Computer Science - Science topic

If there is artificial intelligence, then human intelligence if prior to it. The so called artificial intelligent machines, like computers and robots, only do what they were programed to do by their respective human designers/programers. The so called artificial intelligent machines have rules and grammar, but know nothing about meaning and semantics.

In a nutshell, the so called artificial intelligent machines are not able to go beyond the information they get from their designers/programers. They are blind to some extent.

Here are some of the top journals on computer science education and educational technology:

IYH Dear Sougatamoy Biswas

Kindly find four IEEE SCI journals for computer science with fast publication times (2-3 months) and low impact factors. Generally look for 'fast track' as one indicator wrt fastest publication time.

| Journal Name | Impact Factor | Publication Time |

|--------------|---------------|-----------------|

| IEEE Access | 3.9 | 2-3 months |

| IEEE Transactions on Emerging Topics in Computing | 3.245 | 2-3 months |

| IEEE Transactions on Cloud Computing | 2.876 | 2-3 months |

| IEEE Transactions on Big Data | 2.654 | 2-3 months |

Sources for further research:

[Fast publishing Computer science journals - PhDTalks](https://phdtalks.org/2021/12/fast-publishing-computer-science-journals.html)

[Top Computer Sciences Journals - What And How To Get Published in 2024](https://www.scijournal.org/articles/top-computer-sciences-journals)

Research into the prediction of e-waste generation using AI and ML, while interdisciplinary, can be firmly rooted in the field of computer science by focusing on the challenges and solutions specific to this field. here I present a few ways in which this can be achieved:

1. Optimizing ML algorithms to reduce their energy consumption and carbon footprint : thus contributing to the global effort to decarbonize IT infrastructures.

2. Computational modeling of decarbonization : using simulations to assess the environmental impact of information and communication technologies and their e-waste management.

3. Development of ESG data platforms : to facilitate the collection and analysis of environmental, social and governance data, including e-waste.

4. Application of AI to improve e-waste management : through the design of intelligent systems to optimize waste collection, sorting and recycling.

5. Use of blockchain for e-waste traceability : to improve transparency and combat illegal waste management practices.

6. Assessing the environmental impact of IT : by developing AI/ML-based tools to measure and reduce the impact of IT on the environment.

To overcome the difficulty of accessing data, it is advisable to collaborate with various organizations, to use learning approaches adapted to limited data, and to generate synthetic datasets where necessary.

These directions help to reposition research within computing while addressing pressing environmental issues, offering a significant contribution to both green computing and global sustainability efforts.

Yes you are absolutely right but some students donot know about the right indexing journals

Precisely,Predicting E-Waste Generation using AI & ML is a compelling and interdisciplinary research topic that is well-suited for a Ph.D. in Computer Science. It blends AI and ML innovations with environmental insights, making it both a computer science study and a project with broader, interdisciplinary applications. This focus offers significant potential for advancing recycling efforts, policy-making, and sustainable practices, positioning it as a strong candidate for doctoral research in the field....

I defended my thesis in Applied Mathematics by doing a machine learning model for buildings crack detection. I can say that using CNN, ANN or whatever you want alone will not be enough. In my case, I had to cover the mathematical part behind neural networks. In addition, I included crack detection for pipes as a test case and had to cover image projection on crack part. For short, include as much maths and formulas as you can and you will be fine.

Hmmm,

I am strongly interested in this issue.

Just in case

you find any kind of theoretical answer on this

(gender&& discrimination) issue...

.... I would be glad, to hear from you.

Just drop me note here on ReasearchGate.net

My Motivation:

I am working on an analysis about

what would be translated to English "Cause and Reason".

in order to extend my own research results.. If you like, we can exchange on some relevant scientific opinions and or guesses. l will send you my skype address via PN.

Yours,

- Frank. Haferkorn

Securing a job as a fresher in the UK, especially in the field of Business Analytics with a background in Computer Science, requires a strategic approach and active engagement in the job search process. Here are some tips to help you land a job sooner:

By following these tips and actively engaging in the job search process, you can increase your chances of securing a job in Business Analytics as a fresher in the UK sooner. Good luck with your job search!

The short answer is “no” in foreseeable future.

Regarding some optimism about Generative Pre-trained Transformers (GPTs) and Large Language Models (LLMs), I am sorry to disappoint you. Such technologies are not suitable for automatically generating reliable code. And they are not suitable for this in principal, regardless of their maturity. They are very useful for other tasks: natural language translation, simple refactoring of existing code etc., but not for tasks requiring deep conceptualisation. It’s because GPTsand LLMshave no logical reasoning model.

Current GPTsand LLMs are based on justneurocomputing.To generate programs automatically, AI should be based on neurosymbolic paradigms. But this is a long story.

From my everyday experience, I see how aGPT fails to complete simple coding tasks, generates obscure code and inserts bugs.

However there are some nuances of what we mean by “coders”. If we mean narrow coding, i.e. just coding without other important integrated steps in software development, then such coders have already become obsolete even without involvement of AI. If we mean broad coding, including integrated steps of requirement elicitation, conceptualisation, design, implementation, debugging, testing, evaluation, deployment etc., then the demand for such skills getting higher and higher, especially when AI grows.

Some ideas and associations:

You state “human can only aspire to fluency in so many different languages before mixing up words due to code switching”. I don’t know if this is true at all. On what research is it based? In my own situation, I am fluent in 5 languages and do not mix up words or get confused to which language a word belongs to.

Fluency in language corresponds to numeracy, that has been demonstrated. Children who are read a lot of stories in pre-school, for example, were no better in general subjects compared to other children, but they were better at maths later on. Both implicate logical thinking. Without logical thinking humans cannot string words into a longer narrative either.

People choosing coding or computer sciences may prefer to work individually and not in groups. That is a different dynamic, social vs solo, than proficiency at languages.

The modulo operation (abbreviated “mod”, or “%” in many programming languages) is the remainder when dividing. We can say that all the decimal representations of a prime number in the set N, do not show the congruence 2 and 3, except in two cases: [2]={2} and [3]={3}.

For example, 6 mod mod 2=0 since 6 is even and therefore has no remainder when divided by 2.

not getting publication fund

This is a PhD research topic in its own right. There's absolutely no way it can be answered in a short researchgate text.

Please see:

Yes Dr. absolutely, your thought is very acceptable in the case of background qualifications and priorities of teaching in comparison with engineering and mastering studies.

You will. Anywhere. You are on STEM and have a good profile. Just apply. Give preference to European Universities.

As I mentioned in a recent response to a similar query, MDPI is a publishing platform known for its fast publication process. I recommend you to explore their journals, namely 'Mathematics', 'Applied Sciences' and 'AI', but there are others that may be more appropriate according to what you want to publish in computer sciences. Personally, I have had positive experiences publishing and reviewing articles for the first two journals. The review process is remarkably short, as MDPI gives reviewers a maximum of 10 days to evaluate submitted articles. This agility contributes to the speed of the publication process for researchers.

Dear Dr. Sargiotis!

Your project is very important also from the point of military logistics - cyberspace is already at risk in the context of EU - Russia relations:

1) Simola, J., & Pöyhönen, J. (2022). Emerging Cyber risk Challenges in Maritime Transportation. In R. P. Griffin, U. Tatarand, & B. Yankson (Eds.), ICCWS 2022 : Proceedings of the 17th International Conference on Cyber Warfare and Security (17, pp. 306-314). Academic Conferences International. The proceedings of the ... international conference on cyber warfare and security. https://doi.org/10.34190/iccws.17.1.46 Available at:

2 )Zezhou Wang, Xiang Liu, Cyber security of railway cyber-physical system (CPS) – A risk management methodology, Communications in Transportation Research, Volume 2, 2022, https://doi.org/10.1016/j.commtr.2022.100078, Open access:

Yours sincerely, Bulcsu Szekely

You can choose Seismic retrofitting of regular or plan irregular building with short length BRB on the software openseespy.

Orlov A.I. Methods of reducing the dimension of statistical data space / A.I. Orlov, E.V. Lutsenko // Polythematic network electronic scientific journal of Kuban State Agrarian University (Scientific Journal of KubGAU) [Electronic resource]. – Krasnodar: KubGAU, 2016. – №05(119). P. 92 – 107. – IDA [article ID]: 1191605005. – Access mode: http://ej.kubagro.ru/2016/05/pdf/05.pdf , 1 u.p.l.

You will find readers outside your narrow community.

Thank you very much Peter.

Writing a computer science paper that stands a good chance of being accepted involves several key steps and considerations. Here's a concise guide to help you:

Remember that the peer review process is rigorous, so patience and perseverance are key. By following these steps and continuously improving your writing and research skills, you can increase your chances of having your computer science paper accepted.

Just curious:

Are there any complex and profound differences with the other one:

Well, the Impact Factor for both can be quickly checked

vs

... quite in favor of CS ...

Yes sometimes.

Hi Ankit Sharma

You may work on some IoT based application, in this you will have a chance of using cloud computing as well as machine or deep learning to analyze data and present your work through some web application.

You may search for an nature inspired optimization algorithm or a recent swarm intelligence based optimization algorithm and try improving it. Following steps might be helpful when creating a variant of an existing algorithm:

1. Understand the target algorithm thoroughly. understand the base paper, try reproducing results reported in the original paper.

2. Literature Review: Look at the available variants of the target algorithm as well as others work on "How to improve an optimization algorithm", you may consider variants of PSO or WOA or GWO, these are mature algorithms with a lot of existing variants.

3. Brainstorm modification: it is time to finalize modification for your chosen algorithm.

4. Define a clear Hypothesis: Formulate a clear hypothesis about how your modifications will lead to an improvement in the algorithm's performance. Your hypothesis should be testable and measurable.

5. Implementation: Easiest way is to use matlab, many codes are available online and many tutorials on how to program in matlab. You may use python as well.

6. Test on the common benchmark functions

7. Compare and Analyze results.

8. Iterate and refine: Refine the algorithm based on the testing and comparison.

9. Document the approach.

10. Publish your work in a suitable Journal.

Thank you for sharing Sunny Sharma

I consider myself an interdisciplinary researcher and do enjoy interdisciplinary research. I enjoy mixing research areas from different fields and see the outcome/s. It allows for one to gain knowledge of other fields, whilst also adding your own perspectives to another field. Hence it becomes win-win situation. In addition, some problems cannot be solved from one area/field, but may require different knowledge from multiple research fields to reach a comprehensive solution. However, whilst I do interdisciplinary research, I am also cautious not to overstep boundaries on any specific field that may be totally out of my ambit/expertise.

Selecting optimal parameters for MACD in multivariate time series analysis, especially in financial trading algorithms, is crucial to designing effective trading strategies. Here are some steps and considerations:

1. Understand the MACD Parameters:

2. Parameter Optimization Strategies:

3. Cross-Validation:

4. Objective Function:

5. Be Aware of Overfitting:

6. Trading Considerations:

7. Consider Multivariate Aspects:

8. Backtesting:

9. Sensitivity Analysis:

10. Technology and Tools:

Since you’re using Python, utilize libraries like pandas for data manipulation, matplotlib and seaborn for data visualization, and statsmodels for statistical models and tests.

Python Implementation:

For the MACD calculation and visualization:

{

import pandas as pd

import matplotlib.pyplot as plt

def compute_macd(data, short_window, long_window, signal_window):

short_ema = data['Close'].ewm(span=short_window, adjust=False).mean()

long_ema = data['Close'].ewm(span=long_window, adjust=False).mean()

data['MACD'] = short_ema - long_ema

data['Signal_Line'] = data['MACD'].ewm(span=signal_window, adjust=False).mean()

return data

# Example

# data = pd.read_csv("your_data.csv")

# data = compute_macd(data, 12, 26, 9)

# plt.plot(data['MACD'], label='MACD')

# plt.plot(data['Signal_Line'], label='Signal Line')

# plt.legend(loc='upper left')

# plt.show()

}

Hans Dahn

Thanks for your response sir. My article title is "Enhancing Collaborative AI through Multimodal Contextual Analysis and AI Exploration" and it's field is Computer Science - Artificial Intelligence as I mentioned in the post. And once again thank you sir. I will be very grateful to you if you do it for me.

And if you need more information for this, please mail me personally at - [email protected]

What about comprehensive Review?

An equation can be considered as a sentence in the language of mathematics, at least in its formalized version as can be seen in every book about mathematical logic or ZFC set theory. Programming languages are also formalized languages, and you have to stick to these formalizations in order for a computer to work correctly. However, most mathematicians use a semi formal mathematical language, but when for instance a theorem is correct, meaning that is has a correct proof, then one can write theorem and proof in the formalized language of mathematics, but the result is almost always unreadable by a human being, but " understandable " by a formal proof system, that can be implemented on a computer. In this sense one can let computers prove theorems, but then it needs a lot of input from a human being. But doing mathematics is also an art, and in order to be able to practice this art one needs a lot of practice and mathematical knowledge.

A Gaussian law is a concept in statistics or probability theory. The notion of linear function belongs to the areas of calculus , analysis and linear algebra.

Opportunities for artificial intelligence (AI) in civil engineering are plentiful and expanding. AI-driven tools can increase effectiveness, performance and also safety in every stage of the life cycle of civil engineering projects, from planning, designing & execution to operational and maintenance stages. Here are some specific examples of how AI is being used in civil engineering today: Design: AI can produce and assess numerous ideas faster than traditional methods. This enables engineers to determine the optimal configuration for a specific task considering cost, performance, and green foot print, among others. Construction: AI can also automate and optimize several other construction activities, scheduling, resource allocation, quality control, to name a few. This helps to save cost, to make processes more efficient & to minimize mistakes. Operation and maintenance: Infrastructure: AI can also be deployed for monitoring and analyzing the state of infrastructure assets such as Bridges, Roads and Buildings etc. The earlier the problem is detected, less expensive it will be for you in terms of money, time and effort. AI is being used in many ways also for developing new Civil Engineering solutions. Such as AI-equipped drones examining the bridge and other structures in a faster, safer manner than traditional methods. It’s also developing new materials and techniques of constructing sustainable and long lasting structures.

There are several open areas in which you can work such as,

I did Phd from Banasthali

If the paper is 1 page less than the maximum required limit, does it have a big impact on its acceptance? No. Content matters.

There are seven numbers and you stated that 10000 appears twice, which means that there are eight numbers. In calculation of average denominator is 7 which means that 10000 cannot apper twice. Range is calculated as 23000 - 10000 instead as 54830 - 5603 = 49227. Sum of those numbers is 128573, not 103573. Are you sure about those numbers ?

Anyway, they have a present. To have a future you have to continue working every day of the rest of your life. Otherwise you are dead

IEEE stands for Institute of Electrical and Electronics Engineers. The main AIM of IEEE is to ensure foster technological innovation and excellence for the benefit of humanity. The IEEE standards in computer networks ensure communication between various devices.

The nominee must meet the following three basic qualifications:

-have accomplishments that have contributed importantly to the advancement or application of engineering, science, and technology, bringing the realization of significant value to society;

-hold IEEE Senior member or IEEE Life Senior member grade at the time ...

Writing a computer science paper with a title starting with "Revisit" typically indicates that you are revisiting or reevaluating a previously explored topic, concept, or method. Here's a step-by-step guide on how to write such a paper:

**1. Choose a Specific Topic or Concept:** Begin by selecting a specific topic, concept, algorithm, technique, or area within computer science that you want to revisit. It should be something that has been previously studied or discussed in the field.

**2. Review Existing Literature:** Conduct a comprehensive literature review to gather information about the topic you've chosen. This step is crucial for understanding the current state of knowledge and identifying gaps or areas where a revisit is necessary. Summarize key findings from previous research.

**3. Identify the Need for Revisiting:** Clearly articulate why there is a need to revisit the chosen topic. What has changed since the previous research was conducted? Are there new challenges, technologies, or perspectives that warrant a fresh examination?

**4. Define Your Research Questions or Objectives:** Formulate specific research questions or objectives that your paper will address. These should be focused and aligned with the purpose of revisiting the topic. What do you hope to achieve with this revisit?

**5. Methodology:** Describe the methodology or approach you plan to use in your revisit. Will you conduct experiments, surveys, simulations, or theoretical analyses? Explain how you intend to gather data or information.

**6. Data Collection and Analysis:** If applicable, collect data or perform experiments according to your chosen methodology. Analyze the data using appropriate statistical or computational techniques. Ensure that your analysis is rigorous and unbiased.

**7. Results:** Present the results of your research. Compare and contrast your findings with the results of previous studies. Highlight any significant differences, new insights, or unexpected outcomes.

**8. Discussion:** Interpret the results and discuss their implications. How do your findings contribute to the understanding of the topic? Are there practical applications or theoretical advancements that result from this revisit?

**9. Related Work:** Discuss other relevant work in the field that may not have been part of your revisit but provides additional context or insights.

**10. Conclusion:** Summarize the key points of your paper, restate the significance of revisiting the topic, and highlight the contributions of your work.

**11. References:** Provide a comprehensive list of references to acknowledge and cite all the sources you consulted during your literature review and research.

**12. Title and Abstract:** Craft a meaningful and concise title that starts with "Revisit" and accurately reflects the content of your paper. Write an informative abstract that summarizes the main points of your paper, including the need for the revisit, your methodology, and key findings.

**13. Proofreading and Editing:** Carefully proofread and edit your paper for clarity, coherence, grammar, and formatting. Ensure that it follows the specific formatting and citation style required by the target journal or conference.

**14. Submission:** Submit your paper to a relevant computer science journal or conference. Follow the submission guidelines and address any reviewer comments or feedback during the peer-review process.

Remember that a successful "Revisit" paper should contribute new insights or perspectives to the field, making it valuable to researchers and practitioners in computer science.

Computer science has made significant contributions to the field of chemistry, enabling researchers to model complex molecular systems, predict chemical reactions, and analyze vast datasets. Here are some notable applications of computer science in chemistry:

1. **Monte-Carlo Simulations**: These are used in various areas, including environmental and chemistry fields. Monte-Carlo simulations can help in understanding complex chemical systems by using random variables and state-of-art techniques. [Link to the study](https://doi.org/10.54097/hset.v17i.2430)

2. **Molecular Docking and Dynamics**: Computational methods have been developed to solve the molecular docking problem, which is crucial for understanding molecular interactions and designing drugs. [Link to the study](https://doi.org/10.55522/jmpas.v12i1.4137)

3. **Material Science and Molecular Design**: The combination of material science and computer science has shifted the approach from "how to make a molecule" to "what molecule to make." This involves designing new ligands and complexing them with metals like Ruthenium to develop new materials with diverse applications. [Link to the study](https://doi.org/10.33696/pharmacol.4.042)

4. **Simulation of Practical Works of Crystallography**: Technological innovations have led to the development of computer applications that simulate chemical experiments, especially in the field of crystallography. This allows students and researchers to understand and study different crystalline structures using computer-based virtual experiments. [Link to the study](https://doi.org/10.35940/ijrte.c4665.098319)

5. **Computer-assisted Structure Elucidation (CASE)**: CASE systems have been developed to help in the structure elucidation of natural products using spectroscopic data. These systems mimic the expert's way of thinking during the process of structure elucidation. [Link to the study](https://doi.org/10.1039/9781849735186-00187)

6. **Visualization of Proteins**: Modern biochemists use computational methods to visualize proteins, find ligand binding sites, and understand the three-dimensional interactions with coordinated amino acid residues. [Link to the study](https://dblp.org/rec/journals/cse/PineP20)

7. **Nanoscience and Advanced Computational Methods**: Computational methods have been employed to study and design new compounds at the atomic level, leading to the development of software for targeted simulations. [Link to the study](https://doi.org/10.4018/978-1-4666-1607-3)

8. **Quantum Algorithms for Quantum Chemistry**: Quantum algorithms have been developed for ground-state, dynamics, and thermal-state simulation in quantum chemistry, providing a new dimension to the study of molecular systems. [Link to the study](https://arxiv.org/pdf/2001.03685)

Journal of Supercomputing

AdHoc Networks

Wireless Networks

IEEE Sensors Journal

Hi Mohammad,

If you're using Python, I'd suggest using pow(ct, d, n) instead of pt=(ct ** d) % n. That should improve the time. As Wim suggests, modular exponentiation improves the running time of large exponents.

Cheers

تجربه من

در این امور به من می گوید که هیچکس بهتر از خودتان نمی تواند به خود شما کمک کند.. از این جهت که مواردی که جای کار بیشتر را دارد شناسائی کنید و علاوه بر این علاقه و نگاه به آینده

البته بنده خیلی سال قبل در پونه دنبال قضیه دکترایم بودم و سه پروپوزالی که به هند بردم را نپسندیدند آنچه برایم جلب توجه می کرد نگاه فوق العاده سنتی گروه علم اطلاعات دانشگاه پونه بود که موضوعات جدید را برنمی تابیدند و علاوه برآن بیشتر می خواستند بدانند در کشور کسی که برای تحصیل آمده حول آن رشته تحصیلی چه می گذرد در هر صورت گروه هر سه پروپوزال را رد و آنچه مورد نظر خودشان بود را مطرح و دفاع از پروپوزال و پذیرش

البته بعد من قضیه تحصیل در هند را رها کردم

در هر صورت منظورم این است که اگر در دانشگاه شما هم چنین می کنند اول باید با خود گروه و خصوصا استاد راهنمایتان هماهنگ شوید

سال هشتاد و پنج من کتابی تالیف و منتشر کردم که هنوز هم مهم و تکست آزمون دکترا هست

یعنی این کتاب

بازیابی پیوسته:نظام ها و روش ها

متن کامل را سال بیست بیست و دو در همین ریسرچ گیت آپلود کردم

آن را مطالعه و خصوصا فصل های مربوط به روش جستجو در پایگاه های اطلاعاتی

البته شاید هم به خوبی اینها را بدانیدد

دیگرفصلی از کتاب

نگارش مقاله که آن هم تالیف خودم و یکی از استادانم بود را هم در رابطه با نحوه پیدا کردن موضوع مناسب بر مقاله و تحقیق و رساله برایتان پیوست کرده ام

بنظرم جواب های من به پرسش هایی که ممکن است به نحوی به سوال شما مربوط باشد را هم مطالعه کنید که مهمترین آنها اینها هستند

در کل معمولا اشخاص علاقه ای به اینکه اگر چیزی می دانند که به قول خودشان یونیک هست صحبتی نمی کنند و یا اینکه همکاری نمی کنند پس بهتر است خودتان دست به کار شوید

البته بررسی چند ساله من نشان می دهد که به هر ترتیبی بتوانید بین تحقیقات خود و شبکه های اجتماعی ارتباطی برقرار کنید و مطالعات خود را به آن سمت ببرید بسیار مفید خواهد بود که خود به جهت رشد روز افزون این رسانه ها می باشد

موفق باشید

Adding a research paper to Google Scholar involves a few steps. Google Scholar is a freely accessible search engine that indexes scholarly articles from various disciplines. While you cannot directly upload papers to Google Scholar, you can ensure that your research paper gets indexed and displayed in the search results by following these steps:

Remember that Google Scholar's indexing process is automated, and not all papers may be indexed immediately or at all. It's also worth noting that Google Scholar doesn't verify the quality or credibility of sources, so it's essential to publish in reputable journals and conferences to maintain the integrity of your research.

If you've followed these steps and your paper is not appearing on Google Scholar, you can contact Google Scholar support for assistance or explore their official documentation for more information.

Dear Rishini Hettiarachchi,

a very broad area of research is the use of Artificial Intelligence in the IoT. The idea of Digital Twins is fundamental to this. For details see:

As an appendix I added a small reference list about convergence of Digital Twin, IoT and Machine Learning.

Good luck and best regards

Anatol Badach

Maninder Jeet Kaur,Ved P Mishra, Piyush Maheshwar: „The Convergence of Digital Twin, IoT, and Machine Learning: Transforming Data into Action“; in book: Digital Twin Technologies and Smart Cities, Jan 2020, DOI: 10.1007/978-3-030-18732-3_1

Maninder Jeet Kaur,Ved P Mishra, Piyush Maheshwar: „The Convergence of Digital Twin, IoT, and Machine Learning: Transforming Data into Action“; in book: Digital Twin Technologies and Smart Cities, Jan 2020, DOI: 10.1007/978-3-030-18732-3_1

Muhammad Mazhar Rathore, Syed Attique Shah, Dhirendra Shukla, Elmahdi Bentafat, Spiridon Bakiras: „The Role of AI, Machine Learning, and Big Data in Digital Twinning: A Systematic Literature Review, Challenges, and Opportunities“; IEEE Access, Vol. 9, Feb 2021, DOI: 10.1109/ACCESS.2021.3060863

Xiaoming Li, Hao Liu, Weixi Wang, Ye Zheng, Haibin Lv, Zhihan Lv: Big data analysis of the Internet of Things in the digital twins of smart city based on deep learning; Future Generation Computer Systems, Vol. 128, Mar 2022, DOI: 10.1016/j.future.2021.10.006

Yueyue Dai, Ke Zhang, Sabita Maharjan, Yan Zhang: Deep Reinforcement Learning for StochasticComputation Offloading in Digital Twin Networks; IEEE Transactions on Industrial Informatics, Vol. 17, Issue: 7, Jul 2021, DOI: 10.1109/TII.2020.3016320

Independent researchers can achieve IEEE Fellow status through exceptional contributions in their field, demonstrated leadership, and impact. While supervising students can be beneficial, it's not mandatory. Demonstrating significant research outcomes, publishing influential work, and engaging in professional activities can establish eligibility for the IEEE Fellow recognition.

Victor Ikechukwu Agughasi I just had the 25th Anniversary of The Internet Foundation on 23 Jul 2023. Again and again I see groups working on common topics, but NOT working together. Are there really more than 100 Million chest x-rays in India every year? But that is many separate efforts, and not one common project. Now in the world India is probably not a leader in gathering chest x-ray data into common formats, establishing best algorithms. Rather than cooperating with China and all other countries, groups will want to be "king of the hill" (a children's game, who is strongest). Global research is driven by jealousy and politics, self-interest and massive waste as groups duplicate what is already known. It is the data and models that are most important. If everyone works on their own piece of the puzzle, and does not share, everyone loses. Sounds like you have some good material in your thesis. Again, the thesis is just a step in what you might find is a very long lifetime of exploration and helping others. Try to write from your heart about real issues and what you hope to accomplish. If you think something needs to be done at national or global levels, say so and get people and groups moving in common direction. Be careful not to enable more monopolies and hoarding for self-interests. All I can suggest is so at least try to make sustainable open verifiable truly accessible systems. Then if the systems start to go astray, the tools for accepting feedback and implementing corrections will be there and work as they are supposed to. Systems should not be allowed to lock themselves tightly and ignore all feedback, which is more the norm now than the exception.

Yes, it is possible to become an IEEE Fellow without becoming a doctoral supervisor at a university. To be nominated for the IEEE Fellow title, the individual must meet specific requirements:

1. Accomplishments: They should have made significant contributions to the advancement or application of engineering, science, and technology, bringing valuable benefits to society.

2. IEEE Membership: The nominee must be an IEEE Senior member or IEEE Life Senior member at the time of nomination.

3. Impact: Their contributions should have had a notable impact in their field, paving the way for future contributors.

The nomination process involves submitting materials confidentially to the IEEE Fellow Committee, consisting of 51 members plus a chair. The committee represents the 10 IEEE Regions and possesses expertise in the technical areas represented by IEEE Societies/Technical Councils. The final slate of nominees is then presented to the IEEE Board of Directors for approval.

Hi, I think I can help you with this. You ae absolutely right. AI has revolutionized every industry. Education is changing its landscape at a pace faster than ever.

One great resource for learning about cybersecurity is the Cisco Networking Academy. They offer a free, self-paced course called "Introduction to Cybersecurity" that will teach you the basics of cybersecurity. You can find the course at this link: https://skillsforall.com/course/introduction-to-cybersecurity

Once you've completed the introductory course, you can continue your education with Cisco's other cybersecurity courses. They offer a variety of courses at different levels, so you can find one that's right for your skill level.

ICOAI allows academia to meet industry from the international community to exchange experiences, demonstrate their studies and further advance AI technologies.

To the surprise of many, there is existing so-called emergent computation. There are published on RG several Python softwares--GoL-N24, r-GoL, and GoL, on my research profile that demonstrate the following properties: (i) emergent information processing, which arises through self-organization, (ii) error-resilient properties of some special cases of emergents, (iii) automatic creation of multi-level emergent systems.

This research is capable to reproduce into a certain extent by-the-life- utilized computing and information processing.

See some of many shared animations in my RG data section, software section, and discussion dealing with animations of complex systems, which are available here. The last paper from 2022 and the next one are dealing exactly with those issues. I recommend reading the review "Complexity in medicine ...", which serves as the introduction.

Just one animation to curb your appetite, it is in APNG format which is after its downloading easy to view in any modern web browser as a file (write at the window instead of web page '//' and chose the option to see files).

It's released. You can download from this link https://www.researchgate.net/post/Impact_factor_2023_complete_list

If you look for fees in Elsevier, IEEE or Springer, you can find an Excel with the fees and journals. Their names can help you and they have usually basic specs.

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.

Anyone can evaluate it.English is not my native language, so I hope there is no ambiguity.

The increasing prominence of computer science in various industries, including engineering, is due to its role in automation, data analysis, and efficiency enhancement. However, core engineering subjects such as electrical, electronics, chemical, mechanical, and civil engineering remain crucial. Computer science complements and enhances these disciplines rather than replacing them. Interdisciplinary collaboration, automation and control, simulation and modeling, hardware design, and domain-specific challenges are some areas where core engineering subjects retain their importance. The integration of computer science and engineering leads to advancements and innovations that benefit society. Recognizing the value of both fields and fostering interdisciplinary collaboration is essential for future progress.

I have made a living as a consultant for years using these techniques from TRIZ. (I independently invented Service Oriented Architecture [SOA] and Model Driven Design [MDD] to make practical the results generated.) Unfortunately, the major sticking point is "functional normalization", in other words identifying when a functional entity is performing the same function and/or quantifying how much similarity exists so a decision can be made on the best implementation to be used redundantly.

Dear Onengiyeofori Fenibo,

Choosing the best computer science project topic is critical to the success of any computer science student or employee. After all, the more engaging and interesting topic, the more likely it is that students or employees will be able to stay motivated and focused throughout the duration of the project. However, with so many options out there, it can be tough to decide which one is right for you.

To help you get started, we have compiled a list of best computer science project topics for students and employees. From machine learning algorithms to data mining techniques, these ideas are sure to challenge and engage you. And while thinking about computer science project topics, if you find it difficult to keep up with the latest trends, go for the best online course for Web Development. This is because the coursework is updated frequently, and there are always new things to learn.

In the field of academics, we need to get rid of obsolete ideas and focus on new innovative topics which are fast spreading their arms among the vast global audience. Computer Science students both in bachelors and in masters are studying the same topics and subjects from the past few years. Students don’t even have knowledge about new masters research topics. For project and thesis work also they are relying on outdated topics. Projects like school management system, library management system etc. are now out of date. Students should shift their focus to latest technologies which are highly in demand these days and future depend upon these. Here is the list of latest topics in Computer Science that you can choose and work for your project work or thesis and research:

List of few latest thesis topics in computer science is below:

Data Warehousing is the process of analyzing data for business purposes. Data warehouse store integrated data from multiple sources at a single place which can later be retrieved for making reports. The data warehouse in simple terms is a type of database different and kept isolated from organization’s run-time database. The data in the warehouse is historical data which is helpful in understanding business goals and make decisions for future prospects. It is a relatively new concept and have high growth in future. Data Warehouse provides Online Analytical Processing(OLAP) tools for the systematic and effective study of data in a multidimensional view. Data Warehouse finds its application in the following areas:

So start working on it if you have knowledge of database and data modeling.

Internet of Things(IoT) is a concept of interconnection of various devices, a vehicle to the internet. IOT make use of actuators and sensors for transferring data to and from the devices. This technology is developed for better efficiency and accuracy apart from minimizing human interaction with the devices. The example for this is home heating in some countries when the temperature drops done through motion sensors which automatically detect the weather conditions. Another example for this is the traffic lights which changes its colors depending upon the traffic. Following are the application areas of Internet of Things(IoT):

BELOW IS THE LIST OF FEW LATEST AND TRENDING RESEARCH TOPICS IN IOT:-

Many people are not aware of this concept so you can choose for your project work and learn something new.

Big Data is a term to denote the large volume of data which is complex to handle. The data may be structured or unstructured. Structured data is an organized data while unstructured data is an unorganized data. Big data can be examined for the intuition that can give way to better decisions and schematic business moves. The definition of big data is termed in terms of three Vs. These vs are:

Application areas:

Thus you can prepare your project report or thesis report on this.

Cloud Computing is a comparatively new technology. It is an internet-based service that creates a shared pool of resources for consumers. There are three service models of cloud computing namely:

Characteristics of cloud computing are:

The common examples of cloud computing include icloud from Apple, Google-based Services like Google Drive and many more. The field is very demanding and is growing day by day. You can focus on it if you have interest in innovation.

Semantic Web is also referred to as Web 3.0 and is the next big thing in the field of communication. It is standardized by World Wide Web Consortium(W3C) to promote common data formats and exchange protocols over the web. It is machine-readable information based and is built on XML technology. It is an extension to Web 2.0. In the semantic web, the information is well defined to enable better cooperation between the computers and the people. In the semantic web, the data is interlinked for better understanding. It is different from traditional data sharing technologies.

It can be a good topic for your thesis or project.

MANET stands for mobile ad hoc network. It is an infrastructure-less network with mobile devices connected wirelessly and is self-configuring. It can change locations independently and can link to other devices through a wireless connection. Following are the various types of MANETS:

You can use various simulation tools to study the functionality and working of MANET like OPNET, NS2, NETSIM, NS3 etc.

In MANET there is no need of central hub to receive and send messages. Instead, the nodes directly send packets to each other.

MANET finds its applications in the following areas:

BELOW IS THE LIST OF FEW LATEST AND TRENDING RESEARCH TOPICS IN MANET :-

Just go for it if you have interest in the field of networking and make a project on it.

It is also a relatively new concept in the field of computer science and is a technique of guiding computers to act in a certain way without programming. It makes use of certain complex algorithms to receive an input and predict an output for the same. There are three types of learning;

Machine Learning is closely related to statistics. If you are good at statistics then you should opt this topic.

Data Mining

Data Mining is the process of identifying and establishing a relationship between large datasets for finding a solution to a problem through analysis of data. There are various tools and techniques in Data Mining which gives enterprises and organizations the ability to predict futuristic trends. Data Mining finds its application in various areas of research, statistics, genetics, and marketing. Following are the main techniques used in the process of Data Mining:

BELOW IS THE LIST OF FEW LATEST AND TRENDING RESEARCH TOPICS IN DATA MINING :-

Artificial Intelligence

Artificial Intelligence is the intelligence shown by machines and it deals with the study and creation of intelligent systems that can think and act like human beings. In Artificial Intelligence, intelligent agents are studied that can perceive its environment and take actions according to its surrounding environment.

Following are the main goals of Artificial Intelligence:

Following are the main applications of Artificial Intelligence:

Strong AI – It is a type of artificial intelligence system with human thinking capabilities and can find a solution to an unfamiliar task.

Weak AI – It is a type of artificial intelligence system specifically designed for a particular task. Apple’s Siri is an example of Weak AI.

Turing Test is used to check whether a system is intelligent or not. Machine Learning is a part of Artificial Intelligence. Following are the types of agents in Artificial Intelligence systems:

Natural Language Processing – It is a method to communicate with the intelligent systems using human language. It is required to make intelligent systems work according to your instructions. There are two processes under Natural Language Processing – Natural Language Understanding, Natural Language Generation.

Natural Language Understanding involves creating useful representations from the natural language. Natural Language Generation involves steps like Lexical Analysis, Syntactic Analysis, Semantic Analysis, Integration and Pragmatic Analysis to generate meaningful information.

Image Processing is another field in Computer Science and a popular topic for a thesis in Computer Science. There are two types of image processing – Analog and Digital Image Processing. Digital Image Processing is the process of performing operations on digital images using computer-based algorithms to alter its features for enhancement or for other effects. Through Image Processing, essential information can be extracted from digital images. It is an important area of research in computer science. The techniques involved in image processing include transformation, classification, pattern recognition, filtering, image restoration and various other processes and techniques.

Following are the main purposes of image processing:

Following are the main applications of Image Processing:

Bioinformatics

Bioinformatics is a field that uses various computational methods and software tools to analyze the biological data. In simple words, bioinformatics is the field that uses computer programming for biological studies. It is the current topic of research in computer science and is also a good topic of choice for the thesis. This field is a combination of computer science, biology, statistics, and mathematics. It uses image and signal processing techniques to extract useful information from a large amount of data. Following are the main applications of bioinformatics:

Quantum Computing

Quantum Computing is a computing technique in which computers known as quantum computers use the laws of quantum mechanics for processing information. Quantum Computers are different from digital electronic computers in the sense that these computers use quantum bits known as qubits for processing. A lot of experiments are being conducted to build a powerful quantum computer. Once developed, these computers will be able to solve complex computational problems which cannot be solved by classical computers. Quantum is the current and the latest topic for research and thesis in computer science.

Quantum Computers work on quantum algorithms like Simon’s algorithm to solve problems. Quantum Computing finds its application in the following areas:

The list is incomplete as there are a number of topics to choose from. But these are the trending fields these days. Whether you have any presentation, thesis project or a seminar you can choose any topic from these and prepare a good report.

Hope that one gives you a close feel.

Wasswa Shafik

Further to my above answer, I would like elaborate to use DecisionMentor mobile app

Decision Problem: Appropriate EMBA Thesis Topic for me

1. Appropriate Decision Analysis framework (Integrated with Accounts & Finance, in addition to Technical, Market and HR) - Looking into variety of Multiple Criteria Decision Analysis Tools / Theories available

2. A Case of Digital Transformation: In an existing Business (you may take a case from your own company)

3. Future of Accounts and Finance Professionals with Emerging AI Tools

(or if you have any of your topic of your passion, consider those)

Criteria (Factors) to consider while choosing:

1. My interest

2. Useful in business

3. Knowledge enhancement

4. Skill Enhancement

5. Demand in Market/Business

Now, it would be easy for you to use DecisionMentor mobile app available at:

The DecisionMentor mobile app will help you to compare your criteria to generate criteria weight, then you can compare each options with the Criteria.

This process will give insight to your decision problem, Decision will be Yours, which ever topic option comes at top in ranking.

Best wishes,

Big thanks to both of you Anne-Katharina Weilenmann and Alexander Schniedermann . Will look into these options shortly ^^

Dear Scholar,

I appreciate your interest. I have already volunteered for three research projects and authored three, excluding the analytical research that I am working on now. Among the mentioned, I conducted Literature Review through an LRM a few times. I am a Freshman in Computer Science. I was wondering about joining the initiation.

Dear, it's not possible to do master in computer science after mechanical graduate.

But heay try to robotics, automobile and other computer related course which is the available in Mechanical Engineering domain.

Dear Nameer Hashim Qasim ,

Artificial intelligence design is the new hype. To fully automate their processes, many IT companies are moving toward artificial intelligence today. They do this because they are aware that technologies that can significantly reduce the amount of work required of humans will rule the future.

Regards,

Shafagat

Imagine a primitive man who sees a robot and who wants to figure out what the robot is thinking. This is psychoanalysis, and "what the robot is thinking" is supported by artificial intelligence

Dear Neelesh Mungoli ,

Without a name, it is difficult for people to rally around a cause, so we created the term Research Software Engineer. We are now working to raise awareness of the role and bring the community together.

Regards,

Shafagat