About the Journal

The ISQGD Journal of Artificial Intelligence and Data Science in Engineering and Mathematics (JAIDSEM) is an international, peer-reviewed journal dedicated to advancing research at the intersection of artificial intelligence, data science, engineering, and mathematics.

JAIDSEM provides a scholarly platform for original research articles, survey papers, expository contributions, and interdisciplinary studies that combine rigorous mathematical foundations, computational methodologies, data-driven techniques, and applications in science and engineering. The journal encourages work that connects theoretical developments with practical innovations in modern artificial intelligence, data science, and computational engineering.

The scope of JAIDSEM includes mathematical foundations of artificial intelligence and data science, machine learning and deep learning, statistical and probabilistic methods, optimization, computational and numerical methods, scientific computing, and engineering applications of AI and data science. The journal also welcomes emerging interdisciplinary areas including topological data analysis, quantum computing and quantum machine learning, complex systems, network science, and AI-driven scientific computing.

JAIDSEM encourages contributions that bridge mathematics, artificial intelligence, data science, and engineering and that foster collaboration across disciplines. The journal is committed to maintaining high academic standards through rigorous editorial assessment and peer review.

JAIDSEM currently charges no publication fee.

The journal is published by the International Society for Quality Growth and Development (ISQGD), Houston, Texas, USA. ISQGD is a U.S. 501(c)(3) public charity dedicated to supporting scholarly research, education, international collaboration, and academic development.

Aims and Scope

JAIDSEM welcomes original research articles, survey papers, expository contributions, and interdisciplinary studies in areas including, but not limited to, the following.

1. Mathematical Foundations of AI and Data Science

  • Mathematical analysis of machine learning algorithms
  • Convex, non-convex, and stochastic optimization
  • Probability theory and stochastic processes
  • Statistical learning theory
  • Information theory and entropy methods
  • Functional analysis and operator-theoretic methods in learning
  • Harmonic analysis and signal representations
  • Geometric and topological methods in data analysis
  • Graph theory and network science
  • Dynamical systems and learning dynamics

2. Artificial Intelligence and Machine Learning

  • Supervised, unsupervised, and reinforcement learning
  • Deep learning and neural network architectures
  • Explainable and interpretable AI
  • Generative models
  • Transfer learning and meta-learning
  • Federated and distributed learning
  • AI for scientific computing
  • Symbolic AI and hybrid models
  • AI ethics, fairness, reliability, and robustness

3. Data Science and Statistical Methods

  • Big data analytics and high-dimensional data analysis
  • Statistical inference and modeling
  • Bayesian methods and probabilistic programming
  • Time series analysis and forecasting
  • Data mining and pattern recognition
  • Dimensionality reduction and manifold learning
  • Computational statistics
  • Uncertainty quantification

4. Engineering Applications of AI and Data Science

  • Intelligent systems and automation
  • Signal and image processing
  • Computer vision and pattern analysis
  • Virtual reality applications and simulations
  • Control systems and robotics
  • Smart systems and the Internet of Things
  • Cyber-physical systems
  • AI in electrical, mechanical, civil, and industrial engineering
  • Engineering design optimization
  • AI-assisted engineering design
  • Human-centered intelligent systems

5. Computational and Numerical Methods

  • Numerical linear algebra
  • Scientific computing and simulation
  • Computational optimization
  • High-performance computing for AI
  • Sparse and low-rank methods
  • PDE-based models in data science
  • Computational geometry

6. Interdisciplinary and Emerging Areas

  • AI in physics, biology, medicine, and finance
  • Mathematical biology and bioinformatics
  • Quantum computing and quantum machine learning
  • Topological data analysis
  • Complex systems and network dynamics
  • Fractal geometry and multifractal methods in data analysis
  • AI for sustainability and climate science

7. Applications and Case Studies

  • Real-world applications of AI and data science
  • Industrial and technological innovations
  • Data-driven modeling and decision-making
  • Cross-disciplinary applications bridging mathematics, AI, data science, and engineering

8. Expository and Survey Articles

  • High-quality surveys on emerging topics
  • Mathematical and engineering perspectives on modern AI developments
  • Interdisciplinary expositions accessible to a broad scientific audience

Peer Review and Editorial Independence

Manuscripts submitted to the journal are evaluated through an editorial and peer-review process designed to maintain academic quality, fairness, scholarly rigor, and independence.

Each submission is initially assessed by the Editor-in-Chief or by a member of the editorial team specifically authorized by the Editor-in-Chief for suitability within the journal’s scope and for compliance with basic scholarly and ethical standards. Manuscripts considered suitable for further evaluation are assigned to an appropriate Editor and, as appropriate, to qualified reviewers with relevant subject expertise.

Editorial evaluation may include substantive scholarly assessment by members of the editorial team, together with reports from independently selected reviewers, as appropriate to the manuscript and the needs of the review process.

The selection of Editors and reviewers is an editorial responsibility. Authors should not attempt to influence the assignment of a particular Editor or reviewer. Editorial assignments and reviewer selection are made on the basis of subject expertise, availability, independence, and the needs of the manuscript and the review process.

Editors and reviewers are expected to disclose any actual or potential conflict of interest that could affect, or reasonably appear to affect, the impartiality of the evaluation. When appropriate, the Editor-in-Chief may reassign a manuscript, appoint additional reviewers, or take other measures necessary to preserve the integrity and independence of the editorial process.

Editorial decisions are based on the scholarly quality, originality, relevance, clarity, and contribution of the manuscript, together with the evaluations obtained during the editorial and peer-review process. Personal relationships, institutional affiliations, nationality, or other considerations unrelated to the scholarly merits of the work must not influence editorial decisions.

The Editor-in-Chief retains overall responsibility for the integrity and proper administration of the journal’s editorial and peer-review process.