Journals in Computer science

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Array

ISSN: 2590-0056

Opening Up Computer Science Array is an international open access multidisciplinary journal encompassing a broad spectrum of topics in computer science, including Artificial Intelligence, Machine Learning and Robotics Computer Systems and Architecture Computer Vision, Speech and Pattern Recognition Control & Signal Processing Cyber Security Data, Knowledge and Intelligent Systems Industrial Engineering Interdisciplinary Applications Medical Informatics and Biomedical Engineering Microelectronics and Hardware Multimedia and HCI Networks and Communications Operational Research and Decision Systems Scientific Computing Software Engineering Theoretical Computer Science Submissions must be novel, technically sound, and clearly presented. Array accepts both technical notes (technical notes are limited to a maximum of 10 pages in the standard Elsevier format) and regular papers. In addition to research papers presenting new results, review articles as well as discussion and opinion papers are also welcome. Papers meeting journal criteria will undergo a single-blind review process, utilizing a minimum of two (2) external referees. Our dedicated expert editorial team, together with an editorial board of hundreds of active researchers from all areas of computer science, ensure that papers move through to publication as fast as possible without compromising on the quality of the process. The journal audience comprises academia, industry, and practitioners. Authors are strongly encouraged to make their datasets publicly accessible via a repository of their choosing. Software publication We invite you to convert your open source software into an additional journal publication in Software Impacts, a multi-disciplinary open access journal. Software Impacts provides a scholarly reference to software that has been used to address a research challenge. The journal disseminates impactful and re-usable scientific software through Original Software Publications which describe the application of the software to research and the published outputs. For more information contact us at: [email protected]

Artificial Intelligence

ISSN: 0004-3702
5 Year impact factor: 14.1
Impact factor: 14.4

The journal of Artificial Intelligence (AIJ) welcomes papers on broad aspects of AI that constitute advances in the overall field including, but not limited to, cognition and AI, automated reasoning and inference, case-based reasoning, commonsense reasoning, computer vision, constraint processing, ethical AI, heuristic search, human interfaces, intelligent robotics, knowledge representation, machine learning, multi-agent systems, natural language processing, planning and action, and reasoning under uncertainty. The journal reports results achieved in addition to proposals for new ways of looking at AI problems, both of which must include demonstrations of value and effectiveness. Papers describing applications of AI are also welcome, but the focus should be on how new and novel AI methods advance performance in application areas, rather than a presentation of yet another application of conventional AI methods. Papers on applications should describe a principled solution, emphasize its novelty, and present an indepth evaluation of the AI techniques being exploited. Apart from regular papers, the journal also accepts Research Notes, Research Field Reviews, Position Papers, and Book Reviews (see details below). The journal will also consider summary papers that describe challenges and competitions from various areas of AI. Such papers should motivate and describe the competition design as well as report and interpret competition results, with an emphasis on insights that are of value beyond the competition (series) itself. From time to time, there are special issues devoted to a particular topic. Such special issues must always have open calls-for-papers. Guidance on the submission of proposals for special issues, as well as other material for authors and reviewers can be found at http://aij.ijcai.org/special-issues. Types of Papers Regular Papers AIJ welcomes basic and applied papers describing mature, complete, and novel research that articulate methods for, and provide insight into artificial intelligence and the production of artificial intelligent systems. The question of whether a paper is mature, complete and novel is ultimately determined by reviewers and editors on a case-bycase basis. Generally, a paper should include a convincing motivational discussion, articulate the relevance of the research to Artificial Intelligence, clarify what is new and different, anticipate the scientific impact of the work, include all relevant proofs and/or experimental data, and provide a thorough discussion of connections with the existing literature. A prerequisite for the novelty of a paper is that the results it describes have not been previously published by other authors and have not been previously published by the same authors in any archival journal. In particular, a previous conference publication by the same authors does not disqualify a submission on the grounds of novelty. However, it is rarely the case that conference papers satisfy the completeness criterion without further elaboration. Indeed, even prize-winning papers from major conferences often undergo major revision following referee comments, before being accepted to AIJ. AIJ caters to a broad readership. Papers that are heavily mathematical in content are welcome but should include a less technical high-level motivation and introduction that is accessible to a wide audience and explanatory commentary throughout the paper. Papers that are only purely mathematical in nature, without demonstrated applicability to artificial intelligence problems may be returned. A discussion of the work's implications on the production of artificial intelligent systems is normally expected. There is no restriction on the length of submitted manuscripts. However, authors should note that publication of lengthy papers, typically greater than forty pages, is often significantly delayed, as the length of the paper acts as a disincentive to the reviewer to undertake the review process. Unedited theses are acceptable only in exceptional circumstances. Editing a thesis into a journal article is the author's responsibility, not the reviewers'. Research Notes The Research Notes section of the Journal of Artificial Intelligence will provide a forum for short communications that cannot fit within the other paper categories. The maximum length should not exceed 4500 words (typically a paper with 5 to 14 pages). Some examples of suitable Research Notes include, but are not limited to the following: crisp and highly focused technical research aimed at other specialists; a detailed exposition of a relevant theorem or an experimental result; an erratum note that addresses and revises earlier results appearing in the journal; an extension or addendum to an earlier published paper that presents additional experimental or theoretical results. Reviews The AIJ invests significant effort in assessing and publishing scholarly papers that provide broad and principled reviews of important existing and emerging research areas, reviews of topical and timely books related to AI, and substantial, but perhaps controversial position papers (so-called "Turing Tape" papers) that articulate scientific or social issues of interest in the AI research community. Research Field Reviews: AIJ expects broad coverage of an established or emerging research area, and the articulation of a comprehensive framework that demonstrates the role of existing results, and synthesizes a position on the potential value and possible new research directions. A list of papers in an area, coupled with a summary of their contributions is not sufficient. Overall, a field review article must provide a scholarly overview that facilitates deeper understanding of a research area. The selection of work covered in a field article should be based on clearly stated, rational criteria that are acceptable to the respective research community within AI; it must be free from personal or idiosyncratic bias. Research Field Reviews are by invitation only, where authors can then submit a 2-page proposal of a Research Field Review for confirmation by the special editors. The 2-page proposal should include a convincing motivational discussion, articulate the relevance of the research to artificial intelligence, clarify what is new and different from other surveys available in the literature, anticipate the scientific impact of the proposed work, and provide evidence that authors are authoritative researchers in the area of the proposed Research Field Review. Upon confirmation of the 2-page proposal, the full Invited Research Field Reviews can then be submitted and then undergoes the same review process as regular papers. Book Reviews: We seek reviewers for books received, and suggestions for books to be reviewed. In the case of the former, the review editors solicit reviews from researchers assessed to be expert in the field of the book. In the case of the latter, the review editors can either assess the relevance of a particular suggestion, or even arrange for the refereeing of a submitted draft review. Position Papers: The last review category, named in honour of Alan Turing as a "Turing Tapes" section of AIJ, seeks clearly written and scholarly papers on potentially controversial topics, whose authors present professional and mature positions on all variety of methodological, scientific, and social aspects of AI. Turing Tape papers typically provide more personal perspectives on important issues, with the intent to catalyze scholarly discussion. Turing Tape papers are by invitation only, where authors can then submit a 2-page proposal of a Turing Tape paper for confirmation by the special editors. The 2-page proposal should include a convincing motivational discussion, articulate the relevance to artificial intelligence, clarify the originality of the position, and provide evidence that authors are authoritative researchers in the area on which they are expressing the position. Upon confirmation of the 2-page proposal, the full Turing Tape paper can then be submitted and then undergoes the same review process as regular papers. Competition Papers Competitions between AI systems are now well established (e.g. in speech and language, planning, auctions, games, to name a few). The scientific contributions associated with the systems entered in these competitions are routinely submitted as research papers to conferences and journals. However, it has been more difficult to find suitable venues for papers summarizing the objectives, results, and major innovations of a competition. For this purpose, AIJ has established the category of competition summary papers. Competition Paper submissions should describe the competition, its criteria, why it is interesting to the AI research community, the results (including how they compare to previous rounds, if appropriate), in addition to giving a summary of the main technical contributions to the field manifested in systems participating in the competition. Papers may be supplemented by online appendices giving details of participants, problem statements, test scores, and even competition-related software. Although Competition Papers serve as an archival record of a competition, it is critical that they make clear why the competition's problems are relevant to continued progress in the area, what progress has been made since the previous competition, if applicable, and what were the most significant technical advances reflected in the competition results. The exposition should be accessible to a broad AI audience.

Artificial Intelligence in Agriculture

ISSN: 2589-7217

Artificial Intelligence (AI) techniques are widely used to solve a variety of problems and to optimize the production and operation processes in the fields of agriculture, food and bio-system engineering. Artificial Intelligence in Agriculture is an Open Access journal, publishing original research, reviews and perspectives on the theory and practice of artificial intelligence (AI) in agriculture, food and bio-system engineering and related areas. Artificial Intelligence in Agriculture serves as an interdisciplinary forum to share ideas and solutions related to artificial intelligence and applications in agriculture. The journal welcomes both fundamental science and applied research describing the practical applications of AI methods in the fields of agriculture, food - and bio-system engineering and related areas. Topics of interest to the journal include, but are not limited to: AI-based decision support systems AI-based precision agriculture Smart sensors and Internet of Things Agricultural robotics and automation equipment Agricultural knowledge-based systems Computational intelligence in agriculture, food and bio-systems AI in agricultural optimization management Intelligent interfaces and human-machine interaction Machine vision and image/signal processing Machine learning and pattern recognition Neural networks, fuzzy systems, neuro-fuzzy systems Systems modeling and analysis Intelligent systems for animal feeding Expert systems in agriculture Crop Phenotyping and analysis Remote sensing in agriculture AI technology in aquiculture AI in food engineering and cold chain logistics Big Data and Cloud Computing Automatic navigation and self-driving technology Precision agricultural aviation Distributed ledger technology (Blockchain) The journal welcomes original research articles, review articles, perspective papers and short communications. The journal's editorial leadership welcome suggestions and proposals for special issues.

Artificial Intelligence in Geosciences

ISSN: 2666-5441

Artificial intelligence (AI) is one of the fastest growing disciplines in electronic information technology. Along with diversified data, AI-enabled technologies such as image processing, smart sensors, and intelligent inversion, are being tested by researchers in a wide variety of geosciences domains. These technologies have the potential to help geosciences move from qualitative to quantitative analysis. We believe that taking an interdisciplinary approach will deliver benefits to both geosciences and AI. Artificial Intelligence in Geosciences is an open access journal providing an interdisciplinary forum where ideas and solutions related to artificial intelligence and its applications in geosciences can be shared and discussed. To support this discussion, we encourage authors to open source their code, data, and the labels used in AI. We welcome both fundamental science and applied research describing the practical applications of AI methods in the fields of geology, rock physics, seismicity, hydrology, ecology, marine geosciences, planetary science, environment, volcanology, oceanography, remote sensing and GIS, and related areas. Submissions to Artificial Intelligence in Geosciences may take the form of original research articles, review articles, perspective papers, or short communications, and a variety of topics will be considered. These include, but are not limited to: AI-based decision support systems AI-based precision geosciences Smart sensors and the Internet of Things Geosciences robotics and automation equipment Geosciences knowledge-based systems Computational intelligence in geosciences AI in geosciences optimization management Intelligent interfaces and human-machine interaction Machine vision and image/signal processing Machine learning and pattern recognition Neural networks, fuzzy systems, neuro-fuzzy systems Systems modeling and analysis Expert systems in geosciences Big data and cloud computing in geosciences Automatic navigation and self-driving technology High Performance Computing in the context of Machine Learning Artificial Intelligence in Geosciences also welcomes suggestions and proposals for special issues.

Artificial Intelligence in Medicine

ISSN: 0933-3657
5 Year impact factor: 7.4
Impact factor: 7.5

Artificial Intelligence in Medicine publishes original articles from a wide variety of interdisciplinary perspectives concerning the theory and practice of artificial intelligence (AI) in medicine, medically-oriented human biology, and health care. Artificial intelligence in medicine may be characterized as the scientific discipline pertaining to research studies, projects, and applications that aim at supporting decision-based medical tasks through knowledge- and/or data-intensive computer-based solutions that ultimately support and improve the performance of a human care provider. Artificial Intelligence in Medicine considers for publication manuscripts that have both: • Potential high impact in some medical or healthcare domain; • Strong novelty of method and theory related to AI and computer science techniques. Artificial Intelligence in Medicine papers must refer to real-world medical domains, considered and discussed at the proper depth, from both the technical and the medical points of view. The inclusion of a clinical assessment of the usefulness and potential impact of the submitted work is strongly recommended. Artificial Intelligence in Medicine is looking for novelty in the methodological and/or theoretical content of submitted papers. Such kind of novelty has to be mainly acknowledged in the area of AI and Computer Science. Methodological papers deal with the proposal of some strategy and related methods to solve some scientific issues in specific domains. They must show, usually through an experimental evaluation, how the proposed methodology can be applied to medicine, medically-oriented human biology, and health care, respectively. They have also to provide a comparison with other proposals, and explicitly discuss elements of novelty. Theoretical papers focus on more fundamental, general and formal topics of AI and must show the novel expected effects of the proposed solution in some medical or healthcare field. Following the information explosion brought by the diffusion of Internet, social networks, cloud computing, and big-data platforms, Artificial Intelligence in Medicine has broadened its perspective. Particular attention is given to novel research work pertaining to: AI-based clinical decision making; Medical knowledge engineering; Knowledge-based and agent-based systems; Computational intelligence in bio- and clinical medicine; Intelligent and process-aware information systems in healthcare and medicine; Natural language processing in medicine; Data analytics and mining for biomedical decision support; New computational platforms and models for biomedicine; Intelligent exploitation of heterogeneous data sources aimed at supporting decision-based and data-intensive clinical tasks; Intelligent devices and instruments; Automated reasoning and meta-reasoning in medicine; Machine learning in medicine, medically-oriented human biology, and healthcare; AI and data science in medicine, medically-oriented human biology, and healthcare; AI-based modeling and management of healthcare pathways and clinical guidelines; Models and systems for AI-based population health; AI in medical and healthcare education; Methodological, philosophical, ethical, and social issues of AI in healthcare, medically-oriented human biology, and medicine. If you are considering submitting to Artificial Intelligence in Medicine, make sure that your paper meets the quality requirements mentioned above. English exposition must also be clear and revised with due care. Authors are kindly requested to revise their manuscripts with the help of co-authors that are fluent in English or language editing services before submitting their contribution. Papers written in poor English are likely to be rejected. The mere application of well-known or already published algorithms and techniques to medical data is not regarded as original research work of interest for Artificial Intelligence in Medicine, but it may be suitable for other venues. Artificial Intelligence in Medicine features the following kinds of papers: Original research contributions: Theoretical and/or methodological papers about novel approaches; Methodological reviews/surveys: Papers that collect, classify, describe, and critically analyze research designs, methods and procedures; Position papers: Papers that gather, describe, and analyze the scientific challenges of a specific field, founding them on the related literature; Editorials: Editors will occasionally publish editorials; Guest editorials: Editors can invite guest editors of special issues to publish editorials. Unsolicited editorials will not be considered; Letters to the editor: Letters from readers shortly discussing and commenting on a topic of interest, for example based on recently published articles in the journal Artificial Intelligence in Medicine; Book reviews: A critical review of recently published books; Erratum: Some specific corrections to results previously published in the journal Artificial Intelligence in Medicine; Historical perspectives: Papers that describe and critically review some specific aspects in the history of scientific contributions and applications; In memoriam: Papers describing the life and the main scientific contributions of scientists passed away, having had an important role in the area of artificial intelligence in medicine; PhD projects: Early publications about more recent research trends, having the goal of allowing PhD candidates to explain their PhD research project and to share it with other scientists interested in the topic. Such type of papers should focus on the overall goals and approaches of PhD research projects, without considering in detail the specific scientific results obtained, which would be the focus of other research articles. Special Issues are regularly published and included among regular issues. Artificial Intelligence in Medicine special issues deal with current theoretical/methodological research or convincing applications related to AI in medicine. Special Issues are managed by one or more guest editors who are outstanding experts on the selected topic. Special Issues of Artificial Intelligence in Medicine are directly proposed to potential guest editors by the Editor in Chief, also according to suggestions from the editorial board members. "External" proposals of Special Issues will no longer be considered. Artificial Intelligence in Medicine does not publish conference volumes or conference papers. However, selected and high-quality research results presented earlier at conferences may be published in Artificial Intelligence in Medicine, in the form of a thoroughly revised (rephrased) and extended (including new research results) original research paper. Information for authors and further details about the editorial process can be found in the Guide for Authors section of the Artificial Intelligence in Medicine web page.

Artificial Intelligence in the Life Sciences

ISSN: 2667-3185

Providing a scientific forum for practical applications and theoretical advances of Artificial Intelligence (AI) in the life sciences and related disciplines including (but not restricted to): General, molecular, and systems biology Population and disease genetics Medicinal chemistry and chemical biology Pharmacology and drug discovery Epidemiology and clinical investigations Background: Artificial Intelligence originates from computer science and covers a wide range of approaches intended to enhance the ability of machines to make data-driven decisions and accurate predictions of events. In many scientific fields, AI is being increasingly considered and integrated, especially in the context of Big Data. Given their complexity and highly interdisciplinary nature, the life sciences provide ample opportunities for AI to impact R&D efforts in a variety of ways. Key words: Artificial Intelligence; Life Sciences; Drug Discovery; Bio- and Cheminformatics, Machine Learning; Machine Intelligence; Deep Learning; Data Mining; Big Data.

Artificial Intelligence in the Life Sciences

BenchCouncil Transactions on Benchmarks, Standards and Evaluations

ISSN: 2772-4859

BenchCouncil Transactions on Benchmarks, Standards and Evaluations (TBench) is an open-access journal dedicated to advancing the field of benchmarks, data sets, standards, evaluations and optimizations. We invite submissions covering a wide range of topics from various disciplines, with a particular emphasis on interdisciplinary research. Whether it pertains to computers, AI, medicine, education, finance, business, psychology, or other social disciplines, all relevant contributions are welcome. At TBench, we prioritize the reproducibility of research. We strongly encourage authors to ensure that their articles are prepared for open-source or artifact evaluation before submission. Areas of interest include, but are not limited to: Problem definition as a benchmark Position articles on the definitions of emerging or future problems/ challenges Position articles on opening new research areas Position articles on investigating the impact of new technologies on different disciplines Research articles on instantiating new problem settings Evaluation standard as a benchmark Definition, design, implementation, and validation of evaluation standards Evaluation methodology and metrics New abstractions and tools in evaluation Simulation, emulation, and testbed methodologies and systems in evaluation Industry best practice as a benchmark Searching and summarizing industry best practice Evaluation and optimization of industry practice Retrospective of industry practice Characterizing and optimizing real-world applications and systems Benchmarking State-of-the-art solution as a benchmark State-of-the-art solutions to well-known benchmarks Benchmarking state-of-the-art solutions Preliminary but insightful solutions to new or emerging problems or benchmarks Evaluations of state-of-the-art solutions in the real-world setting Data set as a benchmark Explicit or implicit problem definition deduced from the data set Detailed descriptions of research or industry datasets, including the methods used to collect the data and technical analyses supporting the quality of the measurements Analyses or meta-analyses of existing data Systems, technologies, and techniques that advance data sharing and reuse to support reproducible research Tools that generate large-scale data while preserving their original characteristics Evaluating the rigor and quality of the experiments used to generate the data and the completeness of the data description Workload characterization, evaluation, and retrospective of design/implementation of real-world: Computer or AI applications or systems Finance applications or systems Education applications or systems Business applications or systems Medicine applications or systems Other industry applications or systems Measurement and evaluation: Instrumentation, sampling, tracing, and profiling of large-scale, real-world applications and systems Collection and analysis of measurement data that yield new insights Measurement-based modeling (e.g., workloads, scaling behavior, and assessment of performance bottlenecks) Methods and tools to monitor and visualize measurement and evaluation data Systems and algorithms that build on measurement-based findings Advances in data collection, analysis, and storage, e.g., anonymization, querying, and sharing Reappraisal of previous empirical measurements and measurement-based conclusions Descriptions of challenges and future directions that the measurement and evaluation community should pursue Methodologies, metrics, abstractions, algorithms, and tools for: Analytical modeling techniques and model validation Workload characterization and benchmarking Performance, scalability, power, and reliability analysis Sustainability analysis and power management System measurement, performance monitoring, and forecasting Anomaly detection, problem diagnosis, and troubleshooting Capacity planning, resource allocation, run-time management, and scheduling Experimental design, statistical analysis, and simulation

Big Data Research

ISSN: 2214-5796
5 Year impact factor: 3.6
Impact factor: 3.3

The journal aims to promote and communicate advances in big data research by providing a fast and high quality forum for researchers, practitioners and policy makers from the very many different communities working on, and with, this topic. The journal will accept papers on foundational aspects in dealing with big data, as well as papers on specific Platforms and Technologies used to deal with big data. To promote Data Science and interdisciplinary collaboration between fields, and to showcase the benefits of data driven research, papers demonstrating applications of big data in domains as diverse as Geoscience, Social Web, Finance, e-Commerce, Health Care, Environment and Climate, Physics and Astronomy, Chemistry, life sciences and drug discovery, digital libraries and scientific publications, security and government will also be considered. Occasionally the journal may publish whitepapers on policies, standards and best practices. Benefits to authors We also provide many author benefits, such as free PDFs, a liberal copyright policy, special discounts on Elsevier publications and much more. Please click here for more information on our author services . Please see our Guide for Authors for information on article submission. If you require any further information or help, please visit our Support Center.

Biomaterials Advances

ISSN: 2772-9508

Formerly known as Materials Science and Engineering: C, with a 2022 IF of 7.9. Biomaterials Advances includes topics at the interface of the biomedical sciences and materials engineering. These topics include: • Bioinspired and biomimetic materials for medical applications • Materials of biological origin for medical applications • Materials for "active" medical applications • Self-assembling and self-healing materials for medical applications • "Smart" (i.e., stimulus-response) materials for medical applications • Ceramic, metallic, polymeric, and composite materials for medical applications • Materials for in vivo sensing • Materials for in vivo imaging • Materials for delivery of pharmacologic agents and vaccines • Novel approaches for characterizing and modeling materials for medical applications Manuscripts on biological topics without a materials science component, or manuscripts on materials science without biological applications, will not be considered for publication in Materials Science and Engineering C. New submissions are first assessed for language, scope and originality (plagiarism check) and can be desk rejected before review if they need English language improvements, are out of scope or present excessive duplication with published sources. Biomaterials Advances sits within Elsevier's biomaterials science portfolio alongside Biomaterials, Materials Today Bio and Biomaterials and Biosystems. As part of the broader Materials Today family, Biomaterials Advances offers authors rigorous peer review, rapid decisions, and high visibility. We look forward to receiving your submissions!

Biomimetic Intelligence and Robotics

ISSN: 2667-3797

An official Journal of the Shandong University To promote advances and original contributions to robotics, biomimetics and artificial intelligence, Biomimetic Intelligence and Robotics (BIROB) provides a platform and forum for the exchange and communication of the most innovative and exciting new discoveries and impactful applications in the fields of biomimetic intelligence and robotics.Biomimetic Intelligence and Robotics (BIROB) publishes original, high-quality, peer-reviewed theoretical and applied research achievements and review articles in the fields of robotics, biomimetics, artificial intelligence, and the innovative integration of them.Topics covered by the journal include but are not limited to: autonomous systems, biomimetic actuators, biomimetic and bio-inspired design, biomimetic materials, biomimetics and artificial intelligence, exoskeletons, field robotics, human-robot interaction, humanoids, industrial robotics, kinematics and dynamics, machine learning, material sciences, medical robotics and technology, motion planning and control, micro and nano robotics, multirobot systems, sensors and bionic sensing, service robotics, and soft robotics.