entity. The issue of the scope of large amounts of data acquired from various sources, their storage and processing is related to Big Data Analytics technology. However, in order to significantly increase the efficiency of processing large sets of data and information with the use of this type of analytics to support the management process of the business entity, computerized, multi-module business intelligence applications are particularly helpful in this regard. The combination of database technologies and analytical platforms of Big Data Analytics and Business Intelligence type applications makes it possible, on the basis of large data sets containing not fully structured and organized data, to generate useful information for a specific entity, as well as concretized and sublimated substantive knowledge used to support the management process of an organization, institution, business entity, etc. In terms of the key objectives of the application of knowledge generated in this way, it is distinguished to improve the quality of business decisions, reduce the risk of making errors during the processes of managing the organization, improve risk management systems, increase the effectiveness of early warning systems of new threats and development opportunities, etc. Analytics conducted on large data sets and implemented using Big Data Analytics and Business Intelligence applications can help in the processes of carrying out restructuring, developing a new strategy, investment project, marketing plan, business remodeling, etc. Analytics based on Business Intelligence applications can be helpful in the processes of supporting the management of various spheres of business activity of companies and enterprises and thus supporting the operation of various departments, including procurement, production, distribution, sales, marketing communication with customers, relations with business contractors, financial or public institutions. Multi-module business intelligence information systems can operate as integrated information systems or can be one of the key elements of such information systems digitally integrating many different aspects of companies, enterprises or other types of entities. Besides, multi-module complex Business Intelligence information systems can be dedicated to handle and support the implementation of specific business processes at different levels of an organization's organizational structure, i.e. they can consist of modules dedicated to handling for operational employees, departmental managers, managers, but also the board of directors and the company's president. Besides, in connection with the development of deep learning technologies carried out using artificial neural networks and generative artificial intelligence technologies, there are opportunities to increase the scale of automation of analytical processes through the use of the aforementioned technologies. The application of artificial intelligence technologies to analytics carried out using Big Data Analytics and artificial intelligence can significantly increase the efficiency of analytical processes and in terms of supporting organizational management processes, can speed up decision-making processes and reduce the risk of errors. A particularly important attribute of such solutions is the ability to perform predictive analysis and forecasting, so that an entrepreneur can spot certain business and economic patterns in good time and forecast future financial performance and development trends more accurately. Thanks to the use of generative artificial intelligence technology, the functionality and usefulness of analytics based on Big Data Analytics and Business Intelligence class systems is significantly increasing.

In view of the rapid development of applications of generative artificial intelligence technology and its implementation into applications and information systems supporting business management processes, I addressed the Research Gate community of Researchers, Scientists, Friends with the above question.

I described the key issues of opportunities and threats to the development of artificial intelligence technology in my article below:

I described the applications of Big Data technologies in sentiment analysis, business analytics and risk management in my co-authored article:

In view of the above, I address the following question to the esteemed community of scientists and researchers:

What do you think about this topic?

What is your opinion on this issue?

Please answer,

I invite everyone to join the discussion,

Thank you very much,

Best wishes,

Dariusz Prokopowicz

Generative artificial intelligence technology is used in various fields of economic activity, in business entities operating in different sectors of the economy, in different spheres of human activity. Intelligent chatbots available on the Internet are used, among other things, for the rapid development of plans, projects of various activities, ventures, etc. Since chatGPT and other similar intelligent chatbots were taught, trained using artificial neural networks as part of a deep learning process on large data sets downloaded from the Internet from selected database websites, libraries, indexing databases of scientific publications, articles and books, etc., so they take into account a large body of knowledge from various scientific disciplines. The aforementioned body of knowledge also covers various aspects of agriculture, including organic farming. In this regard, artificial intelligence technology combined with Big Data Analytics can help in the design, planning and development of a crop farm within the framework of the sustainable organic farming formula as an important component of the green transformation of the agricultural sector, the green transformation of the economy and the protection of the climate, biosphere and biodiversity of the planet's natural ecosystems. Planning for the green transformation of agriculture takes into account, first of all, increasing efficiency with a return to traditional agricultural techniques developed several centuries and more ago, i.e. agriculture without the use of pesticides, herbicides and other chemical pesticides; cultivated fields of small areas separated by green belts composed of flower meadows, shrubs, trees, forests; various vegetables and fruits grown side by side; crops characterized by high levels of biodiversity; incompletely regulated rivers, occurring floodplains, marshy areas not drained; additionally created ponds and other water reservoirs to collect rainwater; home gardens and orchards with beehive apiaries containing swarms of honey bees; compost piles with superfluous waste from used crops, etc. In addition, sustainable organic farming is being developed with the goal of decarbonizing agriculture in terms of greenhouse gas emissions, including methane, which is particularly dangerous to the climate. Reducing the emissivity will slow down the planet's greenhouse effect and slow down the progressive process of global warming. Besides, the green transformation of agriculture will reduce the scale of environmental pollution, deforestation, contribute to improving the microclimate, slow the process of soil aridity and reduce the scale of increasingly frequent periods of drought. Thus, thanks to the implemented green transformation of agriculture carried out in accordance with the implementation of the goals of sustainable development, climate and environmental social responsibility, the implementation of the principles of sustainable organic farming will significantly increase the scale of sustainability of agriculture in relation to the climate and the surrounding environment. Since in the framework of the carried out green transformation of agriculture, including the planning of farm organization in accordance with the formula of sustainable organic agriculture, it is necessary to take into account a lot of data, information on the ever-changing factors of climate, environment, soil, surrounding natural ecosystems of nature, different techniques of crop production, different varieties of crops, etc. so helpful in this regard probably is the application of analytical technology of multi-criteria processing of large sets of data and information using big Data Analytics platforms and generative technology of artificial intelligence enabling intelligent processing of large sets of data.

The key issues of opportunities and threats to the development of artificial intelligence technology are described in my article below:

OPPORTUNITIES AND THREATS TO THE DEVELOPMENT OF ARTIFICIAL INTELLIGENCE APPLICATIONS AND THE NEED FOR NORMATIVE REGULATION OF THIS DEVELOPMENT

I am conducting research on this issue. I have included the conclusions of my research in my article below:

IMPLEMENTATION OF THE PRINCIPLES OF SUSTAINABLE ECONOMY DEVELOPMENT AS A KEY ELEMENT OF THE PRO-ECOLOGICAL TRANSFORMATION OF THE ECONOMY TOWARDS GREEN ECONOMY AND CIRCULAR ECONOMY

In view of the above, I address the following question to the esteemed community of scientists and researchers:

What do you think about this topic?

What is your opinion on this issue?

Please answer,

I invite everyone to join the discussion,

Thank you very much,

Best regards,

Dariusz Prokopowicz

Despite the training and updating of internal rules and regulations in many companies and enterprises regarding the proper use of intelligent chatbots, i.e., for example, the ChatGPT made available online by OpenAI and other similar intelligent applications that more technology companies are making available on the Internet, there are still situations where reckless employees enter sensitive data of the companies and enterprises where they are employed into these online tools. In such a situation, there is a high risk that the data and information entered into ChatGPT, Copilot or any other such chatbot may subsequently appear in a reply, an edited report, essay, article, etc. by this application on the smartphone, laptop, computer, etc. of another user of the said chatbot. In this way, another Internet user may accidentally or through a deliberate action of searching for specific data come into possession of particularly important, key, sensitive data for a business entity, public institution or financial institution, which may concern, for example, confidential strategic plans, i.e., information of great value to competitors or intelligence organizations of other countries. This kind of situation has already happened and occurred in some companies characterized by highly recognizable brands in specific markets for the sale of products or services. Such situations clearly indicate that it is necessary to improve internal procedures for data and information protection, improve issues of efficiency of data protection systems, early warning systems informing about the growing risk of loss of key company data, and improve systems for managing the risk of potential leakage of sensitive data and possible cybercriminal attack on internal company information systems. In addition, in parallel to improving the aforementioned systems that ensure a certain level of data and information security, internal regulations should be updated on an ongoing basis according to the scale of the risk, the development of new technologies and their implementation in the business entity, with regard to the issue of correct use by employees of chatbots available on the Internet. In parallel, training should be conducted, during which employees learn about both new opportunities and risks arising from the use of new applications and tools based on generative artificial intelligence technology made available on the Internet. Another solution to this problem may be to order the company to completely ban employees from using smart chatbots made available on the Internet. In such a situation, the company will be forced to create its own, operating as internal such applications and intelligent chatbots, which are not connected to the Internet and operate solely as integral modules of the company's internal information systems. This type of solution will probably involve the company incurring significant financial expenses as a result of creating its own such IT solutions. The costs can be significant and many small companies' financial barrier can be high. However, on the other hand, if the construction of internal IT systems equipped with their own intelligent chatbot solutions becomes an important element of competitive advantage over key direct competitors, the mentioned financial expenses will probably be considered in the category of financial resources allocated to investment and development projects that are important for the future of the company.

The key issues of opportunities and threats to the development of artificial intelligence technology are described in my article below:

OPPORTUNITIES AND THREATS TO THE DEVELOPMENT OF ARTIFICIAL INTELLIGENCE APPLICATIONS AND THE NEED FOR NORMATIVE REGULATION OF THIS DEVELOPMENT

In view of the above, I address the following question to the esteemed community of scientists and researchers:

What do you think about this topic?

What is your opinion on this issue?

Please answer,

I invite everyone to join the discussion,

Thank you very much,

Best regards,

Dariusz Prokopowicz

The development of renewable and carbon-free energy sources is a key element in the green transformation of the economy. On the other hand, the development of renewable and carbon-free energy sources is also an important issue in the context of diversifying energy sources and increasing the scale of the country's energy independence and enhancing energy security. Increasing the share of renewable and emission-free energy sources in the energy source mix is not only the realization of the key objectives of the green transformation of the economy and the achievement of the goals of sustainable economic development, but also a significant reduction in the cost of energy production and energy prices for end users in the coming years. How the structure of different energy sources should be designed is a product of many different determinants. In terms of the aforementioned various determinants of the impact and design of the structure of energy sources, including fossil fuel-based energy and renewable and zero-carbon energy, among others, stand out. : the level of possession by the country of certain energy resources, the energy policy adopted in the country taking into account to a greater or lesser extent the issue of the green transformation of the economy and the implementation of sustainable development goals, the terrain and other natural conditions conducive to the development of wind, hydro and solar power, the scale of geothermal water resources in the earth's crust, the financial feasibility of building large nuclear power plants, the technologies in use for the construction of residential and other buildings, and the most appropriate to the prevailing prosumer building formulas of small power plants that generate energy to power individual buildings, the technologies in place to enable the development of hydrogen power and other technologically innovative types of energy. Since the design of the most appropriate energy mix for a country requires the consideration of many different determinants in the process of research, planning, design and implementation of investment processes, so it is a good solution to involve in this process generative artificial intelligence technology, which has been trained in the process of deep learning on large data sets and Big Data information on various aspects of energy, including different types of energy, different energy technologies, different energy sources, etc., and the above-mentioned determinants of energy development.

I described the key issues of opportunities and threats to the development of artificial intelligence technology in my article below:

OPPORTUNITIES AND THREATS TO THE DEVELOPMENT OF ARTIFICIAL INTELLIGENCE APPLICATIONS AND THE NEED FOR NORMATIVE REGULATION OF THIS DEVELOPMENT

I described the key issues of the problematic sources of Poland's exceptionally deep energy cross in 2022 in my co-authored article below:

POLAND'S 2022 ENERGY CRISIS AS A RESULT OF THE WAR IN UKRAINE AND YEARS OF NEGLECT TO CARRY OUT A GREEN TRANSFORMATION OF THE ENERGY SECTOR

In view of the above, I address the following question to the esteemed community of scientists and researchers:

What do you think about this topic?

What is your opinion on this issue?

Please answer,

I invite everyone to join the discussion,

Thank you very much,

Best wishes,

Dariusz Prokopowicz

In recent years, more and more companies and enterprises, before launching new product and service offerings as part of their market research, commission sentiment analysis of changes in public sentiment, changes in awareness of the company's brand, recognition of the company's mission and awareness of its offerings to specialized marketing research firms. This kind of sentiment analysis is carried out on computerized Big Data Analytics platforms, where a multi-criteria analytical process is carried out on a large set of data and information taken from multiple websites. In terms of source websites from which data is taken, information is dominated by news portals that publish news and journalistic articles on a specific issue, including the company, enterprise or institution commissioning this type of study. In addition to this, the key sources of online data include the pages of online forums and social media, where Internet users conduct discussions on various topics, including product and service offers of various companies, enterprises, financial or public institutions. In connection with the growing scale of e-commerce, including the sale of various types of products and services on the websites of online stores, online shopping portals, etc., as well as the growing importance of online advertising campaigns and promotional actions carried out on the Internet, the importance of the aforementioned analyses of Internet users' sentiment on specific topics is also growing, as playing a complementary role to other, more traditionally conducted market research. A key problem for this type of sentiment analysis is becoming the rapidly growing volume of data and information contained in posts, comments, posts, banners and advertising spots posted on social media, as well as the constantly emerging new social media. This problem is partly solved by the issue of increasing computing power and multi-criteria processing of large amounts of data thanks to the use of increasingly improved microprocessors and Big Data Analytics platforms. In addition, in recent times, the possibilities of advanced multi-criteria processing of large sets of data and information in increasingly shorter timeframes may significantly increase when generative artificial intelligence technology is involved in the aforementioned data processing.

The key issues of opportunities and threats to the development of artificial intelligence technology are described in my article below:

OPPORTUNITIES AND THREATS TO THE DEVELOPMENT OF ARTIFICIAL INTELLIGENCE APPLICATIONS AND THE NEED FOR NORMATIVE REGULATION OF THIS DEVELOPMENT

I described the applications of Big Data technologies in sentiment analysis, business analytics and risk management in my co-authored article:

APPLICATION OF DATA BASE SYSTEMS BIG DATA AND BUSINESS INTELLIGENCE SOFTWARE IN INTEGRATED RISK MANAGEMENT IN ORGANIZATION

The use of Big Data Analytics platforms of ICT information technologies in sentiment analysis for selected issues related to Industry 4.0

In view of the above, I address the following question to the esteemed community of scientists and researchers:

What do you think about this topic?

What is your opinion on this issue?

Please answer,

I invite everyone to join the discussion,

Thank you very much,

Best wishes,

Dariusz Prokopowicz

Generative artificial intelligence technology is currently being used to carry out various complex activities, to solve tasks intelligently, to implement multi-criteria processes, to create multi-faceted simulations and generate complex dynamic models, to creatively perform manufacturing processes that require processing large sets of data and information, etc., which until recently only humans could do. Recently, there have been attempts to create computerized, intelligent analytical platforms, through which it would be possible to forecast complex, multi-faceted, multi-criteria, dynamically changing macroprocesses, including, first of all, long-term objectively realized economic, social, political, climatic, natural and other macroprocesses. Based on the experience to date from research work on the analysis of the development of generative artificial intelligence technology and other technologies typical of the current Fourth Technological Revolution, technologies categorized as Industry 4.0/5.0, the rapidly developing various forms and fields of application of AI technologies, it is clear that the dynamic technological progress that is currently taking place will probably increase the possibilities of building complex intelligent predictive models for multi-faceted, complex macroprocesses in the years to come. The current capabilities of generative artificial intelligence technology in the field of improving forecasting models and carrying out forecasts of the formation of specific trends within complex macroprocesses are still limited and imperfect. The imperfection of forecasting models may be due to the human factor, i.e., their design by humans, the determination by humans of the key criteria and determinants that determine the functioning of certain forecasting models. In a situation where in the future forecasting models will be designed and improved, corrected, adapted to changing, for example, environmental conditions at each stage by artificial intelligence technology then they will probably be able to be much more perfect than the currently functioning and built forecasting models. Another shortcoming is the issue of data obsolescence and data limitation. There is currently no way to connect an AI-equipped analytical platform to the entire resources of the Internet, taking into account the processing of all the data and information contained in the Internet in real time. Even today's fastest quantum computers and the most advanced Big Data Analytics systems do not have such capabilities. However, it is not out of the question that in the future the dynamic development of generative artificial intelligence technology, the ongoing competition among leading technology companies developing technologies for intelligent chatbots, robots equipped with artificial intelligence, creating intelligent control systems for machines and processes, etc., will lead to the creation of general artificial intelligence, i.e. advanced, general artificial intelligence that will be capable of self-improvement. However, it is important that the said advanced general advanced artificial intelligence does not become fully autonomous, does not become completely independent, does not become out of the control of man, because there would be a risk of this highly advanced technology turning against man which would involve the creation of high levels of risks and threats to man, including the risk of losing the possibility of human existence on planet Earth.

I have described the key issues of opportunities and threats to the development of artificial intelligence technology in my article below:

OPPORTUNITIES AND THREATS TO THE DEVELOPMENT OF ARTIFICIAL INTELLIGENCE APPLICATIONS AND THE NEED FOR NORMATIVE REGULATION OF THIS DEVELOPMENT

In view of the above, I address the following question to the esteemed community of scientists and researchers:

And what is your opinion about it?

What is your opinion on this issue?

Please answer,

I invite everyone to join the discussion,

Thank you very much,

Best regards,

Dariusz Prokopowicz

Dear Master Degree, MPhil, and PhD Research Scholars,

We're excited to invite you to the CIMPA School 2024 on Mathematics for Medicine and Health Sciences at Silpakorn University in Thailand.

The CIMPA School will be held from May 20 to May 31 2024 at our esteemed university campus. This school aims to provide participants with in-depth knowledge and practical skills in the application of mathematics in the fields of medicine and health sciences. The program offers a diverse range of courses and training sessions led by renowned experts in their respective fields.

During the school, you will have the opportunity to attend introductory courses that cover various topics such as pharmacokinetics, disease modeling, big data analysis, and numerical implementation of blood flow. Additionally, advanced courses will delve into specialized areas like big data and machine learning for public health, modeling of waterborne diseases, and coagulation-fragmentation modeling.

The CIMPA School at Silpakorn University will serve as a platform for interdisciplinary exchange, fostering collaborations among participants from different countries and backgrounds. You will not only gain valuable knowledge but also have the chance to network with fellow researchers and professionals, opening doors to future collaborations and opportunities.

We are pleased to inform you that CIMPA is offering financial support for eligible participants. If you require financial assistance, we encourage you to apply for CIMPA financial support through their website: https://www.cimpa.info/en/node/40

To learn more about the CIMPA School at Silpakorn University, please visit our official website: https://sites.google.com/view/cimpa-silpakorn2024/. You will find detailed information regarding the registration procedure, program schedule, and other relevant details.

We believe that your expertise and contributions would greatly enrich the learning experience for all participants. Your presence at the CIMPA School would be highly valued, and we look forward to welcoming you to Silpakorn University.

If you have any questions or require further information, please do not hesitate to reach out to us ( [email protected] or [email protected]).

Thank you for your attention, and we hope to see you at the CIMPA School at Silpakorn University.

Best regards,

P. Pornsawad

Coordinators

Among the areas in which applications based on generative artificial intelligence are now rapidly finding application are marketing communication processes realized within the framework of Internet marketing, within the framework of social media advertising campaigns. More and more advertising agencies are using generative artificial intelligence technology to create images, graphics, animations and videos that are used in advertising campaigns. Thanks to the use of generative artificial intelligence technology, the creation of such key elements of marketing communication materials has become much simpler and cheaper and their creation time has been significantly reduced. On the other hand, thanks to the applications already available on the Internet based on generative artificial intelligence technology that enable the creation of photos, graphics, animations and videos, it is no longer only advertising agencies employing professional cartoonists, graphic designers, screenwriters and filmmakers that can create professional marketing materials and advertising campaigns. Thanks to the aforementioned applications available on the Internet, graphic design platforms, including free smartphone apps offered by technology companies, advertising spots and entire advertising campaigns can be designed, created and executed by Internet users, including online social media users, who have not previously been involved in the creation of graphics, banners, posters, animations and advertising videos. Thus, opportunities are already emerging for Internet users who maintain their social media profiles to professionally create promotional materials and advertising campaigns. On the other hand, generative artificial intelligence technology can be used unethically within the framework of generating disinformation, informational factoids and deepfakes. The significance of this problem, including the growing disinformation on the Internet, has grown rapidly in recent years. The deepfake image processing technique involves combining images of human faces using artificial intelligence techniques.

In order to reduce the scale of disinformation spreading on the Internet media, it is necessary to create a universal system for labeling photos, graphics, animations and videos created using generative artificial intelligence technology. On the other hand, a key factor facilitating the development of this kind of problem of generating disinformation is that many legal issues related to the technology have not yet been regulated. Therefore, it is also necessary to refine legal norms on copyright issues, intellectual property protection that take into account the creation of works that have been created using generative artificial intelligence technology. Besides, social media companies should constantly improve tools for detecting and removing graphic and/or video materials created using deepfake technology.

I have described the key issues of opportunities and threats to the development of artificial intelligence technology in my article below:

OPPORTUNITIES AND THREATS TO THE DEVELOPMENT OF ARTIFICIAL INTELLIGENCE APPLICATIONS AND THE NEED FOR NORMATIVE REGULATION OF THIS DEVELOPMENT

In view of the above, I address the following question to the esteemed community of scientists and researchers:

What do you think about this topic?

What is your opinion on this issue?

Please answer,

I invite everyone to join the discussion,

Thank you very much,

Best wishes,

Dariusz Prokopowicz

The above text is entirely my own work written by me on the basis of my research.

In writing this text I did not use other sources or automatic text generation systems.

Copyright by Dariusz Prokopowicz

Full Paper Submission Date: March 19, 2024

Registration Deadline: March 29, 2024

Final Paper Submission Date: April 09, 2024

Conference Dates: April 19-21, 2024

The topics of interest for submission include, but are not limited to:

- The development of artificial intelligence (AI tools, artificial intelligence and evolutionary algorithms, intelligent user interfaces, intelligent information fusion, etc.) and their applications in economic and social development.

- The development of mobile Internet, artificial intelligence, big data and other technologies and their application in economic and social development.

- Artificial intelligence and digital economy development frontier in the Internet era and typical cases.

- Technology, methods and applications of the integration and development of digital economy and artificial intelligence.

- Other topics related to artificial intelligence, Internet and digital economy can be contributed.

All accepted papers will be published in the AHIS-Atlantis Highlights in Intelligent Systems (2589-4919), and submitted to EI Compendex, Scopus for indexing.

I need to extract the x,y coordinates of a PCA plot (generated in R) to plot into excel (my boss prefers excel)

The code to generate the PCA:

pca <- prcomp(data, scale=T, center=T)

autoplot(pca, label=T)

If we take a look at pca$x, the first two PC scores are as follows for an example point is:

29. 3.969599e+01 6.311406e+01

So for sample 29, the PC scores are 39.69599 and 63.11406.

However if you look at the output plot in R, the coordinates are not 39.69599 and 63.11406 but ~0.09 ~0.2.

Obviously some simple algebra can estimate how the PC scores are converted into the plotted coordinates but I can't do this for ~80 samples.

Can someone please shed some light on how R gets these coordinates and maybe a location to a mystery coordinate file or a simple command to generate a plotted data matrix?

Redoing prcomp() without scale and center gives me this for PC1 and PC2 for the first 5 samples

1 -8.9825883 0.0113775

2 -16.3018548 9.1766104

3 -21.0626458 3.0629666

4 5.5305875 4.0334291

5 0.2349433 12.4872609

However the plot ranges from -0.15 to 0.4 for PC1 and -0.35 to 0.15 for PC2

(Plot attached)

Are sciences more of a continuum from hard to soft than those two as discrete categories? How? Why? I think the former. My book here somewhat touches on that question:

In recent years, there has been a growing scale of implementation of Industry 4.0/5.0 technologies, including Big Data Analytics, Data Science, multi-criteria simulation models, digital twins, additive manufacturing, Blockchain, smart technologies and also generative artificial intelligence to business entities in order to improve internal information systems of the Business Intelligence type supporting the management processes of a company, enterprise, corporation or other type of business entity. The Covid-19 pandemic has accelerated the processes of digitizing the economy. The importance and application of analytics conducted via the Internet and/or using data downloaded from the Internet is also growing. An example is sentiment analysis conducted on data downloaded from the Internet implemented on Big Data Analytics platforms being an additional research instrument of conducted market research, marketing research as an additional source of data for conducted Business Intelligence type analysis. This is particularly important because in recent years the importance of Internet marketing, including viral marketing, Real-Time marketing carried out on social media sites is increasing. Accordingly, in many industries and sectors of the economy, there is already an increase in the application of certain Industry 4.0 technologies, i.e., such as Big Data Analytics, Data Science, cloud computing, machine learning, personal and industrial Internet of Things, artificial intelligence, Business Intelligence, autonomous robots, horizontal and vertical data system integration, multi-criteria simulation models, additive manufacturing, Blockchain, cybersecurity instruments, Virtual and Augmented Reality and other advanced data processing technologies Data Mining. Besides, using Big Data Analytics, interesting research is being conducted in the field of the issue: Analysis of changes in the relationship of consumer behavior in the markets for goods and services caused by the impact of advertising campaigns conducted on the Internet, applying new Internet marketing tools used in new online media, including primarily social media. The growth of behavioral economics and finance, including the analysis of the determinants of media formation of consumer opinions on the recognition of the company's brand, product and service offerings, etc., through the growth of Internet information services, including social media portals. Currently, online viral marketing based on social media portals and customer data collected and processed in Big Data Analytics databases is developing rapidly. In recent years, new online marketing instruments have also been developed, applied mainly on social media portals and are also used by e-commerce companies. Internet technology companies and fintechs are also emerging, offering online information services to assist marketing management, including in planning advertising campaigns for products sold via the Internet. For this purpose, the aforementioned sentiment analyses are used to study the opinions of Internet users regarding the prevailing awareness, recognition, brand image, mission, offerings of certain companies. Sentiment analysis is carried out on large data sets taken from various websites, including millions of social media pages, collected in Big Data systems. The analytical data collected in this way is very helpful in the process of planning advertising campaigns carried out in new media, including social media sites. These campaigns advertise, among other things, products and services sold via the Internet, available in online stores. In view of the above, the development of e-commerce is mainly determined by technological advances in ICT information technology and advanced data processing technology Industry 4.0, as well as new technologies used in securing financial transactions carried out over the Internet, including transactions related to e-commerce, i.e. blockchain technology, for example. In my opinion, ongoing scientific research confirms the strong correlation occurring between the development of Big Data technologies, Data Science, Data Analytics and the efficiency of the use of knowledge resources. I believe that the development of Big Data technology and Data Science, Data Analytics and other ICT information technologies, multi-criteria technology, advanced processing of large sets of information, Industry 4.0 technology increases the efficiency of the use of knowledge resources, including in the field of economics, finance and organizational management. In recent years, ICT information technologies, Industry 4.0, etc., have been developing particularly rapidly and are being applied in knowledge-based economies. These technologies are being applied in scientific research and business applications in commercially operating enterprises and in financial and public institutions. In view of the growing importance of this issue in knowledge-based economies, it is important to analyze the correlation between the development of Big Data technologies and analytics of Data Science, Data Analytics, Business Intelligence and the efficiency of using knowledge resources to solve key problems of civilization development. Analytics based on Business Intelligence, in addition to Data Science, Big Data Analytics are increasingly being used in improving business management processes. The development of this analytics based on the implementation of ICT information technologies and Industry 4.0 into analytical processes has a great future in the years to come. In recent years, ICT information technologies, Industry 4.0, etc., have been developing particularly rapidly and are being applied in knowledge-based economies. In addition, the application of artificial intelligence technologies can increase the efficiency of the use of Big Data Analytics and other Industry 4.0/5.0 technologies, which are used to support business management processes.

I have described the issues of application of Big Data and Business Intelligence technologies in the context of enterprise risk management in the following article:

APPLICATION OF DATA BASE SYSTEMS BIG DATA AND BUSINESS INTELLIGENCE SOFTWARE IN INTEGRATED RISK MANAGEMENT IN ORGANIZATION

In addition, I described the issues of opportunities and threats to the development of AI technology applications in my following article:

OPPORTUNITIES AND THREATS TO THE DEVELOPMENT OF ARTIFICIAL INTELLIGENCE APPLICATIONS AND THE NEED FOR NORMATIVE REGULATION OF THIS DEVELOPMENT

In view of the above, I address the following question to the esteemed community of scientists and researchers:

What do you think on this topic?

What is your opinion on this issue?

Please answer,

I invite everyone to join the discussion,

Thank you very much,

Best regards,

Dariusz Prokopowicz

What are the primary issues and problems in a big data workflow's data intake phase, and how can businesses assure efficient and reliable ingestion of varied data sources?

Industry 4.0/5.0 technologies are increasingly being used to manage the organization of modern urban agglomerations developed operating according to the green smart city model. Since artificial intelligence technology has been developing particularly rapidly recently, and numerous new applications of this technology are emerging in various sectors of the economy, so also the opportunities for applying AI technologies to improve the systems of automated management of the organization of modern urban agglomerations are increasing. Besides, the combination of Big Data Analytics, Data Science, Internet of Things, multi-criteria simulation models, digital twins, cloud computing with artificial intelligence technology and other ICT technologies makes it possible to significantly increase the efficiency of operation and improvement of systems of automated management of the organization of modern urban agglomerations.

Smart home technologies and smart city technologies are developing on the basis of new ICT information technologies and Industry 4.0/Industry 5.0. Developed commercial applications of smart home technologies allow remote management and automation of the use processes of certain devices controlling power and generating energy for home use, energy storage and conservation, etc. Such applications are perfectly in line with the development of renewable and zero-carbon energy applications, which are installed in the home to increase the scope of energy self-sufficiency. In such a situation, it is necessary to develop systemic solutions and infrastructure for the collection of surplus energy produced by prosumer citizens. In this regard, a computerized system for managing individual household appliances based on smart home technology can fit perfectly into the current trend of pro-environmental transformation of the economy. Smart technologies based on artificial intelligence or machine learning technology, using cloud computing and the Internet of Things, allow the integration of various household appliances, including household electronics and appliances equipped with microprocessors and smart software. In this way, individual household appliances can be integrated into a central, integrated management system based on smart home technology. This kind of central integrated management system based on smart home technology can be controlled from, for example, a smartphone, a smart tv remote control, a smart watch equipped with the necessary software. With this kind of central integrated management system based on smart home technology, further devices such as home robots can be "modularly" connected, which can be very helpful for the elderly. On the other hand, the development of computerized management systems for individual household appliances based on smart home technology is also determined by the issue of improving cyber security systems and cyber security risk management. This issue is particularly relevant when a central, integrated system for remote management of individual household appliances is connected to the Internet.

In a smart city, on the one hand, many of the city's functions are carried out through automated and centrally managed information systems using new Industry 4.0/Industry 5.0 technologies. On the other hand, citizens of a smart city have the opportunity to use many of the city's information services currently offered mainly through websites and smartphone apps. Where defined certain categories of information appear on the smartphone according to the citizen's location and are automatically added to the calendar, etc. Particularly relevant information applications include systems that alert citizens to unusual weather phenomena, climatic disasters, locally growing pandemic threats, etc. Smart urban information systems can also cooperate with autonomous vehicle systems.

The issues of energy efficiency in buildings, eco-technology and eco-innovative building materials providing high levels of energy efficiency, sustainable construction, green smart city, etc. are some of the important elements for carrying out a pro-environmental transformation of the economy to build a sustainable, green, zero-carbon zero-growth and closed-loop economy. I am conducting research in the problematic of the key determinants of smoothly carrying out the pro-environmental transformation of the classic growth, brown, linear economy of excess to a sustainable, green, zero-carbon zero growth economy and closed loop economy. In view of the above, the issue of green, sustainable construction is one of the key elements for carrying out the pro-environmental transformation of the economy and the development of urban agglomeration developed in the green smart city model. More and more research institutes are working to develop new green technologies and eco-innovations that will make it more efficient and faster to carry out the green transformation of the economy, including the green transformation of the economy. For example, laboratories at research institutes are working on new innovative types of photovoltaic panels. For example, new types of photovoltaic panels are being developed that look like window glass but are also photovoltaic panels. In a situation where these kinds of photovoltaic panels that look like windowpanes are properly refined technologically and come on the market then they could revolutionize the building of energy self-sufficient green smart cities. Such innovative solutions of photovoltaic panel technology could be very useful in buildings that are built or planned to be built in modern sustainable green smart cities.

I described the key issues of opportunities and threats to the development of artificial intelligence technology in my article below:

In view of the above, I address the following question to the esteemed community of scientists and researchers:

How can artificial intelligence technology improve the organizational management process of modern urban agglomerations developed operating according to the green smart city model?

How can artificial intelligence improve the operation of green smart city management systems?

What do you think on this topic?

What is your opinion on this issue?

Please answer,

I invite everyone to join the discussion,

Thank you very much,

Best wishes,

Dariusz Prokopowicz

What aspects of working with data are the most time-consuming in your research activities?

What functional capabilities would you like to see in an ideal data work platform?

Colleagues, good day!

We would like to reach out to you for assistance in verifying the results we have obtained.

We employ our own method for performing deduplication, clustering, and data matching tasks. This method allows us to obtain a numerical value of the similarity between text excerpts (including data table rows) without the need for model training. Based on this similarity score, we can determine whether records match or not, and perform deduplication and clustering accordingly.

This is a direct-action algorithm, relatively fast and resource-efficient, requiring no specific configuration (it is versatile). It can be used for quickly assessing previously unexplored data or in environments where data formats change rapidly (but not the core data content), and retraining models is too costly. It can serve as the foundation for creating personalized desktop data processing systems on consumer-grade computers.

We would like to evaluate the quality of this algorithm in quantitative terms, but we cannot find widely accepted methods for such an assessment. Additionally, we lack well-annotated datasets for evaluating the quality of matching.

If anyone is willing and able to contribute to the development of this topic, please step forward.

Sincerely, The KnoDL Team

The acceleration of the processes of digitization of the economy triggered by the development of the Covid-19 pandemic has resulted in a significant increase in computerization, Internetization, applications of ICT information technologies and Industry 4.0 to various economic processes. There is an increase in applications of specific Industry 4.0 technologies in many industries and sectors of the economy, i.e., such as Big Data Analytics, Data Science, cloud computing, machine learning, personal and industrial Internet of Things, artificial intelligence, Business Intelligence, autonomous robots, horizontal and vertical data system integration, multi-criteria simulation models, digital twins, additive manufacturing, Blockchain, cybersecurity instruments, Virtual and Augmented Reality, and other advanced Data Mining technologies. In my opinion, among others, in the fields of medical therapies, communications, logistics, new online media, life science, ecology, economics, finance, etc., and also in the field of predictive analytics, there is an increase in the applications of ICT information technologies and Industry 4.0/Industry 5.0. Artificial intelligence technologies are growing rapidly as they find applications in various industries and sectors of the economy. It is only up to human beings how and in what capacity artificial intelligence technology will be implemented in various manufacturing processes, analytical processes, etc., where large data sets are processed in the most efficient manner. In addition, various opportunities are opening up for the application of artificial intelligence in conjunction with other technologies of the current fourth industrial revolution referred to as Industry 4.0/5.0. It is expected that in the years to come, applications of artificial intelligence will continue to grow in various areas, fields of manufacturing processes, advanced data processing, in improving manufacturing processes, in supporting the management of various processes, and so on.

I have been studying this issue for years and have presented the results of my research in the article, among others:

In view of the above, I address the following question to the esteemed community of scientists and researchers:

And what is your opinion about it?

What is your opinion on this issue?

Please answer,

I invite everyone to join the discussion,

Thank you very much,

Best wishes,

Dariusz Prokopowicz

The RAY framework is an open-source project that provides a simple, universal API for building distributed applications. It is particularly well-suited for applications that require parallel and distributed computing, making it a popular choice for machine learning, deep learning, and big data processing tasks. Here are some reasons why and scenarios when the RAY framework is used:

𝟯𝗿𝗱 𝗜𝗻𝘁𝗲𝗿𝗻𝗮𝘁𝗶𝗼𝗻𝗮𝗹 𝗖𝗼𝗻𝗳𝗲𝗿𝗲𝗻𝗰𝗲 𝗼𝗻 𝗘𝗹𝗲𝗰𝘁𝗿𝗶𝗰𝗮𝗹, 𝗣𝗼𝘄𝗲𝗿 𝗮𝗻𝗱 𝗚𝗿𝗶𝗱 𝗦𝘆𝘀𝘁𝗲𝗺𝘀 (𝗜𝗖𝗘𝗣𝗚𝗦 𝟮𝟬𝟮𝟰) 𝘄𝗵𝗶𝗰𝗵 𝘄𝗶𝗹𝗹 𝗯𝗲 𝗵𝗲𝗹𝗱 𝗶𝗻 𝗞𝘂𝗮𝗹𝗮 𝗟𝘂𝗺𝗽𝘂𝗿, 𝗠𝗮𝗹𝗮𝘆𝘀𝗶𝗮. 𝗼𝗻 𝗝𝗮𝗻𝘂𝗮𝗿𝘆 𝟮𝟲-𝟮𝟴, 𝟮𝟬𝟮𝟰.

---𝐂𝐚𝐥𝐥 𝐅𝐨𝐫 𝐏𝐚𝐩𝐞𝐫𝐬---

The topics of interest for submission include, but are not limited to:

· Solar Power

· New Energy Power Generation

· Transmission of Electrical Energy

· Signal and System

· Power Systems and Automation

· Electrical System Modeling

· Power System Communication and Control

· Computational Intelligence in Electrical Engineering

· Electrical Materials and Equipment

· Power System Protection and Control

· High Voltage and Insulation

· Smart Grid

· Microgrid

· Smart Sensors

· Smart Networks and Communications

· Materials for Electrical and Optoelectronics

· Machine Learning and Artificial Intelligence in Power Systems

· Big Data Application in Power Systems

All accepted papers will be published in the Conference Proceedings, and submitted to EI Compendex, Scopus for indexing.

𝐈𝐦𝐩𝐨𝐫𝐭𝐚𝐧𝐭 𝐃𝐚𝐭𝐞𝐬:

Full Paper Submission Date: December 10, 2023

Registration Deadline: December 30, 2023

Final Paper Submission Date: January 12, 2024

Conference Dates: January 26-28, 2024

𝐅𝐨𝐫 𝐌𝐨𝐫𝐞 𝐃𝐞𝐭𝐚𝐢𝐥𝐬 𝐩𝐥𝐞𝐚𝐬𝐞 𝐯𝐢𝐬𝐢𝐭:

Since OpenAI brought ChatGPT online in November 2022, interest in the possibilities of using intelligent chatbots for various aspects of business operations has strongly increased among business entities. Intelligent chatbots originally only or mainly enabled conversations, discussions, answered questions using specific data resources, information and knowledge taken from a selection of multiple websites. Then, in the following months, OpenAI released other intelligent applications on the Internet, allowing Internet users to generate images, photos, graphics, videos, solve complex mathematical tasks, create software for new computer applications, generate analytical reports, process various types of documents based on the given commands and formulated commands. In addition to this, in 2023, other technology companies also began to make their intelligent applications available on the Internet, through which certain complex tasks can be carried out to facilitate certain processes, aspects of companies, enterprises, financial institutions, etc., and thus facilitate business. There is a steady increase in the number of intelligent applications and tools available on the Internet that can support the implementation of various aspects of business activities carried out in companies and enterprises. On the other hand, the number of new business applications of said smart applications is growing rapidly.

In view of the above, I address the following question to the esteemed community of scientists and researchers:

And what is your opinion on this topic?

What do you think about this topic?

Please answer,

I invite everyone to join the discussion,

Thank you very much,

Best wishes,

Dariusz Prokopowicz

How are researchers addressing the ethical considerations and privacy concerns associated with collecting and analyzing big data?

The banking sector is among those sectors where the implementation of new ICT, Internet and Industry 4.0/5.0 information technologies, including but not limited to the applications of generative artificial intelligence technology in finance and banking. Commercial online and mobile banking have been among the particularly fast-growing areas of banking in recent years. In addition, the SARS-CoV-2 (Covid-19) coronavirus pandemic, in conjunction with government-imposed lockdowns imposed on selected sectors of the economy, mainly service companies, and national quarantines, the development of online and mobile banking accelerated. Solutions such as contactless payments made with a smartphone developed rapidly. On the other hand, due to the acceleration of the development of online and mobile banking, the increase in the scale of payments made online, the conduct of online settlements related to the development of e-commerce, the scale of cybercriminal activity has increased since the pandemic. When the company OpenAI put its first intelligent chatbot, i.e. ChatGPT, online for Internet users in November 2022 and other Internet-based technology companies accelerated the development of analogous solutions, commercial banks saw great potential for themselves. More chatbots modeled on ChatGPT and new applications of tools based on generative artificial intelligence technology made available on the Internet quickly began to emerge. Commercial banks thus began to adapt the emerging new AI solutions to their needs on their own. The IT professionals employed by the banks thus proceeded with the processes of teaching intelligent chatbots, implementing tools based on generative AI to selected processes and activities performed permanently and repeatedly in the bank. Accordingly, AI technologies are increasingly being implemented by banks into cyber-security systems, processes for analyzing the creditworthiness of potential borrowers, improving marketing communications with bank customers, perfecting processes for automating remote telephone and Internet communications of banks' call center departments, developing market analyses carried out on Big Data Analytics platforms using large sets of data and information extracted from various bank information systems and from databases available on the Internet, online financial portals and thousands of processed posts and comments of Internet users contained in online social media pages, increasingly automated and generated in real time ba based on current large sets of information and data development of industry analysis and analysis and extrapolation into the future of market trends, etc. The scale of new applications of generative artificial intelligence technology in various areas of banking processes carried out in commercial banks is growing rapidly.

In view of the above, I address the following question to the esteemed community of scientists and researchers:

And what is your opinion on this topic?

What is your opinion on this issue?

Please answer,

I invite everyone to join the discussion,

Thank you very much,

Best wishes,

Dariusz Prokopowicz

Well, ChatGPT is not perfect in terms of self-learning new content and perfecting the answers it gives, because it happens to give confirmation answers when there is information or data that is not factually correct in the question formulated by the Internet user. In this way, ChatGPT can learn new content in the process of learning new but also false information, fictitious data, in the framework of the 'discussions' held. Currently, various technology companies are planning to create, develop and implement computerised analytical systems based on artificial intelligence technology similar to ChatGPT, which will find application in various fields of big data analytics, will find application in various fields of business and research work, in various business entities and institutions operating in different sectors and industries of the economy. One of the directions of development of this kind of artificial intelligence technology and applications of this technology are plans to build a system of analysis of large data sets, a system of Big Data Analytics, analysis of information taken from the Internet, an analytical system based on artificial intelligence conducting analytics in real time, integrated with an Internet search engine, but an artificial intelligence system more perfect than ChatGPT, which will, through discussion with Internet users, improve data verification and will learn but only real information and data. Some of the technology companies are already working on this, i.e. on creating this kind of technological solutions and applications of artificial intelligence technology similar to ChatGPT. But presumably many technology start-ups that plan to create, develop and implement business specific technological innovations based on a specific generation of artificial intelligence technology similar to ChatGPPT are also considering undertaking research in this area and perhaps developing a start-up based on a business concept of which technological innovation 4.0, including the aforementioned artificial intelligence technologies, is a key determinant.

In view of the above, I address the following question to the esteemed community of scientists and researchers:

What do you think about this topic?

What is your opinion on this subject?

Please respond,

I invite you all to discuss,

Thank you very much,

Best wishes,

Dariusz Prokopowicz

i need pdf copy of this book Big Data Analytics_ A Hands-On Approach - Arshdeep Bahga & Vijay Madisetti

In the context of the integration of business entities into the currently developing processes of green transformation of the economy, adding the issue of achieving sustainable development goals to the company's mission, implementing green technologies and eco-innovations that contribute to a decrease in the level of emissions in terms of greenhouse gas emissions, exhaust emissions and other pollutants negatively affecting the state of the environment, implementing green investments that reduce the level of energy intensity of buildings and economic processes, etc., the scale of opportunities for improving sustainable business models is also growing. The aforementioned sustainable business models are an important part of green business transformation, conducted in a company or enterprise. On the other hand, the scale of opportunities for improving sustainable business models applied to business entities can be significantly increased by implementing new ICT and Industry 4.0/5.0 information technologies into business, including but not limited to generative artificial intelligence technologies. Recently, the level and generic number of applications of generative artificial intelligence in various business fields of companies and enterprises has been growing rapidly. On the Internet, intelligent applications equipped with generative artificial intelligence technology are appearing in the open, which can be applied to the execution of complex and resource-intensive data and information processing, i.e. such activities that until recently were performed only by humans. In addition, intelligent chatbots and other intelligent applications that enable automation of the execution of complex, multi-faceted, multi-criteria tasks perform the aforementioned tasks in many times less time and with much higher efficiency compared to if the same tasks were to be performed by a human. The ability of tools equipped with generative artificial intelligence to intelligently execute the ordered command is generated by teaching it in the process of deep learning and applying advanced information systems based on artificial neural networks.

In view of the above, I address the following question to the esteemed community of scientists and researchers:

And what is your opinion on this topic?

What is your opinion on this issue?

Please answer,

I invite everyone to join the discussion,

Thank you very much,

Best regards,

Dariusz Prokopowicz

There are different types of websites and sources of data and information on the Internet. The first Internet-accessible intelligent chatbot, i.e. ChatGPT, made available by OpenAI in November 2022, performs certain commands, solves tasks, and writes texts based on knowledge resources, data and information downloaded from the Internet, which were not fully up-to-date, as they were downloaded from selected websites and portals last in January 2022. In addition, the data and information were downloaded from many selected websites of libraries, articles, books, online indexing portals of scientific publications, etc. Thus, these were data and information selected in a certain way. In 2023, more Internet-based leading technology companies were developing and making their intelligent chatbots available on the Internet. Some of them are already based on data and information that is much more up-to-date compared to the first versions of ChatGPT made available on the Internet in open access. In November 2023, social media site X (the former Twiter) released its intelligent chatbot in the US, which reportedly works on the basis of up-to-date information entered into the site through posts, messages, tweets made by Internet users. Also in October 2023, OpenAI announced that it will create a new version of its ChatGPT, which will also draw data and knowledge from updated knowledge resources downloaded from multiple websites. As a result, rival Internet-based leading forms of technology are constantly refining the evolving designs of the intelligent chatbots they are building, which will increasingly use more and more updated data, information and knowledge resources drawn from selected websites, web pages and portals. The rapid technological advances currently taking place regarding artificial intelligence technology may in the future lead to the integration of generative artificial intelligence and general artificial intelligence developed by technology companies. Competing technology companies may strive to build advanced artificial intelligence systems that can achieve a high level of autonomy and independence from humans, which may lead to a situation of the possibility of artificial intelligence technology development slipping out of human control. Such a situation may arise when the emergence of a highly technologically advanced general artificial intelligence that achieves the possibility of self-improvement and, in addition, realizing the process of self-improvement in a manner independent of humans, i.e. self-improvement with simultaneous escape from human control. However, before this happens it is earlier that technologically advanced artificial intelligence can achieve the ability to select data and information, which it will use in the implementation of specific mandated tasks and their real-time execution using up-to-date data and online knowledge resources.

In view of the above, I address the following question to the esteemed community of scientists and researchers:

And what is your opinion about it?

What is your opinion on this issue?

Please answer,

I invite everyone to join the discussion,

Thank you very much,

Best regards,

Dariusz Prokopowicz

Digital transformation seems to be more than just the digitization of data and processes, or digitization in combination with robotisation. It leads to a special kind of socio-economic change. With digital transformation the events gain its momentum and affect functioning of organisations and many aspects of lives of individuals, with consequences as follows:

• the emergence of the almost ubiquitous Internet of Things – subjectivity and objectivity become complex,

• the unreal world becomes a new reality,

• use of smartphones – the need for continuous communication (Fear of Missing Out)

• virtual assistants,

• threats to our private lives through the unauthorized use of security cameras and surveillance equipment

The bank must be safe but fast, cheap, tailored to the customer‘s needs and smart. Today, it is difficult to talk about customer loyalty or sentiment. Today‘sclient is mobile, he comes and goes, does not stay in the bank through sentiment or habit, and because the bank accompanies him in all phases of his life as a consumer and as an economic entity.

I advise you to please take a read of the below chapter and I would be very happy to know your thoughts.

Is it the end of banking as we know it? Will AI be the future of banking? Will banks be soon digitized mechanism and advisors AI?

based on my earlier research:

(PDF) Role of digitization for German savings banks. Available from: https://www.researchgate.net/publication/344808656_Role_of_digitization_for_German_savings_banks [accessed Nov 28 2023].

,,

In the era of big data and artificial intelligence (AI), where aggregated data is used to learn about patterns and for decision-making, quality of input data seems to be of paramount importance. Poor data quality may lead not only to wrong outcomes, which will simply render the application useless, but more importantly to fundamental rights breaches and undermined trust in the public authorities using such applications. In law enforcement as in other sectors the question of how to ensure that data used for the development of big data and AI applications meet quality standards remains.

In law enforcement, as in other sectors, the key element of ensuring quality and reliability of big data and AI apps is the quality of raw material. However, the negative effects of flawed data quality in this context extend far beyond the typical ramifications, since they may lead to wrong and biased decisions producing adverse legal or factual consequences for individuals,Footnote11 such as detention, being a target of infiltration or a subject of investigation or other intrusive measures (e.g., a computer search).

source:

What are the biggest strategic challenges that insurance companies are facing as a result of digitalisation?

How is AI use in medical practice distinguished from big data analytics applications for health care delivery and population health?

,,

The scale of the development of disinformation on the Internet including, among other things, fakenews has been growing in recent years mainly in social media. Disinformation is mainly developing on social media sites that are popular among young people, children and teenagers. The growing scale of disinformation is particularly socially damaging in view of the key objective of its pursuit by cybercriminals and certain organisations using, for example, the technique of publishing posts and banners using fake profiles of fictitious Internet users containing fakenews. The aim is to try to influence public opinion in society, to shape the general social awareness of citizens, to influence the assessment of the activities of specific policies of the government, national and/or international organisations, public or other institutions, to influence the ratings, credibility, reputation, recognition of specific institutions, companies, enterprises, their product and service offerings, individuals, etc., to influence the results of parliamentary, presidential and other elections, etc. In addition to this, the scale of cybercriminal activity and the improvement of cyber security techniques have also been growing in parallel on the Internet in recent years. Therefore, as part of improving techniques to reduce the scale of disinformation spread deliberately by specific national and/or international organisations, computerised platforms are being built to detect fake news and other forms of disinformation on the internet built using Big Data Analytics, artificial intelligence and other Industry 4.0 technologies. Since cybercriminals and organisations generating disinformation use new Industry 4.0 technologies in the creation of fake profiles on popular social networks, new information technologies, Industry 4.0, including but not limited to Big Data Analytics, artificial intelligence, deep learning, machine learning, etc., should also be used to reduce the scale of such harmful activities to citizens.

In view of the above, I address the following question to the esteemed community of scientists and researchers:

And what do you think about it?

What is your opinion on this subject?

Please respond,

I invite you all to discuss,

Thank you very much,

Best wishes,

Dariusz Prokopowicz

Almost from the very beginning of the development of ICT, the rivalry between IT professionals operating on two sides of the barricade, i.e. in the sphere of cybercrime and cyber security, has been realized. In a situation where, within the framework of the technological progress that is taking place, on the one hand, a new technology emerges that facilitates the development of remote communication, digital transfer and processing of data then, on the other hand, the new technology is also used within the framework of hacking and/or cybercrime activities. Similarly, when the Internet appeared then on the one hand a new sphere of remote communication and digital data transfer was created. On the other hand, new techniques of hacking and cybercriminal activities were created, for which the Internet became a kind of perfect environment for development. Now, perhaps, the next stage of technological progress is taking place, consisting of the transition of the fourth into the fifth technological revolution and the development of 5.0 technology supported by the implementation of artificial neural networks based on artificial neural networks subjected to a process of deep learning constantly improved generative artificial intelligence technology. The development of generative artificial intelligence technology and its applications will significantly increase the efficiency of business processes, increase labor productivity in the manufacturing processes of companies and enterprises operating in many different sectors of the economy. Accordingly, after the implementation of generative artificial intelligence and also Big Data Analytics and other technologies typical of the current fourth technological revolution, the competition between IT professionals operating on two sides of the barricade, i.e., in the sphere of cybercrime and cybersecurity, will probably change. However, what will be the essence of these changes?

In view of the above, I address the following question to the esteemed community of scientists and researchers:

What do you think about this topic?

What is your opinion on this issue?

Please answer,

I invite everyone to join the discussion,

Thank you very much,

Best regards,

Dariusz Prokopowicz

Nowadays, Internet marketing is developing rapidly, including viral Internet marketing used on social media sites, among others, in the form of, for example, Real-Time marketing in the formula of viral marketing. It is also marketing aimed at precisely defined groups, audience segments, potential customers of a specific advertised product and/or service offering. In terms of improving Internet marketing, new ICT information technologies and Industry 4.0/5.0 are being implemented. Marketing conducted in this form is usually preceded by market research conducted using, among other things, sentiment analysis of the preferences of potential consumers based on verification of their activity on the Internet, taking into account comments written on various websites, Internet forums, blogs, posts written on social media. In recent years, the importance of the aforementioned sentiment analysis carried out on large data sets using Big Data Analytics has been growing, thanks to which it is possible to study the psychological aspects of the phenomena of changes in the trends of certain processes in the markets for products, services, factor markets and financial markets. The development of the aforementioned analytics makes it possible to study the determinants of specific phenomena occurring in the markets caused by changes in consumer or investor preferences, caused by specific changes in the behavior of consumers in product and service markets, entrepreneurs in factor markets or investors in money and capital markets, including securities markets. The results from these analyses are used to forecast changes in the behavior of consumers, entrepreneurs and investors that will occur in the following months and quarters. In addition to this, sentiment analyses are also conducted to determine the preferences, awareness of potential customers, consumers in terms of recognition of the company's brand, its offerings, description of certain products and services, etc., using textual data derived from comments, entries, posts, etc. posted by Internet users, including social media users on a wide variety of websites. The knowledge gained in this way can be useful for companies to plan marketing strategies, to change the product and service offerings produced, to select or change specific distribution channels, after-sales services, etc. This is now a rapidly developing field of research and the possibilities for many companies and enterprises to use the results of this research in marketing activities, but not only in marketing. Recently, opportunities are emerging to apply generative artificial intelligence and other Industry 4.0/5.0 technologies to analyze large data sets collected on Big Data Analytics platforms. In connection with the development of intelligent chatbots available on the Internet, recently there have been discussions about the possibilities of potential applications of generative artificial intelligence, 5G and other technologies included in the Industry 4.0/5.0 group in the context of using the information resources of the Internet to collect data on citizens, companies, institutions, etc. for their analysis carried out using, among other things, sentiment analysis to determine the opinion of Internet users on certain topics or to define the brand recognition of a company, the evaluation of product or service offerings by Internet users. In recent years, the scope of applications of Big Data technology and Data Science analytics, Data Analytics in economics, finance and management of organizations, including enterprises, financial and public institutions is increasing. Accordingly, the implementation of analytical instruments of advanced processing of large data sets in enterprises, financial and public institutions, i.e. the construction of Big Data Analytics platforms to support organizational management processes in various aspects of operations, including the improvement of customer relations, is also growing in importance. In recent years, ICT information technologies, Industry 4.0/5.0 including generative artificial intelligence technologies are particularly rapidly developing and finding application in knowledge-based economies. These technologies are used in scientific research and business applications in commercially operating enterprises and in financial and public institutions. In recent years, the application of generative artificial intelligence technologies for collecting and multi-criteria analysis of Internet data can significantly contribute to the improvement of sentiment analysis of Internet users' opinions and the possibility of expanding the applications of research techniques carried out on analytical platforms of Business Intelligence, Big Data Analytics, Data Science and other research techniques using ICT information technology, Internet and advanced data processing typical Industry 4. 0/5.0. Most consumers of online information services available on new online media, including social media portals, are not fully aware of the level of risk of sharing information about themselves on these portals and the use of this data by technological online companies using this data for their analytics. I am conducting research on this issue. I have included the conclusions of my research in scientific publications, which are available on Research Gate. I invite you to cooperate with me.

In view of the above, I address the following question to the esteemed community of scientists and researchers:

And what is your opinion on this topic?

What is your opinion on this issue?

Please answer,

I invite everyone to join the discussion,

Thank you very much,

Best wishes,

Dariusz Prokopowicz

Almost every major technological company operating with its offerings on the Internet either already has and has made its intelligent chatbot available on the Internet, or is working on it and will soon have its intelligent chatbot available to Internet users. The general formula for the construction, organization and provision of intelligent chatbots by individual technology companies uses analogous solutions. However, in detailed technological aspects there are specific different solutions. The differentiated solutions include the issue of the timeliness of data and information contained in the created databases of digitized data, data warehouses, Big Data databases, etc., which contain specific data sets acquired from the Internet from various online knowledge bases, publication indexing databases, online libraries of publications, information portals, social media, etc., acquired at different times, data sets having different information characteristics, etc.

In view of the above, I address the following question to the esteemed community of scientists and researchers:

And what is your opinion on this topic?

What is your opinion on this issue?

Please answer,

I invite everyone to join the discussion,

Thank you very much,

Best wishes,

Dariusz Prokopowicz

The topic of my master's thesis is "The use of Big Data and Data Science technologies to assess the investment attractiveness of companies." I plan to design and implement a machine for market analysis, using graphs. I will be grateful to you for links to scientific articles on this topic.

How does AI differ from standard biostatistics?

What is “big data”? How does AI enable big dataset analysis? Can we argue for their synchronization for effective use of these two for efficient delivery in several diverse sectors (business, science, government).

Dear All,

I appreciate your kind help in doing the survey on the role of big data in cybersecurity, which I have given below. Your answers will be a big help in my research and knowledge.

https://docs.google.com/forms/d/e/1FAIpQLSeMa3xJNdU6AM5gW2HOOPUpNGddRouhDqZ8NHfhozECvrVNLQ/viewform?usp=sf_link

Pass it on to all who're knowledgeable about big data and cybersecurity.

Sincerely Regards

Maytha Alshamsi

Generative artificial intelligence technology, which, with the help of deep learning applied to artificial neural networks, is taught specific skills, performing activities previously performed only by humans, is finding more and more new applications in various branches of the economy, in various types of business entities. Generative artificial intelligence technology helps in solving complex tasks that require processing large sets of data in a relatively short period of time, which is already far beyond human capabilities. Therefore, more and more new tools based on generative artificial intelligence technology are being created, which are engaged in solving specific tasks, in which a number of specific criteria are required to be met in order to create a precisely specified product, project, innovative solution, finding a solution to a complex problem, and so on. This type of complex problem solving includes the creation of new solutions for green technology and eco-innovation, which can be helpful in connection with the need to accelerate and increase the efficiency of carrying out the green transformation of the economy, including the green transformation of the energy sector based on, among other things, the development of renewable and emission-free energy sources. However, paradoxically, generative artificial intelligence technology performing certain outsourced tasks i.e. based on large data sets collected in data centers, using Big Data Analytics technological solutions consumes large amounts of electricity. In a situation where these large amounts of electricity are generated by burning fossil fuels through dirty combustion energy, the aforementioned new technological solutions increasingly categorized as Industry 5.0 are unfortunately not described as green, pro-climate, pro-environment, pro-environment, pro-environment, sustainable, pursuing sustainable development goals, etc. Accordingly, Big Data centers assisted by artificial intelligence technology should be developed to fit in with sustainability goals, not to generate high electricity consumption and/or to be powered by renewable and carbon-free energy sources. The aforementioned Big Data centers assisted by artificial intelligence technology should therefore be designed and built in such a way that power plants generating energy from renewable sources are also built next to them or above them if they are built underground, such as wind farms and/or photovoltaic panel installations or other power plants generating energy by other means but emission-free. In the future, these may also include a new generation of nuclear power plants generating energy from currently generated spent fuel waste from currently operating nuclear power plants operating on the basis of widespread traditional nuclear technologies. Besides, in the future, another solution for emission-free clean energy may be the use of a new generation of nuclear power based on cold fusion. In addition to the above, the technologies categorized as energy futures also include energy based on green hydrogen and new types of energy resources, which may be extracted from space. An effective combination of the above-mentioned technologies, i.e. green energy technologies and ICT and Industry 4.0/5.0 information technologies, may lead to the creation of AI-assisted Big Data green data centers.

In view of the above, I address the following question to the esteemed community of scientists and researchers:

And what is your opinion on this topic?

What is your opinion on this topic?

Please answer,

I invite everyone to join the discussion,

Thank you very much,

Best regards,

Dariusz Prokopowicz

Hi,

I need help processing for big data. I am trying figure out ''How does Twitter process big data? Also, what can be done with big data?'' Can you explain briefly and/or can you suggest references for this subject?

Thanks..

Best regards...

With the advent of new technologies (e.g., AI, big data), according to reports, the shortage of technological talents may affect the operations of organizations. So how should the human resources department improve the retention rate of existing talents?

I believe that effective motivational strategies should be adopted, such as employee experiences such as flexible working hours or remote working.

I would like to ask for your opinion on this aspect, thank you very much!

1. Malware execution and analysis on IOT and GPU-based processer devices?

in that IOT and GPU both have different processors in that case how same or different kind of malware will impacts, how to do forensics investigation to club both the technologies ?

2.Malware execution and analysis on IOT and GPU-based processer devices?

in that IOT and GPU both have different processors in that case how same or different kind of malware will impacts in bid data environment

Dear Researchers, I am looking for open-source Gravity/Magnetic data for interpretations via Oasis montaj Software and Voxi Earth Modeling. Please specify some sources where form the data is easily accessible.

Regards,

Ayaz

Hello everyone. I have question about obtaining data from Internet.

In my research I will analyze comments from websites and social media platforms. And I am searching for applications/apps/technologies other tools to download comments from Internet to my computer.

Do you know any tools/apps to download comments for free?

There is around 10.000 comments and if I would copy/paste one by one it would take me a lot of time. I want to obtain data quickly.

Do you have any suggestions for me?

Thank you so much for help.

Regards, Nejc

What are the analytical capabilities of processing large data sets extracted from the Internet and realized by the most powerful quantum computers, which also apply Industry 4.0/5.0 technologies, including generative artificial intelligence and Big Data Analytics technologies?

Can the scale of data processing carried out by the most powerful quantum computers be comparable to the processing that takes place in the billions of neurons of the human brain?

In recent years, the digitization of data and archived documents, the digitization of data transfer processes, etc., has been progressing rapidly.

The progressive digitization of data and archived documents, digitization of data transfer processes, Internetization of communications, economic processes but also of research and analytical processes is becoming a typical feature of today's developing developed economies. Accordingly, developed economies in which information and computer technologies are developing rapidly and finding numerous applications in various economic sectors are called information economies. The societies operating in these economies are referred to as information societies. Increasingly, in discussions of this issue, there is a statement that another technological revolution is currently taking place, described as the fourth and in some aspects it is already the fifth technological revolution. Particularly rapidly developing and finding more and more applications are technologies classified as Industry 4.0/5.0. These technologies, which support research and analytical processes carried out in various institutions and business entities, include Big Data Analytics and artificial intelligence, including generative artificial intelligence with artificial neural network technology also applied and subjected to deep learning processes. As a result, the computational capabilities of microprocessors, which are becoming more and more perfect and processing data faster and faster, are gradually increasing. There is a rapid increase in the processing of ever larger sets of data and information. The number of companies, enterprises, public, financial and scientific institutions that create large data sets, massive databases of data and information generated in the course of a specific entity's activities and obtained from the Internet and processed in the course of conducting specific research and analytical processes is growing. In view of the above, the opportunities for the application of Big Data Analytics backed by artificial intelligence technology in terms of improving research techniques, in terms of increasing the efficiency of the research and analytical processes used so far, in terms of improving the scientific research conducted, are also growing rapidly. By using the combined technologies of Big Data Analytics, other technologies of Industry 4.0/5.0, including artificial intelligence and quantum computers in the processing of large data sets, the analytical capabilities of data processing and thus also conducting analysis and scientific research can be significantly increased.

In view of the above, I address the following question to the esteemed community of scientists and researchers:

What do you think about this topic?

What is your opinion on this issue?

Please answer,

I invite everyone to join the discussion,

Thank you very much,

Thank you,

Warm regards,

Dariusz Prokopowicz

The above text is entirely my own work written by me on the basis of my research.

In writing this text I did not use other sources or automatic text generation systems.

Copyright by Dariusz Prokopowicz