International Journal of Intelligent Systems and Data Science
Building robust, ethical, and scalable data-driven and intelligent systems requires an integrated approach that recognizes the close interdependence between data, analytics, computing in...
A diverse and international author and reviewer network
Real-World Impact
Research that advances theory and practice
Int. J. Intell. Syst. Data Sci.
A global platform for research at the intersection of intelligent systems and data science.
Building robust, ethical, and scalable data-driven and intelligent systems requires an integrated approach that recognizes the close interdependence between data, analytics, computing infrastructure, and decision-making processes. Advances in data science, machine learning, information systems, and computing technologies 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 data science and intelligent systems demands attention to foundational challenges such as data quality, model interpretability, computational efficiency, system scalability, and responsible deployment. Equally important are broader structural considerations, including access to data and computational resources, skills development, data governance, privacy, and the societal implications of data-driven 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 data science, analytics, information systems, machine learning, decision support systems, cloud and distributed computing, and allied domains to deepen understanding of how data-driven and intelligent systems can be designed, evaluated, optimized, 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, responsible data practices, and trustworthy analytics, recognizing the critical role that data science and intelligent systems 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, data science, analytics, information systems, decision support systems, computing infrastructure, or applied machine learning, 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.
3143-2328ISSN
3 daysSubmission to first decision (median)
Continuous PublishingFrequency
CC BY 4.0License
Announcements
Call for Papers – International Journal of Intelligent Systems and Data Science (IJISDS)
The International Journal of Intelligent Systems and Data Science (IJISDS) invites submissions for its inaugural issues from researchers and practitioners working across data science, intelligent information systems, analytics, and data-driven computing.
IJISDS welcomes original research articles, comprehensive review papers, case studies, and applied or practice-oriented studies that present substantive contributions to theory, methodology, systems, or real-world implementation. Submissions may include, but are not limited to:
Novel methods, frameworks, or systems for data science, analytics, and intelligent information systems
Methodological advances in statistical learning, predictive modeling, data mining, and knowledge discovery
Research on decision support systems, recommender systems, expert systems, and information systems
Empirical studies supported by rigorous experimentation, observational data, or real-world datasets
Applied machine learning research addressing practical analytical or decision-making challenges
Research on cloud computing, distributed computing, edge computing, big data technologies, and system optimization
Internet of Things (IoT), cyber-physical systems, and data-intensive computing applications
Research addressing data governance, data privacy, explainable analytics, and responsible data-driven systems
Applied studies demonstrating the deployment, evaluation, or impact of data-driven systems in practical settings
Interdisciplinary work where data, analytics, or computational systems play a central analytical or decision-support role
Critical reviews, systematic reviews, and meta-analyses that synthesize and evaluate developments within the journal's scope
The journal encourages submissions that emphasize technical soundness, methodological clarity, reproducibility, and real-world relevance, while clearly articulating their contribution to existing knowledge, systems, or practice.
Contributions from both academic and industry contexts are welcome, provided they meet scholarly standards and offer verifiable insights.
Submission Status: Open Review Model: Double-blind peer review Publication Model: Open Access
This paper advances a novel IT-centric methodology for evaluating academic reputation within distinct computer science subareas, leveraging a Markov network-based metric known as P-score. Unlike traditional citation-based rankings
This paper presents a probabilistic approach to binary image segmentation that models foreground-background occlusion with pixel-wise Gaussian emissions and mask-conditioned independence, comparing iterated conditional modes, exact expectation maximization, Gibbs-sampled EM, and
The goal of this paper is to develop intelligent agents through fuzzy logic so that they can be capable of adaptability and context awareness in a simulated operational environment. Concentrating on dynamic interactions, fuzzy inference systems help agents to
Apeksha Bhuekar
September 2026 · Vol. 1 · Issue 5
For researchers and authors
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