About the Journal

Journal of Machines and Algorithms is a peer-reviewed, open-access scientific journal in the domain of Computer Science and related disciplines. The journal publishes various types of articles, including original research, short communications, review articles, case studies, and invited commentaries. All submitted articles should report original, previously unpublished research results, experimental or theoretical. Articles submitted to the journal should meet these criteria and must not be under consideration for publication elsewhere. Manuscripts should follow the style of the journal and are subject to both review and editing. Machines and Algorithms encourages authors of original research papers to describe work such as the following:

  • Articles in the areas of computational approaches, artificial intelligence, big data, software engineering, cybersecurity, internet of things, and data analysis.
  • Reports substantive results on a wide range of learning methods applied to a variety of learning problems.
  • Articles providing solid support via empirical studies, theoretical analysis, or comparison to psychological phenomena.
  • Articles that respond to a need in medicine, or rare data analysis with novel methods.
  • Articles that involve healthcare professionals' motivation for the work, with evolutionary results usually necessary.
  • Articles showing how to apply learning methods to solve important application problems.

Machines and Algorithms (MnA) accepts interdisciplinary research that studies and pursues the effective uses of computational and biomedical data, information, and knowledge for scientific inquiry, problem-solving, and decision making, motivated by efforts to improve human health. Novel high-performance computing methods, big data analysis, and artificial intelligence that advance material technologies are especially welcome.

Mission Statement

Our mission is to advance the frontiers of computational sciences, including core and interdisciplinary, by providing a premier platform for disseminating groundbreaking research, innovative methodologies, and transformative applications across all facets of computation. We aim to foster interdisciplinary collaboration, bridge theoretical and applied domains, and address the evolving challenges of science, technology, and society through computational innovations. By uniting diverse perspectives from academia, industry, and beyond, our journal seeks to inspire discovery, fuel innovation, and catalyse impactful solutions that shape the future of computation, applications, and its impact on society.

Potential Audience

The potential audience of Machines and Algorithms refers to the groups of individuals or organizations that may read, cite, or benefit from the published content. Identifying this audience helps shape the journal's scope, marketing strategy, and editorial policies.

  • Academic Researchers: Computer Science, Artificial Intelligence, Data Science, Mathematics, Physics, Engineering, Computational Biology, and related disciplines.
  • Industry Professionals: Software Development, IT, Machine Learning, Robotics, Finance, Healthcare, and Telecommunications.
  • Interdisciplinary Scientists: Computational Biology, Computational Physics, Computational Social Science, Digital Humanities, and more.
  • Policy Makers and Analysts: Computational research impacting cybersecurity, ethical AI, smart cities, and national or global tech strategies.
  • Entrepreneurs and Innovators: Startups and tech entrepreneurs looking for computational breakthroughs and technologies with commercialization potential.
  • Practitioners in Emerging Fields: Quantum Computing, Blockchain, IoT, Edge Computing, AR/VR, and application of computation to push the boundaries of their fields.
  • Corporate Research and Development Teams: Innovative computational methods and technologies to incorporate into products or improve processes.
  • Journalists and Science Communicators: Computational discoveries and advancements to report on or explain to a wider audience.
  • Government and Defence Researchers: Computational technologies related to data security, encryption, simulations, and decision-making systems.

Sponsorship / Source of Funding

The journal has no sponsorship or external funding sources. All operations are funded and maintained by the non-profit academic society Knovell.org.