About the Journal

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

JAIDSEM aims to provide a high-quality platform for original research articles, survey papers, and interdisciplinary contributions that combine rigorous mathematical foundations, computational methodologies, and real-world applications. The journal promotes the integration of theoretical developments with practical innovations in modern data-driven science and engineering.

The scope of the journal includes, but is not limited to, mathematical foundations of artificial intelligence and data science, machine learning and deep learning, statistical and probabilistic methods, optimization theory, computational and numerical methods, and engineering applications of AI. It also welcomes emerging interdisciplinary areas such as topological data analysis, quantum computing and quantum machine learning, complex systems, and AI-driven scientific computing.

JAIDSEM encourages contributions that bridge mathematics, artificial intelligence, and engineering, fostering collaboration across disciplines and supporting the development of innovative methodologies and applications.

The journal is committed to maintaining high academic standards through a rigorous peer-review process. It currently operates under a no publication fee policy to promote broad global participation and accessibility.

The journal is published by the International Society in Quantization, Geometry, and Dynamics, a nonprofit, community-driven global society dedicated to advancing mathematics from K–12 education to frontier research through international collaboration, scholarly exchange, and educational outreach.

Topics Covered

The ISQGD Journal of Artificial Intelligence and Data Science in Engineering and Mathematics (JAIDSEM) welcomes original research articles, survey papers, and interdisciplinary contributions in, but not limited to, the following areas:

1. Mathematical Foundations of AI and Data Science

  • Mathematical analysis of machine learning algorithms
  • Optimization theory (convex, non-convex, stochastic optimization)
  • Probability theory and stochastic processes
  • Statistical learning theory
  • Information theory and entropy methods
  • Functional analysis and operator theory 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 (GANs, diffusion models, VAEs)
  • Transfer learning and meta-learning
  • Federated and distributed learning
  • AI for scientific computing
  • Symbolic AI and hybrid models
  • AI ethics, fairness, 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 IoT (Internet of Things)
  • Cyber-physical systems
  • AI in electrical, mechanical, civil, and industrial engineering
  • Engineering design optimization
  • AI in engineering design methods
  • AI in human-centered design

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 analysis in data
  • 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, and engineering

8. Expository and Survey Articles

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