Machine Learning - Science topic

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

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

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

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?

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,

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

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

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

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

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

Dear ResearchGate Community,

I am in the process of developing a machine learning (ML) predictive model to study the degradation and performance of corroded prestressed steel in concrete structures. The objective is to utilize advanced ML algorithms to predict the long-term effects of corrosion on the mechanical properties of prestressed steel.

For this purpose, I am seeking access to the CEB-FIP database or any similar repository containing comprehensive data on corroded prestressed steel. This data is crucial for training and validating the ML models to ensure accurate predictions. I am particularly interested in datasets that include corrosion rates, mechanical property degradation, fatigue life, and other parameters critical to the structural performance of these materials.

If anyone has access to the CEB-FIP database or knows of similar databases that could serve this research purpose, I would greatly appreciate your assistance in gaining access.

Your support would be invaluable in furthering our understanding of material behavior in civil engineering and developing robust tools for predicting structural integrity.

I am open to collaborations and would be keen to discuss potential joint research initiatives that explore the application of machine learning in civil and structural engineering.

Thank you for your time and consideration. I look forward to any possible assistance or collaboration from the community.

Best regards,

M. Kovacevic

How do you become a Machine Learning(ML) and Artificial Intelligence(AI) Engineer? or start research in AI/ML, Neural Networks, and Deep Learning?

Should I pursue a "Master of Science thesis in Computer Science." with a major in AI to become an AI Engineer?

Please I need the reference about Classification or clustering ,supervised or unsupervised machine learning algorithms ,and specially (J48,Random Forest,Random Tree) please send me best reference which can help me in misbehavior detection in VANET.

How can machine learning algorithms be applied to improve soil health and fertility?

I am researching on automatic modulation classification (AMC). I used the "RADIOML 2018.01A" dataset to simulate AMC and used the convolutional long-short term deep neural network (CLDNN) method to model the neural network. But now I want to generate the dataset myself in MATLAB.

My question is, do you know a good sources (papers or codes) that have produced a dataset for AMC in MATLAB (or Python)? In fact, have they produced the In-phase and Quadrature components for different modulations (preferably APSK and PSK)?

I've recently released a software package that combines my research interests (history of science and statistics) and my day job (machine learning and statistical modelling) It is called timeline_ai (see https://github.com/coppeliaMLA/timeline_ai) It extracts and then visualises timelines from the text of pdfs. It works particularly well on history books and biographies. Here are two examples:

The extraction is done using a large language model so there are occasional inaccuracies and “hallucinations". To counter that I've made the output checkable. You can click on each event and it will take you the page the event was extracted from. So far it has performed very well. I would love some feedback on whether people think it would be useful for research and education.

How does the addition of XAI techniques such as SHAP or LIME impact model interpretability in complex machine learning models like deep neural networks?

We are trying to prepare landslide susceptibility map using ANN through WEKA software. We are facing some technical issue while running the final output in ARCGIS. The boundary of the area is not prominent and some zigzag lines with a dark area is appearing. Is there any tutorial or document that guide us how to perform the ANN through WEKA for susceptibility mapping .

It would be a great help, if someone able to guide us in sort out the technical issue, like where is the problem due to which boundary is not coming or how to fix this zig zag lines?

Thank you.

How can concepts from quantum computing be leveraged to enhance machine learning algorithms?

Perhaps in the future - as a result of the rapid technological advances currently taking place and the rivalry of leading technology companies developing AI technologies - a general artificial intelligence (AGI) will emerge. At present, there are unresolved deliberations on the question of new opportunities and threats that may occur as a result of the construction and development of general artificial intelligence in the future. The rapid technological progress currently taking place in the field of generative artificial intelligence in connection with the already high level of competition among technology companies developing these technologies may lead to the emergence of a super artificial intelligence, a strong general artificial intelligence that can achieve the capabilities of self-development, self-improvement and perhaps also autonomy, independence from humans. This kind of scenario may lead to a situation where this kind of strong, super AI or general artificial intelligence is out of human control. Perhaps this kind of strong, super, general artificial intelligence will be able, as a result of self-improvement, to reach a state that can be called artificial consciousness. On the one hand, new possibilities can be associated with the emergence of this kind of strong, super, general artificial intelligence, including perhaps new possibilities for solving the key problems of the development of human civilization. However, on the other hand, one should not forget about the potential dangers if this kind of strong, super, general artificial intelligence in its autonomous development and self-improvement independent of man were to get completely out of the control of man. Probably, whether this will involve mainly new opportunities or rather new dangers for mankind will mainly be determined by how man will direct this development of AI technology while he still has control over this development.

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 actual scenario of federated learning, the problem of heterogeneity is an inevitable challenge, so what can we do to alleviate the challenges caused by these heterogeneities?

2024 5th International Conference on Artificial Intelligence and Electromechanical Automation (AIEA 2024) will be held in Shenzhen, China, from June 14 to 16, 2024.

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

(1) Artificial Intelligence

- Intelligent Control

- Machine learning

- Modeling and identification

......

(2) Sensor

- Sensor/Actuator Systems

- Wireless Sensors and Sensor Networks

- Intelligent Sensor and Soft Sensor

......

(3) Control Theory And Application

- Control System Modeling

- Intelligent Optimization Algorithm and Application

- Man-Machine Interactions

......

(4) Material science and Technology in Manufacturing

- Artificial Material

- Forming and Joining

- Novel Material Fabrication

......

(5) Mechanic Manufacturing System and Automation

- Manufacturing Process Simulation

- CIMS and Manufacturing System

- Mechanical and Liquid Flow Dynamic

......

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

Full Paper Submission Date: April 1, 2024

Registration Deadline: May 31, 2024

Final Paper Submission Date: May 14, 2024

Conference Dates: June 14-16, 2024

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

Graph Machine Learning Applications for Architecture, Engineering, Construction, and Operation (AECO) Research in 2024

I am inclined to research on EEG classification using ML/DL. The research area seems saturated. Hence, I am confused as to where I can contribute.

We cordially invite you to contribute a book chapter for our edited book entitled "Machine Learning for Drone-Enabled IoT Networks: Opportunities, Developments, and Trends", which will be published by Springer Nature publishers in the Advances in Science, Technology & Innovation series (Scopus indexed). There is no publication fee. This edited book aims to explore the latest developments, challenges, and opportunities in the application of machine learning techniques to enhance the performance and efficiency of IoT networks assisted by aerial unmanned vehicles (UAVs), commonly known as drones.

More information at: https://easychair.org/cfp/MLDroneIoT01

I am seeking an advisor and a place to defend my dissertation in the field of machine learning and artificial intelligence application. I already have a significant amount of material, including publications and developed machine learning tools that are successfully implemented and used in companies. I would like to defend my dissertation specifically based on these developed projects. Please share advice or recommendations regarding finding an advisor and a university that could support me in this endeavor.

Thank you for your attention and assistance!

How is machine learning used in agriculture and how is future farming advancing agriculture with artificial intelligence?

Can anyone recommend Machine learning textbook or any material for analysis/data modeling?

Brief; I have rock drilling experimental data. I would like to use machine learning techniques for modeling of drilling energy. Request any materials/journals, textbooks related modeling please share with me.

With regards

Dr.Vijaya Kumar Chodavarapu.

What are the challenges and opportunities in deploying machine learning models in real-time systems with stringent latency constraints?

#ml #industry5.0

When a user has set notifications or alerts to start an exercise or do the chores, when the notification is delivered, the user may be too engaged in another activity (like social media) which will lead to dismissal of the notification.

Using machine learning.

Can anyone recommend Machine learning textbook for basic level?

Hello everyone, I'm seeking some advice or references related to the optimal number of observations needed per category within a categorical variable for machine learning projects. I've come across a rule of thumb suggesting that a minimum of 20 observations per category is advisable. However, I'm curious about the community's views on this and whether there's any literature or research that could provide more detailed guidance or confirm this rule. Any insights or recommendations for readings on this topic would be greatly appreciated. Thank you!

CCNS was successfully held in Guilin, Xining, Hohhot and Qingdao from 2020 to 2023. The conference covers diverse topics including AI and Machine Learning, Security Challenges in Edge Computing, Quantum Communication Networks, Optical Fiber Sensor Networks for Security, Nano-Photonic Devices in Cybersecurity and so on. We hope that this conference can make a significant contribution to updating knowledge about these latest scientific fields.

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

AI and Machine Learning

Blockchain Applications in Network Defense

Security Challenges in Edge Computing

Cybersecurity in 5G Networks

IoT Security Protocols and Frameworks

Machine Learning in Intrusion Detection

Big Data Analytics for Cybersecurity

Cloud Computing Security Strategies

Mobile Network Security Solutions

Adaptive Security Architectures for Networks

Quantum Communication Networks

Photonics in Secure Data Transmission

Optical Fiber Sensor Networks for Security

Li-Fi Technologies for Secure Communication

Nano-Photonic Devices in Cybersecurity

Laser-Based Data Encryption Techniques

Photonic Computing for Network Security

Advanced Optical Materials for Secure Communication

Nonlinear Optics in Data Encryption

Optical Network Architectures for Enhanced Security

All papers, both invited and contributed, will be reviewed by two or three expert reviewers from the conference committees. After a careful reviewing process, all accepted papers of CCNS 2024 will be published in SPIE - The International Society for Optical Engineering (ISSN: 0277-786X), and indexed by EI Compendex and Scopus.

Full Paper Submission Date: March 17, 2024

Registration Deadline: April 12, 2024

Final Paper Submission Date: April 21, 2024

Conference Dates: May 3-5, 2024

Invitation code: AISCONF

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

Meta-analyses and systematic reviews seem the shortcut to academic success as they usually have a better chance of getting published in accredited journals, be read more, and bring home a lot of citations. Interestingly enough, apart from being time-consuming, they are very easy; they are actually nothing but carefully followed protocols of online data collection and statistical analysis, if any.

The point is that most of this can be easily done (at least in theory) by a simple computer algorithm. A combination of if/thenstatements would simply allow the software to decide on the statistical parameters to be used, not to mention more advanced approaches that can be available to expert systems.

The only part needing a much more advanced algorithm like a very good artificial intelligence is the part that is supposed to search the articles, read them, accurately understand them, include/exclude them accordingly, and extract data from them. It seems that today’s level of AI is becoming more and more sufficient for this purpose. AI can now easily read papers and understand them quite accurately. So AI programs that can either do the whole meta-analysis themselves, or do the heavy lifting and let the human check and polish/correct the final results are on the rise. All needed would be the topic of the meta-analysis. The rest is done automatically or semi-automatically.

We can even have search engines that actively monitor academic literature, and simply generate the end results (i.e., forest plots, effect sizes, risk of bias assessments, result interpretations, etc.), as if it is some very easily done “search result”. Humans then can get back to doing more difficult research instead of putting time on searching and doing statistical analyses and writing the final meta-analysis paper. At least, such search engines can give a pretty good initial draft for humans to check and polish them.

When we ask a medical question from a search engine, it will not only give us a summary of relevant results (the way the currently available LLM chatbots do) but also will it calculate and produce an initial meta-analysis for us based on the available scientific literature. It will also warn the reader that the results are generated by AI and should not be deeply trusted, but can be used as a rough guess. This is of course needed until the accuracy of generative AI surpasses that of humans.

It just needs some enthusiasts with enough free time and resources on their hands to train some available open-source, open-parameter LLMs to do this specific task. Maybe even big players are currently working on this concept behind the scene to optimize their propriety LLMs for meta-analysis generation.

Any thoughts would be most welcome.

Vahid Rakhshan

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

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

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

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

And what is your opinion about it?

What is your opinion on this issue?

Please answer,

I invite everyone to join the discussion,

Thank you very much,

Best regards,

Dariusz Prokopowicz

Dear Scientists and Researchers,

I'm thrilled to highlight a significant update from PeptiCloud: new no-code data analysis capabilities specifically designed for researchers. Now, at www.pepticloud.com, you can leverage these powerful tools to enhance your research without the need for coding expertise.

PeptiCloud's latest update lets you:

This innovation aims to remove the technological hurdles of data analysis, enabling researchers to concentrate on their scientific discoveries. By minimizing the need for programming skills, PeptiCloud is paving the way for more accessible and efficient bioinformatics research.

PeptiCloud is dedicated to empowering the bioinformatics community. Your insights and feedback are invaluable to us as we strive to enhance our platform. Visit us at www.pepticloud.com to explore these new features, and don't hesitate to reach out at [email protected] with your thoughts, suggestions, or questions.

Together, let's embark on a journey towards more accessible and impactful research.

Warm regards,

Chris Lee

Bioinformatics Advocate & PeptiCloud Founder

"From science to law, from medicine to military questions, artificial intelligence is shaking up all our fields of expertise. All?? No?! In philosophy, AI is useless." The Artificial Mind, by Raphaël Enthoven, Humensis, 2024.

Dear ResearchGate Community,

I hope this message finds you well. I am writing to express my strong interest in pursuing a PhD in the field of Optimization in Artificial Intelligence and Machine Learning and to seek a supervisor who shares the same passion for this research area.

I hold a Master's degree in Artificial Intelligence and Robotics, which has provided me with a solid foundation in machine learning. However, to further enhance my knowledge and skills in optimization, I subsequently enrolled in another Master's program in Applied Mathematics. This program has equipped me with a deep understanding of mathematical concepts and techniques that are instrumental in optimizing machine learning algorithms.

I am confident that my profound understanding of the mathematical foundations of machine learning would be a valuable asset to your ML/AI research team. Moreover, my research projects have allowed me to actively engage in the exploration of optimization in AI/ML algorithms. I have developed a particular interest in the intersection of Quantum Computing and its significant implications for AI/ML and optimization.

During my academic journey, I have had the opportunity to work on research projects that focus on applying AI/ML in various domains, such as medicine and environmental sciences. Through these experiences, I have gained practical insights into the challenges and opportunities that arise when optimizing machine learning algorithms for real-world applications.

I am now seeking a PhD supervisor who shares my enthusiasm for optimization in machine learning and who can guide and support me in exploring this fascinating research field. If you are a researcher or know of any potential supervisors who specialize in this area, I would greatly appreciate any recommendations or introductions.

Thank you for taking the time to read my post. I look forward to any suggestions or guidance you may have, and I am eager to contribute to the advancements in optimization in machine learning.

Best regards,

I am tackled with a industrial research issue in which a massive-scale data which is mostly a stream data is about to be processed for the purpose of outlier detection. The problem is that there are some labels for the so-wanted outliers in the data, even though they are not reliable and thus we should discard them.

My approach to resolve the problem is mainly revolving around unsupervised techniques, although my employer insists on finding a trainable supervised technique by which there will be a major need to have outlier label for each individual data point. In other words, he has got trust issues with unsupervised techniques.

Now, my concern is whether there is any official and valid approach to generate outlier labels, at least to some meaningful extent, especially for a massive-scale data? I have done some research in this regard and also have experience in outlier/anomaly detection, nevertheless, it would be an honor to learn from other scholars here.

Much appreciated

learning like a child/baby

I would like to know that prophet time series model is under the category of neural network or machine learning or deep learning? I want to forecast the price of product depending on other influential factors( 7 indicators) and all the data is monthly data with 15 years period.How can I implement with prophet model to get better accuracy? And i also want to compare the result with other time series model.Please suggest me how should I do about my work.thank you.

How can a strong understanding of statistics improve your machine learning models?

I need an idea

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

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

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

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

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

What do you think about this topic?

What is your opinion on this issue?

Please answer,

I invite everyone to join the discussion,

Thank you very much,

Best wishes,

Dariusz Prokopowicz

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

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

Copyright by Dariusz Prokopowicz

As a Cybersecurity Engineering student, I am considering potential thesis topics within the realms of Social Engineering (specifically Phishing attacks), Third-party VPNs, or the Integration of Machine Learning and Deep Learning in advanced cybersecurity. Recognizing the broad scope of these areas, I seek your guidance to refine and specify my research focus. My background includes experience with CCNA and CCNP, a modest exposure to infrastructure and automation (Ansible), and proficiency with both Windows and Linux operating systems. I would appreciate your assistance in identifying a specific problem within these topics that warrants in-depth investigation for my thesis.

..