Computer Science and Engineering - Science topic

Dear Dagim Sileshi Dilla , read this valuable experience. Good luck!

A computer scientist is struggling to trust their postdoctoral research adviser after negative experiences with the previous two. How do you find one that’s right for you?

Thanks for your generosity . Do you have any plan for collaboration ?

@ Dr. Chen, Thank you for consulting with AI bot on behalf of me. It's interesting!

They include:

Mr Duodu, thank you.

Also, mr Amin, can I kindly ask you not to leave URL and ypur email after every comment of yours, its not professional, really.

Plants can be destructive too. For example, an invasive plant species may be introduced by seeds in bird droppings with the result that a waterway may become choked off. Or an invasive plant may force out a native species that provided better protection against erosion. Such possibilities could have deleterious effects on local ecosystems.

Hi Joseph C Lee

I agree with you that it depends on the editor and reviewer. However, could it be better to display this factor (in average or estimated value) on the journal's website. So that authors can get a rough estimate for article.

Hello Aymane Ezzaim,

According to three trusted websites, I am afraid to inform you that journal is not found in known academic journal databases, see below:

JCR profile (with impact factor) was not found in the list of journals

Not found in Master Journal List indexed journals

Not found in Scopus indexed journal list

Not found in DOAJ indexed open access journal list

Going forward, its better in my opinion to get a journal from well established publishers like IEEE, IGI, Plos One, Hindawi, etc.... once you get it, check it before making any submissions.

I think links below might be of help,

Thank you Prof. Masud Ibn Afjal for the interesting question. Research methodology section is a must as this is a blue print of your research covering study area, sampling, data collection methods/tools/analysis etc. More articles can also help;

Dear Yasin,

I propose the following developmental and current research topic in the field of computer engineering: Analysis of the determinants of creating predictive simulation models for multi-faceted, complex economic, social, climate, natural, financial, pandemic, global problems, etc. based on large sets of information and data obtained from various sources including from publicly available websites and social media portals, including the use of Internet users' sentiment analysis, data analysis on Big Data Analytics and Business Intelligence platforms, and with the use of selected, various Industry 4.0 technologies, including cloud computing, artificial intelligence, teaching machines, multi-criteria simulation models, digital twins, etc.

Best wishes,

Dariusz

Editor-in-Chief, i.e. professor Nasser-Eddine Rikli is present here at R^G portal:

Dear Prabhleen Sachdeva:

I respect your perseverance, but you have to go through many researches in a specific field in order to gain experience and correct scientific knowledge, and then move forward..

Good luck ..

Best wishes...

Yes , It is possible

INTERNATIONAL JOURNAL OF HUMAN-COMPUTER STUDIES, INTERACTING WITH COMPUTERS. best of lucks with your manuscript

Dear Vladimir Rotkin,

No. The discussion was not over. The overwhelming majority of the topics discussed in my Research Gate profile are still relevant and the discussions are open. Yes you are right. The issue of varieties of "analytical" artificial intelligence is also important. You added an important point to our discussion.

Thank you, Have a nice day, Stay healthy! Best wishes,

Dariusz Prokopowicz

Good journals accept submission with clear authors contributions to submitted work. First author is considered the main figure for the research item

Infrastructure and technology readiness... Arvanitis, S. (2005). Computerization, workplace organization, skilled labour and firm productivity: Evidence for the Swiss business sector. Economics of innovation and new technology, 14(4), 225-249.

Hi proffesor Damian,

I've seen you have expertise in wavelets and mammography. I'm undergraduate student and I'd be thankful if you recommend me a way to enhance or detect microcalcifications (MCC) in mammograms with DWT. I'm trying with wavelet decomposition at 3 level with Db1 and hard thresholding with Donoho Threshold for subband denoising, but I don't get visually good results, this is to say, it doesn't see the MCC clearly

Voicebots, I guess.

nope, Fales citation may interrupt your relevant data which later on lead to the cancellation of the paper during evaluation.

Normally it is believed that journal publication is superior to conference proceedings. However, we have had new innovations presented/published in journal proceedings. Therefore, in computer science & Engineering, it is always evolving quick and any delay in publication or patenting an idea, you lose. As much as I would prefer to publish in a journal and for you to decide what to publish there, you can always look at the articles published in that journal. Remember, some conference proceedings are of high quality than some journal publications.

I would agree with Professor Edelmann's advice above. However, I think it depends on the norms in your field, the norms for the type of article your target journal publishes, the length of the article, and the type of article you are writing. My field of Social Work is completely different from yours, but my literature review would vary in length depending on the above variables. If in doubt, I would look at your target journal and see what the practice is there.

I suggest that you read

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

You may look at other entropies, search articles from Prof. Michèle Basseville on entropy (of probability measures)

Hey;

I think you can find whatever datasets you are looking for at this website

and I think this is kind of the dataset that you're looking for

https://datasetsearch.research.google.com/search?query=%20traffic%20forecasting&docid=MvPepxWbMzMzrTCbAAAAAA%3D%3D

For me, as well as for majority of other researchers, Mathematics is the language of Science!

All these countries welcome scholarly projects, particularly those with good potential for helping them. They have good schools in technology, petroleum engineering among other hard science fields. You should first, ficus on what you like to do and then see if there is a good school that fits your goals. Good luck.

Dear Sameer Hameed Abdulshaheed

This file will probably help you

Well, I don't know Italian currently. Considering English, I can do some teleworking.

Hyperspectral cameras that I have seen can view wavelengths beyond visible light (into IR and UV), but cannot see the results of sonic (sound pressure) waves inside bodies. The ultrasound images are produced by "microphones" picking up the reflected sound waves from transducers that project the acoustic energy into the target. Very clever wave-directing and detecting systems perform this complex process, which is then rendered into the viewable images with complex post-processing. These sonic internal activities will probably be invisible to cameras that respond to wavelengths near visible light.

Useful link from , thanks for sharing.

All are approaches that exploits the computational intelligence paradigm. Machine learning is refered to data analitics. Evolutionary computation deal with optimization problems.

I have already proposed several improved versions. This book Will be published soon:

Amina Bouraoui

Arranging conference is the business with the intellectual people without risk and harms. So, try to never attend in the seminar , conferences without sponsor. Few days back a conference committee from Sri Lanka sent me a mail for my permission to join there as the keynote speaker. I agreed and the following week , they mailed me for a donation of $1500 ! I didn't reply at all. So, be careful !!!

Computer software applications are already being used to create homes which are movable, for example "AutoCAD". Many engineers would say that many homes which were not planned or built to be movable can be moved any way.

There are:

• homes that were not planned to be moved

• homes that are planned to move once (e.g. installed), often called "mobile home"

• and, homes that are routinely moved

Among homes intended to me routinely moved, there are:

• "tiny homes"

• "motorhomes" and "motorized caravans", most often used for vacation/ holiday

I have put several phrases in quotation marks and recommend you search for these on internet to read more.

Regarding companies that specialize in moving homes which were not intended to be moved, you may also want to explore "house movers" or "structural movers".

Dual band refers to a device which uses two different frequencies to communicate.

Dual radio refers to a device which uses two different protocols to communicate.

Maybe an illustration would help. A dual band device is like a person at a party who can move between two different rooms (different frequencies) to talk with other people, while a single band device would be limited to talking to people in only one room. On the other hand, a dual radio device can speak with people who speak in two different languages, while a single radio device can only speak one language.

A dual radio device is typically used as an intermediary between two different networks like 802.11 (like WiFi) and 802.15.4 (like many wireless sensors).

There are quite a few methodologies: in Croatian research we often use WEF methodology. I find GEM appeoach very interesting.

A very wide field of technologies in a variety of sectors, indeed. For example chemical science and industry. See:

Fruitful research means that the research has already been completed. What about new research ideas?

@Abu Hashan Md Mashud

As a search engine, you can search https://www.scimagojr.com/ that contains the reputation journals but it has different calculations for the SJR factor rather the Thomson Reuters. If you need to check if it's a journal in the master journal list or not you can check the clarivity analysis master journal list (http://mjl.clarivate.com/ ).However, the Journal Citation Report (JCR) managed by Thompson Reuters, releases impact factor each year that can be purchased or some researchers may provide it for free (just needs some deep Googling).

Also, You can search the scopus website for the journal name https://www.scopus.com/home.uri

More over,, you can download the impact factors for journals in 2017 (Thomsen Reuters - uploaded by Murtaza Sayed ) here:

Journal/Conference titles indexed in Scopus: List of Journal/Conference titles listed in Scopus, updated in 2018

(https://docs.google.com/spreadsheets/d/19nxgu_NkycK2A1E5-eJ6oqipkepaZEJJcVFwR8WPWi0/edit#gid=1760483906)

InCite Journal Citation Report (Thomson Reuters Impact Factor) - 2018 (https://sites.google.com/a/koyauniversity.org/dqa/incite-jcr-thomson-reuter

Journal titles indexed by/member of the Directory of Open Access Journal (DOAJ), updated for 2018: https://docs.google.com/spreadsheets/d/1TF4FSlyyNIX6MdVUbD9zoLht2q3XhzerDe9Kf0k4DRs/edit#gid=719492038

Take care that ...many journals gave created their own impact factors which are fake.

Good luck

Samaa Shohieb

Read the following sources:

There are 4 issues I have seen in this discussion:

1. Backstepping can compensate theoretically exactly only uncertainties decreasing together with the state variables. No comparison between backstepping and SMC in this sense.

2. Matched and unmatched uncertainties

For perturbed chain of integrators there are no unmatched perturbations. If you will differentiate the output till the system order all the perturbations will be matched! That is why the only reasonable to control the systems with unmatched uncertainties is a combination between backstepping and SMC, where backstepping compensates state dependent part and SMC(see Estrada et al IJRNC,27(4),2017,DOI : 10.1002/rnc.3590,Automatica 2010(11), TAC 2010(11), J. Davila TAC 2013, Ferreira et al Journal of Franklin Institute, 2014, 351(4),doi:/10.1016/j.jfranklin.2013.12.011, Automatica, September 2015, Vol. 59, 10.1016/j.automatica.2015.06.020)

3. SMC can not be smooth. Sliding mode (for definition!) is a motion on the sliding surface!

4. Chattering

4.a. There is no way to keep the sliding mode and eliminate the chattering.

4.b. It is wrong opinion that it continuous HOSM controllers (like super-twisting, Kamal et al DOI:10.1016/j.automatica.2016.02.001, Torres et al doi :10.1016/j.automatica.2017.02.035, Laghrouche et al TAC,2017) everytime produce less chattering that discontinuous ones (see https://www.researchgate.net/publication/317229996_Is_It_Reasonable_to_Substitute_Discontinuous_SMC_by_Continuous_HOSMC).

It is true for the systems with fast actuators only.

I guess the poster asks about research like

but let him clarify.

You can sum the scores as you would do in a typical knowledge score, with 0 for bad/false or 1 for good/true answers, see example in:

I do not understand why you want to transfer them to a 0 - 100 score though, seems unneccessary to me.

How to use it? You can use it as a dependent variable and for example analyze predictors of software competence. If you have a learning program you can examine change or you can also do an RCT (one group gets the learning program) and compare groups

Best

John-Kåre

you can make some different choice, Wimax is not the best.

Research in comfort

Dear Khan,

I suggest you to see links and attached files in subject.

-Device-to-Device Communication in LTE-Advanced Networks: A ...

- Futuristic device-to-device communication paradigm in vehicular ad ...

- A Scheme of Ad-hoc-Based D2D Communication in Cellular Networks

- Device-to-Device (D2D) Communication: Fundamentals with ... - WNCG

- A Software-Defined Device-to-Device Communication Architecture for ...

- Performance Analysis of mmWave Ad Hoc Networks

https://arxiv.org › cs

Best regards

Mathematicians have a precise definition of infinity that can be used to prove theorems about it. A set S has infinite size if it is possible to create a 1-to-1 correspondence of the elements of S with the elements of a proper subset of S. For example, the positive integers S = {1,2,3,...} is an infinite set because there is a 1-to-1 correspondence between S and the even integers in S: 1 <-> 2, 2 <-> 4, 3 <-> 6, 4 <-> 8, ... Such a 1-to-1 correspondence is impossible for finite sets.

Both, it is science and art, sure of that.

Best,

Litart

PDE are the basis of many "inpainting" image restoration techniques:

NPTEL lectures are lenghthy but are useful to understand all the concepts.

Sharing link;

Regards!!

I think it's only a matter of time before this change would happen as future cutting-edge-technologies and smart cyberspace with ubiquitous access is making learning more flexible.

Best regards,

Debra

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.

For example, search for earthquake victims

or explosives.

Research

Regards,

Joachim

As an Public Health Practitioner, I am not much aware about outcome matrix for computer and engeeneering scince. However, Publications ( Journal?), finacial support ( who and why?) and the research institute and leading figure are also important in my view.

As I understand, Hadoop replicate data based on rack awareness.

you can read more about this here:

TCP is a stream oriented protocol and UDP is a message-oriented protocol.

TCP receives the stream of bytes from application layer protocols and divide it in to segments and pass it to IP. But UDP receives already divided or grouped bytes of data from application protocols and add UDP headers which will become datagram and send it to IP, then application layers has the burden of dividing the streams of data in to messages when they run on top of UDP .

Dear Victor,

To begin with, generally about kinetic equations - as I understand it. The kinetic equation usually takes the form of a power monomial or binomial (homogeneous system) or a hyperbolic function (heterogeneous systems). The form of the equation is determined on the basis of experiments by the differential or integral method using the assumed norm (objective function) in form of RMS, average relative error etc. which we minimize. The effects of temperature, influence of initial substrate concentrations and, possibly, influence of concentrations of selected products (inhibiting effects of products) are taken into account in the studies. In case of reaction A ==> ... we investigate the effect of initial concentration of A, and in case of reaction A + B ==> ....... we will perform experiments at least for several molar ratios B / A. The most common mistake made here is to conduct the experiment for one molar ratio. For example 2: 1 and then the conclusion - the second order reaction with respect to A and B or 20: 1 and then the conclusion - the first reaction order with respect A. This may be, but it does not have to be.

Nothig mixed. I consistently use the term kinetic equation - as a result of kinetic studies (macrokinetic). But what you call the kinetic model, I call the mathematical model of the process. Remember, however, that 30 years ago, the term model and modeling was rarely used.

Besides, the kinetic equation is derived from the experiment, so, of courese, it can be called an empirical equation.

Regards,

Here you can find brain MRI dataset (some include white matter lesions). Hope it helps:

Dear Raju,

You seem to be seriously confused about a distinction that used to puzzle and annoy researchers for years (for centuries, in fact). Only recently it has attracted several groups of prominent researchers that stand on two legs: they are able engineers, AND they are recognized philosophers. I am sure many colleques present on ResearchGate can provide You with the relevant literature (I also work in this area, and I do keep track of these works, but I am a little put off by the hostile wording of your posts; in my view it remains hostile, regardless of how many caveats you attach and how many patents you claim). Namely, the difference is that between SCIENCE (scientific research of the world that is "out there", including dolphins, eggs, etc., mostly using a hypothetico-deductive method), and DESIGN (engaging in changing the world, in our case - by rolling out artefacts, or artificial systems; the hypothetico-deductive system also plays a part in this research, but in a non-trivial and rather indirect way). "System" is that which is composed of at least two proper parts;  otherwise it would be an atom. We normally call these parts "components". Of course, there are different meanings of 'component' - these may be "pieces of junk", but also roles, etc. Design requires, and produces, specific terminology. According to the specificity of design sciences, at least part of this terminology tags and technically describes what designers *intend* it to mean (i.e., it is in a certain way similar to a specification). Its meaning is not "out there" in the first place. This also pertains to "assembling" and other terms that you use in aboundance in your posts. Anyone is free to define technical terms in any way they like. But if this meaning is different from what other people (a lot of people) accept and work with, then, indeed, any convergence towards some intersubjective "truth" is impossible. Not that those other people are involved in some world-wide fallacy or "plot". Claiming THIS would be a fallacy.

By the way - it would indeed be a fraud if an engineering discipline had no methods to validate or correct beliefs, theories or hypotheses. It would be a non-science, or (excusez le mot) a "quack science". Fortunately (what a relief!) this is not the case. Of course, you may disagree with the relevant arguments that can be found in the easily obtainable literature - you have every right to do so. But, mind you, in doing so you are taking dolphins for sharks.

Best regards

Joni Dambre hit the important point in her answers: why exactly do you need your neural net to have only positive weights?

Joni Dambre conjectures that you are involved in a project on "optical" neural network. She rightly points out that is very old stuff: I saw the first optical neural net at Caltech  when visiting John  Hopfield and Dimitri Pasaltis in the mid-1980s. I also remember a lively after-dinner discussion during a conference, in the early 90s, where a participant argued that optical neural nets were the final jolt of the dying body of analog computing. As far as optical neural nets are concerned, the statement was essentially right, but optical computing did find some niches in other areas.

There may be other legitimate reasons why you want positive weights only: for instance, one motivation might be that you know, from expert knowledge, that the output of the network should be a monotonously increasing function of the inputs; this is guaranteed if all weights are positive, if the activation function of the hidden neurons is an increasing function of the potential (typically a tanh or a sigmoid transfer function), and if the output neuron is also an increasing function (tanh or sigmoid if you application is classification, linear if it is nonlinear regression). This is a nice way of capitalizing on prior knowledge.

In the latter case, Turki Younis Abdalla gave the answer above: you want to optimize a given function (in your case, the least squares cost function) under linear inequality constraints; this is a very standard problem for which good solvers exist. Implementation is not a serious problem: use the well-known fmincon function provided in the Matlab optimization toolbox, a proven method that relies on mathematically solid algorithms. Note that, as an option, you may pass to fmincon the value of the gradient of the cost function, which you can compute by backpropagation. Contrary to a misconception for which Matlab is responsible, backpropagation is not an optimization algorithm; it is a computationally efficient method for computing the gradient of the cost function of a neural net. Once the gradient has been computed by backprop, you can use any appropriate gradient-based optimization algorithm, such as simple gradient descent (very inefficient), a second-order optimization algorithm (e.g. Levenberg-Marquardt), or, for constrained optimization, fmincon. Passing the gradient to fmincon will probably be helpful, but it is not mandatory. Also pass the value of the hessian, which is even better.

If the rationale for having positive weights is indeed optical hardware, then fmincon may provide a solution, but if you want the output of the network to produce non-increasing functions, the solution will be very inaccurate, as the network is inherently unable to ouptut a non-increasing function. My suggestion would be to use, in the hidden layer, neurons with tanh transfer function and neurons with (-tanh) transfer function (if your hardware allows you to implement (-tanh)), in equal numbers, and train using fmincon. This is equivalent to having hidden neurons with positive weights only and hidden neurons with negative weights only. This will certainly result in increasing the number of parameters with respect to a "normal" neural net, hence in increasing the risk of overfitting; the price to pay will be the addition of a regularization term to the cost function; there is no free lunch, as usual... My suggestion is a hack, because I have no proof (but maybe it exists somewhere) that the fundamental property of neural nets (the ability to approximate any differentiable function with arbitrary accuracy) is preserved for such a structure.

In either case, do not forget to normalize the inputs (subtract the mean and divide by the stadard deviation, as usual).

Hi Mario,

My two cents:

1) I guess that the change over time is the influencing factor and since it is 2 seconds max in your case, it gives an impression that it is the task time.

2) At a bigger magnitude your case seems similar to sequence scheduling in paint shops. Overall time is optimized in a specific sequence of colours -  a sequence dependent set up time optimization problem.

3) The following papers may give you some lead:

a) A sequential ordering problem in automotive paint shops - this is available in researchgate and you have to request for full text from the authors.

b) Scheduling algorithms by David Karger (MIT), Cliff Stein, Dartmouth college, Joel Wein , Polytechnic University. You can get the full text in google search itself.

Happy if it helps. Good Luck !

Regards,

Guru

Thanks everybody,

your suggestions are really helpful. For now I have successfully increased the data acquisition precision by bypassing analysis and using serial port terminal apps such as puTTy and CoolTerm to capture data and analyze it later.

:)

Best,

Jan

Hi Denis,

I'm so glad to hear from you. Thanks for the reply.Can you tell me what can we do with Articulate Storyline 3.

Sincerly yours,

El Hassane,

Dear Sara,

First of all, let me congratulate you for your achievement.

Do not bother about applications. Go on your research,

But if you want to have an application, you can see

ERP5 - they are using an interesting approach to automate configuration.See

Wölfel K (2014) ERP5 Starter: Open-Source-ERP-Einführung durch standardisierte Beratung. HMD 49:58–67. doi: 10.1007/BF03340663

Wölfel K (2015) Automated ERP Category Configuration Support for Small Businesses: International Journal of Enterprise Information Systems 11:1–23. doi: 10.4018/IJEIS.2015040101

Wölfel K (2017) Approaches for Automating ERP Category Configuration for SMEs. In: Tavana M (ed) Enterprise Information Systems and the Digitalization of Business Functions. IGI Global, Hershey, PA, pp 66–98

Additionally the system is very flexible.

Ahmed, your statement is correct in some approximation. But a lot depends on application and complexity can be higher than ODE or even PDE. In mechanics of finite number (N) of particles, the state variables are coordinates and velocities of each (6N dimensions). The dynamics is governed by Newton's laws and one can write the dynamical system.

Consider another application in economics. Let us have 2 states - employed and unemployed, and probability matrix of transition across those states. This dynamics becomes stochastic and we cannot tell for sure whether at time T there will be exactly N employed and M unemployed. But if the number of people goes to infinity (in reality we never can do that), we can use the law of large numbers to derive almost deterministic dynamics.

So far we were talking about autonomous systems, where dynamics is the function of current state. But there might be systems under external force, with delay, etc.

Dear Lokesh,

I suggset for you a links and attached files in topics.

-Comparison of K-Means and Fuzzy c-Means Algorithm Performance ...

https://www.ncbi.nlm.nih.gov › ... › PubMed Central (PMC)

- Comparative Study of Different Clustering Algorithms | Open Access ...

www.rroij.com/.../comparative-study-of-different-clusteringalgorit...

- ]A Comparison Study between Various Fuzzy Clustering Algorithms

Best regards

evolutionary testing, temporal behavior

yes, must be because your inverting algorithm is linear --> O(n)

Assuming a uniform distribution of addresses, evenly distributing references across cache banks requires dividing the address by the level of associativity.  This is much easier if that is a power of two.  Also, the cache banks usually fit into a more rectangular area when you have power of 2 associativity.  

Some processor designers have decided that a preferred cache size (3 banks, usually) is preferable to maintaining those properties. 

An up to date research topic is data offloading in cellular networks through device-to-device communication, a key enabling technology for the 5G. In such networks data packets can be directly forwarded between devices (e.g., cellphone) enabling to offload the data demand of the base station.

Several predictions could be applied to such networks. For instance, since the devices must be near to each other to communicate directly (D2D), you could use machine learning techniques to predict when, or what are the chances of two given devices getting in communication range in the near future and therefore decide if it is worth to cache a given content instead of transmitting it through the base station directly, enabling the base station offloading. For a more detailed description please take a look at the following paper:

For this type of project you can use real user contact proximity traces. Several are available at:

crawdad.org 

Hope it helps!

Best,

Ivan.

I think what Knuth means is that you should not start optimizing because of a "gut feeling" that some algorithms in the program may run faster. You should first finish the program and then measure the performance. If the performance is sufficient, stop; else identify the problematic parts and optimize only as much as necessary.

Faster code is less readable, and a non-critical perceived problem probably solves itself in one or two processor generations (or your optimization becomes obsolete), so keeping it simple is a very good advice. As Peter implies, the actual most critical parts are often in the most unusual and unexpected places.

So the message is to base the optimization decision on facts (measurements) and not on guessing (programmer intuition).

You might want to check this out:

Hello Manjuprasad,

attached have you some papers as bases of SANs - sorry: only in German

Many success and all the best

Anatol Badach

It is good paper about the complexity of frequent itemset mining.