Algorithm Design - Science topic

Mohammad Imam , thanks for the input, I'll look into this

André Michaud A sensible man indeed! And perhaps we share this outlook because I tend to look at physics through an engineering lens. To me, if one cannot prove their theoretical words with actual equations, it doesn't hold much of a basis at all. I do believe that mathematics and physics are intimately entertained and that physics is simply the study of emergent properties of physical mathematics. It's interesting and quite hillarious you say that you barely have seen a theoretician pick up a calculator in 25years. Unfortunately that used to be me, and you are very right that a more stagnating thing does not exist. With perspective on that I can say my theoretical physics, although thought provoking, rarely had any use, either practical or academic that anyone even wanted to see, cared about, or even could get employed off of. I started getting a lot of success when I realized that the math is fundamental and that I was deluding myself into thinking it wasn't because I wasn't good at it. To be quite honest I found I thought I "wasn't good" at typical mathematics because it was just too abstract for me. "1+1=2." Okay, one what plus one what equals Teo WHAT? Distance? Amount of friends? Weight. It was just way too theoretical for me, abstract numbers with no unit, basis or story attached. Basically unreal axiom that help us understand things. Humans kinda forgot this abstraction is simply a tool to help understand things, not an actual representation of Universal constants. It can indicate those things via representation but that's it. Once I started dealing with physics and engineering in a less theoretical sense, I realized my problem with why I thought I couldn't do math was that pure mathematics (Shoutout Doom) Was just far too abstract. I think a lot of people who are convinced math and physics aren't always intimately entertwined have this problem, a kind of innate fear and of their own competency with math that affects the logic of this assumption. I'm also relatively sure as well that looking at these things I have said here, a lot of people afraid of their mathematic ability would find they are actually very good at it when abstraction is removed. But maybe that is just me. Jixin Chen I also hate to keep referencing back to my paper (if anyone has any info on this let me know) but there are many examples in the mathematics and quantum physics sections that show how mathematical equations are proven to be able to represent absurdly complex quantum physics principles and constants of nature in a neat "package".

Top Paper on Computational Complexity of the Proposed Algorithm?

1. "Computational Complexity Analysis of a Proposed Algorithm" by Murat Kantarcioglu, Mohamed Shehab, and Sushil Jajodia.

2. "Computational Complexity Analysis of a Proposed Algorithm Using Big-O Notation" by Kim T. Nguyen and Neeraj Kumar.

3. "Computational Complexity of a Proposed Algorithm: A Case Study" by David S. Johnson and Shlomo Zilberstein.

4. "Analysis of Computational Complexity of a Proposed Algorithm" by Ling Liu and Shouhuai Xu.

5. "Computational Complexity of the Proposed Algorithm: A Performance Evaluation" by Tsung-Chih Lin, Yi-Chun Huang, and Yen-Hsiang Huang.

Dear Chenyuan,

Here you are a dissertation about it

and on this webpage, you can find most of the algorithms you could need

Cheers,

Ivan

@vadivel SM

Hi.

Im looking for two or more algorithms to optimize and reduce material of concrete and steel structure in legally way

For a humorous treatment, see the "bestiary":

Dear Christoph A Krettler ,

Look the link, maybe useful.

Regards,

Shafagat

Dear Marco Ripà ,

I have done both. I cited my work in the sequences and the sequences in my work.

We hypothesize that future generations of Artificial Intelligence (AI) technologies specifically adapted for biological sciences will help enable the reintegration of biology.

Dear Ohira-san,

Hopefully after the covid-19 I can visit Ohira-san. The ESPAR antenna was really a new direction created by Ohira-san. I was also very lucky to have the opportunity to investigate the ESPAR algorithms. Thank you for the latest papers. I will surely study the lasted papers of Ohira-san.

Mathematics helps AI scientists to solve challenging deep abstract problems using traditional methods and techniques known for hundreds of years. Math is needed for AI because computers see the world differently from humans. Where humans see an image, a computer will see a 2D- or 3D-matrix. With the help of mathematics, we can input these dimensions into a computer, and linear algebra is about processing new data sets.

Here you can find good sources for this:

Teachable neural networks are currently the main direction in the development of artificial intelligence (AI). These systems belong to the category of inductive anthropomorphic, which is associated with their well-known advantages, disadvantages and areas of application. It is generally accepted that anthropomorphism is the exclusive factor in the advantage of AI, however, there are actual areas of AI application, in which the use of deductive systems is more effective, which use less data and computational resources, but are capable of learning. Examples of such systems: knowledge generators, expert systems, active marketing systems, and many others.

The basis of AI deductive imitation systems are extremely general models and algorithms that imitate the objects (objects) under consideration. The generality limit is determined both by the amount of knowledge about the object and by the calculated capabilities. General models (metamodels) are usually formed in the form of systems of equations that link together the variables that describe the object under consideration, as well as parameters that determine the form of partial equations formed when transforming the equations of the general model. The solution of such systems of equations is carried out by methods of global optimization: stochastic (Monte Carlo methods) or heuristic (evolutionary algorithms and others). Simulation AI presupposes the solution of two basic problems: inverse (implementing the system training options) - the formation of partial equations (models) according to the values ​​of certain variable characteristics of objects; direct - definitions of values ​​of variables for particular configurations of models. The use of simulation metamodels in combination with the randomization of variables and parameters provides high flexibility and adaptability of intelligent systems.

I suggest Mendeley and Endnote. But Mendeley is user friendly software.

You have to explain time and space complexity of your algorithm

I have the same problem

Daniel Wright and Remal Al-Gounmeein thanks for the links, I will take a look. We are matching respondents based on their likert scale (5) responses to 16 partisan political positions.

Arash Mazidi PSO is also in various classification problems. I particularly use it for Phishing website datasets.

Hi Dr Behzad Soleimani Neysiani . I agree with Dr Qamar Ul Islam .

Adnan Majeed Thank you for your reply, but the problem still remains. I need an algorithm or MATLAB code related to ADR for LoRaWAN. If you have such details then please share that here. Thank you.

Hi Arijit Nandi ,

Thanks for your input. I have their codes. They have followed the same. I have attached their code below.

Dear Srinivas Talasila

The link posted by Amin shandiz would be very helpful for you.

I'm not keen on calling anything "smart". Any method will fail under some circumstances, such as for some outlier that no-one have thought of.

Please read the paper of Energy and Wiener index of Total graph over Ring Zn . In this paper I calculated the distance between two nodes.

i mean in which class? Wang Ting Dong

Dear Sindhuraj Mukherjee,

I suggest you to see links and attached files on topic.

Best regards

If I understand correctly, this will be part of a commercial product, so I planned to exchange my C program for a four week holiday in Australia, but now Olatunji A Shobande is interested in the algorithm, too, and I guess I cannot expect him to pay for my holiday in Scotland as well :-) ... so, since this is RG let's make the algorithm open source:

There are two main points:

1. Being a visual thinker myself, often I advice my students to redraw the schematics given in exercises in such a way that the height of the electrical potential is reflected by the height in the drawing, i. e. by the y value.

So, first the electrical potentials of the wires are determined or at least upper and lower limits estimated. With sources, this is straightforward; with transistors, we can apply the polarities in normal operation. With resistors in series, one can apply the voltage divider rule. Finally, in cases where only voltage ranges apply the mean value between upper and lower limit is taken. In the example, we get:

p0 = 0V, p1 = 3.7 V, p2 = 12.6 V, p3 = 8.1 V, p4 = 8.1 V, p6 = 7.5 V, p7 = 12 V, p8 = 12 V, p9 = 7.5 V, p10 = 7.5 V, p11 = 15 V

Then y values are assigned to the wires, in the order of increasing potential, the step width being determined by the maximum height of the drawn components. I chose a step width of 2, resulting in:

y0 = 0, y1 = 2, y2 = 18, y3 = 10, y4 = 12, y6 = 4, y7 = 14, y8 = 16, y9 = 6, y10 = 8, y11 = 20

This gives 11 horizontal rails with sufficient distance between adjacent rails for the placement of elements.

The extension in x dimension has to be larger than the number of components (transistors counting 2).

2. Usually, the signal path will start on the left side, and will run through the active components to the right. Therefore, placing starts with the active components. The first transistor placed is simply the first one in the netlist. This placing is triggered by a function which just scans all transistors not yet placed in the netlist. But the placing itself is done by a recursive function which determines if there are further transistors connected directly to the current transistor, and if so, where the largest number of transistors is. Then the function calls itself for one of these transistors.

In order to avoid backtracking to already completely harvested wires, one function parameter is the number of the wire connecting the current transistor to the next, and another parameter is a number which is compared to the number of adjacent transistors at the current wire. During the first call it is set to 2, during further calls it is incremented by 1. If this number equals the number of transistors then the current wire is abandoned.

In the example, the series is: Q1, Q2, Q6, Q5, Q3, Q4

Each transistor is placed on increasing x values, at the moment with step width 2. However, a considerably larger step width might be preferable in conjunction with the final x compression (see below).

If there were further clusters of transistors, the placing would continue with Q7.

The recursive function described above could be supplemented by another with the ability to "look" beyond one resistor or capacitor. In this way, the signal path could be traced even if the active components are coupled by passive ones. (end of point 2)

The placing of the passive components is done in such a way as to avoid increasing the length of wires if possible. The placing of the sources is done last.

Two steps remain which are not yet completely implemented: x compression (decreasing the distances in x dimension) and y compression (placing several rails on the same y value if they are non-overlapping).

Three linked lists are employed: A 1D one for the components and a second 1D one for the wires. The third one is 2D for the placement.

Placing and routing is not my field of expertise, so I don't know how my solution compares to existing ones. Anyway, this was a nice exercise.

Visit to read this article

Perhaps you can visit:

You do not need to change output variable values from continuous to categories (e.g. 0s or 1s). Feature selection works on regression setting too; only difference is the performance measure. For example in classification setting, prediction accuracy, precision, sensitivity, AUC, etc. can be used. For continuous response setting, you can use R square, Mean Squared Error, Root-Mean-Squared-Error, etc.

The proposed algorithm has binary codes and pixels inside an image

Post doc is for those whose Phd is weak.

I suggest to look for some Entrepreneurship in your domains of interest

you could refer to this paper

From my personal experience, Deep Learning (DL) offers NO substantial improvement in time series forecasting accuracy vs. traditional methods, such as Holt-Winters, exponential and double exponential smoothing with or without trends, Holt-Winters with seasonality and trends, autoregressive integrated moving average (ARIMA), digital filtering, etc.

Regardless of the concrete technique (including deep learning and recursive neural networks in AI), all time series forecasting methods are based on the use of the historical past data to extrapolate them into the future. The fundamental assumption of any forecasting technique (implicit or explicit) is that time series represents a stable pattern that can be identified and then extended into the future. If a pattern of the past data-points is not statistically stable, then no meaningful future prediction (forecasting) is possible regardless of the sophistication of the forecasting technique (DL or not).

It seems reasonable to assume that too ‘old’ data-points do not practically affect (correlated to) the most recent data-points, let alone the future data. The data-points that are strongly correlated to the newer ones can be used for making the forecast. The data-points that are weakly correlated to the newer ones (or not correlated at all) should not be included for forecasting; otherwise, the forecast will likely be skewed. Thus, the use of a large number of the available past data-points worth of several years of observations is not needed for making a meaningful forecast; in fact, the large number of the past data-points can be detrimental to producing a reasonably accurate forecast regardless of the used technique (deep learning or not). The problem of the optimal number of the past data points (training set size) of a time series should be the primary focus for industrial applicability of the forecasting rather than a forecasting technique (DL or not).

I'm using MATLAB: PLS toolbox for analyzing my data and i have a problem using EPO preprocessing for PLS-DA.

when im using EPO i get this error which the toolbox cannot perform cross validation. can anyone help me with this situation?

i think powerpoint or word work well too

can be isolated using different spectral signature between cloud cover and ice cover

Dear Muhammad Waseem,

I suggest you to see links and attached files on topic.

A Systematic Method for Designing a PR Controller and Active ...

https://pdfs.semanticscholar.org/.../a9b1ca7f128aec12e365dc826f8...

An Improved Current Control Strategy for a Grid-Connected Inverter ...

https://www.mdpi.com/1996-1073/9/3/190/pdf

A Current-Control Strategy for Voltage-Source Inverters in Microgrids ...

https://www.sheffield.ac.uk/...fs/1.../IEEEPE1103_CurrentRep.pdf

Robust Nonlinear Controller Design for Three Phase Grid Connected ...

https://www.praiseworthyprize.org/jsm/index.php?...page...

Best regards

Genetic Algorithm can be an alternative solution :

There are several methods already published with several publishers

there are people that thinks so. The reason is that apear every week a new algorithm which seeasm to be a copy from other only with different names

There are several journal for algorithms. Some of them are:

Algorithmica

The Computer Journal

Journal of Discrete Algorithms

ACM Journal of Experimental Algorithmics

ACM Transactions on Algorithms

SIAM Journal on Computing

ACM Computing Surveys

Algorithms

Close related:

Theoretical Computer Science

Information Systems

Information Sciences

ACM Transactions on Information Systems

Information Retrieval

International Journal on Foundations of Computer Science

Related:

IEEE Transactions on Information Theory

Information and Computation

Information Retrieval

Knowledge and Information Systems

Information Processing Letters

ACM Computing Surveys

Information Processing and Management

best regards,

rapa

I've found a description with algorithm implemented in R for you. I hope it helps: http://www.joyofdata.de/blog/testing-linear-separability-linear-programming-r-glpk/

Another nice description with implementation in python you can check as well: https://www.tarekatwan.com/index.php/2017/12/methods-for-testing-linear-separability-in-python/

@Nader Chmait

I imagine the approximate entropy notion may apply as an exercise of calculation. I am not sure the entropy and the approximate entropy reflect the same meaning.

The approximate entropy seems closer to the statistics, but Shannon entropy is closer to information content.

The DP algorithm for LCS finds the solution in O(n*m) complexity. This is polynomial time. The reduction you are looking for is the problem itself. If you solve this problem in an alphabet with uncertain size, you solve it for size 2.

There are tons of algorithms. Here a list:

DE

PSO

ABC

CMA-ES, etc

This should be built in base on the particular reuqriments.

nice thinking

Yes, I see. Transaction processing has also a constraint on response time. Optimization then takes more of its canonical form: Your goal "as fast as possible" (this refers to network traversal or RTT) becomes the objective, subject to constraints on benchmark performance which are typically transaction response time and an acceptable range of resource utilization, including link utilization. Actual benchmarks known to me that accomplish such optimization are company-proprietary (I have developed some but under non-disclosure contract). I do not know of very similar standard benchmarks but do have a look at TPC to see how close or how well a TPC standard benchmark would fit your application. I look forward to seeing other respondents who might know actual public-domain sample algorithms.

http://csci.viu.ca/~barskym/teaching/DM2012/labs/LAB5/WekaRecommender.java provides a preliminary example for Collaborative Filtering using k-nearest neighbours.

Sinan Salih Thank you so much i will contact you shortly if i need more clarifications...

I think that it can help you...

SEE THIS LINK I THINK IT IS VERY USFULL

AND THIS CODE IS FOUND IN MATLAB

function D = PandO(Param, Enabled, V, I)

% MPPT controller based on the Perturb & Observe algorithm.

% D output = Duty cycle of the boost converter (value between 0 and 1)

%

% Enabled input = 1 to enable the MPPT controller

% V input = PV array terminal voltage (V)

% I input = PV array current (A)

%

% Param input:

Dinit = Param(1); %Initial value for D output

Dmax = Param(2); %Maximum value for D

Dmin = Param(3); %Minimum value for D

deltaD = Param(4); %Increment value used to increase/decrease the duty cycle D

% ( increasing D = decreasing Vref )

%

persistent Vold Pold Dold;

dataType = 'double';

if isempty(Vold)

Vold=0;

Pold=0;

Dold=Dinit;

end

P= V*I;

dV= V - Vold;

dP= P - Pold;

if dP ~= 0 & Enabled ~=0

if dP < 0

if dV < 0

D = Dold - deltaD;

else

D = Dold + deltaD;

end

else

if dV < 0

D = Dold + deltaD;

else

D = Dold - deltaD;

end

end

else D=Dold;

end

if D >= Dmax | D<= Dmin

D=Dold;

end

Dold=D;

Vold=V;

Pold=P;

Interesting suggestions posted so far. I would have to say this question is too vague. If you check any reputable journal to see what is being published, this is the best indication of what is currently of interest.

I follow answers

regards

Thanks Vincent, a hybrid the proposed methods might be better, but there is only one way to find out, Experiments!

@B. Jourquin

Dear Prof. Jourquin, many thanks for proposing these amazing algorithms. Although they could be applied to this problem under some specific considerations, they cannot handle it in general; these algorithms are designed to handle equilibrium systems (ES), while the above problem is an optimal system (OS), which is not equal to ES in general.

I think that mutation is possible in one parents?

In the case of Euclidean graphs you can use the Euclidean distance between nodes. You can also use random weights. Depending on the application you can use appropriate weights...

You have both, one FN because there is no detection close enough around the annotation, and one FP because there is no annotation close enough around the detection.

Dear Imran,

I suggest you to see links in subject.

-EXPLORING ALGORITHMS FOR EFFECTIVE ... - Semantic Scholar

https://pdfs.semanticscholar.org/.../3f41832b75c6974d6a3a2cc01a...

-Graph-Theoretic Concepts in Computer Science: 24th International ...

-Expanders via Random Spanning Trees

Best regards

Excuse me. Do you have a simulation file of CBRP?

Map-matching algorithms are algorithms created to match the correct link on which a vehicle travels to determine the location of a vehicle on the right link or path. This is achieved by integrating position data with stochastic road network.

See:

packmol will also do the job.

Let p_{ij} denote the profit gained if parcel j is loaded onto truck i. If the parcel cannot be loaded onto that particular truck, we just set p_{ij} to zero. It looks like we just need to solve the following 0-1 linear program:

maximise

\sum_{i=1}^m \sum_{j=1}^k p_{ij} x_{ij}

subject to

\sum_{i=1}^m x_{ij} \le 1 (for all j)

\sum_{j=1}^k x_{ij} \le 1 (for all i)

x_{ij} \in \{0,1\} (for all i and all j).

If that's right, the problem is very easy. As stated by Przemysław and Helmut, it is equivalent to the linear assignment problem, which in turn is equivalent to maximum-weight bipartite matching.

As I said, m and c are fixed. Also, I know both of them.

In a random distribution, the probability of occurrence any one state has to be equal to the probablity occurrence of any other state, and there are supposed to be no consistently repetitive patterns. If pseudo-random, then those same rules apply to a point, before the sequence cycles. Which is a useful feature for things like cryptology, or perhaps even monte carlo simulations, where you want randomness, but you also need to be able to replicate the same random sequence.

You can demonstratet pseudo-randomness for yourself, easily enough. For instance, using a PRNG, paint a sequence of points, coordinates (x,y) on your monitor, where the x and y values are just taken from the sequence generated by the PRNG. You will see how the screen fills up with these dots. You will see that bare spots on the screen will fill in in due course.

Then, change the seed of the PRNG, and run it again. A completely different sequence should be evident, as the screen fills up with dots. What you want to witness is a uniform distribution of dots, over a short period of time.

hi?

Do I know exactly the data test on the bee algorithm that goes through the neural network?

Dear Maysam, why don't you choose a neural network achitecture to be optimised? Afterwards, given a determined training set, choose a metric, like overall accuracy or index kappa, and build your objective funcion. This is the key. Therefore, you will be able to use any metaheuristics-based optimization algorithm, including ABC, and compare their results.

To answer this question best, a few things need clarification such as the grade level of students, are they shortnor long essays and the objective of the essay (Is it a responds essay to a given set of questions? Is it a creative writing essay?). However, I will try my best to explain.

You generally design what is called a rubric. A scoring rubric is an attempt to communicate expectations of quality around a task. In many cases, scoring rubrics are used to delineate consistent criteria for grading.

Scoring rubrics include one or more dimensions on which performance is rated, definitions and examples that illustrate the attribute(s) being measured, and a rating scale for each dimension. Dimensions are generally referred to as criteria, the rating scale as levels, and definitions as descriptors.

The essay should have good grammar and show the right level of vocabulary. It should be organised, and the content should be appropriate and effective. When evaluating specific writing samples, you may also want to include other criteria for the essay based on material you have covered in class. You may choose to grade on the type of essay they have written and whether your students have followed the specific direction you gave. You may want to evaluate their use of information and whether they correctly presented the content material you taught. When you write your own rubric, you can evaluate anything you think is important when it comes to your students’ writing abilities.

The most straightforward evaluation uses a four-point scale for each of the criteria. Taking the criteria one at a time, articulate what your expectations are for an A paper, a B paper and so on. An A paper would be exemplary in all facets of the essay (content, grammar, structure, logic, sources etc). A B paper may show promise in these areas but be unclear in some aspects or lack originality and so on. It's generally up to you what you want the standards to be.

A university level rubric will generally look like the one attached to this answer.

I hope this helps!