Computational Intelligence - Science topic

Matthew Ojo Ayemowa Hello Matthew,

Would you like to collaborate on this? I have recently ventured into authoring research articles and I am looking for co-authors to collaborate with. Please let me know if you'd be interested.

Thank you,

Deepak

The IEEE Symposium on Explainable, Responsible, and Trustworthy Computational Intelligence (CITREx) is a key event within the 2025 IEEE Symposium Series on Computational Intelligence (SSCI), set to take place from March 17-20, 2025, in Trondheim, Norway. This symposium addresses the critical need for transparency, accountability, and user confidence in artificial intelligence (AI) and computational intelligence (CI) systems. It emphasizes the importance of creating AI/CI technologies that are understandable, monitored, and reliable, thus allowing users to make informed decisions that adhere to ethical principles and regulatory frameworks. The event will feature discussions on ethical guidelines surrounding AI technology, the interpretation of AI/CI decisions for enhanced transparency and trust, and the balancing act between technological ecological impacts and economic gains. Topics of interest include the implications of automation on the workforce, privacy protection, and the legal ramifications of AI in autonomous systems. Keynote speakers, such as Federico Cabitza from the Università degli studi di Milano-Bicocca and other notable figures from various prestigious institutions, will contribute to the discourse on pressing issues in the field. With updated important dates, including a new deadline for title and abstract submissions on September 22, 2024, and full/short paper submissions by October 1, 2024, the symposium aims to foster collaboration and innovation through the cross-pollination of ideas and research concepts. As a participant, you'll engage with cutting-edge theories and algorithms in computational intelligence, promoting a deeper understanding of how these technologies can be responsibly integrated into society.

Hi Liew Keng Leong ,

For more details about the conference, please visit the official website of the conference: https://ais.cn/u/IJfQVv

Dear Prof. Gross!

I fully recognise your objectives and you really make a difference. I am a biligual researcher who have studied five different languages. I am also a football coach in Finland at HJK Helsinki, helping children with disabiilities to learn the language of fooball. Football is a language via wich you can get friends, and new opportunities to learn from life:

1) J. Albrecht, K. Elmose-Østerlund, C. Klenk & S. Nagel (2019) Sports clubs as a medium for integrating people with disabilities, European Journal for Sport and Society, 16:2, 88-110, DOI: 10.1080/16138171.2019.1607468, Available at:

2) Dennis Bengtsson, Andreas Stenling, Jens Nygren, Nikos Ntoumanis, Andreas Ivarsson, The effects of interpersonal development programmes with sport coaches and parents on youth athlete outcomes: A systematic review and meta-analysis, Psychology of Sport and Exercise, Volume 70,

2024, https://doi.org/10.1016/j.psychsport.2023.102558, Open access:

Yours sincerely, Bulcsu Szekely

Veryy nice I interesting

Awesome! will share this within my network.

All the best!

Dear Spencer Angus Thomas ,

The world was entrenched in big data before it even realized that big data existed. By the time the term was coined, big data had accumulated a massive amount of stored information that, if analyzed properly, could reveal valuable insights into the industry to which that particular data belonged.

Regards,

Shafagat

Given that the government has already made a wind potential estimation for the region of Ica, you may want to consider focusing on a more specific area within Ocucaje, or looking at a different aspect of wind energy production that hasn't been fully explored yet. For example, you could examine the optimal placement of wind turbines in the district of Ocucaje using computational intelligence techniques, or investigate the feasibility of using wind energy to power a specific industry or application in the area.

Another option could be to focus on the application of computational intelligence tools to optimize the performance of existing wind turbines in the region, which could be a valuable contribution to the field. This could involve developing a machine learning model to predict wind speed and direction, and using this information to adjust the pitch of the turbine blades for optimal energy production.

Dear Dariusz Prokopowicz , mitigating risks from generative AI tools such as ChatGPT means involving humans in final decision-making and establishing guardrails...

There are three significant dangers that could result from generative AI, the first being the creation of killer biological viruses...

Second, bad actors can use generative AI tools to create and target misinformation...

Generative AI can be dangerous when its decision-making is faster than humans, particularly in critical situations...

Now my articles on electronic medicine have been read by 203 readers. This confirms the relevance of the development of this area of science and practice. The Eidos universal intelligent system has 55 operating modes, not counting the Exit mode. Any mistake you make will bring the system to a halt or you will get wrong results. If, without explanation, you begin to study several operating modes of the system per day, then in order to learn how to work in the Eidos system, you will need to spend more than a month. I appreciate your time, so I posted my lectures on the site. And he did it almost for free. For one lecture, the professor receives about $50. Compare this to the cost of a monthly subscription to https://www.patreon.com/user?u=87599532. And you will be pleasantly surprised. The best investment you can make is an investment in education.

Eduard Babulak Yes, the usage of quantum computing is predicted to help enhance artificial intelligence, humanoid robots, automation, and geographic research. Quantum computing ideas can be used to these domains to possibly increase the performance and efficiency of different systems. However, it is vital to highlight that quantum computing is still in its infancy, and more study is required to fully realize its promise in these disciplines.

no need to buy R

Hello Muhammad Yaseen Khan

According to the "Contact us" link at the bottom of their homepage, the address for emailing general enquiries is help@hindawi.com.

Their physical location is listed as:

Hindawi Limited Adam House, Third Floor 1 Fitzroy Square London W1T 5HF United Kingdom

You might try writing (on paper, letter in the post) if email is unsuccessful. I am uncertain if they will write back to you from either angle as these sound like very generic addresses.

Dear Neha Gautam ,

Prestigious might be a bit too strong but Hindawi is more and more considered as a well-established publisher dedicated to open access publishing. Their partnership with Wiley (for numerous titles) is a good sign as well https://www.hindawi.com/publishing-partnerships/wiley-hindawi-publishing-partnership/

The journal “Computational Intelligence and Neuroscience” has some comforting quality features:

-Scopus indexed, CiteScore 3.900

-SCIE indexed, IF 3.100

-PubMed indexed and included in EI Compendex

Personally, I find the APC (at least without a possible waiver) somewhat too high but apart from that this journal is a pretty good and safe choice and worthwhile to publish in.

Best regards.

Dear Edianny Carballo Cruz ,

Innovation is without a doubt one of the hottest buzzwords and most debated topics of the last couple of decades, and while a lot of people are sick and tired of hearing about it everywhere, the term and the concept behind it are here to stay.

Regards,

Shafagat

The number of parameters, the values for the training parameters, the size and topology of the map, all have to be determined in advance. However, there are several settings to bring good results but they are time-consuming.

In general, a heuristic might be suitable when an exact algorithm cannot easily solve a problem. Heuristics have increased in popularity because the problems we want to solve become more complex as time passes. Which one to use would depend on the type of problem being solved, as Tsung-Che Chiang suggests.

Please go through the following RG links.

Due to phase-wise knowledge addition, it is true that each future intelligence system surfaces the previous one. I agree with Muhammad Ali.

Of course not !!! Metaheuristics are here to solve the problem of the time.

Metaheuristics are cleverly done since the exhaustive search with the infinite running time is useless especially for large-scale and real-life optimization problems.

Table one of the following reference gives a detailed answer to this issue:

The Artifical intelligenc (AI) and deep learning with certain limitations is playing an important role in the epidemiological modelling of SARS-CoV-2 transmission, disease prediction, deaths, data analysis of COVID-19 cases X-rays, CT scans, and the viral sequences.

For further reading please have a look at the attached links:

1.

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5.

Stochastic gradient-descent algorithm can lead multiple kernels to converge to similar values while the optimal solution may require different values for them. You can only hope using random initialization and drop-out will reduce the likelihood.

ATEX software is appropriate software to analysis EBSD images. You can download it from beneath link:

Regards,

Behzad

I suggest that you read

"Modeling Information Diffusion in Online Social Networks with Partial Differential Equations".

Good Answer iliasse Hassala

There are actually quite a lot of nice application of machine learning techniques in the context of business process variant analysis, which is a fairly large subset of the process mining literature.

For example, Folino, Cuzzocrea et al. have done a series of studies on variant analysis (or deviance mining) using various machine learning methods, including ensemble learning and clustering:

We recently conducted a literature survey of methods in the field of variant analysis, many of them based on machine learning techniques:

Related to the above, there is work on bayesian networks for delay analysis (explanatory rather than predictive):

The above is related to variant analysis and performance mining. But there is also work on anomaly detection in event logs using bayesian networks:

And using deep learning architectures:

As well as using deep learning models to compute alignments in order to correct anomalies:

And a bit related to the above, there was quite a bit of research on using trace clustering in the context of automated process discovery (e.g. Jochen De Weerdt)

So we can say that process mining and machine learning go well together. One should not forget though that BPM and process mining are application-oriented disciplines - their objective is to design approaches to improve business processes. Whereas machine learning is a horizontal discipline, it seeks to develop methods that can be adapted to a broad range of problems/settings. Process mining has tapped a lot into machine learning, but sure it has a lot more to exploit from it.

Dear Long Nguyen Cong,

Can you able to generate Levy random number in the interval [a, b] using Levy distribution.

Hi

I recommend python-pcl

As a tool, you can use (FreeCAD/Point Workbench, Blender/Import-Export PCL, Meshlab, ROS/Gazebo/RQT).

I hope that's helpful.

If you want to learn

If you need to work with HMMs

Use this link:

https://sci-hub.tw/

Regards

I think you need to narrow very much your search space. Are you interested in image or video processing, signals, networks, big data, cloud, modeling and simulation, high performance computing, etc? It is better to build on your experience and your interest. I can recommend some examples if you are interested in image and video processing, as these are my fields of interest.

Machine learning a probabilistic perspective Murphy

For building a firewall for detection of SQL injection, you model based on neural netowrk (NN) must go through two phases. The former one, is to train the NN with a dataset that contains SQL injection attacks, so NN will build the model or profile of those attacks; it is able to learn and then recognize novel attacks as it have a high capability of generalization. The last phase, it is evaluation of your firewall; after NN leans, you firewall based NN be able to distinguish SQL injection attacks from normal SQL queries.

So, try to find a dataset that contains both SQL injection attacks and normal SQL queries, then divide this dataset in two parts, 70% of that database wil serves as training dataset and the 30% of that database will be used as testing dataset.

good luck.

I am one of Dr. Deb's PhD student. Our Python framework contains a working and benchmarked NSGA-II and NSGA-III implementation.

Please have a look if you are still interested.

To generate rules form data is one of the most popular approaches in fuzzy logic. One of the most known approaches is ANFIS.

I have already done some approches by using matlab

Yes UWSN could be used effectively with fuzzy logic for node energy conservation in multi-hop sink environment. The fuzzy logic can suggest which route to use as a minimum or shortest path towards sink instead of the regular path, which results into confined energy utilization. I hopes @ Dheeman Saha you might understand my point.

Dear Ang,

The answers provided against this question are right. If you are looking more, you may find some interesting answers through the link below.

Thanks,

Sobhan

Nonlinear system modeling has attracted great attention in engineering sciences as well in many other fields such as meteorology, economics, biology, etc. This is simply due to two main reasons. First, the presence of nonlinear phenomena in most physical systems is rather the rule than the exception. Second, nonlinear system analysis and control can only be achieved if the mathematical model of the system is known . This model can be constructed using the physical modeling approach which leads to the white box models. This approach consists in decomposing the considered system in different parts (electrical part, mechanical part, heat conduction part, etc.) and then applying the appropriate first principles that govern each part. This approach can clearly be very complex since it requires detailed specialist knowledge in many different domains of science. In addition, the first principle models are often non linear and non stationary and consequently the obtained models are too complex for most practical purposes. System identification represents an interesting solution of constructing models, known as black box models, since it allows to overcome most of these problems. Furthermore, it can be applied to all physical systems. This approach consists in constructing mathematical models from observed input and output data based on five main steps : data collection, model class selection, model structure estimation , model. parameter estimation and model validation. The model class selection represents the corner stone toward the objective of nonlinear system identification because the model accuracy depends on the used model class. Numerous classes have been proposed in the literature for nonlinear black-box models such as Volterra models, NARMAX (Nonlinear AutoRegressive Moving Average model with eXogenous inputs models), nonlinear state space, block-oriented models structures, etc. All of these models are able to approximate any nonlinear system with arbitrary accuracy. The choice of the best class depends on the model purposes : emulation, prediction, simulation, controller design, fault detection, etc. For control purpose for example, a mathematical model must ensure a compromise between accuracy, model extraction cost and parametric simplicity of model in order to ensure the implementation of the controller.

After writing the rules, run the Pallet reasoner and see the class hierarchy (inferred)

true (1,0,0) > weak true (1,1,0) > contradiction (1,0,1) > {ignorance (0,0,0), saturation (1,1,1)} > neutrality (0,1,0) > weak false (0,1,1) > false (0,0,1).

ignorance (0,1,0) and saturation (1,1,1) are not comparable.

Turing patterns in reaction diffusion equations.

SIR modelling

Visit link for Matlab Code

Artificial Intelligence has been around for a long time – the Greek myths contain stories of mechanical men designed to mimic our own behavior. Very early European computers were conceived as “logical machines” and by reproducing capabilities such as basic arithmetic and memory, engineers saw their job, fundamentally, as attempting to create mechanical brains.

As technology, and, importantly, our understanding of how our minds work, has progressed, our concept of what constitutes AI has changed. Rather than increasingly complex calculations, work in the field of AI concentrated on mimicking human decision making processes and carrying out tasks in ever more human ways.

Artificial Intelligences – devices designed to act intelligently – are often classified into one of two fundamental groups – applied or general. Applied AI is far more common – systems designed to intelligently trade stocks and shares, or manoeuvre an autonomous vehicle would fall into this category.

Neural Networks – Artificial Intelligence And Machine [+]

Generalized AIs – systems or devices which can in theory handle any task – are less common, but this is where some of the most exciting advancements are happening today. It is also the area that has led to the development of Machine Learning. Often referred to as a subset of AI, it’s really more accurate to think of it as the current state-of-the-art.

The Rise of Machine Learning

Two important breakthroughs led to the emergence of Machine Learning as the vehicle which is driving AI development forward with the speed it currently has.

One of these was the realization – credited to Arthur Samuel in 1959 – that rather than teaching computers everything they need to know about the world and how to carry out tasks, it might be possible to teach them to learn for themselves.

The second, more recently, was the emergence of the internet, and the huge increase in the amount of digital information being generated, stored, and made available for analysis.

Once these innovations were in place, engineers realized that rather than teaching computers and machines how to do everything, it would be far more efficient to code them to think like human beings, and then plug them into the internet to give them access to all of the information in the world.

Neural Networks

The development of neural networks has been key to teaching computers to think and understand the world in the way we do, while retaining the innate advantages they hold over us such as speed, accuracy and lack of bias.

A Neural Network is a computer system designed to work by classifying information in the same way a human brain does. It can be taught to recognize, for example, images, and classify them according to elements they contain.

Essentially it works on a system of probability – based on data fed to it, it is able to make statements, decisions or predictions with a degree of certainty. The addition of a feedback loop enables “learning” – by sensing or being told whether its decisions are right or wrong, it modifies the approach it takes in the future.

Machine Learning applications can read text and work out whether the person who wrote it is making a complaint or offering congratulations. They can also listen to a piece of music, decide whether it is likely to make someone happy or sad, and find other pieces of music to match the mood. In some cases, they can even compose their own music expressing the same themes, or which they know is likely to be appreciated by the admirers of the original piece.

These are all possibilities offered by systems based around ML and neural networks. Thanks in no small part to science fiction, the idea has also emerged that we should be able to communicate and interact with electronic devices and digital information, as naturally as we would with another human being. To this end, another field of AI – Natural Language Processing (NLP) – has become a source of hugely exciting innovation in recent years, and one which is heavily reliant on ML.

NLP applications attempt to understand natural human communication, either written or spoken, and communicate in return with us using similar, natural language. ML is used here to help machines understand the vast nuances in human language, and to learn to respond in a way that a particular audience is likely to comprehend.

A Case Of Branding?

Artificial Intelligence – and in particular today ML certainly has a lot to offer. With its promise of automating mundane tasks as well as offering creative insight, industries in every sector from banking to healthcare and manufacturing are reaping the benefits. So, it’s important to bear in mind that AI and ML are something else … they are products which are being sold – consistently, and lucratively.

Machine Learning has certainly been seized as an opportunity by marketers. After AI has been around for so long, it’s possible that it started to be seen as something that’s in some way “old hat” even before its potential has ever truly been achieved. There have been a few false starts along the road to the “AI revolution”, and the term Machine Learning certainly gives marketers something new, shiny and, importantly, firmly grounded in the here-and-now, to offer.

The fact that we will eventually develop human-like AI has often been treated as something of an inevitability by technologists. Certainly, today we are closer than ever and we are moving towards that goal with increasing speed. Much of the exciting progress that we have seen in recent years is thanks to the fundamental changes in how we envisage AI working, which have been brought about by ML. I hope this piece has helped a few people understand the distinction between AI and ML. In my next piece on this subject I go deeper – literally – as I explain the theories behind another trending buzzword – Deep Learning.

Bernard Marr is a best-selling author & keynote speaker on business, technology and bigdata. His new book is Data Strategy. Toread hisfuture posts simply join his network here.

why not smote i think nearest neighbor gives good solutions

Hi Chi-Fan Wang !

Do you have the images of DDSM data base.

I am doing research work in breast cancer diagnosis based on fusion of texture features from MLO and CC view mammograms. Now the

websites

http://ganymed.imib.rwth-aachen.de/irma/datasets_en.php?SELECTED=00010

are not opening.

Hence, if you have this data, help me by sharing that data, it will very much useful for my research.

what is meu that is multiplied with certainity factor (CF)?

Dear Samer

Unfortunately, Your feedback does not help at all

Best wishes, Osman

The stackoverflow section above misses the point. TF/IDF LSA and many similar techniques rely on statistical properties of language. These properties only emerge in the large - so an argument based on keyword matching over two documents is a spurious argument for these techniques. 

So really one of the key shortages and limitation of TF/IDF and the many improved variants is that they will only work well if you have a large document collection to deal with.

Sphinx is a flexible framework for research in speech recognition. use that

Decision tree results in overfitting the data

You may follow this below papers.....

I have used clustering algorithms to predict weather. The above two papers describe two different approaches but the same machine learning concept have been used in these two papers.

Ok then, there is no Information Retrieval tools can do this job by adding actual user clicks with dwell time as features. In addition, Microsoft Bing search engine dataset that extracts from a Bing search engine is the only dataset contains these features.

Dear Rahmanian! 

Thanks for your response ! my main issue is that I do not have any sample of any sort of meta heuristic algorithms  in cloudsim to see how they value parameters .

Hi Precious, 

Sorry for not following up sooner. In view of the details that you have added to the problem description, I suggest that you have a look to Extreme Learning Machines (ELM) in a prediction context ( Section 8.6 from https://www.researchgate.net/profile/Gao_Huang/publication/267339744_Trends_in_Extreme_Learning_Machines_A_Review/links/54744da50cf29afed60f7594/Trends-in-Extreme-Learning-Machines-A-Review.pdf and http://www.jcomputers.us/vol8/jcp0805-31.pdf )

ELM has been proposed for training single hidden layer feedforward neural networks efficiently.

Online/incremental versions exist:

Hoping it will be helpful

Hi Ram,

There are numerous of algorithms in R for feature selection and you can use GUI to use them. Also, deep learning with python contains some neural network example. In addition, you can ECJ or jmetal packages for evolutionary algorithms. Also, you can check https://searchcode.com/ and http://www.artificial-immune-systems.org/algorithms.shtml for immune system. Furthermore, nature-inspired algorithms in https://books.google.co.uk/books?id=iVB_ETlh4ogC&printsec=frontcover&dq=meta+algorithms+oxford&hl=en&sa=X&redir_esc=y#v=onepage&q&f=false and computational intelligence in https://www.crcpress.com/Computational-Intelligence-Paradigms-Theory--Applications-using-MATLAB/Sumathi-Paneerselvam/p/book/9781439809020.

Best wishes,

Osman

@christos k. Aridas...........Thanks

At a macro level the various competitions that invite researchers to submit machine learning solutions for a common problem address the effective comparison of different methods. With each competition, it is hoped that there is enough variety of techniques that a valid enough comparison has been made. Of course, it may be that only popular, quick or easy techniques are used within the various solutions but any other approach requires a research team to spend a very long time trying every possible combination of meta parameter, technique and data feature.  While competitions don't directly address your question about computation time, in a sense they do answer it indirectly in that the various submissions have addressed the same problem within the same timeframe.

Computation time is a factor in my own work and what I have learnt to do is to test ideas using smaller models and then once I think that I have found a reasonable set of circumstances then (sometimes) I scale up, looking for a more optimal solution. However, mostly I am disinterested in proving that one technique is the "best" and more interested in showing that a particular technique works well enough to be usable. This, however, reflects my own research aims and is not intended to be a criticism of researchers who do want to try to prove that their technique is better than someone else's.

In AI, the rules are generally learned.  In general, AI without "learning" collapsed as a field of study and was in some cases such as Terry Winograd explicitly renounced.  Furthermore, most symbolic AI researchers only criticized Perceptrons, not the concept of learning itself - but they found it difficult to learn by purely symbolic means.   

Today most of what we think of as "rule-based AI" survives as Bayesian Networks, which start from a graph that captures the joint-probability relationships between entities.   The fact that we will typically start with a graph that is not fully connected represents the injection of human prior beliefs which are "rule-like."   In its most naive form, this has the drawback that a link initially omitted can never spontaneously generate other than by human intervention.

Exposure to repeated cases can be used to refine the probabilities, and today there are "pretty good" algorithms for learning Bayesian networks.

Can these Bayesian networks and the rules they represent be created and refined purely by hand?  Yes, however, in general, it is an NP-complete problem and trying to solve such problems by hand was the general point of failure for non-learning AI projects.

So, if you insist, the rules can be crafted by hand, but if addressing large problems you may need a lot of time, and therefore first should work on problems related to (a) becoming immortal, and (b) extending the life of the universe exponentially beyond its current practical limitations.

W

This is a good reference for your problem..

This is a good question.

ELKI is open source software written in Java and licensed under AGPLv3.   See

It is observed that  Elki (clustering) does not have much support in the data ming community:

A good discussion about ELKI from a python perspective is given in

Thanks a lot everyone, that certainly helps.

You can follow :

@shamim. Please let me know about A and B. Also if possible send me the issue at Dr.ataurahman@gmail.com. I'll try to help you as much as I can do. 

Thank you very much Proffessor Lafifi.  I am going through your references. Much appreciated.

Dear Dr. Babbar,

I think the need for using event classification, awareness building, situation recognition, context-driven reasoning, unsupervised learning, data-induced strategy development, adaptation planning, and knowledge mining algorithms/mechanisms is most obvious in the field of software development for smart cyber-physical systems. For instance, in our practice, an engagement monitoring and enhancing cyber-physical system solution for stroke rehabilitation or a dynamic context information-based personalized fire evacuation cyber-physical system solution could not be developed without them. As far as the development methodology is concerned, we adopted, adapted and combined algorithms from the fields of AI, HBR and AGI, but we have also developed simple and compound algorithms and mechanisms for specific 'intellectualizing' functionalities. A typical example is the overall mechanism of procedural abduction as decision making and strategy planning enabler for second generation (smart) cyber-physical systems. Please visit our cognitive engineering of cyber-physical systems project on RG for further information, should you need it. Best regards,

Imre Horvath

Apriori Algorithm is made fun here: