About the Journal

Building robust, ethical, and scalable intelligent systems requires an integrated approach that recognizes the close interdependence between algorithmic innovation and data-driven reasoning. Advances in artificial intelligence and data science increasingly influence decision-making across science, industry, governance, and society, making it essential to address not only technical performance but also reliability, transparency, and long-term sustainability. This includes developing methods that are resilient to data uncertainty, bias, and distributional shifts, as well as designing systems that can adapt to evolving real-world conditions.

Achieving meaningful progress in intelligent systems demands attention to foundational challenges such as data quality, model interpretability, computational efficiency, and responsible deployment. Equally important are broader structural considerations, including access to data and computational resources, skills development, and the ethical and societal implications of intelligent technologies. Addressing these challenges requires collaboration across disciplines, combining theoretical advances with applied research and empirical validation.

The International Journal of Intelligent Systems and Data Science (IJISDS) provides a dedicated scholarly platform to advance these objectives. With a multidisciplinary focus, the journal brings together perspectives from artificial intelligence, machine learning, data science, and allied domains to deepen understanding of how intelligent systems can be designed, evaluated, and applied responsibly. IJISDS supports research that bridges theory and practice, encouraging contributions that demonstrate both methodological rigor and real-world relevance.

IJISDS is committed to openness, accessibility, and inclusivity. The journal operates as a fully open-access publication, ensuring that all published research is freely and permanently available to readers worldwide. By prioritizing sustainable and equitable publishing models, IJISDS aims to facilitate the broad dissemination of knowledge and foster global participation in research on intelligent systems and data-driven technologies.

Through its editorial standards and publishing practices, IJISDS actively promotes reproducibility, ethical responsibility, and transparent peer review. The journal also seeks to contribute to broader global priorities by supporting research aligned with sustainable development, digital innovation, and responsible AI, recognizing the critical role that intelligent systems and data science play in shaping resilient and equitable futures.

Publisher: Who supports this journal?

The International Journal of Intelligent Systems and Data Science (IJISDS) is published and supported by Femington, an independent academic publishing organization committed to advancing open, ethical, and high-quality scholarly communication.

Femington ensures that IJISDS operates on transparent, community-driven, and sustainable publishing principles. This open-source backend enables efficient manuscript management, rigorous peer review, and long-term digital preservation, while reinforcing the journal’s commitment to accessibility and academic integrity. The publishing model allows IJISDS to prioritize editorial independence, research quality, and global knowledge dissemination rather than commercial publishing constraints.

IJISDS aims to make academic research available:

• online

• immediately upon publication

• free from most copyright or licensing restrictions

Accepted Types of Articles

The International Journal of Intelligent Systems and Data Science (IJISDS) considers the following categories of scholarly contributions for publication:

• Original Research / Research Articles

• Review Articles

• Case Studies

• Research Notes

• Editorials (by invitation or prior approval)

For detailed formatting requirements and the exact structure, authors are required to use the official journal manuscript template available below.

• Manuscript Template (PDF) - for formatting guidance and Word-based manuscript preparation

• LaTeX Template (ZIP) - includes complete LaTeX source files for manuscript preparation.

Original Research / Research Articles

Original Research Articles report substantial and novel contributions to the field and are considered primary literature. These manuscripts should present original theoretical developments, methodological innovations, empirical analyses, or system-level implementations related to intelligent systems and data-driven technologies.

Submissions are expected to include clearly defined sections such as Introduction, Related Work, Methodology, Experiments or Analysis, Results, and Discussion, along with a concluding section outlining implications and limitations. Articles are typically 6,000–8,000 words in length (excluding references). The research must demonstrate clear relevance to intelligent systems, artificial intelligence, or data science, with appropriate technical depth and rigor.

Review Articles

Review Articles provide a comprehensive and critical synthesis of existing research on a well-defined topic within intelligent systems and data science. These articles should assess the current state of the field, identify gaps and challenges, and offer informed perspectives on future research directions. Review articles are regarded as secondary literature and are often widely read and highly cited.

Review manuscripts should generally be 6,000–8,000 words (excluding references) and must demonstrate analytical depth rather than a descriptive summary of prior work.

IJISDS considers the following main types of review articles:

• Critical Reviews – Authors critically evaluate existing literature, highlighting theoretical, methodological, and empirical contributions, as well as unresolved challenges and limitations.

• Systematic or Scoping Reviews – Authors employ transparent, reproducible, and structured methodologies to identify, screen, and analyse relevant literature, with the aim of reducing bias and informing research or practice.

• Meta-Analyses – Authors use quantitative synthesis techniques to statistically aggregate findings from prior studies in order to derive robust conclusions about trends, effects, or performance across the literature.

Case Studies

Case Studies offer in-depth examinations of real-world applications, deployments, or implementations of intelligent systems and data-driven solutions. These manuscripts should focus on practical insights, lessons learned, and contextual factors influencing system design, performance, and impact.

Authors are expected to follow key stages, including case definition, contextual background, data collection, analytical approach, interpretation of findings, and implications for practice or research. Case studies are typically 3,000–4,000 words in length (excluding references) and must clearly demonstrate relevance to intelligent systems or data science in applied settings.

Research Notes

Research Notes are concise manuscripts presenting preliminary findings, exploratory analyses, novel datasets, new evaluation protocols, or emerging methodological ideas that may not yet warrant a full-length article but are of clear scholarly interest.

These submissions should include a brief abstract and introductory section and may be written in a continuous format to maintain conciseness. Research Notes are generally 2,000–3,000 words (excluding references) and typically include a title, short background, methodology or approach, key results, and a brief conclusion. Research Notes are considered early-stage or proof-of-concept contributions.

Editorials

Editorials are typically commissioned by the Editors to address topical issues, emerging trends, or strategic directions relevant to the journal’s scope. Authors interested in submitting an Editorial should contact the Editor-in-Chief (EIC) in advance to propose their idea for consideration.

Benefits to Authors

All articles published in the International Journal of Intelligent Systems and Data Science (IJISDS) are fully open access. This ensures that published work is available to read, download, and share worldwide.

Key benefits include:

• Flexible publication fees for authors to ensure inclusivity.

• Rigorous double-blind peer-review process

• Rapid editorial screening

• Review decisions are typically communicated within six weeks

• Fast and efficient online submission and review system

• Authors retain copyright of their published work

• The authors grant Femington a license to publish and identify itself as the original publisher.

Aim and Scope

The rapid advancement of artificial intelligence and data-driven technologies has fundamentally transformed scientific research, industrial processes, and societal decision-making. At the same time, this progress has introduced new challenges related to scalability, robustness, interpretability, ethical responsibility, and long-term sustainability of intelligent systems. Failures arising from biased data, opaque models, security vulnerabilities, or poor generalization have underscored the need for principled, transparent, and resilient approaches to intelligent system design.

The International Journal of Intelligent Systems and Data Science (IJISDS) seeks to address these challenges by publishing research that advances the theoretical foundations, methodological frameworks, and applied practices of intelligent computational systems. The journal aims to foster innovation while encouraging critical reflection on the reliability, accountability, and societal implications of AI- and data-driven technologies.

IJISDS welcomes multidisciplinary contributions spanning foundational theory, algorithmic development, system architecture, data engineering, evaluation methodologies, and real-world applications. Submissions may address local, domain-specific problems or broader global challenges, provided they demonstrate clear relevance to intelligent systems and data science.

The principal areas covered by IJISDS include, but are not limited to:

• Machine learning and deep learning

• Data mining and knowledge discovery

• Natural language processing and speech systems

• Computer vision and multimodal intelligence

• Intelligent decision-making and recommender systems

• Scalable data analytics and big data systems

• Trustworthy, explainable, and ethical AI

• Human-centered and socially responsible intelligent systems

• Interdisciplinary applications of AI and data science