how to publish research paper in computer science

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7 steps to publishing in a scientific journal

April 5, 2021 | 10 min read

By Aijaz Shaikh, PhD

Before you hit “submit,” here’s a checklist (and pitfalls to avoid)

As scholars, we strive to do high-quality research that will advance science. We come up with what we believe are unique hypotheses, base our work on robust data and use an appropriate research methodology. As we write up our findings, we aim to provide theoretical insight, and share theoretical and practical implications about our work. Then we submit our manuscript for publication in a peer-reviewed journal. For many, this is the hardest part of research. In my seven years of research and teaching, I have observed several shortcomings in the manuscript preparation and submission process that often lead to research being rejected for publication. Being aware of these shortcomings will increase your chances of having your manuscript published and also boost your research profile and career progression.

Dr Aijaz Shaikh gives a presentation.

In this article, intended for doctoral students and other young scholars, I identify common pitfalls and offer helpful solutions to prepare more impactful papers. While there are several types of research articles, such as short communications, review papers and so forth, these guidelines focus on preparing a full article (including a literature review), whether based on qualitative or quantitative methodology, from the perspective of the management, education, information sciences and social sciences disciplines.

Writing for academic journals is a highly competitive activity, and it’s important to understand that there could be several reasons behind a rejection. Furthermore, the journal peer-review process is an essential element of publication because no writer could identify and address all potential issues with a manuscript.

1. Do not rush submitting your article for publication.

In my first article for Elsevier Connect – “Five secrets to surviving (and thriving in) a PhD program” – I emphasized that scholars should start writing during the early stages of your research or doctoral study career. This secret does not entail submitting your manuscript for publication the moment you have crafted its conclusion. Authors sometimes rely on the fact that they will always have an opportunity to address their work’s shortcomings after the feedback received from the journal editor and reviewers has identified them.

A proactive approach and attitude will reduce the chance of rejection and disappointment. In my opinion, a logical flow of activities dominates every research activity and should be followed for preparing a manuscript as well. Such activities include carefully re-reading your manuscript at different times and perhaps at different places. Re-reading is essential in the research field and helps identify the most common problems and shortcomings in the manuscript, which might otherwise be overlooked. Second, I find it very helpful to share my manuscripts with my colleagues and other researchers in my network and to request their feedback. In doing so, I highlight any sections of the manuscript that I would like reviewers to be absolutely clear on.

2. Select an appropriate publication outlet.

I also ask colleagues about the most appropriate journal to submit my manuscript to; finding the right journal for your article can dramatically improve the chances of acceptance and ensure it reaches your target audience.

Elsevier provides an innovative  Journal Finder opens in new tab/window  search facility on its website. Authors enter the article title, a brief abstract and the field of research to get a list of the most appropriate journals for their article. For a full discussion of how to select an appropriate journal see Knight and Steinbach (2008).

Less experienced scholars sometimes choose to submit their research work to two or more journals at the same time. Research ethics and policies of all scholarly journals suggest that authors should submit a manuscript to only one journal at a time. Doing otherwise can cause embarrassment and lead to copyright problems for the author, the university employer and the journals involved.

3. Read the aims and scope and author guidelines of your target journal carefully.

Once you have read and re-read your manuscript carefully several times, received feedback from your colleagues, and identified a target journal, the next important step is to read the aims and scope of the journals in your target research area. Doing so will improve the chances of having your manuscript accepted for publishing. Another important step is to download and absorb the author guidelines and ensure your manuscript conforms to them. Some publishers report that one paper in five does not follow the style and format requirements of the target journal, which might specify requirements for figures, tables and references.

Rejection can come at different times and in different formats. For instance, if your research objective is not in line with the aims and scope of the target journal, or if your manuscript is not structured and formatted according to the target journal layout, or if your manuscript does not have a reasonable chance of being able to satisfy the target journal’s publishing expectations, the manuscript can receive a desk rejection from the editor without being sent out for peer review. Desk rejections can be disheartening for authors, making them feel they have wasted valuable time and might even cause them to lose enthusiasm for their research topic. Sun and Linton (2014), Hierons (2016) and Craig (2010) offer useful discussions on the subject of “desk rejections.”

4. Make a good first impression with your title and abstract.

The title and abstract are incredibly important components of a manuscript as they are the first elements a journal editor sees. I have been fortunate to receive advice from editors and reviewers on my submissions, and feedback from many colleagues at academic conferences, and this is what I’ve learned:

The title should summarize the main theme of the article and reflect your contribution to the theory.

The abstract should be crafted carefully and encompass the aim and scope of the study; the key problem to be addressed and theory; the method used; the data set; key findings; limitations; and implications for theory and practice.

Dr. Angel Borja goes into detail about these components in “ 11 steps to structuring a science paper editors will take seriously .”

Learn more in Elsevier's free Researcher Academy opens in new tab/window

5. Have a professional editing firm copy-edit (not just proofread) your manuscript, including the main text, list of references, tables and figures.

The key characteristic of scientific writing is clarity. Before submitting a manuscript for publication, it is highly advisable to have a professional editing firm copy-edit your manuscript. An article submitted to a peer-reviewed journal will be scrutinized critically by the editorial board before it is selected for peer review. According to a statistic shared by Elsevier, between 30 percent and 50 percent of articles submitted to Elsevier journals are rejected before they even reach the peer-review stage, and one of the top reasons for rejection is poor language. A properly written, edited and presented text will be error free and understandable and will project a professional image that will help ensure your work is taken seriously in the world of publishing. On occasion, the major revisions conducted at the request of a reviewer will necessitate another round of editing. Authors can facilitate the editing of their manuscripts by taking precautions at their end. These include proofreading their own manuscript for accuracy and wordiness (avoid unnecessary or normative descriptions like “it should be noted here” and “the authors believe) and sending it for editing only when it is complete in all respects and ready for publishing. Professional editing companies charge hefty fees, and it is simply not financially viable to have them conduct multiple rounds of editing on your article. Applications like the spelling and grammar checker in Microsoft Word or Grammarly are certainly worth applying to your article, but the benefits of proper editing are undeniable. For more on the difference between proofreading and editing, see the description in Elsevier’s WebShop.

6. Submit a cover letter with the manuscript.

Never underestimate the importance of a cover letter addressed to the editor or editor-in-chief of the target journal. Last year, I attended a conference in Boston. A “meet the editors” session revealed that many submissions do not include a covering letter, but the editors-in-chief present, who represented renewed and ISI-indexed Elsevier journals, argued that the cover letter gives authors an important opportunity to convince them that their research work is worth reviewing.

Accordingly, the content of the cover letter is also worth spending time on. Some inexperienced scholars paste the article’s abstract into their letter thinking it will be sufficient to make the case for publication; it is a practice best avoided. A good cover letter first outlines the main theme of the paper; second, argues the novelty of the paper; and third, justifies the relevance of the manuscript to the target journal. I would suggest limiting the cover letter to half a page. More importantly, peers and colleagues who read the article and provided feedback before the manuscript’s submission should be acknowledged in the cover letter.

7. Address reviewer comments very carefully.

Editors and editors-in-chief usually couch the acceptance of a manuscript as subject to a “revise and resubmit” based on the recommendations provided by the reviewer or reviewers. These revisions may necessitate either major or minor changes in the manuscript. Inexperienced scholars should understand a few key aspects of the revision process. First, it important to address the revisions diligently; second, is imperative to address all the comments received from the reviewers and avoid oversights; third, the resubmission of the revised manuscript must happen by the deadline provided by the journal; fourth, the revision process might comprise multiple rounds. The revision process requires two major documents. The first is the revised manuscript highlighting all the modifications made following the recommendations received from the reviewers. The second is a letter listing the authors’ responses illustrating they have addressed all the concerns of the reviewers and editors. These two documents should be drafted carefully. The authors of the manuscript can agree or disagree with the comments of the reviewers (typically agreement is encouraged) and are not always obliged to implement their recommendations, but they should in all cases provide a well-argued justification for their course of action.

Given the ever increasing number of manuscripts submitted for publication, the process of preparing a manuscript well enough to have it accepted by a journal can be daunting. High-impact journals accept less than 10 percent of the articles submitted to them, although the acceptance ratio for special issues or special topics sections is normally over 40 percent. Scholars might have to resign themselves to having their articles rejected and then reworking them to submit them to a different journal before the manuscript is accepted.

The advice offered here is not exhaustive but it’s also not difficult to implement. These recommendations require proper attention, planning and careful implementation; however, following this advice could help doctoral students and other scholars improve the likelihood of getting their work published, and that is key to having a productive, exciting and rewarding academic career.

Acknowledgements

I would like to thank Professor Heikki Karjaluoto, Jyväskylä University School of Business and Economics for providing valuable feedback on this article.

Sun, H., & Linton, J. D. (2014).

Structuring papers for success: Making your paper more like a high impact publication than a desk reject opens in new tab/window

Technovation.

Craig, J. B. (2010).

Desk rejection: How to avoid being hit by a returning boomerang opens in new tab/window

Family Business Review

Hierons, R. M. (2016).

The dreaded desk reject opens in new tab/window

, Software Testing, Verification and Reliability .

Borja, A (2014): 

11 steps to structuring a science paper editors will take seriously

Elsevier Connect

Knight, L. V., & Steinbach, T. A. (2008).

Selecting an appropriate publication outlet: a comprehensive model of journal selection criteria for researchers in a broad range of academic disciplines opens in new tab/window

, International Journal of Doctoral Studies .

Tewin, K. (2015).

How to Better Proofread An Article in 6 Simple Steps opens in new tab/window ,

Day, R, & Gastel, B: How to write and publish a scientific paper. Cambridge University Press (2012)

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Aijaz shaikh, phd.

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Top 16 International Computer Science Journals — A Template Guide

MS Word, LaTeX Templates and Author Guidelines

This post is part of a series of blogs with links to Word, LaTeX templates and author instructions of top journals around the world in more than 25 core subjects in academia.

Getting started with your Research Paper Formatting

Writing a successful research paper is more than just communicating your knowledge . Most of the journals prescribe detailed set of authoring guidelines to apply on your content before you submit. Many research papers even get rejected for not following the guidelines of the journal (a reason why we built SciSpace (Formerly Typeset) — a platform to automatically apply 100% journal guidelines on your content).

To get you quickly started with your research paper formatting, this blog article lists journal formats and authoring guidelines of top international journals in Computer Science. You can find the links to MS Word template as well as LaTeX template of each of the journal here. You can also find the access link to the detailed author guidelines set by the journal. Feel free to check it out, share with friends and comment on the article.

Top International Computer Science Journals

We have used "Impact Factor" and various other parameters to rank the journals( Source ).

1. IEEE Transactions on Pattern Analysis and Machine Intelligence

IEEE Transactions on Pattern Analysis and Machine Intelligence is a monthly peer-reviewed scientific journal published by the IEEE Computer Society. It covers research in computer vision and image understanding, pattern analysis and recognition, and machine intelligence. machine learning, search techniques, document and handwriting analysis, medical image analysis, video and image sequence analysis, content-based retrieval of image and video, and face and gesture recognition.

Impact Factor — 5.694 (2013)

Journal Abbreviation — IEEE Trans. Pattern Anal. Mach. Intell.

Download MS Word Template here

Download LaTeX Template here

Check out the detailed Author Guidelines here

2. Artificial Intelligence

Artificial Intelligence is a scientific journal on artificial intelligence research. It was established in 1970 and is published by Elsevier.

Impact Factor — 3.333 (2015)

Find instructions for MS Word Template here

** The journal doesn’t provide you a MS Word template. You have to follow the author guidelines to format your document. You can also write your document on SciSpace and format it to the journal guidelines in a few click s .

3. Communications of the ACM

Communications of the ACM is the monthly Journal of the Association for Computing Machinery (ACM). The focus is on the practical implications of advances in information technology and associated management issues; ACM also publishes a variety of more theoretical journals.

Impact Factor — 3.301 (2015)

Journal Abbreviation — Commun ACM

** The journal doesn’t provide you a MS Word template. You have to follow the author guidelines to format your document. You can also write your document on SciSpace and format it to the journal guidelines in a click.

4. Computer

Computer is an IEEE Computer Society practitioner-oriented magazine and contains peer-reviewed articles, regular columns and interviews on current computing-related issues.

Impact Factor — 1.438 (2013)

5. IEEE Transactions on Computers

IEEE Transactions on Computers is a monthly peer-reviewed scientific journal covering all aspects of computer design. It was established in 1952 and is published by the IEEE Computer Society.

Impact Factor — 1.473 (2013)

Journal Abbreviation — IEEE Trans. Comput.

6. IEEE Transactions on Evolutionary Computation

IEEE Transactions on Evolutionary Computation is a bimonthly peer-reviewed scientific journal published by the IEEE Computational Intelligence Society. It covers evolutionary computation and related areas including nature-inspired algorithms, population-based methods, and optimization where selection and variation are integral, and hybrid systems where these paradigms are combined.

Impact Factor — 5.545 (2013)

Journal Abbreviation — IEEE Trans. Evolut. Comput.

7. IEEE Transactions on Fuzzy Systems

IEEE Transactions on Fuzzy Systems is a bimonthly peer-reviewed scientific journal published by the IEEE Computational Intelligence Society. It covers the theory, design or applications of fuzzy systems ranging from hardware to software, including significant technical achievements, exploratory developments, or performance studies of fielded systems based on fuzzy models.

Impact Factor — 6.701 (2016)

Journal Abbreviation — IEEE Trans. Fuzzy Syst.

8. Journal of Cryptology

The Journal of Cryptology is a scientific journal in the field of cryptology and cryptography. The journal is published quarterly by the International Association for Cryptologic Research.

Impact Factor — 1.021(2015)

** The journal doesn’t provide you a LaTeX template .The journal also doesn’t provide you a MS Word template. You have to follow the author guidelines to format your document. You can also write your document on SciSpace and format it to the journal guidelines in a click and download the LaTeX version.

9. IEEE Transactions on Information Theory

IEEE Transactions on Information Theory is a monthly peer-reviewed scientific journal published by the IEEE Information Theory Society. It covers information theory and the mathematics of communications.

Impact Factor — 2.65 (2013)

Journal Abbreviation — IEEE Trans. Inf. Theory

Check out MS Word Template here

Check out LaTeX Template here

10. IEEE Transactions on Neural Networks and Learning Systems

IEEE Transactions on Neural Networks and Learning Systems is a monthly peer-reviewed scientific journal published by the IEEE Computational Intelligence Society. It covers the theory, design, and applications of neural networks and related learning systems.

Impact Factor — 4.37 (2013)

Journal Abbreviation — IEEE Trans. Neural Netw. Learn. Syst

11. Journal of the ACM

The Journal of the ACM is a peer-reviewed scientific journal covering computer science in general, especially theoretical aspects. It is an official journal of the Association for Computing Machinery.

Impact Factor — 2.353 (2011)

Journal Abbreviation — J. ACM

12. Journal of Artificial Intelligence Research

The Journal of Artificial Intelligence Research is an open access peer-reviewed scientific journal covering research in all areas of artificial intelligence. Paper volumes are printed by the AAAI Press.

Impact Factor — 1.691 (2010)

Journal Abbreviation — J. Artif. Intell. Res

13. Journal of Functional Programming

The Journal of Functional Programming is a peer-reviewed scientific journal covering the design, implementation, and application of functional programming languages, spanning the range from mathematical theory to industrial practice.

Impact Factor — 1.357(2015)

** The journal doesn’t provide you a LaTeX template . The journal also doesn’t provide you a MS Word template. You have to follow the author guidelines to format your document. You can also write your document on SciSpace , format it to the journal guidelines in a click and download your document in LaTeX format.

14. International Journal of Computer Vision

The International Journal of Computer Vision (IJCV) is a journal published by Springer.

Impact Factor — 3.623 (2012)

Journal Abbreviation — IJCV

Find LaTeX instructions here

Find MS Word instructions here

** The journal doesn’t provide you a LaTeX template . The journal also doesn’t provide you a MS Word template. You have to follow the author guidelines to format your document. You can also write your document on SciSpace and format it to the journal guidelines in a click.

15. Journal of Machine Learning Research

The Journal of Machine Learning Research is a peer-reviewed open access scientific journal covering machine learning.

Impact Factor — 2.45(2015)

16. SIAM Journal on Computing (SICOMP)

The SIAM Journal on Computing ( SICOMP ) is a scientific journal focusing on the mathematical and formal aspects of computer science. It is published by the Society for Industrial and Applied Mathematics (SIAM).

Journal Abbreviation — SIAM J. Comput.

If you found this list useful, please do share top computer science journals’ templates with your fellow researchers, academics and colleagues.

A research writing tool that helps you follow 100% guidelines

Adhering to fuzzy journal guidelines that runs to hundreds of pages is every researcher’s nightmare. That’s where SciSpace comes in.

SciSpace has around 14000 journal templates and enables you to format or re-format your research paper to all of the journal guidelines with 100% accuracy. What more, you save loads of your time while doing it .

SciSpace also has various University thesis, assignments and top international Conferences’ templates. Check it out here .

Before you go, SciSpace may be of interest if you are trying to simplify their research workflows. Discover, write, and collaborate on research papers using this comprehensive end-to-end solution. Your scholarly output can be displayed and managed seamlessly with the tools and resources we provide.

By combining writing and publishing tools, copyright detection technology, and searchable indexing, this platform will let you display and manage your scholarly output.It is a good idea to make notes about your citations while conducting a literature review. With SciSpace Discover, it is easy to cite your sources. Using the citation button on an article page, you can generate citation text that is preloaded in multiple formats, so you can copy and paste as you need.

Related Reading

• Top 11 international Biology journals — a template guide
• Top 13 international Material Science journals- a template guide
• Template collection of top Chemistry journals
• How SciSpace simplifies your research writing

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More From Forbes

What is the best way to write a computer science research paper.

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What is the best way to get started in writing research papers? originally appeared on Quora - the place to gain and share knowledge, empowering people to learn from others and better understand the world.

Answer by David J. Malan , Gordon McKay Professor of the Practice of Computer Science, Harvard University, on Quora :

What is the best way to get started in writing research papers?

I’d start by reading papers in your area(s) of interest, as by taking a course or just on your own, taking note of structural similarities and writing conventions. (Here are   some classics , thanks to   Harry Lewis   of   Harvard University .) And, if you don’t already have a research project in mind, reach out to a professor whose area of research appeals to you.

As for the writing itself, it’s not uncommon in computer science for papers to be structured along the lines of:

• Introduction
• Related Work
• Future Work

Or some variation thereof, wherein the ellipsis represents sections on your methodology, arguments, results, and the like.

Writing-wise, perhaps most helpful for me early on was an exercise my   advisor   had us do in graduate school: for each paper we read, read everything but the abstract, then write our own abstract, have others critique it, iteratively improve it, and ultimately compare it against the paper’s actual abstract. It was a helpful way to learn how to distill a paper into its essence and communicate as much.

Collaborating with others on research papers, too, is helpful, as you can then take ownership of just a piece of the paper while learning from (ideally more experienced) collaborators how best to structure the whole.

And do share any drafts you write with others, soliciting feedback, iteratively improving the paper before you submit!

This question originally appeared on Quora - the place to gain and share knowledge, empowering people to learn from others and better understand the world. You can follow Quora on Twitter , Facebook , and Google+ . More questions:

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Writing and Publishing

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Ten simple rules for writing a paper about scientific software

* E-mail: [email protected]

Affiliations Institute for Biomedical Informatics, University of Pennsylvania, Philadelphia, Pennsylvania, United States of America, Department of Epidemiology, Biostatistics, and Informatics, University of Pennsylvania, Philadelphia, Pennsylvania, United States of America, Center of Excellence in Environmental Toxicology, University of Pennsylvania, Philadelphia, Pennsylvania, United States of America

• Joseph D. Romano, 
• Jason H. Moore

Published: November 12, 2020

• https://doi.org/10.1371/journal.pcbi.1008390
• Reader Comments

Papers describing software are an important part of computational fields of scientific research. These “software papers” are unique in a number of ways, and they require special consideration to improve their impact on the scientific community and their efficacy at conveying important information. Here, we discuss 10 specific rules for writing software papers, covering some of the different scenarios and publication types that might be encountered, and important questions from which all computational researchers would benefit by asking along the way.

Author summary

Computational researchers have a responsibility to ensure that the software they write stands up to the same scientific scrutiny as traditional research studies. These 10 simple rules make doing so easier by enhancing usability, reproducibility, transparency, and other crucial characteristics that aren’t taught in most computer science or research methods curricula.

Citation: Romano JD, Moore JH (2020) Ten simple rules for writing a paper about scientific software. PLoS Comput Biol 16(11): e1008390. https://doi.org/10.1371/journal.pcbi.1008390

Editor: Scott Markel, Dassault Systemes BIOVIA, UNITED STATES

Copyright: © 2020 Romano, Moore. This is an open access article distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Funding: This work is funded with support from NIH grants R01LM010098, R01LM012601, R01AI116794, UL1TR001878, UC4DK112217 (PI: Jason Moore), and P30ES013508 (PI: Trevor Penning). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

Competing interests: The authors have declared that no competing interests exist.

Introduction

In recent decades, scientific software has become a critical feature of virtually all research workflows [ 1 ]. Computational researchers and informaticians, therefore, have a responsibility to release and disseminate software in the same scientifically rigorous manner as other research protocols, datasets, and empirical studies released into the scientific community. Writing (and publishing) a peer-reviewed paper about a newly developed scientific software is arguably one of the best ways to do this—“software papers” can reach a massive number of potential users (even acting as advertisement for the software), are a great way to show that the software stands up to scientific scrutiny, and allow users to easily reuse and cite the software in their own research.

However, software papers are fundamentally different from other “traditional” research articles. The process of designing and implementing software is different from designing and carrying out bench experiments, clinical studies, or raw data analyses [ 2 ]. There are also differences in the “final product” of the research: Software studies, obviously, yield a piece of software to be directly reused, whereas other study paradigms provide new protocols, specific findings, and follow-up questions or hypotheses.

There are basically 2 types of software papers: (1) stand-alone papers that solely describe the software, usually in a shorter format than an article written about a traditional research study; and (2) a (more traditional) article describing an original research question that includes development of a piece of new software as one of its critical components. Examples of the former include the original papers describing Biopython [ 3 ], scikit-learn [ 4 ], and SAMtools [ 5 ]. The latter includes the papers that introduced Gene Set Enrichment Analysis [ 6 ], the Connectivity Map tools [ 7 ], and VIPER [ 8 ]. Although these options produce 2 very different styles of paper, the 10 simple rules presented below largely apply to both of them.

Rule 1: Read the other “Ten Simple Rules” papers on coding

In order to have a good software paper, you first need to have good software. All of the other rules for writing great scientific software apply here, especially those that are already covered in other “Ten Simple Rules” articles. All impactful scientific software should aim to be robust [ 9 ], well documented [ 10 ], easy to use [ 11 ], and maintained under version control [ 12 ]. The advantages of making your software open source (with transparent licensing terms) and hosted on public repositories are widely acknowledged [ 13 ] and should be practiced regularly, unless there is a compelling reason not to. Evaluations, use cases, and demonstrative examples should make use of high-quality data that is ideally already well characterized [ 14 , 15 ].

Rule 2: Know the most appropriate publication venues and submission types

Journals and conference proceedings that focus on computational areas of research frequently have article types that are dedicated specifically to descriptions of new software or databases, and these can be a great venue for disseminating information quickly, concisely, and to an audience with an assumed level of technical proficiency. It’s good to think early and reconsider often when finding a specific journal or conference. Make sure to pay special attention to any nonstandard requirements that journals impose—some require evaluations to use real (i.e., nonsynthetic) data [ 16 ], others have special reporting or data/software deposition requirements [ 17 ], and you should always consider whether your software (and desired paper style) match the mission statement and/or goals of the journal or meeting. Discussions with mentors, collaborators, and other colleagues can be hugely beneficial in this context; their past successes and failures can end up saving you from submitting to an unsuitable journal (and all of the headaches that come with it). Some examples of popular submission types and journals for software papers include Bioinformatics (submission type: “original papers” or “application notes”), Nucleic Acid Research’s yearly “Database issue” and “Web Server issue,” and PLOS Computational Biology’s “software articles.”

Rule 3: Publish for users, not developers

In spite of Rule 2, you should always consider submission to noncomputational venues. As computational researchers, we often work in highly interdisciplinary areas, writing software that makes research in other fields easier, more efficient, and more scientifically robust. Scientists working in these fields are often extremely interested in hearing about new software tools that will help them on a daily basis, but they may not frequently search computationally focused journals or conference proceedings. Especially if the software is meant to be easily accessible to bench scientists or other noncomputational stakeholders, describing your software in a domain-specific journal is an excellent way to reach a wider audience. Furthermore, a paper describing an innovative new software tool in one of these journals has a great chance of standing out in comparison to other articles, especially when the field has highlighted a need for new software approaches to long-standing challenges.

However, a few things need to be kept in mind, especially when publishing in a noncomputational journal or conference. If software papers are uncommon in your field, there may not be an ideally suitable publication/article type, and you may need to be creative in how you organize your presentation of the software and your evaluation of its performance. Specifically, think of how your software can address a particular limitation or research question of interest to the field, and show an example demonstrating that it can do so. This can become a primary focus of the paper, or it can be one of several shorter “case studies” that show off useful functionality. Think about the story you want to tell, and what your target audience would find the most useful. Similarly, reviewers might be unfamiliar with how to assess and critique software papers. When in doubt, it never hurts to contact a journal editor or program committee member for guidance—they might even be able to direct the article to a set of reviewers they know have the needed technical expertise. If the publication venue asks authors for reviewer suggestions, you should be able to come up with a similar set yourself. You should also keep your readers in mind: If most of your intended audience have limited computational experience, you should actively cut down on jargon and technical details. These details can be added as supplemental data, published separately as a nontraditional article (e.g., via Zenodo, F1000, or similar), or even be moved entirely to online documentation (see Rule 6).

Good illustrative examples of software papers published in noncomputational journals are plentiful. Many older software papers were published in domain-specific journals, since most of the interdisciplinary fields that eventually led to computationally focused journals were still emerging. This can be seen, for example, in computational phylogenetics and cladistics, a field that began as early as the 1970s [ 18 , 19 ]. More modern examples of highly impactful software papers in domain-specific journals are also plentiful, like those introducing PLINK [ 20 ] and Circos [ 21 ].

Rule 4: Create a long-term software management plan

In academia, affiliations, funding sources, and technology infrastructures change frequently. Researchers therefore assume a level of responsibility for keeping the products of our research available to the rest of the scientific community when things do change. When you release a new piece of software or body of code, you should establish guidelines to help ensure its persistence—otherwise, your papers, and those of others that rely on the software, will be negatively impacted. These guidelines form what can be thought of as a “software management plan” [ 22 , 23 ]. To create one, it can be helpful to ask yourself and your coauthors the following questions:

• Who is responsible for maintaining the software in the future, should affiliations change? The first author on the paper, the lab’s principal investigator (PI), or someone else?
• What is the cost (if any) of keeping the software and any related resources—relevant databases, web apps, application programming interfaces (APIs), etc.—online? What is the funding source? How will it be funded if this source is exhausted?
• Who owns the intellectual property (IP) behind the software? This is often the institution or company that employs the paper’s PI, but it may be different, and it may affect how the software is maintained in the long term. Furthermore, it is crucial to know how ownership of the IP will affect licensing [ 24 ]. Generally, it’s good practice to adopt the most permissive license that doesn’t violate ownership or usage/privacy policies.
• Will updates and bug fixes be provided? If the updates are major, will follow-up papers be published (see Rule 9)? Are any regular maintenance activities necessary, and if so, who will perform them?
• What will happen to the software if data or other resources it relies upon are no longer available?
• When and how will you archive the software? Online code repositories (e.g., via GitHub) make doing so easy, and tools like Zenodo and FigShare let you tie permanent DOIs to specific archives (see Rule 5).

Generally, software management plans aren’t outlined in the actual body of software papers, but an idea of how the lifecycle of the software will be handled—along with general policies and strategies for maintaining the software—are often included in online code repositories, such as in “Contributing” guides or the software’s README (e.g., [ 25 – 27 ]). General tips and guides on developing software management plans are in ample supply online [ 22 , 23 ].

Rule 5: Safeguard against “link rot”

As papers age, it’s unfortunately quite common for hyperlinks to permanently break—the resource they point to has moved, has been taken offline, or affected by some other internet-related issue. This is known as “link rot,” and it is not just contained to academic articles—link rot can affect blogs, social media posts, web pages, and other digital resources [ 28 ]. However, it is especially prevalent in scientific articles—a 2013 study by Hennessey and Ge found that the median uniform resource locator (URL) lifespan is 9.3 years, with some falling far shorter than that mark [ 29 ]. While blogs, README documents, and source code can be edited to point to new links, peer-reviewed papers are static—unless you issue a correction or erratum, the URL you use at the time of publication is the URL that will be in that paper permanently.

Several relatively easy steps can be taken to prevent link rot in papers about scientific software. Institutional affiliations and website structure can change (as mentioned in Rule 4), so it is best to host web apps, APIs, software descriptions, example code, and other digital resources either on a dedicated domain, an independently hosted lab website, or on a free web hosting resource (e.g., using GitHub Pages). However, be familiar with how the host handles persistent links. For example, links on GitHub Pages sites can break if a repository is transferred to a new owner. When digital resources do need to move to a new URL, you should make an effort to set up URL redirection from the old location to the new location, which can usually be arranged with web server administrators. You should also set up persistent versioned releases of software and assign separate DOIs to point to the current software release at the time of the paper’s publication. Zenodo (for software releases tagged on GitHub) and FigShare (for data files, scripts, and other digital resources) are free services that track permanently archived research materials and assign DOIs that basically “solve” link rot when used effectively. Also, having a well-documented system for assigning meaningful URLs to individual resources can help to diagnose the cause if links do break. For example, “http://<domain>/protein/BRCA1” is likely far better than “http://<domain>/540/65df7.php?id=18427,” both from a usability and a maintenance perspective.

Rule 6: Make a clear distinction between code documentation and research results

Whenever software is intended for reuse, high-quality documentation is crucial. However, peer-reviewed papers are arguably not where documentation should be presented. The paper should describe the software (including the design process, technical details, and algorithmic innovations) and any accompanying analyses. Any time you include code in the paper, you’re making a commitment to support the syntax and semantics in the code. Since it’ll be permanently visible to scientific users, changes that break the example code will likely lead to confusion and potentially result in alienating the users. If it’s especially important to show usage examples or other instructions on how to use the software, and they occupy more than a small handful of sentences, they should either (1) be moved to an appendix or supplemental materials document; or (2) be placed in the code’s documentation. If you have dedicated documentation pages online, it’s a great idea to provide a link to those pages in the body of the paper. To ensure consistency, the documentation should also be version controlled, and the link in the paper should point to the version of the documentation that is current at the time of writing. As a side note, sample input/output and example code that support results presented in the paper can also be placed in the software’s version control system and even integrated into the software’s test suite as acceptance tests [ 30 ].

Rule 7: Be current with modern tooling and best coding practices

Many of the choices you make in the development of your software itself can have a profound effect on the longevity and scientific impact of the paper that describes it. If the software solves an important unmet need, yet is challenging to install, written in an obsolete programming language, and filled with bugs, it probably will not attain widespread use. Similarly, the paper would stand a high likelihood of falling by the wayside, if it even passes peer review. Fortunately, a relatively small amount of advance planning during the early stages of development can help avoid this particular issue. We find that the following guidelines are helpful here:

• Use a well-maintained programming language that runs on most modern systems.
• Publish your software on at least 1 packaging index so that users can install it with a single command. Using a continuous integration (CI) service can automate this process and reduce the likelihood of human error [ 31 ].
• Similarly, if possible, distribute your software both as raw source code and as a packaged or compiled version.
• As mentioned before, provide detailed documentation and instructions for use.
• If possible, provide ways for users to contribute to future development, especially in terms of bug fixes and requested features.

Doing so is also important for more fundamentally pragmatic reasons: A good way to encourage widespread use of software is to make it easy to install and use and to give it a fresh, modern look. Although this is not directly related to the scientific quality of the software or the paper, dissemination of research and research tools is an important part of the scientific process and should always be given special thought.

Rule 8: Maintain consistency between code, documentation, websites, and papers

By its very nature, any software paper needs to manage references to (and between) an ecosystem of digital resources describing the software, including websites, source code repositories, documentation, example code, blog articles, and other media types, all of which refer to the same piece of software. Make sure to maintain consistency across this ecosystem. Use the same spelling, punctuation, and capitalization in any names you make up for your software. If you create a logo for the software, use it in multiple places. Make heavy use of links between different sites and resources so readers can find what they are looking for quickly and easily. An easy trick for ensuring version consistency is to include version numbers directly in URLs, where appropriate. For example, documentation pages might be given the URL “http://<domain>/version1/doc.” If the software is part of a larger body of research that has produced other pieces software, it might be a good idea to establish a naming system to indicate the relationship, while ensuring that each can be easily referred to without ambiguity. Don’t force acronyms in your naming either—keep acronyms simple or avoid them entirely.

Rule 9: Plan for follow-up publications and update the software accordingly

More often than not, software development does not end after its first major release—rather, developers add new features and respond to bugs or other performance issues. This definitely applies to scientific software, too, which stems largely from the fact that good research is usually iterative [ 13 ] and conducted in stages that are either planned from the outset or guided by the successes and failures of earlier steps. Writing several papers along the way is more than a ploy to inflate citation metrics or boost a curriculum vitae (CV); it is a demonstration of rigorously following the scientific process, and it allows you to rapidly disseminate new findings to the community.

However, it is also important to know when it’s appropriate to write a follow-up paper. Things like bug fixes or minor usability enhancements are better suited for blog posts, version release notes, or message board/issue tracker threads. Discuss whether a new update constitutes a new scientific advancement and if that advancement solves a need that your user base currently faces. Generally, we aim to publish a new paper for every new major feature that is associated with a specific outstanding research question. For example, an ongoing project within our research group is the development of the Tree-based Pipeline Optimization Tool (TPOT)—an automated machine learning tool that uses genetic programming to automatically find machine learning pipelines that perform well on a given dataset [ 32 ]. In addition to the original publication describing TPOT, we have written follow-up papers for several major additions to the software, including new ways to specify pipeline templates [ 33 ] and support for deep learning [ 34 ].

Rule 10: Prioritize visibility and availability

There is a frustrating scenario that plays out often when performing computational research: You find a paper describing a piece of free, publicly available software that perfectly solves a problem you have been struggling with for several weeks. The paper is a bit old, but the methods seem elegant and robust. However, after finally tracking down a copy of the software, you find that there is no way to make it run on any modern operating system. You spend a few hours trying to track down its (apparently nonexistent) documentation, and eventually give up, deciding that it is either impossible to get the software running or that it will simply take less time to implement it yourself. Problems like these can’t be entirely avoided—software ages, programming languages eventually fall out of favor, and dependencies change in ways that you as a user cannot fully control. However, as a developer, you can take steps to effectively prolong your software’s life, and some of these steps can be implemented directly in the paper that describes the software.

First, redundancy does not hurt. If you have a main informational website for the software, include a link in the paper, as well as on the source repository, in the documentation, and on lab and institutional websites. Make use of social media to promote your work and encourage coauthors to do the same [ 35 ]. A popular metric for determining the social impact of an article is the Altmetric Attention Score [ 36 ], which uses not only citation count but also things like social media mentions, news coverage, and representation in popular science publications. “Badge icons” (sometimes known as “shields”) used on websites, code repositories, and package indexes let you provide rich links to different parts of your software ecosystem (including the paper itself via DOIs) that are dynamic, informationally dense, and visually appealing. Finally, both your software paper and associated media related to the software can be optimized for search engines, which can dramatically increase their scientific impact [ 37 ].

Many of these apply in reverse, too. Once your paper is published (and prior to that, if you release a preprint), your websites and code repositories should point back to the paper, using its DOI when possible. It’s also helpful to explicitly instruct users how to cite your work and provide preformatted citations in several popular styles and/or B ib TEX/EndNote/etc. files that can be exported directly to citation management software.

Software papers are an important component of the scientific research ecosystem, benefiting users in many domains and with widely varying levels of computational expertise. Furthermore, academic publications (and their citations) currently provide the primary means by which scientific software developers and maintainers gain recognition for their work (fortunately, efforts are currently under way to change this—for example, [ 38 , 39 ] show how code contributions can be used as directly citable scholarly works). Following these 10 simple rules will help to ensure your software papers are easy to use, scientifically rigorous, and resistant to future changes in technology.

• 1. Humphreys P. Extending ourselves: Computational science, empiricism, and scientific method. Oxford University Press; 2004.
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• 17. Submitting experimental metadata—Submission Guidelines—Scientific Data; 2020. Available from: https://www.nature.com/sdata/publish/submission-guidelines#metadata .
• 22. Writing and using a software management plan. Available from: https://www.software.ac.uk/resources/guides/software-management-plans .
• 25. Contribute to NumPy—NumPy; 2020. Available from: https://numpy.org/contribute/ .
• 26. Contribution guide—Penn Machine Learning Benchmarks; 2020. Available from: https://epistasislab.github.io/penn-ml-benchmarks/contributing.html .
• 27. Developer’s Guide—Scikit-Learn documentation; 2020. Available from: https://scikit-learn.org/stable/developers/index.html .
• 29. Hennessey J, Ge SX. A cross disciplinary study of link decay and the effectiveness of mitigation techniques. In: BMC bioinformatics. vol. 14. Springer; 2013. p. S5.
• 30. Haugset B, Hanssen GK. Automated acceptance testing: A literature review and an industrial case study. In: Agile 2008 Conference. IEEE; 2008. p. 27–38.
• 31. Hilton M, Tunnell T, Huang K, Marinov D, Dig D. Usage, costs, and benefits of continuous integration in open-source projects. In: 2016 31st IEEE/ACM International Conference on Automated Software Engineering (ASE). IEEE; 2016. p. 426–437.
• 32. Olson RS, Moore JH. TPOT: A tree-based pipeline optimization tool for automating machine learning. In: Automated machine learning. Springer, Cham; 2019. p. 151–160.
• 34. Romano JD, Le TT, Fu W, Moore JH. Is deep learning necessary for simple classification tasks? arXiv preprint arXiv:200606730. 2020.

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I am a CS undegrad. I want to publish a paper, what should I do?

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How to publish a paper?

Being a software engineer for the most part of my life I have absolutley no idea how to start with publishing an "academic" kind of paper. During my latest research I've found an interesting algorithm for the task I've been solving (related to some calculations on financial markets). It's not some great result but I think it can be interesting for people doing the similar tasks and I'd like to publish it.

I'm of course familar with a style of research papers since I use them extensivly at my job (thanks to Google Scholar and all the good people out there) and I'm able to google for free manuals on academic writing style and how to use LaTeX and I have a lot of friends mathematicians who will check my paper and help to make it look ok.

But I have absolutely no idea what to do next! I don't belong to any academic institution or recognizable research entity, I work in a small local company, which will be happy to have its name on some paper published but this name will say nothing to anybody. I don't know anybody who is doing research in this area, I mean I've never communicated with anybody.

How can I found the right place to send paper to? Do I need some sort of recommendation or review and how and where can I try to get them? What are my steps?.. I realize that all these are absolutely obvious things for you if you are a professional sceintist but I have no idea where to start :)

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• 5 $\begingroup$ Plenty of people in industry publish academic papers. Here are some other questions that might also help: How do you decide when you have enough research results to write a paper and to which journal you submit the paper and How to make a paper stronger? and perhaps even What would you advise someone who wants to do research as a hobby? . $\endgroup$ –  Dave Clarke Commented Jul 2, 2011 at 17:43
• $\begingroup$ You can try uploading it to arXiv first. $\endgroup$ –  Kaveh Commented Jul 2, 2011 at 19:50
• $\begingroup$ @Kaveh: What is the real benefit of uploading it to arXiv? I know that I scan it for papers every now and again, but I don't provide any feedback to the authors. $\endgroup$ –  Dave Clarke Commented Jul 2, 2011 at 20:04
• 1 $\begingroup$ @Dave, it would be nice if arXiv had a feedback feature, but even uploading it to arXiv will make it available to others who might be interested in it, who may contact the author directly if they find it interesting. $\endgroup$ –  Kaveh Commented Jul 2, 2011 at 20:08
• $\begingroup$ @Dave Clarke: another benefit of arXiv is the quick dissemination of your work -- as opposed to the time elapsed between a submission to a journal and the acceptance, let alone the final publication. $\endgroup$ –  Anthony Labarre Commented Jul 6, 2011 at 11:47

2 Answers 2

Something to consider: try to figure out if you want to present your work at a scientific conference , or if you would prefer to publish it in a scientific journal .

Pros of conferences:

• A conference talk will typically get more visibility than a journal paper, at least in short term. I guess fairly few researchers read journals regularly, but many of them take part in the main conferences of the field almost every year. At a conference you can also more easily discuss your work with other researchers.

Pros of journals:

Journal reviews are usually much more thorough than conference reviews. If you submit to a journal, you will get useful feedback on your work, regardless of whether it is accepted for publication. If you submit to a conference, this is not necessarily the case.

A conference talk will also mean a nontrivial amount of expenses : flights, hotels, conference registration fees, per diem allowances, etc. can easily be in the ballpark of 1000-2000 EUR, and it might be a good idea to first check if your company is willing to support you. Submitting to a journal is much easier from that perspective: typically, it is 100% free.

• 7 $\begingroup$ On the minus side, journals often have a ridiculously long turn-around time for reviews and for publication. $\endgroup$ –  Dave Clarke Commented Jul 3, 2011 at 12:03

First, you should write up your result so it's comprehensible as you can make it, and send it to a journal. If your write-up looks like it is a real research paper, your paper should be sent out for review and possible publication. How to choose a journal? Look for other papers which are on roughly the same subject matter and around the same quality as yours, and see where they got published.

Don't publish in a fourth-tier journal; if you do, it will never be read; these seem to exist only for the purpose of increasing the number of researcher's publications, and are not subscribed to by many academic libraries. To make sure you're not choosing one of these, check the online databases of a few good academic libraries, and make sure most of them subscribe to the journal you have picked.

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Hiring CS Graduates: What We Learned from Employers

Computer science ( CS ) majors are in high demand and account for a large part of national computer and information technology job market applicants. Employment in this sector is projected to grow 12% between 2018 and 2028, which is faster than the average of all other occupations. Published data are available on traditional non-computer science-specific hiring processes. However, the hiring process for CS majors may be different. It is critical to have up-to-date information on questions such as “what positions are in high demand for CS majors?,” “what is a typical hiring process?,” and “what do employers say they look for when hiring CS graduates?” This article discusses the analysis of a survey of 218 recruiters hiring CS graduates in the United States. We used Atlas.ti to analyze qualitative survey data and report the results on what positions are in the highest demand, the hiring process, and the resume review process. Our study revealed that a software developer was the most common job the recruiters were looking to fill. We found that the hiring process steps for CS graduates are generally aligned with traditional hiring steps, with an additional emphasis on technical and coding tests. Recruiters reported that their hiring choices were based on reviewing resume’s experience, GPA, and projects sections. The results provide insights into the hiring process, decision making, resume analysis, and some discrepancies between current undergraduate CS program outcomes and employers’ expectations.

A Systematic Literature Review of Empiricism and Norms of Reporting in Computing Education Research Literature

Context. Computing Education Research (CER) is critical to help the computing education community and policy makers support the increasing population of students who need to learn computing skills for future careers. For a community to systematically advance knowledge about a topic, the members must be able to understand published work thoroughly enough to perform replications, conduct meta-analyses, and build theories. There is a need to understand whether published research allows the CER community to systematically advance knowledge and build theories. Objectives. The goal of this study is to characterize the reporting of empiricism in Computing Education Research literature by identifying whether publications include content necessary for researchers to perform replications, meta-analyses, and theory building. We answer three research questions related to this goal: (RQ1) What percentage of papers in CER venues have some form of empirical evaluation? (RQ2) Of the papers that have empirical evaluation, what are the characteristics of the empirical evaluation? (RQ3) Of the papers that have empirical evaluation, do they follow norms (both for inclusion and for labeling of information needed for replication, meta-analysis, and, eventually, theory-building) for reporting empirical work? Methods. We conducted a systematic literature review of the 2014 and 2015 proceedings or issues of five CER venues: Technical Symposium on Computer Science Education (SIGCSE TS), International Symposium on Computing Education Research (ICER), Conference on Innovation and Technology in Computer Science Education (ITiCSE), ACM Transactions on Computing Education (TOCE), and Computer Science Education (CSE). We developed and applied the CER Empiricism Assessment Rubric to the 427 papers accepted and published at these venues over 2014 and 2015. Two people evaluated each paper using the Base Rubric for characterizing the paper. An individual person applied the other rubrics to characterize the norms of reporting, as appropriate for the paper type. Any discrepancies or questions were discussed between multiple reviewers to resolve. Results. We found that over 80% of papers accepted across all five venues had some form of empirical evaluation. Quantitative evaluation methods were the most frequently reported. Papers most frequently reported results on interventions around pedagogical techniques, curriculum, community, or tools. There was a split in papers that had some type of comparison between an intervention and some other dataset or baseline. Most papers reported related work, following the expectations for doing so in the SIGCSE and CER community. However, many papers were lacking properly reported research objectives, goals, research questions, or hypotheses; description of participants; study design; data collection; and threats to validity. These results align with prior surveys of the CER literature. Conclusions. CER authors are contributing empirical results to the literature; however, not all norms for reporting are met. We encourage authors to provide clear, labeled details about their work so readers can use the study methodologies and results for replications and meta-analyses. As our community grows, our reporting of CER should mature to help establish computing education theory to support the next generation of computing learners.

Light Diacritic Restoration to Disambiguate Homographs in Modern Arabic Texts

Diacritic restoration (also known as diacritization or vowelization) is the process of inserting the correct diacritical markings into a text. Modern Arabic is typically written without diacritics, e.g., newspapers. This lack of diacritical markings often causes ambiguity, and though natives are adept at resolving, there are times they may fail. Diacritic restoration is a classical problem in computer science. Still, as most of the works tackle the full (heavy) diacritization of text, we, however, are interested in diacritizing the text using a fewer number of diacritics. Studies have shown that a fully diacritized text is visually displeasing and slows down the reading. This article proposes a system to diacritize homographs using the least number of diacritics, thus the name “light.” There is a large class of words that fall under the homograph category, and we will be dealing with the class of words that share the spelling but not the meaning. With fewer diacritics, we do not expect any effect on reading speed, while eye strain is reduced. The system contains morphological analyzer and context similarities. The morphological analyzer is used to generate all word candidates for diacritics. Then, through a statistical approach and context similarities, we resolve the homographs. Experimentally, the system shows very promising results, and our best accuracy is 85.6%.

A genre-based analysis of questions and comments in Q&A sessions after conference paper presentations in computer science

Gender diversity in computer science at a large public r1 research university: reporting on a self-study.

With the number of jobs in computer occupations on the rise, there is a greater need for computer science (CS) graduates than ever. At the same time, most CS departments across the country are only seeing 25–30% of women students in their classes, meaning that we are failing to draw interest from a large portion of the population. In this work, we explore the gender gap in CS at Rutgers University–New Brunswick, a large public R1 research university, using three data sets that span thousands of students across six academic years. Specifically, we combine these data sets to study the gender gaps in four core CS courses and explore the correlation of several factors with retention and the impact of these factors on changes to the gender gap as students proceed through the CS courses toward completing the CS major. For example, we find that a significant percentage of women students taking the introductory CS1 course for majors do not intend to major in CS, which may be a contributing factor to a large increase in the gender gap immediately after CS1. This finding implies that part of the retention task is attracting these women students to further explore the major. Results from our study include both novel findings and findings that are consistent with known challenges for increasing gender diversity in CS. In both cases, we provide extensive quantitative data in support of the findings.

Designing for Student-Directedness: How K–12 Teachers Utilize Peers to Support Projects

Student-directed projects—projects in which students have individual control over what they create and how to create it—are a promising practice for supporting the development of conceptual understanding and personal interest in K–12 computer science classrooms. In this article, we explore a central (and perhaps counterintuitive) design principle identified by a group of K–12 computer science teachers who support student-directed projects in their classrooms: in order for students to develop their own ideas and determine how to pursue them, students must have opportunities to engage with other students’ work. In this qualitative study, we investigated the instructional practices of 25 K–12 teachers using a series of in-depth, semi-structured interviews to develop understandings of how they used peer work to support student-directed projects in their classrooms. Teachers described supporting their students in navigating three stages of project development: generating ideas, pursuing ideas, and presenting ideas. For each of these three stages, teachers considered multiple factors to encourage engagement with peer work in their classrooms, including the quality and completeness of shared work and the modes of interaction with the work. We discuss how this pedagogical approach offers students new relationships to their own learning, to their peers, and to their teachers and communicates important messages to students about their own competence and agency, potentially contributing to aims within computer science for broadening participation.

Creativity in CS1: A Literature Review

Computer science is a fast-growing field in today’s digitized age, and working in this industry often requires creativity and innovative thought. An issue within computer science education, however, is that large introductory programming courses often involve little opportunity for creative thinking within coursework. The undergraduate introductory programming course (CS1) is notorious for its poor student performance and retention rates across multiple institutions. Integrating opportunities for creative thinking may help combat this issue by adding a personal touch to course content, which could allow beginner CS students to better relate to the abstract world of programming. Research on the role of creativity in computer science education (CSE) is an interesting area with a lot of room for exploration due to the complexity of the phenomenon of creativity as well as the CSE research field being fairly new compared to some other education fields where this topic has been more closely explored. To contribute to this area of research, this article provides a literature review exploring the concept of creativity as relevant to computer science education and CS1 in particular. Based on the review of the literature, we conclude creativity is an essential component to computer science, and the type of creativity that computer science requires is in fact, a teachable skill through the use of various tools and strategies. These strategies include the integration of open-ended assignments, large collaborative projects, learning by teaching, multimedia projects, small creative computational exercises, game development projects, digitally produced art, robotics, digital story-telling, music manipulation, and project-based learning. Research on each of these strategies and their effects on student experiences within CS1 is discussed in this review. Last, six main components of creativity-enhancing activities are identified based on the studies about incorporating creativity into CS1. These components are as follows: Collaboration, Relevance, Autonomy, Ownership, Hands-On Learning, and Visual Feedback. The purpose of this article is to contribute to computer science educators’ understanding of how creativity is best understood in the context of computer science education and explore practical applications of creativity theory in CS1 classrooms. This is an important collection of information for restructuring aspects of future introductory programming courses in creative, innovative ways that benefit student learning.

CATS: Customizable Abstractive Topic-based Summarization

Neural sequence-to-sequence models are the state-of-the-art approach used in abstractive summarization of textual documents, useful for producing condensed versions of source text narratives without being restricted to using only words from the original text. Despite the advances in abstractive summarization, custom generation of summaries (e.g., towards a user’s preference) remains unexplored. In this article, we present CATS, an abstractive neural summarization model that summarizes content in a sequence-to-sequence fashion while also introducing a new mechanism to control the underlying latent topic distribution of the produced summaries. We empirically illustrate the efficacy of our model in producing customized summaries and present findings that facilitate the design of such systems. We use the well-known CNN/DailyMail dataset to evaluate our model. Furthermore, we present a transfer-learning method and demonstrate the effectiveness of our approach in a low resource setting, i.e., abstractive summarization of meetings minutes, where combining the main available meetings’ transcripts datasets, AMI and International Computer Science Institute(ICSI) , results in merely a few hundred training documents.

Exploring students’ and lecturers’ views on collaboration and cooperation in computer science courses - a qualitative analysis

Factors affecting student educational choices regarding oer material in computer science, export citation format, share document.

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Home » Journal publications » SCI / SCIE Journals » How to Publish Research Paper in SCI, SCIE, ESCI Journals?

How to Publish Research Paper in SCI, SCIE, ESCI Journals?

• The Social Science Citation Index
• Arts and Humanities Citation Index
• Book Citation Index
• Conference Proceedings Citation Index
• Emerging Sources Citation Index (ESCI)

The ESCI (Emerging Sources Citation Index) was started as a distinct database within the WOS – Web of Science platform. All journal publications submitted to WOS will first be assessed for ESCI and if deemed appropriate indexed in ESCI. It is worth noting that journals indexed in ESCI will not have impact factors. But ESCI journals over time depending on their acceptability and citations will be considered for reviewing impact factors.

Publishing a paper in an SCI, SCIE & ESCI-indexed journal – the process in a nutshell

For the results of scientific research to be published in a journal that is indexed either in the SCI, SCIE or ESCI journal indexing databases. They must be presented in the form of a scientific article. Doing so involves scientific writing skills and know-how. Scientific writing is entirely based on an age-old tradition. That has its roots in the 17th century, back when the very first scientific article was published. And some authors still today consider it to be a very demanding profession. Owing to the high standards imposed on not just SCIE and ESCI-indexed journals but also SCI-indexed journals it is difficult or close to impossible that a poorly written (even though relevant) article could be published in such a journal. Notwithstanding, scientific writing is still not taught these days. And in most cases, scientists have to learn the basic principles of trial and error.

A large number of scholars have written on the art of writing and publishing research papers . On searching for the topic ‘how to write a scientific article’ (whether it be for journals indexed in the Web of Science database, the Scopus database, ARDA publications or otherwise), one will notice hundreds of articles published in hundreds of different journals from various countries. The authors cover several topics such as what constitutes interesting research, publication of theoretical articles, qualitative research and reasons for rejecting articles. Additionally, there are also many relevant books covering the subject as well. For this article, a number of these articles and books have been closely examined and combined.

The following framework is proposed to offer a summary of the most vital suggestions for newbie authors. This framework will help authors to submit articles successfully and publish in SCI, SCIE & ESCI-indexed journals . This framework could facilitate the process of writing and publishing articles in such a journal seamlessly for anyone be they an amateur research professional or an experienced one. The 4C ideology is based on the premise. The following abilities are essential for the successful publication of scientific research work

• Competence: Choose a relevant topic that is explored to the highest standards.
• Course: Targeting the right journal with the right subject.
• Composition: Carefully plan the composition of the article.
• Contents: Linking theory to methodology supported by competence in the mastery of writing. The process is iterative and at any point in time, a researcher can rethink the essence of the previous steps and choose to make further revisions.

The level of mastery of scientific writing determines the category to which the article belongs

• Major results – very significant
• Sound, substantial, engaging work – a definite contribution, minor but positive contribution to knowledge
• Elegant and technically correct but unnecessary
• Neither elegant nor useful, but not false
• False and misleading
• So poorly written that a technical evaluation is impossible

It is crucial to stress that the proposed writing framework is by no means typical of journals indexed in the SCI, SCIE & ESCI journal indexing databases. However, the proposed framework is an appropriate route for the publication of journals indexed in these databases. Since most of them impose very high standards of publication regarding both the pertinence of the content and the writing abilities of the author.

ARDA Professional Publishing Support Services – Facilitating Seamless & Successful Publication

Whether one is a doctoral student, undergrad student or senior researcher time is a precious commodity. Several obstacles may arise in publishing one’s research. However, the experts at ARDA help every step of the way to make a researcher’s publishing journey as smooth as possible. In addition, ARDA also offers a range of service packages to choose from so that researchers can find the one that matches their needs perfectly. ARDA’s panel of scientific publication experts will review an article and provide constructive feedback to help the author of the article improve their manuscript before submitting it to SCI journals , SCIE journals & ESCI journals of their choice.

ARDA Conference team of experts will work with authors to find out which appropriate journals are for their brand of research and assist them with editing the manuscript before submission, performing a technical review and providing post-submission assistance as needed.

30 Replies to “How to Publish Research Paper in SCI, SCIE, ESCI Journals?”

Why I have sent my article’s abstract to ESCI journal, but I have still got an answer from journal.

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Hello, l need assistance to publish my research paper in SCI or SSCI, please. Thank you!

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I want to publish my paper in ssci and I hope I can get help with it

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CVPR 2024 Announces Best Paper Award Winners

This year, from more than 11,500 paper submissions, the CVPR 2024 Awards Committee selected the following 10 winners for the honor of Best Papers during the Awards Program at CVPR 2024, taking place now through 21 June at the Seattle Convention Center in Seattle, Wash., U.S.A.

Best Papers

• “ Generative Image Dynamics ” Authors: Zhengqi Li, Richard Tucker, Noah Snavely, Aleksander Holynski The paper presents a new approach for modeling natural oscillation dynamics from a single still picture. This approach produces photo-realistic animations from a single picture and significantly outperforms prior baselines. It also demonstrates potential to enable several downstream applications such as creating seamlessly looping or interactive image dynamics.
• “ Rich Human Feedback for Text-to-Image Generation ” Authors: Youwei Liang, Junfeng He, Gang Li, Peizhao Li, Arseniy Klimovskiy, Nicholas Carolan, Jiao Sun, Jordi Pont-Tuset, Sarah Young, Feng Yang, Junjie Ke, Krishnamurthy Dj Dvijotham, Katherine M. Collins, Yiwen Luo, Yang Li, Kai J. Kohlhoff, Deepak Ramachandran, and Vidhya Navalpakkam This paper highlights the first rich human feedback dataset for image generation. Authors designed and trained a multimodal Transformer to predict the rich human feedback and demonstrated some instances to improve image generation.

Honorable mention papers included, “ EventPS: Real-Time Photometric Stereo Using an Event Camera ” and “ pixelSplat: 3D Gaussian Splats from Image Pairs for Scalable Generalizable 3D Reconstruction. ”

Best Student Papers

• “ Mip-Splatting: Alias-free 3D Gaussian Splatting ” Authors: Zehao Yu, Anpei Chen, Binbin Huang, Torsten Sattler, Andreas Geiger This paper introduces Mip-Splatting, a technique improving 3D Gaussian Splatting (3DGS) with a 3D smoothing filter and a 2D Mip filter for alias-free rendering at any scale. This approach significantly outperforms state-of-the-art methods in out-of-distribution scenarios, when testing at sampling rates different from training, resulting in better generalization to out-of-distribution camera poses and zoom factors.
• “ BioCLIP: A Vision Foundation Model for the Tree of Life ” Authors: Samuel Stevens, Jiaman Wu, Matthew J. Thompson, Elizabeth G. Campolongo, Chan Hee Song, David Edward Carlyn, Li Dong, Wasila M. Dahdul, Charles Stewart, Tanya Berger-Wolf, Wei-Lun Chao, and Yu Su This paper offers TREEOFLIFE-10M and BIOCLIP, a large-scale diverse biology image dataset and a foundation model for the tree of life, respectively. This work shows BIOCLIP is a strong fine-grained classifier for biology in both zero- and few-shot settings.

There also were four honorable mentions in this category this year: “ SpiderMatch: 3D Shape Matching with Global Optimality and Geometric Consistency ”; “ Image Processing GNN: Breaking Rigidity in Super-Resolution; Objects as Volumes: A Stochastic Geometry View of Opaque Solids ;” and “ Comparing the Decision-Making Mechanisms by Transformers and CNNs via Explanation Methods. ”

“We are honored to recognize the CVPR 2024 Best Paper Awards winners,” said David Crandall, Professor of Computer Science at Indiana University, Bloomington, Ind., U.S.A., and CVPR 2024 Program Co-Chair. “The 10 papers selected this year – double the number awarded in 2023 – are a testament to the continued growth of CVPR and the field, and to all of the advances that await.”

Additionally, the IEEE Computer Society (CS), a CVPR organizing sponsor, announced the Technical Community on Pattern Analysis and Machine Intelligence (TCPAMI) Awards at this year’s conference. The following were recognized for their achievements:

• 2024 Recipient : “ Rich Feature Hierarchies for Accurate Object Detection and Semantic Segmentation ” Authors: Ross Girshick, Jeff Donahue, Trevor Darrell, Jitendra Malik
• 2024 Recipient : Angjoo Kanazawa, Carl Vondrick
• 2024 Recipient : Andrea Vedaldi

“The TCPAMI Awards demonstrate the lasting impact and influence of CVPR research and researchers,” said Walter J. Scheirer, University of Notre Dame, Notre Dame, Ind., U.S.A., and CVPR 2024 General Chair. “The contributions of these leaders have helped to shape and drive forward continued advancements in the field. We are proud to recognize these achievements and congratulate them on their success.”

About the CVPR 2024 The Computer Vision and Pattern Recognition Conference (CVPR) is the preeminent computer vision event for new research in support of artificial intelligence (AI), machine learning (ML), augmented, virtual and mixed reality (AR/VR/MR), deep learning, and much more. Sponsored by the IEEE Computer Society (CS) and the Computer Vision Foundation (CVF), CVPR delivers the important advances in all areas of computer vision and pattern recognition and the various fields and industries they impact. With a first-in-class technical program, including tutorials and workshops, a leading-edge expo, and robust networking opportunities, CVPR, which is annually attended by more than 10,000 scientists and engineers, creates a one-of-a-kind opportunity for networking, recruiting, inspiration, and motivation.

CVPR 2024 takes place 17-21 June at the Seattle Convention Center in Seattle, Wash., U.S.A., and participants may also access sessions virtually. For more information about CVPR 2024, visit cvpr.thecvf.com .

About the Computer Vision Foundation The Computer Vision Foundation (CVF) is a non-profit organization whose purpose is to foster and support research on all aspects of computer vision. Together with the IEEE Computer Society, it co-sponsors the two largest computer vision conferences, CVPR and the International Conference on Computer Vision (ICCV). Visit thecvf.com for more information.

About the IEEE Computer Society Engaging computer engineers, scientists, academia, and industry professionals from all areas and levels of computing, the IEEE Computer Society (CS) serves as the world’s largest and most established professional organization of its type. IEEE CS sets the standard for the education and engagement that fuels continued global technological advancement. Through conferences, publications, and programs that inspire dialogue, debate, and collaboration, IEEE CS empowers, shapes, and guides the future of not only its 375,000+ community members, but the greater industry, enabling new opportunities to better serve our world. Visit computer.org for more information.

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Proceedings of 4th International Conference on Frontiers in Computing and Systems

COMSYS 2023, Volume 1

• Conference proceedings
• © 2024
• Dipak Kumar Kole 0 ,
• Shubhajit Roy Chowdhury 1 ,
• Subhadip Basu 2 ,
• Dariusz Plewczynski 3 ,
• Debotosh Bhattacharjee 4

Department of Computer Science and Engineering, Jalpaiguri Government Engineering College, Jalpaiguri, India

You can also search for this editor in PubMed   Google Scholar

School of Computing and Electrical Engineering, Indian Institute of Technology Mandi, Mandi, India

Department of computer science and engineering, jadavpur university, kolkata, india, faculty of mathematics and information science, warsaw university of technology, warsaw, poland.

• Presents research works in the field of computing and systems
• Gathers the outcomes of COMSYS 2023, held at IIT Mandi, India
• Serves as a reference resource for researchers and practitioners in academia and industry

Part of the book series: Lecture Notes in Networks and Systems (LNNS, volume 974)

Included in the following conference series:

• COMSYS: International Conference on Frontiers in Computing and Systems

Conference proceedings info: COMSYS 2023.

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About this book

This book gathers high-quality research papers presented at the 4th International Conference on Frontiers in Computing and Systems (COMSYS 2023) held at Indian Institute of Technology Mandi, Himachal Pradesh, India, during 16–17 October 2023. The book is divided into two volumes, and it covers research in “cyber-physical systems for real-life applications” pertaining to AI, machine learning and data science; devices, circuits, and systems; computational biology, biomedical informatics, and network medicine; communication networks, cloud computing, and IoT; image, video, and signal processing; and security and privacy.

• Signal Processing
• Machine Learning
• Data Science
• Cloud and Mobile Computing
• Computer Networks
• Biomedical & Bioinformatics
• COMSYS 2023 Proceedings

Table of contents (48 papers)

Front matter, artificial intelligence, machine learning and data science, ai based criminal detection and recognition system for public safety and security using novel criminalnet-228.

• Jamuna S. Murthy, G. M. Siddesh

A Multilayer Framework for Data-Driven Student Modeling

• Mitra Datta Ganapaneni, C. C. Sobin, N. P. Subheesh

Machine Learning-Based Early Epilepsy Diagnosis with Secure EEG Data Sharing Using Blockchain

• G. Lakshmi Sai Bhargavi, R. Shanmukh, Tejas Lokesh, C. C. Sobin

Application of Different Decision Tree Classifier for Diabetes Prediction: A Machine Learning Approach

• Rajendrani Mukherjee, Sudip Kumar Sahana, Siddhant Kumar, Sneha Agrawal, Simran Singh

Exploring Electric Vehicle Adoption Research Through Bibliometric Analysis and Visualization

• Harbansh Singh, Vedant Singh, Bhaskar Dhiman, Nitin Kumar

Optimizing Solar Power Distribution in Microgrids to Reduce Energy Waste

• Rakesh Mondal, Dipanjan Patra, Surajit Kumar Roy, Chandan Giri

GPA: Uni-directional GRU-Based Traffic Prediction Model for Minimizing Air Pollution

• Kalyan Chatterjee, M. Raju, K. Naveen Kumar, R. Praveen Kumar, Bhodigam Akshitha, Sanjana Bandari et al.

A Real-Time Framework for Automatic Sarcasm Detection Using Proposed Tensor-DNN-50 Algorithm

Unveiling the art of music generation with lstm.

• Shashwatha Karkera, Himani Verma, Sakshi Jain, Lisa Verma, Nishtha Srivastava, Sankita J. Patel

Few-Shot Learning with Fine-Tuned Language Model for Suicidal Text Detection

• Sandeep Varma, Shivam Shivam, Biswarup Ray, Ankita Banerjee

A Comparative Analysis of Feature Selection Approaches for Sensor-Based Human Activity Recognition

• Prasanta Sen, Anindita Saha, Saroj Kumari, Chandreyee Chowdhury

GuideBP: Guided Backpropagation in Multi-output Neural Networks by Channeling Gradients Through Weaker Logits

• Swarnendu Ghosh, Bodhisatwa Mandal, Teresa Gonçalves, Paulo Quaresma, Mita Nasipuri, Nibaran Das

A Study on Users Sentiment from Twitter Data and Stock Market During Russia-Ukraine War

• Sutapa Bhattacharya, Gunjan Kumar Biswas, Bibek Roy, Dhrubasish Sarkar, Koushik Majumder, Dipak Kumar Kole

Illegitimate Comment Filtration Method for Social Media Applications Using Logistic Regression

• V. A. Aadhithyanarayanan, Teena George, Abhijith Jaideep, K. S. Divya, M. S. Sumesh

Unsupervised MTS Anomaly Detection with Variational Autoencoders

• M. K. Saravana, M. S. Roopa, J. S. Arunalatha, K. R. Venugopal

SHAPRFs: SHapley Additive eXplanation-Based Random Forests Algorithm for Classification Problems

• Nishant Jain, Shipra Shukla

Predicting Suicidal Behavior Among Indian Adults Using Childhood Trauma, Mental Health Questionnaires and Machine Learning Cascade Ensembles

• Akash K. Rao, Gunjan Y. Trivedi, Riri G. Trivedi, Anshika Bajpai, Gajraj Singh Chauhan, Vishnu K. Menon et al.

Analyzing Students’ Emotion and Activities in the Classroom: A Rural Education Perspective

• Koushik Konar, Shailabh Suman, Diraj Chaurasia, Aniruddha Pal, Sujoy Saha

Other volumes

Editors and affiliations.

Dipak Kumar Kole

Shubhajit Roy Chowdhury

Subhadip Basu, Debotosh Bhattacharjee

Dariusz Plewczynski

About the editors

Dipak K. Kole received his Ph.D. degree in Engineering from Bengal Engineering & Science University (BESU), which is currently known as IIEST, Shibpur, India, in 2012. He also received his M.Tech. and B. Tech. in Computer Science & Engineering and B.Sc. in Mathematics Honors from Calcutta University. He has approximately 21 years of professional experience. Dr. Kole is a faculty member of the Computer Science and Engineering Department of Jalpaiguri Government Engineering College since 2014, where he is currently working as a full professor. His research interest includes Synthesis & Testing of Reversible Circuits, Social Network Analysis, Digital Watermarking, and Agriculture Engineering. He published more than 68 research articles in various international journals, conference proceedings, and book chapters in the areas of VLSI, Reversible Circuits, Social Network Analysis, Agriculture Engineering, Image & Video Processing, and Cryptography.

Shubhajit Roy Chowdhury was born on August 27, 1981. He completed his Ph.D. from the Department of Electronics and Telecommunication Engineering, Jadavpur University, in the year 2010. He is currently an associate professor at the School of Computing and Electrical Engineering, Indian Institute of Technology Mandi. Previously, he also served as an assistant professor at the Centre for VLSI and Embedded Systems Technology, IIIT Hyderabad. He has also taught at Jadavpur University in the capacity of a lecturer from 2006 to 2010. He is a senior member of Institute of Electrical and Electronics Engineers (IEEE), a member of VLSI Society of India, ACM, and a life member of Indian Statistical Institute, Microelectronics Society of India, Institution of Electronic and Telecommunication Engineers and Telemedicine Society of India. He is a member of scientific, technical, and editorial committee of Engineering and Natural Sciences Division of World Academy of Engineering, Science, and Technology. He is the recipient of university gold medals in 2004 and 2006 for his B.E. and M.E., respectively, Altera Embedded Processor Designer Award in 2007, and winner of five best paper awards. He received the award of the Fellow of Society of Applied Biotechnology (FSAB) by the Society of Applied Biotechnology in the year 2012. He is also awarded Young Engineers’ Award 2012-13 by the Institution of Engineers, India, for his outstanding contribution in the field of Electronics and Telecommunication Engineering.

Subhadip Basu is a full professor in the Computer Science and Engineering Department of Jadavpur University, where he joined in 2006. He received his Ph.D. from Jadavpur University and did his postdocs from University of Iowa, USA, and University of Warsaw, Poland. Dr. Basu holds an honorary position as a research scientist at the University of Iowa, USA, since 2016. He is the co-founder and an honorary advisor of Infomaticae, a technology startup headquartered in Kolkata, India. He has also worked in reputed International Institutes like, Hitachi Central Research Laboratory, Japan, Bournemouth University, UK, University of Lorraine, France, Nencki Institute of Experimental Biology, Poland, and Hannover Medical School, Germany. Dr Basu has 250+ international research publications in the areas of Pattern Recognition, Machine Learning, Bioinformatics, Biomedical Image Analysis, etc. He has edited ten books, received two US patents, supervised 10 Ph.D. students, and received several major research grants from UGC, DST, and DBT, Govt. of India. Dr. Basu is the recipient of the ‘Research Award’ from UGC, Govt. of India, in 2016. He also received the DAAD Senior Scientist fellowship from Germany, Hitachi Visiting Research fellowship from Japan, EMMA and CLINK Visiting Researcher fellowships from the European Union, and BOYSCAST and FASTTRACK Young-Scientist fellowships from DST, Govt. of India. He is the past chairperson of the IEEE Computer Society Kolkata, a senior member of IEEE, a member of ACM, and a life member of IUPRAI.

Dariusz Plewczynski, is a professor at University of Warsaw in Center of New Technologies CeNT, Warsaw, Poland, the head of Laboratory of Functional and Structural Genomics, and the principal investigator at Mathematics and Information Science Department at Warsaw University of Technology. His interests are focused on functional and structural genomics attempts to make use of the vast wealth of data produced by high-throughput genomics projects, such as the structural genomics consortia, human genome project, 1000 Genomes Project, ENCODE, and many others. The major tools that are used in this interdisciplinary research endeavor include statistical data analysis (GWAS studies, clustering, machine learning), genomic variation analysis using diverse data sources (karyotyping, confocal microscopy, aCGH microarrays, next-generation sequencing: both whole genome and whole exome), bioinformatics (protein sequence analysis, protein structure prediction), and finally biophysics (polymer theory and simulations) and genomics (epigenetics, genome domains, three-dimensional structure analysis of chromatin).

Debotosh Bhattacharjee is working as a full professor at the Department of Computer Science and Engineering, Jadavpur University with nineteen years of post-Ph.D. experience. His research interests pertain to the applications of machine learning techniques for Face Recognition, Gait Analysis, Hand Geometry Recognition, and Diagnostic Image Analysis. He has authored or co-authored more than 312 journals, and conference publications, including several book chapters in the areas of Biometrics and Medical Image Processing. Two US patents have been granted for his works. Prof. Bhattacharjee has been granted sponsored projects by the Govt. of India funding agencies like Department of Biotechnology(DBT), Department of Electronics and Information Technology (DeitY), University Grants Commission(UGC), with a total amount of around INR 2 Crore. For postdoctoral research, Dr. Bhattacharjee has visited different universities abroad like the University of Twente, The Netherlands; Instituto Superior Técnico, Lisbon, Portugal; University of Bologna, Italy; ITMO National Research University, St. Petersburg, Russia; University of Ljubljana, Slovenia; Northumbria University, Newcastle Upon Tyne, UK; and Heidelberg University, Germany. He is a life member of Indian Society for Technical Education (ISTE, New Delhi), Indian Unit for Pattern Recognition and Artificial Intelligence (IUPRAI), a senior member of IEEE (USA), and a fellow of West Bengal Academy of Science and Technology.

Bibliographic Information

Book Title : Proceedings of 4th International Conference on Frontiers in Computing and Systems

Book Subtitle : COMSYS 2023, Volume 1

Editors : Dipak Kumar Kole, Shubhajit Roy Chowdhury, Subhadip Basu, Dariusz Plewczynski, Debotosh Bhattacharjee

Series Title : Lecture Notes in Networks and Systems

DOI : https://doi.org/10.1007/978-981-97-2611-0

Publisher : Springer Singapore

eBook Packages : Engineering , Engineering (R0)

Copyright Information : The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024

Softcover ISBN : 978-981-97-2610-3 Due: 30 July 2024

eBook ISBN : 978-981-97-2611-0 Published: 28 June 2024

Series ISSN : 2367-3370

Series E-ISSN : 2367-3389

Edition Number : 1

Number of Pages : XXIV, 735

Number of Illustrations : 72 b/w illustrations, 260 illustrations in colour

Topics : Communications Engineering, Networks , Artificial Intelligence , Computational Intelligence , Bioinformatics

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