Big Data - Science topic

As engineering and material science increasingly adopt data-driven approaches, I am intrigued by the rapid advancements in supervised Machine Learning (ML) and Deep Learning (DL). These tools have proven transformative, but I am eager to explore a question at the heart of innovation in this space:

Specifically, I am inviting AI researchers and practitioners to share insights into:

In my concrete durability and sustainability research, I aim to leverage AI tools for academic insights and to provide actionable solutions for designing better, longer-lasting materials. To achieve this, it is critical to understand and integrate the most cutting-edge tools available today. For example:

I am curious to hear from the community:

This is not just a call to share tools or techniques. It is an invitation to discuss how these advancements can be meaningfully applied to solve practical problems in engineering and beyond. I look forward to hearing about your experiences, discoveries, and perspectives on the evolving role of AI in research and practice.

Let’s connect and explore how we can drive innovation with purpose and impact!

What are the effective methods of managing big data and using it in different areas?

“How is AI and advanced technology (such as machine learning and big data analytics) impacting personalized marketing strategies and customer experien?

BIG DATA no necesita calcular el tamaño de la muestras Estadísticas.

Cuáles son sus implicaciones?

adelmormr@gmail.com.

Gracias

Classification or clustering algorithms and how to apply them to existing big data from social networks can help in this. Precise methods and work processes are desired.

Aim is damaged part detection (i.e. scratch, dent, holes, pits, etc.): e.g. Automotive/Industrial part stamping quality inspection, Rolled sheet metal surface inspection, Pit/hole detection in cast iron automotive parts

Would want public data.

Is one of the important factors influencing the valuation of cryptocurrencies the information posted on online social media from the world of politics?

In early December 2024, the price of Bitcoin exceeded the psychological level of $100,000. Besides, the value of the entire Bitcoin market exceeded the global valuation of silver reserves. In 2024, the price of Bitcoin has already risen more than 50 percent. However, investing in cryptocurrencies carries a high risk of incurring financial losses, because just as cryptocurrency valuations can rise quickly they can also fall even faster. Investment strategies vary, and in these strategies speculative investors rely on various types of information appearing in various media, including online social media. It happened more than once that large changes in the valuation of specific cryptocurrencies that took place in a relatively short period of time were influenced by entries, posts, short information posted on online social media by media-recognized individuals. In addition, it was sometimes difficult for Internet users to verify the question of reliability of these entries, posts, not always fully confirmed information.

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

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,

I would like to invite you to join me in scientific cooperation,

Dariusz Prokopowicz

2025 International Conference on Big Data, Communication Technology and Computer Applications(BDCTA 2025) will be held on February 14-16, 2025 in Kuala Lumpur, Malaysia.

The conference will have a variety of academic reports, special seminars and technical presentations, covering multiple hot topics such as large-scale data processing, artificial intelligence and machine learning, 5G/6G communication technology, network security, edge computing, the Internet of Things, and smart cities. Participants will have the opportunity to have in-depth exchanges, share the latest research results and technological breakthroughs, and explore new opportunities for interdisciplinary cooperation. We look forward to meeting you in Kuala Lumpur to join us in a technological feast.

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

◕ Communication and Information Engineering

· Ad hoc & sensor networks

· Embedded networks

· High-speed access networks

· Home and SOHO networks

· IPv6 deployment & migration

· Local area networks

· Optical networks

......

◕ Information and network security

· Network Intrusion Detection and Prevention

· Malware and botnets

· Communication Privacy and Anonymity

· Distributed denial of service

· Web security

· Information leakage

· Information theft

......

All papers submitted to BDCTA 2025 will be reviewed by two or three expert reviewers from the conference committees. After a careful reviewing process, all accepted papers will be published in the Conference Proceedings, and submitted to EI Compendex, Scopus for indexing.

Full Paper Submission Date: December 30, 2024

Notification Date: January 14, 2025

Final Paper Submission Date: January 31, 2025

Conference Dates: Februray 14-16, 2025

Please send the full paper(word+pdf) to Submission System:

In my opinion, AI and Big Data technologies are being combined in a number of areas where analysis of large data sets combined with intelligent algorithms allows for better results and automation of processes. One of the key applications is personalization of services and products, especially in the e-commerce and marketing sectors. By analyzing behavioral data and consumer preferences, AI systems can create personalized product recommendations, dynamic advertisements or tailored pricing strategies. The process is based on the analysis of huge datasets, which allow precise prediction of consumer behavior.

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:

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

In recent years, the development of digitization and Internetization of the economy has accelerated. These processes in particular Digitization and Internetization of the economy are now occurring simultaneously in many areas of economic processes, in the functioning of business entities and public, financial and other institutions operating in economies that are increasingly becoming knowledge-based economies. The Covid-19 pandemic has further accelerated the processes of digitization and Internetization of the economy. More and more companies and enterprises operating in various industries and sectors of the economy are expanding their operations via the Internet, remotely providing their services and selling their products through e-commerce.

The development of information processing technologies in the era of the current Fourth Technological Revolution is determined by the development and growth of the applications of ICT, Internet technologies and advanced data processing technologies Industry 4.0. The current technological revolution associated with the concept of Industry 4. 0 is motivated by the development of such technologies as: Big Data Analytics, Data Science, cloud computing, machine learning, multi-layer artificial neural networks-based deep learning, generative artificial intelligence, personal and industrial Internet of Things, Business Intelligence analytics, autonomous robots, horizontal and vertical data system integration, multi-criteria simulation models, digital twins, Blockchain, smart technologies, cyber security instruments, augmented and virtual reality, and other technologies for advanced multi-criteria processing of large sets of data and information.

The processes of digitalization and Internetization of the economy are determined by “upstream” processes, i.e., those inspired by public institutions, including computerization, Internetization of public offices, and “downstream” determinants, i.e., e.g., the growth of e-commerce, the increase in the share of transactions and payments made electronically via the Internet, the development of Internet banking and the shift of a significant part of business in many companies to remote service conducted via the Internet.

In the future, due to the development of applications of Industry 4.0 technologies, including the ever-improving generative artificial intelligence, the employment of people in certain types of professions, occupations in certain industries and sectors of the economy may significantly decline. It is likely that significant declines in employment in some branches and sectors of the knowledge economy will occur in the next few years. There are many indications that in a few years the scale of applications of generative artificial intelligence in various areas of production processes and services provided will increase strongly. On the other hand, the development of applications of constantly improving generative artificial intelligence and other Industry 4.0 technologies will also create new jobs in analytical and research fields. However, just a few years ago, the prevailing thesis was that the scale of job losses would be much greater than the new jobs being created. In a period of rapid technological advances in recent years in the development of generative artificial intelligence technology and its applications, the emerging new jobs in which generative AI technologies are used is the aforementioned thesis begins to lose its relevance. In this regard, it is not out of the question that the number of new jobs and professions being created may fully manage the potentially emerging gap of lack of employment generated by AI technology replacing human labor. Regardless of the nature of the changes in labor markets that will result from the implementation of new Industry 4.0 technologies into various fields of business activities of companies and enterprises, it is certain that the impact on labor markets will be significant and substantial in the years to come.

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

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

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

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

I invite you to familiarize yourself with the issues described in the publications given above, and to scientific cooperation in these issues.

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

Call for papers: 2025 4th International Conference on Electronic Information Engineering and Data Processing(EIEDP 2025) will be held on January 17-19, 2025 in Kuala Lumpur, Malaysia.

大会官网(投稿网址）：https://ais.cn/u/32mQzi

大会时间：2025年1月17-19日

大会地点：马来西亚-吉隆坡

提交检索：EI Compendex, Scopus

主办单位：马来亚大学

第四届电子信息工程与数据处理国际学术会议（EIEDP 2025）将于2025年1月17-19日在马来西亚首都吉隆坡举办。EIEDP 2025聚焦于电子信息技术的最新进展、大数据处理的创新方法、人工智能应用的深入探索、以及物联网技术的前沿趋势，旨在为全球电子信息工程与数据处理领域的专业人士搭建一个高端交流平台。会议将汇集来自世界各地的顶尖学者、行业领导者、研究人员与工程师，共同分享研究成果、交流实践经验、探讨未来挑战与机遇。会议特色拟定包括主题演讲、分论坛报告、以及口头和海报展示，为参会者提供全方位、深层次的学术交流体验。我们特别邀请了多位国际知名专家进行特邀报告，分享他们在各自研究领域的深刻洞察与独到见解。

算法

计算机网络

计算机软件

计算伦理

计算机模拟

神经网络

计算机安全

图像处理

人工智能

其他相关主题...

电力电子技术

通信信号处理

数字信号处理

微波技术与天线

电磁场与电磁波

信号与图像处理

自动控制和智能控制

计算机与网络技术

网络与办公自动化技术

多媒体技术

单片机技术

电子系统设计工艺

电子设计自动化（EDA）技术

其他相关主题

数据挖掘

大数据技术与应用

大数据运维

数学与应用科学

信息与计算科学

计算机科学

数据科学与大数据技术

控制科学

系统科学

物联网

计算机控制技术

其他相关主题

EIEDP 2025所有的投稿都必须经过2-3位组委会专家审稿，经过严格的审稿之后，会议所录用论文将以论文集的形式交由SPIE - The International Society for Optical Engineering (ISSN: 0277-786X)出版，出版后提交 EI Compendex, Scopus检索。

1、作者参会：一篇录用文章允许一名作者免费参会；

2、主旨报告：可申请主题演讲，由组委会审核；

3、口头报告：可申请口头报告，时间为15分钟；

4、海报展示：可申请海报展示，A1尺寸，彩色打印；

5、听众参会：不投稿仅参会，也可申请演讲及展示；

6、报名参会：https://ais.cn/u/32mQzi

As cyber threats become more sophisticated and persistent, the field of Strategic Cyber Intelligence (SCI) has emerged as a critical discipline to support long-term decision-making at organizational and governmental levels. Given the increasing reliance on Big Data and NoSQL systems for handling vast and diverse datasets, I am curious about how these technologies can be leveraged effectively in SCI.

1. What are the key challenges in integrating Big Data and NoSQL systems into SCI workflows?

2. Are there any existing models or frameworks that combine these technologies for analyzing and predicting cyber threats?

3. How can the community address scalability, data quality, and real-time analysis needs in this context?

I welcome insights, practical examples, or references to recent studies that explore this intersection. Let’s discuss how we can advance SCI research and applications through innovative data-driven approaches.

The function and popularity of Artificial Intelligence are soaring by the day. Artificial Intelligence is the ability of a system or a program to think and learn from experience. AI applications have significantly evolved over the past few years and have found their applications in almost every business sector.

AI enhances decision-making, automates repetitive tasks and drives innovation throughout various industry sectors. AI can answer vital questions, which might not even cross a human mind and process big data in fractions of seconds to spot patterns that humans would never see, resulting in better decision-making.

AI also paves the way for personalization, improves customer experience and might one-day re solve some of the planet's grand challenge problems like climate change or disease prevention. As AI further develops, it has the ability to change our lives and work.

source: 24 Artificial Intelligence Applications in 2025

https://www.simplilearn.com/tutorials/artificial-intelligence-tutorial/artificial-intelligence-applications

This question explores how managers can utilize modern technologies, such as artificial intelligence and big data, as strategic tools to support decision-making processes within organizations. It aims to understand how these technologies can enhance organizational efficiency and improve overall performance.

Big data is a very hot topic right now, I am gathering information to complete my thesis, so kindly answer with proper citations this will help me include it in my thesis and improve my knowledge about big data enabling practices, and the difference between developed countries' big data practices vs developing countries.

One of the earliest applications of game theory to terrorist risk management was implemented half a century ago at the Los Angeles airport. A limited number of hired security guards with dogs to target areas (suspicious passengers) and at times when the risk level is significantly elevated. The number of crimes has dropped in a big way. In Poland, the Ideas NCBiR research team is currently working on developing an AI-based system for SOK, i.e., the Railway Protection Service to improve the work of SOK employees and improve risk management processes. Patrols of SOK, i.e. Railway Protection Service control railroad traction preventing theft of railroad traction and counteracting acts of vandalism consisting of graffiti painting by mischievous youths, etc. must on the one hand move around the railroad area in a random manner, but on the other hand SOK employees having this system should appear in places and times where there will be a much higher level of risk of committing a specific crime. This should significantly increase the level of detected cases of attempted crime, i.e. “in the act of committing a crime”, and effectively prevent a significant proportion of cases of crime that could happen without the use of this system. The system created in this function is intended to support the man and not replace him. It suggests solutions, paths to navigate the area of the turn, the user can use these hints, reject them or ask for others. Besides, the system is supposed to learn from the experience of users - humans. There are more and more applications of this kind in improving risk management systems.

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

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

I invite you to get acquainted with the issues described in the above-mentioned publications, as well as to scientific cooperation in these issues.

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 subject?

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 to mine data, Best analytics tools and best latest practices?

[CFP]2024 4th International Symposium on Artificial Intelligence and Big Data (AIBFD 2024) - December

AIBDF 2024 will be held in Ganzhou during December 27-29, 2024. The conference will focus on the artificial intelligence and big data, discuss the key challenges and research directions faced by the development of this field, in order to promote the development and application of theories and technologies in this field in universities and enterprises, and provide innovative scholars who focus on this research field, engineers and industry experts provide a favorable platform for exchanging new ideas and presenting research results.

◕Track 1：Artificial Intelligence

Natural language processing

Fuzzy logic

Signal and image processing

Speech and natural language processing

Learning computational theory

......

◕Track 2：Big data technology

Decision support system

Data mining

Data visualization

Sensor network

Analog and digital signal processing

......

Full Paper Submission Date: December 23, 2024

Registration Deadline: December 23, 2024

Conference Dates: December 27-29, 2024

The key technology enabling the construction of quantum computers is quantum cryptography. The technology is expensive and available only to the largest corporations and organizations, research and development institutions with large financial capitals enabling the development and implementation of quantum computer technology. The applications of quantum computers are various. Probably, many companies and businesses in various sectors of the economy, which already use various Industry 4.0/5.0 technologies, including cloud computing of large sets of data and information, use analytics based on integrated information systems using Big Data Analytics and/or Business Intelligence, Internet of Things technologies, Blockchain, machine learning, deep learning, generative artificial intelligence, digital twins, etc. would be interested in applying quantum computer technology to their business, to improve it, to improve their computerized management systems, if the price of this technology dropped significantly. The price drop factor is an important determinant of the spread of the implementation of this technology to many companies, enterprises operating in the SME sector, which do not have large financial budgets for the implementation of development and implementation projects involving the implementation of the latest highly advanced digital technologies, etc., into their business activities. At present, such technologies are developed in a small number of research and development centers, research laboratories run by scientific institutes of universities or large technology companies with large financial funds to allocate to such development and implementation projects.

The use of quantum computers makes it possible, among other things, to create microscopes that image very small objects, such as cell fragments, with the ability to view them live in real time. Currently, such observations are made with electron microscopes, with which, for example, cell organelles are observed but frozen cells rather than live, i.e. biologically functioning cells in real time. A typical feature of quantum computers is that quantum software is not written in Java-type programming languages, but the computer systems used in quantum computers rely on quantum circuit design. The results of research in cosmology, astrophysics, theories on the functioning of key cosmic objects in the Universe are concerned with black holes found in space, for example. However, has anyone seen a black hole realistically up close, no one. Of course, by writing these words, I do not intend to undermine any theories about black holes functioning in space. The point is that quanta can be measured only the necessary research infrastructure is needed. The necessary research infrastructure is expensive and therefore available only to some research, development and implementation centers located in a few research institutes of universities and some large technology companies. The quantum technology necessary to build quantum computers can be developed in various ways. Rather, ions, vortices of currents in superconductors will be controlled by photons, so it makes sense to develop quantum technology based on photons. Any kind of microparticles that can be controlled, changed in some respect, intentionally change their form can be used to build quantum computers. With quantum computers, it will be possible to solve complex, multifaceted problems in which large amounts of data are processed. Therefore, when this technology becomes widespread, its price will be significantly reduced then perhaps in the future the world will move to quantum cryptography. The largest financial investments in the development of quantum technology are made in developed countries where large subsidies from the state's public finance system are allocated for R&D purposes, i.e. primarily in the US, China and Europe. A common feature of the various types of applications of quantum computers is that these computers would enable the processing of much larger volumes of data and information in a relatively short period of time within the framework of multi-criteria, advanced data processing carried out on computerized Big Data Analytics platforms and with the involvement also of other technologies typical of Industry 4.0/5.0. Greater capabilities for advanced, multi-criteria processing of large sets of data and information will allow the solution of complex analytical problems concerning various spheres of human activity and various issues operating in various industries and sectors of the economy.

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

I invite you to familiarize yourself with the problems described in the article given above and to scientific cooperation in this field.

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

Link: https://www.techscience.com/cmc/special_detail/AI_data_analysis

The rapid evolution of Artificial Intelligence (AI), data analysis techniques, and big data has significantly transformed various fields. AI technologies have shown tremendous potential in enhancing various applications, predicting trends, and automating complex tasks. Natural Language Processing (NLP) has also advanced, enabling better understanding and generation of human language. However, these advancements come with challenges such as data privacy concerns, algorithmic biases, and the constant need for adaptation to emerging trends. This special issue aims to explore both the advancements and challenges associated with AI, data analysis, and big data, highlighting their impact on various domains and ensuring robust implementations.

This special issue seeks to provide a comprehensive overview of the latest advancements in AI, data analysis, and big data technologies and their applications across different fields. It aims to present innovative solutions, evaluate their effectiveness, and discuss the challenges faced in implementing these technologies in real-world scenarios

The scope includes but is not limited to :

·  AI-driven prediction and automation systems

·  Advanced data analysis techniques

·  Big data processing and Social Media analysis

·  Natural Language Processing (NLP) advancements and applications

·  Ethical and privacy considerations in AI and data analysis

·  Case studies and real-world applications

·  Challenges in integrating AI with existing systems

·  Future trends and emerging technologies in the field

·  AI and Machine Learning for Predictive Analytics

·  Behavioral Analytics an big data

·  Automated Systems and Process Optimization

·  Big Data Analytics and Processing Techniques

·  Natural Language Processing (NLP) for Various Applications

·  Privacy and Ethical Issues in AI Solutions

·  Data Analysis Techniques for Predictive Modelling

·  Challenges in AI-Enhanced Systems

We would be honored if you could consider contributing to our special issue . I will assist you throughout the submission process to ensure everything proceeds smoothly. You can reach me at elaine.lu@techscience.com for any further information.

We look forward to your valuable contribution to this topic.

[CFP]2024 4th International Conference on Artificial Intelligence, Virtual Reality and Visualization(AIVRV 2024) - November

As the leader of the global trend of scientific and technological innovation, China is constantly creating a more open scientific and technological innovation environment, expanding the depth and breadth of academic cooperation, and building a shared innovation community. These efforts are making new contributions to globalization and building a community with a shared future for mankind.

In order to adapt to the changing world in the new era and the rapid development of China, 2024 4th International Conference on Artificial Intelligence, Virtual Reality and Visualization will be held on November 01-03 2024, in Nanji, China. This conference will focus on the latest research fields of "artificial intelligence", "virtual reality" and "visualization technology", and provide a forum for experts, professors, scholars, engineers, etc. from domestic and foreign universities, scientific research institutes, enterprises and institutions. An international platform for sharing professional experience, expanding professional networks, exchanging new ideas face-to-face and presenting research results, discussing key challenges and research directions faced by the development of this field, with a view to promoting the development and application of theories and technologies in this field in universities and enterprises , but also for the participants to establish business or research contacts and to find global partners in future careers.

◕ Artificial Intelligence and Its Applications

Biometric

Pattern recognition

Machine vision

Expert system

Deep learning

Smart search

Automatic programming

Intelligent control

Smart robot

Language and Image Understanding

Genetic programming

Natural language processing

Computer Vision and Robotics

Adaptive system

Smart agent

......

◕Virtual reality and its applications

System Components

Virtual reality platform

AI Platform for VR/AR

Immersive environments and virtual world generation

Optimized and realistic rendering

The semantics and cognition of virtual reality

depth perception

multimodal perception

Multimodal interaction and VR/AR experience

Application of Remote Sensing Image Processing

Application of virtual human body

Augmenting the Customer Experience with Virtual Reality

Virtual Plant Growth Simulation

Human-computer interaction technology

Mobile and Wearable Technologies and Applications

Multi-sensory experience based on virtual reality

Regions and Digital Cities, Digital

3D data acquisition technology

3D reconstruction of medical images

...... ◕ Visualization and its applications

Visualization and Visual Analytics Theory

scientific visualization

information visualization

Visual Analysis

Visual data processing and processing

Interaction Design and Display Technology in Visualization

Visual Design and Systems

Visual Assessment and Cognition

Large (scale) data visual analysis

......

Registration Deadline: October 10, 2024

Full Paper Submission Date: September 29, 2024

Final Paper Submission Date: September 30, 2024

Conference Dates: November 01-03, 2024

When using data collected from sensors to analyze movement patterns in congested metropolises.

While reading articles related to EMG analysis, two concepts caught my attention. One of them is the Hilbert transform, and the other is the envelope. First, I haven't come across this transform often in many software tools (although I might have missed it). Instead, it is stated that rectification and RMS methods can also be used. I have come to the conclusion that the Hilbert transform might be more suitable if you need instantaneous amplitude and phase information or if you want to examine very subtle amplitude changes and instantaneous phase differences in the signal.

I usually used rectification and RMS methods. So, in this case;

I would appreciate your insights on this topic. Thank you in advance.

How do i write an abstract on my research topic called "Analyzing the concept of Data Privacy in big data analytics in banking?

Call for papers: 2024 4th International Conference on Big Data, Artificial Intelligence and Risk Management (ICBAR 2024) will be held in Chengdu, China on November 15-17, 2024.

大会官网(投稿网址）：https://ais.cn/u/yq6zii

大会时间：2024年11月15-17日

大会地点：中国-成都

收录检索：EI Compendex，Scopus

第四届大数据、人工智能与风险管理国际学术会议（ICBAR2024）将于2024年11月15-17日在中国成都隆重举行。大会由四川省人工智能学会、中国民用航空飞行学院联合主办，中国民航飞行学院理学院、民航飞行技术与飞行安全重点实验室共同承办，上海海事大学、吉隆坡大学、AEIC学术交流中心共同协办。进入21世纪以来，大数据、人工智能与风险管理科学的进步，推动了社会经济的繁荣发展，众多高校与企业研发了许多相关的技术和产品，取得了丰硕的学术成果和应用转化。大会旨在为从事大数据、人工智能与风险管理科技研究的专家学者、工程技术人员、研发人员提供一个共享科研成果和前沿技术，了解学术发展趋势，拓宽研究思路，加强学术研究和探讨，促进学术成果产业化合作的平台。

基于大数据的科学研究

大数据处理的算法与编程技术

大数据搜索算法与系统

移动和普适计算中的大数据分析

数据挖掘与机器学习工具

大数据分析管理...

人工智能技术与应用

人工智能的基本理论与应用

智能化、知识化系统

智能与人工智能控制

智能自动化

机器学习...

风险管理

风险管理理论、方法与应用

风险识别、分析和应对

风险规避

风险自留

风险分担

风险转移...

会议投稿经过2-3位组委会专家严格审核后，最终所录用的论文将被ACM ICPS (ACM International Conference Proceeding Series)出版论文集，并提交至ACM Digital library，EI Compendex, Scopus，谷歌学术检索。

1、作者参会：一篇录用文章允许一名作者免费参会；

2、主讲嘉宾：申请主题演讲，由组委会审核；

3、口头演讲：申请口头报告，时间为15分钟；

4、海报展示：申请海报展示，A1尺寸；

5、听众参会：不投稿仅参会，也可申请演讲及展示。

6、投稿参会链接：https://ais.cn/u/yq6zii

How advances in technology such as AI, IoT and big data analytics can optimise crop yields, reduce resource use and increase resilience to climate change, ultimately contributing to global food securi

Dear colleagues,

I am honoured to co-edit a new special issue on data driven approaches for safety in industrial sites.

More in detail, this Special Issue aims at collecting powerful evidence of new methods and datasets in the field of industrial safety and environmental risks related to industrial processes, specifically through data mining techniques and big data.

Check this link and let me know your interest!

Modern artificial intelligence systems, cannot function without access to large databases when processing a specific type of information. Examples of types of such information could be speech, texts or video information. How do living organisms manage to survive without such databases when processing various types of information simultaneously?

I'm currently seeking postdoctoral research opportunities in multidisciplinary areas within Computer Science, with an interest in both academic and industry settings. My research interests include advanced cloud-based data management for smart buildings, NLP for low-resource languages like Amharic, AI and machine learning, data science and big data, human-computer interaction, and robotics. I'm open to discussing potential opportunities and collaborations in these fields. Please feel free to contact me if you are aware of any suitable positions.

Do experts have journals in the field of artificial intelligence and big data that are not indexed by SCI or EI?

IEEE 2024 5th International Conference on Big Data & Artificial Intelligence & Software Engineering (ICBASE 2024) will be held on September 20-22, 2024 in Wenzhou, China.

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

· Big Data Analysis

· Deep Learning、Machine Learning

· Artificial Intelligence

· Pattern Recognition

· Data Mining

· Cloud Computing Technologies

· Internet of Things

· AI Applied to the IoT

· Clustering and Classificatio

· Soft Computing

· Natural Language Processing

· E-commerce and E-learning

· Wireless Networking

· Network Security

· Big Data Networking Technologies

· Graph-based Data Analysis

· Signal Processing

· Online Data Analysis

· Sequential Data Processing

All papers, both invited and contributed, the accepted papers, will be published and submitted for inclusion into IEEE Xplore subject to meeting IEEE Xplore’s scope and quality requirements, and also submitted to EI Compendex and Scopus for indexing.

Full Paper Submission Date: July 10,2024

Registration Deadline: August 5, 2024

Final Paper Submission Date: August 20, 2024

Conference Dates: September 20-22, 2024

Please send the full paper(word+pdf) to Submission System:

The key issue in this kind of consideration is to answer the question of what is a fully new solution, what is an innovation. Generative artificial intelligence technology, combined with other Industry 4.0/5.0 technologies, including Big Data Analytics and computers equipped with high-performance microprocessors, enable multi-criteria, advanced processing of large information datasets in many times less time than if a human were to do it without the use of the aforementioned technologies. Advanced information systems equipped with generative artificial intelligence technology backed by high computing power computers make it possible, through a process of deep learning, to train intelligent chatbots to carry out specific tasks and commands much faster and more efficiently than a human can do the same. In a situation where intelligent advanced language models that enable a machine to carry on a conversation with a human were learned on large collections of data and information, including online databases of scientific knowledge that contain millions of scientific texts and/or databases of other publications, the texts generated by intelligent chatbots will be created much faster than a human would and, in addition, will be generated on the basis of processing, analysis, inference, etc. of thousands or millions of different source texts. This is virtually impossible for a human to do. However, whether the texts generated by intelligent chatbots will contain innovative solutions, whether they will be created in an innovative way, whether they will contain proposals for innovative implementation of a specific task, command, etc., this will already depend mainly on how this issue will be programmed in these machines by a human. Unless, in the future, autonomously functioning highly intelligent robots will be created, which will be equipped with a strong general artificial intelligence and will thus be able to act independently within a certain range of independence, will be able to self-improve, repair their own faults, will be able to learn just like a human being, over time will become better and better at performing various types of activities previously performed exclusively by humans then perhaps they will also learn to solve certain tasks in a highly innovative manner themselves. But this is a matter for consideration for the perspective of the next dozen or so years of dynamic development of AI technology and its applications.

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

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

I invite you to familiarize yourself with the issues described in the publications given above, and to scientific cooperation in these issues.

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

Dear Colleagues,

The Faculty of Computer Science, Universitas Singaperbangsa Karawang, Karawang, Indonesia, proudly presents:

The 1st International Conference on Network, Information Technology, and Computer Science of Singaperbangsa (ICONICSS 2024)

🗓️ 03 October 2024

🏠 Hybrid conference combining both online participation through virtual meetings and in-person attendance in Bandung, West Java, Indonesia.

Theme:

"The Impact of Digital Innovation on Advancing Sustainable Development Objectives"

Opening Speaker:

Prof. Ade Maman Suherman, S.H., M.Sc. (Rector of Universitas Singaperbangsa Karawang)

Keynote Speakers:

1. Prof. João Saraiva (Green Software Laboratory, Universidade do Minho, Braga, Portugal)

2. Prof. Ir. Teddy Mantoro, M.Sc., Ph.D. (Senior Member, IEEE, Professor of Computer Science, Sampoerna University, Indonesia)

3. Prof. Anton Satria Prabuwono, Ph.D. (Professor at the Faculty of Computing and Information Technology in Rabigh, King Abdulaziz University)

We invite all academicians and professionals to participate in this conference. Accepted articles will be processed and published in International Proceeding indexed by SCOPUS after reviewing the process.

ICONICSS 2024 accepts full papers in these areas, but not limited to:

* Artificial Intelligence

* Augmented and Virtual Reality

* Bioinformatics

* Big Data

* Cloud Computing

* Computational Modeling

* Computer Graphics

* Computing in Medicine and Biology

* Computing in Social Sciences

* Computer Optimization

* Computer Vision

* Data Communication Networking

* Data Mining

* Decision Support Systems

* Digital Image Processing

* Distributed Systems

* E-Commerce

* E-Government

* E-Health

* Embedded Systems

* Geographic Information Systems

* High-Performance Computing

* Human-Computer Interaction

* Information Retrieval

* Information Security

* Information Systems

* Internet of Things

* IT Governance

* Linked Data

* Machine Learning

* Natural Language Processing

* Numerical Analysis in Science & Engineering

* Semantic Web

* Socio-Informatics

* Soft Computing

* Software Engineering

* Smart City

* Speech Recognition

* User Experience and Design

* Wireless Sensor Networks

Important Dates - Times in UTC:

🗓️ 25 May 2024 (Abstract Submission Opens)

🗓️ 17 July 2024 (Abstract Submission Deadline)

🗓️ 20 July 2024 (Acceptance Notification)

🗓️ 31 July 2024 (Payment Deadline)

🗓️ 17 August 2024 (Full Paper Submission)

🗓️ 03 October 2024 (Conference Day)

For further information and to register:

🌐 [ICONICSS Website](https://iconicss.unsika.ac.id/)

✉️ iconicss@cs.unsika.ac.id

Contact Persons:

📞 Ratna Mufidah, M.Kom. (085215957346)

📞 Mina (https://s.id/iconicss_group)

Please share this event!

BCM systems, i.e., operating according to the Business Continuity Management concept, consist of managing business continuity. The implementation of Business Continuity Management in an enterprise is an important part of taking anti-crisis measures. Under Business Continuity Management, an organization, business entity, company, enterprise, financial or public institution takes steps to ensure that its critical business functions are available to customers, suppliers and regulators in the event of a crisis. Business Continuity Management is the concept of managing an organization, including taking the actions that an organizational unit must perform to ensure the continuity of its business. Depending on what type of entity it is, this will include continuity in the flow of data, raw materials and/or liquidity. In Polish corporate jargon, you may come across a situation to refer to this function as "CDN" which simply means "continuity will follow."

The purpose of Business Continuity Management is to build mechanisms to protect companies from the negative impact of disruptions, so that if a crisis occurs, business processes can continue. Business Continuity Management is not a process that a business unit implements during a failure, disruption or disaster just refers to activities that are carried out on a daily basis. This plan of action is aimed at eliminating or reducing the risk of an emergency, crisis situation and maintaining readiness for immediate response should the said crisis situation occur.

Ensuring the continuity of the operation of an economic entity or institution is one of the priority tasks of participants, including companies and enterprises operating in different types of markets: financial, logistics, suppliers of raw materials, recipients of finished goods, etc. The special role of ensuring business continuity in the context of the effective functioning of economic entities concerns institutions that are participants in financial systems, including, among others, commercial banks that co-create what is referred to as the "bloodstream of the economy."

In a situation of economic crisis in many companies and enterprises there is an escalation of various types of problems and an increase in risks. In crisis situations, the level of risk and likelihood of business interruption increases. Business interruption causes a spiral of consequences, resulting in a negative reaction from stakeholders and having an adverse impact on society. The consequence can be a lowering of the company's reputation, diminished value and brand. The consequence can also be a deterioration of the company's valuation by the market and rating agencies. This type of situation can also generate difficulties in raising the financial capital necessary for the continued development of the business entity. Besides, Business Continuity Management is part of corporate management. Accordingly, Business Continuity Management is a set of good practices providing guidelines for redesigning the processes of manufacturing products and providing services in such a way as to increase the organization's resilience to the occurrence of harmful disruptions of interrupted processes and incurring losses.

In recent years, the implementation of new ICT, Internet and Industry 4.0/5.0 communication technologies, including generative artificial intelligence and Big Data Analytics technologies to business entities contributes to increasing the efficiency of processes carried out within various spheres of business activity. The aforementioned technologies also support the management processes of companies, enterprises, financial or public institutions. The application of generative artificial intelligence and Big Data Analytics technologies makes it possible to improve computerized support systems for business continuity management processes.

The key issues of opportunities and threats to the development of artificial intelligence technology are described 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

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!

International Journal of Complexity in Applied Science and Technology, IJCAST aims to address various emerging complexities in applied science and technology by disseminating novel intelligent methodologies and techniques. These new methods and technologies will reveal the principles underlying various engineering applications. No Fee for publications.

Topics covered include

As information systems that allow the largely automated performance of Business Intelligence analytics become an important factor in supporting the process of business management, so the importance of the new technologies of Industry 4.0/5.0, including generative artificial intelligence and Big Data Analytics, to improve the said analytical processes is growing. On the one hand, the obvious point is that the application of generative artificial intelligence technology and Big Data Analytics can improve the processes of predictive analytics carried out within the framework of Business Intelligence, and thus the effectiveness of business, economic and financial analytics supporting the management process of an organization, enterprise, company, corporation, etc. can be increased. However, on the other hand, it is also important to precisely define the determinants that determine the performance of such analytical processes, to point out the role of the new technologies of Industry 4.0/5.0, including generative artificial intelligence and Big Data Analytics technologies in the processes of predictive analytics carried out within the framework of Business Intelligence, and to estimate the extent of the influence of these technologies on the improvement of the said analytical processes.

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

I invite you to familiarize yourself with the problems described in the publications given above and to cooperate with me in scientific research on these problems.

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

Can the application of generative artificial intelligence technology and Big Data Analytics improve the processes of predictive analytics carried out within the framework of Business Intelligence and thus the effectiveness of business, economic and financial analytics supporting the management process of an organization, enterprise, company, corporation, etc., can be increased? And if so, how and to what extent?

Can the use of generative artificial intelligence and Big Data Analytics technologies improve the processes of predictive analytics performed as part of Business Intelligence?

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

financial forcast

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

International Journal of Complexity in Applied Science and Technology,

Topics covered include

My research title is Enhancing Business Intelligence: Leveraging Big Data Analytics and Machine Learning in ERP Systems for Strategic Decision Making for Logistic Businesses.

I would like to ask fellow researchers to share some insights on what research methodology I can or should use for my particular topic.

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

As the computing power of processors and the ability to process large and increasingly large data sets and information grows rapidly year on year thanks to technological advances, so also the sets of data and information, so also the possibilities for multi-criteria, automated analysis of large data sets on Big Data Analytics platforms, including the use of Industry 4.0 technologies, including machine learning, deep learning and artificial intelligence are also growing rapidly. Therefore, through the use of the aforementioned technologies, including artificial intelligence and Big Data Analytics, it is also possible to improve the processes of analysing the sentiment of large collections of publications and conducting semi-automated bibliometric research on large collections of publications, including scientific publications collected in online indexing databases of scientific publications. In view of the above, the application of new Industry 4.0 technologies, including new generations of artificial intelligence and computerised analytical Big Data Analytics platforms, may increase the scale of automation of the processes of conducting sentiment analysis on large sets of data and information contained in publications included in online indexing databases of scientific and/or professional publications and in the framework of bibliometric research carried out. The key determinants limiting the possibility of conducting multi-criteria analyses, including sentiment analyses of the content of multiple online publications, professional and/or scientific journals, in addition to technological limitations, include the issue of IT compatibility of Big Data Analytics platforms and the structural and technical conditions of online journal portals and online indexing databases of specific publications, including scientific publications.

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

What is your opinion on this topic?

What is your opinion on this subject?

Please respond,

I invite you all to discuss,

Thank you very much,

Warm regards,

Dariusz Prokopowicz

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.

This questionnaire will take no more than 5 minutes to complete, and your participation is confidential!

Thank you in advance for your time and contribution!

To take part, please click on the link below: https://forms.gle/fzzHq4iNqGUiidTWA

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