Machine Learning - Science topic

I asked some LLMs to craft some meaningless but beautiful word salads for me. Then tested many LLMs with those word salads (just gave them the word salads as prompts). Every single tested LLM praised the passage and then gave an in-depth analysis of the "beautiful poem" they had received! Reminded me of the critiques of many post-modern or modern paintings that praise pieces of ***.

Then I told the AIs that they were fooled by word salads created by their mates. Their responses were really impressive.

Think we can use this word salad challenge as a new (or if not new, as a good) way of testing LLM intelligence and understanding. Also the ability of LLMs in creating good-enough word salads was impressive.

Hello! I am pursuing Ph.D. in Environmental Sciences from Sambalpur University, India. My thesis topic broadly focuses on physico-chemical analysis of water and soil samples. I eagerly want to incorporate a modeling approach into pre-existing databases for water sample and soil sample parameters. Which type of machine-learning languages will be helpful for me?

By: Giovanni Cerulli

Timberlake Consultants

financial forcast

Dear Researchers,

Our book, "Climate-Smart Water Resource Management: A Paradigm Shift from Ancient to Modern Practices with Integrated Technologies," has been accepted by Elsevier! We're inviting chapter contributions from global experts and researchers on AI-ML, remote sensing, GIS, LLMs, and more. The abstract submission deadline is 30th June, while the full chapter submission is due 31st October. For more details on guidelines, get the Book Flyer (also attached) from here:

Sincerely,

Book Volume Editors

In recent days (end of May 2024) in the country where I operate, weather aura conditions have been favorable for the production of clean energy generated mainly through renewable and emission-free energy sources. Accordingly, it was reported that a record amount of clean, emission-free energy was generated in Poland on 27.5.2024 from renewable and emission-free energy sources, including mainly photovoltaics and wind. It's just a pity that much of this energy has gone to waste, as the dominant companies in the energy market have for many years ignored the need to invest in the development of transmission grids and storage facilities for energy from new photovoltaic and windmill installations put up on rooftops by prosumer citizens. Paradoxically, however, the share of renewable and zero-emission energy in the overall energy mix in Poland is still relatively low, amounting to only about 1/4 of the energy source mix, as a result of years of subsidizing from the state's public finance system the development of dirty combustion energy based on the generation of energy from the combustion of fossil fuels while blocking and restricting the development of RES. Paradoxically, when the development of onshore wind energy was blocked in Poland in 2016, it was already clear from a number of analyses and studies conducted in various research centers operating in different parts of the world that since the middle of the 2nd decade of the 21st century, clearly the cheapest energy is wind and solar energy, and the most expensive energy production is dirty combustion energy based on burning coal and lignite. Since the middle of the 2nd decade of the 21st century, the aforementioned gap between steadily, year by year cheap photovoltaic technology and other energy eco-technologies, cheap RES energy production and more expensive energy produced from burning fossil fuels has been growing steadily. This issue is particularly important because there may be further energy crises in the future, such as those that occurred in the 1970s and in 2022. The demand for electricity production will also grow in the future. Paradoxically, the ongoing process of global warming will also contribute to an increase in energy demand in the future. The various types of projects, processes and activities that are being launched to reduce the scale of the negative effects of the progressive global warming process, such as the production and use of refrigeration equipment, equipment for obtaining clean water in the situation of increasingly frequent periods of drought, desalination of seawater, in addition to the development of electromobility, etc., will consume large amounts of additional energy, which is already starting to run out at times. In the context of these aggravating problems, there is a growing importance of systems and instruments to help and/or facilitate the management of energy production and use, taking into account the optimization of production from specific, different types of energy sources, under conditions of changing weather and climatic conditions and the existing structure of different energy sources, including renewable and non-renewable, sustainable and combustion, emission-free and emission-free energy, etc., within a specific, occurring mix of energy sources in the country. The various different energy sources are significantly different in terms of various determinants, which include varying levels of variability in energy production caused by objective external factors, e.g. changes in weather conditions, significant variation in the necessary financial outlays that are incurred in the investment processes for the construction of a particular type of power plant or electric and/or thermal power plant, variation in the period of implementation of investment projects for the construction of power plants generating energy within the framework of renewable or non-renewable energy sources, local and national natural and geological conditions conditioning the possibility of building a particular type of power plant, variation in the economic and technological development of the country, equipment of the financial system with financial capital that can be applied to support the development of investments in the energy sector, etc. In view of the above, new ICT and Industry 4.0/5.0 information technologies, including but not limited to generative artificial intelligence and Big Data Analytics technologies, may prove helpful in optimizing energy production under different types of energy sources and optimizing the investment processes carried out within the power sector. The research shows that new technologies of artificial intelligence and Big Data Analytics can help optimize the production and use of energy generated from various energy sources within the existing specific structure of the mix of energy sources in the national energy sector, as well as optimize investment processes for the construction of specific types of power plants, energy-generating power plants, investments in the development of transmission networks, energy storage, etc. The key issue, therefore, will be how the integrated information systems built from modules equipped with the aforementioned new technologies will be designed and built so that the processes of optimizing the level of energy production from certain different sources operating within the national energy source mix; optimizing energy transmission, consumption and storage; and optimizing the investment processes in which various investment projects for the construction of power plants and energy-generating power plants within different types of energy sources are implemented simultaneously.

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:

I described the key issues concerning the problem of green transformation of the economy in the following article:

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

I invite you to familiarize yourself with the issues described in the above-mentioned publications 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:

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

I have a dataset and want to study the factors that positively or negatively affect a target variable. Based on features related to academic performance, demographics, etc., I want to aggregate these features into different factors and determine which factors most influence the target variable.

thank you very much in advance

I understand that there are many machine learning predictive model, I just want to know the best model that can be used to predict customer behavior with the least amount of error .

With the rapid advancements in artificial intelligence, many aspects of academic research are expected to undergo significant transformations. I’m particularly interested in discussing:

I’d love to hear your insights and opinions on these topics. How should the academic community prepare for these changes, and what steps can be taken to ensure a smooth transition?

There are many indications that artificial intelligence technology can be of great help in terms of discovering and creating new drugs. Artificial intelligence can help reduce the cost of developing new drugs, can significantly reduce the time it takes to design and create new drug formulations, the time it takes to conduct research and testing, and can thus provide patients with new therapies for treating various diseases and saving lives faster. Thanks to the use of new technologies and analytical methods, the way healthcare professionals treat patients has been changing rapidly in recent times. As scientists manage to overcome the complex problems associated with lengthy research processes, and the pharmaceutical industry seeks to reduce the time it takes to develop life-saving drugs, so-called precision medicine is coming to the rescue. It takes a lot of time to develop, analyze, test and bring a new drug to market. Artificial intelligence technology is particularly helpful in this regard, including reducing the aforementioned time to create a new drug. When creating most drugs, the first step is to synthesize a compound that can bind to a target molecule associated with the disease. The molecule in question is usually a protein, which is then tested for various influencing factors. In order to find the right compound, researchers analyze thousands of potential candidates of different molecules. When a compound that meets certain characteristics is successfully identified, then researchers search through huge libraries of similar compounds to find the optimal interaction with the protein responsible for the specific disease. In contrast, many years of time and many millions of dollars of funding are required to complete this labor-intensive process today. In a situation where artificial intelligence, machine learning and deep learning are involved in this process, then the entire process can be significantly reduced in time, costs can be significantly reduced and the new drug can be brought to the pharmaceutical market faster by pharmaceutical companies. However, can an artificial intelligence equipped with artificial neural networks that has been taught through deep learning to carry out the above-mentioned processes get it wrong when creating a new drug? What if the drug that was supposed to cure a person of a particular disease produces a number of new side effects that prove even more problematic for the patient than the original disease from which it was supposed to be cured? What if the patient dies due to previously unforeseen side effects? Will insurance companies recognize the artificial intelligence's mistake and compensate the family of the deceased patient? Who will bear the legal, financial, ethical, etc. responsibility for such a situation?

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

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

Recently, I spent a significant amount of time developing a new model, but its accuracy is lower than some existing models. My model's accuracy is above 90%, while some existing models achieve 95-96% accuracy. Is this work still publishable? If so, why? Additionally, how should I handle the recent work and model comparison part?

I would appreciate any insights or guidance on this matter.

Thank you.

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.

I'm currently in my project year, keen solutions would be appreciated and acknowledged. Thank you

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

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

I am currently working on a financial project and require an expert in machine learning. Specifically, I need expertise in tree boosting, neural networks, random forests, linear and nonlinear frailty models, as well as ensemble methods. In addition to discussing the work, I will need the source code files of the models and analyses we develop. I hope this clarifies my requirements.

I'm thinking to do some optimisation of geometric parameters using OpenFOAM to find 'best' design - for a non-profit project. Thinking of using CFD in combo with Machine Learning. Basically a massive parameter sweep using CFD then using those (hopefully) empirical results to predict in-between results via ML. Has anyone tried this before?

Hello everyone,

I am currently 26 years old (feeling like I am making a lot of mistakes at this young age), finishing my Master's degree, and planning to continue onto a PhD and possibly a PostDoc. I am very passionate about pursuing a career in research. Given the vast experience within this community, I would appreciate any advice on this journey.

Thank you for your time and insights. I look forward to learning from all of you!

In a situation where companies, enterprises, financial and public institutions have a built in cybercrime risk management system, including email anti-spam applications, anti-virus systems, complex login tools, backap systems for data contained on hard drives, firewalls, cyber threat early warning systems, etc., then most cybercrime attacks targeting these business entities prove to be ineffective, and those that are effective cause very limited problems, financial losses, etc. However, there are still many business entities, especially companies and SMEs, that do not have complex, high-tech, integrated systems built to manage the risk of cybercrime and/or loss of sensitive data stored in databases. In recent years, one of the most serious cybercrime problems causing serious financial losses in some companies, enterprises, public institutions include cyberattacks used by cybercriminals with ransomware-type viruses. A successful attack carried out using ransomware viruses results in infecting a computer, blocking users, company employees from accessing the company's internal systems, stealing or blocking access to data collected in the company's databases, information stored on hard drives, etc., with a simultaneous demand to pay a ransom to remove the imposed blockades. In Poland, of the companies attacked with ransomware viruses, as many as 77 percent agree to pay the ransom. So security systems are still too poorly organized in many companies and institutions. In many business entities, systems for managing the risk of cybercrime and/or loss of sensitive data archived in internal databases are still not professionally built. Cybercrime risk management in many companies and enterprises apparently works poorly or not at all. Since generative artificial intelligence technology is being applied in many areas of cyber-security, so the question arises, could the application of this technology to detect cyber-crime attacks carried out with ransomware-type viruses significantly increase the level of cyber-security in many companies, enterprises, financial and public institutions?

I am conducting research in the problems of analyzing cybercriminal attacks conducted using ransomware viruses and in improving cyber security systems. I have included the conclusions of my research in the following articles:

Analysis of the security of information systems protection in the con-text of the global cyberatomy ransomware conducted on June 2, 2017

Development of malware ransomware as a new dimension of cybercrime taking control of IT enterprise and banking systems

Determinants of the development of cyber-attacks on IT systems of companies and individual clients in financial institutions

The Impact of the COVID-19 Pandemic on the Growing Importance of Cybersecurity of Data Transfer on the Internet

Cybersecurity of Business Intelligence Analytics Based on the Processing of Large Sets of Information with the Use of Sentiment Analysis and Big Data

THE QUESTION OF THE SECURITY OF FACILITATING, COLLECTING AND PROCESSING INFORMATION IN DATA BASES OF SOCIAL NETWORKING

I invite you to get acquainted with the issues described in the above-mentioned publications 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:

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

IEEE 2024 4th International Symposium on Computer Technology and Information Science(ISCTIS 2024) will be held during July 12-14, 2024 in Xi’an, China.

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

1. Computer Engineering and Technology

Computer Vision & VR

Multimedia & Human-computer Interaction

Image Processing & Understanding

PDE for Image Processing

Video compression & Streaming

Statistic Learning & Pattern Recognition

......

2. Information Science

Digital Signal Processing (DSP)

Advanced Adaptive Signal Processing

Optical communication technology

Communication and information system

Physical Electronics and Nanotechnology

Wireless communication technology·

......

All accepted papers of ISCTIS 2024 will be published in conference proceedings by IEEE, which will be submitted to IEEE Xplore,EI Compendex, Scopus for indexing.

Full Paper Submission Date: June 20, 2024

Registration Deadline: June 25, 2024

Final Paper Submission Date: June 26, 2024

Conference Dates: July 12-14, 2024

Dear Research Community,

We are Team Tech Wing from the American International University-Bangladesh (AIUB), and we are excited to announce that we are looking for collaborators to join us in our research endeavors. Our team specializes in various aspects of data science, including data preprocessing, analysis, and visualization, with proficiency in R and Python.

- Expertise in Data Preprocessing: Our team excels in cleaning, transforming, and preparing data for analysis, ensuring high-quality and reliable datasets.

- Advanced Data Analysis: We apply sophisticated statistical methods and algorithms to uncover insights and trends.

- Visualization Proficiency: We create compelling visualizations to present data in an understandable and impactful way.

- Algorithm Development and Performance Analysis: We are experienced in developing and assessing the performance of various algorithms, particularly in string manipulation and algorithm efficiency.

- Machine Learning Projects: We have a strong background in implementing and fine-tuning machine learning models to solve complex problems.

- Programming Languages: R, Python, C++

- Technical Writing: LaTeX for professional and well-formatted documentation and paper writing.

- Machine learning: Development and application of ML algorithms for diverse research purposes.

We are dedicated to delivering timely and precise work, ensuring that all projects meet the highest standards of quality and rigor. Our team is passionate about contributing to meaningful research and is always ready to tackle new challenges.

If you have any ideas or projects, particularly those involving string manipulation, algorithm analysis, or any innovative machine learning applications, we would love to hear from you. We are open to collaborating on survey research, developing new methodologies, or applying existing techniques to novel problems.

Please feel free to reach out to us to discuss potential collaborations. We look forward to the possibility of working together and contributing to groundbreaking research.

Best regards,

Lead, Team Tech Wing

American International University-Bangladesh (AIUB)

Email: redwan.stu@gmail.com

Dear Research Community,

We are Team Tech Wing from the American International University-Bangladesh (AIUB), and we are excited to announce that we are looking for collaborators to join us in our research endeavors. Our team specializes in various aspects of data science, including data preprocessing, analysis, and visualization, with proficiency in R and Python.

- Expertise in Data Preprocessing: Our team excels in cleaning, transforming, and preparing data for analysis, ensuring high-quality and reliable datasets.

- Advanced Data Analysis: We apply sophisticated statistical methods and algorithms to uncover insights and trends.

- Visualization Proficiency: We create compelling visualizations to present data in an understandable and impactful way.

- Algorithm Development and Performance Analysis: We are experienced in developing and assessing the performance of various algorithms, particularly in string manipulation and algorithm efficiency.

- Machine Learning Projects: We have a strong background in implementing and fine-tuning machine learning models to solve complex problems.

- Programming Languages: R, Python, C++

- Technical Writing: LaTeX for professional and well-formatted documentation and paper writing.

- Machine learning: Development and application of ML algorithms for diverse research purposes.

We are dedicated to delivering timely and precise work, ensuring that all projects meet the highest standards of quality and rigor. Our team is passionate about contributing to meaningful research and is always ready to tackle new challenges.

If you have any ideas or projects, particularly those involving string manipulation, algorithm analysis, or any innovative machine learning applications, we would love to hear from you. We are open to collaborating on survey research, developing new methodologies, or applying existing techniques to novel problems.

Please feel free to reach out to us to discuss potential collaborations. We look forward to the possibility of working together and contributing to groundbreaking research.

Can we expect new results on dynamical systems using artificial intelligence?

Machine learning algorithms can be less accurate and powerful when working with small datasets because their ability to recognize patterns is generally proportional to the dataset's size. How much data is enough for Machine learning? What are the ways to use Machine learning when we have very limited data set?

Choosing the Right Tool: CPU vs GPU vs TPU for Machine Learning Optimization https://youtu.be/6OeicarGRlc In this video, we delve into the world of hardware choices for optimizing machine learning tasks: CPU, GPU, and TPU. Choosing the right tool can significantly impact the performance and efficiency of your machine learning models. We explore the strengths, weaknesses, and ideal use cases for CPUs, GPUs, and TPUs, helping you make informed decisions to maximize ML capabilities. 1. Understanding CPU, GPU, and TPU architectures 2. Comparative analysis of compute capabilities for ML workloads 3. When to use CPUs, GPUs, or TPUs based on dataset size and complexity 4. Cost considerations and budget-friendly options 5. Real-world examples and performance benchmarks Join us as we uncover the secrets behind selecting the optimal hardware for machine learning optimization! #CPU #GPU #TPU #MachineLearning #Hardware #Optimization #DeepLearning #NeuralNetworks #DataScience #Performance #MLModels Feedback link: https://maps.app.goo.gl/UBkzhNi7864c9BB1A LinkedIn link for professional queries: https://www.linkedin.com/in/professorrahuljain/ Join my Telegram link for Free PDFs: https://t.me/+xWxqVU1VRRwwMWU9 Connect with me on Facebook: https://www.facebook.com/professorrahuljain/ Watch Videos: Professor Rahul Jain  Link: https://www.youtube.com/@professorrahuljain

Welcome to our comprehensive guide on the classification of machine learning! In this video, we dive deep into the fundamental categories of machine learning, exploring both supervised and unsupervised learning.

#MachineLearning #SupervisedLearning #UnsupervisedLearning #AI #DataScience #KMeans #LinearRegression #LogisticRegression #DecisionTrees #Clustering #SupportVectorMachines #MachineLearningAlgorithms #TechEducation #LearnAI

Feedback link: https://maps.app.goo.gl/UBkzhNi7864c9BB1A

LinkedIn link for professional queries: https://www.linkedin.com/in/professorrahuljain/

Join my Telegram link for Free PDFs: https://t.me/+xWxqVU1VRRwwMWU9

Connect with me on Facebook: https://www.facebook.com/professorrahuljain/

Watch Videos: Professor Rahul Jain  Link: https://www.youtube.com/@professorrahuljain

Space supertelescopes, including one of the most modern and powerful space telescopes, which is the James Webb supertelescope, take many images of galaxies, suns, nebulae, etc., millions of light years distant. In distant galaxies, of which there are millions if not more in the Universe, there are many constellations numbering in the billions, planetary systems that contain many exoplanets. Many of these billions of exoplanets orbiting other suns in other planetary systems are similar in many ways to our plaenta Earth. For many thousands or millions of these exoplanets, the similarity of chemical element composition, physico-chemical conditions, temperature levels, chemical composition of inorganic compounds, atmospheric processes, surface formation, possible presence of water or highly alternative chemical-physical structures, etc. with what is found on Earth may be so great that it is highly likely that life is or has been found on many of these exoplanets. Most likely, these are different life forms to those we know. The dissimilarity of these life forms is determined by different conditions of physical and chemical processes, different composition of chemical elements, different chemical compounds, different atmospheric processes, different temperature ranges, different calendar of rotation around other suns, etc. Perhaps on some of these exoplanets where other life forms arose other intelligent beings also arose. Perhaps on some of these exoplanets where evolved life created other intelligent beings are also present advanced civilizations created by said other intelligent beings. Humanity has been searching for many years for answers to questions about the possible existence of other forms of life, other intelligent beings, other civilizations on distant exoplanets.For years, space supertelescopes have been involved for this purpose, which successively over time as space exploration technology advances, take more and more perfect photographs of more and more distant celestial bodies, galaxies, constellations, planetary systems, exoplanets. This produces a huge amount of data contained in the thousands or millions of photographs taken in this way. It would take many years for a human to analyze such a large amount of data contained in these photographs. Industry 4.0/5.0 technologies, including Big Data Analytics and generative artificial intelligence, can help analyze these large data sets contained in the aforementioned many photographs.

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

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

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

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

I am currently working on a research project that involves analyzing #ecological_momentary_assessment (#EMA) . Given the complexity and volume of real-time data collected, I am interested in leveraging #machine_learning techniques to enhance the analysis and interpretation of these data. Specifically, I am looking to identify key predictors and understand the interactions among multiple variables. I am reaching out to connect with researchers, data scientists, or professionals who have experience in applying machine learning to EMA data. If you have worked on similar projects or know someone who has, I would greatly appreciate your insights, advice, or any relevant resources you could share. Your expertise and guidance will be invaluable in advancing my research and ensuring robust, meaningful results. Please feel free to comment below or reach out to me directly if you can help or recommend someone who can.

I am trying to analyze the size of agarose beads in a cell counter chip. The image was taken using brightfield so I am having a hard time processing the image well enough to where ImageJ can differentiate between the different droplets. It would be a lot easier if there was a machine learning algorithm or open source code that can help me differentiate between the different droplets and record the size of each droplet. I attached the images with the droplets that need to be processed.

I am currently exploring research opportunities in data science, C++ string manipulation, algorithm hybrid approaches, and healthcare-related machine learning. Are there any ongoing projects or research initiatives in these domains where my skills in data analysis using R and Python, coupled with expertise in algorithmic string manipulation, could be of value? Additionally, I am eager to contribute to collaborative efforts or co-authorship opportunities in these areas. If you have any relevant projects or suggestions, I would greatly appreciate your insights and potential for collaboration.

Thank you for your consideration.

Machine Learning (ML)

When data are brought together for a given model, whether, Machine Learning (ML) will be able to figure out the areas, where field measurements are required?

Whether ML could accommodate the application of physical laws to field data, which would possibly reveal additional information about 'unmeasured' or 'difficult to measure' field properties?

Whether ML could offer insight to the system being modelled?

At least, ML could act as a Parsimonious model for any given physical system, which are based on the simplest conceptual mechanisms and employ fewest parameters, while also providing an acceptable representation of a given physical system by providing the basic insights to the system functioning and critical processes?

Whether the forecasts from ML could test hypotheses about system responses and allow quantitative comparisons of alternative proposed scenarios?

How exactly ML is expected to improve the model performance, given the fact that the complexity of petroleum reservoir systems and the uneven spread, poor quality or even absence of observed data present considerable difficulties for oil/gas drainage modelling?

Hi everyone, I'm a Masters student in Earthquake Engineering. As this is my 1st thesis, I am having a hard time selecting a topic to my thesis in. I am Seeking suggestions for a thesis topic that explores Earthquake engineering which includes Machine learning. Any ideas on this are welcome. Thanks!

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

On the one hand, the development of modern and safe nuclear power technology is needed especially in the situation of creating an important source of electricity as an intermediate step in the green transformation of the energy sector, which consists of shifting power generation based mainly on emission-intensive fossil fuel combustion to clean power generation based on emission-free, fully renewable energy sources. The application of new Industry 4.0/5.0 technologies, including but not limited to machine learning, deep learning, generative artificial intelligence, Big Data Analytics, multi-criteria simulation models, Internet of Things, Blockchain, digital twins, cloud computing, autonomous robots, etc., enables the generation of new innovative solutions, improvement of applications and safety systems of energy technologies, including nuclear energy technologies. Besides, thanks to the application of the above-mentioned technologies, there are new opportunities for optimization both in the design and implementation of investment projects for the construction of new power plants and optimization in the processes of managing energy production from various energy sources within the applied mix of energy sources, taking into account the changing conditions of energy consumption, forecasting changes in energy demand in the future, changes in the economic environment, climatic conditions and other factors modeling changes in energy production from various energy sources, including those that depend on specific atmospheric and climatic conditions such as wind and solar power.

However, on the other hand, the application of Industry 4.0/5.0 technologies, including primarily generative artificial intelligence in improving technology development processes, supporting research processes, supporting management processes, etc. concerning nuclear technology, including nuclear technology applied in fields other than energy, i.e., e.g., medicine, geology, construction analytics, etc., and in the military field, may generate various kinds of very serious risks.

A cinematic vision of the kind of risks that can result from the application of artificial intelligence technology in the military sphere, including in terms of supporting the management processes of military systems taking into account ballistic missiles equipped with nuclear payloads, i.e. carried by transcontinental missiles and/or launched from submarines and strategic bombers was presented many years ago in films of the drama, thriller and at the same time science fiction genre, such as the 1983 film titled “War Games”, directed by John Badham, and the continuation of this theme in a similar film titled “War Games: The Code of Death”. “War Games: Code of Death” from 2008, directed by Stuart Gillard.

In view of the above, for at least 4 decades now, there have been considerations about the serious risks arising from the application of artificial intelligence technology in the field of computerized weapons management systems, in the field of military defense of the country, including with regard to nuclear weapons.

In view of the above, in view of the fact that the application of Industry 4.0/5.0 technologies, including, first of all, generative artificial intelligence in the military field can generate various kinds of very serious risks so it is still topical to consider the validity and scope of optimal, safe application of AI technologies in this field.

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

I described the key issues concerning the problem of green transformation of the economy 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 invite you to discuss this important topic for the future of the planet's biosphere and climate.

The key issues of the problematic sources of Poland's exceptionally deep energy cross in 2022 are described 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:

Is it safe to apply generative artificial intelligence technology in conjunction with Big Data and other Industry 4.0/5.0 technologies to support the development of nuclear technology?

Is it safe to apply generative artificial intelligence technology in the development of nuclear technology?

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

How do we evaluate the importance of individual features for a specific property using ML algorithms (say using GBR) and construct an optimal features set for our problem.

image taken from: 10.1038/s41467-018-05761-w

Which Machine learning algorithms suits best in the material science for the problems that aims to determine the properties and functions of existing materials. Eg. typical problem of determination of band gap of solar cell materials using ML.

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

Evaluation Metrics | L-01 | Basic Overview

Welcome to our playlist on "Evaluation Matrices in Machine Learning"! In this series, we dive deep into the key metrics used to assess the performance and effectiveness of machine learning models. Whether you're a beginner or an experienced data scientist, understanding these evaluation metrics is crucial for building robust and reliable ML systems.

📷 Check out our comprehensive guide to Evaluation Matrices in Machine Learning, covering topics such as:

Accuracy

Precision and Recall

F1 Score

Confusion Matrix

ROC Curve and AUC

MSE (Mean Squared Error)

RMSE (Root Mean Squared Error)

MAE (Mean Absolute Error)

Stay tuned as we explore each metric in detail, discussing their importance, calculation methods, and real-world applications. Whether you're working on classification, regression, or another ML task, these evaluation matrices are fundamental to measuring model performance accurately.

Don't forget to subscribe for more insightful content on machine learning and data science! 📷

#MachineLearning #DataScience #EvaluationMetrics #ModelPerformance #DataAnalysis #AI #MLAlgorithms #Precision #Recall #Accuracy

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I am preparing a chapter for my research paper and I would like to know your opinion on the possible difference between the notion of interpretability and explainability of machine learning models. There is no one clear definition of these two concepts in the literature. What is your opinion about it?

I have a question that I would like to ask, for a data-driven task (for example, based on machine learning, etc.), what kind of data set is the advantage data set? Is there a qualitative or quantitative way to describe the quality of the data set?

Dear all,

I would like to publish my papers in a journal. Since it is strongly required to publish the paper in an international journal indexed by Scopus, I face some difficulties due to some fees that must be paid (which is very high) by the author.

My research areas are computer science, artificial intelligence, machine learning, Pattern recognition, natural language processing and Social Media Analytics.

Are there any Scopus-indexed journals without without any article processing charge or other hidden charges for publication and suitable for my research areas?

I would like to thanks for your kind help.

With best regards,

Amit

Conducting a SWOT analysis of the applications of artificial intelligence technology in business, in the business activities of companies and enterprises, shows that there are both many already and developing many more business applications of the said technology, i.e., many potential development opportunities are recognized in this field of using the achievements of the current fourth and/or fifth technological revolution in various spheres of business activity, as well as there are many risks arising from inappropriate, incompatible with the prevailing social norms, standards of reliable business activity, incompatible with business ethics use of new technologies. Among some of the most recognized negative aspects of improper use of generative artificial intelligence technology is the use of AI-equipped graphic applications available on the Internet that allow for the simple and easy generation of photos, graphics, images, videos and animations that, in the form of very realistically presented images, photos, videos, etc., depict something that never happened in reality, i.e., they graphically present images or videos presenting what could be described as “fictitious facts” in a very professional manner. In this way, Internet users can become disinformation generators in online social media, where they can post the said generated images, photos, videos, etc. with added descriptions, posts, comments, in which the said “fictitious facts” presented in the photos or videos will also be described in an editorially correct manner. Besides, the mentioned descriptions, posts, entries, comments, etc. can also be edited with the help of intelligent chatbots available on the Internet like Chat GPT, Copilot, Gemini, etc. However, misinformation is not the only serious problem as it has significantly intensified after OpenAI released the first versions of ChatGPT chatbot online in November 2021. A new category of technical operational risk associated with the new AI technology applied has emerged in companies and enterprises that implement generative artificial intelligence technology into various spheres of business. In addition, there is a growing scale of risks arising from conflicts of interest between business entities related to not fully regulated copyright issues of works created using applications and information systems equipped with generative artificial intelligence technology. Accordingly, there is a demand for the development of a standard of a kind of digital signature with the help of which works created with the help of AI technology will be electronically signed, so that each such work will be unique, unrepeatable and whose counterfeiting will thus be seriously hampered. However, these are only some of the negative aspects of the developing applications of AI technologies, for which there are no functioning legal norms. In the middle of 2023 and then in the spring of 2024, European Union bodies made public the preliminary versions of the developed legal norms on the proper, business-ethical use of technology in business, which were given the name AI Act. The legal normatives, referred to as the AIAct, contain a number of specific, defined types of AI technology applications deemed inappropriate, unethical, i.e. those that should not be used. The AIAct contains classified according to different levels of negative impact on society various types and specific examples of inappropriate and unethical use of AI technologies in the context of various aspects of business as well as non-business activities. An important issue to consider is the scale of the commitment of technology companies developing AI technologies to respect such regulations so that issues of ethical use of this technology are also defined as much as possible in technological aspects in companies that create, develop and implement these technologies. Besides, in order for AIACT's legal norms, when they come into force, not to be dead, it is necessary to introduce both sanction instruments in the form of specific penalties for business entities that use artificial intelligence technologies unethically, antisocially, contrary to AIAct. On the other hand, it would also be a good solution to introduce a system of rewarding those companies and businesses that make the most proper, pro-social, in accordance with the provisions of the AIAct, fully ethical use of AI technologies. In view of the fact that AIACT is to come into force only in more than 2 years so it is necessary to constantly monitor the development of AI technology, verify the validity of the provisions of AIAct in the face of dynamically developing AI technology, successively amend the provisions of the said legal norms, so that when they come into force they do not turn out to be outdated. In view of the above, it is to be hoped that, despite the rapid technological progress, the provisions on the ethical applications of artificial intelligence technology will be constantly updated and the legal normatives shaping the development of AI technology will be amended accordingly. If AIAct achieves the above-mentioned goals to a significant extent, ethical applications of AI technology should be implemented in the future, and the technology can be referred to as ethical generative artificial intelligence, which is finding new applications.

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 regards,

Dariusz Prokopowicz

I am trying to apply a machine-learning classifier to a dataset. But the dataset is in the .pcap file extension. How can I apply classifiers to this dataset?

Is there any process to convert the dataset into .csv format?

Thanks,

Since the release of an intelligent chatbot built on the ChatGPT language model on the Internet in November 2021, the scale of ongoing discussions on the topic of the impact of the development of artificial intelligence technology on labor markets has increased again. Each successive technological revolution has largely generated changes in labor markets. The increase in the scale of automation of manufacturing processes carried out as part of business operations was motivated by the reduction of operational personnel costs resulting from hired personnel. Automation of manufacturing processes, including processes of production and offering services, may also have reduced the level of personnel operational risk. As a result, companies, firms and, in recent years, financial institutions and public entities, through the implementation of ICT, Internet and Industry 4.0/5.0 technologies in various business processes, are improving the efficiency of business processes and increasing the economic profitability of these processes. In each of the previous four technological revolutions, in spite of changing technical solutions and emerging new technologies, analogous processes of using these new technological advances to increase the scale of automation of economic processes worked. In the era of the current fourth or fifth technological revolution, in which a special role is played by the development of generative artificial intelligence technology, applications of this technology in the development of robotics, building autonomous robots, increasing the scale of cooperation between humans and highly intelligent androids is also making a new appearance and another stage of increasing the scale of automation of manufacturing processes. However, what from the point of view of entrepreneurs thanks to the applied new technologies, the achieved automation of production processes is an increase in the efficiency of manufacturing processes, increasing the scale of economic profitability, etc., is, on the other hand, generating serious effects on labor markets, including, among other things, a reduction in employment in certain jobs. The largest scale of applied automation of economic processes and, at the same time, the largest scale of employment reduction was and is generated for those jobs that are characterized by a high level of repetition of certain activities. The activities carried out by employees that are characterized by a high level of repetitiveness were usually the first ones that could be and have been replaced by technology in a relatively simple way. this is also the case today in the era of the fifth technological revolution, in which highly advanced intelligent information systems and autonomous androids equipped with generative artificial intelligence technologies contribute to the reduction of employment in companies and enterprises where humans are replaced by such technology. A particular manifestation of these trends are the group layoffs announced starting in 2022 of employees, including IT specialists in technology companies that the aforementioned advanced technologies of Industry 4.0/5.0 are also creating, developing and implementing into their economic processes carried out in the aforementioned technology companies. Recently, there have been a lot of different kinds of predictive analysis results in the media suggesting which occupations and professions previously performed by people are most at risk of increasing unemployment in the future due to the development of business applications of generative artificial intelligence technologies. In the first months of ChatGPT's release, the Internet was dominated by a number of publications suggesting that a significant portion of jobs in many industries will be replaced by AI technology over the next few decades. Then, after another few months of the development of applications of intelligent chatbots, but also the revelation of many controversies and risks associated with it such as the development of cybercrime and disinformation on the Internet, this dominant opinion began to change in the direction of slightly less pessimistic. these less pessimistic opinions suggest that the technology of generative artificial intelligence does not necessarily deprive the majority of employees in companies and enterprises of their jobs only the majority of employees will be forced to use these new tools, applications, information systems equipped with AI technology as part of their work. Besides, the scale of the impact of new technologies on labor markets will probably not be the same across industries and sectors of the economy.

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

In the context of machine learning models for healthcare that predominantly handle discrete data and require high interpretability and simplicity, which approach offers more advantages:

Rough Set Theory or Neutrosophic Logic?

I invite experts to share their insights or experiences regarding the effectiveness, challenges, and suitability of these methodologies in managing uncertainties within health applications.

I am developing a machine-learning model for a Network Intrusion Detection System (IDS) and have experimented with several ensemble classifiers including Random Forest, Bagging, Stacking, and Boosting. In my experiments, the Random Forest classifier consistently outperformed the others. I am interested in conducting a statistical analysis to understand the underlying reasons for this performance disparity.

Could anyone suggest the appropriate statistical tests or analytical approaches to compare the effectiveness of these different ensemble methods? Additionally, what factors should I consider when interpreting the results of such tests?

Thank you for your insights.

Hello Researchers & Professors,

Limited research has been done on effect of high strain rate on concrete due to blast loading using machine learning techniques. According to study we want to collect experimental data ie database of high strain rate to apply new machine learning techniques. Humble request if someone have data about strain rate , kindly share us . so that we can use new approach for better results.

thanks & Regards

In recent years, more and more perfect robots equipped with artificial intelligence have been developed. New generations of artificial intelligence and/or machine learning technologies, when equipped with software that enables the creation of unique works, new creations, creative solutions, etc., can create a kind of artwork in the chosen field of creativity and artistry. If we connect a 3D printer to a robot equipped with an artificial intelligence system that is capable of designing and producing beautiful sculptures, can we thus obtain a kind of work of art?

When a robot equipped with an artificial intelligence system paints beautiful pictures, can the resulting works be considered fully artistic works of art?

If NO, why not?

And if YES, then who is the artist of the works of art created in this way, is it a robot equipped with artificial intelligence that creates them or a human being who created this artificial intelligence and programmed it accordingly?

What is your opinion on this topic?

What do you think about this topic?

Please reply,

I invite you all to discuss,

Thank you very much,

Best regards,

Dariusz Prokopowicz

..

In the context of online learning platforms, how can machine learning algorithms be utilized to analyze and predict student behavior patterns, and what are the potential applications of this predictive analysis in improving educational outcomes?

This question delves into the intersection of online learning and machine learning, focusing specifically on how predictive analytics can be leveraged to understand and influence student behavior.

Greetings everyone,

I am a BTech student pursuing my bachelor's degree in Information Technology with a keen interest in machine learning. I am actively seeking mentors or co-authors for collaborative research endeavors in this domain. If you are currently engaged in research on machine learning or related topics and are open to collaboration, I would greatly appreciate it if you could reach out to me.

While I possess a solid understanding of machine learning concepts and proficiency in Python, I find myself at a juncture where I am seeking guidance on how to delve into a more focused research topic. I am enthusiastic about the prospect of working under the mentorship of experienced researchers in this field to further develop my skills and contribute meaningfully to ongoing projects.

If you are interested in exploring potential collaborations or if you have any advice to offer on initiating research in machine learning, please feel free to message me. I am eager to engage in fruitful discussions and collaborative efforts within the research community.

Thank you for your attention, and I'm excited about the prospect of collaborating and learning from fellow enthusiasts in the research community.

Dear RG group,

We are going to examine different AI models on large datasets of ultrasound focal lesions with definitive (patological examination after surgery in malignant leasions and biopsy and follow up in benign ones) final diagnosis. I am looking for images obtained with different us scanners with application of different image optimisation techniques as eg harmonic imaging, compound ultrasound etc. with or without segmentation.

Thank you in advance for your suggestions,

RZS

..

Dear Colleagues,

Does anyone know about Universities that are offering (a) Ph.D. by prior publication (b) Ph.D. by portfolio?

I have two publications viz."Regression Testing in Era of Internet of Things and Machine Learning" and "Regression Testing and Machine Learning". The former has touched 1k+ copies and has a rating of 4.04 and the latter is a recent publication with 200+ copies with a rating of 4.04. This data is as per BookAuthority.org.

Also, the former is indexed in prestigious searches such as Deutsche Nationalbibliothek (DNB), GND Network, Crossref Metadata Search, and OpenAIRE Explore.

Any leads or pointers would be greatly appreciated.

Best Regards,

Abhinandan(919886406214).

References

1) https://orcid.org/0000-0002-8425-1493

2) https://bookauthority.org/

Hello everyone and thank you for reading my question.

I have a data set that have around 2000 data point. It have 5 inputs (4 wells rate and the 5th is the time) and 2 ouputs ( oil cumulative and water cumulative). See the attached image.

I want to build a Proxy model to simualte the cumulative oil & water.

I have made 5 models ( ANN, Extrem Gradient Boost, Gradient Boost, Randam forest, SVM) and i have used GridSearch to tune the hyper parameters and the results for training the models are good. Of course I have spilited the training data set to training, test and validation sets.

So I have another data that I haven't include in either of the train,test and validation sets and when I use the models to predict the output for this data set the models results are bad ( failed to predict).

I think the problem lies in the data itself because the only input parameter that changes are the (days) parameter while the other remains constant.

But the problem is I can't remove the well rate or join them into a single variable because after the Proxy model has been made I want to optimize the well rates to maximize oil and minimize water cumulative respectively.

Is there a solution to suchlike issue?

When a model is trained using a specific dataset with limited diversity in labels, it may accurately predict labels for objects within that dataset. However, when applied to real-time recognition tasks using a webcam, the model might incorrectly predict labels for objects not present in the training data. This poses a challenge as the model's predictions may not align with the variety of objects encountered in real-world scenarios.

Are there any solutions or opinions that experts can share to guide me further in improving the model?

Thank you for considering your opinion on my problems.

I'm looking for datasets for my research project based on smartphone addiction. Is there any dataset available based on Smartphone addiction?

I have come across packages that specialize in fitting energy and forces, but none seem to include stress. I would greatly appreciate it if you could recommend packages that are capable of fitting all three parameters—force, energy, and stress—for neural network interatomic potentials.

Dear researchers,

I am trying to fit a FTIR spectrum with a reference spectrum using linear regression. However, I ended up with errors regarding the shape mismatch of the files used. I have tried my best to solve it but I have exhausted the best of my knowledge. I seek your advice on this Python code or how to handle this dataset. Considering the size of the query, I am sharing the Stackoverflow link here.

Any help is highly appreciated.

I am working on the project to detect credit card fraud using machine learning. Looking for a latest dataset .

Thanks in advance

..

2024 4th International Conference on Machine Learning and Intelligent Systems Engineering (MLISE 2024) will be held on June 28- June 30, 2024 in Zhuhai China.

MLISE is conducting exciting series of symposium programs that connect researchers, scholars and students to industry leaders and highly relevant information. The conference will feature world-class presentations by internationally renowned speakers, cutting-edge session topics and provide a fantastic opportunity to network with like-minded professionals from around the world. MLISE propose new ideas, strategies and structures, innovating the public sector, promoting technical innovation and fostering creativity in development of services.

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

1. Machine Learning

- Deep and Reinforcement learning

- Pattern recognition and classification for networks

- Machine learning for network slicing optimization

- Machine learning for 5G system

- Machine learning for user behavior prediction

......

2. Intelligent Systems Engineering

- Intelligent control theory

- Intelligent control system

- Intelligent information systems

- Intelligent data mining

- AI and evolutionary algorithms

......

All papers, both invited and contributed, will be reviewed by two or three experts from the committees. After a careful reviewing process, all accepted papers of MLISE 2024 will be published in the MLISE 2024 Conference Proceedings by IEEE (ISBN: 979-8-3503-7507-7), which will be submitted to IEEE Xplore, EI Compendex, Scopus for indexing.

Submission Deadline: April 26, 2024

Registration Deadline: May 26, 2024

Conference Dates: June 28-30, 2024

*Using the invitation code on submission system/registration can get priority review and feedback

In my opinion, I could say:

As part of today's rapid technological advances, new technologies are being developed for Industry 4.0, including but not limited to artificial intelligence, machine learning, robotization, Internet of Things, cloud computing, Big Data Analytics, etc. The aforementioned technologies are being applied in various industries and sectors. The development of artificial intelligence generates opportunities for its application in various spheres of companies, enterprises and institutions; in various industries and services; improving the efficiency of business operations by increasing the scale of process automation; increasing the scale of business efficiency, increasing the ability to process large sets of data and information; increasing the scale of implementation of new business models based on large-scale automation of manufacturing processes, etc.

However, developing artificial intelligence uncontrollably generates serious risks, such as increasing the scale of disinformation, emerging fake news, including banners, memes containing artificial intelligence crafted photos, graphics, animations, videos presenting "fictitious facts", i.e. in a way that apparently looks very realistic describing, depicting events that never happened. In this way, intelligent but not fully perfect chatbots create so-called hallucinations. Besides, by analogy, just like many other technologies, applications available on the Internet equipped with generative artificial intelligence technology can be used not only in positive but also in negative applications.

On the one hand, there are new opportunities to use generative AI as a new tool to improve the work of computer graphic designers and filmmakers. On the other hand, there are also controversies about the ethical aspects and the necessary copyright regulations for works created using artificial intelligence. Sometimes copyright settlements are not clear-cut. This is the case when it cannot be precisely determined whether plagiarism has occurred, and if so, to what extent. Ambiguity on this issue can also generate various court decisions regarding, for example, the recognition or non-recognition of copyrights granted to individuals using Internet applications or information systems equipped with certain generative artificial intelligence solutions, who act as creators who create a kind of cultural works and/or works of art in the form of graphics, photos, animations, films, stories, poems, etc. that have the characteristics of uniqueness and uniqueness.

However, this is probably not the case since, for example, the company OpenAI may be in serious trouble because of allegations by the editors of the New York Times Journal suggesting that ChatGPT was trained on data and information from, among other things, online news portals run by the editors of the aforementioned journal. Well, in December 2023, the New York Times filed a lawsuit against OpenAI and Microsoft accusing them of illegally using the newspaper's articles to train its chatbots, ChatGPT and Bing. According to the newspaper, the companies used millions of texts in violation of copyright laws, creating a service based on them that competes with the newspaper. The New York Times is demanding billions of dollars in damages.In view of the above, there are all sorts of risks of potentially increasing the scale of influence on public opinion, the formation of the general public consciousness by organizations operating without respect for the law. On the one hand, it is necessary to create digital computerized and standardized tools, diagnostic information systems, to build a standardized system of labels informing users, customers, citizens using certain solutions, products and services that they are the products of artificial intelligence, not man. On the other hand, on the other hand, there should be regulations obliging to inform that a certain service or product was created as a result of work done not by humans, but by artificial intelligence. Many issues concerning the socially, ethically and business-appropriate use of artificial intelligence technology will be normatively regulated in the next few years.

Regulations defining the proper use of artificial intelligence technologies by companies developing applications based on these technologies, making these applications available on the Internet, as well as Internet users, business entities and institutions using intelligent chatbots to improve the operation of certain spheres of economic, business activities, etc., are being processed, enacted, but will come into force only in a few years.

On June 14, 2023, the European Parliament passed a landmark piece of legislation regulating the use of artificial intelligence technology. However, since artificial intelligence technology, mainly generative artificial intelligence, is developing rapidly and the currently formulated regulations are scheduled to be implemented between 2026 and 2027, so on the one hand, operators using this technology have plenty of time to bring their procedures and products in line with the supported regulations. On the other hand, one cannot exclude the scenario that, despite the attempt to fully regulate the development of applications of this technology through the implementation of a law on the proper, safe and ethical use of artificial intelligence, it will again turn out in 2027 that the dynamic technological progress is ahead of the legislative process that rapidly developing technologies are concerned with.

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