Computer Science - Science topic

Data mining used for extraction of hidden information from large databases while Swarm intelligence are artificial intelligence techniques used with machine learning. data mining can use some artificial intelligent techniques like swarm computing methods

The fast two journals in cybersecurity which are Q1:

Sensor MDPI and IEEE Access; however, these two journals are open source (paid journal)

For Q2 Journals, Wireless Networks (Springer), and Computer Networks (Elsevier) but these two journal needs three months to get response.

http://lc.kubagro.ru/aidos/How_to_make_your_own_cloud_Eidos-application.htm

http://aidos.byethost5.com/Source_data_applications/WebAppls.htm?i=1

That seems quite close really.

If you want to really get them closer, you could add a Dropout/SpatialDropout layer, which would help prevent overfitting.

You can find the available opportunities via https://www.findaphd.com/

Some projects are funded by organisations while others are self-funded.

From my experience (studying PhD in the UK), It is a bit tricky to get accepted into a funded project especially for students out of the UK and EU. Good Luck

There is a European grant but is very hard to get. you can check more information on the link below

https://ec.europa.eu/info/research-and-innovation/funding/funding-opportunities_en

Read as much indexed articles as you can then begin writing. This will let you see how things go on.

thanks a lot.

Mohamed-Mourad Lafifi

Muhammad Ali

Mac OS. It is easy to use and has fewer security issues.

How can it be so costly that code cannot be re-executed, with known seed? I would think that personal computer, running just your Python code, would be able to grind through the set of computations in an orderly fashion, iterating for each possible seed. How big is the seed?

They are almost the same, however., you have to use it according to the fitness/coherence nature, style, and style of your manuscript. However., the tiny differences still arises with respect different situitions. For example:

1) Model may be mathematical/statistical/physical/nominal/real/exact/approximate/optimal/maximal/minimal etc.

2) Approach may be some way of data processing, computation, scaling, idea, technique etc.

3) Method may be one of the chosen procedure for data processing, computation, calculations etc.

4) Algorithm is just step by step procedure for computation, calculation, computer or programming coding etc.

Related to you query you may follow : https://www.quora.com/What-is-the-threshold-word-frequency-that-makes-a-word-crucial-to-learn

Dear Frank Vega

Wish you good luck. I hope to see the results of the work when they appear.

Best regards

This will not be easy. The countries that still have tuition-free education are few and mostly found in Europe, you can see them here https://www.study.eu/article/study-in-europe-for-free-or-low-tuition-fees . If you come from a non-EU/EEA-country there often are fees, but sometimes lower, in these countries. For participation, you also have to qualify for a student visa for that country as a non-EU/EEA citizen, which involves showing that you have funds enough also for supporting yourself for living costs. In the category of non-tuition master programmes, there are almost no "distance educations" where you can remain in your home country. For master education, however, you can look for "Erasmus Mundus" university cooperations and master programmes. They often have, as part of the concept, some student places with a grant, enabling students from third countries to study,

For PhD candidate education, there are sometimes, as in Sweden, PhD student positions that you apply for, and if accepted, you have both tuition-free education and a monthly salary and a student visa for 4 years. it is like this in some other countries as well, as in other Nordic countries, Germany, Switzerland and Belgium. See this RG discussion https://www.researchgate.net/post/which_countries_offer_PhD_positions_as_paid_jobs_in_English

These PhD positions are advantageous, but not often "on distance", whatever that means. They can however sometimes be used in a half-flexible way. If you want a PhD "on distance", try Walden university

There is another option that may come close. The MOOC platforms have for long worked with nano-master, specialisations, micro-masters and similar course packages in interesting fields and certificates from these are often attractive for employers as a part of a CV. Not cost-free, but rather cheap, and fees are sometimes adapted after country economy. And, more and more, these platforms are offering also BSc and master programmes with ordinary credits and exams. Not free, but not very expensive, and with a lot of flexibility. begin here https://www.coursera.org/degrees and continue search other MOOC platforms as EdX, Futurelearn etc, and search them overall at classcentral.com.

There are also on some US universities online PhD and masters programmes, but they come with a cost - but sometimes cheaper than a campus option.

Hi Jovanka,

at first glance, nothing seriously applicable comes to mind but just for exercise, and if you don't mind reducing the wide field of electronics to ordinary resistors:

You could define functions like

fs8(1.0, 1.2, 1.5, .. 8.2) which decide whether a set of resistors in series results in a total resistance of 8 Ohm or not. The arguments are taken from the E12 series, for example. The argument "1.0" has the value 1 if a resistor of 1.0 Ohm is present, and 0 otherwise. For example, fs8(0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0) = 1 because 1.2 + 6.8 = 8, or fs8(0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1) = 0 because 8.2 != 8.

For such functions, you could sketch (part of) the binary tree, then the BDD, and at last the ZBDD.

Hi. I think you can use a lot of tools according to circumstances:

LMS (with password for your students) in case of official courses and for limited number;

Website (containing all course program and some quizz, with external links for technical tools and usefull softwares)+using e-mail/other ICT for continuous contact and eventual questions;

MOOC for fast courses for large public+using e-mail/other ICT for continuous contact and eventual questions;...

Good luck.

Dear Wasnaa,

AHP and TOPSIS are both the most known and effective MCDM techniques. In fact many applications of them in computer science until now. You can search for that in Journal of Computers and Industrial Engineering

With my experience, I think this all depends upon your luck. Many times editors are busy, some times they can't find reviewer, and then reviewer doesn't review in time. Once my paper in so called fast journal took 6 months to review. Last time I got a paper to review from one of the reputed IEEE Transactions and it was submitted to journal 2 months before it reached me for review. Although I gave review on 2 weeks other reviewers took time and decision was given to authors after nearly 5 months.

Similarly once for a Wiley journal which is not known to be fast, me and other reviewers gave decision quickly and decision to authors was given in less than three weeks.

So it is all luck factor that works or otherwise editor is known else you can't be sure of time.

There is no need to travel to any country to acquire the knowledge that you can easily get on internet, books

I would suggest for you to work on projects on different platforms like GitHub on which you can even collaborate and apply while learning to build your resume and gain practical experience,

You can then apply for internship on various sites that can be easily found on internet.

Foremost the syllabus must be flexible to adapt the current advances in Computer Science, such as React Js and the use of Node.

Topics must include:

(1) Programming Languages such as Python extended to it's useful applications such as in the field of Data Science, AI

(2) Operating Systems: Linux and how to use virtual machines (such as Ubuntu Studio, Kali linux) and bootable pendrive such as that of Tails OS

(3) Arduino Uno , Raspberry pi to learn how to make use of the programming (software)- hardware (physical objects) interaction[for example: making VPN router or EEG for measurement and recording],

(4) Background and history of Computer Science with the inclusion of the current syllabus but giving less priority to the old concepts which are replaced by better practical ones

(5) Students must be taught how to develop their own website, mobile application, software pc apps, and many other practical applications of Computer Science

(6) AI, data Science, machine learning

(7) GitHub should gain more priority and importance

The list isn't over yet, it will never be as the future technology keeps on evolving, for example quantum technology could be the next big thing that can be studied in Computer Science,

I think, The best Syllabus must include topics which allows Computer Science students to think for themselves and create a learning attitude which will allow them to learn different subtopics such as learning about Tensorflow , opencv and how Python can be used to analyse videos.

Also the syllabus must be designed with more emphasis on practical work for which rote learning is not possible and which allows every student after having completed the syllabus to have skills that are of practical value and in demand in the market.

I'm now thinking more than five. Wrt liberal arts, use of computers in design of graphic arts, music/sound arts, movie arts, and art arts. Computers in medical science, urban planning, forensics, one could go on and on...

Hi,

If you have any academic email address, you will not need to be endorsed. So, it suffices you to register via your academic email address. If you do not have an academic email address, you have to ask someone having such a thing to endorse you. By getting the endorsement from that person you can upload your preprints to arXiv.

The Purdue Homeland Security Institute has been using AnyLogic ABM software to garner insights related to active shooter events, test "run, hide, fight" methodologies, and establish effective evacuation procedures for theme parks. I'm only familiar with this software, but I know there are other good ones out there. Look to see if there are libraries available for your intended use. We rely on the logic for the pedestrian libraries pretty often that AnyLogic provides.

Dear Adekunle Ajibode,

You can use Test Driven Development technique easily.

I would suggest you to try with this address:

https://wiki.owasp.org/index.php/OWASP_Testing_Guide_v4_Table_of_Contents

Thanks a million Martin. I am trying for a topics in which I invested time a effort at work.

Regards,

Sanjay

Object recognition is a computer vision technique for identifying objects in images or videos. ... Using object recognition to identify different categories of objects. Object recognition is a key technology behind driverless cars, enabling them to recognize a stop sign or to distinguish a pedestrian from a lamppost.

Size, color, and shape are some commonly used features. system depend on the types of objects to be recognized and the organization of the model database. Using the detected features in the image, the hypothesizer assigns likelihoods to objects present in the scene.

Refer the following link:

https://www.learnopencv.com/image-recognition-and-object-detection-part1/

Machine learning algorithms could help determine key behavioral biomarkers to aid mental health professionals in deciding if a patient is at risk of developing a particular mental health disorder. Additionally the algorithms may assist in tracking effectiveness of a treatment plan. ( https://towardsdatascience.com/machine-learning-and-mental-health-7981a6001bd5 )

Dear Colleagues and Friends from RG,

The above discussion inspired me to formulate the following question:

On the basis of the above considerations and conclusions from the discussion on interesting issues discussed, I formulated the following thesis that the unlimited development of artificial intelligence may lead in the future to the creation of artificial awareness by combining cybernetics, ICT information technologies, bioinformatics, cyberbergetics, neuroinformatics and through the development and implementation of achievements from range of machine learning technologies and other advanced data processing technologies Industry 4.0.

Below I have described the key determinants confirming the formulated research thesis. To the above discussion I would like to add the following conclusion formulated as a summary of my previous considerations on this topic: Axiomatic and technological implications of the possibility of building artificial awareness.

For several years, artificial intelligence researchers and scientists have been discussing the axiomatic and technological implications of building artificial consciousness, one of whose goals is to seek the answer to the question: Will artificial intelligence evolve into artificial consciousness in the future?

Technologies of advanced data processing Industry 4.0, including above all Learning machines and Artificial Intelligence is also used in the attempt to build machines equipped with the ability to self-improve the performed tasks and programmed activities.

Perhaps in the future there will be an attempt to build artificial awareness in which supercomputers will be equipped. In my opinion, consciousness can only be mathematically modeled in theory. Even if a mathematical model of artificial consciousness were built using ICT and Industry 4.0 and in the future Industry 5.0 and based on this model artificial intelligence would be created in quantum computers installed e.g. in autonomous robots, androids, it will still be only artificial intelligence without emotions and the essence of human consciousness. An analysis of the nature of human thoughts is necessary to distinguish between human intelligence and various artificial intelligence technologies being developed.

In advanced computerized systems of neural networks, artificial intelligence systems are created, whose task will be to solve tasks consisting of complex sequences of many algorithms and self-learning systems for solving complex problems with the help of many algorithms. In these systems, man will try to create a structure that solves complex analytical tasks and learns from his mistakes. The advantage of artificial intelligence systems over their creator, i.e. man, is to rely on a much smaller number of mistakes made during repeated processes of solving complex tasks and learning new complex formulas to apply specific increasingly complex algorithms. However, after developing these artificial intelligence systems and applying them in many computerized fields of modern economies, what will be the next stage of technological progress in this field?

Therefore, will the age of artificial intelligence and artificial consciousness come after the age of artificial intelligence? In my opinion, this is impossible. In my opinion, despite the rapid progress in the development and creation of new generations of artificial intelligence, it will never be possible to create an artificial creation that can be the equivalent of human intelligence taking into account human emotional intelligence and the specifics of human thoughts, human consciousness, human feelings. Therefore, the thesis can be formulated that in some respects artificial intelligence will probably never match human intelligence. The machine will be able to solve very complex problems and tasks but will not know why it does it, who it is, in what world it operates, it will not be able to realize its existence in the Universe etc. Machines in the form of autonomous androids can perform physically difficult works that a man cannot is able to perform.

Quantum computers equipped with Big Data Analytics will be able to solve analytical tasks many times faster than the most powerful human minds. However, they will not be aware of their existence. Human awareness of its existence has been evolved in millions of years of evolution of the human mind and also of human ancestors that preceded the human being, i.e. human-like primates belonging to primates. Human consciousness was created in a process of evolution lasting millions of years, during which the process of continuous interaction of a complex biological organism with the environment has evolved. While artificial intelligence is based on systems of neural networks in a simplified way, to a small extent mimicking the human central nervous system and the computational power of performing specific elementary tasks exceeding the analytical abilities of a human being, however, the level of complexity of the living organism of mammals is still many times higher than the most advanced computers.

In line with the above, in my opinion, the unlimited development of artificial intelligence may lead to the creation of artificial awareness in the future by combining cybernetics, ICT information technologies, bioinformatics, cyberbergetics, neuroinformatics and through the development and implementation of technology achievements learning machine and other advanced data processing technologies Industry 4.0.

In view of the above, the following question arises:

Will artificial neural structures become such advanced artificial intelligence that artificial consciousness will arise? Theoretically, you can consider this type of projects, however, to verify it realistically, you would need to create this type of artificial neural structures. Research on the human brain shows that it is a very complex and not fully understood neural structure. The brain has various centers, areas that manage the functioning of specific organs and processes of the human body. In addition, consciousness is also complex and consists of elements of emotional, abstract, creative intelligence, etc., which also function in separate sectors of the human brain.

Best wishes.

Dariusz Prokopowicz

Dear Mokhaled Al-Hamadani, You can check the web sites of Scopus and Web of science to identify the journal titles. Similarly, you may check the www.doaj.org for identification of indexed journals which are not charging the authors.

Kindly elaborate sir

If you want to work on the Vehicular communicationnetwork system. then go for the VANET, and work on the VCC (Vehicular cloud computing)

HR (Human Resource) Industry will be HR ( Human Responsability) Industry. Beyond AI, the changes should be analyzed with the perspective of a sustainable future building. Our research should be as much contributive as possible.

Read the documents produced by ITU-T-FG-NET2030 (Networks by 2030). Several use cases refer to AI for the management of advanced network features.

Another source is ETSI-ISG-NFV (network function virtualisation) where AI might help manage complexity beyond human (control layer).

Let's start with a field that I know the best: biosignals processing, clarification, and prediction. There are many unresolved problems like

* Prediction of arrhythmias

* Prediction of epileptic seizures

* Reconstruction of physiological interdependencies of various physiological processes using biosignals.

* Reconstruction of physiological paths in the brain from biosignals.

* Assessing the health condition of people using biosignals.

We can do the following research

* Data mining for disease-genome dependencies from available databases.

* Prediction of disease spreads using internet searches.

* Assessment of the population health using internet searches.

* Cross AI methods with deep knowledge of complex systems theory and apply it to medicine -- this is my area of research.

* Study and predict drug interactions and side effects in patients. This will save a lot of unnecessary suffering in those using medical drugs.

* The above can be supported by an active search through all available data for possible, future drug interactions prior to their application to patients.

* Such research can help medical doctors to avoid deadly or highly damaging drug interactions. Each patient reacts differently to the same drugs! We need to know why and especially when it happens!

* Start development of advanced AI methods tailored towards the needs of bio-medicine.

All the above depends on how reliable databases of biosignals, medical records, bio-imaging, laboratory results, and many other database build.

When you want to have successful research in the field of AI, perfect databases that are open-access are a must. We have an extreme shortage of those databases. You can build a very successful carrier by building such a database(s). :-)

This is just a short list of all possibilities. :-)

thank you, all the best to come

License plate reading is a very well solved problem in computer vision and probably not worth a good PhD research project. You should at least try to find a problem that is not already solved by commercial libraries or software and that would require at least some small theoretical innovation/discovery.

My suggestion, look for some recent "state of the art" review papers on deep learning application to find:

1) An application domain that has a great potential future;

2) Has lots of unsolved problems;

3) That is of interest to you.

The secret of a great PhD research subject is at the intersection of these three sets!

Good luck!

Well, there are some journals indexed in Scopus and web of science that are free but in the real sense nothing is actually free.

Firstly, free journals take a very long time to publish. When i first publish in a free inderscience journal, the review period was 13months.

Secondly, after publication, you are placed on an embargo not to share the journal for some months.

These are the difficulties with approaching free high indexed journals. However, besides all these limitations, one can still get funding for high impact journals that are not free.

read this

A magazine is definitely not the same as a Journal. Magazines normally carry popular articles, not original work. A Scientific Journal publishes original scientific work not published elsewhere. 'Letters' or 'Notes' are of shorter length. It may include a comment on an already published recent paper by the author or someone else. Normally, Editor takes decisions on 'Letters' and 'Notes'. Seldom are they are sent to reviewers.They get published quickly. A full length paper in a reputed Journal has to undergo peer review process and may take up to an year or so to get published.

It is in the master list and Scopus now as well but has no impact factor.

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

Dear Maurizio,

Although I find your question really interesting and important and hope that somebody will give a more positive answer to it, I'm afraid it's not possible to let all packages use multiple CPU cores and GPU as well with a simple package loading. A workaround instead of solution: Microsoft R Open provides built-in multithreaded matrix algebra which is used by a lot of packages. You may find that this software is a useful alternative to R.

HTH,

Ákos

As a person from computer science field, you are more focused on the technological aspects of the project.

usually such projects starts with defining a problem (monitoring dryness of soil, monitoring ripeness of fruits, monitoring specific diseases of the plants, monitoring cattles or sheep, ...etc.), then you deploy the appropriate sensors for the selected application, collecting data from these sensors, analyse the data with different ML/AI tools to give reports or predictions about the required value of the project to the farmers.

This is only one example and you can find more examples here:

good luck

Sorry

1)IoT and Data: data fusion from multiple sensors, and extraction of behaviours/patterns

2) IoT and people

People can be beneficiaries or victims (my neighbours automatic vacuum cleaner, etc)

3) IoT and AI

AI supporting IoT (data analysis, decision, action)

IoT supporting AI: IoT google cars gather data, serving as input for Google map AI

Dear Raju and Shadi,

Excellent idea that to enter more in depth in these subjects, studying your work Raju, and provide Shadi team with a first article suite for SDE 2020, so it can introduce to the book with an extensive work. I will work on this in a few days and come back to you.

Kind regards,

Laurent

PS one article gives an idea of this work going from a political point of view to a metaphysical one (may be efficiently translated from french by automatic translators): https://une-vraie-politique-pour-notre-pays.net/2019/01/04/limposture-intellectuelle-face-cachee-dun-desastre-clef-dune-reussite-a-venir/

With classical biological sequence alignment algorithms such as blast, psiblast and fastA, the number of gaps inserted is controlled by two parameters, by the gap insertion penalty and gap extension penalty, which contribute to the alignment score. A high gap insertion penalty and low gap extension penalty gives preference to few large gaps rather then many short gaps.

Advances in Data Analysis and Classification

The international journal Advances in Data Analysis and Classification (ADAC) is designed as a forum for high standard publications on research and applications concerning the extraction of knowable aspects from many types of data. It publishes articles on such topics as structural, quantitative, or statistical approaches for the analysis of data; advances in classification, clustering, and pattern recognition methods; strategies for modeling complex data and mining large data sets; methods for the extraction of knowledge from data, and applications of advanced methods in specific domains of practice. Articles illustrate how new domain-specific knowledge can be made available from data by skillful use of data analysis methods. The journal also publishes survey papers that outline, and illuminate the basic ideas and techniques of special approaches.

Supported by the International Federation of Classification Societies, and funded by the Italian, German, and Japanese Classification Societies (CLADAG, GfKl, JCS).

Please share the Conference website link further details

Pl consult the article that may be useful

https://www.nature.com/articles/s41598-018-33874-1

Narrow your topic down to your area of interest

Networking is broad

Sat Solvers likely will not work well for the purpose of trying to mine Bitcoin. That said it can have many other useful purposes in a blockchain context.

As others said, you need a real life problem, I suggest looking at your place of work and what data analysis applications can improve it.

Dblp computer science bibliography?

OOPS -- speaking of glitches, the correct position of this piece on RG is:

Dear Hossein Homayoun ,

Machine learning provides a new means of screening novel materials with good performance, developingquantitative structure-activity relationships (QSARs) and other models, predicting the properties ofmaterials, discovering new materials and performing other materials-relateds studies.

Regards,

Shafagat

Hi Renata,

This paper details about it.

https://www.nature.com/articles/s41598-018-28084-8

"A solution to detect a malware at packet level and directing it to a honeypot in order to further analyze and find the attack vector or the attack source" would be a great use of machine learning and Cyber Security.

Dear Arjun R ,

It is advisable that the paper is related to some cross-fertilization of AI and Blockchain, otherwise it risks being rejected for being out of scope.

That said, if the work involves Blockchain to some extent even if it is not core to the proposal, I still suggest that you submit the paper for consideration.

Best!

IDE would be an overview of multiple tools. Off hand, I can think of:

a. Choose an optimized compiler. Its important to make sure you have the correct flags;

b. The editor should be helpful, but must not get in your way. I like vi and emacs.

c. Use of a good debugger is highly recommended.

d. Use of memory checking tools such as Valgrind is recommended.

e. A good profiler will inform you about bottlenecks in your code.

f. I found that a paper based log helps a lot. You may wish to have your log in tex if easier.

For documenting, you can use MS Word, but of course TeX is free and very versatile.

Often, design comes from some diagrams or imaging data. You need a good image segmentation tool, which should also mesh the model for you.

The AMSTAR (Assessment of Multiple Systematic Reviews) and PRISMA are basically very good and applicable across different research areas.

My recommendation for reading literature reviews, systematic reviews and meta-analysis:

► Green, B. N., Johnson, C. D., & Adams, A. (2006). Writing narrative literature reviews for peer-reviewed journals: Secrets of the trade. Journal of Chiropratic Medicine, 5, 101-117.

This is very basic overview of writing literature reviews which is focused towards biomedicine, but is very accessible and covers lots of things that are so basic they are not covered in the other recommended reading texts.

Well, what I believe, it is may be depending on the situations and level of educational institutions as well as grades of student. And certainly professor have to be experienced in their own discipline.

Regards,

MJ Khan

time() returns the total CPU time in milliseconds that was spent by the current MuPAD® process.

Greetings professor and great question!

I do hope to see interesting answers here. I will contribute based on my brief experience in the area.

I think that we can see several classical knowledge representation forms in modern applications, for example semantic networks is really popular. In the other hand, vector representation seems the most widespread representation form for machine learning. Vectorial knowledge representation is a different (bottom-up) approach compared to semantic networks. Fussing the top-down approach of semantic networks with the conventional bottom-up approach of machine learning seems to be a current-to-future trend in the area.

No matter what, I think that is more important to discuss the limitations of

vectorial data representation despite its widespread. Maybe that would be a good opportunity to explain how this is one of the core issues in pattern recognition and the role of the sub discipline of learning representations from data (and the incredible hypo that deep learning have had in this area due to its ability to learn several layers of representation from raw sensorial data). The current trend is clear when enough data is available: skip manually handcrafted features and learn the representation automatically.

Academic access to HPC resources that are not available on campus is typically at a national shared facility rather than on another academic campus. In Korea, you might check out KISTI (Korea Institute of Science and Technology Information), Also, CUDA is a software system from NVIDIA. You are probably looking for hardware like a GPU or Tensorflow chip.

The main stakes are probably connected to the development of a circular exconomy which is likely to change completelmy the way goods are designed, manufactured, used by consumers, repaired, reused, recycled, etc. Some exemples in the following papers:

Following

Springer has just released my new book on the subject: Sonification Design: From data to intelligible soundfields. It contains an extensive overview ++ https://www.springer.com/gp/book/9783030014964

(also available on Amazon)

For researchers, there's the Sonification Handbook: https://sonification.de/handbook/

May not be possible I feel

Dear Sir

I just suggest you before designing your proposal see some sample proposals it will help you to make your outer frame for the proposal. You may go through previous research design of the department. I think it will be helpful for you.

Dear Surendra Kumar ,

You can try

(1) MDPI journals https://www.mdpi.com/

(2) Hindawi journals https://www.hindawi.com/

(3) Frontier journals https://www.frontiersin.org/

(4) IEEE access journal https://ieeeaccess.ieee.org/

Research design is a plan to answer the research question in effective way which divided into two groups: exploratory and conclusive.

A research method is a strategy used the data obtain to archive that plan which divided into: qualitative and quantitative

Hi,

Kindly Refer This link:

https://www.google.com/search?rlz=1C1GCEU_en-GBIN821IN821&ei=DB4jXd7eGZL59QOM4pmACQ&q=What+would+be+the+time+taken+by+Malaysian+Journal+of+Computer+Science+%28MJCS%29+for+First+review%3F&oq=What+would+be+the+time+taken+by+Malaysian+Journal+of+Computer+Science+%28MJCS%29+for+First+review%3F&gs_l=psy-ab.3...55381.55381..56399...0.0..0.135.135.0j1......0....2j1..gws-wiz.......0i71.8GV6lmNkju4#

Best Wishes..

Dear Karim Mahfouz,

They are more susceptible to false positives when they want to rule in an association and to false negatives when they want to rule out an association. Since most experiments are aimed to rule in, the scientific community demands smaller type I error (false positives) than type II error (false negative) to counter balance such tendency (also because mis-information may be worse than missed information).

Sudhakar - For writing an effective research proposal for a grant you need to follow certain steps which are common to all subjects whether it is computer science and engineering or sociology 1) Download, read carefully and follow guidelines very strictly, 2) Pick a catchy title among the themes given in guidelines, 3) Write a clear concept note and highlight your objectives very clearly, 4) Cite international and national literature relevant to the title, 5) Highlight the significance of the study and how it will have an impact in the advancement of knowledge or policy making 6) Keep budget as low as possible and don't cross your limits, 7) Present your strong bio-data.

Thank you so much,Mr Mahamat Moussa...That was really helpful, I really appreciate it...Thank you

Read this article

What is impact of your paper getting published in Springer Lecture notes?

The GA in WEKA is from Goldberg (1989)

Hi, for structural segmentation one would parcellate the grey matter (cortex) and use the white matter (fiber bundles) for structural connectome construction via tractography, as the links between. For that you could use MRtrix3 (https://mrtrix.readthedocs.io/en/latest/quantitative_structural_connectivity/structural_connectome.html).

You could look into Freesurfer (https://surfer.nmr.mgh.harvard.edu) for atlas based parcellations, which outputs as well the different cortical regions (enables you to investigate function via the labels). If you’re interested into the function of different fiber bundles (white matter) you could review those via literature. These fiber bundles can be automated tracked with TractSeg (https://github.com/MIC-DKFZ/TractSeg).

Me agree

Think increase the intelligence level of the sensory robot

Deep learning is a good alternative

Dear Wasiu,

I suggest you to see links on subject.

possible environmental impacts of drilling exploratory ... - Orkustofnun

https://orkustofnun.is/gogn/unu-gtp.../UNU-GTP-2001-05.pdf

Assessment of Groundwater Investigations and Borehole Drilling ...

https://knowledgepoint.org/upfiles/14311010428865177.pdf

(PDF) Borehole Drilling, Usage, Maintenance and Sustainability in ...

https://www.academia.edu/.../Borehole_Drilling_Usage_Maintenan...

Professional Water Well Drilling - Unicef

https://www.unicef.org/wash/files/Guidance_Note_001.pdf

Professional Management of Water Well Drilling ... - Skat Consulting

skat.ch/.../Online_course_Report_FINAL_FOR_PARTICIPANTS.p...

Carbonate Systems During the Olicocene-Miocene Climatic Transition

https://books.google.dz/books?isbn=1444349155

Best regards

I have used Tetcos NetSim and would recommend it. This is the link to a recent paper we published using NetSim (https://www.mdpi.com/1424-8220/19/9/2191/pdf)

Following

The following link is good,

Amit Das You can work on Deep learning with supervised approaches, semi-supervised and unsupervised techniques on various domains such as :

Automation and prediction in all of the above domains with high precision is in high demands. Further, below links will be helpful for you :

Fast journals are often of low quality or even predatory, don't trust speed as an effective metric.

If you take a wide angle view of statistics, data science is statistics https://www.amazon.com/gp/product/1119570700/ref=dbs_a_def_rwt_bibl_vppi_i0

let's express our thanks for your answers

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

HI; I agree with Dairi Abdelkader , I think you will need some background in mathematics. But again you need to choose which field you want to apply machine learning in it (e.g. machine learning in finance) so that you can refresh your mind with the needed background. Also to which extent you want to apply machine learning, only modeling or you want also to develop it yourself, in this case you will need some python skills for example.

Dear Deleted profile ,

Look the link, maybe useful.

Regards,

Shafagat

For recruiting candidates from the UK a good place to start is www.jobs.ac.uk. A generic site but can be filtered according to subject area etc.

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/

It may be tempting to buy older processors like core i5 or i3, however, micro-architecture has changed a lot even over a few years. Also, don't focus on anything trivial like clock speed, there are lots of more important attributes. You should get at minimum 2GB of RAM per physical processor. Check any series of processor you are considering on https://www.cpubenchmark.net/ or just browse the all time CPU value page to get an idea of performance vs price. Definitely get one or more solid state hard drives for daily use and just dump archives onto a standard HDD.