Aims and Scope
Journal Scope
Aims and Scope
Bayanika Journal of Artificial Intelligence and Data Science is an open access, peer-reviewed scholarly journal that publishes high-quality research in artificial intelligence, machine learning, data science, intelligent systems, computational modeling, and their interdisciplinary applications. The journal aims to provide an academic platform for researchers, lecturers, practitioners, and professionals to disseminate innovative theories, methods, algorithms, models, systems, and applications in AI and data-driven research.
Aims
The primary aim of the journal is to advance scientific knowledge and practical innovation in artificial intelligence and data science. The journal encourages submissions that contribute to theoretical development, methodological innovation, algorithmic improvement, computational experimentation, intelligent system design, data-driven decision-making, and real-world applications across diverse domains.
The journal particularly welcomes manuscripts that demonstrate originality, methodological rigor, reproducibility, interpretability, and clear contribution to the advancement of artificial intelligence, machine learning, data science, and intelligent computational systems.
Scope of the Journal
The journal publishes original research articles, review articles, systematic literature reviews, methodological papers, case studies, short communications, and technical reports within the following areas:
Artificial Intelligence
- Artificial intelligence theory and applications
- Knowledge representation and reasoning
- Expert systems and intelligent agents
- AI-based decision support systems
- Human-centered and responsible AI
Machine Learning
- Supervised, unsupervised, and semi-supervised learning
- Classification, regression, clustering, and prediction
- Ensemble learning and hybrid learning models
- Feature selection and feature engineering
- Model evaluation, validation, and benchmarking
Deep Learning
- Neural networks and deep neural architectures
- Convolutional neural networks
- Recurrent neural networks and sequence models
- Transformers and attention-based models
- Deep learning applications in real-world domains
Data Science and Analytics
- Data mining and knowledge discovery
- Big data analytics
- Predictive analytics and prescriptive analytics
- Statistical learning and computational statistics
- Data visualization and exploratory data analysis
Explainable and Trustworthy AI
- Explainable artificial intelligence
- Interpretable machine learning
- Transparent decision-making models
- Fairness, accountability, and ethical AI
- Uncertainty-aware and reliable AI systems
Fuzzy Systems and Soft Computing
- Fuzzy logic and fuzzy inference systems
- Fuzzy clustering and fuzzy classification
- Rough sets, fuzzy rough sets, and uncertainty modeling
- Evolutionary computation and swarm intelligence
- Hybrid soft computing models
Natural Language Processing
- Text mining and document classification
- Sentiment analysis and opinion mining
- Language models and transformer-based NLP
- Information extraction and retrieval
- Computational linguistics and applied NLP
Computer Vision and Pattern Recognition
- Image classification and object detection
- Medical image analysis
- Pattern recognition and feature extraction
- Remote sensing image analysis
- Vision-based intelligent systems
Optimization and Computational Intelligence
- Metaheuristic optimization
- Evolutionary algorithms
- Multi-objective optimization
- Optimization for machine learning
- Computational intelligence for complex systems
Time Series and Forecasting
- Time series modeling and prediction
- Forecasting with machine learning and deep learning
- Hybrid forecasting models
- Anomaly detection in temporal data
- Applications in energy, finance, health, and business
AI Applications
- AI in health and biomedical data analysis
- AI in education and learning analytics
- AI in business, finance, and management
- AI in engineering and industrial systems
- AI for social science and public policy
Data-Driven Decision Support
- Decision support systems
- Intelligent recommendation systems
- Multi-criteria decision-making
- Risk prediction and decision analytics
- Data-driven policy and strategic decision-making
Types of Manuscripts
The journal accepts several types of manuscripts, including original research articles, review articles, systematic literature reviews, methodological papers, applied research papers, technical notes, short communications, and special issue articles. All manuscripts must be original, scientifically sound, clearly written, and relevant to the journal scope.
Article Selection Priorities
Manuscripts that propose novel models, algorithms, frameworks, or computational methods.
Manuscripts that provide strong experimental design, appropriate evaluation metrics, and reproducible analysis.
Manuscripts that offer meaningful theoretical, methodological, or practical contributions to AI and data science.
Manuscripts that address real-world problems using rigorous data-driven or intelligent computational approaches.
Manuscripts that discuss interpretability, transparency, uncertainty, robustness, fairness, or ethical implications of AI systems.
Out of Scope
The journal does not prioritize manuscripts that merely apply standard algorithms without sufficient novelty, methodological contribution, strong analysis, or meaningful interpretation. Submissions consisting only of routine software implementation, simple system development, superficial algorithm comparison, or small-scale case studies without scientific contribution may be rejected during initial editorial screening.
Target Readership
The journal is intended for researchers, lecturers, graduate students, data scientists, AI practitioners, software engineers, computational scientists, policymakers, and professionals interested in artificial intelligence, machine learning, data science, intelligent systems, and data-driven decision-making.
